{"schema_version":2,"generated_at":"2026-09-10T06:04:24Z","scope":"Global","bounds":[-145.9294444,-71.6667,172.9333333,71.692107],"papers":[{"id":"doc-208156599179ea3586ceca01","title":"Can the dataset of field based recharge estimates in Australia be used to predict recharge in data-poor areas?","display_title":"Empirical Recharge Estimates in Australia's MDB","doi":"https://doi.org/10.5194/hess-14-2023-2010","theme":"Groundwater","color":"#a8b9ff","year":2010,"publication_date":"2010-10-25","authors":["R. S. Crosbie","I. D. Jolly","F. W. Leaney","C. Petheram"],"summary":"This study compiles 4386 field-based groundwater recharge estimates from 172 studies across Australia, focusing on the Murray-Darling Basin (MDB). It evaluates factors affecting recharge including vegetation type, climate, surface material, and estimation methods. Empirical relationships relating average annual rainfall to recharge were developed for combinations of soil and vegetation types to estimate recharge in data-poor areas.","finding":"The study found that vegetation type and soil order were critical determinants of recharge, more so than climate zones or surface geology. Recharge under annual vegetation is significantly higher than under perennial or tree vegetation. Empirical relationships using rainfall, vegetation type, and soil group explained about 60% of the variance in recharge. However, uncertainty is high, with prediction intervals greater than an order of magnitude.","finding_evidence":{"passage_id":"p014-000","page":14,"quote":"2036 R. S. Crosbie et al.: Recharge estimates in Australia to predict recharge in data-poor areas Fig. 12. Relationship between the field estimates of recharge and the estimates of recharge from the regression equations in Fig. 11. used to build the relationships between rainfall and recharge where the author of the study that provided the recharge estimate also provided a vegetation type to associate with it. About a quarter of all recharge estimates used here had a vegetation type associated with it (Fig. 1). The recharge estimates that did not have a vegetation type associated with it were used in the “all” category, this category consists of all of the data (irrespective of whether vegetation type has been specified or not) and has limited value due to the dominance of vegetation type over recharge. The relationships developed here had a lot of scatter about the line of best fit.","verified_in_page":true},"topics":["groundwater recharge","empirical modeling","vegetation impact","soil influence","climate classification","recharge estimation methods","data-poor area estimation","hydrological uncertainty"],"study_period":null,"locations":[{"name":"Murray-Darling Basin","kind":"basin","role":"primary study area for groundwater recharge analyses","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"They used 41 studies to develop relationships between rainfall and recharge for different combinations of land use and soil type but the relationships were based on data of limited geographical extent and were confined to just two land uses types. Tolmie and Silburn (2003) reviewed studies from the Queensland portion of the Murray-Darling Basin but did not attempt to generalise the results. Silberstein (2010) reviewed all the data from the Gnangara groundwater mound in Western Australia with an emphasis on plantation forestry. Interestingly this region was excluded by Petheram et al. (2002) because the recharge measurements were so much greater than those measured elsewhere in the country. The most recent review of Australian recharge field studies, conducted by Crosbie et al. (2010a), collated 4386 recharge estimates from 172 studies nationally and but did not attempt to produce any robust relationships from this data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.530275,-31.195835],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Gnangara groundwater mound, Western Australia","kind":"region","role":"comparison site with high recharge rates under native vegetation","river_name":null,"geocode_query":"Gnangara groundwater mound, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"They used 41 studies to develop relationships between rainfall and recharge for different combinations of land use and soil type but the relationships were based on data of limited geographical extent and were confined to just two land uses types. Tolmie and Silburn (2003) reviewed studies from the Queensland portion of the Murray-Darling Basin but did not attempt to generalise the results. Silberstein (2010) reviewed all the data from the Gnangara groundwater mound in Western Australia with an emphasis on plantation forestry. Interestingly this region was excluded by Petheram et al. (2002) because the recharge measurements were so much greater than those measured elsewhere in the country. The most recent review of Australian recharge field studies, conducted by Crosbie et al. (2010a), collated 4386 recharge estimates from 172 studies nationally and but did not attempt to produce any robust relationships from this data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.865707,-31.835704],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413000,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gnangara WA 6077, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-1","true_coordinates":[115.8657072,-31.7757036],"spread_km":6.7},{"name":"South-west Murray Basin, South Australia","kind":"region","role":"location of earlier recharge studies cited","river_name":null,"geocode_query":"South-west Murray Basin, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"2024 R. S. Crosbie et al.: Recharge estimates in Australia to predict recharge in data-poor areas studies was conducted by Scanlon et al. (2006) who reviewed 140 recharge studies in (semi-) arid regions from all continents. They produced a single relationship between rainfall and recharge under native vegetation based on studies from different continents. Most previous reviews of Australian recharge studies have been of limited geographical extent or constrained by a paucity of data. Kennett-Smith et al. (1994) reviewed 18 studies conducted in the south-west Murray Basin and produced a relationship between percentage clay content of the soil and deep drainage. Cook et al. (2001) revisited this work some years later but did not update the relationship. Petheram et al. (2002) undertook the most comprehensive review to that time with a national focus.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Queensland portion of Murray-Darling Basin","kind":"region","role":"site of recharge studies reviewed but not generalized","river_name":null,"geocode_query":"Queensland portion of Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"2024 R. S. Crosbie et al.: Recharge estimates in Australia to predict recharge in data-poor areas studies was conducted by Scanlon et al. (2006) who reviewed 140 recharge studies in (semi-) arid regions from all continents. They produced a single relationship between rainfall and recharge under native vegetation based on studies from different continents. Most previous reviews of Australian recharge studies have been of limited geographical extent or constrained by a paucity of data. Kennett-Smith et al. (1994) reviewed 18 studies conducted in the south-west Murray Basin and produced a relationship between percentage clay content of the soil and deep drainage. Cook et al. (2001) revisited this work some years later but did not update the relationship. Petheram et al. (2002) undertook the most comprehensive review to that time with a national focus.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.543541,-31.194751],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-3","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Swan Coastal Plain and wheatbelt, Western Australia","kind":"region","role":"area with dry summer, hot summer climate with sandy soils affecting recharge","river_name":null,"geocode_query":"Swan Coastal Plain, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-004","page":8,"quote":"The recharge estimates in the Csa class were from the Swan Coastal Plain and wheatbelt in Western Australia, which have predominantly sandy soils whereas the Cfa class includes many recharge estimates from heavy textured soils in New South Wales and Queensland. The same problems exist with the warm summer classes Csb and Cfb. Recharge estimates made under Csb were mainly on sand in the south-east of South Australia and recharge estimates made under Cfb were mainly from Victoria on heavier textured soils. We do not have enough information to conclusively say that winter dominated rainfall produces more recharge than equi-seasonal rainfall for a given mean annual rainfall. The temperature classes can also be used to investigate whether recharge was greater under a hot climate. The Arid Steppe Hot (BSh) and Cold (Bsk) both have a relationship developed between rainfall and recharge for the annual vegetation class but they are not significantly different from each other.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.784337,-32.079938],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412826,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Swan Coastal Plain, Bicton WA 6157, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-4","true_coordinates":[115.7843368,-32.0199382],"spread_km":6.7},{"name":"New South Wales and Queensland","kind":"region","role":"humid climate regions with heavier soils influencing recharge","river_name":null,"geocode_query":"New South Wales, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-004","page":8,"quote":"The recharge estimates in the Csa class were from the Swan Coastal Plain and wheatbelt in Western Australia, which have predominantly sandy soils whereas the Cfa class includes many recharge estimates from heavy textured soils in New South Wales and Queensland. The same problems exist with the warm summer classes Csb and Cfb. Recharge estimates made under Csb were mainly on sand in the south-east of South Australia and recharge estimates made under Cfb were mainly from Victoria on heavier textured soils. We do not have enough information to conclusively say that winter dominated rainfall produces more recharge than equi-seasonal rainfall for a given mean annual rainfall. The temperature classes can also be used to investigate whether recharge was greater under a hot climate. The Arid Steppe Hot (BSh) and Cold (Bsk) both have a relationship developed between rainfall and recharge for the annual vegetation class but they are not significantly different from each other.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"South-east South Australia","kind":"region","role":"region representing dry summer warm summer climate class impacting recharge","river_name":null,"geocode_query":"South-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-004","page":8,"quote":"The recharge estimates in the Csa class were from the Swan Coastal Plain and wheatbelt in Western Australia, which have predominantly sandy soils whereas the Cfa class includes many recharge estimates from heavy textured soils in New South Wales and Queensland. The same problems exist with the warm summer classes Csb and Cfb. Recharge estimates made under Csb were mainly on sand in the south-east of South Australia and recharge estimates made under Cfb were mainly from Victoria on heavier textured soils. We do not have enough information to conclusively say that winter dominated rainfall produces more recharge than equi-seasonal rainfall for a given mean annual rainfall. The temperature classes can also be used to investigate whether recharge was greater under a hot climate. The Arid Steppe Hot (BSh) and Cold (Bsk) both have a relationship developed between rainfall and recharge for the annual vegetation class but they are not significantly different from each other.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.957348,-35.144099],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677591,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"S Eastern Fwy, South Australia, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-6","true_coordinates":[138.9573476,-35.0840992],"spread_km":6.7},{"name":"Victoria","kind":"region","role":"region representing temperate equi-seasonal rainfall with heavier soils affecting recharge","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-004","page":8,"quote":"The recharge estimates in the Csa class were from the Swan Coastal Plain and wheatbelt in Western Australia, which have predominantly sandy soils whereas the Cfa class includes many recharge estimates from heavy textured soils in New South Wales and Queensland. The same problems exist with the warm summer classes Csb and Cfb. Recharge estimates made under Csb were mainly on sand in the south-east of South Australia and recharge estimates made under Cfb were mainly from Victoria on heavier textured soils. We do not have enough information to conclusively say that winter dominated rainfall produces more recharge than equi-seasonal rainfall for a given mean annual rainfall. The temperature classes can also be used to investigate whether recharge was greater under a hot climate. The Arid Steppe Hot (BSh) and Cold (Bsk) both have a relationship developed between rainfall and recharge for the annual vegetation class but they are not significantly different from each other.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.457929,-37.148056],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-208156599179ea3586ceca01-site-7","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7}],"limitations":["Prediction intervals indicate uncertainty greater than an order of magnitude in recharge estimates.","Vegetation type was assigned only when original data provided it; many points had no vegetation class.","Soil types were assigned based on national scale soil maps that do not capture local heterogeneity.","Climate classification data was insufficient to definitively determine climate influence on recharge variations.","Surface geology classification lacked detail on karst and fractures, reducing predictive 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040056460,5040073420,5040591010,5040591050],"main_basin_ids":[5040056460,5040073410,5040073420]},{"id":"doc-9deefa71cbd912cbafe379e9","title":"The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments","display_title":"Climatic and Terrain Controls on Tropical Baseflow","doi":"https://doi.org/10.5194/hessd-7-4059-2010","theme":"Flow & ecology","color":"#80edc2","year":2010,"publication_date":"2010-07-01","authors":["J. L. Peña-Arancibia","A. I. J. M. van Dijk","M. Mulligan","L. A. Bruijnzeel"],"summary":"This study used daily streamflow data from the Global River Discharge Center and 167 unregulated tropical and subtropical catchments to analyze how climatic and terrain attributes influence baseflow recession coefficients (kbf). It found that climatic variables, particularly mean annual rainfall (MAR) and aridity index (AI), explain nearly half the variance in kbf, with terrain factors playing a lesser but still notable role, especially in smaller, geographically clustered catchments. Linear reservoir modeling allowed parameter estimation relevant for ungauged catchments and hydrologic modeling in tropical regions.","finding":"Mean annual rainfall and aridity index together explain 49% of the spatial variation in baseflow recession coefficients across 167 tropical and subtropical unregulated catchments.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"Regression equations were computed for kbf vs. MAR and AI, results are shown in Fig. 6 (no power or exponential regression were possible for negative values of TMI). A two-parameter exponential relationship of MAR and AI explained 49% of the variance in kbf. Only marginal improvement was achieved with the stepwise regression when including the weakly correlated catchment elongation (CE, r ∗ = 0.138). The other catchment terrain attributes with explanatory value were cross-correlated to climatic attributes and therefore not used in the multivariate analysis. The equations and summary statistics of all regressions are shown in Table 3. A subset of catchments that were smaller and geographically close to each other, contiguous in some cases, were analysed to see whether the correlation of catchment terrain properties with kbf was confounded by the large geographical Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010 www.hydrol-earth-syst-sci.net/14/2193/2010/","verified_in_page":true},"topics":["baseflow dynamics","streamflow recession","climate-hydrology interactions","terrain influence on hydrology","hydrological modeling","tropical catchments","water resources management","catchment morphology"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Region containing many tropical and subtropical catchments analyzed for baseflow recession coefficients using GRDC and DERM Queensland data","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"After selecting the best equation, the same types of regression were computed for both the absolute and relative residual variance and the remaining potential predictors, until no further variation was explained by adding these. 4 Results 4.1 Assembling a pan-tropical dataset for baseflow modelling Catchment boundaries were obtained from the Hydrosheds 1 km river network (Lehner et al., 2010). Only catchments with a relative error of less than 10% between the GRDC reported surface areas and the river network derived areas were considered in the analysis. After controlling for regulation, snow and lake influence, urban and irrigation areas, and land use change; the analysis resulted in a database comprising 167 catchments worldwide (Fig. 2a). Of the 167 catchments, 50% had a catchment area <1000 km2 and 90% <6000 km2 . The median was 850 km2 . The catchment assemblage encompassed many tropical climates (Fig. 2b). A large number of stream gauging stations were located in Australia.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland, Australia","kind":"region","role":"Region with additional 272 tropical and subtropical gauging stations included in the dataset for analysis","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The relationship between initial storage (S0 in mm) and S after t days is defined by: S = S0e −kbft (3) By combining Eqs. (2) and (3) for time step t = 1, provided that both Q and Q∗ represent baseflow and Q0 = Q∗ , baseflow recession can be represented by the exponential decay function: Q=Qe−kbft (4) 3 Methodology Time-series of catchment streamflow hydrographs for 1175 tropical and sub-tropical stations with >5 years of data were obtained from the Global Runoff Database (GRDB) from the GRDC. Additional data for 272 stations in tropical and subtropical Queensland (Australia) were obtained from the Department of Environment and Resource Management Queensland (DERM). Stations with more than 30 runoff events (defined as the number of times that daily average streamflow is exceeded) and more than 30 data pairs Q–Q∗ characteristic of low flow conditions were selected for the analysis. Data in m3 s −1 were subsequently converted to mm d−1 .","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Southeast Asia","kind":"region","role":"Tropical/subtropical region included in the catchment dataset where baseflow recession coefficients were estimated","river_name":null,"geocode_query":"Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.66283,-2.27797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-2","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"Malayan Peninsula","kind":"region","role":"Fully humid subregion in Southeast Asia where baseflow recession coefficients range from 0.02 to 0.06 day−1","river_name":null,"geocode_query":"Malayan Peninsula, Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.54509739999999,8.7158321],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Malay Peninsula, Thailand","basin_id":4040018350,"main_basin_id":4040018350,"basin_region":"Central and South-East Asia","basin_area_km2":293068.4,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-3"},{"name":"Andes Mountains","kind":"region","role":"Region where catchments showed intermediate kbf values (0.03–0.08 day−1) consistent with tropical mountain hydrology","river_name":null,"geocode_query":"Andes Mountains, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.7752471,-21.1607667],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Andes","basin_id":6040040470,"main_basin_id":6040040470,"basin_region":"South America","basin_area_km2":326937.3,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-4"},{"name":"Namibia","kind":"region","role":"Arid African region with catchments exhibiting high baseflow recession coefficients (∼0.20 day−1), representing fast drainage in arid basins","river_name":null,"geocode_query":"Namibia, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sahel","kind":"region","role":"Northernmost part of the Sahel with catchments showing relatively high kbf (~0.17 day−1), reflecting arid zone hydrology","river_name":null,"geocode_query":"Sahel, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.543691,19.004317],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sahel","basin_id":1040041030,"main_basin_id":1040041030,"basin_region":"Africa","basin_area_km2":172561.6,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-6","true_coordinates":[13.5436913,19.0643174],"spread_km":6.7},{"name":"West and Central Africa (Congo and Zambia)","kind":"region","role":"Regions with catchments closer to coastlines showing lower baseflow recession coefficients around 0.035 day−1","river_name":null,"geocode_query":"West Africa and Central Africa including Congo and Zambia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"South Africa","kind":"region","role":"Region with temperate catchments used for comparison, showing kbf similar to tropical wet catchments (∼0.035 day−1)","river_name":null,"geocode_query":"South Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"In Southeast Asia, the fully humid Malayan Peninsula had values of 0.02–0.06 day−1 . Continental Southeast Asia showed values of 0.04–0.07 day−1 . The highest values in Africa were found in Namibia (0.20 day−1 ) and in the catchments located in the northernmost of the Sahel (0.17 day−1 ). Catchments located closer to the coastline in West Africa and Central Africa (Congo and Zambia) generally showed values of ∼0.035 day−1 , as did temperate catchments in South Africa. Catchments located in the Andes had values of 0.03– 0.08 day−1 . Catchments in Panama, Costa Rica Nicaragua www.hydrol-earth-syst-sci.net/14/2193/2010/ Hydrol. Earth Syst. Sci., 14, 2193–2205, 2010","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North Queensland, Central Queensland, South Queensland, Australia","kind":"region","role":"Smaller geographically clustered groups of catchments analyzed for residual terrain effects on kbf","river_name":null,"geocode_query":"North Queensland, Central Queensland, South Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"J. L. Pena-Arancibia: The role of climatic and terrain attributes in estimating baseflow 2199 ̃ 27 1 2 3 4 5 6 Figure 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q-Q* 7 data pairs (dots) with and increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y-1 8 ), aridity index (AI) and seasonality index (SI) is show for each catchment 9 gauge data. (a) (b) (c) (d) (e) (f) Fig. 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q–Q∗ data pairs (dots) with an increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y−1 ), aridity index (AI) and seasonality index (SI) is show for each catchment gauge data. area and the different climates covered by the overall dataset (cf. Fig. 2a). Relatively smaller groups of catchments (<300 km2 ) were selected in north, central and south Queensland and in Puerto Rico.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.761524,-19.358215],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652762,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Queensland, QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-9deefa71cbd912cbafe379e9-site-9","true_coordinates":[146.7615238,-19.2982152],"spread_km":6.7},{"name":"Malaysia","kind":"region","role":"Region with geographically close catchments used to analyse residual variation in kbf linked to terrain factors","river_name":null,"geocode_query":"Malaysia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"J. L. Pena-Arancibia: The role of climatic and terrain attributes in estimating baseflow 2199 ̃ 27 1 2 3 4 5 6 Figure 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q-Q* 7 data pairs (dots) with and increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y-1 8 ), aridity index (AI) and seasonality index (SI) is show for each catchment 9 gauge data. (a) (b) (c) (d) (e) (f) Fig. 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q–Q∗ data pairs (dots) with an increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y−1 ), aridity index (AI) and seasonality index (SI) is show for each catchment gauge data. area and the different climates covered by the overall dataset (cf. Fig. 2a). Relatively smaller groups of catchments (<300 km2 ) were selected in north, central and south Queensland and in Puerto Rico.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Senegal","kind":"region","role":"Region with catchments analyzed for terrain influence on residuals of baseflow recession coefficient models","river_name":null,"geocode_query":"Senegal","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"J. L. Pena-Arancibia: The role of climatic and terrain attributes in estimating baseflow 2199 ̃ 27 1 2 3 4 5 6 Figure 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q-Q* 7 data pairs (dots) with and increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y-1 8 ), aridity index (AI) and seasonality index (SI) is show for each catchment 9 gauge data. (a) (b) (c) (d) (e) (f) Fig. 4. Variation of estimated recession coefficient kbf (closed lines) and number of Q–Q∗ data pairs (dots) with an increase in TQF from 0 to 20 days. Values for mean annual rainfall (MAR in mm y−1 ), aridity index (AI) and seasonality index (SI) is show for each catchment gauge data. area and the different climates covered by the overall dataset (cf. Fig. 2a). Relatively smaller groups of catchments (<300 km2 ) were selected in north, central and south Queensland and in Puerto Rico.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Amazon Basin","kind":"basin","role":"Large tropical basin noted as lacking suitable daily streamflow data for inclusion but critical for future studies","river_name":null,"geocode_query":"Amazon Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"After selecting the best equation, the same types of regression were computed for both the absolute and relative residual variance and the remaining potential predictors, until no further variation was explained by adding these. 4 Results 4.1 Assembling a pan-tropical dataset for baseflow modelling Catchment boundaries were obtained from the Hydrosheds 1 km river network (Lehner et al., 2010). Only catchments with a relative error of less than 10% between the GRDC reported surface areas and the river network derived areas were considered in the analysis. After controlling for regulation, snow and lake influence, urban and irrigation areas, and land use change; the analysis resulted in a database comprising 167 catchments worldwide (Fig. 2a). Of the 167 catchments, 50% had a catchment area <1000 km2 and 90% <6000 km2 . The median was 850 km2 . The catchment assemblage encompassed many tropical climates (Fig. 2b). A large number of stream gauging stations were located in Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.659944,-1.704128],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-12","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Congo Basin","kind":"basin","role":"Major tropical basin missing from dataset due to absence of sufficient daily data, important for future hydrological characterizations","river_name":null,"geocode_query":"Congo Basin, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"After selecting the best equation, the same types of regression were computed for both the absolute and relative residual variance and the remaining potential predictors, until no further variation was explained by adding these. 4 Results 4.1 Assembling a pan-tropical dataset for baseflow modelling Catchment boundaries were obtained from the Hydrosheds 1 km river network (Lehner et al., 2010). Only catchments with a relative error of less than 10% between the GRDC reported surface areas and the river network derived areas were considered in the analysis. After controlling for regulation, snow and lake influence, urban and irrigation areas, and land use change; the analysis resulted in a database comprising 167 catchments worldwide (Fig. 2a). Of the 167 catchments, 50% had a catchment area <1000 km2 and 90% <6000 km2 . The median was 850 km2 . The catchment assemblage encompassed many tropical climates (Fig. 2b). A large number of stream gauging stations were located in Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.779508,-2.00689],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1041213640,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":894488.8,"hydrology_source":"hydrobasins","site_id":"doc-9deefa71cbd912cbafe379e9-site-13","true_coordinates":[18.77950841330257,-1.946890029824763],"spread_km":6.7}],"limitations":["Limited quality and spatial density of daily streamflow data in tropical regions, especially in Amazon and Congo Basins, impacting representativeness.","Potential bias due to use of long-term climatologies which do not capture seasonal and interannual variability in baseflow recession.","Lack of detailed soil and geological data globally limits accounting for subsurface attributes influencing baseflow.","Negative correlation of baseflow recession coefficient with rainfall weighted slope contrary to expectations indicates possible confounding factors or scale effects.","General limitations inherent in estimating recession coefficients using a linear reservoir model which may not capture non-linear baseflow behavior in all catchments."],"river_ids":[],"site_ids":["doc-9deefa71cbd912cbafe379e9-site-2","doc-9deefa71cbd912cbafe379e9-site-3","doc-9deefa71cbd912cbafe379e9-site-4","doc-9deefa71cbd912cbafe379e9-site-6","doc-9deefa71cbd912cbafe379e9-site-9","doc-9deefa71cbd912cbafe379e9-site-12","doc-9deefa71cbd912cbafe379e9-site-13"],"anchor":[99.54509739999999,8.7158321],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[1040041030,1041213640,4040018350,5040015660,5040067860,6040040470,6040280400],"main_basin_ids":[1040020040,1040041030,4040018350,5040015660,5040067860,6040007000,6040040470]},{"id":"doc-dea931d179b50540aa49858a","title":"The within-day behaviour of 6 minute rainfall intensity in Australia","display_title":"Within-day 6-min Rainfall Intensity in Australia","doi":"https://doi.org/10.5194/hess-15-2561-2011","theme":"Climate","color":"#98d4f1","year":2011,"publication_date":"2011-08-18","authors":["A. W. Western","B. Anderson","L. Siriwardena","F. H. S. Chiew","A. Seed","G. Blöschl"],"summary":"This study analyzed high-resolution (6-minute) rainfall intensity data from 42 pluviograph stations across Australia to compare the performance of nine theoretical distribution functions (TDFs) in fitting the within-day rainfall intensity distributions. The three-parameter Generalised Pareto distribution (GPT3) was identified as the best fit, followed by two-parameter Generalised Pareto (GPT2), Exponential, and Gamma distributions. Trends in rainfall intensity statistics with latitude and climate zones were observed, reflecting changes in rainfall generation mechanisms.","finding":"The three-parameter Generalised Pareto distribution consistently provided the best fit to within-day rainfall intensity data at 42 Australian stations, outperforming other tested theoretical distributions across diverse climates and locations.","finding_evidence":{"passage_id":"p013-005","page":13,"quote":"However because the range of mCOE and RMSE90 values overlaps with the box plots of other TDFs, it is possible that at particular stations one of the other TDFs yields a better fit. Thus, on a station-by-station basis the TDF and fitting method showing the highest mCOE and, independently, the lowest RMSE90 were identified. The aggregated results are summarised in Table 4 and demonstrate that GPT3LS is unequivocally the best fitting TDF, with GPT2 and WEBL distributions providing a lower RMSE90 result at only 3 and 1 pluviometer stations, respectively. www.hydrol-earth-syst-sci.net/15/2561/2011/ Hydrol. Earth Syst. Sci., 15, 2561–2579, 2011","verified_in_page":true},"topics":["rainfall intensity distribution","statistical distribution fitting","hydrometeorology","rainfall variability","parameter estimation","climate influence on rainfall","distribution function modeling","rainfall duration"],"study_period":null,"locations":[{"name":"Halls Creek","kind":"station","role":"study site","river_name":null,"geocode_query":"Halls Creek, Western Australia, Australia","latitude":-18.2,"longitude":127.7,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[127.7,-18.2],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102210041,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059450,"main_basin_id":5040059450,"basin_region":"Australasia","basin_area_km2":199214.0,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-0"},{"name":"Broome","kind":"station","role":"study site","river_name":null,"geocode_query":"Broome, Western Australia, Australia","latitude":-17.9,"longitude":122.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[122.2,-17.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"none","site_id":"doc-dea931d179b50540aa49858a-site-1"},{"name":"Port Hedland","kind":"station","role":"study site","river_name":null,"geocode_query":"Port Hedland, Western Australia, Australia","latitude":-20.4,"longitude":118.6,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.6,-20.4],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":80911193,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040057720,"main_basin_id":5040057720,"basin_region":"Australasia","basin_area_km2":175726.2,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-2"},{"name":"Carnarvon","kind":"station","role":"study site","river_name":null,"geocode_query":"Carnarvon, Western Australia, Australia","latitude":-24.9,"longitude":113.7,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[113.7,-24.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":80910087,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056750,"main_basin_id":5040056750,"basin_region":"Australasia","basin_area_km2":48217.5,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-3"},{"name":"Meekatharra","kind":"station","role":"study site","river_name":null,"geocode_query":"Meekatharra, Western Australia, Australia","latitude":-26.6,"longitude":118.5,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.5,-26.6],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":80911147,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056740,"main_basin_id":5040056740,"basin_region":"Australasia","basin_area_km2":102560.9,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-4"},{"name":"Geraldton","kind":"station","role":"study site","river_name":null,"geocode_query":"Geraldton, Western Australia, Australia","latitude":-28.8,"longitude":114.7,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.7,-28.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":80910291,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-5"},{"name":"Perth","kind":"station","role":"study site","river_name":null,"geocode_query":"Perth, Western Australia, Australia","latitude":-31.9,"longitude":116,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116,-31.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70417296,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-6"},{"name":"Albany","kind":"station","role":"study site","river_name":null,"geocode_query":"Albany, Western Australia, Australia","latitude":-34.9,"longitude":117.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[117.8,-34.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70417159,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040076890,"main_basin_id":5040076890,"basin_region":"Australasia","basin_area_km2":5390.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-7"},{"name":"Esperance","kind":"station","role":"study site","river_name":null,"geocode_query":"Esperance, Western Australia, Australia","latitude":-33.8,"longitude":121.9,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.9,-33.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":61119787,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040076520,"main_basin_id":5040076520,"basin_region":"Australasia","basin_area_km2":4123.4,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-8"},{"name":"Kalgoorlie-Boulder","kind":"station","role":"study site","river_name":null,"geocode_query":"Kalgoorlie-Boulder, Western Australia, Australia","latitude":-30.8,"longitude":121.5,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.5,-30.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":61119362,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087690,"main_basin_id":5040087690,"basin_region":"Australasia","basin_area_km2":127703.4,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-9"},{"name":"Giles Meteorological Office","kind":"station","role":"study site","river_name":null,"geocode_query":"Giles Meteorological Office, Western Australia, Australia","latitude":-25,"longitude":128.3,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[128.3,-25],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":90880936,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040088170,"main_basin_id":5040088170,"basin_region":"Australasia","basin_area_km2":83025.1,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-10"},{"name":"Darwin Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Darwin Airport, Northern Territory, Australia","latitude":-12.4,"longitude":130.9,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.9,-12.4],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102217176,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-11"},{"name":"Gove Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Gove Airport, Northern Territory, Australia","latitude":-12.3,"longitude":136.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.8,-12.3],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102217104,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-12"},{"name":"Tennant Creek Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Tennant Creek Airport, Northern Territory, Australia","latitude":-19.6,"longitude":134.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[134.2,-19.6],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102216316,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087620,"main_basin_id":5040087620,"basin_region":"Australasia","basin_area_km2":104013.1,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-13"},{"name":"Alice Springs Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Alice Springs Airport, Northern Territory, Australia","latitude":-23.8,"longitude":133.9,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.9,-23.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":115715301,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-14"},{"name":"Woomera Aerodrome","kind":"station","role":"study site","river_name":null,"geocode_query":"Woomera Aerodrome, South Australia, Australia","latitude":-31.2,"longitude":136.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.8,-31.2],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":50287923,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087760,"main_basin_id":5040087760,"basin_region":"Australasia","basin_area_km2":146408.6,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-15"},{"name":"Oodnadatta Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Oodnadatta Airport, South Australia, Australia","latitude":-27.6,"longitude":135.4,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[135.4,-27.6],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":119369740,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-16"},{"name":"Ceduna AMO","kind":"station","role":"study site","river_name":null,"geocode_query":"Ceduna AMO, South Australia, Australia","latitude":-32.1,"longitude":133.7,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.7,-32.1],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":61126755,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040074330,"main_basin_id":5040074330,"basin_region":"Australasia","basin_area_km2":100682.8,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-17"},{"name":"Adelaide Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Adelaide Airport, South Australia, Australia","latitude":-35,"longitude":138.5,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.5,-35],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"none","site_id":"doc-dea931d179b50540aa49858a-site-18"},{"name":"Mount Gambier Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Mount Gambier Airport, South Australia, Australia","latitude":-37.7,"longitude":140.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.8,-37.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556987,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-19"},{"name":"Coen Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Coen Airport, Queensland, Australia","latitude":-13.8,"longitude":143.1,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.1,-13.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":129257113,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-20"},{"name":"Thursday Island MO","kind":"station","role":"study site","river_name":null,"geocode_query":"Thursday Island MO, Queensland, Australia","latitude":-10.6,"longitude":142.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.2,-10.6],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":129256793,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-21"},{"name":"Normanton Post Office","kind":"station","role":"study site","river_name":null,"geocode_query":"Normanton Post Office, Queensland, Australia","latitude":-17.7,"longitude":141.1,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.1,-17.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":122859581,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-22"},{"name":"Mount Isa AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Mount Isa AERO, Queensland, Australia","latitude":-20.7,"longitude":139.5,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.5,-20.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":120771104,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064140,"main_basin_id":5040064140,"basin_region":"Australasia","basin_area_km2":178643.3,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-23"},{"name":"Koombooloomba Dam","kind":"station","role":"study site","river_name":null,"geocode_query":"Koombooloomba Dam, Queensland, Australia","latitude":-17.8,"longitude":145.6,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.6,-17.8],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":16651456,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-24"},{"name":"Townsville AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Townsville AERO, Queensland, Australia","latitude":-19.2,"longitude":146.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8,-19.2],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","hydrology_source":"none","site_id":"doc-dea931d179b50540aa49858a-site-25"},{"name":"Mackay MO","kind":"station","role":"study site","river_name":null,"geocode_query":"Mackay MO, Queensland, Australia","latitude":-21.1,"longitude":149.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.2,-21.1],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":16654237,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040068460,"main_basin_id":5040068460,"basin_region":"Australasia","basin_area_km2":23350.6,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-26"},{"name":"Longreach AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Longreach AERO, Queensland, Australia","latitude":-23.4,"longitude":144.3,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.3,-23.4],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":111006238,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-27"},{"name":"Rockhampton AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Rockhampton AERO, Queensland, Australia","latitude":-23.4,"longitude":150.5,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.5,-23.4],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":12126667,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-28"},{"name":"Brisbane AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Brisbane AERO, Queensland, Australia","latitude":-27.4,"longitude":153.1,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1171687,-27.3906786],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130590,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Aero Rd, Brisbane Airport QLD 4008, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-29"},{"name":"Charleville AERO","kind":"station","role":"study site","river_name":null,"geocode_query":"Charleville AERO, Queensland, Australia","latitude":-26.4,"longitude":146.3,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.3,-26.4],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43677366,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040533380,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":53422.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-30"},{"name":"Cobar MO","kind":"station","role":"study site","river_name":null,"geocode_query":"Cobar MO, New South Wales, Australia","latitude":-31.5,"longitude":145.8,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.8,-31.5],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43664539,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-31"},{"name":"Gunnedah SCS","kind":"station","role":"study site","river_name":null,"geocode_query":"Gunnedah SCS, New South Wales, Australia","latitude":-31,"longitude":150.3,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.3,-31],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43674577,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name 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Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1,-30.3],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":26971371,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name 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Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.1816955,-33.950033],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054959,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney Airport (SYD), Mascot NSW 2020, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-34"},{"name":"Canberra Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Canberra Airport, Australian Capital Territory, Australia","latitude":-35.3,"longitude":149.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.1933066,-35.3053031],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43671849,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canberra Airport (CBR), 21 Terminal Ave, Canberra Airport ACT 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2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.5,-35.2],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43667743,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-36"},{"name":"Mildura Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Mildura Airport, Victoria, Australia","latitude":-34.2,"longitude":142.1,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.0845819,-34.2306752],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661231,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mildura Airport Pty Ltd, Alan Mathews Dr, Mildura VIC 3500, 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57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.1,-38.1],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21560758,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name 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2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145,-37.8],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21562828,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-39"},{"name":"Launceston Airport","kind":"station","role":"study site","river_name":null,"geocode_query":"Launceston Airport, Tasmania, Australia","latitude":-41.5,"longitude":147.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Station Station Lati- Long Period Elev Koppen Annual Max Max Min Min Annual ̈ Code Name tude itude (m) Climate Rainfall Monthly Month Monthly Month Rain Class Rainfall Rainfall Days 2012 Halls Creek −18.2 127.7 1955– 422 BSh 546 152.6 Jan 2.2 Aug 47 Airport 2005 3003 Broome −17.9 122.2 1948– 7 BSh 593 177 Jan 1.5 Sep 35 Airport 2005 4032 Port Hedland −20.4 118.6 1953– 6 BWh 313 98.5 Feb 0.9 Sep 20 Airport 2005 6011 Carnarvon −24.9 113.7 1956– 4 BWh 234 49.1 Jun 2.1 Dec 25 Airport 2005 7045 Meekatharra −26.6 118.5 1953– 517 BWh 235 34.6 Jun 4.9 Sep 29 Airport 2005 8051 Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.2105629,-41.543102],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31291993,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Launceston Airport (LST), 201 Evandale Rd, Western Junction TAS 7212, 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Geraldton −28.8 114.7 1953– 33 Csa 467 107 Jun 5.7 Dec 60 Airport 2005 9021 Perth −31.9 116.0 1961– 15 Csa 795 172.6 Jun 9.2 Jan 87 Airport 2005 9741 Albany −34.9 117.8 1965– 68 Csb 804 123.2 Jul 23.4 Feb 82 Airport 2005 9789 Esperance −33.8 121.9 1969– 25 Csb 625 98.5 Jul 17.4 Dec 91 2005 12038 Kalgoorlie-Boulder −30.8 121.5 1939– 365 BSh 271 31.4 Jun 14.4 Sep 40 Airport 2005 13017 Giles Meteorological −25.0 128.3 1956– 598 BWh 273 48.8 Feb 10.1 Aug 32 Office 2005 14015 Darwin −12.4 130.9 1953– 30 Aw 1715 428.5 Jan 1 Jul 94 Airport 2005 14508 Gove −12.3 136.8 1966– 52 Aw 1430 284.8 Feb 4.2 Sep 56 Airport 2005 15135 Tennant Creek −19.6 134.2 1969– 376 BSh 435 119.6 Feb 1.6 Aug 37 Airport 2005 15590 Alice Springs −23.8 133.9 1951– 546 BWh 282 44.4 Feb 9 Sep 30 Airport 2005 16001 Woomera −31.2 136.8 1955– 167 BWh 192 20.8 May 11.9 Apr 28 Aerodrome 2005 17043 Oodnadatta −27.6 135.4 1961– 117 BWh 176 28.9 Feb 9 Aug 22 Airport 2004 18012 Ceduna −32.1 133.7 1954– 15 BSk 304 41.1 Jul 11.9 Jan 57 AMO 2005 23034 Adelaide −35.0 138.5 1967– 6 Csb 455 63 Jul 18.2 Jan 79 Airport 2005 26021 Mount Gambier −37.7 140.8 1942– 63 Csb 707 99.3 Jul 25.6 Feb 119 Airport 2005 AERO 27006 Coen −13.8 143.1 1967– 161 Aw 1192 308.5 Jan 0.9 Sep 75 Airport 2002 27022 Thursday −10.6 142.2 1961– 58 Aw 1746 418.6 Jan 3.5 Sep 84 Island MO 1993 29041 Normanton −17.7 141.1 1964– 8 Aw 919 259.5 Jan 1.7 Aug 44 Post Office 1999 29127 Mount Isa −20.7 139.5 1967– 340 BSh 443 102.9 Jan 3.8 Jun 37 AERO 2005 31083 Koombooloomba −17.8 145.6 1960– 760 Cfa 2739 481.7 Mar 85.6 Oct 138 Dam 2005 Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.51161,-42.8375245],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31292299,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hobart International Airport (HBA), Strachan St, Cambridge TAS 7170, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-dea931d179b50540aa49858a-site-41"}],"limitations":["Lack of uncertainty quantification in parameter fits due to ignoring temporal structure and serial correlation of rainfall data.","Chi-squared goodness-of-fit test applied without accounting for temporal dependence, possibly overestimating power of test.","Some technical challenges prevented use of least squares fitting for Gamma and 3-parameter Lognormal distributions."],"river_ids":[],"site_ids":["doc-dea931d179b50540aa49858a-site-0","doc-dea931d179b50540aa49858a-site-1","doc-dea931d179b50540aa49858a-site-2","doc-dea931d179b50540aa49858a-site-3","doc-dea931d179b50540aa49858a-site-4","doc-dea931d179b50540aa49858a-site-5","doc-dea931d179b50540aa49858a-site-6","doc-dea931d179b50540aa49858a-site-7","doc-dea931d179b50540aa49858a-site-8","doc-dea931d179b50540aa49858a-site-9","doc-dea931d179b50540aa49858a-site-10","doc-dea931d179b50540aa49858a-site-11","doc-dea931d179b50540aa49858a-site-12","doc-dea931d179b50540aa49858a-site-13","doc-dea931d179b50540aa49858a-site-14","doc-dea931d179b50540aa49858a-site-15","doc-dea931d179b50540aa49858a-site-16","doc-dea931d179b50540aa49858a-site-17","doc-dea931d179b50540aa49858a-site-18","doc-dea931d179b50540aa49858a-site-19","doc-dea931d179b50540aa49858a-site-20","doc-dea931d179b50540aa49858a-site-21","doc-dea931d179b50540aa49858a-site-22","doc-dea931d179b50540aa49858a-site-23","doc-dea931d179b50540aa49858a-site-24","doc-dea931d179b50540aa49858a-site-25","doc-dea931d179b50540aa49858a-site-26","doc-dea931d179b50540aa49858a-site-27","doc-dea931d179b50540aa49858a-site-28","doc-dea931d179b50540aa49858a-site-29","doc-dea931d179b50540aa49858a-site-30","doc-dea931d179b50540aa49858a-site-31","doc-dea931d179b50540aa49858a-site-32","doc-dea931d179b50540aa49858a-site-33","doc-dea931d179b50540aa49858a-site-34","doc-dea931d179b50540aa49858a-site-35","doc-dea931d179b50540aa49858a-site-36","doc-dea931d179b50540aa49858a-site-37","doc-dea931d179b50540aa49858a-site-38","doc-dea931d179b50540aa49858a-site-39","doc-dea931d179b50540aa49858a-site-40","doc-dea931d179b50540aa49858a-site-41"],"anchor":[128.3,-25],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040049720,5040056460,5040056470,5040056740,5040056750,5040057720,5040059450,5040061670,5040064140,5040067860,5040068460,5040069590,5040069970,5040070710,5040074330,5040076520,5040076890,5040077640,5040087620,5040087690,5040087760,5040088170,5040491370,5040491510,5040524870,5040533380,5040591010,5040591050],"main_basin_ids":[5040049720,5040056460,5040056470,5040056740,5040056750,5040057720,5040059450,5040061670,5040064140,5040067860,5040068460,5040069590,5040069970,5040070710,5040073410,5040074330,5040076520,5040076890,5040077640,5040087590,5040087620,5040087690,5040087760,5040088170]},{"id":"doc-0291e2731ee8682cce108f73","title":"Climatic controls on diffuse groundwater recharge across Australia","display_title":"Climatic Controls on Diffuse Groundwater Recharge in Australia","doi":"https://doi.org/10.5194/hess-16-4557-2012","theme":"Groundwater","color":"#a8b9ff","year":2012,"publication_date":"2012-12-04","authors":["O. V. Barron","R. S. Crosbie","W. R. Dawes","S. P. Charles","T. Pickett","M. J. Donn"],"summary":"This study investigates how climatic variables influence diffuse groundwater recharge across Australia's diverse Koppen-Geiger climate zones using numerical modeling. Diffuse recharge is affected by rainfall, rainfall intensity, seasonality, as well as non-rainfall climate variables like vapour pressure deficit and solar radiation, with variations depending on soil permeability and vegetation cover. Recharge correlates strongly with annual rainfall in wetter climates but this correlation weakens in arid and low rainfall zones where other climate factors become more important. Recharge also varies seasonally, with higher recharge under winter-dominated rainfall regions for certain vegetation types. The findings are relevant for groundwater resource assessments and climate change impact studies in Australia.","finding":"Under similar annual rainfall, regions with winter-dominated rainfall have greater annual groundwater recharge than those with summer-dominated rainfall, particularly for perennial and tree vegetation.","finding_evidence":{"passage_id":"p008-003","page":8,"quote":"Similar results were found for trees, as the land cover, and for K = 0.1 m d−1 (and heavy soils are not present in Cs zone). For Cf climate types, the recharge is greater for Cfb than for Cfa where under overall equi-seasonal conditions the proportion of winter rainfall is greater. This relationship may Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/","verified_in_page":true},"topics":["groundwater recharge","rainfall intensity","climate variability","soil permeability","vegetation effects","climate seasonality","climate zones","climate change impacts"],"study_period":"1930–2009","locations":[{"name":"Australia","kind":"region","role":"primary study area encompassing multiple Koppen-Geiger climate zones","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Specifically it will – examine the influence of rainfall, including total rainfall, rainfall intensity and rainfall seasonality, on recharge as these climate parameters are considered to be the most influential in recharge estimation (Crosbie et al., 2010a; Owor et al., 2009; and Petheram et al., 2002); – examine the influence of other climate variables, such as vapour pressure deficit, temperature and solar radiation, on recharge as these parameters affect evapotranspiration and therefore indirectly the amount of rainfall which becomes recharge (Monteith, 1967); – make recommendations for climate change impact studies. In the following discussion the term “climate type” will be used when the climate characteristics are discussed, while the term “climate zone” will be used when the geographical extent of a climate type occurrence is referred to. 2 Climate types in Australia There are 15 Koppen-Geiger climate types in Australia, ̈ shown in Fig. 1.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Murray-Darling Basin, Australia","kind":"region","role":"example region illustrating recharge response to climate variables and climate change impact studies","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Furthermore, recharge can be influenced by variability in solar radiation, vapour pressure deficit and CO2 concentration as these climate characteristics influence the vegetation water demands and water use efficiency (McCallum et al., 2010). Natural vegetation is largely adapted to local climatic conditions, and some titles of climate types reflect this (e.g. tundra or steppe or savannah). Changes in climatic conditions, which lead to changes in the vegetation and/or their water use efficiency, can have a follow-on impact on recharge. For instance, increased recharge was simulated in parts of the Murray-Darling Basin despite a decrease in rainfall, and this was attributed to a reduction in transpiration; i.e. the transpiration reduced due to the effect of temperature on vegetation when the optimum temperature for vegetation growth was exceeded (Crosbie et al., 2010b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.556328,-31.191537],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-0291e2731ee8682cce108f73-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Tropical savannah (Aw) climate zone, northern Australia","kind":"region","role":"study climate zone characterized by tropical summer-dominated rainfall with monsoon influence","river_name":null,"geocode_query":"Tropical savannah Aw climate zone, northern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The northern regions (under climate type Aw and partly the northern part of arid BSh zone; see Table 1) are greatly influenced by monsoons and tropical cyclones, both of which bring heavy rains during the summer months. Rainfall in the south-west (Csa/Csb climate types) is dominated by heavy rain events occurring during winter. In the south and south-east, frontal weather systems and east coast lows during winter, in combination with localised troughs, bring occasional heavy rains with prolonged periods of lower intensity rainfall, mainly during winter. The sub-tropical ridge brings dry and stable conditions to large parts of middle Australia. To some extent, the approximate position of the ridge separates the summer-dominated rainfall in the north from the winter-dominated rainfall in the south. The southern regions also experience an overall cooler climate.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Arid hot desert (BWh) climate zone, Australia","kind":"region","role":"study climate zone characterized by hot arid conditions with low annual rainfall","river_name":null,"geocode_query":"Arid hot desert BWh climate zone, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Rainfall seasonality: summer rainfall as Rainfall proportion of annual Mean temperature Climate types Annuala (mm) Rangeb (mm) Annuala Rangeb Annuala ( ◦C) Rangeb ( ◦C) Tropical savannah Aw 1125 758–2038 0.92 0.67–0.96 26.7 22.3–29.5 Arid desert hot BWh 254 138–417 0.67 0.26–0.88 22.5 18.0–28.2 Arid steppe hot BSh 483 225–870 0.75 0.15–0.96 23.4 18.0–29.7 Arid steppe cold BSk 342 235–498 0.44 0.26–0.69 16.9 14.2–18.0 Temperate without Cfa 762 439–3493 0.63 0.37–0.79 18.6 14.1–23.4 dry season with hot summer Temperate without Cfb 953 433–3219 0.49 0.33–0.72 12.8 6.7–18.5 dry season with warm summer Temperate with Csa 557 341–1517 0.22 0.15-0.77 17.5 14.8–21.5 dry hot summer Temperate with Csb 665 347–1200 0.30 0.15–0.40 15.0 9.3–17.3 dry warm summer a Mean value across the climate zone, estimated using mean annual values of the relevant parameter within each climate zone. b The range of mean annual values within each climate zone. et al., 2010a; Petheram et al., 2002), and this has been carried forward into modelling studies (Crosbie et al., 2010b, 2012b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Arid hot steppe (BSh) climate zone, Australia","kind":"region","role":"study climate zone adjacent to tropical climate, exhibiting influence of summer rainfall and variable recharge response","river_name":null,"geocode_query":"Arid hot steppe BSh climate zone, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"Within individual climate types, RIP changes are related to the distribution of annual mean rainfall within the zone and are influenced by the other climate types located in the neighbouring regions. For instance, temperate climate Cfa covers the eastern regions of the country stretching from the northeast to south-east. It is characterised by the greatest variation in rainfall and its relative importance in recharge estimation. The higher RIP -values are related to the most northern modelled points that are similar to RIP in tropical climate (Aw), while the lowest RIP -values are found for the most southern modelled points that are similar to RIP under arid climate (BSk) (Fig. 4). Another example is related to RIP variability within the arid climate BSh, which is also greatly influenced by the position of this climate zone in relation to other climate zones.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Arid cold steppe (BSk) climate zone, Australia","kind":"region","role":"arid climate zone with low rainfall intensity and lowest relative importance of rainfall in recharge estimation","river_name":null,"geocode_query":"Arid cold steppe BSk climate zone, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The lower PRI indicates that other variables, which are not incorporated into the considered annual mean values, have a greater impact on recharge in such conditions. P The only climate type where RI of rainfall is lower than RISR,VDP,T is the arid (BSk) climate under low permeable soils and perennial vegetation or trees. Under this climate type, PRI values in recharge estimation are the overall lowest across all other climate types (Fig. 3). The highest values of PRISR,VDP,T are related to (i) the climates with summer-dominated rainfall (Aw and Cfa) and (ii) the cooler climate types (Cfb and Csb) where the ranges of the solar radiation and temperature are greater and their mean values are lower than in other climate types.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Temperate dry summer hot summer (Csa) climate zone, southwestern Australia","kind":"region","role":"study climate zone with winter rainfall dominance and cooler temperatures influencing recharge","river_name":null,"geocode_query":"Temperate Csa climate zone, southwestern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Similar results were found for trees, as the land cover, and for K = 0.1 m d−1 (and heavy soils are not present in Cs zone). For Cf climate types, the recharge is greater for Cfb than for Cfa where under overall equi-seasonal conditions the proportion of winter rainfall is greater. This relationship may Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.041389,-34.115683],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-0291e2731ee8682cce108f73-site-6","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"Temperate dry summer warm summer (Csb) climate zone, southwestern Australia","kind":"region","role":"study climate zone with distinct winter rainfall and cooler temperatures affecting recharge patterns","river_name":null,"geocode_query":"Temperate Csb climate zone, southwestern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The northern regions (under climate type Aw and partly the northern part of arid BSh zone; see Table 1) are greatly influenced by monsoons and tropical cyclones, both of which bring heavy rains during the summer months. Rainfall in the south-west (Csa/Csb climate types) is dominated by heavy rain events occurring during winter. In the south and south-east, frontal weather systems and east coast lows during winter, in combination with localised troughs, bring occasional heavy rains with prolonged periods of lower intensity rainfall, mainly during winter. The sub-tropical ridge brings dry and stable conditions to large parts of middle Australia. To some extent, the approximate position of the ridge separates the summer-dominated rainfall in the north from the winter-dominated rainfall in the south. The southern regions also experience an overall cooler climate.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.09801,-34.093092],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-0291e2731ee8682cce108f73-site-7","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"Temperate without dry season warm summer (Cfb) climate zone, southeastern Australia","kind":"region","role":"study climate zone with equi-seasonal rainfall and cooler conditions affecting recharge and its climatic control","river_name":null,"geocode_query":"Temperate Cfb climate zone, southeastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Similar results were found for trees, as the land cover, and for K = 0.1 m d−1 (and heavy soils are not present in Cs zone). For Cf climate types, the recharge is greater for Cfb than for Cfa where under overall equi-seasonal conditions the proportion of winter rainfall is greater. This relationship may Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Queensland, Australia","kind":"region","role":"location of CSIRO Ecosciences Precinct and climate data source center","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/ doi:10.5194/hess-16-4557-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Climatic controls on diffuse groundwater recharge across Australia O. V. Barron1 , R. S. Crosbie2 , W. R. Dawes1 , S. P. Charles1 , T. Pickett3 , and M. J. Donn1 1CSIRO Land and Water, Private Bag 5, Wembley, Western Australia 6913, Australia 2CSIRO Land and Water, Gate 4 Waite Road, Urrbrae, South Australia 5064, Australia 3CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Dutton Park, Queensland 4102, Australia Correspondence to: O. V. Barron (olga.barron@csiro.au) Received: 1 March 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 9 May 2012 Revised: 19 October 2012 – Accepted: 26 October 2012 – Published: 4 December 2012 Abstract.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia, Australia","kind":"region","role":"location of CSIRO Urrbrae climate data source center","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/ doi:10.5194/hess-16-4557-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Climatic controls on diffuse groundwater recharge across Australia O. V. Barron1 , R. S. Crosbie2 , W. R. Dawes1 , S. P. Charles1 , T. Pickett3 , and M. J. Donn1 1CSIRO Land and Water, Private Bag 5, Wembley, Western Australia 6913, Australia 2CSIRO Land and Water, Gate 4 Waite Road, Urrbrae, South Australia 5064, Australia 3CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Dutton Park, Queensland 4102, Australia Correspondence to: O. V. Barron (olga.barron@csiro.au) Received: 1 March 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 9 May 2012 Revised: 19 October 2012 – Accepted: 26 October 2012 – Published: 4 December 2012 Abstract.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia, Australia","kind":"region","role":"location of CSIRO Land and Water headquarters and climate data source center","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 4557–4570, 2012 www.hydrol-earth-syst-sci.net/16/4557/2012/ doi:10.5194/hess-16-4557-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Climatic controls on diffuse groundwater recharge across Australia O. V. Barron1 , R. S. Crosbie2 , W. R. Dawes1 , S. P. Charles1 , T. Pickett3 , and M. J. Donn1 1CSIRO Land and Water, Private Bag 5, Wembley, Western Australia 6913, Australia 2CSIRO Land and Water, Gate 4 Waite Road, Urrbrae, South Australia 5064, Australia 3CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Dutton Park, Queensland 4102, Australia Correspondence to: O. V. Barron (olga.barron@csiro.au) Received: 1 March 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 9 May 2012 Revised: 19 October 2012 – Accepted: 26 October 2012 – Published: 4 December 2012 Abstract.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Some spatial coordinates are unavailable, limiting precise geolocation.","Recharge modeling results are mainly relative due to lack of verification data for absolute recharge amounts.","Broad climate zones used, some climate types cover limited area less than 1% of 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040055960,5040591010],"main_basin_ids":[5040055960,5040073410]},{"id":"doc-21068c3b01a9b9b59e6221f9","title":"Land cover and water yield: inference problems when comparing catchments with mixed land cover","display_title":"Land Cover Influence on Streamflow in Australian Catchments","doi":"https://doi.org/10.5194/hess-16-3461-2012","theme":"Land & ecosystems","color":"#c8df98","year":2012,"publication_date":"2012-09-26","authors":["A. I. J. M. van Dijk","J. L. Peña-Arancibia","L. A. (Sampurno) Bruijnzeel"],"summary":"This study investigates why strong evidence from controlled experiments showing that land cover changes affect catchment streamflow (Q) has not been corroborated by analyses of multiple mixed land-cover catchments. Using data from 278 Australian catchments and synthetic experiments with a hydrological process model (AWRA-L), the authors find that methodological issues with the Budyko framework commonly used to analyse such data result in underestimation or lack of detection of land cover influence in mixed catchments. Additional factors including measurement noise, other climatic controls, covariance between land cover and climate, and lateral water redistribution also complicate detection of land cover effects on Q.","finding":"Random measurement errors in land cover, precipitation, and streamflow data can significantly reduce the likelihood of detecting a land cover influence on streamflow when analysed using a Budyko-type model in catchments with mixed land cover.","finding_evidence":{"passage_id":"p007-005","page":7,"quote":"This shows that previous contrasting findings can also be reproduced with the synthetic Q data. 3.2 Measurement errors are at least partly responsible The introduction of noise in the data led to higher average optimised w values than for the experiment without noise added: 2.7 (range 0.6–9.4) for forest and 2.3 (1.3–9.2) for herbaceous cover. Importantly, for 39 % of the 3000 replicates, the optimised w value for forest was actually lower than for herbaceous cover. It follows that random errors in the observations reduce the likelihood that land cover influence is detected, let alone accurately quantified. 3.3 Underlying climate factors may be responsible The distribution of w values calculated from simulated Q for individual catchments appeared approximately log-normally distributed and therefore all values were log-transformed www.hydrol-earth-syst-sci.net/16/3461/2012/ Hydrol. Earth Syst. Sci., 16, 3461–3473, 2012","verified_in_page":true},"topics":["land cover change","streamflow","hydrological modelling","Budyko framework","measurement error","climate influence","water yield","vegetation"],"study_period":"1990-2006","locations":[{"name":"Australian catchments","kind":"basin","role":"study area","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"3464 A. I. J. M. van Dijk et al.: Land cover and water yield 766 767 Fig. 1. Location of the 278 Australian catchments for which streamflow data were used in the 768 analysis. 769 Fig. 1. Location of the 278 Australian catchments for which streamflow data were used in the analysis. form of the Budyko model is able to accurately detect land cover influence. 2 Methods 2.1 Data The Q data used here were identical to the data used by van Dijk and Warren (2010), which is a subset of 278 out of around 326 records used in previous studies (Guerschman et al., 2008, 2009; van Dijk, 2010a, c) and very similar in composition to Australian catchment data used in other studies (e.g. Zhang et al., 2004; Peel et al., 2010). Catchment boundaries were derived from a 900 resolution digital elevation model (Fig. 1) and catchments with major water regulation infrastructure were excluded.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Murray-Darling Basin","kind":"basin","role":"example reference area in context of land cover and water balance","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-002","page":11,"quote":"Carrying out synthetic experiments with the process model, we could reproduce the absence of a detectable influence in mixed land-cover catchments as well as the presence of such an influence in individual catchments. In other words, previous contrasting findings could be reconciled. 2. Several potential methodological problems with the Budyko framework based inference approach applied in previous studies were investigated. The apparent absence of a detectable influence when comparing mixed land-cover catchments could, at least partially, be explained by the three factors investigated, viz. (i) noise in land cover, precipitation and Q data; (ii) additional catchments climate characteristics more important than -10% -5% 0% 5% 10% 15% 20% Es Et Ei Difference (non-forest-forest) as percentage of precipitation P/PE=0.54 (N=167) P/PE=0.95 (N=87) P/PE=1.60 (N=24) 804 805 Fig. 6.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.568173,-31.18631],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-21068c3b01a9b9b59e6221f9-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7}],"limitations":["Absence of daily streamflow and meteorological data for paired catchment experiments limited validation of process model 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[5040591010],"main_basin_ids":[5040073410]},{"id":"doc-488efc1a43e331109e9fd7e8","title":"Potential climate change impacts on the water balance of regional unconfined aquifer systems in south-western Australia","display_title":"Climate Change Impacts on SW Australia Aquifers","doi":"https://doi.org/10.5194/hess-16-4581-2012","theme":"Groundwater","color":"#a8b9ff","year":2012,"publication_date":"2012-12-04","authors":["R. Ali","D. McFarlane","S. Varma","W. Dawes","I. Emelyanova","G. Hodgson"],"summary":"This study evaluates the potential impacts of projected climate change on the water balance of regional unconfined aquifer systems in south-western Australia, a region experiencing significant rainfall decline. Using dynamically linked recharge and groundwater models for the Central Perth Basin, Peel-Harvey Area, and Southern Perth Basin, the study quantifies reductions in rainfall, recharge, groundwater discharge, and changes in storage under various climate scenarios up to 2030. It also assesses impacts of groundwater abstraction and land development.","finding":"Reductions of 15 to 18% in mean annual rainfall under dry future climate scenarios for 2030 are projected to reduce groundwater recharge by 33 to 49% compared to historical climate, leading to up to 50% reductions in groundwater discharge to oceans and drainage systems and increased risk of seawater intrusion in areas such as the Peel-Harvey Area.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"This study assesses climate change impacts on water balance components of the regional unconfined aquifer systems in south-western Australia, an area that has experienced a marked decline in rainfall since the mid 1970s and is expected to experience further decline due to global warming. Compared with the historical period of 1975 to 2007, reductions in the mean annual rainfall of between 15 and 18 percent are expected under a dry variant of the 2030 climate which will reduce recharge rates by between 33 and 49 percent relative to that under the historical period climate. Relative to the historical climate, reductions of up to 50 percent in groundwater discharge to the ocean and drainage systems are also expected. Sea-water intrusion is likely in the PeelHarvey Area under the dry future climate and net leakage to confined systems is projected to decrease by up to 35 percent which will cause reduction in pressures in confined systems under current abstraction.","verified_in_page":true},"topics":["climate change impacts","groundwater recharge","unconfined aquifers","water balance components","hydrogeology","groundwater discharge","seawater intrusion","land use impact"],"study_period":"1975-2030","locations":[{"name":"South-western Australia","kind":"region","role":"study area encompassing regional unconfined aquifer systems","river_name":null,"geocode_query":"South-western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This study assesses climate change impacts on water balance components of the regional unconfined aquifer systems in south-western Australia, an area that has experienced a marked decline in rainfall since the mid 1970s and is expected to experience further decline due to global warming. Compared with the historical period of 1975 to 2007, reductions in the mean annual rainfall of between 15 and 18 percent are expected under a dry variant of the 2030 climate which will reduce recharge rates by between 33 and 49 percent relative to that under the historical period climate. Relative to the historical climate, reductions of up to 50 percent in groundwater discharge to the ocean and drainage systems are also expected. Sea-water intrusion is likely in the PeelHarvey Area under the dry future climate and net leakage to confined systems is projected to decrease by up to 35 percent which will cause reduction in pressures in confined systems under current abstraction.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.111994,-34.042332],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-488efc1a43e331109e9fd7e8-site-0","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"Central Perth Basin, Western Australia","kind":"basin","role":"major study sub-region with groundwater modelling for water balance and recharge","river_name":null,"geocode_query":"Central Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Spatial distribution of mean annual historical (1975 to 2007) rainfall, areal evapotranspiration (APET) and rainfall deficit (rainfall less areal potential evapotranspiration) across the study area. 7 141 142 Fig. 3 Major land cover types in the study area (using satellite imagery from 2005) 143 2.3 Hydrogeology 144 The Central Perth Basin extends 150 km north and 70 km south of Perth and comprises the Swan 145 Coastal Plain and southern Dandaragan Plateau. The three main aquifers are the Superficial, 146 Leederville and Yarragadee. Locally important aquifers include the Parmelia, Rockingham, Mirrabooka 147 and Poison Hill aquifers (Fig. 4). The lithology and hydraulic conductivity of geological units are listed 148 in Table 1. The sub-surface geology and sub-cropping strata for the Central Perth Basin (Fig. 4a) 149 shows the degree of connectivity with underlying aquifers such as the Leederville Aquifer.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.861678,-32.011399],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-488efc1a43e331109e9fd7e8-site-1","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Peel-Harvey Area, Western Australia","kind":"region","role":"major study sub-region vulnerable to seawater intrusion under future climate","river_name":null,"geocode_query":"Peel-Harvey Area, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Further details about hydrogeology and confined aquifer systems of the Central Perth Basin can be found in CSIRO (2009) and Davidson (1995). The Peel-Harvey Area extends between the coastal cities of Mandurah and Bunbury and the Darling Scarp (Fig. 5a). The major aquifers are the Superficial, Leederville and Cockleshell Gully (Fig. 5b) and their lithology and hydraulic conductivity is listed in Table 1. The Superficial Aquifer extends over the entire region and consists of the Bassendean Sands and the Ascot, Guildford and Yoganup formations. Its thickness ranges from 20 to 30 m. The Superficial Aquifer is underlain by a number of confined aquifer systems including the Leederville, Cockleshell Gully and Yarragadee. The water table is generally shallow in the area. Recharge mainly occurs through rainfall infiltration but some is rejected due to shallow water tables.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.6483509,-32.6135821],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412763,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Peel-Harvey Estuarine System, Dawesville WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-488efc1a43e331109e9fd7e8-site-2"},{"name":"Southern Perth Basin, Western Australia","kind":"basin","role":"major study sub-region included in groundwater and recharge modelling","river_name":null,"geocode_query":"Southern Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Further details about 168 hydrogeology and confined aquifer systems of the Central Perth Basin can be found in CSIRO (2009) 169 and Davidson (1995). 170 171 Fig. 4 Sub-surface geology of the Central Perth Basin (a) and geological cross-section A-A’ (b) 172 (adapted from Davidson and Yu, 2008) Fig. 4. Sub-surface geology of the Central Perth Basin (a) and geological cross-section A-A’ (b) (adapted from Davidson and Yu, 2008). the Busselton Fault (Fig. 6a, b). Major aquifers include the Superficial, Leederville and Yarragadee (Baddock, 2005) (Fig. 6b); their lithology and hydraulic conductivities are listed in Table 1. The Lesueur Sandstone, Cockelshell Gully and Sue Coal Measures are minor aquifers. The Superficial Aquifer extends over the Swan Coastal and Scott Coastal plains.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.8504979,-31.9725411],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70412885,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-488efc1a43e331109e9fd7e8-site-3"},{"name":"Greater Perth area, Western Australia","kind":"settlement","role":"population growth centre influencing future groundwater demand","river_name":null,"geocode_query":"Greater Perth, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The future population growth within the study area is likely to be centred on the Greater Perth area, the Bunbury area, the coastal area between Bunbury and Busselton and the Shire of Augusta-Margaret River, and will therefore have increased demand for water (DoW, 2007). 3 Methods A flow diagram of the main methods (models and datasets) used in this study is shown in Fig. 8. Recharge was estimated using a vertical flux model (VFM) (Barr et al., 2003) linked with three groundwater models: the Perth Regional Aquifer Modelling System (PRAMS) of the Central Perth Basin; the South West Aquifer Modelling System (SWAMS) of the Southern Perth Basin; and the Peel-Harvey Regional Aquifer Modelling System (PHRAMS) of the Peel-Harvey Area. The PRAMS was previously dynamically coupled with the VFM.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.907132,-31.997362],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Perth WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-488efc1a43e331109e9fd7e8-site-4","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7}],"limitations":["Some uncertainty in recharge estimates due to limited field data for all land cover and soil types","Peel-Harvey Regional Aquifer Model (PHRAMS) is relatively new and less validated than other models used","Coordinates for locations not explicitly provided, lat/long set as null","Broad regional scale geographies with heterogeneous hydrogeology imply local variability not captured explicitly"],"river_ids":[],"site_ids":["doc-488efc1a43e331109e9fd7e8-site-0","doc-488efc1a43e331109e9fd7e8-site-1","doc-488efc1a43e331109e9fd7e8-site-2","doc-488efc1a43e331109e9fd7e8-site-3","doc-488efc1a43e331109e9fd7e8-site-4"],"anchor":[115.6483509,-32.6135821],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040055960,5040056460],"main_basin_ids":[5040055960,5040056460]},{"id":"doc-491dd89977bbd28dd5dfc782","title":"High-resolution projections of surface water availability for Tasmania, Australia","display_title":"Tasmania Water Projections Using Climate Models","doi":"https://doi.org/10.5194/hess-16-1287-2012","theme":"Flow & ecology","color":"#80edc2","year":2012,"publication_date":"2012-05-07","authors":["J. C. Bennett","F. L. N. Ling","D. A. Post","M. R. Grose","S. P. Corney","B. Graham","G. K. Holz","J. J. Katzfey","N. L. Bindoff"],"summary":"This study assesses future surface water availability in Tasmania, Australia, by coupling high-resolution regional climate model (RCM) simulations with hydrological models. Using six bias-corrected downscaled GCM simulations under the A2 scenario and five different hydrological models, streamflow projections are made for 2070–2099 compared to 1961–1990. The study finds spatially variable changes in runoff, with projected decreases in central Tasmania and increases in the east. Variability of streamflows is projected to increase, with greater uncertainty arising from RCM differences than hydrological model differences. The study demonstrates the benefit of directly coupling bias-corrected RCM rainfall and potential evapotranspiration with hydrological models for impact assessments.","finding":"Runoff is projected to decrease up to 30% annually in central Tasmania and increase in eastern Tasmania by over 60%, with daily and inter-annual variability increasing across much of Tasmania by 2070–2099.","finding_evidence":{"passage_id":"p012-000","page":12,"quote":"1298 J. C. Bennett et al.: High-resolution projections of surface water availability Fig. 12. Change in runoff simulated by SIMHYD from 1961–1990 to 2070–2099. (a) Change in mean annual runoff. (b) Change in mean DJF runoff. (c) Change in mean JJA runoff. (d) Change in Q75 runoff. (e) Change in Q1 runoff. Changes are calculated from the mean of the six RCM simulations. Stippling shows regions where at least five of the six RCM simulations agree on the sign of change. rainfall increases of <20 % (Fig. 10a) are projected to increase runoff by >60 % (Fig. 12a). Low runoff events generally decrease more than mean runoff, while high runoff events increase similarly to mean runoff. The RCM simulations agree strongly on a decrease in low runoff (represented by runoff with 75 % exceedance probability, Q75) over most of Tasmania (Fig. 12d). Q75 runoff decreases more and over a wider area than decreases to mean runoff (Fig. 12d).","verified_in_page":true},"topics":["regional climate modeling","bias correction","hydrological modeling","rainfall variability","streamflow projections","climate change impacts","surface water availability","runoff variability"],"study_period":"1961–1990 baseline, 2070–2099 future","locations":[{"name":"Tasmania","kind":"region","role":"Study area for climate impact on surface water availability","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"To find if the RCM simulations are a greater source of uncertainty than the hydrological models, we couple an ensemble of RCM simulations to an ensemble of hydrological models. 2 Study area: Tasmania Tasmania is Australia’s smallest (∼70 000 km2 ) and most southerly state, in addition to being Australia’s only island state. Tasmania is mountainous, with mountain ranges in the north-east (Ben Lomond Plateau), centre (central highlands), west and south all exceeding 1000 m in elevation. Tasmania lies in the path of the “Roaring Forties” winds (Fig. 1), and the prevailing westerly weather combines with the mountains to make western Tasmania one of the wettest places in Australia. Mean annual rainfalls exceed 2000 mm for much of the west and rise to more than 4000 mm on some mountain peaks (Fig. 2a). Rainfall in the west is highest in the austral winter (June-July-August, JJA) and lowest in summer (DecemberJanuary-February, DJF).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Ben Lomond Plateau, Tasmania, Australia","kind":"landscape","role":"North-east mountainous area with high runoff and study reference","river_name":null,"geocode_query":"Ben Lomond Plateau, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"To find if the RCM simulations are a greater source of uncertainty than the hydrological models, we couple an ensemble of RCM simulations to an ensemble of hydrological models. 2 Study area: Tasmania Tasmania is Australia’s smallest (∼70 000 km2 ) and most southerly state, in addition to being Australia’s only island state. Tasmania is mountainous, with mountain ranges in the north-east (Ben Lomond Plateau), centre (central highlands), west and south all exceeding 1000 m in elevation. Tasmania lies in the path of the “Roaring Forties” winds (Fig. 1), and the prevailing westerly weather combines with the mountains to make western Tasmania one of the wettest places in Australia. Mean annual rainfalls exceed 2000 mm for much of the west and rise to more than 4000 mm on some mountain peaks (Fig. 2a). Rainfall in the west is highest in the austral winter (June-July-August, JJA) and lowest in summer (DecemberJanuary-February, DJF).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.6258515,-41.5064523],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31292383,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ben Lomond TAS 7212, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-1"},{"name":"Central Highlands, Tasmania, Australia","kind":"landscape","role":"Mountainous central region with projected runoff decreases","river_name":null,"geocode_query":"Central Highlands, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"1298 J. C. Bennett et al.: High-resolution projections of surface water availability Fig. 12. Change in runoff simulated by SIMHYD from 1961–1990 to 2070–2099. (a) Change in mean annual runoff. (b) Change in mean DJF runoff. (c) Change in mean JJA runoff. (d) Change in Q75 runoff. (e) Change in Q1 runoff. Changes are calculated from the mean of the six RCM simulations. Stippling shows regions where at least five of the six RCM simulations agree on the sign of change. rainfall increases of <20 % (Fig. 10a) are projected to increase runoff by >60 % (Fig. 12a). Low runoff events generally decrease more than mean runoff, while high runoff events increase similarly to mean runoff. The RCM simulations agree strongly on a decrease in low runoff (represented by runoff with 75 % exceedance probability, Q75) over most of Tasmania (Fig. 12d). Q75 runoff decreases more and over a wider area than decreases to mean runoff (Fig. 12d).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8547703,-42.2117324],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31291504,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Highlands, TAS, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-2"},{"name":"Eastern Tasmania, Australia","kind":"region","role":"Region with projected runoff increases and agricultural importance","river_name":null,"geocode_query":"Eastern Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"1298 J. C. Bennett et al.: High-resolution projections of surface water availability Fig. 12. Change in runoff simulated by SIMHYD from 1961–1990 to 2070–2099. (a) Change in mean annual runoff. (b) Change in mean DJF runoff. (c) Change in mean JJA runoff. (d) Change in Q75 runoff. (e) Change in Q1 runoff. Changes are calculated from the mean of the six RCM simulations. Stippling shows regions where at least five of the six RCM simulations agree on the sign of change. rainfall increases of <20 % (Fig. 10a) are projected to increase runoff by >60 % (Fig. 12a). Low runoff events generally decrease more than mean runoff, while high runoff events increase similarly to mean runoff. The RCM simulations agree strongly on a decrease in low runoff (represented by runoff with 75 % exceedance probability, Q75) over most of Tasmania (Fig. 12d). Q75 runoff decreases more and over a wider area than decreases to mean runoff (Fig. 12d).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Black River Catchment, Tasmania, Australia","kind":"basin","role":"Study catchment representing wet western Tasmania for model validation and projections","river_name":"Black River","geocode_query":"Black River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Comparison of coefficients of variation (CV) of daily streamflows generated by SIMHYD at 86 streamflow gauges for 1961–2007. (a) CV of daily streamflows generated by SIMHYD forced with the RCM (RCM-runoff) and observations (OBS). (b) CV of daily streamflows generated by RCM-runoff and SIMHYD forced with SILO (SILO-runoff). Points show the mean of the six RCM simulations, bars show the range from the six simulations. 4.2.3 SIMHYD model performance SIMHYD exhibited the lowest biases of the hydrological models, and accordingly we focus on SIMHYD projections to report changes to future streamflows. We describe several additional performance tests of the SIMHYD model here. SIMHYD RCM-runoff tends to underestimate the daily variance (measured as the coefficient of variation, CV) of observed streamflows at the 86 gauge sites (Fig. 7a). However, when daily CV of SIMHYD RCM-runoff is compared to daily CV of SIMHYD SILO-runoff at the same sites, there is strong agreement (Fig. 7b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.3210132,-40.855664],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31289816,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-4"},{"name":"Rubicon River Catchment, Tasmania, Australia","kind":"basin","role":"Study catchment in central north with projected streamflow increases","river_name":"Rubicon River","geocode_query":"Rubicon River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The Rubicon River in the central north of Tasmania is projected to experience increases in streamflows in all seasons, particularly JJA (Fig. 13). Projections for rivers in the drier regions, including the north-east (South Esk River), east (Little Swanport River) and centre (Clyde River), are characterised by a high degree of variation between RCM simulations. The South Esk River and Little Swanport River are projected to experience increases in streamflow (Fig. 13), largely during February to April. A major feature of these projections is reduced runoff over Tasmania’s central mountains in all seasons (Fig. 12a–c). This contrasts with projected increases in mean annual runoff in many low-elevation areas in the east and in coastal areas (Fig. 12a). The high-elevation Nive River catchment is projected to experience decreases in streamflow year round, particularly in May and June (Fig. 13). Catchments in central Tasmania that span both high and low elevations (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.5686638192816,-41.31353388944518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31291166,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-5"},{"name":"Nive River Catchment, Tasmania, Australia","kind":"basin","role":"High elevation catchment with projected year-round streamflow decreases","river_name":"Nive River","geocode_query":"Nive River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The Rubicon River in the central north of Tasmania is projected to experience increases in streamflows in all seasons, particularly JJA (Fig. 13). Projections for rivers in the drier regions, including the north-east (South Esk River), east (Little Swanport River) and centre (Clyde River), are characterised by a high degree of variation between RCM simulations. The South Esk River and Little Swanport River are projected to experience increases in streamflow (Fig. 13), largely during February to April. A major feature of these projections is reduced runoff over Tasmania’s central mountains in all seasons (Fig. 12a–c). This contrasts with projected increases in mean annual runoff in many low-elevation areas in the east and in coastal areas (Fig. 12a). The high-elevation Nive River catchment is projected to experience decreases in streamflow year round, particularly in May and June (Fig. 13). Catchments in central Tasmania that span both high and low elevations (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.4448392227625,-42.1930610895498],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31291033,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-6"},{"name":"Franklin River Catchment, Tasmania, Australia","kind":"basin","role":"Western Tasmanian river with modelled streamflows used for validation","river_name":"Franklin River","geocode_query":"Franklin River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Comparison of coefficients of variation (CV) of daily streamflows generated by SIMHYD at 86 streamflow gauges for 1961–2007. (a) CV of daily streamflows generated by SIMHYD forced with the RCM (RCM-runoff) and observations (OBS). (b) CV of daily streamflows generated by RCM-runoff and SIMHYD forced with SILO (SILO-runoff). Points show the mean of the six RCM simulations, bars show the range from the six simulations. 4.2.3 SIMHYD model performance SIMHYD exhibited the lowest biases of the hydrological models, and accordingly we focus on SIMHYD projections to report changes to future streamflows. We describe several additional performance tests of the SIMHYD model here. SIMHYD RCM-runoff tends to underestimate the daily variance (measured as the coefficient of variation, CV) of observed streamflows at the 86 gauge sites (Fig. 7a). However, when daily CV of SIMHYD RCM-runoff is compared to daily CV of SIMHYD SILO-runoff at the same sites, there is strong agreement (Fig. 7b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9003115240611,-42.35645120277396],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31290513,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-7"},{"name":"Huon River Catchment, Tasmania, Australia","kind":"basin","role":"South-west Tasmanian river showing reasonable streamflow model agreement","river_name":"Huon River","geocode_query":"Huon River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Comparison of coefficients of variation (CV) of daily streamflows generated by SIMHYD at 86 streamflow gauges for 1961–2007. (a) CV of daily streamflows generated by SIMHYD forced with the RCM (RCM-runoff) and observations (OBS). (b) CV of daily streamflows generated by RCM-runoff and SIMHYD forced with SILO (SILO-runoff). Points show the mean of the six RCM simulations, bars show the range from the six simulations. 4.2.3 SIMHYD model performance SIMHYD exhibited the lowest biases of the hydrological models, and accordingly we focus on SIMHYD projections to report changes to future streamflows. We describe several additional performance tests of the SIMHYD model here. SIMHYD RCM-runoff tends to underestimate the daily variance (measured as the coefficient of variation, CV) of observed streamflows at the 86 gauge sites (Fig. 7a). However, when daily CV of SIMHYD RCM-runoff is compared to daily CV of SIMHYD SILO-runoff at the same sites, there is strong agreement (Fig. 7b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.7147565302938,-43.13045734103447],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31291357,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-8"},{"name":"South Esk River Catchment, Tasmania, Australia","kind":"basin","role":"Drier region catchment with considerable model projection variation","river_name":"South Esk River","geocode_query":"South Esk River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The Rubicon River in the central north of Tasmania is projected to experience increases in streamflows in all seasons, particularly JJA (Fig. 13). Projections for rivers in the drier regions, including the north-east (South Esk River), east (Little Swanport River) and centre (Clyde River), are characterised by a high degree of variation between RCM simulations. The South Esk River and Little Swanport River are projected to experience increases in streamflow (Fig. 13), largely during February to April. A major feature of these projections is reduced runoff over Tasmania’s central mountains in all seasons (Fig. 12a–c). This contrasts with projected increases in mean annual runoff in many low-elevation areas in the east and in coastal areas (Fig. 12a). The high-elevation Nive River catchment is projected to experience decreases in streamflow year round, particularly in May and June (Fig. 13). Catchments in central Tasmania that span both high and low elevations (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.1183323619952,-41.44638565757682],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31291883,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-9"},{"name":"Little Swanport River Catchment, Tasmania, Australia","kind":"basin","role":"Dry eastern catchment with projected increases in flow mainly Feb-April","river_name":"Little Swanport River","geocode_query":"Little Swanport River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The Rubicon River in the central north of Tasmania is projected to experience increases in streamflows in all seasons, particularly JJA (Fig. 13). Projections for rivers in the drier regions, including the north-east (South Esk River), east (Little Swanport River) and centre (Clyde River), are characterised by a high degree of variation between RCM simulations. The South Esk River and Little Swanport River are projected to experience increases in streamflow (Fig. 13), largely during February to April. A major feature of these projections is reduced runoff over Tasmania’s central mountains in all seasons (Fig. 12a–c). This contrasts with projected increases in mean annual runoff in many low-elevation areas in the east and in coastal areas (Fig. 12a). The high-elevation Nive River catchment is projected to experience decreases in streamflow year round, particularly in May and June (Fig. 13). Catchments in central Tasmania that span both high and low elevations (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.9665299396023,-42.33110383630528],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31292879,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-10"},{"name":"Clyde River Catchment, Tasmania, Australia","kind":"basin","role":"Catchment spanning elevations with complex runoff responses","river_name":"Clyde River","geocode_query":"Clyde River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The Rubicon River in the central north of Tasmania is projected to experience increases in streamflows in all seasons, particularly JJA (Fig. 13). Projections for rivers in the drier regions, including the north-east (South Esk River), east (Little Swanport River) and centre (Clyde River), are characterised by a high degree of variation between RCM simulations. The South Esk River and Little Swanport River are projected to experience increases in streamflow (Fig. 13), largely during February to April. A major feature of these projections is reduced runoff over Tasmania’s central mountains in all seasons (Fig. 12a–c). This contrasts with projected increases in mean annual runoff in many low-elevation areas in the east and in coastal areas (Fig. 12a). The high-elevation Nive River catchment is projected to experience decreases in streamflow year round, particularly in May and June (Fig. 13). Catchments in central Tasmania that span both high and low elevations (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8222091,-42.5751085],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":31291461,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-491dd89977bbd28dd5dfc782-site-11"}],"limitations":["Potential sensitivity of quantile mapping bias-correction to the climate training period","Uncertainty and bias in hydrological modeling especially for low and high flow extremes","Limited representation of temporal and spatial correlation of rainstorms by bias-corrected RCM inputs","IHACRES hydrological model shows poor performance and unstable sensitivity with bias-corrected inputs"],"river_ids":[],"site_ids":["doc-491dd89977bbd28dd5dfc782-site-1","doc-491dd89977bbd28dd5dfc782-site-2","doc-491dd89977bbd28dd5dfc782-site-4","doc-491dd89977bbd28dd5dfc782-site-5","doc-491dd89977bbd28dd5dfc782-site-6","doc-491dd89977bbd28dd5dfc782-site-7","doc-491dd89977bbd28dd5dfc782-site-8","doc-491dd89977bbd28dd5dfc782-site-9","doc-491dd89977bbd28dd5dfc782-site-10","doc-491dd89977bbd28dd5dfc782-site-11"],"anchor":[146.8547703,-42.2117324],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040077640],"main_basin_ids":[5040077640]},{"id":"doc-7e43d427ef85c8abfa7e104c","title":"Changes in Köppen-Geiger climate types under a future climate for Australia: hydrological implications","display_title":"Projected Shifts in Australia's Climate Zones","doi":"https://doi.org/10.5194/hess-16-3341-2012","theme":"Climate","color":"#98d4f1","year":2012,"publication_date":"2012-09-18","authors":["R. S. Crosbie","D. W. Pollock","F. S. Mpelasoka","O. V. Barron","S. P. Charles","M. J. Donn"],"summary":"This study projects changes in Koppen-Geiger climate types across Australia under future global warming scenarios (2030 and 2050 relative to 1990) using 17 global climate models and five warming scenarios. It finds a general shift with an expansion of arid and tropical climates and contraction of temperate and cold climates, implying significant vegetation shifts with hydrological impacts.","finding":"The study projects that by 2050 with a +2.4°C warming, the arid climate zone in Australia will expand from covering 76.5% to 81.7% of the continent, while the temperate climate zone will contract significantly from 14.7% to 9.2%, causing major transitions in vegetation that affect water resources such as recharge and runoff.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"At the highest level of classification for a +2.4 ◦C future climate (the upper limit projected for 2050) relative to the historical baseline, it was projected that the area of the continent covered by – tropical climate types would increase from 8.8 % to 9.1 %; – arid climate types would increase from 76.5 % to 81.7 %; – temperate climate types would decrease from 14.7 % to 9.2 %; – cold climate types would decrease from 0.016 % to 0.001 %. Previous climate change impact studies on water resources in Australia have assumed a static vegetation distribution. If the change in projected climate types is used as a surrogate for a change in vegetation, then the major transition in climate from temperate to arid in parts of Australia under a drier future climate could cause indirect effects on water resources. A transition from annual cropping to perennial grassland would have a compounding effect on the projected reduction in recharge.","verified_in_page":true},"topics":["climate change impacts","regional climate classification","vegetation cover","groundwater recharge","runoff","climate modeling","hydrological cycle","land use change"],"study_period":"1970–2010 historical baseline; projections to 2030 and 2050","locations":[{"name":"Australia","kind":"region","role":"study region and subject of climate classification and projections","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 3341–3349, 2012 www.hydrol-earth-syst-sci.net/16/3341/2012/ doi:10.5194/hess-16-3341-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Changes in Koppen-Geiger climate types under a future climate for ̈ Australia: hydrological implications R. S. Crosbie1 , D. W. Pollock2,*, F. S. Mpelasoka3 , O. V. Barron2 , S. P. Charles2 , and M. J. Donn2 1CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, PMB 2, Glen Osmond SA 5064, Australia 2CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Private Bag 5, Wembley, WA 6913, Australia 3CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, G.P.O. Box 1666, Canberra ACT 2601, Australia *now at: Rio Tinto Iron Ore, 152–158 St George’s Terrace Perth 6000, Western Australia Correspondence to: R. S. Crosbie (russell.crosbie@csiro.au) Received: 17 May 2012 – Published in Hydrol. Earth Syst. Sci.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Victoria","kind":"region","role":"region with observed high elevation climate types and projected climate shifts including presence of arid climate types by 2050","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Jones et al. (2009) had access to more than three times as many rainfall stations and more than twice as many temperature stations compared to Peel et al. (2007) to create the gridded climate surfaces used here, and the resolution of the interpolation is also higher from Jones et al. (2009) compared to that used by Peel et al. (2007) or Kottek et al. (2006). Jones et al. (2009) also used an interpolation algorithm that included elevation and so the major differences in climate types are in the higher elevation areas of Victoria (Vic), Tasmania (Tas) and New South Wales (NSW) where the climate types Cfc, Dfb and Dfc are present in this analysis but not that of Peel et al. (2007) or Kottek et al. (2006) (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.516735,-37.125465],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7e43d427ef85c8abfa7e104c-site-1","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7},{"name":"Tasmania","kind":"region","role":"region with higher elevation temperate and cold climate types and projected changes under warming scenarios","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Jones et al. (2009) had access to more than three times as many rainfall stations and more than twice as many temperature stations compared to Peel et al. (2007) to create the gridded climate surfaces used here, and the resolution of the interpolation is also higher from Jones et al. (2009) compared to that used by Peel et al. (2007) or Kottek et al. (2006). Jones et al. (2009) also used an interpolation algorithm that included elevation and so the major differences in climate types are in the higher elevation areas of Victoria (Vic), Tasmania (Tas) and New South Wales (NSW) where the climate types Cfc, Dfb and Dfc are present in this analysis but not that of Peel et al. (2007) or Kottek et al. (2006) (Fig. 1).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales","kind":"region","role":"region with presence of temperate and cold climate types, subject to contractions and shifts to arid and tropical climates","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Jones et al. (2009) had access to more than three times as many rainfall stations and more than twice as many temperature stations compared to Peel et al. (2007) to create the gridded climate surfaces used here, and the resolution of the interpolation is also higher from Jones et al. (2009) compared to that used by Peel et al. (2007) or Kottek et al. (2006). Jones et al. (2009) also used an interpolation algorithm that included elevation and so the major differences in climate types are in the higher elevation areas of Victoria (Vic), Tasmania (Tas) and New South Wales (NSW) where the climate types Cfc, Dfb and Dfc are present in this analysis but not that of Peel et al. (2007) or Kottek et al. (2006) (Fig. 1).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland","kind":"region","role":"northern region with expanding tropical climate type area along the coast and replacement of temperate climate","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The tropical (A) climate types occur across the north of the continent and are expanding overall with increasing global warming, this is due to an expansion along the Queensland (Qld) coast (displacing temperate) that is progressing faster than the contraction inland (being replaced by arid). Overall the tropical (A) area is projected to expand from 8.8 % of the continent under the historical baseline up to 9.1 % for +2.4 ◦C. The arid (B) climate types are expanding in every direction with increasing global warming; the area is projected to increase from 76.5 % to 81.7 % of the continent for +2.4 ◦C. The greatest area of expansion is in the southeast of Queensland, but the more surprising result is that by +1.0 ◦C, the arid climate types have spread into southern Victoria.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern wheatbelt, Western Australia","kind":"region","role":"annual cropping area projected to experience shift to hot arid climate (BWh) and large reductions in recharge under future climate","river_name":null,"geocode_query":"Northern wheatbelt, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"This will occur when it is no longer profitable to grow crops. Under the historical baseline climate only the northern wheatbelt (WA) has a significant area of annual vegetation in the BSh climate type (Fig. 7). The northern wheatbelt is currently amongst the lowest yielding grain growing areas in the country (Kokic et al., 2005). The majority of annual cropping is undertaken in temperate (C) or BSk climate types, and under the current distribution of cropping zones there is nowhere in Australia where there is annual vegetation under the BWh climate type. Under a +2.4 ◦C future climate BWh has moved into the northern wheatbelt (WA), far west, mid north, riverland (SA) and the sunraysia (Vic) areas of annual vegetation. The BSh climate type is projected to be dominant in the southern wheatbelt (WA), Eyre Peninsula, Yorke Peninsula, Mallee (SA), northern plains (Vic), the northern, central and southern slopes (NSW), and the Darling Downs (Qld).","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.095273,-32.093416],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70414962,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wheatbelt, WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-7e43d427ef85c8abfa7e104c-site-5","true_coordinates":[118.0952729,-32.0334157],"spread_km":6.7},{"name":"Mallee region, South Australia","kind":"region","role":"area of projected dominance of BSh (hot arid steppe) climate impacting vegetation and land use","river_name":null,"geocode_query":"Mallee, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"This will occur when it is no longer profitable to grow crops. Under the historical baseline climate only the northern wheatbelt (WA) has a significant area of annual vegetation in the BSh climate type (Fig. 7). The northern wheatbelt is currently amongst the lowest yielding grain growing areas in the country (Kokic et al., 2005). The majority of annual cropping is undertaken in temperate (C) or BSk climate types, and under the current distribution of cropping zones there is nowhere in Australia where there is annual vegetation under the BWh climate type. Under a +2.4 ◦C future climate BWh has moved into the northern wheatbelt (WA), far west, mid north, riverland (SA) and the sunraysia (Vic) areas of annual vegetation. The BSh climate type is projected to be dominant in the southern wheatbelt (WA), Eyre Peninsula, Yorke Peninsula, Mallee (SA), northern plains (Vic), the northern, central and southern slopes (NSW), and the Darling Downs (Qld).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.5067103,-34.3234209],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661405,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mallee NSW 2738, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7e43d427ef85c8abfa7e104c-site-6"},{"name":"Darling Downs, Queensland","kind":"region","role":"region projected to have dominance of BSh climate type affecting cropping and vegetation","river_name":null,"geocode_query":"Darling Downs, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"This will occur when it is no longer profitable to grow crops. Under the historical baseline climate only the northern wheatbelt (WA) has a significant area of annual vegetation in the BSh climate type (Fig. 7). The northern wheatbelt is currently amongst the lowest yielding grain growing areas in the country (Kokic et al., 2005). The majority of annual cropping is undertaken in temperate (C) or BSk climate types, and under the current distribution of cropping zones there is nowhere in Australia where there is annual vegetation under the BWh climate type. Under a +2.4 ◦C future climate BWh has moved into the northern wheatbelt (WA), far west, mid north, riverland (SA) and the sunraysia (Vic) areas of annual vegetation. The BSh climate type is projected to be dominant in the southern wheatbelt (WA), Eyre Peninsula, Yorke Peninsula, Mallee (SA), northern plains (Vic), the northern, central and southern slopes (NSW), and the Darling Downs (Qld).","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.582068,-27.589991],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674536,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darling Downs, QLD, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7e43d427ef85c8abfa7e104c-site-7","true_coordinates":[150.5820676,-27.5299906],"spread_km":6.7},{"name":"Murray-Darling Basin","kind":"basin","role":"major hydrological basin where cold climate types form energy limited zones with high runoff, projected to experience reduced runoff with climate shifts","river_name":"Murray-Darling","geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The change in climate type from cold (D) to temperate (C) is the result of a projected increase in temperature and therefore potential evapotranspiration, combined with a projected decrease in rainfall (Chiew et al., 2009a) and the area that is energy limited is reduced resulting in less runoff. The area classified as cold (D) under the historical baseline climate is projected to be amongst the areas with the greatest reduction in runoff in the MDB (Chiew et al., 2009b). The transition of areas from temperate (C) to arid (B) climate types could cause either an increase or decrease in recharge. Barron et al. (2012) showed that groundwater recharge under BSh is higher for a given annual rainfall than Cfa but BSh by definition experiences a lower rainfall than Cfa (Peel et al., 2007).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.578648,-31.179259],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-7e43d427ef85c8abfa7e104c-site-8","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7}],"limitations":["No precise latitude/longitude coordinates provided for regions; geographic extents defined only by regional names.","Study relies on downscaled GCM outputs subject to model uncertainties, especially for tropical climate projections.","Vegetation change only inferred indirectly via climate classification; actual vegetation shifts and temporal dynamics can vary.","Simplifications in linking climate classification changes to hydrological impacts acknowledge complexity but do not model full ecosystem feedbacks or human land use 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-6","basin_ids":[5040056460,5040524870,5040591010,5040591050],"main_basin_ids":[5040056460,5040073410]},{"id":"doc-87eb54ae86b0d69b22bab746","title":"Modelling the effects of climate and land cover change on groundwater recharge in south-west Western Australia","display_title":"Climate and land cover impacts on groundwater recharge SW Australia","doi":"https://doi.org/10.5194/hess-16-2709-2012","theme":"Groundwater","color":"#a8b9ff","year":2012,"publication_date":"2012-08-14","authors":["W. Dawes","R. Ali","S. Varma","I. Emelyanova","G. Hodgson","D. McFarlane"],"summary":"This study models the effects of climate change and land cover alterations on groundwater recharge across south-west Western Australia, focusing on the Perth Basin and surrounding areas. Using linked unsaturated/saturated groundwater models and the WAVES model for regions without full models, the study investigates spatially variable recharge responses under different future climate scenarios based on GCM outputs and land cover changes such as pine plantation removal and urban expansion. Recharge changes depend strongly on soil type, vegetation cover, and regional climate, highlighting areas of increased and decreased recharge.","finding":"Recharge changes are spatially variable and influenced by both climate and land cover. Sandy soils with shallow groundwater show reduced sensitivity to rainfall declines, while clayey soils with deep-rooted vegetation exhibit greater sensitivity, with recharge reductions up to 50 percent for modest rainfall declines. In some cases, land use changes like pine plantation removal lead to increased recharge despite rainfall declines. Recharge reductions in the northern Perth Basin under drier scenarios may approach zero, threatening groundwater resources. Overall, recharge changes generally exceed rainfall change percentages by a factor between 1 and 3.","finding_evidence":{"passage_id":"p011-004","page":11,"quote":"For areas with low conductivity surface soils, such as the Jarrahdale climate zone in the central region and Donnybrook climate zone in the south, recharge can decrease by up to 50 percent with a much smaller decrease in rainfall. In areas where only a vertical unsaturated model was used, results were for a consistently large reduction in recharge with future drier climates. In the more arid north, recharge under grass cover could potentially reduce by between 60 and 99 percent, while it may increase under trees on coastal areas if average rainfall increases. Along the wet southern coast near Albany, modelled recharge reductions were 30 to 50 percent, which would be extremely serious as the area is already over-allocated for groundwater extraction. Relative changes in recharge compared to rainfall indicate that sandy soils are less sensitive to projected climate changes than the same land cover on clay soils.","verified_in_page":true},"topics":["groundwater recharge","climate change impact","land cover change","hydrological modelling","soil hydrology","vegetation influence","regional climate variability","water resources management"],"study_period":"1975-2030","locations":[{"name":"Swan Coastal Plain, Western Australia","kind":"region","role":"Primary study area for modelling groundwater recharge under climate and land cover change","river_name":null,"geocode_query":"Swan Coastal Plain, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 2709–2722, 2012 www.hydrol-earth-syst-sci.net/16/2709/2012/ doi:10.5194/hess-16-2709-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Modelling the effects of climate and land cover change on groundwater recharge in south-west Western Australia W. Dawes1 , R. Ali1 , S. Varma2 , I. Emelyanova1 , G. Hodgson1 , and D. McFarlane1 1CSIRO Land and Water, Floreat, Western Australia 2CSIRO Earth Science and Resource Engineering, Kensington, Western Australia Correspondence to: W. Dawes (warrick.dawes@csiro.au) Received: 17 April 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 10 May 2012 Revised: 10 July 2012 – Accepted: 22 July 2012 – Published: 14 August 2012 Abstract. The groundwater resource contained within the sandy aquifers of the Swan Coastal Plain, south-west Western Australia, provides approximately 60 percent of the drinking water for the metropolitan population of Perth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.784337,-31.959938],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412826,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Swan Coastal Plain, Bicton WA 6157, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-0","true_coordinates":[115.7843368,-32.0199382],"spread_km":6.7},{"name":"Perth Basin, Western Australia","kind":"basin","role":"Study region encompassing sand plains and plateaux with major aquifers, focus of recharge modelling","river_name":null,"geocode_query":"Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"It is therefore vital that modelling studies are undertaken locally that assess the specific combination of climatic, land cover and human activity changes present. The geographic area of interest is the fresh groundwater resources of the Perth Basin from Geraldton in the north, down the west coast of Western Australia, and east to Albany (Fig. 1). It covers approximately 62 500 km2 and contains 1.9 million Fig. 1. Location map showing groundwater model regions and climate zones in the south-west of Western Australia. people, with groundwater being the major water source for all human purposes, including more than 60 percent of drinking water. The Perth Basin comprises the flat sandy Swan and Scott Coastal Plains, and the more elevated and clayey Blackwood and Dandaragan Plateaux. Several previous studies of recharge alone (Crosbie et al., 2010; Ng et al., 2010) and recharge to a groundwater model (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.931317,-31.961818],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-1","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"City of Perth, Western Australia","kind":"settlement","role":"Metropolitan center reliant on groundwater from Swan Coastal Plain aquifers, area of highest recharge from urban runoff","river_name":null,"geocode_query":"Perth, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 16, 2709–2722, 2012 www.hydrol-earth-syst-sci.net/16/2709/2012/ doi:10.5194/hess-16-2709-2012 © Author(s) 2012. CC Attribution 3.0 License. Hydrology and Earth System Sciences Modelling the effects of climate and land cover change on groundwater recharge in south-west Western Australia W. Dawes1 , R. Ali1 , S. Varma2 , I. Emelyanova1 , G. Hodgson1 , and D. McFarlane1 1CSIRO Land and Water, Floreat, Western Australia 2CSIRO Earth Science and Resource Engineering, Kensington, Western Australia Correspondence to: W. Dawes (warrick.dawes@csiro.au) Received: 17 April 2012 – Published in Hydrol. Earth Syst. Sci. Discuss.: 10 May 2012 Revised: 10 July 2012 – Accepted: 22 July 2012 – Published: 14 August 2012 Abstract. The groundwater resource contained within the sandy aquifers of the Swan Coastal Plain, south-west Western Australia, provides approximately 60 percent of the drinking water for the metropolitan population of Perth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.922918,-31.921399],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Perth WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-2","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Northern Perth Basin (NPB), Western Australia","kind":"basin","role":"Study sub-region modelled with WAVES, with complex faulting and low population, investigated for recharge sensitivity","river_name":null,"geocode_query":"Northern Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Present also are outcropping areas of the deepest regional aquifer, with associated soils of high conductivity providing direct recharge. The linking with VFM and calibration of the model is described in Cymod (2009). The NPB and AA do not have a VFM linked MODFLOW model; these were modelled using WAVES as a stand-alone product. The NPB covers an area from Dandaragan, at the northern edge of the PRAMS region, north of Geraldton, bounded by the natural coastline and Darling Fault. It has similar aquifers to PRAMS, including the LeedervilleParmelia and Yarragadee, but has complex faulting that makes them locally discontinuous. The area has low rainfall and low population, which leads to groundwater usage that is around 40 percent of the total amount available for licensing. Texture contrast soils are common in the basin with sandy and loamy top soils over clayey lower layers. Water tables are generally not close to the surface, being 10m or deeper away from coastal dunes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.852492,-31.987384],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-3","true_coordinates":[115.852492,-31.9273844],"spread_km":6.7},{"name":"Albany area (AA), Western Australia","kind":"region","role":"Smaller groundwater management unit near Albany studied for recharge using WAVES with dune terrain aquifers","river_name":null,"geocode_query":"Albany area, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"In keeping with the PRAMS and SWAMS modelling, the leaf area index (LAI) and rooting distribution of the cover were fixed, along with the groundwater depth. The cover types are bare ground with no vegetation cover, perennial grass with an LAI of 1.0 and rooting depth of 1 m over a 10 m water table, and trees with an LAI of 0.5 and rooting depth of 5 m over a 10 m water table. At the Three Springs site with 15 percent less rainfall, the tree LAI was reduced to 0.33. The AA is a 212 km2 groundwater management unit 420 km south-east of the city of Perth on the south coast, at the edge of the geological Bremer Basin. The aquifer of interest here is in dune terrain over Tamala limestone up to 190m thick, overlying layers of clay and siltstone aquitard and minor aquifers down to a granite basement. The city of Albany is a regional population centre, and the area’s groundwater resource is currently licensed for 120 percent of its allocation limit of 4.2 GL yr−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[117.8837114,-35.0268148],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70414880,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Albany WA 6330, Australia","basin_id":5040076890,"main_basin_id":5040076890,"basin_region":"Australasia","basin_area_km2":5390.7,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-4"},{"name":"Dandaragan Plateau, Western Australia","kind":"landscape","role":"Part of Perth Basin with clayey soils and deep-rooted vegetation influencing recharge responses","river_name":null,"geocode_query":"Dandaragan Plateau, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-003","page":11,"quote":"Where this was not possible, the unsaturated vertical model was used to estimate recharge to a suitably deep water table. In the central part of the Perth Basin, recharge changes are affected by both change in land cover and climate patterns. In the Dandaragan Plateau, recharge in cleared areas is greater now and into the future than under trees have been historically, so water levels that have been rising since the 1970s will continue to rise into the future. Recharge in the area around Wanneroo primarily responds to removal of pine plantations thus causing recharge to increase despite a reduction in rainfall into the future. In areas where future pumping pressure results in an increase in bore abstraction, recharge can increase due to a greater soil buffer being created. This applies both to the central and southern Perth Basin models on soils that are primarily sand dunes or have sand-dominated soil profiles, such as Lancelin, Busselton and Margaret River climate zones.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.7028717,-30.6751401],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412705,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dandaragan WA 6507, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-5"},{"name":"Wanneroo climate zone, Western Australia","kind":"region","role":"Climate zone within Perth Basin where removal of pine plantations increases recharge despite rainfall declines","river_name":null,"geocode_query":"Wanneroo, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"A groundwater modelling study of the area used both chloride profiles and steady-state groundwater modelling to estimate recharge over the sand dunes (URS and Dames and Moore, 2000). They reported an average recharge rate of 220 mm yr−1 , which accords well with the grass and tree recharge estimates from WAVES under historical climate scenario. 2.3 Discussion 2.3.1 Recharge changes under climate scenarios In general in the areas modelled, a decline in rainfall has led to a decline in recharge, and vice versa, in the absence of other human activities. Two areas of note where human activity has impacted on recharge, contrary to the climatic signal, are Wanneroo in PRAMS and Busselton in SWAMS. In the Wanneroo climate zone, there is a significant area of pine plantation that is legislated for removal over the next two decades.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.8027929,-31.7509712],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412826,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wanneroo WA 6065, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-6"},{"name":"Blackwood Plateau, Western Australia","kind":"landscape","role":"Area in southern Perth Basin with clay-rich soils, exhibiting large decreases in recharge with rainfall decline","river_name":null,"geocode_query":"Blackwood Plateau, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"SWAMS has less recharge overall than PRAMS, and this is mainly due to the large areas in the region with low conductivity surface soils and deep- rooted native vegetation away from the coastal dune systems. There are few areas in SWAMS with large positive modelled recharge values. These do include some of the southern Scott Coastal Plain areas, and the intake area for the outcropping Yarragadee Aquifer. Both areas have highly transmissive sandy soils and capacity within the aquifer for recharge to enter. Along the northern and western edges of the Blackwood Plateau, there are also smaller areas where large amounts of recharge can occur. www.hydrol-earth-syst-sci.net/16/2709/2012/ Hydrol. Earth Syst. Sci., 16, 2709–2722, 2012","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Margaret River climate zone, Western Australia","kind":"region","role":"Small but significant recharge contributor in the southern Perth Basin with sandy soil profiles","river_name":null,"geocode_query":"Margaret River, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"SWAMS has less recharge overall than PRAMS, and this is mainly due to the large areas in the region with low conductivity surface soils and deep- rooted native vegetation away from the coastal dune systems. There are few areas in SWAMS with large positive modelled recharge values. These do include some of the southern Scott Coastal Plain areas, and the intake area for the outcropping Yarragadee Aquifer. Both areas have highly transmissive sandy soils and capacity within the aquifer for recharge to enter. Along the northern and western edges of the Blackwood Plateau, there are also smaller areas where large amounts of recharge can occur. www.hydrol-earth-syst-sci.net/16/2709/2012/ Hydrol. Earth Syst. Sci., 16, 2709–2722, 2012","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.062967,-34.013547],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417189,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Margaret River WA 6285, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-8","true_coordinates":[115.0629667,-33.9535468],"spread_km":6.7},{"name":"Busselton climate zone, Western Australia","kind":"region","role":"Southern Perth Basin area with less affected recharge, may see recharge increase under some land use changes","river_name":null,"geocode_query":"Busselton, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-003","page":9,"quote":"In previous field and modelling studies, the recharge under plantation pines has been estimated at close to zero, while under less dense native scrub, it may be more than 100 mm yr−1 (DOW, 2009; Silberstein et al., 2005). The modelling predictions suggest that, as rainfall increases under wet scenario by 1.7 percent, recharge increases by 11 percent; this is a greater change in both absolute and percentage terms. Under the median scenario, rainfall is projected to reduce by 4.8 percent and recharge by 5 percent, which is a smaller change in absolute recharge. Had future land use included expansion, rather than reduction, of the area of pine plantation we might expect the effect of recharge reduction due to rainfall decline to be amplified. In the Busselton climate zone, human activities that increase the soil water deficit act to increase net recharge.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.3473335,-33.6516465],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412695,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Busselton WA 6280, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-9"},{"name":"Geraldton Airport climate zone, Western Australia","kind":"region","role":"Northern Perth Basin climate station area used for WAVES recharge modelling and climate scenario application","river_name":null,"geocode_query":"Geraldton Airport, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Busselton with the smallest contribution is the least affected by future changes, but with the greatest capacity for future development will allow rainfall that was previously rejected due to saturated surface conditions, to become recharge under development. For the overall SWAMS region, the simulated decline in average recharge is up to 50 percent. 2.2.3 Recharge in northern Perth Basin (WAVES Model) In NPB the WAVES model was run for various land covers on the dominant soil type within the Geraldton Airport and Three Springs climate zones. The recharge predictions for the future climate scenarios are summarised in Table 5. The entries of negative recharge are an artefact of running the WAVES model with a fixed groundwater lower boundary condition in keeping with PRAMS and SWAMS. However, the result is consistent with the work of Bekele et al. (2006) who did not encounter water tables below most areas of native vegetation in the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.7023672,-28.7960639],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":80910291,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Geraldton Airport, Geraldton-Mount Magnet Rd, Moonyoonooka WA 6532, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-10"},{"name":"Three Springs climate zone, Western Australia","kind":"region","role":"Northern Perth Basin climate station area showing potential near-zero recharge under drier future scenarios","river_name":null,"geocode_query":"Three Springs, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Busselton with the smallest contribution is the least affected by future changes, but with the greatest capacity for future development will allow rainfall that was previously rejected due to saturated surface conditions, to become recharge under development. For the overall SWAMS region, the simulated decline in average recharge is up to 50 percent. 2.2.3 Recharge in northern Perth Basin (WAVES Model) In NPB the WAVES model was run for various land covers on the dominant soil type within the Geraldton Airport and Three Springs climate zones. The recharge predictions for the future climate scenarios are summarised in Table 5. The entries of negative recharge are an artefact of running the WAVES model with a fixed groundwater lower boundary condition in keeping with PRAMS and SWAMS. However, the result is consistent with the work of Bekele et al. (2006) who did not encounter water tables below most areas of native vegetation in the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.761719,-29.5331741],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412865,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Three Springs WA 6519, Australia","basin_id":5040087740,"main_basin_id":5040087740,"basin_region":"Australasia","basin_area_km2":65879.0,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-11"},{"name":"Jarrahdale climate zone, Western Australia","kind":"region","role":"Central Perth Basin zone with high clay soils sensitive to rainfall reductions causing recharge declines","river_name":null,"geocode_query":"Jarrahdale, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"In the Wanneroo climate zone, there is significant land use change, mainly the legislated removal of pine plantations under which it is estimated there is close to zero recharge (Sharma and Pionke, 1984; DOW, 2009). In this zone, 41 percent of rainfall becomes modelled recharge due to a combination of the removal of pines, large areas of market gardens that have extensive sprinkler irrigation, and the presence of urban areas. The phenomenon when recharge increases due to land-use change effects are greater than rainfall decline due to climate change has been reported by Favreau et al. (2009) in Niger, Africa, and occurs again in this study in the NPB. In the Jarrahdale climate zone, there are the largest areas of high clay content, low conductivity soil types and deep-rooted perennial vegetation. In this climate zone, 13 percent of rainfall becomes recharge despite the average rainfall of 1052 mm under the historical climate.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.0611059,-32.3368026],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417213,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jarrahdale WA 6124, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-12"},{"name":"Donnybrook climate zone, Western Australia","kind":"region","role":"Southern Perth Basin climate zone with significant recharge declines under dry future scenarios","river_name":null,"geocode_query":"Donnybrook, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Scenario A is historical 1975–2007, B is historical 1997–2007, Cdry is historical 1984–2006 modified by INMCM +1.3◦C, Cmid is historical 1984–2006 modified by MIROC +1.0◦C, Cwet is historical 1984–2006 modified by GFDL +1.3◦C. The contribution to overall recharge from the Donnybrook climate zone, in which much of the low conductivity soil and regolith appears, is affected greatest by simulated future climates. Here the contribution drops by up to 59 percent under dry as rainfall declines by 18 percent. With drier future climate sequences, the recharge across much of the Blackwood Plateau is close to zero, and with large areas of native vegetation it may become a net discharge area. It is of interest that the much smaller Margaret River climate zone makes the largest contribution to the regions recharge under all future climate scenarios.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.8275492,-33.5797259],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Donnybrook WA 6239, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-87eb54ae86b0d69b22bab746-site-13"}],"limitations":["Broad regional scale of climate zones limits fine spatial resolution.","Recharge modelling in some areas based on 1D unsaturated model without full groundwater linkage.","Future climate scenarios depend on GCM outputs with inherent uncertainties.","Land cover change assumptions limited to known/planned changes; actual future changes may differ.","Exact coordinates for many locations not provided in source text."],"river_ids":[],"site_ids":["doc-87eb54ae86b0d69b22bab746-site-0","doc-87eb54ae86b0d69b22bab746-site-1","doc-87eb54ae86b0d69b22bab746-site-2","doc-87eb54ae86b0d69b22bab746-site-3","doc-87eb54ae86b0d69b22bab746-site-4","doc-87eb54ae86b0d69b22bab746-site-5","doc-87eb54ae86b0d69b22bab746-site-6","doc-87eb54ae86b0d69b22bab746-site-8","doc-87eb54ae86b0d69b22bab746-site-9","doc-87eb54ae86b0d69b22bab746-site-10","doc-87eb54ae86b0d69b22bab746-site-11","doc-87eb54ae86b0d69b22bab746-site-12","doc-87eb54ae86b0d69b22bab746-site-13"],"anchor":[115.784337,-31.959938],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040055960,5040056460,5040056470,5040076890,5040087740],"main_basin_ids":[5040055960,5040056460,5040056470,5040076890,5040087740]},{"id":"doc-ba70c10507a8a3be80cef9ba","title":"Deep drainage estimates using multiple linear regression with percent clay content and rainfall","display_title":"Deep Drainage Predictors in Murray Darling Basin","doi":"https://doi.org/10.5194/hess-16-563-2012","theme":"Groundwater","color":"#a8b9ff","year":2012,"publication_date":"2012-02-24","authors":["D. L. Wohling","F. W. Leaney","R. S. Crosbie"],"summary":"This study evaluates deep drainage rates, which are crucial for groundwater recharge management, using multiple linear regression models incorporating measured soil clay content (average over 0–2 m) and mean annual rainfall. Utilizing an extensive database of field recharge and deep drainage measurements mainly from the southwestern Murray Darling Basin (Mallee and Wimmera regions) as well as areas in New South Wales and Queensland, significant predictive relationships were found for annual vegetation and combined tree and perennial vegetation. The study quantifies the uncertainty of deep drainage estimates with 95% prediction intervals for the first time, improving reliability in managing groundwater recharge.","finding":"The average clay content of the top 2 meters of soil combined with mean annual rainfall are significant predictors of point-scale deep drainage, with the best correlations found for the average clay content (0–2 m) over other soil clay metrics.","finding_evidence":{"passage_id":"p005-004","page":5,"quote":"However when we investigated the statistical fit more thoroughly we found that even though there was an overall significance to the MLR for average clay content (0–1 m), (Significant F = 2.17E-12), there was no correlation between log deep drainage and clay (Pvalue = 0.837). It was similarly true for the average clay content (0–0.5 m) and highest clay content (0–2 m) MLR results. Despite having a lower degree of fit to the data (R 2 = 0.3521), the MLR statistical results for average clay content (0–2 m) showed a relationship was very likely to exist between average clay content (0–2 m), rainfall and log deep drainage (Significant F = 3.99E-17). Moreover, there was evidence for correlations between log deep drainage and clay (P-value = 3.7E-05); and log deep drainage and rainfall (P-value = 5.68E-18). From the evidence provided in Table 1, we conclude that the best metric of clay content to use as a surrogate for determining deep drainage is the average between zero and two metres.","verified_in_page":true},"topics":["groundwater recharge","soil texture","rainfall variability","multiple linear regression","deep drainage estimation","statistical modelling","uncertainty quantification","land use effects"],"study_period":null,"locations":[{"name":"Mallee region, south-west Murray Darling Basin, Victoria, Australia","kind":"region","role":"primary study region, extensive field data collected for deep drainage estimation","river_name":null,"geocode_query":"Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Kennett-Smith et al. (1994) used the chloride front displacement (CFD), water balance, and water table fluctuation (WTF) methods to determine deep drainage and recharge associated with the clearing of Eucalyptus mallee woodland (in the form of shrub land, scrub or heath) between 1900–1914 and 1920–1930 for rotational cereal cropping, sheep and beef cattle grazing. Leaney and Herczeg (1999) and Cook et al. (2001) studied the Mallee region of the south-west Murray Darling Basin including the Little Desert and Big Desert using soil-water chloride concentration profiles, groundwater chloride, stable isotopes of water and carbon-14 to determine recharge processes and provide deep drainage estimates beneath Mallee vegetation and cleared pastures and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.587375,-31.170638],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Wimmera region, south-west Murray Darling Basin, Victoria, Australia","kind":"region","role":"primary study region, field recharge estimates and clay content data source","river_name":null,"geocode_query":"Wimmera region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Kennett-Smith et al. (1994) used the chloride front displacement (CFD), water balance, and water table fluctuation (WTF) methods to determine deep drainage and recharge associated with the clearing of Eucalyptus mallee woodland (in the form of shrub land, scrub or heath) between 1900–1914 and 1920–1930 for rotational cereal cropping, sheep and beef cattle grazing. Leaney and Herczeg (1999) and Cook et al. (2001) studied the Mallee region of the south-west Murray Darling Basin including the Little Desert and Big Desert using soil-water chloride concentration profiles, groundwater chloride, stable isotopes of water and carbon-14 to determine recharge processes and provide deep drainage estimates beneath Mallee vegetation and cleared pastures and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.594038,-31.16076],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Tintinara area, South Australia","kind":"region","role":"field site for recharge and deep drainage studies using chloride front displacement and other methods","river_name":null,"geocode_query":"Tintinara, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Similarly, Leaney (2000) investigated recharge processes and provided deep drainage estimates for the Tintinara area of South Australia using the CFD and steady state soil chloride mass balance (CMB) methods for mallee vegetation and cleared dry land cropping and pastures. Cook et al. (2004) investigated deep drainage and aquifer recharge in the north-east Mallee region of South Australia and determined that clearing of native vegetation (Eucalyptus mallee) and replacement with dryland cropping and pastures lead to increased salt loads to the Murray River. Cook et al. (2004) used the CFD method for estimating point estimates of deep drainage and developed equations for estimating the time lag between an increase in deep drainage and increase in aquifer recharge. They then spatially extrapolated the data to provide a deep drainage map and predicted time delays for transport through the unsaturated zone.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.0553914,-35.8831534],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556934,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tintinara SA 5266, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-2"},{"name":"Padthaway region, south-east South Australia","kind":"region","role":"field site for pasture deep drainage studies and soil texture data collection","river_name":null,"geocode_query":"Padthaway, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Wohling et al. (2005) used the CFD method to estimate deep drainage beneath pastures in the Padthaway region of south east South Australia. Leaney et al. (2006) used the CMB method under native vegetation, dry land pasture, and tree plantations in the lower south-east of South Australia. In the Border Designated Area and Keith region of southeast South Australia, Wohling (2007) used the water balance, daily soil water balance, CMB, CFD and LEACHM (Leaching Estimation and Chemistry Model, Hutson 2003) to determine recharge and deep drainage rates beneath native vegetation, pastures and crops. Unpublished data used in Wohling (2009) followed on from the work of Wohling et al. (2005) to estimate additional deep drainage rates under pastures using the CFD in the Padthaway region of south-east South Australia. Tolmie et al. (2004) used a transient CMB method in south-east Queensland to determine recharge beneath native vegetation, cropping and pastures, and perennial lucerne.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.4909548,-36.5973422],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556934,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Padthaway SA 5271, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-3"},{"name":"North-east Mallee region, South Australia","kind":"region","role":"investigated region for deep drainage and groundwater recharge studies","river_name":null,"geocode_query":"North-east Mallee region, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Similarly, Leaney (2000) investigated recharge processes and provided deep drainage estimates for the Tintinara area of South Australia using the CFD and steady state soil chloride mass balance (CMB) methods for mallee vegetation and cleared dry land cropping and pastures. Cook et al. (2004) investigated deep drainage and aquifer recharge in the north-east Mallee region of South Australia and determined that clearing of native vegetation (Eucalyptus mallee) and replacement with dryland cropping and pastures lead to increased salt loads to the Murray River. Cook et al. (2004) used the CFD method for estimating point estimates of deep drainage and developed equations for estimating the time lag between an increase in deep drainage and increase in aquifer recharge. They then spatially extrapolated the data to provide a deep drainage map and predicted time delays for transport through the unsaturated zone.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.279703,-34.823791],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Mallee, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-4","true_coordinates":[142.2797032,-34.7637906],"spread_km":6.7},{"name":"Border Designated Area and Keith region, south-east South Australia","kind":"region","role":"study area for multi-method deep drainage and recharge estimation including soil texture data","river_name":null,"geocode_query":"Keith, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Wohling et al. (2005) used the CFD method to estimate deep drainage beneath pastures in the Padthaway region of south east South Australia. Leaney et al. (2006) used the CMB method under native vegetation, dry land pasture, and tree plantations in the lower south-east of South Australia. In the Border Designated Area and Keith region of southeast South Australia, Wohling (2007) used the water balance, daily soil water balance, CMB, CFD and LEACHM (Leaching Estimation and Chemistry Model, Hutson 2003) to determine recharge and deep drainage rates beneath native vegetation, pastures and crops. Unpublished data used in Wohling (2009) followed on from the work of Wohling et al. (2005) to estimate additional deep drainage rates under pastures using the CFD in the Padthaway region of south-east South Australia. Tolmie et al. (2004) used a transient CMB method in south-east Queensland to determine recharge beneath native vegetation, cropping and pastures, and perennial lucerne.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.353141,-36.0993321],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556934,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Keith SA 5267, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-5"},{"name":"Tomago Sand Beds near Newcastle, New South Wales, Australia","kind":"region","role":"site of groundwater recharge estimation under perennial vegetation using water table fluctuation method","river_name":null,"geocode_query":"Newcastle, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Crosbie et al. (2005) investigated recharge in the Tomago Sand Beds near Newcastle, NSW. The WTF method was used to predict gross recharge under perennial vegetation in heath and mining revegetation areas. 2.2 Statistical methods Regression analysis explains movements in the dependant variable (Y ) as a function of the independent variables (X) through the quantification of an equation. The simplest measure of the quality of fit is the coefficient of determination (R 2 ), which is the proportion of variation of Y about its mean (Y ) that is explained by the model. The higher the R 2 the closer the estimated regression fits the sample data. The level of significance (significant F) tests whether the hypothesis, that the data forms a relationship, is null, specifically testing whether the data has no relationship. If the hypothesis is null it can be concluded that the equation does have a significant overall fit.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.78168,-32.988271],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26967317,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Newcastle NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-ba70c10507a8a3be80cef9ba-site-6","true_coordinates":[151.7816802,-32.9282712],"spread_km":6.7},{"name":"south-eastern Queensland, Australia","kind":"region","role":"location of studies determining deep drainage beneath native vegetation, cropping, and perennial pasture","river_name":null,"geocode_query":"south-eastern Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Crosbie et al. (2005) investigated recharge in the Tomago Sand Beds near Newcastle, NSW. The WTF method was used to predict gross recharge under perennial vegetation in heath and mining revegetation areas. 2.2 Statistical methods Regression analysis explains movements in the dependant variable (Y ) as a function of the independent variables (X) through the quantification of an equation. The simplest measure of the quality of fit is the coefficient of determination (R 2 ), which is the proportion of variation of Y about its mean (Y ) that is explained by the model. The higher the R 2 the closer the estimated regression fits the sample data. The level of significance (significant F) tests whether the hypothesis, that the data forms a relationship, is null, specifically testing whether the data has no relationship. If the hypothesis is null it can be concluded that the equation does have a significant overall fit.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Data is strongly biased towards southwestern Murray Darling Basin with limited data from other regions.","Soil clay content data was available for only 202 of 4386 recharge/deep drainage estimates (~5%), limiting spatial coverage.","Latitude and longitude coordinates for field sites not provided; regional names only.","Uncertainty quantifications pertain to point-scale measurements; upscaling introduces additional uncertainties.","Some clay content and rainfall data for perennial vegetation are 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040070710,5040591010,5040591050],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-d22bef3d1e7a3541c087a1c4","title":"Sensitivity of groundwater recharge using climatic analogues and HYDRUS-1D","display_title":"Groundwater Recharge Sensitivity Using Climatic Analogues","doi":"https://doi.org/10.5194/hess-16-2485-2012","theme":"Groundwater","color":"#a8b9ff","year":2012,"publication_date":"2012-08-06","authors":["B. Leterme","D. Mallants","D. Jacques"],"summary":"This study assesses how groundwater recharge near a proposed radioactive waste disposal facility in Dessel, Belgium, responds to future climate changes over millennia using climatic analogue stations and HYDRUS-1D soil water balance modeling. It evaluates recharge for warmer subtropical and colder tundra climates by simulating with meteorological data from analogue stations representing these conditions, considering soil profiles and groundwater depths reflective of the site.","finding":"Groundwater recharge near Dessel generally decreases under warmer subtropical analogue climates, except for the Gijon (Northern Spain) analogue station which shows a slight increase of 3 % recharge with a 5 % precipitation increase. Climates analogous to colder tundra conditions can reduce recharge by up to 85 % (e.g., Sisimiut, Greenland).","finding_evidence":{"passage_id":"p010-011","page":10,"quote":"However, these soils are mostly buried or truncated soils (i.e. soil development could not continue) while unburied podzols could further develop in the next few thousands of years. 4 Conclusions Groundwater recharge response to transition to a warmer, subtropical climate was studied using HYDRUS-1D and the approach of climatic analogue stations. Although no optimal choice of analogue stations may exist due to different weather circulation systems, this approach allows including observed variations of all meteorological parameters in a transparent and straightforward way. The results showed a decrease of groundwater recharge in the vicinity of the Dessel disposal site (typical podzol with a grass cover) for all analogue stations except Gijon. Based on projected temperature and precipitation increases the Gijon station is considered a good representative analogue for Dessel for the next 10 000 yr. For that station, groundwater recharge increases slightly with +3 %.","verified_in_page":true},"topics":["groundwater recharge","climate change impact","soil water balance","hydrological modelling","climatic analogues","subtropical climate","tundra climate","long-term hydrology"],"study_period":"1984–2009 for climate data; projection up to 10,000 years","locations":[{"name":"Dessel site","kind":"settlement","role":"Study site for groundwater recharge assessment","river_name":null,"geocode_query":"Dessel, Flanders, Belgium","latitude":51.2167,"longitude":5.1167,"evidence":{"passage_id":"p001-001","page":1,"quote":"The study was conducted in the context of a safety assessment of a future near-surface disposal facility for low and intermediate level short-lived radioactive waste in Belgium; this includes estimation of groundwater recharge for the next millennia. Groundwater recharge was simulated using the Richards based soil water balance model HYDRUS1D and meteorological time series from analogue stations. This study used four analogue stations for a warmer subtropical climate with changes of average annual precipitation and potential evapotranspiration from −42 % to +5 % and from +8 % to +82 %, respectively, compared to the present-day climate. Resulting water balance calculations yielded a change in groundwater recharge ranging from a decrease of 72 % to an increase of 3 % for the four different analogue stations.","verified_in_page":true},"resolution_status":"resolved","coordinates":[5.112921,51.179217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2480 Dessel, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-0","true_coordinates":[5.1129213,51.2392168],"spread_km":6.7},{"name":"Gijon analogue station","kind":"station","role":"Representative analogue station for projected near future subtropical climate affecting Dessel","river_name":null,"geocode_query":"Gijon, Asturias, Spain","latitude":43.5333,"longitude":-5.65,"evidence":{"passage_id":"p001-002","page":1,"quote":"The Gijon analogue station (Northern Spain), considered as the most representative for the near future climate state in the study area, shows an increase of 3 % of groundwater recharge for a 5 % increase of annual precipitation. Calculations for a colder (tundra) climate showed a change in groundwater recharge ranging from a decrease of 97 % to an increase of 32 % for four different analogue stations, with an annual precipitation change from −69 % to −14 % compared to the present-day climate. 1 Introduction In the context of disposal of low and intermediate level shortlived radioactive waste (LILW-SL), the Belgian Agency for Radioactive Waste and Enriched Fissile Materials (ONDRAF/NIRAS) aims at developing a disposal facility in Dessel, northeast Belgium. Demonstrating safety of the repository requires investigating the short and long-term impact of the evolution of environmental conditions on the performance of the facility.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-5.660973299999999,43.5322493],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gijón, Asturias, Spain","basin_id":2040019160,"main_basin_id":2040019160,"basin_region":"Europe and Middle East","basin_area_km2":60291.6,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-1"},{"name":"Ourense analogue station","kind":"station","role":"Bounding analogue station for warmer climate scenario with precipitation variability","river_name":null,"geocode_query":"Ourense, Galicia, Spain","latitude":42.34,"longitude":-7.87,"evidence":{"passage_id":"p004-000","page":4,"quote":"2488 B. Leterme et al.: Groundwater recharge using climatic analogues Table 1. Characteristics of selected analogue stations for different climate classes (DO = temperate oceanic, Cs/Cr = subtropical with/without rainfall seasonality, FT = tundra (with permafrost)). The Dessel site is the reference station for current climate. Cadiz is the station with the ́ smallest rank deviation from Cs class average temperature (16.8 ◦C) and precipitation (607 mm yr−1 ). Ourense and Huelva were selected as bounding cases for the precipitation. For the Cr class, sufficient input data were found only for Gijon. Sisimiut and Nuuk are the stations with the smallest rank deviations from FT class average temperature (−2.0 ◦C) and precipitation (571 mm yr−1 ). Paamiut and Ilulissat were selected as bounding cases for the precipitation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-7.863880999999998,42.33578929999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ourense, Province of Ourense, Spain","basin_id":2040019160,"main_basin_id":2040019160,"basin_region":"Europe and Middle East","basin_area_km2":60291.6,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-2"},{"name":"Cadiz analogue station","kind":"station","role":"Bounding analogue station for warmer climate scenario with low precipitation","river_name":null,"geocode_query":"Cadiz, Andalusia, Spain","latitude":36.75,"longitude":-6.2,"evidence":{"passage_id":"p004-001","page":4,"quote":"Climate Mean annual Mean annual Altitude Distance (km) from class Station Latitude and longitude temperature precipitation (m a.s.l.) humidity source ( ◦C) (mm yr−1 ) (shoreline) DO Dessel, Belgium 51◦130 N, 05◦060 E 10.3 899 20 120 Cs Ourense, Spain 42◦200 N, 07◦520 W 14.5 807 143 90 Cs Cadiz, Spain 36 ́ ◦450 N, 06◦040 W 17.7 536 27 0 Cs Huelva, Spain 37◦170 N, 06◦550 W 18.1 479 19 0 Cr Gijon, Spain 43◦320 N, 05◦390 W 13.8 947 3 0 FT Nuuk, Greenland 64◦100 N, 51◦450 W −1.4 740 54 / 80 0 FT Ilulissat, Greenland 69◦130 N, 51◦030 W −5.0 268 39 0 FT Sisimiut, Greenland 66◦550 N, 53◦400 W −3.9 319 12 0 FT Paamiut, Greenland 62◦000 N, 49◦430 W −0.8 794 15 0 HYDRUS-1D was used, for which the discharge rate q(n) at the bottom of the soil profile at node n is defined as a function of the position of the groundwater table (Hopmans and Stricker, 1989): q(n) = q(h) = Aqh exp(Bqh × |h − GWL0L|) (1) where q(h) [cm day−1 ] is the discharge rate, h [cm] is the pressure head at the bottom of the soil profile, Aqh [cm day−1 ] and Bqh [cm−1 ] are empirical parameters and GWL0L [cm] is the reference groundwater depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-6.2804565,36.5210142],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cádiz, Spain","basin_id":2040018240,"main_basin_id":2040018240,"basin_region":"Europe and Middle East","basin_area_km2":5869.8,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-3"},{"name":"Huelva analogue station","kind":"station","role":"Bounding analogue station for warmer climate scenario with low precipitation","river_name":null,"geocode_query":"Huelva, Andalusia, Spain","latitude":37.2614,"longitude":-6.9447,"evidence":{"passage_id":"p004-001","page":4,"quote":"Climate Mean annual Mean annual Altitude Distance (km) from class Station Latitude and longitude temperature precipitation (m a.s.l.) humidity source ( ◦C) (mm yr−1 ) (shoreline) DO Dessel, Belgium 51◦130 N, 05◦060 E 10.3 899 20 120 Cs Ourense, Spain 42◦200 N, 07◦520 W 14.5 807 143 90 Cs Cadiz, Spain 36 ́ ◦450 N, 06◦040 W 17.7 536 27 0 Cs Huelva, Spain 37◦170 N, 06◦550 W 18.1 479 19 0 Cr Gijon, Spain 43◦320 N, 05◦390 W 13.8 947 3 0 FT Nuuk, Greenland 64◦100 N, 51◦450 W −1.4 740 54 / 80 0 FT Ilulissat, Greenland 69◦130 N, 51◦030 W −5.0 268 39 0 FT Sisimiut, Greenland 66◦550 N, 53◦400 W −3.9 319 12 0 FT Paamiut, Greenland 62◦000 N, 49◦430 W −0.8 794 15 0 HYDRUS-1D was used, for which the discharge rate q(n) at the bottom of the soil profile at node n is defined as a function of the position of the groundwater table (Hopmans and Stricker, 1989): q(n) = q(h) = Aqh exp(Bqh × |h − GWL0L|) (1) where q(h) [cm day−1 ] is the discharge rate, h [cm] is the pressure head at the bottom of the soil profile, Aqh [cm day−1 ] and Bqh [cm−1 ] are empirical parameters and GWL0L [cm] is the reference groundwater depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-6.9427327,37.2619585],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Huelva, Spain","basin_id":2040018370,"main_basin_id":2040018370,"basin_region":"Europe and Middle East","basin_area_km2":5061.3,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-4"},{"name":"Sisimiut analogue station","kind":"station","role":"Representative analogue station for colder tundra climate showing a large decrease in groundwater recharge","river_name":null,"geocode_query":"Sisimiut, Qeqqata, Greenland","latitude":66.5667,"longitude":-53.4,"evidence":{"passage_id":"p010-012","page":10,"quote":"Other analogue stations are considered as bounding cases accounting for uncertainty about climate time evolution. Although no colder climate is foreseen in the next 10 000 yr, the approach was also tested with analogue stations for a colder climate state. Groundwater recharge simulated for the representative analogue Sisimiut showed a decrease by 85 %. Hydrol. Earth Syst. Sci., 16, 2485–2497, 2012 www.hydrol-earth-syst-sci.net/16/2485/2012/","verified_in_page":true},"resolution_status":"resolved","coordinates":[-53.6631554,66.9394119],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sisimiut 3911, Greenland","hydrology_source":"none","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-5"},{"name":"Ilulissat analogue station","kind":"station","role":"Analogue station for colder tundra climate with low annual precipitation and recharge","river_name":null,"geocode_query":"Ilulissat, Qaasuitsup, Greenland","latitude":69.2167,"longitude":-51.05,"evidence":{"passage_id":"p004-001","page":4,"quote":"Climate Mean annual Mean annual Altitude Distance (km) from class Station Latitude and longitude temperature precipitation (m a.s.l.) humidity source ( ◦C) (mm yr−1 ) (shoreline) DO Dessel, Belgium 51◦130 N, 05◦060 E 10.3 899 20 120 Cs Ourense, Spain 42◦200 N, 07◦520 W 14.5 807 143 90 Cs Cadiz, Spain 36 ́ ◦450 N, 06◦040 W 17.7 536 27 0 Cs Huelva, Spain 37◦170 N, 06◦550 W 18.1 479 19 0 Cr Gijon, Spain 43◦320 N, 05◦390 W 13.8 947 3 0 FT Nuuk, Greenland 64◦100 N, 51◦450 W −1.4 740 54 / 80 0 FT Ilulissat, Greenland 69◦130 N, 51◦030 W −5.0 268 39 0 FT Sisimiut, Greenland 66◦550 N, 53◦400 W −3.9 319 12 0 FT Paamiut, Greenland 62◦000 N, 49◦430 W −0.8 794 15 0 HYDRUS-1D was used, for which the discharge rate q(n) at the bottom of the soil profile at node n is defined as a function of the position of the groundwater table (Hopmans and Stricker, 1989): q(n) = q(h) = Aqh exp(Bqh × |h − GWL0L|) (1) where q(h) [cm day−1 ] is the discharge rate, h [cm] is the pressure head at the bottom of the soil profile, Aqh [cm day−1 ] and Bqh [cm−1 ] are empirical parameters and GWL0L [cm] is the reference groundwater depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-51.0986032,69.2198118],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ilulissat, Greenland","basin_id":9040010430,"main_basin_id":9040010430,"basin_region":"Greenland","basin_area_km2":7071.9,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-6"},{"name":"Nuuk analogue station","kind":"station","role":"Analogue station for colder tundra climate with higher recharge due to low evapotranspiration","river_name":null,"geocode_query":"Nuuk, Sermersooq, Greenland","latitude":64.1833,"longitude":-51.75,"evidence":{"passage_id":"p004-001","page":4,"quote":"Climate Mean annual Mean annual Altitude Distance (km) from class Station Latitude and longitude temperature precipitation (m a.s.l.) humidity source ( ◦C) (mm yr−1 ) (shoreline) DO Dessel, Belgium 51◦130 N, 05◦060 E 10.3 899 20 120 Cs Ourense, Spain 42◦200 N, 07◦520 W 14.5 807 143 90 Cs Cadiz, Spain 36 ́ ◦450 N, 06◦040 W 17.7 536 27 0 Cs Huelva, Spain 37◦170 N, 06◦550 W 18.1 479 19 0 Cr Gijon, Spain 43◦320 N, 05◦390 W 13.8 947 3 0 FT Nuuk, Greenland 64◦100 N, 51◦450 W −1.4 740 54 / 80 0 FT Ilulissat, Greenland 69◦130 N, 51◦030 W −5.0 268 39 0 FT Sisimiut, Greenland 66◦550 N, 53◦400 W −3.9 319 12 0 FT Paamiut, Greenland 62◦000 N, 49◦430 W −0.8 794 15 0 HYDRUS-1D was used, for which the discharge rate q(n) at the bottom of the soil profile at node n is defined as a function of the position of the groundwater table (Hopmans and Stricker, 1989): q(n) = q(h) = Aqh exp(Bqh × |h − GWL0L|) (1) where q(h) [cm day−1 ] is the discharge rate, h [cm] is the pressure head at the bottom of the soil profile, Aqh [cm day−1 ] and Bqh [cm−1 ] are empirical parameters and GWL0L [cm] is the reference groundwater depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-51.7372787,64.17432339999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nuuk, Greenland","hydrology_source":"none","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-7"},{"name":"Paamiut analogue station","kind":"station","role":"Analogue station for colder tundra climate with higher recharge due to low evapotranspiration","river_name":null,"geocode_query":"Paamiut, Sermersooq, Greenland","latitude":61.9969,"longitude":-49.4333,"evidence":{"passage_id":"p004-001","page":4,"quote":"Climate Mean annual Mean annual Altitude Distance (km) from class Station Latitude and longitude temperature precipitation (m a.s.l.) humidity source ( ◦C) (mm yr−1 ) (shoreline) DO Dessel, Belgium 51◦130 N, 05◦060 E 10.3 899 20 120 Cs Ourense, Spain 42◦200 N, 07◦520 W 14.5 807 143 90 Cs Cadiz, Spain 36 ́ ◦450 N, 06◦040 W 17.7 536 27 0 Cs Huelva, Spain 37◦170 N, 06◦550 W 18.1 479 19 0 Cr Gijon, Spain 43◦320 N, 05◦390 W 13.8 947 3 0 FT Nuuk, Greenland 64◦100 N, 51◦450 W −1.4 740 54 / 80 0 FT Ilulissat, Greenland 69◦130 N, 51◦030 W −5.0 268 39 0 FT Sisimiut, Greenland 66◦550 N, 53◦400 W −3.9 319 12 0 FT Paamiut, Greenland 62◦000 N, 49◦430 W −0.8 794 15 0 HYDRUS-1D was used, for which the discharge rate q(n) at the bottom of the soil profile at node n is defined as a function of the position of the groundwater table (Hopmans and Stricker, 1989): q(n) = q(h) = Aqh exp(Bqh × |h − GWL0L|) (1) where q(h) [cm day−1 ] is the discharge rate, h [cm] is the pressure head at the bottom of the soil profile, Aqh [cm day−1 ] and Bqh [cm−1 ] are empirical parameters and GWL0L [cm] is the reference groundwater depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-49.6677591,61.9938596],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Paamiut, Greenland","basin_id":9040005890,"main_basin_id":9040005890,"basin_region":"Greenland","basin_area_km2":154297.2,"hydrology_source":"hydrobasins","site_id":"doc-d22bef3d1e7a3541c087a1c4-site-8"}],"limitations":["Use of climatic analogue stations restricts representation to locations with available instrumental data, possibly missing some local influences.","Unsaturated zone model (HYDRUS-1D) is uncoupled from groundwater flow model, limiting dynamic groundwater table response.","Soil freezing and thawing processes were not simulated, possibly overestimating recharge in tundra analogues.","Deep drainage bottom boundary condition used may not accurately reflect actual groundwater dynamics.","Vegetation and land use feedbacks to climate change (e.g., CO2 effects on stomatal resistance) were not mechanistically modeled but only considered in sensitivity analysis.","No validation with observed future data—results are model-based projections only."],"river_ids":[],"site_ids":["doc-d22bef3d1e7a3541c087a1c4-site-0","doc-d22bef3d1e7a3541c087a1c4-site-1","doc-d22bef3d1e7a3541c087a1c4-site-2","doc-d22bef3d1e7a3541c087a1c4-site-3","doc-d22bef3d1e7a3541c087a1c4-site-4","doc-d22bef3d1e7a3541c087a1c4-site-5","doc-d22bef3d1e7a3541c087a1c4-site-6","doc-d22bef3d1e7a3541c087a1c4-site-7","doc-d22bef3d1e7a3541c087a1c4-site-8"],"anchor":[-7.863880999999998,42.33578929999999],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[2040018240,2040018370,2040019160,2040022160,9040005890,9040010430],"main_basin_ids":[2040018240,2040018370,2040019160,2040022160,9040005890,9040010430]},{"id":"doc-08d279d5edf87966f25e6c27","title":"The usefulness of outcrop-analogue air-permeameter measurements for analysing aquifer heterogeneity: testing outcrop hydrogeological parameters with independent borehole data","display_title":"Outcrop Permeameter Analysis of Belgian Neogene Aquifer","doi":"https://doi.org/10.5194/hess-17-5155-2013","theme":"Groundwater","color":"#a8b9ff","year":2013,"publication_date":"2013-12-18","authors":["B. Rogiers","K. Beerten","T. Smeekens","D. Mallants","M. Gedeon","M. Huysmans","O. Batelaan","A. Dassargues"],"summary":"This study evaluates the use of air-permeameter measurements on outcrops of Neogene unconsolidated sediments in northern Belgium as analogues to quantify aquifer heterogeneity and saturated hydraulic conductivity (K). The outcrop K values and grain-size distributions are compared with laboratory analyses on borehole cores and large-scale pumping tests. Systematic differences, attributed to weathering, bioturbation, clay migration and compaction differences, necessitate linear rescaling of outcrop data to represent subsurface conditions. Spatial variability from outcrop data complements borehole data, providing enhanced insight for groundwater modeling.","finding":"Linear rescaling can effectively correct systematic overestimations of hydraulic conductivity from outcrop air-permeameter measurements compared to borehole core data, enabling the use of outcrop measurements to characterize aquifer heterogeneity and improve groundwater flow models.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"Therefore, differences in K distributions between outcrop and aquifer sediments can be attributed to the scale of investigation (a single outcrop with a typical measurement grid of a few m2 vs seven ∼ 50 m-deep vertical transects through the different lithostratigraphical formations, Fig. 1), different evolutionary states of the outcropping and subsurface sediments, and possibly different sedimentation conditions. 3.3 Linear rescaling correction To investigate the (dis)similarities between the outcrop and borehole data across these five lithological units, the minimum and maximum values are plotted in Fig. 4, with all deciles (10th, 20th, . . . , 90th percentile) in between. This shows that linear scaling of the outcrop values to the corresponding borehole distributions is possible for all outcrops. The extreme values are however not always in line with the centre of the distributions (as indicated by the deviation of the overall shape of the first and last line segments).","verified_in_page":true},"topics":["hydraulic conductivity heterogeneity","groundwater flow modeling","aquifer heterogeneity","scale effects in hydraulic conductivity","grain-size analysis","spatial variability","hydrogeological parameter measurement","outcrop analogues"],"study_period":null,"locations":[{"name":"Mol/Dessel area","kind":"region","role":"main study region with boreholes and aquifer sediment characterization","river_name":null,"geocode_query":"Mol Dessel Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This methodology was tested on five outcrops from three key formations of the Neogene aquifer in north-eastern Belgium (from top to bottom): the Mol formation (the abbreviation Fm. will be used in the subsequent discussions), sandy and clayey parts of the Kasterlee Fm., and the clayey and sandy parts of the Diest Fm. For these five formations additional geological and hydrogeological data is available from a recent characterization campaign (Beerten et al. 2010) of the shallow aquifer sediments in Mol/Dessel (up to about 40 m depth), including seven cored boreholes (Fig. 2 in Rogiers et al., 2013a). This lithostratigraphical succession and its main characteristics are presented in Fig. 1. Apart from the minimum and maximum unit thickness obtained from this recent characterization campaign, a typical borehole core from the clayey Kasterlee Fm. is displayed, as well as a grain-size and glauconite content profile through most of the units.","verified_in_page":true},"resolution_status":"resolved","coordinates":[5.195918,51.269217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2480 Dessel, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-08d279d5edf87966f25e6c27-site-0","true_coordinates":[5.1129213,51.2392168],"spread_km":6.7},{"name":"Heist-op-den-Berg","kind":"settlement","role":"outcrop location for clayey Kasterlee Formation used as surrogate for aquitard sediments","river_name":null,"geocode_query":"Heist-op-den-Berg Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"The corresponding grain-size distributions for boreholes show no overlap between the clay and sand samples, an illustration of the existence of two distinctly different materials within the clayey part of the Kasterlee Fm. (i.e. heavy clay lenses embedded in coarse sands characterized by a sharp interface) (Beerten et al., 2010). In conclusion, weathering, clay migration, and bioturbation may have influenced the lower end of the outcrop samples’ grain-size distribution considerably. Furthermore, dissimilarities in palaeogeographic conditions and sediment source regions between the outcrop and borehole locations may equally explain such differences.","verified_in_page":true},"resolution_status":"resolved","coordinates":[4.7238688,51.0725819],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Heist-op-den-Berg, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-08d279d5edf87966f25e6c27-site-1"}],"limitations":["Limited number and size of studied outcrops restrict broader representativity","Incomplete spatial coordinates for study sites","Some outcrops located several tens of kilometres away from aquifer borehole sites possibly invoking variability","Complex stratigraphy results in depth vs compaction trends being hard to detect"],"river_ids":[],"site_ids":["doc-08d279d5edf87966f25e6c27-site-0","doc-08d279d5edf87966f25e6c27-site-1"],"anchor":[5.195918,51.269217],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040022160],"main_basin_ids":[2040022160]},{"id":"doc-10ca86633c661b1a69152122","title":"The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments","display_title":"Forest Regeneration Impact on Streamflow in Puerto Rico","doi":"https://doi.org/10.5194/hess-17-2613-2013","theme":"Land & ecosystems","color":"#c8df98","year":2013,"publication_date":"2013-07-09","authors":["H. E. Beck","L. A. Bruijnzeel","A. I. J. M. van Dijk","T. R. McVicar","F. N. Scatena","J. Schellekens"],"summary":"The study investigates the impact of secondary forest regeneration on streamflow characteristics in 12 humid tropical catchments in Puerto Rico ranging from 23 to 346 km², using long-term (33–51 years) simultaneous records of streamflow, precipitation, potential evaporation, and land cover. A conceptual rainfall-runoff model (HBV-light) was applied controlling for climate variability and carry-over effects. Despite large increases in forest cover, no significant relationships were found between forest or urban area changes and streamflow characteristics, suggesting complex catchment responses and highlighting data limitations. The work emphasizes that hydrological impacts of forest regeneration at the mesoscale are currently unpredictable and further detailed process studies are needed.","finding":"No significant relationship was found between changes in forest cover or urban area and changes in multiple streamflow characteristics across 12 mesoscale catchments in Puerto Rico, despite substantial forest regeneration over several decades.","finding_evidence":{"passage_id":"p011-005","page":11,"quote":"Clear decreases in the Q characteristic related to peak flows (Qp95) were found for catchments B, C, I, and J, whereas strong increases were found for catchments D, H, and L. 5.5 Impacts of land-cover change on streamflows Figure 7 shows regressions between the amount of urban and forest area change vs. the cumulative change in annual time series of D prior to 2000 (expressed by Aˆ in Eq. 5) for each Q characteristic. In spite of strong increases in forest and urban area, and pronounced changes in D over time for the Q characteristics of several catchments, all correlations were insignificant (p ≥ 0.389).","verified_in_page":true},"topics":["forest regeneration","streamflow","rainfall-runoff modeling","land-use change","tropical hydrology","climate variability","catchment hydrology","hydrological modeling"],"study_period":"1955–2000","locations":[{"name":"Puerto Rico","kind":"region","role":"main study area","river_name":null,"geocode_query":"Puerto Rico","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Indeed, as long-term Q records for large, once degraded catchment areas are becoming available, evidence of improved baseflows (Qbf) following large-scale land rehabilitation is beginning to be documented (Wilcox and Huang, 2010; Zhou et al., 2010). The tropical island of Puerto Rico provides a unique opportunity to study the impacts of natural forest regeneration on Q at the mesocatchment scale (1–10 000 km2 ), as highquality and long-term hydro-climatic records and sequential land-cover data are available. Since the 1950s, Puerto Rico has seen widespread secondary forest regrowth on abandoned pastures, agricultural land (mostly sugar cane), and coffee plantations (Thomlinson et al., 1996; Aide et al., 2000; cf. Del Mar Lopez et al. ́ , 1998). Although previous work on the relationship between land-cover change and Q using lumped meso- and macro-scale catchment data has experienced some difficulty demonstrating unequivocal results (e.g.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Tanamá catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Tanamá River","geocode_query":"Tanamá catchment, Puerto Rico","latitude":18.301,"longitude":-66.782,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.782,18.301],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-1"},{"name":"Bauta catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Bauta River","geocode_query":"Bauta catchment, Puerto Rico","latitude":18.236,"longitude":-66.455,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.455,18.236],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-2"},{"name":"Grande de Manatí catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Grande de Manatí River","geocode_query":"Grande de Manatí catchment, Puerto Rico","latitude":18.323,"longitude":-66.459,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.459,18.323],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-3"},{"name":"Cibuco catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Cibuco River","geocode_query":"Cibuco catchment, Puerto Rico","latitude":18.352,"longitude":-66.336,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.336,18.352],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-4"},{"name":"Grande de Loíza catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Grande de Loíza River","geocode_query":"Grande de Loíza catchment, Puerto Rico","latitude":18.242,"longitude":-66.01,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.01,18.242],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-5"},{"name":"Valenciano catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Valenciano River","geocode_query":"Valenciano catchment, Puerto Rico","latitude":18.217,"longitude":-65.925,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-65.925,18.217],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-6"},{"name":"Canóvanas catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Canóvanas River","geocode_query":"Canóvanas catchment, Puerto Rico","latitude":18.318,"longitude":-65.889,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-65.889,18.318],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-7"},{"name":"Fajardo catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Fajardo River","geocode_query":"Fajardo catchment, Puerto Rico","latitude":18.296,"longitude":-65.692,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-65.692,18.296],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-8"},{"name":"Grande de Patillas catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Grande de Patillas River","geocode_query":"Grande de Patillas catchment, Puerto Rico","latitude":18.035,"longitude":-66.034,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.034,18.035],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-9"},{"name":"Inabón catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Inabón River","geocode_query":"Inabón catchment, Puerto Rico","latitude":18.086,"longitude":-66.562,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.562,18.086],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-10"},{"name":"Portugués catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Portugués River","geocode_query":"Portugués catchment, Puerto Rico","latitude":18.078,"longitude":-66.634,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-66.634,18.078],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-11"},{"name":"Culebrinas catchment, Puerto Rico","kind":"river","role":"study catchment with flow and land cover data","river_name":"Culebrinas River","geocode_query":"Culebrinas catchment, Puerto Rico","latitude":18.36,"longitude":-67.089,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchments are identified by the letters A–L in the header. ́a A: Tanam B: Bauta ı ́ C: Grande de Manat D: Cibuco ıza ́ E: Grande de Lo F: Valenciano ́ovanas G: Can H: Fajardo I: Grande de Patillas ́on J: Inab ́es K: Portugu L: Culebrinas Catchment details USGS ID (last six digits) 028000 034000 035000 038320 055000 056400 061800 071000 092000 112500 115000 147800 Gauge latitude [ ◦ N] 18.301 18.236 18.323 18.352 18.242 18.217 18.318 18.296 18.035 18.086 18.078 18.360 Gauge longitude [ ◦ W] 66.782 66.455 66.459 66.336 66.010 65.925 65.889 65.692 66.034 66.562 66.634 67.089 Catchment area a [km2 ] 47.1 43.5 345.9 39.8 233.0 41.5 26.5 39.3 46.3 24.5 22.9 177.1 Catchment mean elevation [m a.s.l.] 571 734 578 247 265 170 461 272 442 558 568 167 Catchment elongation ratio b [−] 0.66 0.57 0.69 0.84 0.73 0.72 0.71 0.69 0.76 0.61 0.52 0.62 Catchment mean slope [ ◦ ] 16.2 21.5 19.6 13.1 13.3 8.9 14.4 16.0 20.1 24.1 22.8 10.0 Record c start year 1960 1970 1957 1970 1960 1971 1968 1962 1966 1965 1965 1968 Record c end year 2010 2010 2010 2006 2010 2010 2010 2009 2010 2010 1997 2010 Record c length [yr] 51 34 51 35 51 40 43 48 45 44 33 43 Dominant crop(s) in 1951 d c c c c, sc, p c, sc, f c, sc, f c, sc, f sc c, sc, f c c c, sc Dominant geology e v, gd, ls v v, gd v, gd v, gd, q gd v v, gd, q v, gd v, gd v, gd v, ls Long-term catchment means of hydroclimatic parameters calculated for record c period Baseflow recession constant (kbf [d −1 ]) 0.956 0.942 0.949 0.935 0.940 0.927 0.943 0.919 0.925 0.926 0.929 0.959 Precipitation ( P [mm yr −1 ]) 2102 2038 2004 1827 1963 2039 3422 2875 1720 1781 1875 2178 Total streamflow ( Qtot [mm yr −1 ]) 953 812 653 642 845 1067 942 1529 1185 681 743 1509 Baseflow ( Qbf [mm yr −1 ]) 680 389 341 320 420 394 463 656 601 441 388 673 Quickflow ( Qqf [mm yr −1 ]) 273 423 312 322 426 673 480 872 584 241 354 836 P − Q [mm yr −1 ] 1149 1225 1351 1185 1118 972 2480 1347 535 1100 1132 669 Potential evaporation (PET [mm yr −1 ]) 1487 1407 1456 1535 1542 1548 1399 1449 1514 1502 1519 1658 Runoff coefficient ( Qtot/P [−]) 0.453 0.399 0.326 0.351 0.431 0.523 0.275 0.532 0.689 0.383 0.396 0.693 Aridity index ( P/PET [−]) 1.42 1.46 1.38 1.18 1.28 1.33 2.46 1.97 1.15 1.18 1.23 1.31 Baseflow index f (Qbf/Qtot [−]) 0.714 0.479 0.522 0.499 0.496 0.369 0.491 0.429 0.507 0.647 0.522 0.446 a Computed catchment area; see Sect. 3.2. b Calculated by dividing the diameter of a circle having the same area as the catchment, by the maximum length of the catchment (Schumm, 1956). c Record is defined as the period of simultaneous P, PET, and Q data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-67.089,18.36],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040072800,"main_basin_id":7040072800,"basin_region":"North America and Caribbean","basin_area_km2":13805.0,"hydrology_source":"hydrobasins","site_id":"doc-10ca86633c661b1a69152122-site-12"}],"limitations":["Land cover maps derived with differing resolutions and methodologies across years may introduce classification uncertainties.","Station density for precipitation data may be insufficient to fully capture spatial variability, especially for convective storms in tropics.","Potential evaporation estimates use empirical method (Hargreaves) without full consideration of radiation, humidity, and wind, which may limit accuracy.","Hydrological model calibration required precipitation scaling parameter in some catchments indicating possible biases in input or model structural errors.","Anthropogenic water extractions and inter-basin groundwater flows are not fully quantified, possibly confounding streamflow data.","Catchment-scale lumped climatic and hydrological values may mask local heterogeneity in precipitation and runoff response.","Study catchments may have baseline forested headwaters before records began, limiting ability to detect impacts of subsequent regeneration.","Observational and measurement errors in precipitation, streamflow, and land cover data may mask subtle land-cover change effects on streamflow."],"river_ids":[],"site_ids":["doc-10ca86633c661b1a69152122-site-1","doc-10ca86633c661b1a69152122-site-2","doc-10ca86633c661b1a69152122-site-3","doc-10ca86633c661b1a69152122-site-4","doc-10ca86633c661b1a69152122-site-5","doc-10ca86633c661b1a69152122-site-6","doc-10ca86633c661b1a69152122-site-7","doc-10ca86633c661b1a69152122-site-8","doc-10ca86633c661b1a69152122-site-9","doc-10ca86633c661b1a69152122-site-10","doc-10ca86633c661b1a69152122-site-11","doc-10ca86633c661b1a69152122-site-12"],"anchor":[-66.336,18.352],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[7040072800],"main_basin_ids":[7040072800]},{"id":"doc-2013c62c6ac74ec11e4ad159","title":"Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis","display_title":"Comprehensive Guide to Evaporation Estimation","doi":"https://doi.org/10.5194/hess-17-1331-2013","theme":"Flow & ecology","color":"#80edc2","year":2013,"publication_date":"2013-04-10","authors":["T. A. McMahon","M. C. Peel","L. Lowe","R. Srikanthan","T. R. McVicar"],"summary":"This paper provides a rigorous and pragmatic synthesis of methods to estimate daily and monthly actual, potential, reference crop, and pan evaporation using standard meteorological data. It serves researchers, consulting hydrologists, and engineers by unifying theory and practice for a variety of application contexts, such as deep and shallow lakes, reservoirs, farm dams, catchment water balance, rainfall-runoff modeling, and irrigation management. The article reviews and compares classical and modern evaporation models including Penman, Penman–Monteith, Priestley–Taylor, Morton’s complementary relationship models, and the FAO-56 reference crop evapotranspiration, emphasizing model applicability, data requirements, and practical guidelines across climates and hydrological settings.","finding":"Morton’s evaporation models, based on the complementary relationship, provide a robust framework for estimating actual evaporation across landscapes and water bodies, and they outperform simpler empirical models especially in heterogeneous and limited-data environments.","finding_evidence":{"passage_id":"p009-004","page":9,"quote":"The shape of the CR relationship for the observed pan data, assuming a pan represents an infinitesimal point as required in the CR relationship, is similar to the shape in Fig. 2a. Using a mesoscale model over an irrigation area in south-eastern Turkey, Ozdogan et al. (2006) concluded that their results lend credibility to the CR hypothesis. However, research is underway into understanding whether the constant of proportionality (“2” in Eq. 15) varies and, if so, what is the nature of the asymmetry in the relationship (Ram ́ırez et al., 2005; Szilagyi, 2007; Szilagyi and Jozsa, 2008). Some other references of relevance include Hobbins et al. (2001a); Yang et al. (2006); Kahler and Brutsaert (2006); Lhomme and Guilioni (2006); Yu et al. (2009) and Han et al. (2011). 2.5.2 Morton’s models F. I. Morton was at the forefront of evaporation analyses from about 1965 culminating in the mid-80s with the publication of the program WREVAP (Morton et al., 1985).","verified_in_page":true},"topics":["evaporation estimation","potential evaporation","reference crop evapotranspiration","complementary relationship","hydrological modeling","water balance","Penman-Monteith model","climate data interpolation"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"location of extensive testing, calibration and application of evaporation models, including pan coefficients","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Program WREVAP (Morton 1983a, b, 1986) Environment Land environment Shallow lake Deep lake Radiation input α = 0.10–0.30 α = 0.05 α = 0.05 (if not using Morton, depending on vegetation εs = 0.97 εs = 0.97 1983a method) εs = 0.92 Models CRAE CRWE CRLE Data Latitude, elevation, mean annual precipitation, As for CRAE plus lake salinity As for CRAE plus lake salinity and and daily temperature, humidity (Morton, 1986, Sect. 4, item 2) average depth (Morton, 1986, Sect. 4) and sunshine hours Component models ETMO Pot EPot∗ and variable values Potential evapotranspiration Potential evaporation Morton (1983a) For Australia (in the land environment) b0 = 1.0 (p. 64) (Chiew and Leahy, 2003, or pan-size wet surface fZ = 28 W m−2 Sect. 2.3) b0 = 1.0 evaporation Morton (1983a, p. 26) mbar−1 (p. 25) fZ = 29.2 W m−2 mbar−1 b0 = 1.12 fZ = 25 W m−2 mbar−1 ETMO Wet ETMO Wet ETMO Wet Wet environment areal evapotranspiration Shallow lake evaporation Deep lake evaporation Morton (1983a, p. 25) For Australia Morton (1983a, p. 26) b1 = 13 W m−2 b2 = 1.12 b1 = 14 W m−2 (Chiew and Leahy, 2003, Sect. 2.3) b1 = 13 W m−2 Rne (net radiation at T ◦ e C) b2 = 1.2 b1 = 13.4 W m−2 b2 = 1.12 with seasonal adjustment of solar b2 = 1.13 Rne (net radiation at Te ◦C) and water borne inputs Outcome Actual areal evapotranspiration ESL EDL ETMO Act = 2ETMO Wet − ETMO Pot Shallow lake evaporation Deep lake evaporation * According to Morton (1986, p. 379, item 4) in the context of estimating lake evaporation, EPot has no “. . . real world meaning. . . ” because the estimates are sensitive to both the lake energy environment and the land temperature and humidity environment which are significantly out of phase.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North America","kind":"region","role":"location of lake evaporation validation studies and water balance comparisons","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"Morton (1979, 1983b) validated his approach for estimating lake evaporation against water budget estimates for ten major lakes in North America and East Africa. The average absolute percentage deviation between the model of lake evaporation and water budget estimates was 3.7 % of the water budget estimates (Morton, 1979, p. 72). Morton (1986, p. 378) notes that, because the complementary relationship takes into account the differences in surrounding, for the CRLE model it matters little where the meteorological measurements are made in relation to the lake; they can be land-based or from a floating raft. Because routing of solar and water-borne energy is incorporated in the CRLE model, a monthly time step is adopted (Morton, 1983b, Sect. 9). Land-based meteorological data would normally be used (Morton, 1983b, p. 82) but as noted above data measured over water has only a minor effect on the estimate of lake evaporation (Morton, 1983b, p. 96; 1986, p. 378).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.255119,54.465961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-2013c62c6ac74ec11e4ad159-site-1","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Puerto Rico","kind":"region","role":"location of hydrologic water balance studies supporting complementary relationship hypothesis","river_name":null,"geocode_query":"Puerto Rico","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-005","page":9,"quote":"WREVAP, which is summarised in Table 2, combines three models namely CRAE (Morton, 1983a), CRWE (Morton, 1983b) and CRLE (Morton, 1986), typically at a monthly time step. The CRAE model computes actual evapotranspiration for land environments, the CRWE model computes evaporation for shallow lakes and the CRLE model computes evaporation for deep lakes. Details of the models are discussed briefly in this section, in Sects. 3.1.2 and 3.2, and in detail in Sect. S7. Nash (1989, Abstract) concluded that Morton’s analysis based mainly on the complementary relationship provides a valuable extension to Penman in that it allows one to estimate actual evapotranspiration under a limiting water supply. As air passes from a land environment to a lake environment it is modified and the complementary relationship takes this into account.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Ontario, Canada","kind":"region","role":"region of field studies for shallow lake evaporation estimation models","river_name":null,"geocode_query":"Ontario, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Values, which can vary between 0.47 and 2.19 seasonally and between 0.68 and 1.00 annually, are listed in the Tables S11 and S12. Garrett and Hoy (1978, Table III) modelled annual pan coefficients based on a simple numerical lake model incorporating energy and vapour fluxes. The results show that for the seven reservoirs examined, the annual pan coefficients change little with lake depth. 3.2 Shallow lakes, small lakes and farm dams For large shallow lakes, less than a meter or so in depth, where advected energy and changes in seasonal stored energy can be ignored, the Penman equation with the 1956 wind function or Morton’s CRLE model (Morton, 1983a, b) may be used to estimate lake evaporation. The upwind transition from the land environment to the large lake is also ignored (Morton, 1983b). Stewart and Rouse (1976) recommended the Priestley– Taylor model for estimating daily evaporation from shallow lakes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"South-eastern Turkey","kind":"region","role":"location of mesoscale modelling supporting complementary relationship hypothesis","river_name":null,"geocode_query":"South-eastern Turkey","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"The shape of the CR relationship for the observed pan data, assuming a pan represents an infinitesimal point as required in the CR relationship, is similar to the shape in Fig. 2a. Using a mesoscale model over an irrigation area in south-eastern Turkey, Ozdogan et al. (2006) concluded that their results lend credibility to the CR hypothesis. However, research is underway into understanding whether the constant of proportionality (“2” in Eq. 15) varies and, if so, what is the nature of the asymmetry in the relationship (Ram ́ırez et al., 2005; Szilagyi, 2007; Szilagyi and Jozsa, 2008). Some other references of relevance include Hobbins et al. (2001a); Yang et al. (2006); Kahler and Brutsaert (2006); Lhomme and Guilioni (2006); Yu et al. (2009) and Han et al. (2011). 2.5.2 Morton’s models F. I. Morton was at the forefront of evaporation analyses from about 1965 culminating in the mid-80s with the publication of the program WREVAP (Morton et al., 1985).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Western Victoria, Australia","kind":"region","role":"region with studies validating lake evaporation models and water level modelling","river_name":null,"geocode_query":"Western Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-005","page":13,"quote":"As noted in Sect. 2.5.2, for annual evaporation estimates, there is no difference in magnitude between deep and shallow lake evaporation (see also Sacks et al., 1994, p. 331). In Morton’s procedure, seasonal heat changes in deep lakes are incorporated through linear routing. Details are presented in Sect. S7. The data for Morton’s WREVAP program are mean monthly air temperature, mean dew point temperature (or mean monthly relative humidity) and monthly sunshine hours as well as latitude, elevation and mean annual precipitation at the site. The broad computational steps are set out in Sect. S7 and details can be found in Appendix C of Morton (1983a). A Fortran 90 listing of a slightly modified version of the Morton WREVAP program is provided in Sect. S20 and a worked example is available in Sect. S21. The complementary relationship lake evaporation model of Morton (1983b, 1986) and Morton et al. (1985) may be www.hydrol-earth-syst-sci.net/17/1331/2013/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.40393,-37.757584],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21557393,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western District of Victoria, VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2013c62c6ac74ec11e4ad159-site-5","true_coordinates":[142.4039296,-37.6975835],"spread_km":6.7},{"name":"China","kind":"region","role":"region with studies on pan evaporation trends, spatial interpolation of meteorological data, and modeling of reference evapotranspiration","river_name":null,"geocode_query":"China","latitude":null,"longitude":null,"evidence":{"passage_id":"p030-001","page":30,"quote":"A., Nathan, R. J., Etchells, T., and Malano, H. M.: Evaporation from water supply reservoirs: An assessment of uncertainty, J. Hydrol., 376, 261–274, 2009. Lu, J., Sun, G. McNulty, S. G., and Amatya, D. M.: A comparison of six potential evapotranspiration methods for regional use in the Southeastern United States, J. Am. Water Resour. As., 41, 621–633, 2005. Luo, Y. F., Peng, S. Z., Klan, S., Cui, Y. L., Wang, Y., and Feng, Y. H.: A comparative study of groundwater evapotranspiration functions, 18th IMACS World Congress MODSIM09 Proceedings, 3095–3101, 2009. Maidment, D. R.: Handbook of Hydrology, McGraw-Hill Inc., New York, 1992. Masoner, J. R., Stannard, D. I., and Christenson, S. C.: Differences in evaporation between a floating pan and Class-A pan on land, J. Am. Water Resour. As., 44, 552–561, 2008. McJannet, D. L., Webster, I. T., Stenson, M. P., and Sherman, B. S.: Estimating open water evaporation for the Murray-Darling Basin.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States","kind":"region","role":"sites of numerous hydrologic studies verifying evaporation models and pan versus lake evaporation","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"This is not so with lake evaporation as the model accounts for the impact of overpassing air. CRAE model The CRAE model estimates the three components: potential evapotranspiration, wet-environment areal evapotranspiration and actual areal evapotranspiration. Estimating potential evapotranspiration (ETPot in Fig. 2). Because Morton’s (1983a, p. 15) model does not require wind data, it has been used extensively in Australia (where historical wind data were unavailable until recently; see McVicar et al., 2008) to compute time series estimates of historical potential evaporation.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Minnesota, United States","kind":"region","role":"location of modeling of lake evaporation errors using energy budgets","river_name":null,"geocode_query":"Minnesota, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p020-001","page":20,"quote":"Replacing raft-based air temperature or humidity data with those from a land-based near-shore site affected computed estimates of annual evaporation between +3.7 % and −3.6 % (averaged −1.2 %). 2. Neglecting heat transfer from the bottom sediments to the water resulted in an increase in lake evaporation of +7 %. 3. For this local climate, lake shortwave solar radiation, air temperature and atmospheric vapour pressure with values from a site 110 km away resulted in errors of +6 % to +8 %. 4.4 Dealing with a climate change environment: increasing annual temperature but decreasing pan evaporation Based on analysis of regions, across seven countries, with more than 10 pan evaporation stations, Roderick et al. (2009a, Table 1) reported negative trends in pan evaporation measures over the last 30 to 50 yr. Recently, McVicar et al. (2012a, Table 5) showed that declining evaporative demand, as measured by pan evaporation rates, was globally widespread.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-94.506947,44.367499],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":7040569650,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":446352.2,"hydrology_source":"hydrobasins","site_id":"doc-2013c62c6ac74ec11e4ad159-site-8"},{"name":"Florida, US","kind":"region","role":"location of lysimeter and energy budget studies comparing evaporation estimates","river_name":null,"geocode_query":"Florida, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p022-005","page":22,"quote":"Bold values are the ratios of PET estimates compared to the Priestley–Taylor estimates. 16. Xu and Chen (2005) Table 1: comparisons of actual evapotranspiration based on 12 yr of parenthesis are the average standard errors of estimate (mm day Kovoor (2006), Table 6: monthly and daily analysis for four climate stations in India. Bold values are the average percentage differences from FAO-56 ref. crop of the four sites and the average monthly values. Values in annual lysimeter data of grass in Germany. Bold values are the mean estimates as ratios of observed values. Values in parenthesis are the average standard deviation of annual errors in percent. 17. Nandagiri and −1 ) of regression with PM values of the four sites. 18. Rosenberry et al. (2007), Fig. 4: based on 37 months of data for small lake in North Hampshire. values and the Bowen ratio estimates (mm day Estimates are compared with Bowen ratio energy budget measurements.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Northern China","kind":"region","role":"site of semi-arid climate modeling comparing evaporation methods","river_name":null,"geocode_query":"Northern China","latitude":null,"longitude":null,"evidence":{"passage_id":"p022-005","page":22,"quote":"Bold values are the ratios of PET estimates compared to the Priestley–Taylor estimates. 16. Xu and Chen (2005) Table 1: comparisons of actual evapotranspiration based on 12 yr of parenthesis are the average standard errors of estimate (mm day Kovoor (2006), Table 6: monthly and daily analysis for four climate stations in India. Bold values are the average percentage differences from FAO-56 ref. crop of the four sites and the average monthly values. Values in annual lysimeter data of grass in Germany. Bold values are the mean estimates as ratios of observed values. Values in parenthesis are the average standard deviation of annual errors in percent. 17. Nandagiri and −1 ) of regression with PM values of the four sites. 18. Rosenberry et al. (2007), Fig. 4: based on 37 months of data for small lake in North Hampshire. values and the Bowen ratio estimates (mm day Estimates are compared with Bowen ratio energy budget measurements.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Uses standard meteorological data averaged over 24 h; effects of shorter time-step averaging or daylight-only averaging not fully addressed","Spatially interpolated data may introduce errors when at-site observations are unavailable","Model parameters (e.g. pan coefficients, resistance factors) require local calibration for best accuracy","Models like Morton’s require assumptions that may not hold in all landscape or climate conditions","The paper is a synthesis of existing literature and methods, not a new experimental study"],"river_ids":["ne-809"],"site_ids":["doc-2013c62c6ac74ec11e4ad159-site-1","doc-2013c62c6ac74ec11e4ad159-site-5","doc-2013c62c6ac74ec11e4ad159-site-8"],"anchor":[-105.255119,54.465961],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070710,7040021740,7040569650],"main_basin_ids":[5040070710,7040021740,7040047060]},{"id":"doc-257979d61da66d3fd27aacfb","title":"A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions","display_title":"Bayesian Post-Processor of Australian Streamflow","doi":"https://doi.org/10.5194/hess-17-795-2013","theme":"Flow & ecology","color":"#80edc2","year":2013,"publication_date":"2013-02-22","authors":["P. Pokhrel","D. E. Robertson","Q. J. Wang"],"summary":"This study presents a Bayesian joint probability (BJP) statistical post-processor to reduce errors and quantify uncertainty in monthly streamflow predictions generated by a monthly water balance model (WAPABA). Applied to 18 catchments in eastern Australia across Queensland, Victoria, and Tasmania, the BJP method effectively corrects systematic bias and updates predictions using recent observations. It generates reliable probabilistic streamflow forecasts with robust performance across time and event magnitudes, showing particular improvements in catchments with slow-responding runoff processes.","finding":"The Bayesian joint probability post-processor significantly reduces systematic bias and transient errors in monthly streamflow forecasts, enhancing accuracy and providing reliable uncertainty quantification, especially in catchments with persistent error structures.","finding_evidence":{"passage_id":"p008-002","page":8,"quote":"This can be better appreciated in Fig. 7, which shows monthly percentage bias obtained by WAPABA predictions and its elimination by method A. Furthermore, it is interesting to note that the bias correction is not just due to linear changes in slope or intercepts but also due to non-linear changes as illustrated by Fig. 8. The figures demonstrate non-linear compensations to WAPABA predictions by the BJP post-processor. The log-sinh transformation in combination with the BJP model parameter inference allow for the non-linear corrections of errors, thus allowing for corrections of conditional as well as unconditional biases. Our results show that further error reductions can be possible through prediction updating. This contradicts the assumptions made by Li et al. (2011), who assume that persistence in error structure at monthly time step is negligible. However, we note that the results tend to be catchment specific.","verified_in_page":true},"topics":["statistical post-processing","streamflow prediction","uncertainty quantification","bias correction","Bayesian modeling","hydrological modeling","seasonal forecasting","error reduction"],"study_period":"1950–2008","locations":[{"name":"Burdekin River","kind":"river","role":"study catchment representing large semi-arid area","river_name":"Burdekin River","geocode_query":"Burdekin River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.53419,-20.16003],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":16652564,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040068450,"main_basin_id":5040068450,"basin_region":"Australasia","basin_area_km2":130431.0,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-0","true_coordinates":[146.53419,-20.10003],"spread_km":6.7},{"name":"Cape River","kind":"river","role":"study catchment in Queensland semi-arid climate","river_name":"Cape River","geocode_query":"Cape River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.482978,-20.466107],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651490,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cape River, Homestead QLD 4816, Australia","basin_id":5040068450,"main_basin_id":5040068450,"basin_region":"Australasia","basin_area_km2":130431.0,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-1"},{"name":"Lake Hume","kind":"facility","role":"upper Murray region catchment and water storage","river_name":null,"geocode_query":"Lake Hume, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.037327,-36.160148],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666929,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Hume Village NSW 3691, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-2","true_coordinates":[147.0373272,-36.100148],"spread_km":6.7},{"name":"Dartmouth Reservoir","kind":"facility","role":"upper Murray catchment reservoir","river_name":null,"geocode_query":"Dartmouth Reservoir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River 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1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.5228482,-36.5566993],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667718,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dartmouth Dam, Dartmouth VIC 3701, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-3"},{"name":"Kiewa River","kind":"river","role":"upper Murray study catchment","river_name":"Kiewa River","geocode_query":"Kiewa River, Victoria, 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Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9999188,-36.8751645],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665306,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nillahcootie Dam, Victoria, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-6"},{"name":"Lake Eildon","kind":"facility","role":"central Victoria reservoir catchment, example site for detailed results","river_name":null,"geocode_query":"Lake Eildon, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Figure 2 is an example of the post-processed predictions generated by the BJP post-processor. This example is to provide the reader with an appreciation of how the postprocessed predictions from the BJP post-processor may look. A detailed evaluation of the post-processor, with respect to the post-processing qualities, will be presented in Sect. 4. The example is drawn from Lake Eildon in central Victoria and shows [0.1, 0.25, 0.5, 0.75, 0.9] quantiles and observed streamflow values plotted chronologically. In this case the post-processed predictions do not show any obvious trend with time, and the widths of the quantile intervals seem to cover the expected number of observed values. 4 Results In this section, we assess the quality of the probabilistic predictions generated by using the three methods and evaluate Hydrol. Earth Syst. 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Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.1789802,-36.7291107],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43664145,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Goulburn Weir, VIC 3608, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-8"},{"name":"Eppalock Reservoir","kind":"facility","role":"central Victoria catchment reservoir","river_name":null,"geocode_query":"Eppalock Reservoir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River 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1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Cairn Curran Reservoir","kind":"facility","role":"central Victoria catchment reservoir","river_name":null,"geocode_query":"Cairn Curran Reservoir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 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Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.970474,-37.0767],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662539,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cairn Curran Reservoir, Victoria, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-10","true_coordinates":[143.9704739,-37.0167004],"spread_km":6.7},{"name":"Tullaroop Reservoir","kind":"facility","role":"central Victoria catchment reservoir","river_name":null,"geocode_query":"Tullaroop Reservoir, Victoria, 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0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.914978,-37.745405],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559583,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Upper Yarra Reservoir, Reefton VIC 3799, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-13","true_coordinates":[145.9149785,-37.6854051],"spread_km":6.7},{"name":"Maroondah Reservoir","kind":"facility","role":"southern Victoria catchment reservoir","river_name":null,"geocode_query":"Maroondah Reservoir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.564924,-37.69365],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559147,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Maroondah Reservoir, Healesville VIC 3777, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-14","true_coordinates":[145.5649243,-37.6336501],"spread_km":6.7},{"name":"O’Shannassy Reservoir","kind":"facility","role":"southern Victoria catchment reservoir","river_name":null,"geocode_query":"O’Shannassy Reservoir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.807177,-37.731838],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559443,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"O'Shannassy Reservoir, Mcmahons Creek VIC 3799, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-15","true_coordinates":[145.8071772,-37.671838],"spread_km":6.7},{"name":"Mersey-Forth Hydro Electric Scheme","kind":"facility","role":"Tasmanian catchment studied, hydroelectric scheme","river_name":null,"geocode_query":"Mersey-Forth Hydro Electric Scheme, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"King Hydro Electric Scheme","kind":"facility","role":"Tasmanian catchment studied, hydroelectric scheme","river_name":null,"geocode_query":"King Hydro Electric Scheme, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Burdekin River Queensland 1967–2008 36 260 567 76 (2765 GL) 0.13 Cape River Queensland 1967–2008 16 074 456 23 (372 GL) 0.05 Lake Hume upper Murray 1950–2008 12 184 819 227 (2764 GL) 0.28 Dartmouth Reservoir upper Murray 1950–2008 3193 1042 279 (890 GL) 0.27 Kiewa River upper Murray 1965–2008 1748 1099 248 (433 GL) 0.23 Ovens River upper Murray 1959–2008 7543 963 175 (1320 GL) 0.18 Lake Nillahcootie central Victoria 1950–2008 422 942 150 (63 GL) 0.16 Lake Eildon central Victoria 1950–2008 3877 1104 373 (1447 GL) 0.34 Goulburn Weir central Victoria 1950–2008 7166 769 188 (1349 GL) 0.24 Eppalock Reservoir central Victoria 1950–2008 1749 630 98 (172 GL) 0.16 Cairn Curran Reservoir central Victoria 1950–2008 1603 617 72 (115 GL) 0.12 Tullaroop Reservoir central Victoria 1950–2008 702 633 77 (54 GL) 0.12 Thompson Reservoir southern Victoria 1950–2008 487 1299 485 (236 GL) 0.37 Upper Yarra Reservoir southern Victoria 1950–2008 336 1387 443 (149 GL) 0.32 Maroondah Reservoir southern Victoria 1950–2008 129 1351 577 (74 GL) 0.43 O’Shannassy Reservoir southern Victoria 1950–2008 127 1404 766 (97 GL) 0.55 Mersey-Forth HES Tasmania 1950–2008 2698 1900 793 (2141 GL) 0.42 King HES Tasmania 1950–2008 731 2703 1724 (1260 GL) 0.64 Note: HES stands for Hydro Electric Scheme.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.937076,-39.935348],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31289394,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"King Island, Tasmania 7256, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-257979d61da66d3fd27aacfb-site-17","true_coordinates":[143.9370758,-39.87534770000001],"spread_km":6.7}],"limitations":["Latitude and longitude coordinates are not provided for catchments and reservoirs.","Climate and hydro data span large regions which may limit detailed local inference.","Study focuses on eastern Australian catchments only, limiting wider spatial applicability."],"river_ids":["43639557","43639567","43639570","43639579","43639618","43639641","43640241","43640344","43643199","43643227","43643345","43643771","43644030","43644470","43644997","43645007","43645094","43645616","43645755","43645787","43645803","43645906","43645925","43646027","43646036","43646136","43646451","43646488","43646586","43646716","43646800","43646834","43646866","43646976","43646999","43647042","43647060","43647075","43647242","43647450","43647513","43647646","43648328","43648338","43648350","43648354","43648403","43648454","43648456","43649191","43650161","43650481","43660233","43660234","43660236","43660242","43660251","43660252","43660256","ne-1082"],"site_ids":["doc-257979d61da66d3fd27aacfb-site-0","doc-257979d61da66d3fd27aacfb-site-1","doc-257979d61da66d3fd27aacfb-site-2","doc-257979d61da66d3fd27aacfb-site-3","doc-257979d61da66d3fd27aacfb-site-4","doc-257979d61da66d3fd27aacfb-site-5","doc-257979d61da66d3fd27aacfb-site-6","doc-257979d61da66d3fd27aacfb-site-7","doc-257979d61da66d3fd27aacfb-site-8","doc-257979d61da66d3fd27aacfb-site-10","doc-257979d61da66d3fd27aacfb-site-11","doc-257979d61da66d3fd27aacfb-site-12","doc-257979d61da66d3fd27aacfb-site-13","doc-257979d61da66d3fd27aacfb-site-14","doc-257979d61da66d3fd27aacfb-site-15","doc-257979d61da66d3fd27aacfb-site-17"],"anchor":[145.1789802,-36.7291107],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040068450,5040070710,5040077640,5040591050],"main_basin_ids":[5040068450,5040070710,5040073410,5040077640]},{"id":"doc-2e526861ed0d1a197900a413","title":"Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting","display_title":"Post-processing Rainfall Forecasts in Ovens Catchment","doi":"https://doi.org/10.5194/hess-17-3587-2013","theme":"Flow & ecology","color":"#80edc2","year":2013,"publication_date":"2013-09-27","authors":["D. E. Robertson","D. L. Shrestha","Q. J. Wang"],"summary":"This study develops and evaluates a new statistical post-processing approach for sub-daily rainfall forecasts from the ACCESS-R numerical weather prediction (NWP) model applied to 33 rain gauges in the Ovens catchment within the south-east Murray–Darling Basin, Australia. The method uses a Bayesian joint probability (BJP) modeling approach with a log-sinh transformation and Schaake shuffle to generate ensemble rainfall forecasts that are bias corrected and reliably quantify uncertainty, including spatial and temporal correlations. Verification shows improved skill, bias correction, discrimination, and reliability compared with raw NWP outputs, supporting their use for short-term and flood-related streamflow forecasting. Limitations include data availability and regional climatic variability affecting generalizability.","finding":"The post-processing method produces ensemble rainfall forecasts with significantly improved skill and reduced bias compared to raw NWP forecasts, effectively correcting mean and nonlinear conditional biases, accurately discriminating events, and reliably quantifying uncertainty while preserving spatial-temporal correlations through the Schaake shuffle.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"Post-processing results in marked improvements in skill over the raw NWP predictions, with the skill of the post-processed forecast being on average 37 % higher than the raw NWP predictions. The skill of post-processed forecasts of cumulative rainfall totals (Figs. 4 and 5), increases for the first three lead times and then remains relatively stable at a CRPS skill score of approximately 50 % out to 57 h. The raw NWP rainfall predictions display similar behaviour, but skill scores are approximately 20 % lower than the post-processed forecasts for all lead times. The skill of the cumulative forecasts is greater than forecasts for individual periods because errors in individual periods will tend to compensate for each other. Figure 5 presents the skill of the post-processed forecasts conditioned on the forecast mean exceeds a range of thresholds ranging between 0 and 5 mm for 0–3 and 30–33 h lead times.","verified_in_page":true},"topics":["ensemble forecasting","rainfall post-processing","numerical weather prediction","forecast bias correction","forecast uncertainty quantification","hydrological forecasting","statistical calibration","spatial-temporal correlation"],"study_period":"January 2010 to August 2011","locations":[{"name":"Ovens catchment","kind":"basin","role":"primary study catchment where rainfall post-processing and forecast verification is conducted","river_name":null,"geocode_query":"Ovens catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"Section 6 provides a summary of the paper and draws conclusions. 2 Study catchment and data For this study we focus on the Ovens catchment in south-east Murray Darling Basin in Australia. A continuous flood and short-term flow forecasting system is being developed for the catchment because it provides a significant source of unregulated inflow to the Murray River and has several urban centres that have experienced significant economic damage from Hydrol. Earth Syst. Sci., 17, 3587–3603, 2013 www.hydrol-earth-syst-sci.net/17/3587/2013/","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.787795,-36.666702],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43666411,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2e526861ed0d1a197900a413-site-0","true_coordinates":[146.7877948,-36.6067022],"spread_km":6.7},{"name":"Carboor Upper","kind":"station","role":"representative rain gauge site used for detailed analysis and presentation of results within the Ovens catchment","river_name":null,"geocode_query":"Carboor Upper, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"D. E. Robertson et al.: Post-processing rainfall forecasts from numerical weather prediction models 3589 flooding. The time of concentration to the catchment outlet is of the order of four to five days; however the time of concentration to some flood sensitive areas within the catchment can be less than 24 h and therefore hydrological models are run at sub-daily time steps (Pagano et al., 2011). Hourly observed precipitation data were obtained from the operational flood forecasting database of Australian Bureau of Meteorology for 33 rain gauges located in the Ovens catchment (Fig. 1). Carboor Upper is highlighted in Fig. 1 as many of the results presented focus on this site. Mean annual rainfall at the 33 gauges locations varies between 550 mm, near the catchment outlet, and 1950 mm in the catchment headwaters. An historical archive of hourly precipitation data is available from September 1991.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.53,-36.72],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666138,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Carboor Upper, Carboor VIC 3678, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2e526861ed0d1a197900a413-site-1","true_coordinates":[146.53,-36.6599999],"spread_km":6.7}],"limitations":["Limited length of retrospective NWP forecast archive (20 months) restricting comprehensive assessment especially of rare extreme rainfall events.","The study focuses on a temperate catchment dominated by large-scale synoptic weather systems, so performance in regions influenced by convective rainfall or other climatic regimes remains untested.","The post-processing method currently models only concurrent NWP predicted and observed rainfall for individual lead times, not explicitly modeling temporal lag correlations between lead times.","Potential reduced reliability in high rainfall exceedance probabilities at longer lead times due to small sample sizes.","Lack of hindcast datasets limits the evaluation of longer retrospective forecast periods needed for robust calibration and assessment."],"river_ids":[],"site_ids":["doc-2e526861ed0d1a197900a413-site-0","doc-2e526861ed0d1a197900a413-site-1"],"anchor":[146.53,-36.72],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-55a6ee7b647aae97bac92945","title":"Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data","display_title":"Woody Encroachment and Vegetation Changes in Global Drylands","doi":"https://doi.org/10.5194/bg-10-6657-2013","theme":"Land & ecosystems","color":"#c8df98","year":2013,"publication_date":"2013-10-24","authors":["N. Andela","Y. Y. Liu","A. I. J. M. van Dijk","R. A. M. de Jeu","T. R. McVicar"],"summary":"This study analyzed global dryland vegetation dynamics from 1988 to 2008 using two complementary satellite-based vegetation indices: the reflective Normalized Difference Vegetation Index (NDVI), sensitive primarily to herbaceous vegetation greenness and canopy cover; and the passive microwave Vegetation Optical Depth (VOD), sensitive mainly to woody biomass water content. The study modeled vegetation responses to antecedent precipitation and examined residual trends attributable to other drivers such as fire, grazing, agriculture and CO2 fertilization. Results show differential sensitivities of NDVI and VOD, with widespread evidence of woody encroachment in arid drylands at the expense of herbaceous cover, likely driven by global factors including increasing atmospheric CO2. Semi-arid dryland trends are more heterogeneous and influenced by local factors including fire regime changes and agriculture. The combined use of NDVI and VOD provides new insights into dryland vegetation changes at a global scale beyond precipitation influences.","finding":"Woody encroachment in global arid drylands is evidenced by increasing VOD trends and decreasing NDVI trends, suggesting a relative increase in woody aboveground biomass at the expense of herbaceous vegetation, driven largely by global drivers such as CO2 fertilization rather than precipitation variability alone.","finding_evidence":{"passage_id":"p012-001","page":12,"quote":"Both the relatively constant or increasing VOD trends and most negative trends in NDVI remain after precipitation-induced variation is accounted for (Fig. 7e and f). This results in many regions showing opposite trends in NDVI and VOD (Fig. 8; e.g., arid drylands of Argentina, southern Africa, northern Africa and Australia). In those regions, it seems that woody encroachment takes place at the expense of the herbaceous understory. Woody encroachment has been attributed to climate (Fensham et al., 2005), fire regime (Sankey et al., 2012), grazing (Asner et al., 2004) and CO2 fertilization (Buitenwerf et al., 2011). Clear trends in the NDVI and VOD residuals indicate that next to precipitation, other drivers also play a role (Fig. 7e and f). A possible explanation for residual trends could be changing fire regimes (Fig. 10b) through its impact on competitiveness of the herbaceous and woody vegetation components (Sankey et al., 2012).","verified_in_page":true},"topics":["vegetation dynamics","remote sensing","vegetation indices","precipitation effects","woody encroachment","drylands","fire effects","climate change impacts"],"study_period":"1988–2008","locations":[{"name":"Southern Africa drylands","kind":"region","role":"study region exhibiting vegetation index dynamics demonstrating woody encroachment","river_name":null,"geocode_query":"Southern Africa, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"6664 N. Andela et al.: Global changes in dryland vegetation dynamics Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (a) Grassland (b) Savanna (c) Woody savanna (d) Study areas Fig. 3. Long-term (1988–2008) average monthly NDVI and VOD signals for three common land cover classes of southern Africa (5–35◦ S, 10–50◦ E): (a) grassland (733 0.25◦ resolution grid cells), (b) savanna (4041 0.25◦ resolution grid cells), and (c) woody savanna (3826 0.25◦ resolution grid cells). The annual average and range of both vegetation indices are reported on each sub-plot. (d) Shows an overview of the study areas used in Figs. 3, 6 and 10. Australia is shown at half the scale of southern Africa. The three regions, based on land cover, used in (a–c) are shown in yellow (grassland), blue (savanna) and pink (woody savanna). 37 Fig. 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.56871,-24.417077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-55a6ee7b647aae97bac92945-site-0","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7},{"name":"Eastern Australia drylands","kind":"region","role":"study region with observed vegetation anomalies and precipitation influence analysis","river_name":null,"geocode_query":"Eastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-000","page":10,"quote":"6666 N. Andela et al.: Global changes in dryland vegetation dynamicsDiscussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Eastern Australia Southern Africa Fig. 6. Time series of estimated and observed vegetation anomalies. Estimated NDVI (green) and observed NDVI (black) vegetation anomalies are shown for regions in (a) eastern Australia (25–30◦ S, 145–150◦ E) and (b) southern Africa (25–30◦ S, 20–25◦ E), (c) and (d) are as (a) and (b) except for the estimated VOD (red) and observed VOD (black) vegetation anomalies. The average Spearman’s ranked correlation coefficient (Rs) and averaging period (T) of all 0.25◦ resolution grid cells in each 5◦ region are listed in each sub-plot. Locations of both 5◦ regions are shown in Fig. 3d. 40 Fig. 6. Time series of estimated and observed vegetation anomalies.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.37169,-27.732817],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677010,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eastern states of Australia, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-55a6ee7b647aae97bac92945-site-1","true_coordinates":[148.3716902,-27.6728168],"spread_km":6.7},{"name":"Northern Africa drylands","kind":"region","role":"study region showing increasing NDVI and VOD trends associated with herbaceous vegetation increase and changing fire regimes","river_name":null,"geocode_query":"Northern Africa, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"The median trend in NDVI is around zero for semi-arid drylands, showing positive trends over savannas and croplands and negative median trends for other land cover classes. VOD shows positive median trends for all land cover classes except forest (see Fig. 9a and d). In most semi-arid areas, where savanna grasslands dominate, resources are available to support forests which are suppressed by frequent fire (Bond and Keeley, 2005). Fire is thought to be the next most important driver of vegetation variation in savannas after precipitation (Sankaran et al., 2005). Although northern and southern African semi-arid drylands experience similar increasing trends in precipitation (Fig. 10a), the changes in the vegetation indices are very different. In southern Africa (i.e., south of 5◦S), NDVI trends are declining, while VOD trends are increasing– likely caused by an increase in the relative fraction of woody AGB.","verified_in_page":true},"resolution_status":"resolved","coordinates":[32.277834,26.0197776],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Africa","basin_id":1040034260,"main_basin_id":1040034260,"basin_region":"Africa","basin_area_km2":797088.8,"hydrology_source":"hydrobasins","site_id":"doc-55a6ee7b647aae97bac92945-site-2"},{"name":"Argentina arid drylands","kind":"region","role":"study region showing woody encroachment with inverse NDVI and VOD trends likely unrelated to grazing by domestic livestock","river_name":null,"geocode_query":"Argentina, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-003","page":12,"quote":"Decreasing NDVI trends have been interpreted as a proxy for land degradation (Wessels et al., 2007; Bai et al., 2008) which might be caused by grazing. Grazing has also been associated with woody encroachment (e.g., Asner et al., 2004). Although grazing might cause contradicting trends between NDVI and VOD in some regions (e.g., arid parts of northern Africa) similar trends are observed in regions of limited or no grazing by domestic livestock (e.g., compare Figs. 10c and 8 for arid drylands of Argentina and Australia). The spatial patterns and scale at which trends occur suggest that grazing by domestic livestock is not the main driver in these cases, impact of grazing by non-domesticated animals was not included here, as no information was available. Sankaran et al. (2005) suggest that woody vegetation receiving less than 350 mm annual precipitation is largely constrained by water availability.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mongolian Steppe semi-arid drylands","kind":"region","role":"study region with vegetation trends attributed partially to grazing and overgrazing pressures","river_name":null,"geocode_query":"Mongolian Steppe, Mongolia","latitude":null,"longitude":null,"evidence":{"passage_id":"p018-003","page":18,"quote":"Lett., 34, L15401, doi:10.1029/2007GL030311, 2007. Liu, Y. Y., de Jeu, R. A. M., McCabe, M. F., Evans, J. P., and van Dijk, A. I. J. M.: Global long-term passive microwave satellitebased retrievals of vegetation optical depth, Geophys. Res. Lett., 38, L18402, doi:10.1029/2011GL048684, 2011a. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, doi:10.5194/hess-15-425-2011, 2011b. Liu, Y. Y., Dijk, A. I. J. M., McCabe, M. F., Evans, J. P., and de Jeu, R. A. M.: Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers, Global Ecol. Biogeogr., 22, 692–705, doi:10.1111/geb.12024, 2013a. Liu, Y. Y., Evans, J. P., McCabe, M. F., de Jeu, R. A. M., van Dijk, A. I. J. M., Dolman, A.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Northern Australia drylands","kind":"region","role":"study region with varied NDVI and VOD trends reflecting local drivers beyond precipitation","river_name":null,"geocode_query":"Northern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"VOD, on the other hand, is usually based on longer averaging periods and therefore follows the interannual variation in precipitation, being less sensitive to small intra-annual variability. 5.3 Global dryland vegetation trends For some regions, observed annual trends in NDVI and VOD (1988–2008) showed similar trends: e.g., India, North America, eastern Australia, and northern African savannas (Fig. 7a and b). Other areas showed contrasting trends between the data sets: e.g., southern Africa, northern Australia, Argentina and central Asia. While observed trends in NDVI and VOD differ considerably, the estimated trends in NDVI and VOD were relatively similar and strong decreasing trends were found in southeastern Australia and Mongolia, while increasing trends were found in most of Africa and northern Australia (Fig. 7c and d). Figure 7e and f show residual trends, calculated as observed anomalies minus estimated vegetation anomalies.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southern America dry forests","kind":"region","role":"study region where negative vegetation trends correspond to deforestation reducing woody biomass","river_name":null,"geocode_query":"South America dry forests","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"NDVI can also be affected by grazing; while any given area might support livestock during most times, the highest pressure on vegetation occurs during longer dry seasons. The negative trends in NDVI and VOD over the South American dry forests are likely caused by deforestation, that is reducing the woody vegetation component and therefore total AGB (Grau et al., 2005). Trends in VOD occurred in areas of frequent fire, but no coherent spatial pattern was apparent (compare Fig. 7f with Fig. 10b and 10d). Although fire suppresses woody encroachment, dryland fires generally have a relatively low temperature and flame height and therefore do not necessarily affect established woody vegetation (Bond and Keeley, 2005). Woody vegetation increased in the southern African savannas despite recently increased burning (Figs. 10b and 8).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Indian subcontinent agricultural drylands","kind":"region","role":"area with strong increasing NDVI and VOD trends linked to agricultural intensification and irrigation","river_name":null,"geocode_query":"India, Pakistan, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-003","page":14,"quote":"This may be explained by increasing precipitation (Fig. 7; Sankaran et al., 2005), CO2 fertilization (Buitenwerf et al., 2011; Higgins and Scheiter, 2012) and/or grazing by nondomesticated herbivores. A clear increasing NDVI and VOD trend was found for croplands and adjacent grasslands in the USA. Woody encroachment in US grasslands has been widely reported (see Archer et al., 2001, and the references therein) and although there is no straightforward single driver, grazing is understood to play an important role (Van Auken, 2000; Briggs et al., 2005). Increasing agricultural activity could also play a role in explaining those trends (Neigh et al., 2008). The strongest positive median NDVI and VOD trends were found in agricultural areas (Fig. 9). Increases of both indices over the world’s agricultural regions are explained by advances in agricultural practice including mechanization, irrigation and fertilization (Liu et al., 2013a).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North China Plain croplands","kind":"region","role":"agricultural dryland region exhibiting positive NDVI trends due to improved land management","river_name":null,"geocode_query":"North China Plain, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-003","page":14,"quote":"This may be explained by increasing precipitation (Fig. 7; Sankaran et al., 2005), CO2 fertilization (Buitenwerf et al., 2011; Higgins and Scheiter, 2012) and/or grazing by nondomesticated herbivores. A clear increasing NDVI and VOD trend was found for croplands and adjacent grasslands in the USA. Woody encroachment in US grasslands has been widely reported (see Archer et al., 2001, and the references therein) and although there is no straightforward single driver, grazing is understood to play an important role (Van Auken, 2000; Briggs et al., 2005). Increasing agricultural activity could also play a role in explaining those trends (Neigh et al., 2008). The strongest positive median NDVI and VOD trends were found in agricultural areas (Fig. 9). Increases of both indices over the world’s agricultural regions are explained by advances in agricultural practice including mechanization, irrigation and fertilization (Liu et al., 2013a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[119.15036,32.26771],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North China, Yizheng, Yangzhou, Jiangsu, China, 211414","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-55a6ee7b647aae97bac92945-site-8","true_coordinates":[119.15036,32.32771],"spread_km":6.7},{"name":"USA dryland agricultural zones","kind":"region","role":"study area with increasing woody encroachment and positive vegetation trends, influenced by grazing and agriculture","river_name":null,"geocode_query":"United States of America","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-004","page":13,"quote":"Outside the African savannas and woody savannas, the residual trends in NDVI are not easily attributed to changes www.biogeosciences.net/10/6657/2013/ Biogeosciences, 10, 6657–6676, 2013","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Precipitation and burned area trends used are at coarse spatial and temporal resolutions limiting attribution precision.","Lack of time series data for grazing and comprehensive fire regimes constrains full assessment of these drivers.","Some vegetation index retrievals have limitations in open water, sandy desert and frost-affected areas, reducing reliability there.","Land cover used is static from 2005, hence changes in land cover during the study period cannot be fully accounted for.","Spatial resolution of vegetation products (0.25°) is coarse, limiting local scale inference."],"river_ids":[],"site_ids":["doc-55a6ee7b647aae97bac92945-site-0","doc-55a6ee7b647aae97bac92945-site-1","doc-55a6ee7b647aae97bac92945-site-2","doc-55a6ee7b647aae97bac92945-site-8"],"anchor":[119.15036,32.26771],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[1040034260,1041624310,4040694350,5040511570],"main_basin_ids":[1040015850,1040034260,4040009880,5040073410]},{"id":"doc-852c4d5b58e20e0d49a74795","title":"Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose","display_title":"Assessing NWP Precipitation Forecasts in Australia","doi":"https://doi.org/10.5194/hess-17-1913-2013","theme":"Flow & ecology","color":"#80edc2","year":2013,"publication_date":"2013-05-21","authors":["D. L. Shrestha","D. E. Robertson","Q. J. Wang","T. C. Pagano","H. A. P. Hapuarachchi"],"summary":"This study evaluates the skill of precipitation forecasts from four Australian Numerical Weather Prediction models (ACCESS-G, ACCESS-R, ACCESS-A, ACCESS-VT) over the Ovens catchment, Southeast Australia, using continuous and categorical verification scores, and analysis of diurnal cycles and forecast lead times. The study reveals significant spatial and temporal variability in forecast skill, model biases related to elevation, and improved skill at longer temporal accumulation scales and catchment averages compared to station-level forecasts.","finding":"High resolution NWP models overestimate precipitation in low elevation dry areas and underestimate in high elevation wet areas, while the global model consistently underestimates precipitation, with biases increasing with elevation.","finding_evidence":{"passage_id":"p009-003","page":9,"quote":"In general, the RMSE score does not exhibit any strong spatial pattern with respect to the altitude of the stations. Figure 5b depicts the bias of the ACCESS model forecasts as a percentage of the observed values. The ACCESS-VT and ACCESS-A models overestimate dry (low elevation) areas by up to 60 % and underestimate wet (high elevation) areas by up to 30 %. This finding supports the hypothesis that orographically enhanced precipitation is underestimated by NWP models. The ACCESS-R model also shows a similar pattern, but the bias is much greater than that of high resolution models. The coarse resolution model ACCESSG has a systematic positive bias (underforecasting) for all stations and bias generally increases with altitude. The bias of the coarse resolution NWP model is up to 70 %. Other studies have reported the NWP biases on the order of 100 % (see e.g. Clark and Hay, 2004).","verified_in_page":true},"topics":["numerical weather prediction","precipitation forecasting","forecast verification","hydrological modeling","bias correction","spatial variability","temporal variability","diurnal cycle"],"study_period":"2010–2011","locations":[{"name":"Ovens catchment","kind":"basin","role":"study area for evaluation of precipitation forecasts","river_name":null,"geocode_query":"Ovens catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"Sci., 17, 1913–1931, 2013","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.852524,-36.576702],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43666411,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-0","true_coordinates":[146.7877948,-36.6067022],"spread_km":6.7},{"name":"Mount Hotham","kind":"landscape","role":"high elevation reference peak influencing precipitation patterns in the catchment","river_name":null,"geocode_query":"Mount Hotham, Victoria, Australia","latitude":-37.05,"longitude":147.33,"evidence":{"passage_id":"p005-000","page":5,"quote":"D. L. Shrestha et al.: Evaluation of numerical weather prediction model precipitation forecasts 1917 Fig. 2. Schematic of NWP model runs. several urban centres that have experienced significant economic damage from flooding. The Ovens river rises in the Victorian Alps and the catchment is bounded by several significant peaks, including Mount Hotham (elevation 1861 m, longitude 147.33◦ , latitude −37.05◦ ), Mount Feathertop (elevation 1922 m, longitude 147.13◦ , latitude −36.9◦ ) and Mount Buller (elevation 1805 m, longitude 146.41◦ , latitude −37.14◦ ). The Wangaratta streamflow gauge (elevation 140 m, longitude 146.30◦ , latitude −36.42◦ ) drains an area of 5552 km2 . The upper catchment is steep and hilly, and covered by native forest and tree plantations. The lower catchment is relatively flat with a wide floodplain and is mainly used for grazing and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.33,-37.05],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43667541,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-1"},{"name":"Mount Feathertop","kind":"landscape","role":"high elevation reference peak influencing precipitation patterns in the catchment","river_name":null,"geocode_query":"Mount Feathertop, Victoria, Australia","latitude":-36.9,"longitude":147.13,"evidence":{"passage_id":"p005-000","page":5,"quote":"D. L. Shrestha et al.: Evaluation of numerical weather prediction model precipitation forecasts 1917 Fig. 2. Schematic of NWP model runs. several urban centres that have experienced significant economic damage from flooding. The Ovens river rises in the Victorian Alps and the catchment is bounded by several significant peaks, including Mount Hotham (elevation 1861 m, longitude 147.33◦ , latitude −37.05◦ ), Mount Feathertop (elevation 1922 m, longitude 147.13◦ , latitude −36.9◦ ) and Mount Buller (elevation 1805 m, longitude 146.41◦ , latitude −37.14◦ ). The Wangaratta streamflow gauge (elevation 140 m, longitude 146.30◦ , latitude −36.42◦ ) drains an area of 5552 km2 . The upper catchment is steep and hilly, and covered by native forest and tree plantations. The lower catchment is relatively flat with a wide floodplain and is mainly used for grazing and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.13,-36.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43667140,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-2"},{"name":"Mount Buller","kind":"landscape","role":"high elevation reference peak influencing precipitation patterns in the catchment","river_name":null,"geocode_query":"Mount Buller, Victoria, Australia","latitude":-37.14,"longitude":146.41,"evidence":{"passage_id":"p005-000","page":5,"quote":"D. L. Shrestha et al.: Evaluation of numerical weather prediction model precipitation forecasts 1917 Fig. 2. Schematic of NWP model runs. several urban centres that have experienced significant economic damage from flooding. The Ovens river rises in the Victorian Alps and the catchment is bounded by several significant peaks, including Mount Hotham (elevation 1861 m, longitude 147.33◦ , latitude −37.05◦ ), Mount Feathertop (elevation 1922 m, longitude 147.13◦ , latitude −36.9◦ ) and Mount Buller (elevation 1805 m, longitude 146.41◦ , latitude −37.14◦ ). The Wangaratta streamflow gauge (elevation 140 m, longitude 146.30◦ , latitude −36.42◦ ) drains an area of 5552 km2 . The upper catchment is steep and hilly, and covered by native forest and tree plantations. The lower catchment is relatively flat with a wide floodplain and is mainly used for grazing and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.41,-37.14],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43665952,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-3"},{"name":"Wangaratta streamflow gauge","kind":"station","role":"catchment outlet and hydrological data point for streamflow forecasting","river_name":"Ovens River","geocode_query":"Wangaratta, Victoria, Australia","latitude":-36.42,"longitude":146.3,"evidence":{"passage_id":"p005-000","page":5,"quote":"D. L. Shrestha et al.: Evaluation of numerical weather prediction model precipitation forecasts 1917 Fig. 2. Schematic of NWP model runs. several urban centres that have experienced significant economic damage from flooding. The Ovens river rises in the Victorian Alps and the catchment is bounded by several significant peaks, including Mount Hotham (elevation 1861 m, longitude 147.33◦ , latitude −37.05◦ ), Mount Feathertop (elevation 1922 m, longitude 147.13◦ , latitude −36.9◦ ) and Mount Buller (elevation 1805 m, longitude 146.41◦ , latitude −37.14◦ ). The Wangaratta streamflow gauge (elevation 140 m, longitude 146.30◦ , latitude −36.42◦ ) drains an area of 5552 km2 . The upper catchment is steep and hilly, and covered by native forest and tree plantations. The lower catchment is relatively flat with a wide floodplain and is mainly used for grazing and cropping.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.3,-36.42],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43677356,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-4"},{"name":"Falls Creek AWS","kind":"station","role":"high elevation precipitation station for observation comparison","river_name":null,"geocode_query":"Falls Creek, Victoria, Australia","latitude":-36.87,"longitude":147.27,"evidence":{"passage_id":"p009-002","page":9,"quote":"Clearly, downscaling and bias adjustment are operationally recommended for the ACCESS-R and ACCESS-G models. Figure 5 shows the evaluation scores of the ACCESS models for forecasts of 24 h precipitation accumulations at measurement stations. The RMSE score is shown in Fig. 5a. ACCESS-VT model has a minimum RMSE score of about 5.41 mm day−1 for Wangaratta station (4) and a maximum value of 12.06 mm day−1 for Falls Creek station (26). ACCESS-A model has the highest RMSE score of 14.64 mm day−1 at Cheshunt station (23) and the lowest value of 6.4 mm day−1 at Rocky Point station (8). ACCESS-R model has a minimum RMSE value of 6.5 mm day−1 at Loombah Reservoir station (15) and a maximum RMSE value of 13.24 mm day−1 at Falls Creek station (26). ACCESS-G model has a minimum RMSE value of 4.67 mm day−1 at Bloomfield station (5) and a maximum RMSE value of 13.24 mm day−1 at Mount Buffalo station (17). Average RMSE values of ACCESS-VT, ACCESS-R and ACCESS-G models for all stations are comparable.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.279182,-36.8646894],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667338,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Falls Creek VIC 3699, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-5"},{"name":"Mongans Bridge station","kind":"station","role":"measurement station with outlier precipitation event affecting skill analysis","river_name":null,"geocode_query":"Mongans Bridge, Victoria, Australia","latitude":-36.6,"longitude":147.1,"evidence":{"passage_id":"p010-001","page":10,"quote":"Thus, the spatial variability of correlation coefficients (Fig. 5c) comes mainly from the variability of the observed precipitation across the stations and, but not necessarily from the ACCESS-G forecasts. For example, Wangaratta AWS (4) and Mount Buffalo (17) stations share the same value of precipitation forecasts as they lie in the same grid cell of the ACCESS-G model, but have quite different observed precipitation (mean daily values of 2.77 mm vs. 8.81 mm). The variability of mean daily precipitation across the stations (standard deviation of 1.41) is much higher than that of the ACCESS-G model (standard deviation of 0.32). The very low value of correlation at Mongans Bridge (11) may be due to forecast and/or observation outlier in a month of March 2011 (forecast of 150 mm against observation of 5 mm precipitation). Further analysis has been done to understand the contribution of bias and variance to RMSE (see Eq. 2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.0933795,-36.5847249],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666985,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mongans Bridge VIC 3691, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-6"},{"name":"Carboor Upper station","kind":"station","role":"central station for forecast skill analysis and lead-time evaluation","river_name":null,"geocode_query":"Carboor Upper, Victoria, Australia","latitude":-36.64,"longitude":146.55,"evidence":{"passage_id":"p010-003","page":10,"quote":"We present analysis of forecasts from the ACCESS-G model because it has the longest lead time. We focus on a single precipitation station, Carboor Upper (13), which is close to the centre of the Ovens catchment and ACCESS-G model grid cell, and analyse forecast skill of 3 h precipitation accumulations. Analysis of other models and locations produces similar results. The score for 3 h precipitation accumulations at 3 h lead time means the score of total precipitation for the period 09:00– 12:00 LT. Figure 6 shows the forecast skill of 3 h precipitation accumulations for the ACCESS-G model at Carboor Upper station. The RMSE score standardised by standard deviation of observations (sRMSE) is shown in Fig. 6a. The sRMSE value will be greater than 1 when the mean-square error (MSE) exceeds the variance of the observation. This is analogue to a negative value of Nash–Sutcliffe efficiency (Nash and Sutcliffe, 1970) when the MSE exceeds the variance of the observation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.53,-36.6],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666138,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Carboor Upper, Carboor VIC 3678, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-7","true_coordinates":[146.53,-36.6599999],"spread_km":6.7},{"name":"Rosenwhite station","kind":"station","role":"station with low correlation for precipitation forecast evaluation","river_name":null,"geocode_query":"Rosewhite, Victoria, Australia","latitude":-36.58,"longitude":146.82,"evidence":{"passage_id":"p009-004","page":9,"quote":"As the model resolution becomes progressively coarser (i.e. regional and global models), large systematic biases emerge. Unlike RMSE score, the bias shows some spatial pattern. Spearman rank correlation coefficients between the station precipitation and the corresponding ACCESS model forecasts are shown in Fig. 5c. The correlation coefficients of high resolution models ACCESS-VT, ACCESS-A, and the regional model ACCESS-R are comparable and vary between about 0.7 and 0.8. In some stations like Rosenwhite (10), all these models give consistently lower correlation values (about 0.7) and stations like Cheshnut (23), all models give consistently higher correlation values (about 0.8). The www.hydrol-earth-syst-sci.net/17/1913/2013/ Hydrol. Earth Syst. Sci., 17, 1913–1931, 2013","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8786243,-36.5848151],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666633,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rosewhite VIC 3737, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-8"},{"name":"Cheshunt station","kind":"station","role":"station with high correlation for precipitation forecast evaluation","river_name":null,"geocode_query":"Cheshunt, Victoria, Australia","latitude":-36.83,"longitude":146.4,"evidence":{"passage_id":"p009-004","page":9,"quote":"As the model resolution becomes progressively coarser (i.e. regional and global models), large systematic biases emerge. Unlike RMSE score, the bias shows some spatial pattern. Spearman rank correlation coefficients between the station precipitation and the corresponding ACCESS model forecasts are shown in Fig. 5c. The correlation coefficients of high resolution models ACCESS-VT, ACCESS-A, and the regional model ACCESS-R are comparable and vary between about 0.7 and 0.8. In some stations like Rosenwhite (10), all these models give consistently lower correlation values (about 0.7) and stations like Cheshnut (23), all models give consistently higher correlation values (about 0.8). The www.hydrol-earth-syst-sci.net/17/1913/2013/ Hydrol. Earth Syst. Sci., 17, 1913–1931, 2013","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.4327839,-36.8291292],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665997,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cheshunt VIC 3678, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-852c4d5b58e20e0d49a74795-site-9"}],"limitations":["Year of data is limited to one year (2010–2011), limiting conclusions on seasonality.","Spatial resolution of global NWP model too coarse to resolve small catchments accurately.","Observed precipitation datasets required infilling and quality control which may differ from operational real-time data.","Coordinates only given for selected landmarks and stations; many catchment scale locations lack explicit coordinates.","Analysis focused on precipitation from large scale synoptic systems; convective precipitation skill not assessed."],"river_ids":["43639557","43639567","43639570","43640241","43640344","43643199","43643227","43643345","43643771","43644030","43644470","43644997","43645007","43645094","43645755","43645787","43645803","43645925","43646976","43646999","43647042","43647060","43647075","43647242","43647646","43648328","43648338","43648350","43648354","43648403","43648454","43648456","43649191","43650481","43660233","43660234","43660236","43660242","43660251","43660252","43660256"],"site_ids":["doc-852c4d5b58e20e0d49a74795-site-0","doc-852c4d5b58e20e0d49a74795-site-1","doc-852c4d5b58e20e0d49a74795-site-2","doc-852c4d5b58e20e0d49a74795-site-3","doc-852c4d5b58e20e0d49a74795-site-4","doc-852c4d5b58e20e0d49a74795-site-5","doc-852c4d5b58e20e0d49a74795-site-6","doc-852c4d5b58e20e0d49a74795-site-7","doc-852c4d5b58e20e0d49a74795-site-8","doc-852c4d5b58e20e0d49a74795-site-9"],"anchor":[147.13,-36.9],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-98979cbe50b92475d849783c","title":"Improving statistical forecasts of seasonal streamflows using hydrological model output","display_title":"Hybrid Seasonal Streamflow Forecasting in Eastern Australia","doi":"https://doi.org/10.5194/hess-17-579-2013","theme":"Flow & ecology","color":"#80edc2","year":2013,"publication_date":"2013-02-08","authors":["D. E. Robertson","P. Pokhrel","Q. J. Wang"],"summary":"This study assesses a hybrid seasonal streamflow forecasting system in 21 catchments across eastern Australia, combining dynamic hydrological model outputs with statistical approaches to improve forecasts of three-month streamflow totals. The approach uses WAPABA model simulations representing initial catchment conditions and lag-1 streamflow as predictors, compared with operational practice using selected antecedent streamflow and rainfall predictors. The hybrid system generally produces more skillful and reliable forecasts, particularly during catchment wetting, drying, or intermittent saturation periods, with evaluation using strict cross-validation techniques.","finding":"Replacing selected predictors representing initial catchment conditions with hydrological model simulations and lag-1 streamflow improves forecast skill by up to 25%, especially when catchments are wetting up, drying, or near saturation intermittently, without loss of reliability.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"However when the reliability is assessed for each season and catchment separately, deviations from uniformity are within the range expected by sample variability. 4.3 Reasons for improvements in forecast skill The improvements in forecast skill occur under three main sets of conditions: (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall; (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow; and (3) when the initial catchment condition is near saturation intermittently throughout Hydrol. Earth Syst. Sci., 17, 579–593, 2013 www.hydrol-earth-syst-sci.net/17/579/2013/","verified_in_page":true},"topics":["seasonal streamflow forecasting","hydrological modeling","catchment wetness","statistical forecasting","hydrological model calibration","forecast skill","forecast reliability","cross-validation"],"study_period":"1950–2008","locations":[{"name":"Barron River","kind":"river","role":"study catchment","river_name":"Barron River","geocode_query":"Barron River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.585912,-17.047495],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651465,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barron River, Queensland, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-0","true_coordinates":[145.5859120481276,-16.98749451142494],"spread_km":6.7},{"name":"South Johnstone River","kind":"river","role":"study catchment","river_name":"South Johnstone River","geocode_query":"South Johnstone River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.8581302583786,-17.60309243315682],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651933,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Johnstone River, Queensland, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-1"},{"name":"Burdekin River","kind":"river","role":"study catchment","river_name":"Burdekin River","geocode_query":"Burdekin River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.589522,-20.07003],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":16652564,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040068450,"main_basin_id":5040068450,"basin_region":"Australasia","basin_area_km2":130431.0,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-2","true_coordinates":[146.53419,-20.10003],"spread_km":6.7},{"name":"Brisbane River","kind":"river","role":"study catchment","river_name":"Brisbane River","geocode_query":"Brisbane River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.3331008092553,-26.87816882571065],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129112,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane River, Queensland, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-3"},{"name":"Somerset Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Somerset Reservoir, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Hume Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Hume Reservoir, Upper Murray, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Dartmouth Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Dartmouth Reservoir, Upper Murray, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Kiewa River","kind":"river","role":"study catchment","river_name":"Kiewa River","geocode_query":"Kiewa River, Upper Murray, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.055556,-36.468056],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43666973,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-7","true_coordinates":[147.0555556600001,-36.52805551999995],"spread_km":6.7},{"name":"Ovens River","kind":"river","role":"study catchment","river_name":"Ovens River","geocode_query":"Ovens River, Upper Murray, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.931177,-36.652358],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676905,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-8","true_coordinates":[146.85638899200012,-36.65235757999994],"spread_km":6.7},{"name":"Nillahcootie Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Nillahcootie Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Eildon Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Eildon Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9166667,-37.1833333],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665201,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eildon Reservoir, Lake Eildon VIC 3713, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-10"},{"name":"Goulburn Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Goulburn Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.0209892,-37.7230058],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558717,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Goulburn Ave, Reservoir VIC 3073, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-11"},{"name":"Eppalock Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Eppalock Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Cairn Curran Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Cairn Curran Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.970474,-36.9567],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662539,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cairn Curran Reservoir, Victoria, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-13","true_coordinates":[143.9704739,-37.0167004],"spread_km":6.7},{"name":"Tullaroop Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Tullaroop Reservoir, Central Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.864471,-37.048242],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662378,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tullaroop Reservoir, Victoria 3465, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-14","true_coordinates":[143.8644705,-37.1082424],"spread_km":6.7},{"name":"Thompson Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Thompson Reservoir, Southern Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Upper Yarra Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Upper Yarra Reservoir, Southern Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.914978,-37.625405],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559583,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Upper Yarra Reservoir, Reefton VIC 3799, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-16","true_coordinates":[145.9149785,-37.6854051],"spread_km":6.7},{"name":"Maroondah Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"Maroondah Reservoir, Southern Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.564924,-37.57365],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559147,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Maroondah Reservoir, Healesville VIC 3777, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-17","true_coordinates":[145.5649243,-37.6336501],"spread_km":6.7},{"name":"O'Shannassy Reservoir","kind":"facility","role":"study catchment reservoir","river_name":null,"geocode_query":"O'Shannassy Reservoir, Southern Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.807177,-37.611838],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559443,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"O'Shannassy Reservoir, Mcmahons Creek VIC 3799, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-18","true_coordinates":[145.8071772,-37.671838],"spread_km":6.7},{"name":"Mersey-Forth Hydroelectric Scheme","kind":"facility","role":"study catchment hydroelectric scheme","river_name":null,"geocode_query":"Mersey-Forth Hydroelectric Scheme, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"King Hydroelectric Scheme","kind":"facility","role":"study catchment hydroelectric scheme","river_name":null,"geocode_query":"King Hydroelectric Scheme, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"We use the observed monthly streamflow data obtained from various water resource management agencies and the Bureau of Meteorology. For most catchments, with the exception of some in Queensland and Victoria, the data are available from 1950 to 2008 (see Table 1). The monthly catchment average rainfall and potential evapotranspiration for each catchment are calculated from 5 km gridded data available from the Australian Water Availability Project (Jones et al., 2009). The monthly values of the 13 climate indices are obtained from the Bureau of Meteorology and described in Appendix A. 4 Results 4.1 Forecast skill improvements Forecasts made using WAPABA simulations and lag-1 streamflow as predictors to represent initial catchment conditions generally have greater skill than forecasts made using selected predictors (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.937076,-39.815348],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31289394,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"King Island, Tasmania 7256, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-98979cbe50b92475d849783c-site-20","true_coordinates":[143.9370758,-39.87534770000001],"spread_km":6.7}],"limitations":["Coordinates of study sites not provided; latitude and longitude values unavailable for all catchments and facilities","Use of monthly aggregated data may limit resolution of catchment dynamics","Hydrological modelling uses a simplified lumped monthly water balance model which may not capture all process complexities","Climate indices as predictors are surrogates and may not fully capture true climate predictability sources","Cross-validation approach (leave-one-plus-four-years-out) might not eliminate all dependencies in data for parameter inference"],"river_ids":["43639557","43639567","43639570","43639579","43639618","43639641","43640241","43640344","43643199","43643227","43643345","43643771","43644030","43644470","43644997","43645007","43645094","43645616","43645755","43645787","43645803","43645906","43645925","43646027","43646036","43646136","43646451","43646488","43646586","43646716","43646800","43646834","43646866","43646976","43646999","43647042","43647060","43647075","43647242","43647450","43647513","43647646","43648328","43648338","43648350","43648354","43648403","43648454","43648456","43649191","43650161","43650481","43660233","43660234","43660236","43660242","43660251","43660252","43660256","ne-1082"],"site_ids":["doc-98979cbe50b92475d849783c-site-0","doc-98979cbe50b92475d849783c-site-1","doc-98979cbe50b92475d849783c-site-2","doc-98979cbe50b92475d849783c-site-3","doc-98979cbe50b92475d849783c-site-7","doc-98979cbe50b92475d849783c-site-8","doc-98979cbe50b92475d849783c-site-10","doc-98979cbe50b92475d849783c-site-11","doc-98979cbe50b92475d849783c-site-13","doc-98979cbe50b92475d849783c-site-14","doc-98979cbe50b92475d849783c-site-16","doc-98979cbe50b92475d849783c-site-17","doc-98979cbe50b92475d849783c-site-18","doc-98979cbe50b92475d849783c-site-20"],"anchor":[145.9166667,-37.1833333],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040067860,5040068450,5040069970,5040070710,5040077640,5040591050],"main_basin_ids":[5040067860,5040068450,5040069970,5040070710,5040073410,5040077640]},{"id":"doc-3817075ef2e7fea5d7fbac2c","title":"Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps","display_title":"Management and Environment Effects on Australian Wheat Yields","doi":"https://doi.org/10.1088/1748-9326/9/4/044005","theme":"Land & ecosystems","color":"#c8df98","year":2014,"publication_date":"2014-04-01","authors":["Brett A. Bryan","Darran King","Gang Zhao"],"summary":"This study used the APSIM crop model to simulate wheat yields under 225 fertilization and residue management scenarios across 179 Mha of Australian arable land from 1900 to 2010. It quantified the influence of management and environmental variables on wheat yield, finding fertilization strongly improves yields up to ~100 kg N/ha/yr, especially in cooler, wetter climates with higher soil water holding capacity. Residue management showed little yield effect under the conditions studied. The results provide spatially explicit, agronomically detailed information to support sustainable intensification and closing yield gaps in Australian wheat production.","finding":"Fertilization strongly increased wheat yield up to around 100 kg N ha−1 yr−1, but this effect was highly dependent on local climate and soil conditions, with stronger influences in cooler, wetter climates and soils with higher water holding capacity.","finding_evidence":{"passage_id":"p002-001","page":2,"quote":"We quantified the ability for agricultural management to increase wheat yields across 179 Mha of potentially arable land in Australia. Using the Agricultural Production Systems Simulator (APSIM), we simulated the impact on wheat yield of 225 fertilization and residue management scenarios at a high spatial, temporal, and agronomic resolution from 1900 to 2010. The influence of management and environmental variables on wheat yield was then assessed using Spearman’s non-parametric correlation test with bootstrapping. While residue management showed little correlation, fertilization strongly increased wheat yield up to around 100 kg N ha−1 yr−1 . However, this effect was highly dependent on the key environment variables of rainfall, temperature, and soil water holding capacity. The influence of fertilization on yield was stronger in cooler, wetter climates, and in soils with greater water holding capacity.","verified_in_page":true},"topics":["yield gap","crop modeling","fertilization","soil water holding capacity","climate influence","sustainable intensification","wheat production","nutrition management"],"study_period":"1900-2010","locations":[{"name":"Wimmera cropping region, Victoria, Australia","kind":"region","role":"Example yield gap region for comparison and context","river_name":null,"geocode_query":"Wimmera cropping region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Yield gaps vary considerably between regions following differences in access to capital, technology, and labor. Larger yield gaps tend to occur in developing nations, although yield improvements are also possible in industrialized agricultural systems (Neumann et al 2010, Mueller et al 2012). For example, Hochman et al (2012) recently reported that on average actual yields were about half of attainable yields in the Wimmera cropping region of Australia. Within regions, yield gaps also vary between individual farms as farmers manage land according to their managerial skill, access to capital, attitude to risk, and other factors outside of their control (Hochman et al 2009, 2013, Monjardino et al 2013, van Ittersum et al 2013). However, simply quantifying the difference between actual and attainable yields provides little information on how yield gaps might be closed.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.3613378,-36.3131369],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677796,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wimmera, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-0"},{"name":"Western Australian wheatbelt, Western Australia, Australia","kind":"region","role":"Key wheat-growing region analyzed for model validation and yield patterns","river_name":null,"geocode_query":"Western Australian wheatbelt, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Results were collected and stored in a MS SQL Server database. 2.7. Validation Census and survey data offers an opportunity for validation of large scale model outputs (Bryan et al 2011b). We validated our simulations against census-reported yields sourced from the Australian Bureau of Statistics. Nitrogen application rates—a key management variable—were estimated from state government agricultural extension information (gross margin handbooks) for three key wheat-growing regions including the Western Australian wheatbelt, the South Australian and western Victorian cropping districts, and northern New South Wales. Wheat yields simulated at the level of nitrogen application specified in the gross margin handbooks were compared against census-reported yields by Statistical Local Area for 2006 for the three regions using a student’s t-test to assess differences between the means. 2.8.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.156567,-32.003416],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70414962,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wheatbelt, WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-1","true_coordinates":[118.0952729,-32.0334157],"spread_km":6.7},{"name":"South Australian and western Victorian cropping districts, South Australia and Victoria, Australia","kind":"region","role":"Key wheat-growing regions analyzed for model validation and yield patterns","river_name":null,"geocode_query":"South Australian and western Victorian cropping districts, South Australia and Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Results were collected and stored in a MS SQL Server database. 2.7. Validation Census and survey data offers an opportunity for validation of large scale model outputs (Bryan et al 2011b). We validated our simulations against census-reported yields sourced from the Australian Bureau of Statistics. Nitrogen application rates—a key management variable—were estimated from state government agricultural extension information (gross margin handbooks) for three key wheat-growing regions including the Western Australian wheatbelt, the South Australian and western Victorian cropping districts, and northern New South Wales. Wheat yields simulated at the level of nitrogen application specified in the gross margin handbooks were compared against census-reported yields by Statistical Local Area for 2006 for the three regions using a student’s t-test to assess differences between the means. 2.8.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Northern New South Wales cropping districts, New South Wales, Australia","kind":"region","role":"Key wheat-growing region analyzed for model validation and yield patterns","river_name":null,"geocode_query":"Northern New South Wales cropping districts, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Results were collected and stored in a MS SQL Server database. 2.7. Validation Census and survey data offers an opportunity for validation of large scale model outputs (Bryan et al 2011b). We validated our simulations against census-reported yields sourced from the Australian Bureau of Statistics. Nitrogen application rates—a key management variable—were estimated from state government agricultural extension information (gross margin handbooks) for three key wheat-growing regions including the Western Australian wheatbelt, the South Australian and western Victorian cropping districts, and northern New South Wales. Wheat yields simulated at the level of nitrogen application specified in the gross margin handbooks were compared against census-reported yields by Statistical Local Area for 2006 for the three regions using a student’s t-test to assess differences between the means. 2.8.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Murray–Darling Basin, Australia","kind":"basin","role":"Region exhibiting significant yield benefits from fertilization","river_name":"Murray–Darling Basin","geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"A similar effect was found in the influence of fertilization and soil water holding capacity as the influence of fertilization was greatly reduced in soils with lower water holding capacity (figure 4). 3.3. Yield benefits of fertilization The benefit of fertilization in increasing yields varied over the study area with the complex geographic interplay of environmental variables. At fertilization rates up to 100 kg N ha−1 yr−1 , wheat yields were most responsive in the wheatbelt of Western Australia, in South Australia’s agricultural districts, and in the Murray–Darling Basin. Further yield increases were achieved with higher fertilization rates (>100 kg N ha−1 yr−1 ), especially in the higher rainfall areas of southern Victoria and 6","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.598397,-31.149981],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-4","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Wheatbelt of Western Australia, Western Australia, Australia","kind":"region","role":"Region with high wheat yield responsiveness to fertilization","river_name":null,"geocode_query":"Wheatbelt of Western Australia, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"A similar effect was found in the influence of fertilization and soil water holding capacity as the influence of fertilization was greatly reduced in soils with lower water holding capacity (figure 4). 3.3. Yield benefits of fertilization The benefit of fertilization in increasing yields varied over the study area with the complex geographic interplay of environmental variables. At fertilization rates up to 100 kg N ha−1 yr−1 , wheat yields were most responsive in the wheatbelt of Western Australia, in South Australia’s agricultural districts, and in the Murray–Darling Basin. Further yield increases were achieved with higher fertilization rates (>100 kg N ha−1 yr−1 ), especially in the higher rainfall areas of southern Victoria and 6","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.033979,-32.003416],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70414962,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wheatbelt, WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-5","true_coordinates":[118.0952729,-32.0334157],"spread_km":6.7},{"name":"Agricultural districts of South Australia, South Australia, Australia","kind":"region","role":"Region with high wheat yield responsiveness to fertilization","river_name":null,"geocode_query":"Agricultural districts of South Australia, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"A similar effect was found in the influence of fertilization and soil water holding capacity as the influence of fertilization was greatly reduced in soils with lower water holding capacity (figure 4). 3.3. Yield benefits of fertilization The benefit of fertilization in increasing yields varied over the study area with the complex geographic interplay of environmental variables. At fertilization rates up to 100 kg N ha−1 yr−1 , wheat yields were most responsive in the wheatbelt of Western Australia, in South Australia’s agricultural districts, and in the Murray–Darling Basin. Further yield increases were achieved with higher fertilization rates (>100 kg N ha−1 yr−1 ), especially in the higher rainfall areas of southern Victoria and 6","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Higher rainfall areas of southern Victoria, Victoria, Australia","kind":"region","role":"Region achieving further yield increases with higher fertilization rates","river_name":null,"geocode_query":"Southern Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"A similar effect was found in the influence of fertilization and soil water holding capacity as the influence of fertilization was greatly reduced in soils with lower water holding capacity (figure 4). 3.3. Yield benefits of fertilization The benefit of fertilization in increasing yields varied over the study area with the complex geographic interplay of environmental variables. At fertilization rates up to 100 kg N ha−1 yr−1 , wheat yields were most responsive in the wheatbelt of Western Australia, in South Australia’s agricultural districts, and in the Murray–Darling Basin. Further yield increases were achieved with higher fertilization rates (>100 kg N ha−1 yr−1 ), especially in the higher rainfall areas of southern Victoria and 6","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.9559035,-37.83434430000001],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Melbourne VIC 3205, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-7"},{"name":"Coastal New South Wales, New South Wales, Australia","kind":"region","role":"Region achieving further yield increases with higher fertilization rates","river_name":null,"geocode_query":"Coastal New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"A similar effect was found in the influence of fertilization and soil water holding capacity as the influence of fertilization was greatly reduced in soils with lower water holding capacity (figure 4). 3.3. Yield benefits of fertilization The benefit of fertilization in increasing yields varied over the study area with the complex geographic interplay of environmental variables. At fertilization rates up to 100 kg N ha−1 yr−1 , wheat yields were most responsive in the wheatbelt of Western Australia, in South Australia’s agricultural districts, and in the Murray–Darling Basin. Further yield increases were achieved with higher fertilization rates (>100 kg N ha−1 yr−1 ), especially in the higher rainfall areas of southern Victoria and 6","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.656449,-36.74061],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29052296,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Coast, NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-8","true_coordinates":[149.6564494,-36.6806101],"spread_km":6.7},{"name":"Arable cropping regions south of the Tropic of Capricorn, eastern states, Australia","kind":"region","role":"Study area covering most Australian wheat production areas","river_name":null,"geocode_query":"Eastern states south of Tropic of Capricorn, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"The individual effects of the four most influential management (fertilization) and environmental parameters (effective growing season rainfall, average maximum temperature, soil water holding capacity) on yield were quantified using boxplots. The effect of fertilization on yield was also assessed in the context of the three most influential environmental parameters using bivariate contour plots. Finally, we mapped the yield benefits of fertilization across a range of effective growing season rainfall. The results provide information sensitive to local environmental conditions that can support farm management decisions for improving wheat yields and thereby help to close yield gaps in Australia. 2. Methods 2.1. Study area Wheat is the most widely produced crop in Australia and can be grown throughout most of the eastern states south of the tropic of Capricorn, in the southern parts of South Australia and in south-west Western Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.43944,-27.672817],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677010,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eastern states of Australia, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-9","true_coordinates":[148.3716902,-27.6728168],"spread_km":6.7},{"name":"South-west Western Australia, Australia","kind":"region","role":"Study area for wheat production and model simulation","river_name":null,"geocode_query":"South-west Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"The individual effects of the four most influential management (fertilization) and environmental parameters (effective growing season rainfall, average maximum temperature, soil water holding capacity) on yield were quantified using boxplots. The effect of fertilization on yield was also assessed in the context of the three most influential environmental parameters using bivariate contour plots. Finally, we mapped the yield benefits of fertilization across a range of effective growing season rainfall. The results provide information sensitive to local environmental conditions that can support farm management decisions for improving wheat yields and thereby help to close yield gaps in Australia. 2. Methods 2.1. Study area Wheat is the most widely produced crop in Australia and can be grown throughout most of the eastern states south of the tropic of Capricorn, in the southern parts of South Australia and in south-west Western Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.072811,-34.001625],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-3817075ef2e7fea5d7fbac2c-site-10","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"South-eastern Australia cropping districts, Australia","kind":"region","role":"Region with highest simulated and census wheat yields","river_name":null,"geocode_query":"South-eastern Australia cropping districts, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The break in gray shade is the median, the ends of the shaded area represent the 25th percentile (lower) and 75th percentile (upper), and the whiskers extend to the most extreme data point within 1.5 times the inter-quartile range. variable) on potential wheat yield benefit was then mapped across the range of effective growing season rainfall (the most influential environmental variable). 3. Results 3.1. Yield estimates and validation The major cropping districts in south-eastern Australia and south-western Australia were the highest-yielding regions, with the drier, inland areas producing the lowest yields. For 2006, census-reported yields ranged from 540 to 2310 kg ha−1 yr−1 (median 1260 kg ha−1 yr−1 ) across the three validation areas. These estimates were comparable with modeled yields which ranged from 639 to 2906 kg ha−1 yr−1 (median 1553 kg ha−1 yr−1 ).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Soil mapping quality and extent is variable in parts of the study area reducing confidence in these areas","Soil data lacks topographical detail such as slope and aspect which affect crop yields","Management scenarios discretized in increments of 25 kg N/ha/yr may miss finer management effects","Other crop management techniques like crop rotation were not considered","Inter-annual carryover effects of nitrogen, organic matter, or water were not modeled","Economic viability and environmental sustainability considerations were identified but not quantitatively integrated in this 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-6","basin_ids":[5040055960,5040056460,5040070710,5040511570,5040591010,5040591050],"main_basin_ids":[5040055960,5040056460,5040070710,5040073410]},{"id":"doc-38c0b978cbb7ac3f4baf3c00","title":"Modelling overbank flood recharge at a continental scale","display_title":"Large-scale modeling of overbank flood recharge in Australia","doi":"https://doi.org/10.5194/hess-18-1273-2014","theme":"Groundwater","color":"#a8b9ff","year":2014,"publication_date":"2014-04-03","authors":["R. Doble","R. Crosbie","L. Peeters","K. Joehnk","C. Ticehurst"],"summary":"The study applied and validated a continental-scale model to estimate groundwater recharge from overbank floods in seven hydrologically distinct Australian catchments during the 2010/2011 flood period. The model combined analytical flood recharge equations with satellite-derived flood maps and national soil datasets, and its outputs were compared against independent point-scale borehole recharge estimates, 1-D soil-vegetation-atmosphere model simulations, and catchment-scale groundwater storage changes. Overbank flood recharge was found to be a significant though episodic contributor to groundwater recharge, accounting for at least 4% of total recharge and 15% of riparian recharge in the Loddon catchment. The model sufficiently captured spatial flood recharge patterns but tended to underpredict absolute recharge volumes, with limitations due to satellite flood detection accuracy, soil property data, and groundwater observation frequency.","finding":"Overbank flood recharge represented a minimum of 4% of the total groundwater recharge and 15% of riparian recharge in the Loddon catchment during the 2010/2011 floods, indicating it as a significant component to include in continental scale water balance and groundwater sustainable yield models.","finding_evidence":{"passage_id":"p008-004","page":8,"quote":"It should be noted that the water budget presented, while it is the best possible for the data available, is not an accurate representation of the water budget during flood conditions. Due to the changes in soil saturation during flood inundation, irrigation and rainfall, there is a high uncertainty associated with using a linear water balance. 5 Discussion 5.1 How important is the overbank flood recharge process? Given the uncertainty in modelling groundwater recharge from overbank flooding, and the input data required to do so, it is reasonable to ask whether there is value considering this process in large-scale water balance models. Comparison between the OFR modelling and WAVES modelling suggested that overbank flood recharge is not an insignificant volume of recharge to a catchment.","verified_in_page":true},"topics":["groundwater recharge","floodplain hydrology","remote sensing","satellite imagery","soil hydraulic properties","water balance modeling","water resources management","hydrological modeling"],"study_period":"2010-11-01 to 2011-03-31","locations":[{"name":"Loddon catchment","kind":"basin","role":"primary study area for model application, validation and comparison with bore data, recharge modeling, and catchment-scale storage change","river_name":"Loddon River","geocode_query":"Loddon River catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1276 R. Doble et al.: Modelling overbank flood recharge at a continental scale Fig. 1. Location map for the catchments used in this study. Equations (2)–5) were coded in R, and the script run with a daily time step from 1 November 2010 until the 31 March 2011, a time frame that adequately captured the 2010/2011 floods for all of the catchments modelled. Recharge to groundwater was calculated for each day, then summed for the 5 months to give total recharge for each of the floods. 2.4 Test catchments Seven catchments were chosen within Australia from areas that are climatically distinct and within regions that experienced overbank flooding during the period from November 2010 until March 2011 (Fig. 1). The catchments ranged from a tropical savannah environment in northern Australia, to sub-tropical, arid and temperate catchments within the Murray Darling Basin of south-eastern Australia. The hydrological characteristics of the catchments are outlined in Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.888996,-36.29693],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43676983,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-0","true_coordinates":[143.8889961117272,-36.23693026199377],"spread_km":6.7},{"name":"Campaspe catchment","kind":"basin","role":"study area for model application and validation against borehole recharge estimates","river_name":"Campaspe River","geocode_query":"Campaspe River catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Maps of the flood recharge for the seven catchments for the floods between 1 November 2010 and 31 March 2011. 4.1 Comparison of modelled and estimated recharge at a point scale There appears to be a nominal increase in recharge calculated from bore records in the floodplain region of the Loddon and Campaspe catchments (the area indicated to be flooded from modelled data) compared with bores in other areas; for example the southern highland (Fig. 5). Flooded areas were defined by a modelled OFR of greater than 0.1, 0.5, or 1 mm over the flood period (box plot of log recharge shows results for a 1 mm threshold), and the log of negative recharge redefined to zero (Fig. 6). The box plots were found to be very insensitive to the threshold for flooding within this range.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.631576,-36.548183],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43663477,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-1","true_coordinates":[144.6315759260196,-36.48818284354748],"spread_km":6.7},{"name":"Condamine catchment","kind":"basin","role":"study area for model application and spatial comparison of recharge patterns with bore data","river_name":"Barwon-Condamine-Culgoa Rivers","geocode_query":"Condamine River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"In the Condamine catchment, there was evidence of groundwater recharge in bores in the eastern part of the catchment that is close to, but does not align with areas of modelled recharge (Fig. 8). Using 1 mm OFR to separate flooded and non-flooded areas, both the box plots (Fig. 6) and a comparison of hydrographs (Fig. 7) showed very little difference between the two data sets. Many of the shallow bores in the catchment are located in the highly permeable volcanic soils surrounding the upland valley, and were probably not flooded, but showed a response to the high rainfall in this area. The spatial frequency of bore records in the westFig. 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.7489734496872,-27.698022485044],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43676603,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-2"},{"name":"Murrumbidgee catchment","kind":"basin","role":"study area with model application but limited groundwater monitoring data for validation","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Shallow bore hydrograph data were available for the Loddon, Campaspe, and Condamine catchments for the period modelled. The Loddon catchment was selected for more detailed study of point-scale recharge modelling and catchment-scale recharge estimates due to the higher density of bores available for analysis within the catchment. 2.5 Estimation of recharge at a point scale To validate the results from the OFR modelling with estimations from field data, recharge was calculated for the Loddon, Campaspe and Condamine catchments from shallow bore hydrographs using the water table fluctuation method. In the Daly catchment, bore information was too sparse for rigorous analysis. In the Lachlan and Murrumbidgee catchments, bore information for shallow aquifers was not publicly available. Frequently logged data were not available for any bores within these catchments. Recharge was therefore calculated from bores monitored manually, approximately once every 1–2 months.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.201665,-35.312873],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665563,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-3","true_coordinates":[146.2016652340651,-35.2528729004432],"spread_km":6.7},{"name":"Lachlan catchment","kind":"basin","role":"study area for model application with limited MODIS satellite flood data due to cloud cover","river_name":"Lachlan River","geocode_query":"Lachlan River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"In the Lachlan catchment, cloud cover and areas of no satellite signal were found to obstruct mapping of open water bodies early in the flood period (1–9 December) and this may have led to the flood peak being missed (Fig. 4). There are, however, very few open water bodies observed in the period immediately following the flood (11–31 December), and very little change in these water bodies in time. This suggests that even with perfect satellite coverage and absence of clouds, detection of open water bodies is likely to have been underestimated. 4 Tests against independent data The modelled OFR data were compared against recharge calculated at a point scale, at a catchment scale, and with pointscale recharge modelled with the WAVES model. Fig. 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.691375,-33.950919],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43666222,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-4","true_coordinates":[146.6913748976105,-33.89091850027836],"spread_km":6.7},{"name":"Logan–Albert catchments","kind":"basin","role":"study area for model application, low evidence of flood recharge detected by satellite imagery","river_name":null,"geocode_query":"Logan River and Albert River catchments, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"1278 R. Doble et al.: Modelling overbank flood recharge at a continental scale Fig. 2. Time series of flood recharge maps for the Loddon catchment showing the progression of flood recharge down the catchment with time. in particular the Condamine, Daly and Lachlan catchments. There is some confidence that this represents real recharge in areas where the recharge pixels are concentrated together, for example, in small parts of the Murrumbidgee catchment along the river and at the western (outflow) end of the Daly River. Overbank flood recharge was difficult to discern from unexplained flooding in the Logan or Lachlan catchments. There is little evidence of flood-induced open water in the Lachlan, Daly, Logan and Campaspe catchments. This may have been due to the cloud cover and null data due to satellite synchronisation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1325563996681,-27.94778087735968],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":12130581,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-5"},{"name":"Daly catchment","kind":"basin","role":"study area for model application in tropical savannah climate, with sparse bore data and limited flood detection in satellite imagery","river_name":"Daly River","geocode_query":"Daly River catchment, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Shallow bore hydrograph data were available for the Loddon, Campaspe, and Condamine catchments for the period modelled. The Loddon catchment was selected for more detailed study of point-scale recharge modelling and catchment-scale recharge estimates due to the higher density of bores available for analysis within the catchment. 2.5 Estimation of recharge at a point scale To validate the results from the OFR modelling with estimations from field data, recharge was calculated for the Loddon, Campaspe and Condamine catchments from shallow bore hydrographs using the water table fluctuation method. In the Daly catchment, bore information was too sparse for rigorous analysis. In the Lachlan and Murrumbidgee catchments, bore information for shallow aquifers was not publicly available. Frequently logged data were not available for any bores within these catchments. Recharge was therefore calculated from bores monitored manually, approximately once every 1–2 months.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.710743,-13.824953],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102212844,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-38c0b978cbb7ac3f4baf3c00-site-6","true_coordinates":[130.7107425,-13.7649526],"spread_km":6.7}],"limitations":["Satellite MODIS data for flood inundation mapping affected by cloud cover and flight path gaps leading to data gaps and unexplained recharge prediction.","Coarse spatial resolution and uncertainty in continental scale soil hydraulic conductivity, clogging layer thickness, and specific yield data.","Low frequency groundwater bore observations limiting accurate point-scale recharge estimation and validation.","Unexplained recharge due to false positives in satellite flood detection, including saturated soil surfaces misclassified as water.","No incorporation of dynamic water table updates in prototype model runs, which may affect accuracy.","GRACE satellite gravity data resolution insufficient for detailed validation at the model grid scale (500 m)."],"river_ids":[],"site_ids":["doc-38c0b978cbb7ac3f4baf3c00-site-0","doc-38c0b978cbb7ac3f4baf3c00-site-1","doc-38c0b978cbb7ac3f4baf3c00-site-2","doc-38c0b978cbb7ac3f4baf3c00-site-3","doc-38c0b978cbb7ac3f4baf3c00-site-4","doc-38c0b978cbb7ac3f4baf3c00-site-5","doc-38c0b978cbb7ac3f4baf3c00-site-6"],"anchor":[146.691375,-33.950919],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040061670,5040069970,5040511570,5040591050],"main_basin_ids":[5040061670,5040069970,5040073410]},{"id":"doc-c59584f9b3cd28b59d8ae84c","title":"Linkages between ENSO/PDO signals and precipitation, streamflow in China during the last 100 years","display_title":"ENSO/PDO Impacts on China's Precipitation & Streamflow","doi":"https://doi.org/10.5194/hess-18-3651-2014","theme":"Climate","color":"#98d4f1","year":2014,"publication_date":"2014-09-17","authors":["R. Ouyang","W. Liu","G. Fu","C. Liu","L. Hu","H. Wang"],"summary":"This study examines the separate and combined impacts of El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) on precipitation and streamflow across China over the last century (1901–2009). Using data from four major river basins and corresponding gauging stations, it finds that precipitation and streamflow generally decrease during El Niño/PDO warm phases and increase during La Niña/PDO cool phases in most parts of China, with notable regional and seasonal variation. The study highlights important spatial differences in ENSO/PDO impacts between northern and southern China and among the four major river basins: Songhua, Yellow, Yangtze, and Pearl Rivers. It also identifies that streamflow responses under combined ENSO and PDO phases show opposite patterns between northern and southern regions.","finding":"Precipitation and streamflow in the Yellow, Yangtze, and Pearl River basins are more significantly influenced by El Niño and La Niña events than in the Songhua River basin, especially in October and November. Moreover, when considering the combined ENSO and PDO influence, precipitation and streamflow responses are enhanced in northern China and decreased in southern China during warm PDO phases, with opposite patterns during cool PDO phases.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Liu (liuwb@itpcas.ac.cn) Received: 4 March 2014 – Published in Hydrol. Earth Syst. Sci. Discuss.: 15 April 2014 Revised: 16 July 2014 – Accepted: 12 August 2014 – Published: 17 September 2014 Abstract. This paper investigates the single and combined impacts of El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) on precipitation and streamflow in China over the last century. Results indicate that the precipitation and streamflow overall decrease during El Niño/PDO warm phase periods and increase during La Niña/PDO cool phase periods in the majority of China, although there are regional and seasonal differences. Precipitation and streamflow in the Yellow River basin, Yangtze River basin and Pearl River basin are more significantly influenced by El Niño and La Niña events than is precipitation and streamflow in the Songhua River basin, especially in October and November.","verified_in_page":true},"topics":["climate variability","teleconnections","streamflow variability","precipitation patterns","ENSO","PDO","seasonal variability","regional hydrology"],"study_period":"1901–2009","locations":[{"name":"Songhua River basin","kind":"basin","role":"major study basin representing northern China hydrology","river_name":null,"geocode_query":"Songhua River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[108.268232,21.759129],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040013000,"main_basin_id":4040013000,"basin_region":"Central and South-East Asia","basin_area_km2":89832.6,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-0"},{"name":"Harbin Station","kind":"station","role":"streamflow gauging station on Songhua River basin","river_name":"Songhua River","geocode_query":"Harbin, Heilongjiang, China","latitude":45.75,"longitude":126.7667,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[126.6424173,45.7567307],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Harbin, Heilongjiang, China","basin_id":4040186800,"main_basin_id":4040000880,"basin_region":"Central and South-East Asia","basin_area_km2":552626.5,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-1"},{"name":"Yellow River basin","kind":"basin","role":"major study basin with significant ENSO/PDO influence","river_name":"Yellow River","geocode_query":"Yellow River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.610556,36.459334],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040007850,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":77127.5,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-2","true_coordinates":[116.610556,36.519334],"spread_km":6.7},{"name":"Sanmenxia Station","kind":"station","role":"streamflow gauging station on Yellow River basin","river_name":"Yellow River","geocode_query":"Sanmenxia, Henan, China","latitude":34.8167,"longitude":111.3667,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[111.2000526,34.7727068],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sanmenxia, Henan, China","basin_id":4040007850,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":77127.5,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-3"},{"name":"Hankou Station","kind":"station","role":"streamflow gauging station on Yangtze River basin","river_name":"Yangtze River","geocode_query":"Wuhan (Hankou), Hubei, China","latitude":30.6167,"longitude":114.25,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.3664924,30.7194246],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Han Kou Bei Da Dao, Huang Pi Qu, Wu Han Shi, Hu Bei Sheng, China","basin_id":4040773130,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":33943.4,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-4"},{"name":"Yangtze River basin","kind":"basin","role":"major study basin influenced by ENSO/PDO","river_name":"Yangtze River","geocode_query":"Yangtze River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[112.650141,29.660415],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-5","true_coordinates":[112.650141,29.720415],"spread_km":6.7},{"name":"Wuzhou Station","kind":"station","role":"streamflow gauging station on Pearl River basin","river_name":"Pearl River","geocode_query":"Wuzhou, Guangxi, China","latitude":23.7167,"longitude":111.3167,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[111.27917,23.4769099],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wuzhou, Guangxi, China","basin_id":4040012990,"main_basin_id":4040012990,"basin_region":"Central and South-East Asia","basin_area_km2":403804.7,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-6"},{"name":"Pearl River basin","kind":"basin","role":"major study basin in subtropical-tropical climate zone","river_name":"Pearl River","geocode_query":"Pearl River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The location of the gauging stations and the four river basins can be seen in Fig. 1. Songhua River basin, Yellow River basin, Yangtze River basin and Pearl River basin, being the four major large river basins in China, range approximately from the north to the south of China and cover almost all climate types of China. Songhua River basin is located in the north of northern China and lies in the temperate monsoon climate zone. The Yellow River basin can be divided into three sub-regions (i.e. the eastern monsoon sub-region, the arid and semi-arid sub-region, and the high-elevation sub-region), in accordance with the three natural zones of China (Liang et al., 2014). The southern part of the Yangtze River basin is close to the tropical zone and the northern part is close to the temperate region. The Pearl River basin covers a region of subtropical to tropical monsoon climate straddling the Tropic of Cancer. The selected basins are expected to be able to represent the streamflow Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[113.6216667,22.7694444],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"none","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-7"},{"name":"North China Plain","kind":"region","role":"region showing enhanced precipitation and streamflow during La Niña periods","river_name":null,"geocode_query":"North China Plain, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"For example, the overall “annual” precipitation increased in the North China Plain, Southwest China as well as the Tibetan Plateau while it declined in Northeast China, Southeast China and Northwest China during La Niña periods (Fig. 3). However, the trends in El Niño periods over these regions are clearly reversed. The Yangtze River can be spatially treated as a dividing line of ENSO influences on precipitation for eastern China, with the “annual” average precipitation obviously less (differences < −5 %) in the southern regions of the Yangtze River and greater (differences > 5 %) in the northern regions (including the Yellow River, Hai River and Huai River) in La Niña periods than that in El Niño years. It should be noted that the results obtained in the Yellow River basin (similar to the North China Plain) are consistent with many previous studies (Gong and Wang, 1999; Fu et al., 2007; Hao et al., 2008).","verified_in_page":true},"resolution_status":"resolved","coordinates":[119.211853,32.35771],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North China, Yizheng, Yangzhou, Jiangsu, China, 211414","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-8","true_coordinates":[119.15036,32.32771],"spread_km":6.7},{"name":"South China","kind":"region","role":"southern region showing distinct responses to ENSO/PDO compared to northern China","river_name":null,"geocode_query":"South China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-005","page":7,"quote":"Results show that the “annual” precipitation changes in El Niño/La Niña period compared to multi-year average in cool PDO phase are similar to Fig. 3, which indicates that the cool PDO phase does not significantly modulate the ENSO influence on precipitation. However, in the warm PDO phase, the percentage changes for the precipitation are clearly related to El Niño/La Niña. For instance, in Northeast China and Northwest China, the precipitation in La Niña periods is obviously higher than that in El Niño periods during PDO warm phase while the reverse is the case during PDO cool phase. However, the precipitation responses to the two PDO phases are almost opposite in South China and Central China, including most parts of the Yangtze River basin and the upper stream of the Pearl River basin. El Niño/La Niña-related streamflows in the four basins show different responses during different PDO phases (Fig. 9).","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.404754,15.488092],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South China Sea","hydrology_source":"none","site_id":"doc-c59584f9b3cd28b59d8ae84c-site-9"}],"limitations":["Coordinates set to null when not explicitly given","Study uses selected gauging stations as proxies for large basins but these may not capture full basin variability","Limited availability of monthly streamflow data for some basins (Yellow and Pearl Rivers) restricts temporal resolution analysis"],"river_ids":["ne-530"],"site_ids":["doc-c59584f9b3cd28b59d8ae84c-site-0","doc-c59584f9b3cd28b59d8ae84c-site-1","doc-c59584f9b3cd28b59d8ae84c-site-2","doc-c59584f9b3cd28b59d8ae84c-site-3","doc-c59584f9b3cd28b59d8ae84c-site-4","doc-c59584f9b3cd28b59d8ae84c-site-5","doc-c59584f9b3cd28b59d8ae84c-site-6","doc-c59584f9b3cd28b59d8ae84c-site-7","doc-c59584f9b3cd28b59d8ae84c-site-8","doc-c59584f9b3cd28b59d8ae84c-site-9"],"anchor":[114.3664924,30.7194246],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[4040007850,4040012990,4040013000,4040186800,4040694350,4040773130,4040783120],"main_basin_ids":[4040000880,4040007850,4040009880,4040012990,4040013000]},{"id":"doc-e503e7fc1b483dea291758a3","title":"A strategy to overcome adverse effects of autoregressive updating of streamflow predictions","display_title":"Improving Autoregressive Streamflow Forecast Updates","doi":"https://doi.org/10.5194/hessd-11-6035-2014","theme":"Flow & ecology","color":"#80edc2","year":2014,"publication_date":"2014-06-10","authors":["M. Li","Q. J. Wang","J. C. Bennett","D. E. Robertson"],"summary":"This study evaluates autoregressive (AR) error updating models used to correct streamflow forecasts generated by rainfall–runoff models. Analyzing seven catchments across Australia and the US, the paper identifies two common AR approaches—applying AR to either raw errors or normalized errors—and finds these methods can lead to forecast over-corrections at different hydrograph phases and degrade base model performance. To address these issues, the authors propose a restricted AR model applied to normalized errors (RAR-Norm), which limits error corrections to below previous observed errors. The RAR-Norm improves forecast accuracy, reduces over-correction during rapidly rising or falling streamflow, and maintains robust base hydrological model performance.","finding":"The proposed restricted AR-Norm model reduces over-correction in streamflow forecasts compared to conventional AR models applied to raw or normalized errors, thus improving both updated forecast accuracy and the standalone performance of the underlying hydrological model.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 11 5 Discussion and conclusions For streamflow forecasting, rainfall–runoff models are often augmented with an updating procedure that corrects the forecast using information from recent simulation errors. The most popular updating approach uses autoregressive (AR) models that exploit the “memory” in model errors. AR models may be applied to raw errors directly or to normalised errors. We demonstrate three adverse effects of AR error updating procedures on seven catchments. The first adverse effect is possible over-correction on the rising limb of the hydrograph. The AR-Norm model can exhibit the tendency to over-correct the peaks or on the rise of a hydrograph, because error updating can be (overly) amplified by the backtransformation. The second adverse effect is the tendency to over-correct receding hydrographs.","verified_in_page":true},"topics":["streamflow forecasting","autoregressive models","error updating","hydrological modeling","forecast accuracy","forecast uncertainty","model calibration","time series analysis"],"study_period":null,"locations":[{"name":"Australian catchments","kind":"region","role":"study area for model evaluation","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The probability density function is replaced by the cumulative probability function when evaluating events of zero flow occurrences (Wang and Robertson, 2011; Li et al., 2013). The shuffled complex evolution (SCE) algorithm (Duan et al., 1994) is used to minimise the log likelihood. 3 Data We use daily data from four Australian catchments and three catchments from the US (Fig. 1, Table 1). Australian streamflow data are taken from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). Australian rainfall and potential evaporation data are derived from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments come from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"US catchments","kind":"region","role":"study area for model evaluation","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The probability density function is replaced by the cumulative probability function when evaluating events of zero flow occurrences (Wang and Robertson, 2011; Li et al., 2013). The shuffled complex evolution (SCE) algorithm (Duan et al., 1994) is used to minimise the log likelihood. 3 Data We use daily data from four Australian catchments and three catchments from the US (Fig. 1, Table 1). Australian streamflow data are taken from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). Australian rainfall and potential evaporation data are derived from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments come from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mitta Mitta catchment, Victoria, Australia","kind":"basin","role":"example catchment showing forecast over-correction by AR models","river_name":null,"geocode_query":"Mitta Mitta catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"In this study the forecast rainfall is observed rainfall, so the terms forecast and simulation are interchangeable. 4 Results 4.1 Over-correction of forecasts as the hydrograph rises The first adverse effect of the conventional AR models is over-correction of errors in updating as streamflows are rising. By over-correction, we mean that the AR model updates the hydrological model simulations too much. Overcorrection is difficult to define precisely; however, we will demonstrate the concept with two examples in the Mitta Mitta catchment: the first example illustrates over-correction by the AR-Norm model, and the second example illustrates over-correction by the AR-Raw model. To illustrate the problem of over-correction caused by the AR-Norm model, Fig. 2 presents a 1 week time series for the Mitta Mitta catchment, showing streamflow forecasts with GR4J before error updating (referred to as streamflow forecast with the base hydrological model) and after error updating.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.376281,-36.597458],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43667518,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-e503e7fc1b483dea291758a3-site-2","true_coordinates":[147.3762806,-36.5374584],"spread_km":6.7},{"name":"Orara catchment, New South Wales, Australia","kind":"basin","role":"example catchment with poor base model performance corrected by RAR-Norm model","river_name":null,"geocode_query":"Orara catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Furthermore, if the base hydrological model does not replicate important catchment processes realistically, the performance of the hydrological model outside the calibration period may be less robust. Figure 7 presents the Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970) calculated from the base hydrological model and the error models. When the AR-Norm model is used, the forecasts from the base hydrological model are very poor for the Orara catchment (NSE < 0). The scatter plot in Fig. 8 shows a serious over-estimation of the streamflow simulation for the Orara catchment. When the AR-Norm model is used, the base hydrological model greatly over-estimates discharge, and the AR-Norm model then attempts to correct this systematic over-estimation. This is also shown in Fig. 4, where the base hydrological model has a strong tendency to over-estimate streamflows for a range of streamflow magnitudes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.92064,-29.979556],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26970323,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070700,"main_basin_id":5040070700,"basin_region":"Australasia","basin_area_km2":22150.2,"hydrology_source":"geofabric","site_id":"doc-e503e7fc1b483dea291758a3-site-3","true_coordinates":[152.920639772319,-29.91955586275683],"spread_km":6.7},{"name":"Abercrombie River catchment, New South Wales, Australia","kind":"basin","role":"example of intermittent catchment with challenging flow conditions for model evaluation","river_name":"Abercrombie River","geocode_query":"Abercrombie River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The selected US catchments are amongst the 12 catchments used by Evin et al. (2014) to compare joint and postprocessor approaches to estimating hydrological uncertainty, and allow us to compare results with that study (the other catchments used by Evin et al. (2014) are influenced by snowmelt, which is not considered in the hydrological model used in this study). The Abercrombie River and the Guadalupe River intermittently experience periods of very low (to zero) flow, while the other rivers flow perennially (Table 1). Such dry catchments are challenging for hydrological simulations and error modelling. All catchments have high-quality streamflow records with very few missing data. We forecast daily streamflow with the GR4J rainfall– runoff model (Perrin et al., 2003). We apply updating procedures to correct these forecasts. All results presented in this paper are based on cross-validation to ensure the results can be generalised to independent data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.4960357265003,-34.04157429068727],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43672927,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-e503e7fc1b483dea291758a3-site-4"},{"name":"Guadalupe River catchment, Texas, United States","kind":"basin","role":"study catchment with intermittent flows and model evaluation","river_name":"Guadalupe River","geocode_query":"Guadalupe River catchment, Texas, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The selected US catchments are amongst the 12 catchments used by Evin et al. (2014) to compare joint and postprocessor approaches to estimating hydrological uncertainty, and allow us to compare results with that study (the other catchments used by Evin et al. (2014) are influenced by snowmelt, which is not considered in the hydrological model used in this study). The Abercrombie River and the Guadalupe River intermittently experience periods of very low (to zero) flow, while the other rivers flow perennially (Table 1). Such dry catchments are challenging for hydrological simulations and error modelling. All catchments have high-quality streamflow records with very few missing data. We forecast daily streamflow with the GR4J rainfall– runoff model (Perrin et al., 2003). We apply updating procedures to correct these forecasts. All results presented in this paper are based on cross-validation to ensure the results can be generalised to independent data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-98.068674,29.198809],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-e503e7fc1b483dea291758a3-site-5","true_coordinates":[-98.06867363467566,29.25880925311428],"spread_km":6.7},{"name":"San Marcos River catchment, Texas, United States","kind":"basin","role":"study catchment for streamflow prediction assessment","river_name":"San Marcos River","geocode_query":"San Marcos River catchment, Texas, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The selected US catchments are amongst the 12 catchments used by Evin et al. (2014) to compare joint and postprocessor approaches to estimating hydrological uncertainty, and allow us to compare results with that study (the other catchments used by Evin et al. (2014) are influenced by snowmelt, which is not considered in the hydrological model used in this study). The Abercrombie River and the Guadalupe River intermittently experience periods of very low (to zero) flow, while the other rivers flow perennially (Table 1). Such dry catchments are challenging for hydrological simulations and error modelling. All catchments have high-quality streamflow records with very few missing data. We forecast daily streamflow with the GR4J rainfall– runoff model (Perrin et al., 2003). We apply updating procedures to correct these forecasts. All results presented in this paper are based on cross-validation to ensure the results can be generalised to independent data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.69863364757904,29.69085721650198],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-e503e7fc1b483dea291758a3-site-6"},{"name":"Tarwin catchment, Victoria, Australia","kind":"basin","role":"study catchment used for model comparisons and validation","river_name":null,"geocode_query":"Tarwin catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Furthermore, if the base hydrological model does not replicate important catchment processes realistically, the performance of the hydrological model outside the calibration period may be less robust. Figure 7 presents the Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970) calculated from the base hydrological model and the error models. When the AR-Norm model is used, the forecasts from the base hydrological model are very poor for the Orara catchment (NSE < 0). The scatter plot in Fig. 8 shows a serious over-estimation of the streamflow simulation for the Orara catchment. When the AR-Norm model is used, the base hydrological model greatly over-estimates discharge, and the AR-Norm model then attempts to correct this systematic over-estimation. This is also shown in Fig. 4, where the base hydrological model has a strong tendency to over-estimate streamflows for a range of streamflow magnitudes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.970208,-38.664959],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21559641,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-e503e7fc1b483dea291758a3-site-7","true_coordinates":[145.9702078,-38.6049594],"spread_km":6.7}],"limitations":["Lack of explicit latitude and longitude coordinates for study catchments","Broad regional scale for catchments limits spatial specificity","Study focuses on one-step-ahead forecasts only, no multi-step lead time results yet"],"river_ids":[],"site_ids":["doc-e503e7fc1b483dea291758a3-site-2","doc-e503e7fc1b483dea291758a3-site-3","doc-e503e7fc1b483dea291758a3-site-4","doc-e503e7fc1b483dea291758a3-site-5","doc-e503e7fc1b483dea291758a3-site-6","doc-e503e7fc1b483dea291758a3-site-7"],"anchor":[145.970208,-38.664959],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070700,5040070710,5040591050,7040048420],"main_basin_ids":[5040070700,5040070710,5040073410,7040048420]},{"id":"doc-f757c886485f9e08c7c16075","title":"The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia","display_title":"Forecasting High Streamflows in SE Australia","doi":"https://doi.org/10.5194/nhess-14-219-2014","theme":"Floodplains","color":"#e9bd77","year":2014,"publication_date":"2014-02-13","authors":["J. C. Bennett","Q. J. Wang","P. Pokhrel","D. E. Robertson"],"summary":"This study evaluates statistical forecasts of high streamflows in six mesoscale catchments in southeast Australia, using Bayesian joint probability modeling combined with Bayesian model averaging. The forecasts use prior catchment wetness and lagged climate indices to predict maximum 1-day and 5-day streamflows over coming 1-month and 3-month periods. Forecasts are skilful for the 1-month horizon, especially in wetter months, with skill largely deriving from catchment wetness rather than lagged climate indices. Skill at 3-month lead times is generally low. The results suggest that improving catchment wetness estimation is key to enhancing forecasts, and that lagged climate indices contribute little additional predictive power.","finding":"Including lagged ocean–atmosphere climate indices as predictors adds little skill to the forecasts of high streamflows over 1- and 3-month horizons; catchment wetness is by far the most important predictor.","finding_evidence":{"passage_id":"p009-004","page":9,"quote":"This finding is also supported by Robertson and Wang (2013), who found that climate indices made only weak contributions to the skill of forecasts of seasonal streamflow totals in the MMH and MUR catchments. The contribution of catchment wetness to forecast skill declines over longer forecast periods (Mahanama et al., 2012; Shukla and Lettenmaier, 2011; Li et al., 2009). Thus forecasts for longer periods are less accurate than for shorter forecast periods. This effect is also evident www.nat-hazards-earth-syst-sci.net/14/219/2014/ Nat. Hazards Earth Syst. Sci., 14, 219–233, 2014","verified_in_page":true},"topics":["hydrological-forecasting","streamflow-forecasting","statistical-modeling","catchment-wetness","climate-teleconnections","forecast-skill","probabilistic-forecast","lagged-predictors"],"study_period":null,"locations":[{"name":"Orara River near Bawden Bridge, New South Wales, Australia","kind":"river","role":"study catchment","river_name":"Orara River","geocode_query":"Orara River near Bawden Bridge, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.810316,-29.782537],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26971389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bawden Bridge, Old Glen Innes Rd, Chambigne NSW 2460, Australia","basin_id":5040070700,"main_basin_id":5040070700,"basin_region":"Australasia","basin_area_km2":22150.2,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-0","true_coordinates":[152.8103156,-29.7225366],"spread_km":6.7},{"name":"Nowendoc River near Rocks Crossing, New South Wales, Australia","kind":"river","role":"study catchment","river_name":"Nowendoc River","geocode_query":"Nowendoc River near Rocks Crossing, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.0341040474295,-31.76085016410882],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26967947,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nowendoc River, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-1"},{"name":"Abercrombie River near Hadley No. 2, New South Wales, Australia","kind":"river","role":"study catchment","river_name":"Abercrombie River","geocode_query":"Abercrombie River near Hadley No. 2, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.464167,-34.070555],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43672613,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-2","true_coordinates":[149.46416679000004,-34.01055549999995],"spread_km":6.7},{"name":"Murray River near Biggara, Victoria, Australia","kind":"river","role":"study catchment","river_name":"Murray River","geocode_query":"Murray River near Biggara, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.63888899800008,-35.95861107099995],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43667934,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-3"},{"name":"Mitta Mitta River near Hinnomunjie, Victoria, Australia","kind":"river","role":"study catchment","river_name":"Mitta Mitta River","geocode_query":"Mitta Mitta River near Hinnomunjie, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.459722,-36.593889],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676927,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-4","true_coordinates":[147.4597223300001,-36.53388885299995],"spread_km":6.7},{"name":"Tarwin River near Meeniyan, Victoria, Australia","kind":"river","role":"study catchment","river_name":"Tarwin River","geocode_query":"Tarwin River near Meeniyan, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"We present the reliability and skill of these forecasts, and discuss the prospects for improving long lead forecasts of high streamflows. We conclude with a summary of the paper. 2 Data and methods 2.1 Study sites Forecasts are generated for six catchments in southeast Australia shown in Fig. 1. Characteristics of the six catchments are summarised in Table 1 and Fig. 2. The catchments are selected as they have long (> 40 yr) streamflow records, are free of diversions or impoundments, and are minimally impacted by human activities. Streamflow data are taken from the quality-controlled Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). All the catchments are of a size we describe as mesoscale, with drainage areas between 1000 km2 and 2000 km2 . The catchments are large enough to minimise the influence of highly localised storms (e.g. localised convective storms) on the streamflow records.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.014677,-38.637737],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559672,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Meeniyan VIC 3956, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-f757c886485f9e08c7c16075-site-5","true_coordinates":[146.0146765,-38.5777372],"spread_km":6.7}],"limitations":["Catchment wetness estimated only from prior month total streamflow, which is a coarse measure.","Exact latitudes/longitudes for study catchments are not provided, so coordinates are set null.","Forecast skill may vary with catchment and season; large event forecasts limited by sample size at low exceedance probabilities.","Use of cross-validation includes data unavailable in real-time (future observations)."],"river_ids":["43639566","43639591","43639599","43639846","43639869","43639888","43639915","43640282","43640312","43640364","43640440","43640498","43640508","43640573","43640722","43640925","43640947","43641203","43641218","43641527","43641846","43642141","43642305","43644008","43644763","43645117","43645312","43645398","43645403","43645571","43645602","43645620","43645673","43645730","43646066","43646073","43646110","43646226","43646303","43646577","43646746","43646801","43646913","43646978","43647013","43647034","43647048","43647112","43647198","43647284","43647322","43647414","43647439","43647472","43647605","43647696","43647721","43647723","43647744","43647754","43647762","43647765","43647772","43647782","43647784","43647788","43647790","43647802","43647912","43648012","43648100","43648102","43648266","43648317","43648324","43648355","43648373","43648380","43648387","43648391","43648412","43648441","43648491","43648499","43648506","43648547","43648558","43648576","43648589","43648615","43648621","43648622","43648626","43648629","43648631","43648632","43648633","43648636","43648644","43648647","43648648","43648655","43648661","43648696","43648806","43648821","43648830","43648831","43648843","43648845","43648846","43648847","43648849","43648885","43648886","43648909","43648999","43649001","43649018","43649027","43649054","43649084","43649085","43649093","43649209","43649217","43649224","43649239","43649249","43649253","43649261","43649264","43649267","43649269","43649279","43649311","43649316","43649327","43649380","43649384","43649389","43649395","43649417","43649427","43649429","43649442","43649444","43649445","43649446","43649455","43649466","43649477","43649484","43649488","43649489","43649497","43649506","43649551","43649603","43649612","43650096","43650097","43650098","43650099","43650103","43650162","43650163","43650164","43650182","43650227","43650480","43650484","43650485","43650489","43652752","43654642","43657763","43658998","43659000","43659003","43659004","43659005"],"site_ids":["doc-f757c886485f9e08c7c16075-site-0","doc-f757c886485f9e08c7c16075-site-1","doc-f757c886485f9e08c7c16075-site-2","doc-f757c886485f9e08c7c16075-site-3","doc-f757c886485f9e08c7c16075-site-4","doc-f757c886485f9e08c7c16075-site-5"],"anchor":[152.0341040474295,-31.76085016410882],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070700,5040070710,5040591050],"main_basin_ids":[5040070700,5040070710,5040073410]},{"id":"doc-f8a56ac79c984f9ef3455cc2","title":"Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects","display_title":"Sensitivity Analysis of Southern Murray–Darling Basin Model","doi":"https://doi.org/10.5194/hess-18-3777-2014","theme":"Water management","color":"#eeabac","year":2014,"publication_date":"2014-09-29","authors":["L. J. M. Peeters","G. M. Podger","T. Smith","T. Pickett","R. H. Bark","S. M. Cuddy"],"summary":"The study applies a robust, density-based global sensitivity analysis method to an idealized river management model representative of the southern Murray–Darling Basin, Australia. It assesses the main and interaction effects of three driving forces—inflow, rainfall, and potential evapotranspiration—on hydrological and socio-economic model outcomes, highlighting dominant control by inflow and significant nonlinear interactions, particularly between inflow and potential evapotranspiration.","finding":"The main finding is that inflow is the dominant driver affecting river management model outcomes, while potential evapotranspiration has significant nonlinear interaction effects with inflow; rainfall and potential evapotranspiration have minor main effects but show subtle nonlinear interactions impacting socio-economic and environmental outputs.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"These indices confirm the dominant influence of Inflow on most output variables. They provide a relative ranking of the influence of the input variable Inflow on the various output variables. MiddleFlow, EndFlow and GenSec respond to a similar degree to changes in Inflow and the same is true for the output variables related to monetary value ($AlgalBloom, $Stor and $TotalAg). HydroPower is least influenced by Inflow, which, from Fig. 2, is clearly related to the threshold-induced nonlinear behaviour. The methodology is also able to quantify the often small and nonlinear effects of the other forcing variables. This is especially noticeable for PET. There is a clear but highly nonlinear effect of PET on $Stor, which is reflected in a higher ˆ δˆ. The output variable HydroPower has a bimodal distribution where the majority of simulations have an Mˆ close to zero.","verified_in_page":true},"topics":["river management modeling","global sensitivity analysis","hydrological modeling","nonlinear interactions","water allocation","Murray–Darling Basin","environmental economics","model calibration"],"study_period":"1895–2006","locations":[{"name":"Southern Murray–Darling Basin","kind":"region","role":"Study area representative of modeled regulated river system","river_name":null,"geocode_query":"Southern Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"The case study is an idealized river management model representing typical conditions of the southern Murray– Darling Basin in Australia for which the sensitivity of a variety of model outcomes to variations in the driving forces, inflow to the system, rainfall and potential evapotranspiration, is examined. The model outcomes are most sensitive to the inflow to the system, but the sensitivity analysis identified minor effects of potential evapotranspiration and nonlinear interaction effects between inflow and potential evapotranspiration. 1 Introduction Water managers rely heavily on models to predict future water availability, optimize water use and evaluate water management strategies in order to find a balance between environmental, social and economic demands on the system. It is therefore crucial to be aware of the ability of a model to capture the dynamics of the hydrological cycle relevant to the water management question.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.600294,-31.13869],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-f8a56ac79c984f9ef3455cc2-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Murrumbidgee River","kind":"river","role":"Basis for idealized hypothetical river system model","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The sensitivity index δˆ for X2, δˆX2 , is computed for each interval. If there is no interaction effect between X1 and X2, then δˆX2 will not vary with the level of X1. To quantify this, the variance of δˆX2 is computed over all n levels of X1. Small variances indicate small interaction effects and vice versa. 3 Model description and setup The case study is a hypothetical river system model (Fig. 1), based on a simplified version of the Murrumbidgee River model in New South Wales, Australia (Dutta et al., 2012; Podger et al., 2014). Using the full version of the Murrumbidgee River model was not warranted, not only because of the complexity of the system and the management rules, but, more importantly, because of legal issues with regard to model licensing and confidentiality.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.368028,-35.159972],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677696,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f8a56ac79c984f9ef3455cc2-site-1","true_coordinates":[147.36802785600003,-35.09997214699996],"spread_km":6.7},{"name":"Lake Hume","kind":"facility","role":"Reference site for recreational use values and socio-economic indicators","river_name":null,"geocode_query":"Lake Hume, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are three storage volume categories (< 10, < 50 and > 50%) for recreational usage based on visitor numbers. Recreational benefits are calculated for periods of time the model is at each threshold, using the Crase and Gillespie (2008) 100 000 visitor estimate to Lake Hume. Estimates of visitor numbers at high and low storage volumes are based on this estimate and the actual Tourism Research Australia (TRA) average, low and high visitor numbers in the Murrumbidgee catchment in the period 2003–2010 (DRET, 2010). Benefit transfer recreation values are taken from the same study (updated to 2012 Australian dollars (AUD) using the Australian Consumer Price Index, CPI). There are three risk of algal bloom categories (no bloom, alert and bloom) – no bloom occurs if there is a flow of at least 11.6 m3 s −1 in the previous 7 days and alert if this flow occurs within the previous 14 days; if flow does not exceed 11.6 m3 s −1 in the previous 14 days, algal bloom is simulated to occur.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.037327,-36.040148],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666929,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Hume Village NSW 3691, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f8a56ac79c984f9ef3455cc2-site-2","true_coordinates":[147.0373272,-36.100148],"spread_km":6.7},{"name":"Murrumbidgee Catchment","kind":"region","role":"Area for tourism and recreation data to estimate visitor numbers","river_name":null,"geocode_query":"Murrumbidgee Catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are three storage volume categories (< 10, < 50 and > 50%) for recreational usage based on visitor numbers. Recreational benefits are calculated for periods of time the model is at each threshold, using the Crase and Gillespie (2008) 100 000 visitor estimate to Lake Hume. Estimates of visitor numbers at high and low storage volumes are based on this estimate and the actual Tourism Research Australia (TRA) average, low and high visitor numbers in the Murrumbidgee catchment in the period 2003–2010 (DRET, 2010). Benefit transfer recreation values are taken from the same study (updated to 2012 Australian dollars (AUD) using the Australian Consumer Price Index, CPI). There are three risk of algal bloom categories (no bloom, alert and bloom) – no bloom occurs if there is a flow of at least 11.6 m3 s −1 in the previous 7 days and alert if this flow occurs within the previous 14 days; if flow does not exceed 11.6 m3 s −1 in the previous 14 days, algal bloom is simulated to occur.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.275139,-35.252873],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665563,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murrumbidgee River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f8a56ac79c984f9ef3455cc2-site-3","true_coordinates":[146.2016652340651,-35.2528729004432],"spread_km":6.7}],"limitations":["Coordinates for geographic entities are not provided in the source text.","The idealized river system model is simplified and based on aggregated or hypothetical data rather than detailed observations.","Study relies on multipliers on three forcing variables; other parameter sensitivities and management rule parameter impacts not 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-1b345e3b1196656a2ff90a34","title":"Non-stationarity driven by long-term change in catchment storage: possibilities and implications","display_title":"Hydrologic Non-Stationarity in SW Australia","doi":"https://doi.org/10.5194/piahs-371-7-2015","theme":"Flow & ecology","color":"#80edc2","year":2015,"publication_date":"2015-06-12","authors":["J. D. Hughes","J. Vaze"],"summary":"This study examines hydrologic non-stationarity in south-western Australia (SWA), a Mediterranean climate region experiencing long-term rainfall decline and runoff reduction. It highlights that traditional rainfall–runoff (RR) models with static storage capacities fail to capture persistent changes in groundwater storage and runoff, especially after droughts. Based on extensive observations and rainfall–runoff modeling challenges, the authors propose a new modeling approach incorporating a dynamically variable unsaturated storage zone linked to groundwater levels to improve runoff prediction under changing climatic conditions.","finding":"Runoff and groundwater storage in south-western Australia have shown persistent declines following droughts, which traditional rainfall–runoff models with static storage capacities cannot reproduce; a dynamically changing unsaturated zone storage concept is needed to capture long-term runoff changes.","finding_evidence":{"passage_id":"p002-002","page":2,"quote":"Petrone et al. (2010) noted a changing runoff–rainfall (Q ∼ P) relationship across various SWA catchments during the post-1970 period. An update of their plot is shown below for a selection of catchments (Fig. 1). These relationships show that a single Q ∼ P relationship is inadequate, and this could be interpreted as a type of “non-stationarity”. Obvious changes in the relationship follow drought years 2001 and 2010. More recent analysis (Hughes et al., 2012; Kinal and Stoneman, 2012) show that permanent groundwater plays a critical role in runoff generation. Hughes et al. (2012), demonstrated that runoff ratio (Q/P) was strongly related to groundwater elevation in the catchment, particularly in the riparian zone. As such, the groundwater acts as the catchment “memory”, and runoff produced from an average year now produces around 150 mm less runoff than the same rainfall in the 1970’s. In years following droughts runoff coefficient is reduced in subsequent years.","verified_in_page":true},"topics":["hydrological-modelling","groundwater-storage","rainfall-runoff-relationship","non-stationarity","catchment-hydrology","climate-change","drought-effects","model-structure"],"study_period":"1970s to post 2010","locations":[{"name":"South-western Australia","kind":"region","role":"study area for long-term hydrologic observations and model application","river_name":null,"geocode_query":"South-western Australia, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Proc. IAHS, 371, 7–12, 2015 proc-iahs.net/371/7/2015/ doi:10.5194/piahs-371-7-2015 © Author(s) 2015. CC Attribution 3.0 License. Open Access Hydrologic Non-Stationarity and Extrapolating Models to Predict the Future (HS02 – IUGG2015) Non-stationarity driven by long-term change in catchment storage: possibilities and implications J. D. Hughes and J. Vaze CSIRO Land and Water, Canberra, ACT, Australia Correspondence to: J. Hughes (justin.hughes@csiro.au) Received: 11 March 2015 – Accepted: 11 March 2015 – Published: 12 June 2015 Abstract. “Non-stationarity” with reference to hydrology is a term applied to many situations (Milly et al., 2008). While climate change non-stationarity is often examined, these effects can provide a test for assumptions of runoff generation process impliedin rainfall–runoff (RR) models. Observations from South-western Australia (SWA) over the past 40 years show a decline in rainfall and reductions in runoff.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.009967,-34.001625],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-1b345e3b1196656a2ff90a34-site-0","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"Perth metropolitan area catchment","kind":"basin","role":"main water catchment within SWA under study","river_name":null,"geocode_query":"Perth metropolitan area catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"8 J. D. Hughes and J. Vaze: Non-stationarity and catchment storage forested, and includes the main water catchment for the Perth metropolitan areas (see Hughes et al., 2012). The regolith of the area is deep ( ∼ 40 m) and is very transmissive. Annual rainfall in the area varies between 900 and 1300 mm, while annual potential evaporation is 1200–1400 mm. The SWA has experienced a drying trend since the mid 1970’s, although this has been described as more of a step change than gradual drying (IOCI, 2002). Rainfall had declined by 15–20 % since the 1970’s with a concurrent reduction in runoff of greater than 70 % (Bates et al., 2008). Petrone et al. (2010) showed that runoff fell dramatically following droughts within the 1988–2009 time period, although no significant trend in rainfall could be indentified throughout this period. This trend in declining runoff has continued with a major drought in 2010.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.9467997,-31.8092824],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70413000,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-1b345e3b1196656a2ff90a34-site-1"},{"name":"Bates Experimental Catchment","kind":"field_site","role":"experimental site used for rainfall-runoff modeling and groundwater observations","river_name":null,"geocode_query":"Bates Experimental Catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Runoff is calculated as the difference between Pn and Ps ; Q = Pn − Ps . (3) A critical observation from the data sets of SWA is that the persistent decline in runoff coefficient is related to a persistent reduction in catchment storage or groundwater levels for catchments (where groundwater remains “connected” to the surface), particularly following droughts (Fig. 2). We used the S1S model to explore the dynamics of drought induced storage decline and recovery. Data from Bates experimental catchment was used to test the model for its ability to represent annual runoff. A matrix of parameter values was tested (n > 5000), using a Monte Carlo approach and assessed for goodness of fit using root mean square error (RMSE). Parameter values were selected from a grid of values that covered what was considered reasonable parameter bounds. A synthetic input data set was created that consisted of an annual cycle of rainfall and evaporation that was repeated Figure 3.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Waterfall Gully catchment","kind":"basin","role":"one of the monitored catchments showing runoff-rainfall relationships in SWA","river_name":null,"geocode_query":"Waterfall Gully catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Petrone et al. (2010) noted a changing runoff–rainfall (Q ∼ P) relationship across various SWA catchments during the post-1970 period. An update of their plot is shown below for a selection of catchments (Fig. 1). These relationships show that a single Q ∼ P relationship is inadequate, and this could be interpreted as a type of “non-stationarity”. Obvious changes in the relationship follow drought years 2001 and 2010. More recent analysis (Hughes et al., 2012; Kinal and Stoneman, 2012) show that permanent groundwater plays a critical role in runoff generation. Hughes et al. (2012), demonstrated that runoff ratio (Q/P) was strongly related to groundwater elevation in the catchment, particularly in the riparian zone. As such, the groundwater acts as the catchment “memory”, and runoff produced from an average year now produces around 150 mm less runoff than the same rainfall in the 1970’s. In years following droughts runoff coefficient is reduced in subsequent years.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.6768408,-34.9637451],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":50289778,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-1b345e3b1196656a2ff90a34-site-3"},{"name":"Del Park catchment","kind":"basin","role":"monitored catchment providing runoff-rainfall data in SWA","river_name":null,"geocode_query":"Del Park catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Petrone et al. (2010) noted a changing runoff–rainfall (Q ∼ P) relationship across various SWA catchments during the post-1970 period. An update of their plot is shown below for a selection of catchments (Fig. 1). These relationships show that a single Q ∼ P relationship is inadequate, and this could be interpreted as a type of “non-stationarity”. Obvious changes in the relationship follow drought years 2001 and 2010. More recent analysis (Hughes et al., 2012; Kinal and Stoneman, 2012) show that permanent groundwater plays a critical role in runoff generation. Hughes et al. (2012), demonstrated that runoff ratio (Q/P) was strongly related to groundwater elevation in the catchment, particularly in the riparian zone. As such, the groundwater acts as the catchment “memory”, and runoff produced from an average year now produces around 150 mm less runoff than the same rainfall in the 1970’s. In years following droughts runoff coefficient is reduced in subsequent years.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.016846,-32.6157803],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70413030,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-1b345e3b1196656a2ff90a34-site-4"},{"name":"Bates catchment","kind":"basin","role":"monitored catchment showing declining runoff and storage in SWA","river_name":null,"geocode_query":"Bates catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Petrone et al. (2010) noted a changing runoff–rainfall (Q ∼ P) relationship across various SWA catchments during the post-1970 period. An update of their plot is shown below for a selection of catchments (Fig. 1). These relationships show that a single Q ∼ P relationship is inadequate, and this could be interpreted as a type of “non-stationarity”. Obvious changes in the relationship follow drought years 2001 and 2010. More recent analysis (Hughes et al., 2012; Kinal and Stoneman, 2012) show that permanent groundwater plays a critical role in runoff generation. Hughes et al. (2012), demonstrated that runoff ratio (Q/P) was strongly related to groundwater elevation in the catchment, particularly in the riparian zone. As such, the groundwater acts as the catchment “memory”, and runoff produced from an average year now produces around 150 mm less runoff than the same rainfall in the 1970’s. In years following droughts runoff coefficient is reduced in subsequent years.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Lack of specific geographic coordinates for study sites and catchments","Wide regional scale limits precise spatial attribution","Study focuses on a regional climatic and hydrologic setting, limiting broader geographic applicability"],"river_ids":[],"site_ids":["doc-1b345e3b1196656a2ff90a34-site-0","doc-1b345e3b1196656a2ff90a34-site-1","doc-1b345e3b1196656a2ff90a34-site-3","doc-1b345e3b1196656a2ff90a34-site-4"],"anchor":[116.009967,-34.001625],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040055960,5040056460,5040073420],"main_basin_ids":[5040055960,5040056460,5040073420]},{"id":"doc-1d767e5eec8d98f00e5830c8","title":"Water dynamics under changing land cover","display_title":"Water Flow Impacts of Land Cover Change","doi":"https://doi.org/10.5194/piahs-371-215-2015","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-06-12","authors":["J. Vaze","Y. Q. Zhang","L. Zhang"],"summary":"This study investigates how changes in forest cover due to afforestation, deforestation, and bushfires affect streamflow in forested headwater catchments in southeast Australia. Using multiple hydrological methods, including sensitivity-based approaches and hydrological modelling, the impacts on catchment water yield were quantified in two catchments: Crawford River and Starvation Creek. The study highlights that vegetation dynamics can significantly alter streamflow, with afforestation reducing and deforestation or bushfires initially increasing streamflow. Climate variability also influences streamflow changes but often to a lesser extent than vegetation changes immediately after disturbance events.","finding":"Vegetation dynamics caused by afforestation, deforestation, and bushfires significantly affect streamflow in catchments; for example, afforestation in the Crawford River catchment reduced streamflow by approximately 40%, while the 1983 Ash Wednesday bushfires in the Starvation Creek catchment increased streamflow by about 27% in the first 15 years post-fire, primarily due to reduction in leaf area and evapotranspiration.","finding_evidence":{"passage_id":"p005-001","page":5,"quote":"Within each of the four panels, the yellow/orange dash represent total streamflow change in the post-bushfire period relative to pre-bushfire period, the first boxplot represent vegetation change impact on streamflow and the second boxplot represent climate variability impact on streamflow. The results for the first 15 years post-bushfire show that the total increase in streamflow (when compared to the prebushfire period) due to the 1983 bushfires and climate variability for the Starvation Creek catchment is 111 mm which represent about 27 % increase in streamflow. The increase in streamflow during the first 15 years post-bushfire due to vegetation dynamics is 156 mm (38 % of the pre-bushfire streamflow) for the AWRA-L model, 80 mm (19 % of the pre-bushfire streamflow) for Xinanjiang model and 160 mm (39 % of the pre-bushfire streamflow) for the GR4J model.","verified_in_page":true},"topics":["land cover change","hydrological modelling","streamflow","vegetation dynamics","climate variability","forest disturbances","water yield","catchment hydrology"],"study_period":"1973–2004","locations":[{"name":"Crawford River catchment, Victoria, Australia","kind":"basin","role":"afforestation impact study site","river_name":null,"geocode_query":"Crawford River catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The observed and simulated streamflow over the afforested/deforested period is then used to quantify the change in runoff due to plantation differences (afforestation/deforestation) and climate variability between the pre-afforestation and afforested or pre-deforestation and deforestation periods. 3 Case studies We present the results from two case studies conducted for catchments in southeast Australia. The first case study evaluates afforestation impacts on water yield and the second study is carried out to evaluate deforestation/bushfire impacts. 3.1 Afforestation case study The Crawford River catchment is about 700 km2 and located in Victoria, Australia. The climate is temperate, with cool and wet winters and mild to warm and dry summers. Mean annual rainfall is 762 mm (pre-afforestation 775 mm and after afforestation 736 mm) and mean annual areal potential evapotranspiration (APET) is 1091 mm (pre-afforestation 1087 mm and after afforestation 1106 mm).","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.5785956293002,-37.89244651464738],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21557122,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-1d767e5eec8d98f00e5830c8-site-0"},{"name":"Starvation Creek catchment, Central Highlands, Victoria, Australia","kind":"basin","role":"deforestation/bushfire impact study site","river_name":null,"geocode_query":"Starvation Creek catchment, Central Highlands, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The daily time series of impacted streamflow can also be used to investigate the impacts of plantation expansion or reduction and climate change/variability on different runoff characteristics. 3.2 Deforestation/bushfire case study The Starvation Creek catchment is about 31 km2 and located in the Central Highlands of Victoria, east of Melbourne. The climate is temperate, with cool and wet winters and mild to warm and dry summers. The streamflow and climate records for this catchment cover the period of 1973 to 2004. Mean annual rainfall and mean annual areal potential evapotranspiration (APET) is 1644, 1604, 1400 and 1091, 1073, 1079 mm for 1973–1982, 1983–1998 and 1999–2004 respectively. The vegetation cover is a mix of pure E. regnans (mountain ash) and mixed damp eucalypt species, predominantly E. obliqua, E. cypellocarpa and E. sieberi with about 51 % of ash. The ash stands were all regrowth originating from the 1939 wildfires.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.8217784718671,-37.73372651594104],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21559439,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-1d767e5eec8d98f00e5830c8-site-1"}],"limitations":["Exact latitude and longitude not provided for catchment locations","Coordinates could not be inferred from provided geographic names","Study focuses only on two catchments in southeast Australia, limiting geographic breadth"],"river_ids":[],"site_ids":["doc-1d767e5eec8d98f00e5830c8-site-0","doc-1d767e5eec8d98f00e5830c8-site-1"],"anchor":[141.5785956293002,-37.89244651464738],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-2c1a3cc3e117e7c08ba711b4","title":"Designer policy for carbon and biodiversity co-benefits under global change","display_title":"Optimizing Carbon and Biodiversity Policies in Australia","doi":"https://doi.org/10.1038/nclimate2874","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-11-30","authors":["Brett A. Bryan","Rebecca K. Runting","Tim Capon","Michael P. Perring","Shaun C. Cunningham","Marit E. Kragt","Martin Nolan","Elizabeth A. Law","Anna R. Renwick","Sue Eber","Rochelle Christian","Kerrie A. Wilson"],"summary":"This study evaluated 14 policy mechanisms to supply carbon sequestration and biodiversity co-benefits through reforestation on Australia's 85.3 Mha of agricultural land under global change scenarios. Using the LandUse Trade-Offs (LUTO) model, the study assessed combinations of payment schemes (uniform, discriminatory), land-use policies (competition, regulation), and targeting strategies (carbon, biodiversity, multifunctional). Results show discriminatory payments combined with land-use competition and multifunctional targeting greatly increase biodiversity co-benefits with substantial carbon sequestration, achieving near Pareto efficiency. Complementary incentives like biodiversity premiums and carbon levies further enhance biodiversity but involve trade-offs and costs. The findings suggest a clear policy direction favoring discriminatory payments with targeting to efficiently achieve desired co-benefits in Australia and globally.","finding":"Discriminatory payment schemes under land-use competition with multifunctional targeting can increase biodiversity co-benefits nearly 100-fold compared to uniform payments targeting carbon, while maintaining substantial carbon sequestration at lower cost, representing an optimal policy approach for carbon and biodiversity incentives in Australia's agricultural lands.","finding_evidence":{"passage_id":"p005-002","page":5,"quote":"The discriminatory payment scheme with land-use competition and multifunctional targeting (D-C-M) optimally traded-off carbon sequestration and biodiversity co-benefits assuming relative change in each is valued equally by society. Under the 292 $B budget, D-C-M achieved over 96 times the biodiversity co-benefits (34.70%) and roughly the same carbon sequestration (108.09 MtCO2 yr -1) as the reference policy (Figure 1). D-C-M involved a 17% larger area of reforestation with 56 times the area of EP concentrated in highbenefit biodiversity areas (Figure 2b). A number of caveats apply. Our analyses do not consider activities beyond CP and EP which may offer cost-effective carbon sequestration and /or biodiversity co-benefits such as avoided deforestation, bioenergy crops, or changed agricultural management. While sensitivity of the model to variation in individual parameters such as climate change has been covered","verified_in_page":true},"topics":["carbon sequestration","biodiversity conservation","land-use policy","payment schemes","reforestation","economic modeling","climate change adaptation","policy efficiency"],"study_period":"2013-2050","locations":[{"name":"Australia's agricultural land","kind":"landscape","role":"study area covering 85.3 Mha of intensive agricultural lands eligible for reforestation","river_name":null,"geocode_query":"Australia's agricultural land, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-000","page":10,"quote":"10 Methods Policy mechanisms Supplementary Table 5 details the payment schemes, land-use policy, targeting strategies, and complementary incentives assessed in this study. <Insert Supplementary Table 5 about here> Study area and policy settings This analysis was undertaken for the entire intensive agricultural land of Australia—a noncontiguous area of privately-owned and managed cleared land stretching across eastern and southern Australia from Queensland to south-west Western Australia (Supplementary Figure 21). We do not consider potential land-use change in native vegetation, wetland/riparian, or urban areas. <Insert Supplementary Figure 21 about here> Agricultural policy in Australia has long been focused on increasing agricultural productivity and competitiveness, and has included relatively low levels of public subsidy.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North-east extensive grazing lands","kind":"region","role":"region within study area where carbon plantings dominated reforestation due to low agricultural profitability and climate favorable to carbon sequestration","river_name":null,"geocode_query":"north-east Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"As CP sequestered carbon more cost-effectively than EP7 , land-use change was dominated by CP, particularly in the extensive grazing lands in the north-east (Figure 2a). For example, the reference policy sequestered 109.54 MtCO2 yr -1 under the 292 $B budget, but achieved biodiversity co-benefits of only 0.36% of the maximum amount possible (Figure 1; Supplementary Tables 3 & 4). This demonstrates the potential of carbon markets to crowd out land-uses with potential to contribute to conservation goals16. <Insert Figure 2 about here>","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southern Australia","kind":"region","role":"region within study area where environmental plantings were more prevalent in optimal policy for biodiversity co-benefits","river_name":null,"geocode_query":"Southern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p029-002","page":29,"quote":"Under both policies land-use change was concentrated in the north-east of the study area due to the relatively low agricultural profitability in these extensive beef cattle grazing lands, and a climate conducive to carbon sequestration. In the optimal policy, EP were more widely distributed but were prominent in southern Australia where biodiversity co-benefits could be cost-effectively achieved. (Figure sizing – 1 column width) Figure 3 – Cost of a biodiversity premium payment and impact on the supply of carbon sequestration and biodiversity co-benefits. The $0 premium payment points indicate the supply achieved under the reference policy (U-C-C) under each global outlook (Supplementary Table 3). The colour of the dots indicates the additional cost of the biodiversity premium payments compared to the reference policy. The figure illustrates the high cost and carbon trade-offs involved in achieving biodiversity co-benefits through a","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Coordinates of study area and subregions are not specified explicitly, limiting precise geolocation.","Study region is described broadly at continental and regional scales without local site coordinates.","Uncertainties in some model parameters and environmental trade-offs beyond carbon and biodiversity were acknowledged but not fully quantified."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[],"main_basin_ids":[]},{"id":"doc-434c7d4d9a2359ffb5df52cb","title":"Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes","display_title":"Satellite Soil Moisture Assimilation Improves Flood Forecasts","doi":"https://doi.org/10.5194/hess-19-1659-2015","theme":"Flow & ecology","color":"#80edc2","year":2015,"publication_date":"2015-04-09","authors":["C. Alvarez-Garreton","D. Ryu","A. W. Western","C.-H. Su","W. T. Crow","D. E. Robertson","C. Leahy"],"summary":"This study assesses the use of satellite-derived soil moisture data assimilation (SM-DA) to improve streamflow and flood prediction in the large semi-arid Warrego River catchment (>40,000 km²) in Queensland, Australia. Using both lumped and semi-distributed Probability Distributed Models (PDM) coupled with an Ensemble Kalman Filter, the authors assimilate soil moisture retrievals from AMSR-E, ASCAT, and SMOS satellites. They demonstrate that incorporating spatial rainfall distribution and channel routing (semi-distributed scheme) enhances flood ensemble prediction. SM-DA improves ensemble skill and reliability at the catchment outlet and shows some improvement in ungauged inner subcatchments, though systematic model errors limit full accuracy there. The study highlights the importance of model error representation and the need for further development in soil moisture data processing and assimilation methods.","finding":"Assimilating satellite soil moisture into rainfall-runoff models using a semi-distributed scheme that accounts for channel routing and spatial precipitation distribution improves streamflow ensemble prediction and reduces forecast errors more effectively than a lumped model, enhancing operational flood forecast skill in a sparsely monitored semi-arid catchment.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24 %, respectively; the false alarm ratio was reduced by 9 % in both cases; the peak volume error was reduced by 58 and 1 %, respectively; the ensemble skill was improved (evidenced by 12 and 13 % reductions in the continuous ranked probability scores, respectively); and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for.","verified_in_page":true},"topics":["soil moisture","data assimilation","rainfall-runoff modeling","ensemble prediction","flood forecasting","hydrological modeling","remote sensing","error modeling"],"study_period":"1967-2014","locations":[{"name":"Warrego River catchment","kind":"basin","role":"main study catchment","river_name":"Warrego River","geocode_query":"Warrego River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Among the latter, semi-arid catchments present distinct rainfall-runoff processes which have been rarely studied in SM-DA. Here we address this gap by studying the Warrego River catchment in Australia, a large and sparsely monitored semiarid basin. We set up the probability distributed model (PDM) within the catchment, and assimilate passive and active satellite SM products using an ensemble Kalman filter (EnKF) (Evensen, 2003) for the purpose of improving operational flood prediction. We devise an operational SM-DA scheme to answer three main questions. (1) While rainfall is presumably the main driver of flood generation in semiarid catchments, can we effectively improve streamflow prediction by correcting the antecedent soil water state of the model? (2) What is the impact of accounting for channel routing and the spatial distribution of forcing data on SMDA performance? (3) What are the prospects for improving streamflow prediction within ungauged sub-catchments using satellite SM?","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.59798,-26.704133],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673349,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-0","true_coordinates":[149.5979804,-26.6441333],"spread_km":6.7},{"name":"Warrego at Binnowee (N1)","kind":"station","role":"inner subcatchment gauging station for evaluation","river_name":"Warrego River","geocode_query":"Binnowee, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Regarding the governing runoff mechanisms within the study catchment, Alvarez-Garreton et al. (2014) showed that streamflow has a negligible baseflow component and the surface runoff is generated only when a wetness threshold is exceeded. They concluded that soil moisture exerts an important control on the runoff generation mechanisms. In this work, the runoff mechanisms analysis is deepened by looking at model predictions (Sect. 3.1). Daily rainfall data was computed from the Australian Water Availability Project (AWAP), which has a grid resolution of 0.05◦ (Jones et al., 2009). Hourly streamflow records were collected from the State of Queensland, Department of Natural Resources and Mines (http://watermonitoring.dnrm. qld.gov.au) (Fig. 1). Daily discharge was calculated based on the daily AWAP time convention (9.00 a.m.–9.00 a.m. local time, UTC +10 h).","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Warrego at Augathella (N3)","kind":"station","role":"inner subcatchment gauging station for evaluation","river_name":"Warrego River","geocode_query":"Augathella, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Regarding the governing runoff mechanisms within the study catchment, Alvarez-Garreton et al. (2014) showed that streamflow has a negligible baseflow component and the surface runoff is generated only when a wetness threshold is exceeded. They concluded that soil moisture exerts an important control on the runoff generation mechanisms. In this work, the runoff mechanisms analysis is deepened by looking at model predictions (Sect. 3.1). Daily rainfall data was computed from the Australian Water Availability Project (AWAP), which has a grid resolution of 0.05◦ (Jones et al., 2009). Hourly streamflow records were collected from the State of Queensland, Department of Natural Resources and Mines (http://watermonitoring.dnrm. qld.gov.au) (Fig. 1). Daily discharge was calculated based on the daily AWAP time convention (9.00 a.m.–9.00 a.m. local time, UTC +10 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.5847126,-25.7952481],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666019,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Augathella QLD 4477, Australia","basin_id":5040533380,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":53422.7,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-2"},{"name":"Warrego at Wyandra (N7)","kind":"station","role":"catchment outlet gauging station for model calibration and evaluation","river_name":"Warrego River","geocode_query":"Wyandra, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Regarding the governing runoff mechanisms within the study catchment, Alvarez-Garreton et al. (2014) showed that streamflow has a negligible baseflow component and the surface runoff is generated only when a wetness threshold is exceeded. They concluded that soil moisture exerts an important control on the runoff generation mechanisms. In this work, the runoff mechanisms analysis is deepened by looking at model predictions (Sect. 3.1). Daily rainfall data was computed from the Australian Water Availability Project (AWAP), which has a grid resolution of 0.05◦ (Jones et al., 2009). Hourly streamflow records were collected from the State of Queensland, Department of Natural Resources and Mines (http://watermonitoring.dnrm. qld.gov.au) (Fig. 1). Daily discharge was calculated based on the daily AWAP time convention (9.00 a.m.–9.00 a.m. local time, UTC +10 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9823083,-27.2460736],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665048,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wyandra QLD 4489, Australia","basin_id":5040533380,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":53422.7,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-3"},{"name":"Warrego River catchment subcatchment SC1","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC1, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.536095,-30.009272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-4","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7},{"name":"Warrego River catchment subcatchment SC2","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC2, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.596064,-29.979272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-5","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7},{"name":"Warrego River catchment subcatchment SC3","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC3, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.596064,-29.919272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-6","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7},{"name":"Warrego River catchment subcatchment SC4","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC4, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Warrego River catchment subcatchment SC5","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC5, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.536095,-29.889272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-8","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7},{"name":"Warrego River catchment subcatchment SC6","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC6, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.476125,-29.919272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-9","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7},{"name":"Warrego River catchment subcatchment SC7","kind":"basin","role":"semi-distributed model subcatchment","river_name":"Warrego River","geocode_query":"Warrego River catchment SC7, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1662 C. Alvarez-Garreton et al.: Assimilation of satellite soil moisture to improve flood prediction 115°E 120°E 125°E 130°E 135°E 140°E 145°E 150°E 155°E 10°S 15°S 20°S 25°S 30°S 35°S 40°S 1:80,000,000 \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") \") #\") # !( # !( !( !( 145°E 146°E 147°E 148°E 25°S 26°S 27°S 1:7,000,000 Warrego River Catchment SC1 SC7 SC6 SC5 SC4 SC2 SC3 \") Rainfall gauge !( Node without streamflow gauge # Node 1: Warrego at Binnowee (N1) # Node 3: Warrego at Augathella (N3) # Node 7: Warrego at Wyandra (N7) Figure 1. The Warrego River basin located in Queensland, Australia (left panel). A close-up of the area is presented in the right panel.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.476125,-29.979272],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664856,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-434c7d4d9a2359ffb5df52cb-site-10","true_coordinates":[145.5360946904291,-29.94927174676144],"spread_km":6.7}],"limitations":["Exact coordinates of study locations not provided in text or corrupted in PDF glyphs.","Model error parameters calibration is highly underdetermined due to limited data.","Satellite soil moisture profile estimation uses empirical exponential filter which may violate data assimilation assumptions.","Assumption of seasonal but not inter-annual variability in observation error may limit accuracy.","SM-DA performance limited by systematic model errors in ungauged subcatchments."],"river_ids":["43640178","43647483","43647600","43647663","43647843","43648096","43648165","43648196","43648254","43648291","43648337","43648427","43648519","43648674","43658073","43659946","43659947","43659970","43659974","43659992","43660013","43660037","43660040","43660042","43660046","43660047","43660048","43660049","43660050","43660070","43660079","43660080","43660087","43660093","43660099","43660101","43660102","43660103","43660105","43660106"],"site_ids":["doc-434c7d4d9a2359ffb5df52cb-site-0","doc-434c7d4d9a2359ffb5df52cb-site-2","doc-434c7d4d9a2359ffb5df52cb-site-3","doc-434c7d4d9a2359ffb5df52cb-site-4","doc-434c7d4d9a2359ffb5df52cb-site-5","doc-434c7d4d9a2359ffb5df52cb-site-6","doc-434c7d4d9a2359ffb5df52cb-site-8","doc-434c7d4d9a2359ffb5df52cb-site-9","doc-434c7d4d9a2359ffb5df52cb-site-10"],"anchor":[146.5847126,-25.7952481],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040511570,5040533380,5040533450],"main_basin_ids":[5040073410]},{"id":"doc-449baa8e73415f6340a9a9cc","title":"Multi-Action Planning for Threat Management: A Novel Approach for the Spatial Prioritization of Conservation Actions","display_title":"Prioritizing Conservation Actions in Mitchell River","doi":"https://doi.org/10.1371/journal.pone.0128027","theme":"Flow & ecology","color":"#80edc2","year":2015,"publication_date":"2015-05-28","authors":["Lorenzo Cattarino","Virgilio Hermoso","Josie Carwardine","Mark J. Kennard","Simon Linke"],"summary":"This study develops a novel algorithm to spatially prioritize multiple conservation actions addressing different threats independently within the same site, accounting for river connectivity. Applied to freshwater fish in the Mitchell River catchment, northern Australia, the approach uses simulated annealing to minimize costs while ensuring species conservation targets. It compares efficiency against traditional fixed-action site prioritization methods under varying connectivity importance.","finding":"The independent prioritization algorithm that selects multiple alternative actions within the same site outperforms traditional fixed prioritization, especially as the importance of connectivity increases, resulting in nearly 100% higher cost-efficiency when connectivity is highly considered.","finding_evidence":{"passage_id":"p009-000","page":9,"quote":"When the degree of connectivity achieved was low (i.e., the importance of achieving connectivity was low), the efficiency of the independent prioritization approach was similar to the one of the fixed prioritization approach (Fig 5). As the degree of connectivity achieved increased (i.e., the importance of achieving connectivity increased), the efficiency of both approaches decreased. However, the efficiency of the fixed prioritization approach decreased faster than the efficiency of the individual prioritization approach (Fig 5). When the degree of connectivity achieved was close to one (i.e., the importance of achieving connectivity was very high), the individual prioritization approach delivered a solution with an efficiency almost 100% higher than the fixed prioritization approach. Discussion Finding a cost-effective set of actions, and sites where to implement them, to improve biodiversity persistence, represents a key challenge in systematic conservation planning [7].","verified_in_page":true},"topics":["spatial conservation prioritization","connectivity","freshwater ecology","threat management","cost-effectiveness","systematic conservation planning","species vulnerability","river ecosystems"],"study_period":null,"locations":[{"name":"Mitchell River catchment, northern Australia","kind":"basin","role":"primary study area where the multi-action spatial prioritization algorithm was applied to conserve freshwater fish species by addressing multiple threats","river_name":null,"geocode_query":"Mitchell River catchment, northern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"where cik is the costs of the action required to abate threat k in site i, xki 2 [0,1] is the abetment status of threat k, Sp is the species penalty (Eq 2), CSM (Connectivity Strength Modifier) is a scaling factor which controls the importance of minimizing the connectivity penalty in relation to the cost of the actions and the species target, and Cp is the connectivity penalty (Eq 3). When connectivity is not important in the optimization (CSM = 0), actions are selected in sites regardless of the location of those sites along the river network. As connectivity becomes more important (CSM > 0), actions tend to be selected in most of the sites upstream of the sites already in the solution. Action prioritization algorithm We developed an action prioritization algorithm, which uses simulated annealing [32] to approximate the minimum value of the objective function (Eq 4).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.9887494,-12.5257236],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-449baa8e73415f6340a9a9cc-site-0"},{"name":"Queensland, Australia","kind":"region","role":"location of the research institutions and broader regional context relevant to the study area","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"RESEARCH ARTICLE Multi-Action Planning for Threat Management: A Novel Approach for the Spatial Prioritization of Conservation Actions Lorenzo Cattarino1 *, Virgilio Hermoso1,2, Josie Carwardine3 , Mark J. Kennard1,2, Simon Linke1,2 1 Australian Rivers Institute, Griffith University, Nathan, Queensland, Australia, 2 National Environmental Research Program Northern Australia Hub, Griffith University, Nathan, Queensland, Australia, 3 CSIRO Ecosystem Sciences, Dutton Park, Queensland, Australia * l.cattarino@gmail.com Abstract Planning for the remediation of multiple threats is crucial to ensure the long term persistence of biodiversity. Limited conservation budgets require prioritizing which management actions to implement and where. Systematic conservation planning traditionally assumes that all the threats in priority sites are abated (fixed prioritization approach). However, abating only the threats affecting the species of conservation concerns may be more cost-effective.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Assumption that each threat abatement action is fully effective and binary (implemented or not) without varying intensity","Benefit to species from threat abatement considered full only if all threats at site are addressed, which may underestimate partial benefits","Connectivity considered only between sites where threats are abated but not among sites where the same threat is abated, which may omit important spatial processes","Cost of actions assumed constant across sites regardless of threat intensity, which may misrepresent real implementation costs","Study focused only on freshwater fish in one catchment; transferability to other taxa or regions not demonstrated"],"river_ids":[],"site_ids":["doc-449baa8e73415f6340a9a9cc-site-0"],"anchor":[130.9887494,-12.5257236],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040061670],"main_basin_ids":[5040061670]},{"id":"doc-602e7358fe676f4fc7a83271","title":"Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding","display_title":"Hydrologic Nonstationarity in Southeastern Australia","doi":"https://doi.org/10.5194/piahs-371-17-2015","theme":"Climate","color":"#98d4f1","year":2015,"publication_date":"2015-06-12","authors":["F. H. S. Chiew","J. Vaze"],"summary":"This paper overviews the challenge of hydrologic nonstationarity, particularly under anthropogenic climate change and altered physical systems, which complicates hydrological model extrapolation to predict future conditions. The prolonged Millennium Drought (1997–2009) in far south-eastern Australia exemplifies nonstationarity, showing unprecedented runoff decline driven by multiple altered climate and hydrologic factors. Models calibrated on pre-drought data failed during the drought, but incorporating these extreme conditions improves model reliability. The paper summarizes approaches to account for nonstationarity, including updated conceptualizations, parameterizations, and use of climate model ensembles, within the framework of IAHS initiatives.","finding":"Hydrological models calibrated against historical data failed to accurately simulate runoff during the Millennium Drought in far south-eastern Australia because the drought introduced significantly different climate-runoff relationships and hydrological processes, but models adapted to include these conditions can better represent such extremes moving forward.","finding_evidence":{"passage_id":"p003-001","page":3,"quote":"Because of the significantly different climate-runoff relationship and model conceptualisations that do not adequately represent surface-groundwater connection through long dry spells, it is not surprising then that models developed and calibrated against the pre-1997 data were not able to estimate the flow volumes and runoff characteristics during the drought. However, because the Millennium Drought has exposed these extreme conditions, models can now be developed or adapted to also represent these conditions. There are many similar examples of models not being able to simulate the hydrology of a period with very different hydroclimate characteristics from the period used to develop the models.","verified_in_page":true},"topics":["hydrological modelling","climate change impact","runoff variability","hydrologic processes","parameterization","drought impact","model extrapolation","eco-hydrology"],"study_period":null,"locations":[{"name":"Far south-eastern Australia","kind":"region","role":"study area","river_name":null,"geocode_query":"far south-eastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"F. H. S. Chiew and J. Vaze: Overview of session and proceeding 19 Chiew et al. (2014) presents an example of hydrologic nonstationarity and the implications on hydrologic prediction exposed by the prolonged 1997–2009 Millennium Drought in far south-eastern Australia (Fig. 1). The unprecedented runoff decline during the drought was caused not only by the lower annual rainfall, but also by changes in other climate characteristics (lack of high rainfall years, change in rainfall seasonality and higher temperatures) and dominant hydrological processes (reduced surface-groundwater connection and farm dams intercepting proportionally more water during dry periods).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southern Murray-Darling Basin","kind":"region","role":"example region for climate-runoff relationship studies","river_name":"Murray-Darling Basin","geocode_query":"Southern Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"C. & Bloschl, G.: Hydrological modelling in a changing world, Prog. Phys. Geogr., 35, 249–261, doi:10.1177/0309133311402550, 2011. Peterson, T. J., Argent, R. M., Western, A. W., and Chiew, F. H. S.: Multiple stable states in hydrological models: an ecohydrological investigation, Water Resour. Res., 45, W03406, doi:10.1029/2008WR006886, 2009. Potter, N. J. and Chiew, F. H. S.: An investigation into changes in climate characteristics causing the recent very low runoff in the southern Murray-Darling Basin using rainfall-runoff models, Water Resour. Res., 47, W00G10, doi:10.1029/2010WR010333, 2011. Puspalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andreassian, V.: A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, J. Hydrol., 411, 66– 76, doi:10.1016/j.jhydrol.2011.09.034, 2011. Raisanan, J.: How reliable are climate models?, Tellus, 59A, 2–29, doi:10.1111/j.1600-0870.2006.00211.x, 2007. Roderick, M. L., Hobbins, M.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.59966,-31.127296],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-602e7358fe676f4fc7a83271-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7}],"limitations":["No precise coordinates provided for study locations","Some regional geographies are broad geographic descriptions without specific 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040591010],"main_basin_ids":[5040073410]},{"id":"doc-685d4c77fee1445c824e5262","title":"Implication of calibration period on modelling climate change impact on future runoff","display_title":"Effect of Calibration Period on Future Runoff Modeling","doi":"https://doi.org/10.5194/piahs-371-3-2015","theme":"Climate","color":"#98d4f1","year":2015,"publication_date":"2015-06-12","authors":["F. H. S. Chiew","H. Zheng","J. Vaze"],"summary":"This study investigates how the choice of historical calibration period impacts hydrological model predictions of future runoff under climate change. Using two Australian catchments with different climates and runoff regimes, two hydrological models, and future climate projections from 14 global climate models under RCP4.5, the study compares model calibrations against the entire historical data period (1961–2009) versus subsets matching future rainfall projections. Results indicate that calibration period influences modeled runoff, but the uncertainty due to calibration choice is small relative to the wide range of future rainfall projections.","finding":"Using parameter values calibrated against historical periods similar to projected future rainfall can increase or decrease the range and uncertainty in projected future runoff compared to calibrations using the entire historical dataset, but this effect is small compared to uncertainties in future rainfall projections from global climate models.","finding_evidence":{"passage_id":"p004-001","page":4,"quote":"This is particularly so where a single hydrological model has been chosen or developed specifically for a particular region and/or study objective or where a model can be developed or conceptualised differently to better represent different hydroclimate conditions. Nevertheless, the results from the limited modelling experiments here (e.g. only two catchments and two hydrological models, with a very simplistic consideration of changed future climate inputs) show that the difference in runoff responses from different model calibration considerations is likely to be small relative to the uncertainty/range in the future rainfall projections. This is particularly so when ensembles from multiple hydrological models are used as the different models can show biased responses in different directions. 6 Conclusions The modelled runoff and hydrologic responses can be influenced by the choice of historical data period used to calibrate and develop the hydrological model.","verified_in_page":true},"topics":["hydrological-modeling","climate-change-impact","runoff-projection","model-calibration","rainfall-projection","climate-uncertainty","rainfall-runoff-modeling","parameter-optimization"],"study_period":"1961–2009 for historical data; 2046–2065 for future projections","locations":[{"name":"Barron River at Picnic Crossing","kind":"river","role":"primary catchment study site with tropical climate and summer dominated runoff","river_name":"Barron River","geocode_query":"Barron River at Picnic Crossing, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"4 F. H. S. Chiew et al.: Modelling climate change impact on future runoff Figure 1 Locations of the two catchments used in the study and their annual rainfall and runoff time series Catchment 110003 Barron River at Picnic Crossing Catchment area: 228 km2 Tropical climate Mean annual rainfall: 1628 mm Mean annual runoff: 586 mm Summer dominated rainfall and runoff Catchment 405219 Goulburn River at Dohertys Catchment area: 694 km2 Temperate climate Mean annual rainfall: 1212 mm Mean annual runoff: 458 mm Winter dominated runoff 0 500 1000 1500 2000 2500 3000 1960 1970 1980 1990 2000 0 500 1000 1500 2000 2010 1960 1970 1980 1990 2000 2010 Rainfall and runoff (mm) 110003 405219 Rainfall Runoff Figure 1. Locations of the two catchments used in the study and their annual rainfall and runoff time series. ulated and largely unimpaired catchments come from the respective state water agencies. Daily rainfall, PET and streamflow data from 1961 to 2009 are used for this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.585912,-16.927495],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651465,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barron River, Queensland, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-685d4c77fee1445c824e5262-site-0","true_coordinates":[145.5859120481276,-16.98749451142494],"spread_km":6.7},{"name":"Goulburn River at Dohertys","kind":"river","role":"primary catchment study site with temperate climate and winter dominated runoff","river_name":"Goulburn River","geocode_query":"Goulburn River at Dohertys, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"4 F. H. S. Chiew et al.: Modelling climate change impact on future runoff Figure 1 Locations of the two catchments used in the study and their annual rainfall and runoff time series Catchment 110003 Barron River at Picnic Crossing Catchment area: 228 km2 Tropical climate Mean annual rainfall: 1628 mm Mean annual runoff: 586 mm Summer dominated rainfall and runoff Catchment 405219 Goulburn River at Dohertys Catchment area: 694 km2 Temperate climate Mean annual rainfall: 1212 mm Mean annual runoff: 458 mm Winter dominated runoff 0 500 1000 1500 2000 2500 3000 1960 1970 1980 1990 2000 0 500 1000 1500 2000 2010 1960 1970 1980 1990 2000 2010 Rainfall and runoff (mm) 110003 405219 Rainfall Runoff Figure 1. Locations of the two catchments used in the study and their annual rainfall and runoff time series. ulated and largely unimpaired catchments come from the respective state water agencies. Daily rainfall, PET and streamflow data from 1961 to 2009 are used for this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.874167,-37.315556],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43664960,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-685d4c77fee1445c824e5262-site-1","true_coordinates":[145.87416676200007,-37.25555552599997],"spread_km":6.7}],"limitations":["Only two catchments studied which may limit generalizability.","Only two hydrological models used, possibly limiting model diversity.","Future climate inputs considered via simplistic delta scaling approach with no changes in sequence, distribution, or seasonality.","No explicit coordinate data available for catchments, coordinates set as null."],"river_ids":["43639555","43639562","43639604","43639619","43640246","43640255","43640367","43640372","43640606","43640741","43640767","43641225","43641428","43641738","43642972","43643044","43643136","43643327","43644769","43644968","43645529","43645552","43645940","43646164","43646210","43646273","43646295","43646309","43646330","43646395","43646400","43646427","43646432","43646472","43646490","43646493","43647165","43647196","43647220","43647236","43647673","43648171","43648218","43648764","43648765","43648769","43648786","43648801","43648826","43648874","43648875","43649124","43649144","43649164","43649185","43649194","43649202","43649205","43649211","43649308","43649310","43649369","43649374","43649375","43649408","43649486","43649556","43649599","43649678","43649679","43649681","43649682","43649683","43649743","43649749","43649836","43653302","43660286","43660287","43660291","43660295","43660299","43660301","43660303","43660305","43660306","43660307","43660309"],"site_ids":["doc-685d4c77fee1445c824e5262-site-0","doc-685d4c77fee1445c824e5262-site-1"],"anchor":[145.585912,-16.927495],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040067860,5040591050],"main_basin_ids":[5040067860,5040073410]},{"id":"doc-80c1783d0f971832a6a29974","title":"Convergent modelling of past soil organic carbon stocks but divergent projections","display_title":"Uncertainty in Soil Carbon Projections in Australian Croplands","doi":"https://doi.org/10.5194/bg-12-4373-2015","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-07-28","authors":["Z. Luo","E. Wang","H. Zheng","J. A. Baldock","O. J. Sun","Q. Shao"],"summary":"This study quantifies uncertainty in soil organic carbon (SOC) projections across 90 experiments at 26 sites in Australian cereal-growing regions. Using the APSIM model optimized via Bayesian methods, the authors demonstrate that while models accurately simulate past SOC changes, forward projections over 100 years diverge significantly due to uncertainty in microbial carbon use efficiency, decomposability of soil organic matter, and initialization parameters. Uncertainty increases with projection duration, carbon input levels, and temperature, but decreases with rainfall. The study highlights the need for better representation of microbial processes and soil carbon heterogeneity to improve future SOC projections.","finding":"Uncertainty in SOC projections increases exponentially with the variability in model parameters, correlates positively with temperature and negatively with rainfall, and rises with longer projection periods and greater carbon input, indicating that future SOC projections under warming and drying climate scenarios will be subject to greater uncertainty.","finding_evidence":{"passage_id":"p008-002","page":8,"quote":"The variance in model parameters (GV) across experiments had a major effect on the intercepts (positive at P < 0.001) and slopes (positive at P < 0.001) of the regression model linking UP to C input (Table 1). As GV was logarithmically transformed when fitting the model, the increase in UP with GV was exponential across experiments. This result highlights the crucial role to improve the representation of the sensitive microbial processes (Zhou et al., 2012; Xu et al., 2014) and the heterogeneous SOM (Sierra et al., 2011) in biogeochemical SOM models, and to constrain the space of relevant model parameters. For example, we assumed a relatively wide range of CUE (0.2–0.8) as the prior information for the Bayesian optimization. Sinsabaugh et al. (2013) suggested that CUE prediction should consider resource composition, stoichiometry constraints and biomass composition, as well as environmental drivers. A more informative prior of CUE could help reduce the uncertainty in soil C projections.","verified_in_page":true},"topics":["soil carbon storage","microbial carbon use efficiency","soil organic matter decomposition","biogeochemical modeling","soil carbon sequestration","model uncertainty quantification","climate impact on soil carbon","carbon input management"],"study_period":null,"locations":[{"name":"Australian cereal-growing regions","kind":"region","role":"primary study region comprising the 90 agricultural experiments used in modeling soil organic carbon dynamics and projection","river_name":null,"geocode_query":"Australian cereal-growing regions, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We further investigated how the projection uncertainty can be quantified by using these drivers, so that future SOC projections can become more useful with attached and well-quantified uncertainties. 2 Materials and methods 2.1 Study sites and data sets Data from a total of 90 experimental plots located within 26 different sites (Fig. S1 in the Supplement) and compiled and described by Skjemstad and Spouncer (2003) were used in this study. The experimental duration of these trials ranged from 5 to 82 years; the experiments cover diverse climate, soil and agricultural management conditions and are representative of Australian cereal-growing regions (Table S1 in the Supplement). The data set included detailed records on crop sequence, crop yield, crop residue production (estimated according to harvest index) and agricultural management practices such as residue management (removal or retention) and fertilizer application over each year.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Wagga Wagga, New South Wales, Australia","kind":"region","role":"representative site used to conduct sensitivity analysis of key soil carbon model parameters","river_name":null,"geocode_query":"Wagga Wagga, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4376 Z. Luo et al.: Uncertainty in soil C projections and the most important parameters were identified and optimized to obtain the best agreement between simulated and observed SOC dynamics for each of the 90 experiments. As the relative importance of those parameters was independent of soil and climate conditions, the typical soil and climate at Wagga Wagga (a major cropping area in Australia, and one of the 26 sites used in the main text), New South Wales, Australia, were selected to conduct above analyses. 2.4 Model optimization The differential evolution (DE) algorithm (which belongs to the class of genetic algorithms) was used to optimize the most influential parameters identified. The optimization was performed in R 3.0.3 using the DEoptim function in the “DEoptim” package (Mullen et al., 2011).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.36551,-35.162598],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wagga Wagga NSW 2650, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-80c1783d0f971832a6a29974-site-1","true_coordinates":[147.3655103,-35.1025977],"spread_km":6.7},{"name":"Tarlee, South Australia, Australia","kind":"settlement","role":"case study site for model parameter optimization and soil organic carbon projection uncertainty assessment","river_name":null,"geocode_query":"Tarlee, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Our optimization procedure enabled accurate simulation of SOC change during the optimization period (Fig. 1a) using distinct ensembles of model parameters for each experiment (Fig. 1b). Pooling together all data of the 90 experiments, the modelled average SOC of the 700 simulations could explain 96 % (P < 0.001) of the variance in observed SOC (Fig. 1a). For each experiment, model performance was nearly identical (Fig. 1a) when the simulations (using different parameter sets) were intercompared. At the Tarlee site (Fig. 2a), for example, the RMSE between modelled and observed SOC ranged from 0.44 to 0.52 t ha−1 , compared with the range of 3.11 to 3.12 t ha−1 at the Brigalow site (Fig. 2b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.7698855,-34.2723168],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289624,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tarlee SA 5411, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-80c1783d0f971832a6a29974-site-2"},{"name":"Brigalow, Queensland, Australia","kind":"settlement","role":"case study site for model parameter optimization and soil organic carbon projection uncertainty assessment","river_name":null,"geocode_query":"Brigalow, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Our optimization procedure enabled accurate simulation of SOC change during the optimization period (Fig. 1a) using distinct ensembles of model parameters for each experiment (Fig. 1b). Pooling together all data of the 90 experiments, the modelled average SOC of the 700 simulations could explain 96 % (P < 0.001) of the variance in observed SOC (Fig. 1a). For each experiment, model performance was nearly identical (Fig. 1a) when the simulations (using different parameter sets) were intercompared. At the Tarlee site (Fig. 2a), for example, the RMSE between modelled and observed SOC ranged from 0.44 to 0.52 t ha−1 , compared with the range of 3.11 to 3.12 t ha−1 at the Brigalow site (Fig. 2b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.7909259,-26.8442671],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43675267,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brigalow QLD 4412, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-80c1783d0f971832a6a29974-site-3"}],"limitations":["Broad regional scale for main study area without specific site coordinate details","Exact coordinates missing for most sites; latitude and longitude set to null","Uncertainty assessment limited to Australian cereal-growing environments, lacking global coverage"],"river_ids":["43647620"],"site_ids":["doc-80c1783d0f971832a6a29974-site-1","doc-80c1783d0f971832a6a29974-site-2","doc-80c1783d0f971832a6a29974-site-3"],"anchor":[147.36551,-35.162598],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040073420,5040511570,5040591050],"main_basin_ids":[5040073410,5040073420]},{"id":"doc-8f744c8faecbc651890281d3","title":"What Actually Confers Adaptive Capacity? Insights from Agro-Climatic Vulnerability of Australian Wheat","display_title":"Adaptive Capacity in Australian Wheat Farming","doi":"https://doi.org/10.1371/journal.pone.0117600","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-02-10","authors":["Brett A. Bryan","Jianjun Huai","Jeff Connor","Lei Gao","Darran King","John Kandulu","Gang Zhao"],"summary":"This study empirically assessed adaptive capacity to climate variability in Australian wheat production over the period 1991–2010 across 12 regions in Australia's wheat-sheep zone. Using a crop model to estimate expected yields and actual farm survey yields, the study developed an adaptive capacity index reflecting relative performance of actual to expected yields. It tested 24 capital indicators (social, human, physical, natural, financial) commonly used to represent adaptive capacity, finding only some were significantly related to adaptive capacity, many contrary to theoretical expectations. The results suggest the need for evidence-based selection of indicators in vulnerability assessments based on sustainable livelihoods theory (SLT), to effectively target adaptation policy and investment.","finding":"Among 24 capital indicator variables tested for explaining adaptive capacity of Australian wheat farming to climatic variability, only 10 showed significant relationships to an empirical adaptive capacity index, with six aligning positively as predicted by sustainable livelihoods theory, and four showing opposite (negative) relationships. Specifically, positive associations included fertilizer use, growing season rainfall, total annual rainfall, solar radiation, native vegetation cover, and net primary productivity; whereas telephone expenditure, education level, cropped land area, and soil water holding capacity showed negative associations to adaptive capacity. This challenges assumptions that increasing capital metrics invariably enhance adaptive capacity.","finding_evidence":{"passage_id":"p015-000","page":15,"quote":"relationships between the empirical metric of adaptive capacity (ACI) and 10 capital indicators. For the remaining 14 capital indicators, no statistically significant relationship to adaptive capacity could be established. Notably however, we could only verify the theory-predicted and a priori expected sign of the relationship with adaptive capacity for six of the 10 significant capital indicators. Specifically, we found adaptive capacity significantly and positively related to one physical capital variable fertilization (PC_Fert), and five natural capital variables: growing season rainfall (NC_SHRain), total annual rainfall (NC_TRain), solar radiation (NC_SRad), the presence of native vegetation (NC_NVeg), and net primary productivity (NC_NPP). This supports our earlier hypotheses, that these six variables that may confer adaptive capacity by directly influencing crop growth and, in the case of NC_NVeg, by supporting other ecosystem services that enhance crop yields.","verified_in_page":true},"topics":["adaptive capacity","climate variability","crop yield modeling","socioeconomic indicators","vulnerability assessment","regression analysis","sustainable livelihoods theory","wheat production"],"study_period":"1991-2010","locations":[{"name":"Wheat-Sheep Zone","kind":"region","role":"study region","river_name":null,"geocode_query":"Wheat-Sheep Zone, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Exported to over 40 countries, Australian wheat plays an important role in global food security. In Australia, total wheat production in 2006 was just over 25 million tonnes from 12.443 million ha, with about 90% produced in the wheat-sheep zone—our study area (Fig. 1). We used the 12-region Australia Bureau of Agricultural and Resource Economics and Sciences (ABARES) classification as the Components of Adaptive Capacity in Australian Wheat Farming PLOS ONE | DOI:10.1371/journal.pone.0117600 February 10, 2015 3 / 20","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mallee","kind":"region","role":"study region within wheat-sheep zone","river_name":null,"geocode_query":"Mallee, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Overall, the actual WYI was more variable (SD = 0.367) than the expected (SD = 0.246). Variance in actual and expected WYI differed between regions (Fig. 2). For example, North West Slopes and Plains (NSW) and Eastern Darling Downs (Qld) had low variance in expected WYI but a high variance in the actual WYI. Mallee (Vic) had high variance in both expected and actual WYI, with actual WYI tracking closely to expected. Actual WYI also tracked closely to expected in the two WA and SA regions, with Central and South Wheat Belt (WA) exhibiting low variance in both actual and expected WYI. Mean ACI over all regions and years was positive but low (mean = 0.038) and highly variable (SD = 0.424). In most regions, the ACI tends to exhibit runs of a few years with positive or negative ACI, with a noticeable run of negative ACI years coinciding with drought in the late 2000s (Fig. 2). On average, most regions had a near zero but positive ACI with Mallee (Vic) the highest (mean ACI = 0.112) (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.349165,-34.782332],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Mallee, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f744c8faecbc651890281d3-site-1","true_coordinates":[142.2797032,-34.7637906],"spread_km":6.7},{"name":"North West Slopes and Plains","kind":"region","role":"study region within wheat-sheep zone","river_name":null,"geocode_query":"North West Slopes and Plains, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Overall, the actual WYI was more variable (SD = 0.367) than the expected (SD = 0.246). Variance in actual and expected WYI differed between regions (Fig. 2). For example, North West Slopes and Plains (NSW) and Eastern Darling Downs (Qld) had low variance in expected WYI but a high variance in the actual WYI. Mallee (Vic) had high variance in both expected and actual WYI, with actual WYI tracking closely to expected. Actual WYI also tracked closely to expected in the two WA and SA regions, with Central and South Wheat Belt (WA) exhibiting low variance in both actual and expected WYI. Mean ACI over all regions and years was positive but low (mean = 0.038) and highly variable (SD = 0.424). In most regions, the ACI tends to exhibit runs of a few years with positive or negative ACI, with a noticeable run of negative ACI years coinciding with drought in the late 2000s (Fig. 2). On average, most regions had a near zero but positive ACI with Mallee (Vic) the highest (mean ACI = 0.112) (Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Eastern Darling Downs","kind":"region","role":"study region within wheat-sheep zone","river_name":null,"geocode_query":"Eastern Darling Downs, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Overall, the actual WYI was more variable (SD = 0.367) than the expected (SD = 0.246). Variance in actual and expected WYI differed between regions (Fig. 2). For example, North West Slopes and Plains (NSW) and Eastern Darling Downs (Qld) had low variance in expected WYI but a high variance in the actual WYI. Mallee (Vic) had high variance in both expected and actual WYI, with actual WYI tracking closely to expected. Actual WYI also tracked closely to expected in the two WA and SA regions, with Central and South Wheat Belt (WA) exhibiting low variance in both actual and expected WYI. Mean ACI over all regions and years was positive but low (mean = 0.038) and highly variable (SD = 0.424). In most regions, the ACI tends to exhibit runs of a few years with positive or negative ACI, with a noticeable run of negative ACI years coinciding with drought in the late 2000s (Fig. 2). On average, most regions had a near zero but positive ACI with Mallee (Vic) the highest (mean ACI = 0.112) (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.582068,-27.469991],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674536,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darling Downs, QLD, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-8f744c8faecbc651890281d3-site-3","true_coordinates":[150.5820676,-27.5299906],"spread_km":6.7},{"name":"Central North","kind":"region","role":"baseline reference region for regression within wheat-sheep zone","river_name":null,"geocode_query":"Central North, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-001","page":14,"quote":"Interval] Social Family share of farm income SC_FSFI - 6.16E-07 6.45E-07 0.96 0.341 -6.56E-7 1.89E-06 Telephone charges SC_Phone Inv. sqrt – 50.36992 15.7503 3.2 0.002 19.32515 81.41469 Accessibility and Remoteness SC_Remote Inverse -0.49933 0.3465744 -1.44 0.151 -1.18245 0.183791 Human Advisory services HC_Advis Sqrt 0.003471 0.002235 1.55 0.122 -0.00093 0.007876 Crop diversity HC_CropDiv - 0.304657 0.4048501 0.75 0.453 -0.49333 1.102641 Total labor used HC_Labor - -0.00058 0.0027764 -0.21 0.834 -0.00605 0.00489 Education and occupation HC_Educ - – -0.00407 0.0017318 -2.35 0.020 -0.00748 -0.00065 Physical Electricity use PC_Elec - 3.89E-05 0.0000447 0.87 0.385 -4.9E-05 0.000127 Fertilizer use PC_Fert Log + 0.37311 0.0850215 4.39 0.000 0.205528 0.540692 Area cropped PC_Land Log – -0.51106 0.136713 -3.74 0.000 -0.78053 -0.2416 Natural Gowing season rainfall NC_SHRain - + 0.002167 0.0004953 4.37 0.000 0.001191 0.003143 Total rainfall NC_TRain - + 0.001149 0.0003908 2.94 0.004 0.000379 0.001919 Solar radiation NC_SRad - + 0.000459 0.0002305 1.99 0.048 4.71E-06 0.000913 Soil water holding capacity NC_SWHC - – -87.0139 20.55461 -4.23 0.000 -127.528 -46.4996 Native vegetation NC_NVeg Log + 1.399986 0.364436 3.84 0.000 0.681661 2.118311 Net primary productivity NC_NPP Inverse + -34.5693 8.929121 -3.87 0.000 -52.1691 -16.9695 Financial Access to credit FC_AccCred Log 0.098395 0.0652794 1.51 0.133 -0.03027 0.227064 Recent family income FC_RFInc - 2.11E-06 1.57E-06 1.35 0.180 -9.80E-7 5.20E-06 Region Central West (NSW) -1.28521 0.3320974 -3.87 0.000 -1.93979 -0.63062 Central and South Wheat Belt (WA) -4.31125 1.040959 -4.14 0.000 -6.36304 -2.25946 Darling Downs & Cent.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Use of regional aggregated data may cause ecological fallacy limiting inference at farm/household level","Farm survey data (ABARES AgSurf) have limited sample size causing uncertainty in actual yield estimates","Crop model (APSIM) assumes fixed management practices, limiting accuracy of expected yield projections","Some capital indicator data are time-invariant and only represent between-region variability, not temporal changes","Multicollinearity persists for certain variables inflating standard error estimates but not affecting directionality conclusions"],"river_ids":[],"site_ids":["doc-8f744c8faecbc651890281d3-site-1","doc-8f744c8faecbc651890281d3-site-3"],"anchor":[150.582068,-27.469991],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040524870,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-92101b8d0fd9874102983849","title":"Streamflow predictions in regulated river systems: hydrological non-stationarity versus anthropogenic water use","display_title":"Streamflow Predictions in the Regulated Murray-Darling Basin","doi":"https://doi.org/10.5194/piahs-371-35-2015","theme":"Water management","color":"#eeabac","year":2015,"publication_date":"2015-06-12","authors":["D. Dutta","S. Kim","J. Vaze","J. Hughes"],"summary":"This study used the AWRA-R river system model to assess the influence of anthropogenic development, water use, and climate variability/change on streamflow non-stationarity in the highly regulated Murray-Darling Basin (MDB), Australia, during 1950–2010. The model was calibrated and validated against observed data, and streamflow was simulated under current and pre-development conditions. Results demonstrated that water storages and irrigation diversions greatly influenced declining streamflow trends, contributing to over 30% of the decline in some sub-basins during recent decades. The study emphasizes the need to incorporate anthropogenic impacts in hydrologic modeling and water resources management in regulated basins.","finding":"Anthropogenic developments such as storages and irrigation diversions contributed more than one-third of the decline in streamflow in highly regulated sub-basins like the Condamine-Balonne and Goulburn-Broken during 1970–2010, exacerbating the non-stationarity of rainfall-streamflow relationships in the MDB.","finding_evidence":{"passage_id":"p006-004","page":6,"quote":"For example, in the Condamine-Balonne and Goulburn-Broken systems, anthropogenic development and water use have contributed to more than one-third of the overall decline in streamflow. Some of the smaller storages (such as farm dams, ring tanks) and water diversions (such as urban and stock and domestic) were not incorporated in AWRA-R modelling due to non-availability of suitable data. The influence of anthropogenic development and water use is likely to be even bigger on streamflow non-stationarity in regulated systems with incorporation of these additional information in the modelling. 6 Summary This study used the newly developed AWRA-R river system model to analyse the influence of anthropogenic development and water use on streamflow and to partition this influence from climate variability/change impact on streamflow in a regulated river system. The model was implemented in the Murray-Darling Basin, a highly regulated basin with a Proc.","verified_in_page":true},"topics":["streamflow","hydrological non-stationarity","river regulation","water use","irrigation","climate variability","hydrological modeling","water resources management"],"study_period":"1950–2010","locations":[{"name":"Murray-Darling Basin","kind":"basin","role":"primary study area, highly regulated river basin for modeling streamflow under natural and developed conditions","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"The paper briefly introduces the model and the method used for assessing streamflow variability under natural and developed conditions and discusses the results and findings. 2 Study area The study area is the Murray Darling Basin (MDB) in Australia, which covers an area of about 1 million km2 (1450 km long and 1000 km wide) and consists largely of plains rising to the Great Dividing Range on its eastern and southern rim. The MDB river system is a highly complex and mostly regulated system covering four states (Queensland, New South Wales (NSW), Victoria and South Australia) and one territory (ACT) (CSIRO, 2008). The MDB is often referred to as Australia’s “food bowl”, with agriculture covering up to 80 % of the land area in the Basin (Connell and Grafton, 2011; Griffiths and Edraki, 2013). It utilises about 70 % of all water used for agriculture across the nation and accounts for 40 % of Australia’s agricultural production.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.596518,-31.116211],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-92101b8d0fd9874102983849-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Paroo","kind":"basin","role":"unregulated catchment selected for comparison of rainfall and streamflow trends under current and pre-development scenarios","river_name":null,"geocode_query":"Paroo, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"This can be clearly seen from the plots of annual average rainfall and streamflow in the two basins as shown in Fig. 5. In Paroo, the trends in rainfall and streamflow are very similar. In Ovens, rainfall-streamflow relationships do exhibit non-stationarity, but to a much lesser extent compared to the overall MDB. As expected, there is no difference between annual average streamflow for the current and pre-development scenarios in these two catchments. As shown in Fig. 6, rainfall and streamflow relationships in the two highly regulated catchments (Condamine-Balonne and Goulburn-Broken) show non-stationarity with very distinct trends in rainfall and streamflow over the period of 1950–2010. The decreasing trends in streamflow are highly influenced by the development of storages and irrigation diversion starting from the early 1970s. The influence is relatively more prominent in Goulburn-Broken, where irrigation diversion is relatively larger compared to CondamineBalonne.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.846805,-28.056939],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665440,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524780,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":15576.0,"hydrology_source":"geofabric","site_id":"doc-92101b8d0fd9874102983849-site-1","true_coordinates":[145.8468053,-27.996939],"spread_km":6.7},{"name":"Ovens","kind":"basin","role":"largely unregulated catchment selected for comparison of rainfall and streamflow trends under current and pre-development scenarios","river_name":null,"geocode_query":"Ovens, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"This can be clearly seen from the plots of annual average rainfall and streamflow in the two basins as shown in Fig. 5. In Paroo, the trends in rainfall and streamflow are very similar. In Ovens, rainfall-streamflow relationships do exhibit non-stationarity, but to a much lesser extent compared to the overall MDB. As expected, there is no difference between annual average streamflow for the current and pre-development scenarios in these two catchments. As shown in Fig. 6, rainfall and streamflow relationships in the two highly regulated catchments (Condamine-Balonne and Goulburn-Broken) show non-stationarity with very distinct trends in rainfall and streamflow over the period of 1950–2010. The decreasing trends in streamflow are highly influenced by the development of storages and irrigation diversion starting from the early 1970s. The influence is relatively more prominent in Goulburn-Broken, where irrigation diversion is relatively larger compared to CondamineBalonne.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.270647097326,-36.21191667063577],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665726,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-92101b8d0fd9874102983849-site-2"},{"name":"Condamine-Balonne","kind":"basin","role":"highly regulated catchment used to analyze anthropogenic influence on streamflow trend with irrigation diversions and storages","river_name":null,"geocode_query":"Condamine-Balonne, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"As can be seen from Table 2, differences in streamflow between the current and pre-development scenarios are relatively low (∼ 2 %) for the early period of 1950–1977, but increased significantly (by over 10 %) for the two subsequent periods. The results presented in Table 2 show that one third of the decreasing trends in the streamflow in the two basins can be attributed to the anthropogenic development and water use. proc-iahs.net/371/35/2015/ Proc. IAHS, 371, 35–42, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.136457,-26.987087],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43677463,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-92101b8d0fd9874102983849-site-3","true_coordinates":[150.1364566,-26.9270865],"spread_km":6.7},{"name":"Goulburn-Broken","kind":"basin","role":"highly regulated catchment analyzed for impact of irrigation diversions and storages on streamflow declines","river_name":null,"geocode_query":"Goulburn-Broken, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"As can be seen from Table 2, differences in streamflow between the current and pre-development scenarios are relatively low (∼ 2 %) for the early period of 1950–1977, but increased significantly (by over 10 %) for the two subsequent periods. The results presented in Table 2 show that one third of the decreasing trends in the streamflow in the two basins can be attributed to the anthropogenic development and water use. proc-iahs.net/371/35/2015/ Proc. IAHS, 371, 35–42, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.583398,-36.881256],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43677721,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-92101b8d0fd9874102983849-site-4","true_coordinates":[145.5833978884901,-36.82125562451294],"spread_km":6.7},{"name":"Chaffey Dam","kind":"facility","role":"large storage constructed between mid-1950s and 1990 that contributed to altered streamflow","river_name":null,"geocode_query":"Chaffey Dam, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"As can be seen from the streamflow plots and trend lines for the current and predevelopment scenarios, the decreasing trend in streamflow is exacerbated by the anthropogenic development and water use in the basin over the modelling period. There is more than 5 % difference in the mean value of streamflow under the current and pre-development scenario. Much of the storage capacity (other than farm dams) within the Basin was constructed between the mid-1950s and 1990 and now account for about 80 % of the water-storage capacity in the Basin (Connell and Grafton, 2011) The analysis period (1950–2010) can be divided into three periods based on climatic characteristics in South-east Australia over this period: (i) persistent wet period (1950– 1977), (ii) average climate (1978–1996) and (iii) millennium drought period (1997–2009).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Split Rock Dam","kind":"facility","role":"large storage constructed/enlarged during study period influencing streamflow","river_name":null,"geocode_query":"Split Rock Dam, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"As can be seen from the streamflow plots and trend lines for the current and predevelopment scenarios, the decreasing trend in streamflow is exacerbated by the anthropogenic development and water use in the basin over the modelling period. There is more than 5 % difference in the mean value of streamflow under the current and pre-development scenario. Much of the storage capacity (other than farm dams) within the Basin was constructed between the mid-1950s and 1990 and now account for about 80 % of the water-storage capacity in the Basin (Connell and Grafton, 2011) The analysis period (1950–2010) can be divided into three periods based on climatic characteristics in South-east Australia over this period: (i) persistent wet period (1950– 1977), (ii) average climate (1978–1996) and (iii) millennium drought period (1997–2009).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Pindari Dam","kind":"facility","role":"large storage built/enlarged impacting irrigation water use and streamflow in MDB","river_name":null,"geocode_query":"Pindari Dam, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"As can be seen from the streamflow plots and trend lines for the current and predevelopment scenarios, the decreasing trend in streamflow is exacerbated by the anthropogenic development and water use in the basin over the modelling period. There is more than 5 % difference in the mean value of streamflow under the current and pre-development scenario. Much of the storage capacity (other than farm dams) within the Basin was constructed between the mid-1950s and 1990 and now account for about 80 % of the water-storage capacity in the Basin (Connell and Grafton, 2011) The analysis period (1950–2010) can be divided into three periods based on climatic characteristics in South-east Australia over this period: (i) persistent wet period (1950– 1977), (ii) average climate (1978–1996) and (iii) millennium drought period (1997–2009).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Some small reservoirs and farm dams not included due to data unavailability, potentially underestimating anthropogenic 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040511570,5040524780,5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-a20f70ce62886154936afb03","title":"Methodologies for Pre-Validation of Biofilters and Wetlands for Stormwater Treatment","display_title":"Pre-Validation Methods for Stormwater Treatment","doi":"https://doi.org/10.1371/journal.pone.0125979","theme":"Water management","color":"#eeabac","year":2015,"publication_date":"2015-05-08","authors":["Kefeng Zhang","Anja Randelovic","Larissa M. Aguiar","Declan Page","David T. McCarthy","Ana Deletic"],"summary":"This study develops a pre-validation framework for stormwater treatment systems, specifically biofilters and wetlands, within Water Sensitive Urban Design (WSUD) for potable water reuse. It identifies target micropollutants in stormwater exceeding drinking water guidelines, establishes treatment targets, removal mechanisms, surrogate parameters, and operational challenge conditions across three Australian climate zones using 30-year rainfall data and hydrological modeling with MUSIC software. The findings inform validation monitoring to ensure treated stormwater meets potable standards.","finding":"A number of micropollutants are present in stormwater at levels exceeding worldwide drinking water guidelines, including eight pesticides, benzene, benzo(a)pyrene, pentachlorophenol, di-(2-ethylhexyl)-phthalate, and total polychlorinated biphenyls; these should be considered as target micropollutants for treatment validation.","finding_evidence":{"passage_id":"p011-003","page":11,"quote":"The estimated 95th percentile concentrations of benzene (10μg/L) and benzo(a)pyrene (BaP) (8μg/L) were above all DWG values (i.e. 1 μg/L for benzene and 0.01 μg/L for BaP according to the most lenient guideline value in Australia). The 95th percentile concentrations of di-(2-ethylhexyl)-phthalate (50μg/L) and pentachlorophenol (90μg/L) exceeded all DWG values except those for the EU which has not defined any guideline values for them. The estimated 95th percentile concentrations of Aldrin and Dieldrin (2 μg/L and 0.2 μg/L respectively) were above Australian and WHO values (i.e. 0.3 μg/L for the sum of these two pesticides). EU DWGs has a maximum value of 0.1μg/L for an individual pesticide and 0.5μg/L for the sum of all pesticides. Hence, all 95th percentile values for pesticides in Table 3 exceeded EU DWG values. Estimated 95th percentile concentration of total PCB was 0.9μg/L, which is above the limit of USEPA guideline value (0.5μg/L).","verified_in_page":true},"topics":["micropollutants","stormwater treatment","water quality guidelines","biofilters","wetlands","validation framework","hydrological modelling","operational conditions"],"study_period":"1980-2010 (rainfall data)","locations":[{"name":"Brisbane, Queensland, Australia","kind":"region","role":"Climate zone for operational condition modeling in subtropical climate","river_name":null,"geocode_query":"Brisbane, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Removal mechanisms of micropollutant have been extensively reported in wetlands [21–26] stormwater bioretention systems [27, 28] and related soil-based systems such as aquifers [29, 30]. These removal processes are largely dependent on the physical-chemical properties of the micropollutants, therefore the latter were also reviewed [38]. Step 4: Identification potential surrogate parameters Identification of potential surrogates parameters was conducted by reviewing the current literature on the surrogates for micropollutants during different treatment systems, such as aquifer recharge systems [39], chemical oxidation [40, 41] and ozonation [42], as no studies on surrogates have been reported for stormwater biofilters and wetlands. Step 5: Determination of operational and challenge conditions for biofilters and wetlands in three different climates The following parameters were considered to be the key operational variables to be considered in defining the boundaries of validation: 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.026034,-27.530453],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-a20f70ce62886154936afb03-site-0","true_coordinates":[153.0260341,-27.4704528],"spread_km":6.7},{"name":"Perth, Western Australia, Australia","kind":"region","role":"Climate zone for operational condition modeling in Mediterranean climate","river_name":null,"geocode_query":"Perth, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Removal mechanisms of micropollutant have been extensively reported in wetlands [21–26] stormwater bioretention systems [27, 28] and related soil-based systems such as aquifers [29, 30]. These removal processes are largely dependent on the physical-chemical properties of the micropollutants, therefore the latter were also reviewed [38]. Step 4: Identification potential surrogate parameters Identification of potential surrogates parameters was conducted by reviewing the current literature on the surrogates for micropollutants during different treatment systems, such as aquifer recharge systems [39], chemical oxidation [40, 41] and ozonation [42], as no studies on surrogates have been reported for stormwater biofilters and wetlands. Step 5: Determination of operational and challenge conditions for biofilters and wetlands in three different climates The following parameters were considered to be the key operational variables to be considered in defining the boundaries of validation: 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.885864,-31.895018],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Perth WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-a20f70ce62886154936afb03-site-1","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Melbourne, Victoria, Australia","kind":"region","role":"Climate zone for operational condition modeling in mild oceanic climate and site of Monash University where study was conducted","river_name":null,"geocode_query":"Melbourne, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"RESEARCH ARTICLE Methodologies for Pre-Validation of Biofilters and Wetlands for Stormwater Treatment Kefeng Zhang1,2*, Anja Randelovic3 , Larissa M. Aguiar1,2, Declan Page4 , David T. McCarthy1,2,5, Ana Deletic1,2 1 Monash Water for Liveability, Department of Civil Engineering, Monash University, Melbourne, VIC, Australia, 2 CRC for Water Sensitive Cities, Melbourne, VIC, Australia, 3 Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia, 4 CSIRO Land and Water Research Flagship, Waite Laboratories, Adelaide SA, Australia, 5 Environmental and Public Health Microbiology Laboratory, Department of Civil Engineering, Monash University, Melbourne, VIC, Australia * kefeng.zhang@monash.edu Abstract Background Water Sensitive Urban Design (WSUD) systems are frequently used as part of a stormwater harvesting treatment trains (e.g. biofilters (bio-retentions and rain-gardens) and wetlands).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.963058,-37.873628],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Melbourne VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-a20f70ce62886154936afb03-site-2","true_coordinates":[144.9630576,-37.8136276],"spread_km":6.7},{"name":"Australian urban catchments","kind":"region","role":"Source area for data and model parameterization of stormwater micropollutant concentrations and biofilter/wetland designs","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-001","page":17,"quote":"This paper focused on the pre-validation stage and developed a specific roadmap consisting of five steps with detailed methodologies: (1) identification of target micropollutants, (2) specification of treatment targets, (3) identification of potential removal mechanisms and influential factors, (4) identification of potential surrogates and (5) determination of operational and challenge conditions. A literature search was undertaken to identify and quantify micropollutants in stormwater. Statistical analysis revealed that challenge concentrations of 8 pesticides, benzene, benzo(a)pyrene, pentachlorophenol (PCP), di-(2-ethylhexyl)-phthalate (DEHP) and total polychlorinated biphenyls (PCBs), were above different worldwide drinking water guideline limits, and hence set the treatment targets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates for locations were not provided in the text and thus not included.","Study areas are broad regional climate zones rather than specific catchments or field sites.","Micropollutant data compiled from multiple international studies, not only from Australian sites.","Operational parameters derived from model simulations using MUSIC, not direct field measurements."],"river_ids":[],"site_ids":["doc-a20f70ce62886154936afb03-site-0","doc-a20f70ce62886154936afb03-site-1","doc-a20f70ce62886154936afb03-site-2"],"anchor":[144.963058,-37.873628],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040056460,5040069970,5040070710],"main_basin_ids":[5040056460,5040069970,5040070710]},{"id":"doc-a2e7d146d3a9fcc77c52d491","title":"Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments","display_title":"QR and UNEEC Uncertainty in UK Catchments","doi":"https://doi.org/10.5194/hess-19-3181-2015","theme":"Flow & ecology","color":"#80edc2","year":2015,"publication_date":"2015-07-23","authors":["N. Dogulu","P. López López","D. P. Solomatine","A. H. Weerts","D. L. Shrestha"],"summary":"This study compares two methods for estimating predictive uncertainty in hydrological models—quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC)—applied to four UK catchments with contrasting hydrological characteristics. The catchments include the Brue catchment in southwest England and three sub-catchments of the Upper Severn River (Yeaton, Llanyblodwel, and Llanerfyl) in the Midlands. UNEEC, a nonlinear and more complex method using additional predictors including groundwater level and soil moisture deficit, generally performs better than QR in fast-responding catchments with changing hydrological dynamics, such as Llanerfyl. Both methods provide similar uncertainty estimates overall, appropriate for operational flood forecasting, though model errors are heteroscedastic and often non-normal. Inclusion of groundwater level as a predictor improves UNEEC's performance.","finding":"UNEEC outperforms quantile regression in small, rapid-response catchments with high hydrological variability, demonstrating better predictive uncertainty estimation, while both methods perform similarly in larger or slower catchments.","finding_evidence":{"passage_id":"p018-004","page":18,"quote":"We can recommend both methods for use in flood forecasting. – In one case study, Llanerfyl, we found that UNEEC was giving more adequate estimates than QR. This catchment has a shorter basin lag time and the model outputs for this catchment were characterized by a relatively high error, so our conclusion was that probably in such a rapid response catchment the UNEEC’s more sophisticated non-linear models were able to capture relationships between the hydrometeorological and state variables, and the quantiles better than the QR’s linear model. – A useful finding is that inclusion of a variable representing groundwater level (GW) as a predictor in UNEEC improves its performance for the Upper Severn catchments. This can be explained by the fact that this variable has a high level of information content about the state of a catchment. However, it should be noted that, in other catchments, using such information can be misleading due to slow (and long) response times Hydrol. Earth Syst.","verified_in_page":true},"topics":["hydrological-modelling","predictive-uncertainty","quantile-regression","machine-learning","flood-forecasting","hydrological-model-evaluation","clustering","groundwater"],"study_period":null,"locations":[{"name":"Brue catchment","kind":"basin","role":"study catchment and model test site","river_name":"Brue River","geocode_query":"Brue catchment, Southwest England, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Additional analysis may be carried out to see whether the methods developed for the assessment of the probabilistic forecast quality can be used (Laio and Tamea, 2007) (it is not exactly the same as the residual uncertainty analysed here, but the mathematical apparatus seems to be transferrable). In this paper, however, we have not considered these, so they can be recommended for exploration and testing in the future studies. It is also worth mentioning that all considered measures are averages and so should be used together with the uncertainty bound plots of which visual analysis reveals more information on the capacity of different uncertainty prediction methods during particular periods. 3 Application 3.1 Case studies 3.1.1 Brue catchment Located in the southwest of England, the Brue River catchment has a history of severe flooding.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.675532832192663,51.15086325533978],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-a2e7d146d3a9fcc77c52d491-site-0"},{"name":"Upper Severn catchments: Yeaton, Llanyblodwel, Llanerfyl","kind":"basin","role":"study catchments and model test sites representing different hydrological regimes","river_name":"Severn River","geocode_query":"Upper Severn catchments, Midlands, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"Earth Syst. Sci., 19, 3181–3201, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.937876225001142,52.15145822238388],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-a2e7d146d3a9fcc77c52d491-site-1"},{"name":"Yeaton sub-catchment","kind":"basin","role":"study sub-catchment with longest basin lag time and slow hydrologic response","river_name":"Severn River","geocode_query":"Yeaton, Midlands, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"Earth Syst. Sci., 19, 3181–3201, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.8411839,52.7692554],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-a2e7d146d3a9fcc77c52d491-site-2"},{"name":"Llanyblodwel sub-catchment","kind":"basin","role":"study sub-catchment with moderate basin lag time","river_name":"Severn River","geocode_query":"Llanyblodwel, Midlands, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"Earth Syst. Sci., 19, 3181–3201, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[-3.1281,52.797087],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-a2e7d146d3a9fcc77c52d491-site-3"},{"name":"Llanerfyl sub-catchment","kind":"basin","role":"study sub-catchment with shortest basin lag time and flash flood potential","river_name":"Severn River","geocode_query":"Llanerfyl, Midlands, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"Earth Syst. Sci., 19, 3181–3201, 2015","verified_in_page":true},"resolution_status":"resolved","coordinates":[-3.429018,52.675484],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-a2e7d146d3a9fcc77c52d491-site-4"}],"limitations":["Model residuals often exhibit heteroscedasticity and non-normality, limiting strict statistical assumptions.","Groundwater level and soil moisture proxies come from hydrological models and may not fully capture real catchment states, especially in extreme events.","The study compares only two uncertainty estimation methods; other methods were not included.","The data sets have varied time steps (hourly and 12 hours) which affect model error characteristics and uncertainty estimation.","Geographic coordinates for catchments/stations are not provided, hence latitude and longitude are null."],"river_ids":[],"site_ids":["doc-a2e7d146d3a9fcc77c52d491-site-0","doc-a2e7d146d3a9fcc77c52d491-site-1","doc-a2e7d146d3a9fcc77c52d491-site-2","doc-a2e7d146d3a9fcc77c52d491-site-3","doc-a2e7d146d3a9fcc77c52d491-site-4"],"anchor":[-2.937876225001142,52.15145822238388],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[2040048790],"main_basin_ids":[2040048790]},{"id":"doc-a55ae488f114d6e4e3a389d1","title":"Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock","display_title":"Multiscale Image Fusion for Shale Rock","doi":"https://doi.org/10.1038/srep15880","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-11-02","authors":["Kirill M. Gerke","Marina V. Karsanina","Dirk Mallants"],"summary":"This study develops a robust and general stochastic methodology to merge multiscale spatial images into a single dataset by rescaling correlation functions and stochastic reconstruction. It is demonstrated using images of shale rock from the macroscale, microscale, and nanoscale scales, capturing mineral, organic matter, and pore distributions. This fusion approach addresses challenges in reliably quantifying petrophysical properties important for hydrocarbon production optimization and environmental impact minimization.","finding":"The fusion method effectively merges macro, micro, and nanoscale images of shale rock from distinct geological formations, enabling a multiscale pore-network continuum model that captures the spatial distribution of mineral, kerogen, and porosity phases important for flow simulations.","finding_evidence":{"passage_id":"p003-000","page":3,"quote":"www.nature.com/scientificreports/ Scientific Reports | 5:15880 | DOI: 10.1038/srep15880 3 micro and nanoscale pore-system have to be merged into a single network continuum with proper connections between scales. Three different images of shale structure are shown on Fig. 1 comprising macroscale, microscale and nanoscale. Images represent shale samples from Domanic (macroscale) and Upper Jurasic Bazhenov (micro and nanoscale) formations, respectively from the Tatar Basin and West Siberian Basin, Russia (see brief description in SI). The Bazhenov formation generated about 90 percent of oil reserves of the West Siberian basin33. The microscale image displays basically the same solids and kerogen phase as the macroscale, whereas the porosity now excludes the fractures (Fig. 1). Finally, the nanoscale image provides the kerogen nanoporosity. Both examples consider merging three multiscale images with scale ratios of 16:4:1.","verified_in_page":true},"topics":["stochastic reconstruction","multiscale image fusion","shale rock","petrophysical properties","porosity network","correlation functions","geostatistics","hydrocarbon reservoirs"],"study_period":null,"locations":[{"name":"Domanic Formation, Tatar Basin","kind":"region","role":"source rock sampling location for macroscale shale images","river_name":null,"geocode_query":"Domanic Formation, Tatar Basin, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"www.nature.com/scientificreports/ Scientific Reports | 5:15880 | DOI: 10.1038/srep15880 3 micro and nanoscale pore-system have to be merged into a single network continuum with proper connections between scales. Three different images of shale structure are shown on Fig. 1 comprising macroscale, microscale and nanoscale. Images represent shale samples from Domanic (macroscale) and Upper Jurasic Bazhenov (micro and nanoscale) formations, respectively from the Tatar Basin and West Siberian Basin, Russia (see brief description in SI). The Bazhenov formation generated about 90 percent of oil reserves of the West Siberian basin33. The microscale image displays basically the same solids and kerogen phase as the macroscale, whereas the porosity now excludes the fractures (Fig. 1). Finally, the nanoscale image provides the kerogen nanoporosity. Both examples consider merging three multiscale images with scale ratios of 16:4:1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Upper Jurassic Bazhenov Formation, West Siberian Basin","kind":"region","role":"source rock sampling location for micro and nanoscale shale images","river_name":null,"geocode_query":"Bazhenov Formation, West Siberian Basin, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"www.nature.com/scientificreports/ Scientific Reports | 5:15880 | DOI: 10.1038/srep15880 3 micro and nanoscale pore-system have to be merged into a single network continuum with proper connections between scales. Three different images of shale structure are shown on Fig. 1 comprising macroscale, microscale and nanoscale. Images represent shale samples from Domanic (macroscale) and Upper Jurasic Bazhenov (micro and nanoscale) formations, respectively from the Tatar Basin and West Siberian Basin, Russia (see brief description in SI). The Bazhenov formation generated about 90 percent of oil reserves of the West Siberian basin33. The microscale image displays basically the same solids and kerogen phase as the macroscale, whereas the porosity now excludes the fractures (Fig. 1). Finally, the nanoscale image provides the kerogen nanoporosity. Both examples consider merging three multiscale images with scale ratios of 16:4:1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.196656,60.95371],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Siberia, Russia","basin_id":3040369320,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":237680.1,"hydrology_source":"hydrobasins","site_id":"doc-a55ae488f114d6e4e3a389d1-site-1","true_coordinates":[99.1966559,61.01370970000001],"spread_km":6.7}],"limitations":["Study uses 2D images only; extension to 3D is stated as straightforward but not performed.","Some scale ratios in synthetic examples differ from original images but are used for demonstration.","Nanometer scale tiling applied due to computational constraints, potentially affecting spatial continuity.","Cross-correlations between phases during merging not accounted for, possibly affecting interfaces.","Accuracy depends on stochastic reconstruction quality and available input data.","Coordinates for sampling formations are not provided; locations are regional only."],"river_ids":[],"site_ids":["doc-a55ae488f114d6e4e3a389d1-site-1"],"anchor":[99.196656,60.95371],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[3040369320],"main_basin_ids":[3040004740]},{"id":"doc-a98ddca1bbe257f09384eaa6","title":"A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts","display_title":"Improving Streamflow Forecast Updating","doi":"https://doi.org/10.5194/hess-19-1-2015","theme":"Flow & ecology","color":"#80edc2","year":2015,"publication_date":"2015-01-06","authors":["M. Li","Q. J. Wang","J. C. Bennett","D. E. Robertson"],"summary":"This study analyses the effects of applying autoregressive (AR) models for updating streamflow forecasts in seven catchments across Australia and the US. Two conventional approaches—applying AR models to raw errors and normalized errors—can cause over-correction in forecasts particularly when streamflow changes rapidly, either rising or falling, and may degrade the standalone performance of hydrological models during joint parameter estimation. The authors introduce a restricted AR model (RAR-Norm) that limits corrections to avoid excessive updates, resulting in improved forecast reliability and model performance.","finding":"The RAR-Norm model effectively reduces the tendency of conventional AR updating methods to over-correct streamflow forecasts during rapid rises and falls, and improves the base hydrological model's standalone performance, particularly in challenging catchments such as the intermittent Abercrombie River and subtropical Orara River.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 11 5 Discussion and conclusions For streamflow forecasting, rainfall–runoff models are often augmented with an updating procedure that corrects the forecast using information from recent simulation errors. The most popular updating approach uses autoregressive (AR) models that exploit the “memory” in model errors. AR models may be applied to raw errors directly or to normalised errors. We demonstrate three adverse effects of AR error updating procedures on seven catchments. The first adverse effect is possible over-correction on the rising limb of the hydrograph. The AR-Norm model can exhibit the tendency to over-correct the peaks or on the rise of a hydrograph, because error updating can be (overly) amplified by the backtransformation. The second adverse effect is the tendency to over-correct receding hydrographs.","verified_in_page":true},"topics":["streamflow forecasting","autoregressive models","error updating","hydrological modeling","forecast bias","variance stabilization","parameter estimation","forecast uncertainty"],"study_period":null,"locations":[{"name":"Abercrombie River","kind":"river","role":"study catchment for assessing AR models on intermittent streamflows","river_name":"Abercrombie River","geocode_query":"Abercrombie River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.464167,-33.950555],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43672613,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-a98ddca1bbe257f09384eaa6-site-0","true_coordinates":[149.46416679000004,-34.01055549999995],"spread_km":6.7},{"name":"Mitta Mitta River at Hinnomunjie","kind":"river","role":"study catchment in Australia for evaluating AR updating procedures","river_name":"Mitta Mitta River","geocode_query":"Mitta Mitta River at Hinnomunjie, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.524391,-36.503889],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676927,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-a98ddca1bbe257f09384eaa6-site-1","true_coordinates":[147.4597223300001,-36.53388885299995],"spread_km":6.7},{"name":"Orara River at Bawden Bridge","kind":"river","role":"study catchment with subtropical climate demonstrating model overestimation and AR-Norm issues","river_name":"Orara River","geocode_query":"Orara River at Bawden Bridge, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Furthermore, if the base hydrological model does not replicate important catchment processes realistically, the performance of the hydrological model outside the calibration period may be less robust. Figure 7 presents the Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970) calculated from the base hydrological model and the error models. When the AR-Norm model is used, the forecasts from the base hydrological model are very poor for the Orara catchment (NSE < 0). The scatter plot in Fig. 8 shows a serious over-estimation of the streamflow simulation for the Orara catchment. When the AR-Norm model is used, the base hydrological model greatly over-estimates discharge, and the AR-Norm model then attempts to correct this systematic over-estimation. This is also shown in Fig. 4, where the base hydrological model has a strong tendency to over-estimate streamflows for a range of streamflow magnitudes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.810316,-29.662537],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26971389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bawden Bridge, Old Glen Innes Rd, Chambigne NSW 2460, Australia","basin_id":5040070700,"main_basin_id":5040070700,"basin_region":"Australasia","basin_area_km2":22150.2,"hydrology_source":"geofabric","site_id":"doc-a98ddca1bbe257f09384eaa6-site-2","true_coordinates":[152.8103156,-29.7225366],"spread_km":6.7},{"name":"Tarwin River at Meeniyan","kind":"river","role":"Australian catchment included for cross-validation of the AR models","river_name":"Tarwin River","geocode_query":"Tarwin River at Meeniyan, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.014677,-38.517737],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21559672,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Meeniyan VIC 3956, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-a98ddca1bbe257f09384eaa6-site-3","true_coordinates":[146.0146765,-38.5777372],"spread_km":6.7},{"name":"Amite River","kind":"river","role":"US catchment used for hydrological model comparison and AR model evaluation","river_name":"Amite River","geocode_query":"Amite River, Louisiana/Mississippi, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-90.7896005584126,30.62829141646093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amite River, Louisiana, USA","basin_id":7040046750,"main_basin_id":7040046750,"basin_region":"North America and Caribbean","basin_area_km2":42962.1,"hydrology_source":"hydrobasins","site_id":"doc-a98ddca1bbe257f09384eaa6-site-4"},{"name":"Guadalupe River","kind":"river","role":"US catchment with intermittent flow dynamics tested against AR updating methods","river_name":"Guadalupe River","geocode_query":"Guadalupe River, Texas, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-98.068674,29.318809],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Guadalupe River, Texas, USA","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-a98ddca1bbe257f09384eaa6-site-5","true_coordinates":[-98.06867363467566,29.25880925311428],"spread_km":6.7},{"name":"San Marcos River","kind":"river","role":"perennial US catchment evaluated with AR error models","river_name":"San Marcos River","geocode_query":"San Marcos River, Texas, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"M. Li et al.: A strategy to overcome adverse effects of autoregressive updating of streamflow forecasts 3 forecasts. Motivated to overcome the potential for overcorrection, we introduce a modification of the AR-Norm model, called the restricted AR-Norm model (referred to as RAR-Norm). A condition |Qe∗ t − Qet | ≤ |Qt−1 − Qet−1| is used to limit the correction to an amount not exceeding the raw error at the last time step. The updated streamflow is given by Qe∗ t = \u001a f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 if Dt ≤ Qt−1 − Qet−1 Qet + Qt−1 − Qet−1 \u0001 otherwise (6) where Dt = f −1 \b Zet + ρ Zt−1 − Zet−1 \u0001 − Qet . (7) The full RAR-Norm model in the transformed space is given by Zt = \u001a Zet + ρ Zt−1 − Zet−1 \u0001 + εt if Dt ≤ Qt−1 − Qet−1 f Qet + Qt−1 − Qet−1 \u0001 + εt otherwise . (8) 2.2 Estimation The AR-Norm, AR-Raw and RAR-Norm models are each calibrated jointly with the hydrological model.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.941394,29.823275],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"San Marcos, TX, USA","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-a98ddca1bbe257f09384eaa6-site-6","true_coordinates":[-97.9413941,29.8832749],"spread_km":6.7}],"limitations":["No exact latitude/longitude coordinates provided for catchments resulting in null spatial coordinates.","Study focus on single step ahead forecasts; longer lead times and different rainfall forecast sources not assessed.","Results potentially influenced by use of the GR4J hydrological model; other models (e.g. HBV) might behave differently."],"river_ids":["43639591","43639599","43639846","43640282","43640364","43640498","43640508","43640573","43640722","43640925","43641218","43641527","43641846","43642305","43644008","43645312","43645398","43645571","43645602","43645620","43645673","43646073","43646577","43647013","43647048","43647605","43647696","43647721","43647723","43647744","43647754","43647762","43647765","43647772","43647782","43647784","43647788","43647790","43647802","43647912","43648012","43648100","43648102","43648266","43648317","43648324","43648355","43648373","43648380","43648387","43648391","43648412","43648441","43648491","43648499","43648547","43648558","43648576","43648589","43648615","43648621","43648622","43648626","43648629","43648631","43648632","43648633","43648636","43648644","43648647","43648648","43648655","43648661","43648696","43648806","43648821","43648830","43648831","43648843","43648845","43648846","43648847","43648849","43648885","43648886","43648909","43648999","43649001","43649018","43649027","43649054","43649084","43649085","43649093","43649209","43649217","43649224","43649239","43649249","43649253","43649484","43650097"],"site_ids":["doc-a98ddca1bbe257f09384eaa6-site-0","doc-a98ddca1bbe257f09384eaa6-site-1","doc-a98ddca1bbe257f09384eaa6-site-2","doc-a98ddca1bbe257f09384eaa6-site-3","doc-a98ddca1bbe257f09384eaa6-site-4","doc-a98ddca1bbe257f09384eaa6-site-5","doc-a98ddca1bbe257f09384eaa6-site-6"],"anchor":[-90.7896005584126,30.62829141646093],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070700,5040070710,5040591050,7040046750,7040048420],"main_basin_ids":[5040070700,5040070710,5040073410,7040046750,7040048420]},{"id":"doc-b73955ddfd766108c0ad0ef2","title":"How does bias correction of regional climate model precipitation affect modelled runoff?","display_title":"Bias Correction of RCM Precipitation in MDB","doi":"https://doi.org/10.5194/hess-19-711-2015","theme":"Climate","color":"#98d4f1","year":2015,"publication_date":"2015-02-04","authors":["J. Teng","N. J. Potter","F. H. S. Chiew","L. Zhang","B. Wang","J. Vaze","J. P. Evans"],"summary":"This study reviews and assesses bias correction methods for precipitation data from regional climate models (RCMs), focusing on impacts to runoff modelling in eight catchments within the southern Murray–Darling Basin, Australia. Using Weather Research and Forecasting (WRF) model simulations and observed data for 1985–2000, the study tests linear scaling and distribution mapping methods, including a newly proposed double gamma distribution approach, evaluating their ability to correct precipitation biases and the subsequent runoff modelling errors using hydrological models. The research highlights challenges due to non-stationarity of biases over time and limitations in simulating precipitation sequence.","finding":"Distribution-based bias correction methods (quantile mapping and double gamma distribution mapping) outperform linear scaling in correcting RCM precipitation biases. However, inconsistencies in bias over time (non-stationarity) limit correction success and errors in rainfall are amplified in runoff simulations. Bias correction does not greatly affect precipitation mean change signals but increases uncertainty in extremes and runoff change signals.","finding_evidence":{"passage_id":"p016-000","page":16,"quote":"726 J. Teng et al.: How does bias correction of RCM precipitation affect modelled runoff? However, the difference between the distribution mapping methods tested here is small because of the large corrections required and the inconsistent errors in the calibration and validation periods (non-stationarity). The errors in bias corrected precipitation lead to amplified errors in modelled runoff. The bias correction methods tested here cannot overcome the limitations of the RCM in simulating all precipitation features that influence runoff, in particular, daily precipitation sequence. The errors in modelled runoff are strongly influenced by the inconsistent RCM errors over time, although this can be partially overcome by using a long calibration data set. Results further show that whereas bias correction does not seem to affect the change signals in precipitation means, it can introduce extra uncertainty to the change signals in high precipitation amounts, and consequently, in runoff.","verified_in_page":true},"topics":["bias-correction","regional-climate-model","precipitation","runoff-modelling","hydrological-modelling","climate-change-impacts","statistical-methods","non-stationarity"],"study_period":"1985–2000; additionally 1950–2009 for validation","locations":[{"name":"southern Murray–Darling Basin","kind":"region","role":"study area","river_name":null,"geocode_query":"southern Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"There were two possible outcomes from this investigation: if bias correction does not alter the change signals in the hydro-climatic projections, then the use of bias correction should be considered either unnecessary or safe to use, depending on the circumstance (Muerth et al., 2013). If it does alter the change signal, bias correction could be reducing or increasing errors in the change signals, either way, it introduces an extra level of uncertainty in the modelling chain. This paper contributes to the present lively discussion on whether bias correction methods should be applied to global and regional climate model data, a conversation initiated by Christensen et al. (2008), stimulated by Ehret et al. (2012), and continued by more recent studies such as Muerth et al. (2013) and Teutschbein and Seibert (2013). 2 Study area and data 2.1 Study area The study area was located in the southern Murray–Darling Basin, Australia (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.590982,-31.105835],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-b73955ddfd766108c0ad0ef2-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Loddon River catchment","kind":"basin","role":"study catchment within Murray–Darling Basin","river_name":"Loddon River","geocode_query":"Loddon River, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"There were two possible outcomes from this investigation: if bias correction does not alter the change signals in the hydro-climatic projections, then the use of bias correction should be considered either unnecessary or safe to use, depending on the circumstance (Muerth et al., 2013). If it does alter the change signal, bias correction could be reducing or increasing errors in the change signals, either way, it introduces an extra level of uncertainty in the modelling chain. This paper contributes to the present lively discussion on whether bias correction methods should be applied to global and regional climate model data, a conversation initiated by Christensen et al. (2008), stimulated by Ehret et al. (2012), and continued by more recent studies such as Muerth et al. (2013) and Teutschbein and Seibert (2013). 2 Study area and data 2.1 Study area The study area was located in the southern Murray–Darling Basin, Australia (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.888996,-36.17693],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43676983,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-b73955ddfd766108c0ad0ef2-site-1","true_coordinates":[143.8889961117272,-36.23693026199377],"spread_km":6.7},{"name":"Campaspe River catchment","kind":"basin","role":"study catchment within Murray–Darling Basin","river_name":"Campaspe River","geocode_query":"Campaspe River, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"There were two possible outcomes from this investigation: if bias correction does not alter the change signals in the hydro-climatic projections, then the use of bias correction should be considered either unnecessary or safe to use, depending on the circumstance (Muerth et al., 2013). If it does alter the change signal, bias correction could be reducing or increasing errors in the change signals, either way, it introduces an extra level of uncertainty in the modelling chain. This paper contributes to the present lively discussion on whether bias correction methods should be applied to global and regional climate model data, a conversation initiated by Christensen et al. (2008), stimulated by Ehret et al. (2012), and continued by more recent studies such as Muerth et al. (2013) and Teutschbein and Seibert (2013). 2 Study area and data 2.1 Study area The study area was located in the southern Murray–Darling Basin, Australia (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.631576,-36.428183],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43663477,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-b73955ddfd766108c0ad0ef2-site-2","true_coordinates":[144.6315759260196,-36.48818284354748],"spread_km":6.7},{"name":"Goulburn River catchment","kind":"basin","role":"study catchment within Murray–Darling Basin","river_name":"Goulburn River","geocode_query":"Goulburn River, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"There were two possible outcomes from this investigation: if bias correction does not alter the change signals in the hydro-climatic projections, then the use of bias correction should be considered either unnecessary or safe to use, depending on the circumstance (Muerth et al., 2013). If it does alter the change signal, bias correction could be reducing or increasing errors in the change signals, either way, it introduces an extra level of uncertainty in the modelling chain. This paper contributes to the present lively discussion on whether bias correction methods should be applied to global and regional climate model data, a conversation initiated by Christensen et al. (2008), stimulated by Ehret et al. (2012), and continued by more recent studies such as Muerth et al. (2013) and Teutschbein and Seibert (2013). 2 Study area and data 2.1 Study area The study area was located in the southern Murray–Darling Basin, Australia (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.583398,-36.761256],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43677721,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-b73955ddfd766108c0ad0ef2-site-3","true_coordinates":[145.5833978884901,-36.82125562451294],"spread_km":6.7},{"name":"Canberra, Australian Capital Territory, Australia","kind":"settlement","role":"CSIRO Land and Water Flagship location, research institution and authors' affiliation","river_name":null,"geocode_query":"Canberra, Australian Capital Territory, Australia","latitude":-35.2809,"longitude":149.13,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 19, 711–728, 2015 www.hydrol-earth-syst-sci.net/19/711/2015/ doi:10.5194/hess-19-711-2015 © Author(s) 2015. CC Attribution 3.0 License. How does bias correction of regional climate model precipitation affect modelled runoff? J. Teng1 , N. J. Potter1 , F. H. S. Chiew1 , L. Zhang1 , B. Wang1 , J. Vaze1 , and J. P. Evans2 1CSIRO Land and Water Flagship, Canberra, Australia 2Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia Correspondence to: J. Teng (jin.teng@csiro.au) Received: 29 August 2014 – Published in Hydrol. Earth Syst. Sci. Discuss.: 23 September 2014 Revised: 11 December 2014 – Accepted: 8 January 2015 – Published: 4 February 2015 Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Sydney, New South Wales, Australia","kind":"settlement","role":"University of New South Wales, affiliation of co-author J.P. Evans","river_name":null,"geocode_query":"Sydney, New South Wales, Australia","latitude":-33.8688,"longitude":151.2093,"evidence":{"passage_id":"p001-000","page":1,"quote":"Hydrol. Earth Syst. Sci., 19, 711–728, 2015 www.hydrol-earth-syst-sci.net/19/711/2015/ doi:10.5194/hess-19-711-2015 © Author(s) 2015. CC Attribution 3.0 License. How does bias correction of regional climate model precipitation affect modelled runoff? J. Teng1 , N. J. Potter1 , F. H. S. Chiew1 , L. Zhang1 , B. Wang1 , J. Vaze1 , and J. P. Evans2 1CSIRO Land and Water Flagship, Canberra, Australia 2Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia Correspondence to: J. Teng (jin.teng@csiro.au) Received: 29 August 2014 – Published in Hydrol. Earth Syst. Sci. Discuss.: 23 September 2014 Revised: 11 December 2014 – Accepted: 8 January 2015 – Published: 4 February 2015 Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Study limited to eight catchments in southern Murray–Darling Basin; results may not generalize to other regions.","Streamflow validation period limited to 1985–2000 due to data availability; longer-term assessments limited.","Coordinate data for study catchments undefined, precise mapping is limited.","Non-stationarity of biases complicates bias correction and validation 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-f375cd71f3de79279c019245","title":"Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure","display_title":"Spatial Correlation Functions in Soil Microstructure","doi":"https://doi.org/10.1371/journal.pone.0126515","theme":"Land & ecosystems","color":"#c8df98","year":2015,"publication_date":"2015-05-26","authors":["Marina V. Karsanina","Kirill M. Gerke","Elena B. Skvortsova","Dirk Mallants"],"summary":"The study introduces and applies novel spatial correlation functions—including two-point probability, linear, and cluster functions computed directionally—to characterize and reconstruct 2D soil microstructures from thin-section images of eight distinct Russian soils. Using simulated annealing optimization, the soil structures were reconstructed based on these functions, preserving porosity and some morphological features. While reconstructions captured isolated pore structures well, they had limitations in reproducing complex, highly connected pore networks such as cracks and fissures, highlighting the need for additional correlation functions and addressing statistical inhomogeneity.","finding":"Directional two-point probability and linear correlation functions can successfully describe and reconstruct anisotropic soil pore structures, but current sets lack sufficient information content to fully capture connectivity in soils with dual porosity (pores plus microcracks).","finding_evidence":{"passage_id":"p017-000","page":17,"quote":"Several types of discrepancies were evident from the above analysis. The discrepancies are especially important as concerns the connectivity of the pore space, which is consistently lower for the reconstructed image. This in turn results in a different number of total pores and different pore-size distributions, with the degree of discrepancy depending on the soil type (see S1 File). This can be explained in part by 1) the fact that the set of correlation functions employed contains insufficient information about pore structure [86], and in part by 2) the inappropriate weighing of different correlation functions employed based on their information content according to Eq 10 [68]. The remaining challenge, thus, is to determine which sets of correlation functions contain enough information to characterize a given soil structure. The existence of degenerated states of the S2 correlation function was demonstrated for hypothetical structures and some simple composite materials [86–87].","verified_in_page":true},"topics":["soil microstructure","spatial correlation functions","soil porosity","pore connectivity","anisotropy","simulated annealing","soil morphology","image reconstruction"],"study_period":null,"locations":[{"name":"Russian Plane, Russia","kind":"region","role":"source region of soil thin-section samples for structural analysis","river_name":null,"geocode_query":"Russian Plane, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"The five shape classes are as follows (Fig 3B): 1) fissure-like with 0 < F < 0.2, 2) elongated dissected with 0.2 < F <0.4, 3) isometric dissected with 0.4 < F < 0.6, 4) isometric slightly dissected 0.6 < F < 0.8, and 5) round with 0.8 < F < 1, 6). The three orientation classes are (Fig 3B): 1) vertical, with inclinations of 0–30° and 150–180°, 2) oblique, with inclinations between 30–60° and 120–150°, and 3) horizontal, with inclinations between 60– 120° All eight classes together are used for comparison of original and reconstructed images (see further). Soil thin-section images, analysis and reconstruction procedure In order to study reconstruction techniques and test applicability of correlation functions to describe soil structure, we have chosen eight 2.1x2.1 cm2 soil thin-section binary images (segmented into solid material and pores) of soils of the Russian Plane (Table 1). These soil types (images) are marked I-VIII.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["All soil images and analyses are 2D thin-section based; no 3D validation","Coordinates of soil sampling sites are not specified","Reconstruction procedure computationally intensive, especially for cluster function inclusion","Statistical homogeneity assumption not fully valid due to soil structural inhomogeneities","Correlation functions used insufficient alone to uniquely characterize soil pore connectivity"],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[],"main_basin_ids":[]},{"id":"doc-05e0d304509e0ae487b290a7","title":"Optimising seasonal streamflow forecast lead time for operational decision making in Australia","display_title":"Streamflow Forecast Lead Time Optimization in Australia","doi":"https://doi.org/10.5194/hess-20-4117-2016","theme":"Water management","color":"#eeabac","year":2016,"publication_date":"2016-10-10","authors":["Andrew Schepen","Tongtiegang Zhao","Q. J. Wang","Senlin Zhou","Paul Feikema"],"summary":"This study evaluates the potential to produce seasonal streamflow forecasts in Australia with lead times of up to 21 days, aiming to release forecasts before the forecast season starts. Using Bayesian joint probability models and daily streamflow and sea surface temperature data for 23 catchments across Australia, the study analyzes how forecast skill and reliability change with increased lead time. The study finds that forecast reliability remains stable across lead times but accuracy skill decreases monotonically as lead time increases. Nevertheless, forecasts with about 7-day lead time show only a small skill reduction while providing operational advantages.","finding":"Forecasts for Australian catchments with 7-day lead time have only about a 4 percentage point mean reduction in skill compared to 0-day lead time forecasts, while maintaining similar reliability, enabling earlier forecast release and more timely decision making.","finding_evidence":{"passage_id":"p011-002","page":11,"quote":"Thus for the bureau to consider releasing forecasts prior to the beginning of the target season, it would be a safe choice to prepare forecasts with 7-day lead time. Compared with forecasts with 0-day lead time, the mean reduction in CRPS skill scores in skilful cases is approximately 4 pp (Fig. 9), which is likely to be tolerated by forecast users in exchange for earlier forecast release. However, it is to be reasonably expected that CRPS skill scores will reduce by up to 10–15 pp in some instances (Fig. 9). The significant reduction in skill scores at 21-day lead time (Fig. 9) highlights the importance of short-lead-time forecasts for Australian catchments and confirms that simply switching to a 1-month-ahead forecast system is undesirable. A better, more flexible operational forecasting system for Australia could be built upon a flexible strategy that allows for any number of lead times (in days). Such a system allows for multiple forecast runs prior to forecast release.","verified_in_page":true},"topics":["seasonal streamflow forecasting","forecast lead time","predictability","Bayesian joint probability models","forecast skill","forecast reliability","sea surface temperature predictors","water management decision making"],"study_period":"1982–2012","locations":[{"name":"Green Ant Creek at Tipperary","kind":"river","role":"forecast location","river_name":null,"geocode_query":"Green Ant Creek at Tipperary Northern Territory Australia","latitude":-13.7,"longitude":131.1,"evidence":{"passage_id":"p003-001","page":3,"quote":"ID (km2 ) G8140161 GAT Green Ant Creek at Tipperary NT 416 131.1 −13.7 922101B CNC Coen River above Coen Racecourse QLD 170 143.2 −13.9 110003A BRP Barron River above Picnic Crossing QLD 239 145.5 −17.3 116006B HRA Herbert River above Abergowrie QLD 7486 145.9 −18.5 120002 BRS Burdekin River above Sellheim QLD 36230 146.4 −20 143303A STP Stanley River above Peachester QLD 102 152.8 −26.8 203005 RCW Richmond River above Wiangaree QLD 712 153 −28.5 419005 NMN Namoi River above North Cuerindi QLD 2532 150.8 −30.7 206014 WLC Wollomombi River above Coninside NSW 377 152 −30.5 208005 NWR Nowendoc River above Rocks Crossing NSW 1893 152.1 −31.8 412066 ABH Abercrombie River above Hadley No. 2 NSW 1631 149.6 −34.1 410024 GDW Goodradigbee River above Wee Jasper NSW 990 148.7 −35.2 410057 GBL Goobarragandra River above Lacmalac NSW 668 148.4 −35.3 410730 CTG Cotter River above Gingera NSW 130 148.8 −35.6 401012 MRB Murray River above Biggara NSW 1257 148.1 −36.3 401203 MTH Mitta Mitta River above Hinnomunjie VIC 1518 147.6 −37 405219 GBD Goulburn River above Dohertys VIC 700 146.1 −37.3 223202 TMS Tambo River above Swifts Creek VIC 899 147.7 −37.3 14213 BSF Black River at South Forest TAS 3191 145.3 −40.9 473 DVC Davey River above D/S Crossing Rv TAS 698 146 −43.1 A5050517 NPP North Para River at Penrice SA 121 139.1 −34.5 613002 HRD Harvey River above Dingo Road WA 148 116 −33.1 616013 HRN Helena River at Ngangaguringuring WA 316 116.4 −31.9 of lead time.","verified_in_page":true},"resolution_status":"resolved","coordinates":[131.1,-13.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102217206,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-05e0d304509e0ae487b290a7-site-0"},{"name":"Coen River above Coen Racecourse","kind":"river","role":"forecast location","river_name":"Coen River","geocode_query":"Coen River above Coen Racecourse Queensland Australia","latitude":-13.9,"longitude":143.2,"evidence":{"passage_id":"p003-001","page":3,"quote":"ID (km2 ) G8140161 GAT Green Ant Creek at Tipperary NT 416 131.1 −13.7 922101B CNC Coen River above Coen Racecourse QLD 170 143.2 −13.9 110003A BRP Barron River above Picnic Crossing QLD 239 145.5 −17.3 116006B HRA Herbert River above Abergowrie QLD 7486 145.9 −18.5 120002 BRS Burdekin River above Sellheim QLD 36230 146.4 −20 143303A STP Stanley River above Peachester QLD 102 152.8 −26.8 203005 RCW Richmond River above Wiangaree QLD 712 153 −28.5 419005 NMN Namoi River above North Cuerindi QLD 2532 150.8 −30.7 206014 WLC Wollomombi River above Coninside NSW 377 152 −30.5 208005 NWR Nowendoc River above Rocks Crossing NSW 1893 152.1 −31.8 412066 ABH Abercrombie River above Hadley No. 2 NSW 1631 149.6 −34.1 410024 GDW Goodradigbee River above Wee Jasper NSW 990 148.7 −35.2 410057 GBL Goobarragandra River above Lacmalac NSW 668 148.4 −35.3 410730 CTG Cotter River above Gingera NSW 130 148.8 −35.6 401012 MRB Murray River above Biggara NSW 1257 148.1 −36.3 401203 MTH Mitta Mitta River above Hinnomunjie VIC 1518 147.6 −37 405219 GBD Goulburn River above Dohertys VIC 700 146.1 −37.3 223202 TMS Tambo River above Swifts Creek VIC 899 147.7 −37.3 14213 BSF Black River at South Forest TAS 3191 145.3 −40.9 473 DVC Davey River above D/S Crossing Rv TAS 698 146 −43.1 A5050517 NPP North Para River at Penrice SA 121 139.1 −34.5 613002 HRD Harvey River above Dingo Road WA 148 116 −33.1 616013 HRN Helena River at Ngangaguringuring WA 316 116.4 −31.9 of lead time.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.2,-13.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":129257150,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-05e0d304509e0ae487b290a7-site-1"},{"name":"Barron River above Picnic Crossing","kind":"river","role":"forecast location","river_name":"Barron River","geocode_query":"Barron River above Picnic Crossing Queensland Australia","latitude":-17.3,"longitude":145.5,"evidence":{"passage_id":"p003-001","page":3,"quote":"ID (km2 ) G8140161 GAT Green Ant Creek at Tipperary NT 416 131.1 −13.7 922101B CNC Coen River above Coen Racecourse QLD 170 143.2 −13.9 110003A BRP Barron River above Picnic Crossing QLD 239 145.5 −17.3 116006B HRA Herbert River above Abergowrie QLD 7486 145.9 −18.5 120002 BRS Burdekin River above Sellheim QLD 36230 146.4 −20 143303A STP Stanley River above Peachester QLD 102 152.8 −26.8 203005 RCW Richmond River above Wiangaree QLD 712 153 −28.5 419005 NMN Namoi River above North Cuerindi QLD 2532 150.8 −30.7 206014 WLC Wollomombi River above Coninside NSW 377 152 −30.5 208005 NWR Nowendoc River above Rocks Crossing NSW 1893 152.1 −31.8 412066 ABH Abercrombie River above Hadley No. 2 NSW 1631 149.6 −34.1 410024 GDW Goodradigbee River above Wee Jasper NSW 990 148.7 −35.2 410057 GBL Goobarragandra River above Lacmalac NSW 668 148.4 −35.3 410730 CTG Cotter River above Gingera NSW 130 148.8 −35.6 401012 MRB Murray River above Biggara NSW 1257 148.1 −36.3 401203 MTH Mitta Mitta River above Hinnomunjie VIC 1518 147.6 −37 405219 GBD Goulburn River above Dohertys VIC 700 146.1 −37.3 223202 TMS Tambo River above Swifts Creek VIC 899 147.7 −37.3 14213 BSF Black River at South Forest TAS 3191 145.3 −40.9 473 DVC Davey River above D/S Crossing Rv TAS 698 146 −43.1 A5050517 NPP North Para River at Penrice SA 121 139.1 −34.5 613002 HRD Harvey River above Dingo Road WA 148 116 −33.1 616013 HRN Helena River at Ngangaguringuring WA 316 116.4 −31.9 of lead time.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.5,-17.3],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":16654479,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-05e0d304509e0ae487b290a7-site-2"},{"name":"Herbert River above Abergowrie","kind":"river","role":"forecast location","river_name":"Herbert River","geocode_query":"Herbert River above Abergowrie Queensland Australia","latitude":-18.5,"longitude":145.9,"evidence":{"passage_id":"p003-001","page":3,"quote":"ID (km2 ) G8140161 GAT Green Ant Creek at Tipperary NT 416 131.1 −13.7 922101B CNC Coen River above Coen Racecourse QLD 170 143.2 −13.9 110003A BRP Barron River above Picnic Crossing QLD 239 145.5 −17.3 116006B HRA Herbert River above Abergowrie QLD 7486 145.9 −18.5 120002 BRS Burdekin River above Sellheim QLD 36230 146.4 −20 143303A STP Stanley River above Peachester QLD 102 152.8 −26.8 203005 RCW Richmond River above Wiangaree QLD 712 153 −28.5 419005 NMN Namoi River above North Cuerindi QLD 2532 150.8 −30.7 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040049720,5040055960,5040056460,5040061670,5040067860,5040068450,5040069970,5040070710,5040073420,5040077640,5040524870,5040591050],"main_basin_ids":[5040049720,5040055960,5040056460,5040061670,5040067860,5040068450,5040069970,5040070710,5040073410,5040073420,5040077640]},{"id":"doc-0db61b9f42a684cb9bc07546","title":"Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting","display_title":"ERRIS Method for Streamflow Forecasting","doi":"https://doi.org/10.5194/hess-20-3561-2016","theme":"Flow & ecology","color":"#80edc2","year":2016,"publication_date":"2016-09-06","authors":["Ming Li","Q. J. Wang","James C. Bennett","David E. Robertson"],"summary":"This study develops the error reduction and representation in stages (ERRIS) method for ensemble short-term, real-time streamflow forecasting, using a sequence of simple error models to progressively improve forecast accuracy and reliability. ERRIS applies four stages: transformation and hydrological model parameterization, linear bias correction, autoregressive updating, and Gaussian mixture distribution to capture residual errors. The method is validated with catchments in southeastern Australia and the United States, showing improved reliability and accuracy in ensemble forecasts.","finding":"The ERRIS method significantly improves streamflow forecast accuracy and reliability through staged modeling: bias correction improves forecast mean accuracy, autoregressive updating reduces forecast residuals but creates non-Gaussian residual distributions leading to poor reliability, and final residual representation with a Gaussian mixture distribution restores forecast reliability while maintaining accuracy.","finding_evidence":{"passage_id":"p009-002","page":9,"quote":"The PIT plots show that the forecasts at the first two stages are reliable (as with the α index in Fig. 3e). However, for Stage 3 the points on the PIT plots deviate substantially from the 1 : 1 line, with a clear S-shape pattern for almost all catchments (the exception is the Tarwin catchment). A traditional interpretation of this S shape is that the forecasts are underconfident (Laio and Tamea, 2007). However, in this case, the S shape is caused by the high level of kurtosis in the distribution of the residuals, as we will show www.hydrol-earth-syst-sci.net/20/3561/2016/ Hydrol. Earth Syst. Sci., 20, 3561–3579, 2016","verified_in_page":true},"topics":["ensemble forecasting","streamflow forecasting","hydrological modeling","error modeling","autoregressive processes","bias correction","residual distribution","forecast reliability"],"study_period":"1992–2005 (Australia), 1981–1998 (US)","locations":[{"name":"Mitta Mitta catchment","kind":"basin","role":"primary case study catchment used for detailed examples and performance evaluation","river_name":null,"geocode_query":"Mitta Mitta catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"3568 M. Li et al.: ERRIS for ensemble streamflow forecasting Table 3. AWCI and CRPS calculated from the reference forecast for each catchment. Abercrombie Mitta Mitta Emu Hope Orara Tarwin Amite Guadalupe San Marcos AWCIREF (m3 s −1 ) 18.00 49.68 9.41 5.04 62.83 38.81 409.63 70.25 59.69 CRPSREF (m3 s −1 ) 2.20 6.42 0.79 0.46 10.25 4.65 41.69 9.29 7.64 Figure 2. An example of streamflow time-series plots for the Mitta Mitta catchment in the period between 1 July 2000 and 31 December 2000. 4 Results Figure 2 compares forecasts at different stages for an example period. In this example, we generate daily streamflow forecasts for the Mitta Mitta catchment in the period between 1 July 2000 and 31 December 2000. The forecast mean and the 95 % forecast interval are plotted against observations. The forecast at Stage 1 (the base hydrological model forecast) frequently overestimates low flows, such as in the period between July and September. For high-flow periods (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.376281,-36.477458],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43667518,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-0db61b9f42a684cb9bc07546-site-0","true_coordinates":[147.3762806,-36.5374584],"spread_km":6.7},{"name":"Abercrombie catchment","kind":"basin","role":"case study catchment exhibiting zero flows and abrupt high flows challenging forecasting","river_name":null,"geocode_query":"Abercrombie catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Stage 3 is based on the time persistence of the residuals and may introduce more errors when flows change abruptly, which sometimes occurs in the Abercrombie catchment. In addition, residuals tend to be larger at higher flows and because NSE is a measure of squared residuals, it tends to give more weights to residuals at high flows. This causes the Abercrombie Stage 3 forecasts to be less efficient than those of Stage 2. As we expect, Stage 3 forecasts are notably sharper than those at Stage 2 (Fig. 3d). However, this sharpness is not supported by reliability: Stage 3 forecasts tend to be much less reliable than all other stages (Fig. 3e). Figure 4 illustrates the reliability of the forecasts at each stage in more detail with the PIT plots. As PIT values are highly autocorrelated, we have to “thin” them in order to make the Kolmogorov– Smirnov significant band applicable (Zhao et al., 2015). We generate PIT plots from every 30th forecast to eliminate the autocorrelation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.549599,-33.3901487],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673186,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-0db61b9f42a684cb9bc07546-site-1"},{"name":"Emu catchment","kind":"basin","role":"study catchment in the evaluation of ERRIS performance","river_name":null,"geocode_query":"Emu catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum of CRPS_SS is 1, corresponding to a perfectly skillful forecast. While a decomposition of CRPS is available that gives an indication of reliability (Hersbach, 2000), we do not use this. PIT plots are a stronger test of reliability (Candille and Talagrand, 2005), and accordingly we focus on PIT plots to discuss reliability. 3 Case study 3.1 Study region and data We select six catchments in southeastern Australia and three catchments in the United States (US) for this study (Fig. 1), from a range of climatic and hydrological conditions. The streamflow data for the Australian catchments are obtained from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). The rainfall and potential evaporation data for the Australian catchments are taken from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments are taken from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.454327,-36.7308493],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43661918,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-0db61b9f42a684cb9bc07546-site-2"},{"name":"Hope catchment","kind":"basin","role":"study catchment with frequent zero flow occurrences influencing forecast bias evaluations","river_name":null,"geocode_query":"Hope catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Stage 3 is based on the time persistence of the residuals and may introduce more errors when flows change abruptly, which sometimes occurs in the Abercrombie catchment. In addition, residuals tend to be larger at higher flows and because NSE is a measure of squared residuals, it tends to give more weights to residuals at high flows. This causes the Abercrombie Stage 3 forecasts to be less efficient than those of Stage 2. As we expect, Stage 3 forecasts are notably sharper than those at Stage 2 (Fig. 3d). However, this sharpness is not supported by reliability: Stage 3 forecasts tend to be much less reliable than all other stages (Fig. 3e). Figure 4 illustrates the reliability of the forecasts at each stage in more detail with the PIT plots. As PIT values are highly autocorrelated, we have to “thin” them in order to make the Kolmogorov– Smirnov significant band applicable (Zhao et al., 2015). We generate PIT plots from every 30th forecast to eliminate the autocorrelation.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Orara catchment","kind":"basin","role":"study catchment in the ERRIS evaluation","river_name":null,"geocode_query":"Orara catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum of CRPS_SS is 1, corresponding to a perfectly skillful forecast. While a decomposition of CRPS is available that gives an indication of reliability (Hersbach, 2000), we do not use this. PIT plots are a stronger test of reliability (Candille and Talagrand, 2005), and accordingly we focus on PIT plots to discuss reliability. 3 Case study 3.1 Study region and data We select six catchments in southeastern Australia and three catchments in the United States (US) for this study (Fig. 1), from a range of climatic and hydrological conditions. The streamflow data for the Australian catchments are obtained from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). The rainfall and potential evaporation data for the Australian catchments are taken from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments are taken from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.92064,-29.859556],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26970323,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070700,"main_basin_id":5040070700,"basin_region":"Australasia","basin_area_km2":22150.2,"hydrology_source":"geofabric","site_id":"doc-0db61b9f42a684cb9bc07546-site-4","true_coordinates":[152.920639772319,-29.91955586275683],"spread_km":6.7},{"name":"Tarwin catchment","kind":"basin","role":"study catchment used in forecasting reliability assessments","river_name":null,"geocode_query":"Tarwin catchment, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"The PIT plots show that the forecasts at the first two stages are reliable (as with the α index in Fig. 3e). However, for Stage 3 the points on the PIT plots deviate substantially from the 1 : 1 line, with a clear S-shape pattern for almost all catchments (the exception is the Tarwin catchment). A traditional interpretation of this S shape is that the forecasts are underconfident (Laio and Tamea, 2007). However, in this case, the S shape is caused by the high level of kurtosis in the distribution of the residuals, as we will show www.hydrol-earth-syst-sci.net/20/3561/2016/ Hydrol. Earth Syst. Sci., 20, 3561–3579, 2016","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.970208,-38.544959],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21559641,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-0db61b9f42a684cb9bc07546-site-5","true_coordinates":[145.9702078,-38.6049594],"spread_km":6.7},{"name":"Amite catchment","kind":"basin","role":"United States study catchment for multi-region validation","river_name":null,"geocode_query":"Amite catchment, Louisiana, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum of CRPS_SS is 1, corresponding to a perfectly skillful forecast. While a decomposition of CRPS is available that gives an indication of reliability (Hersbach, 2000), we do not use this. PIT plots are a stronger test of reliability (Candille and Talagrand, 2005), and accordingly we focus on PIT plots to discuss reliability. 3 Case study 3.1 Study region and data We select six catchments in southeastern Australia and three catchments in the United States (US) for this study (Fig. 1), from a range of climatic and hydrological conditions. The streamflow data for the Australian catchments are obtained from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). The rainfall and potential evaporation data for the Australian catchments are taken from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments are taken from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-90.50898149999999,30.7265747],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040046750,"main_basin_id":7040046750,"basin_region":"North America and Caribbean","basin_area_km2":42962.1,"hydrology_source":"hydrobasins","site_id":"doc-0db61b9f42a684cb9bc07546-site-6"},{"name":"Guadalupe catchment","kind":"basin","role":"US study catchment for ERRIS method validation","river_name":null,"geocode_query":"Guadalupe catchment, Texas, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum of CRPS_SS is 1, corresponding to a perfectly skillful forecast. While a decomposition of CRPS is available that gives an indication of reliability (Hersbach, 2000), we do not use this. PIT plots are a stronger test of reliability (Candille and Talagrand, 2005), and accordingly we focus on PIT plots to discuss reliability. 3 Case study 3.1 Study region and data We select six catchments in southeastern Australia and three catchments in the United States (US) for this study (Fig. 1), from a range of climatic and hydrological conditions. The streamflow data for the Australian catchments are obtained from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). The rainfall and potential evaporation data for the Australian catchments are taken from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments are taken from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.87216,29.6477307],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-0db61b9f42a684cb9bc07546-site-7"},{"name":"San Marcos catchment","kind":"basin","role":"US study catchment included to assess ERRIS performance in different hydrological conditions","river_name":null,"geocode_query":"San Marcos catchment, Texas, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum of CRPS_SS is 1, corresponding to a perfectly skillful forecast. While a decomposition of CRPS is available that gives an indication of reliability (Hersbach, 2000), we do not use this. PIT plots are a stronger test of reliability (Candille and Talagrand, 2005), and accordingly we focus on PIT plots to discuss reliability. 3 Case study 3.1 Study region and data We select six catchments in southeastern Australia and three catchments in the United States (US) for this study (Fig. 1), from a range of climatic and hydrological conditions. The streamflow data for the Australian catchments are obtained from the Catchment Water Yield Estimation Tool (CWYET) data set (Vaze et al., 2011). The rainfall and potential evaporation data for the Australian catchments are taken from the Australian Water Availability Project (AWAP) data set (Jones et al., 2009). All data for the US catchments are taken from the Model Intercomparison Experiment (MOPEX) data set (Duan et al., 2006).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.941394,29.943275],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040048420,"main_basin_id":7040048420,"basin_region":"North America and Caribbean","basin_area_km2":115495.7,"hydrology_source":"hydrobasins","site_id":"doc-0db61b9f42a684cb9bc07546-site-8","true_coordinates":[-97.9413941,29.8832749],"spread_km":6.7}],"limitations":["Geographic coordinates for catchments are not provided in the text.","Study regions are broad catchment scales without finer geographic details.","Results are based on one-day lead forecasts using perfect observed rainfall; performance for longer lead times or uncertain forecasts remains to be tested."],"river_ids":[],"site_ids":["doc-0db61b9f42a684cb9bc07546-site-0","doc-0db61b9f42a684cb9bc07546-site-1","doc-0db61b9f42a684cb9bc07546-site-2","doc-0db61b9f42a684cb9bc07546-site-4","doc-0db61b9f42a684cb9bc07546-site-5","doc-0db61b9f42a684cb9bc07546-site-6","doc-0db61b9f42a684cb9bc07546-site-7","doc-0db61b9f42a684cb9bc07546-site-8"],"anchor":[143.454327,-36.7308493],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070700,5040070710,5040524870,5040591050,7040046750,7040048420],"main_basin_ids":[5040070700,5040070710,5040073410,7040046750,7040048420]},{"id":"doc-2fdc1abf3e1d4f1ce233a848","title":"Land-use and sustainability under intersecting global change and domestic policy scenarios: Trajectories for Australia to 2050","display_title":"Projected Land-Use Change and Sustainability in Australia","doi":"https://doi.org/10.1016/j.gloenvcha.2016.03.002","theme":"Land & ecosystems","color":"#c8df98","year":2016,"publication_date":"2016-03-24","authors":["Brett A. Bryan","Martin Nolan","Lisa McKellar","Jeffery D. Connor","David Newth","Tom Harwood","Darran King","Javier Navarro Garcia","Yiyong Cai","Lei Gao","Mike Grundy","Paul Graham","Andreas Ernst","Simon Dunstall","Florian Stock","Thomas S. Brinsmead","Ian N. Harman","Nicky Grigg","Michael Battaglia","B. A. Keating","Alex Wonhas","Steve Hatfield–Dodds"],"summary":"This study conducted a comprehensive, integrated, high-resolution scenario analysis of land-use and sustainability across Australia's agricultural land from 2013 to 2050. Using the Land-Use Trade-Offs (LUTO) model combined with global and domestic policy scenarios, the research evaluated land competition among agriculture, carbon plantings, environmental plantings, biofuels, and bioenergy. It assessed impacts on economic returns, food/fiber production, emissions abatement, water use, biodiversity services, and energy production under varying global emissions abatement efforts, domestic land-use policies, productivity growth, adoption behaviors, climate models, and land-use constraints.","finding":"Strong global emissions abatement incentives combined with biodiversity-focused domestic land-use policy can substantially increase and diversify economic returns and ecosystem services from Australian agricultural land, notably increasing emissions abatement, biodiversity, and energy production without major reductions in agricultural outputs. However, this results in significant increased water resource use necessitating improved governance.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"However, the type, magnitude, timing, and location of land-use responses and their impacts were highly dependent on scenario parameter assumptions including global outlook and emissions abatement effort, domestic land-use policy settings, land-use change adoption behaviour, productivity growth, and capacity constraints. With strong global abatement incentives complemented by biodiversity-focussed domestic land-use policy, land-use responses can substantially increase and diversify economic returns to land and produce a much wider range of ecosystem services such as emissions abatement, biodiversity, and energy, without major impacts on agricultural production. However, better governance is needed for managing potentially significant water resource impacts. The results have wide-ranging implications for land-use and sustainability policy and governance at global and domestic scales and can inform strategic thinking and decision-making about land-use and sustainability in Australia.","verified_in_page":true},"topics":["land-use change","climate change","ecosystem services","economic returns to land","emissions abatement","water resource use","biodiversity services","energy production"],"study_period":"2013-2050","locations":[{"name":"Australia","kind":"region","role":"study area for comprehensive land-use and sustainability scenario analysis","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Study area Our 85.3 million hectare study area incorporated the intensive agricultural land of Australia (a non-contiguous area from southwestern Western Australia to eastern Queensland; Fig. 1). We considered only cleared agricultural land and excluded other landuses such as urban land, water bodies, and forest and woodland. Around 60% of Australian agricultural production is exported, making the region a globally significant contributor to food security. Land-use in the study area is diverse (Table 1) with dryland agriculture (mixed grazing, cropping) dominating agricultural land-use, punctuated by localised areas of high-value irrigation (ABARES, 2010; Australian Bureau of Statistics, 2006; Bryan et al., 2009a; Marinoni et al., 2012). Climate ranges from cool-temperate and Mediterranean in the south to semi-arid in the interior and sub-tropical in the north. 2.2.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Southern Queensland, Australia","kind":"region","role":"key hotspot area for land-use change from livestock grazing to carbon plantings","river_name":null,"geocode_query":"Southern Queensland, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-001","page":17,"quote":"For example, a dominant effect occurred in the economically marginal beef grazing area of southern Queensland which can also support fairly high rates of carbon sequestration (Evans et al., 2015). When the profitability of carbon plantings exceeded beef grazing, it tended to do so over the whole area which had similar land-use and economic profiles within a fairly narrow combination of parameter settings. Depending on the scenario settings (i.e. global outlook, domestic land-use policy, productivity growth, adoption hurdle rate etc.) and the associated trajectories in key parameters such as the carbon price, the profitability threshold was met and land-use change ensued at different dates. Stronger incentives for emissions abatement under L1 and M3 also translated into much higher total economic returns to land.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.0134124,-27.4743523],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Brisbane QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-2fdc1abf3e1d4f1ce233a848-site-1"},{"name":"Northern New South Wales, Australia","kind":"region","role":"key hotspot area for land-use change from livestock grazing to carbon plantings","river_name":null,"geocode_query":"Northern New South Wales, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-001","page":17,"quote":"For example, a dominant effect occurred in the economically marginal beef grazing area of southern Queensland which can also support fairly high rates of carbon sequestration (Evans et al., 2015). When the profitability of carbon plantings exceeded beef grazing, it tended to do so over the whole area which had similar land-use and economic profiles within a fairly narrow combination of parameter settings. Depending on the scenario settings (i.e. global outlook, domestic land-use policy, productivity growth, adoption hurdle rate etc.) and the associated trajectories in key parameters such as the carbon price, the profitability threshold was met and land-use change ensued at different dates. Stronger incentives for emissions abatement under L1 and M3 also translated into much higher total economic returns to land.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Central New South Wales, Australia","kind":"region","role":"illustrative example of high spatial resolution potential future land-use change","river_name":null,"geocode_query":"Central New South Wales, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-005","page":12,"quote":"Illustration of the high spatial resolution of land-use modelling showing potential 2050 land-use under the M3 global outlook for central New South Wales (Fig. 1) (Balanced focus, Medium productivity growth, 2 \u0001 adoption hurdle rate, MPI-ESM-LR, unconstrained). The legend is shown in Fig. 7. ã 2016 CSIRO. All rights reserved. B.A. Bryan et al. / Global Environmental Change 38 (2016) 130–152 141","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Projections are scenario-based, not predictions, and depend on many assumptions about policy, productivity, adoption behaviours, and climate models.","Residual uncertainties exist from technical parameter specifications and natural variability.","Competition between agricultural land-uses and intensification were not explicitly modelled.","Temporal variability and price volatility (e.g., in oil prices) were simplified and not captured in full detail.","The study excluded natural lands and recent changes in land clearance regulations, focusing only on cleared agricultural lands.","Social constraints and desirability of large-scale land-use changes are not considered.","Water governance impacts were not fully addressed; water resource costs might be underestimated.","Impacts on ecosystem services like soil carbon, landscape amenity, and regional socioeconomics were not fully explored."],"river_ids":[],"site_ids":["doc-2fdc1abf3e1d4f1ce233a848-site-1"],"anchor":[153.0134124,-27.4743523],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040069970],"main_basin_ids":[5040069970]},{"id":"doc-3cb45846a7566b3dd94619f4","title":"Time series of tritium, stable isotopes and chloride reveal short-term variations in groundwater contribution to a stream","display_title":"Groundwater Contributions and Transit Times in Upper Teviot Brook","doi":"https://doi.org/10.5194/hess-20-257-2016","theme":"Groundwater","color":"#a8b9ff","year":2016,"publication_date":"2016-01-18","authors":["C. Duvert","M. K. Stewart","D. I. Cendón","M. Raiber"],"summary":"This study in the upper Teviot Brook catchment, southeast Queensland, Australia, investigated temporal variations in groundwater contributions to streamflow using time series of tritium (3H), stable isotopes (δ2H, δ18O), and chloride. A lumped convolution modelling approach quantified transit times (TTs) and residence times (RTs) of young and older water components contributing to streamflow. The study found distinct young components with short TTs (~2 weeks quickflow and ~0.3 years fractured rock discharge) and an older groundwater component with TTs of 16–25 years, varying over time and flow conditions. The older component TT was consistently younger than the groundwater RT (76–102 years), indicating different age distributions of water stored and water in transit. Results highlight the complexity and time-variant nature of groundwater-stream interactions in subtropical settings.","finding":"The transit time (TT) of the older groundwater component contributing to streamflow in the downstream catchment varied substantially over time, averaging 17 ± 6 years at low flow and increasing to 38 ± 15 years after major recharge events, reflecting flushing of older, deeper groundwater following recharge.","finding_evidence":{"passage_id":"p012-001","page":12,"quote":"Input series τo (years) RMSE (TU) A1 15.8 ±0.15 A2 20.2 ±0.15 A3 24.5 ±0.15 B1 15.8 ±0.14 B2 19.8 ±0.16 B3 24.4 ±0.16 The mean TT of younger components (τy) was constrained to 0.33 years, the dispersion parameter of older components was constrained to 0.3, and the ratio of older water was constrained to 82 %. tracers, hydrograph separation based on chloride generally yielded lower variations in φ over time (Fig. 10a). Generally, the older component was lowest during high flow conditions and greatest during recession periods. The simulated τo values varied considerably over time, and variations exceeded the uncertainties related to measurement uncertainties, chemical mass balance calculation errors and input estimates (Fig. 10b–d). 18O was the least accurate in evaluating the variations in τo (wider range for the red shaded area in Fig. 10c), while chloride was the most accurate despite less pronounced τo variations (narrower range for the red shaded area in Fig. 10d).","verified_in_page":true},"topics":["groundwater residence time","transit time distributions","hydrological modelling","tritium tracer","stable isotopes","subtropical catchment hydrology","evapotranspiration effects","groundwater-stream interactions"],"study_period":"2012–2014","locations":[{"name":"Upper Teviot Brook catchment","kind":"region","role":"primary study catchment","river_name":null,"geocode_query":"Upper Teviot Brook catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The stream gauging station corresponds to Teviot Brook at Croftby (145011A; operated by the Queensland Department of Natural Resources and Mines). The rainfall gauges correspond to Wilsons Peak Alert (040876), Carneys Creek The Ranch (040490) and Croftby Alert (040947), all run by the Bureau of Meteorology. ii. Can short-term variations in older (5–100 years) groundwater contributions be captured by 3H timeseries data? iii. How dissimilar are the RT of aquifers adjacent to streams (i.e. storage water) and the transit time of streamwater (i.e. exiting water)? 2 Study area 2.1 Physical setting The upper Teviot Brook catchment is located south-west of Brisbane (south-eastern Queensland, Australia), with its headwaters in the Great Dividing Range (Fig. 1). It covers an area of 95 km2 , and elevations range between 160 and 1375 m a.s.l..","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.538503,-28.239463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129502,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Teviot Brook (Ickkaybin), Queensland 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-0","true_coordinates":[152.5385032460705,-28.17946347953258],"spread_km":6.7},{"name":"Croftby gauging station","kind":"station","role":"streamflow monitoring station 2 km upstream of S2","river_name":"Teviot Brook","geocode_query":"Croftby, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The stream gauging station corresponds to Teviot Brook at Croftby (145011A; operated by the Queensland Department of Natural Resources and Mines). The rainfall gauges correspond to Wilsons Peak Alert (040876), Carneys Creek The Ranch (040490) and Croftby Alert (040947), all run by the Bureau of Meteorology. ii. Can short-term variations in older (5–100 years) groundwater contributions be captured by 3H timeseries data? iii. How dissimilar are the RT of aquifers adjacent to streams (i.e. storage water) and the transit time of streamwater (i.e. exiting water)? 2 Study area 2.1 Physical setting The upper Teviot Brook catchment is located south-west of Brisbane (south-eastern Queensland, Australia), with its headwaters in the Great Dividing Range (Fig. 1). It covers an area of 95 km2 , and elevations range between 160 and 1375 m a.s.l..","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.583297,-28.19723],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129587,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Croftby QLD 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-1","true_coordinates":[152.5832966,-28.1372299],"spread_km":6.7},{"name":"Sampling site S1","kind":"station","role":"upstream streamwater sampling site in fractured igneous headwaters","river_name":"Teviot Brook","geocode_query":"Upper Teviot Brook catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Climate in the region is humid subtropical with extremely variable rainfall: mean annual precipitation for the catchment is 970 mm (1994–2014 period), of which 76% falls from November to April. While Teviot Brook is a perennial stream, the distribution of discharge is uneven throughout the year: the mean annual discharge is 120 mm (1994– 2014 period), with highest and lowest streamflow occurring in February (average 40 mm) and September (average 2 mm), respectively. The headwaters support undisturbed subtropical rainforest, while the valley supports open woodland and grassland. The first sampling location (S1) is situated in a steep, narrow valley where the stream erodes into the fractured, silicarich igneous rocks forming the headwaters. At this upstream location, boulders, gravel and sand constitute the streambed substrate as well as near-channel deposits. The second sampling location (S2) lies further downstream where the valwww.hydrol-earth-syst-sci.net/20/257/2016/ Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.606571,-28.179463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129502,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Teviot Brook (Ickkaybin), Queensland 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-2","true_coordinates":[152.5385032460705,-28.17946347953258],"spread_km":6.7},{"name":"Sampling site S2","kind":"station","role":"downstream streamwater sampling site in alluvial plain section","river_name":"Teviot Brook","geocode_query":"Upper Teviot Brook catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Climate in the region is humid subtropical with extremely variable rainfall: mean annual precipitation for the catchment is 970 mm (1994–2014 period), of which 76% falls from November to April. While Teviot Brook is a perennial stream, the distribution of discharge is uneven throughout the year: the mean annual discharge is 120 mm (1994– 2014 period), with highest and lowest streamflow occurring in February (average 40 mm) and September (average 2 mm), respectively. The headwaters support undisturbed subtropical rainforest, while the valley supports open woodland and grassland. The first sampling location (S1) is situated in a steep, narrow valley where the stream erodes into the fractured, silicarich igneous rocks forming the headwaters. At this upstream location, boulders, gravel and sand constitute the streambed substrate as well as near-channel deposits. The second sampling location (S2) lies further downstream where the valwww.hydrol-earth-syst-sci.net/20/257/2016/ Hydrol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.538503,-28.119463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129502,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Teviot Brook (Ickkaybin), Queensland 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-3","true_coordinates":[152.5385032460705,-28.17946347953258],"spread_km":6.7},{"name":"Groundwater borehole G1","kind":"facility","role":"alluvial aquifer groundwater sampling point near S2","river_name":null,"geocode_query":"Upper Teviot Brook catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"260 C. Duvert et al.: Variations in groundwater contribution to a stream ley is flatter and forms a wide alluvial plain. At this downstream location the stream is incised into the alluvial deposits, which at G1 are composed of fine-grained material, i.e. mostly gravel and silty clay. Underlying the alluvial deposits is a sedimentary bedrock formation (Walloon Coal Measures) consisting of irregular beds of sandstone, siltstone, shale and coal, some of which contain significant volumes of groundwater. Duvert et al. (2015b, a) reported high Fe concentrations and low 3H activities for some groundwaters of the sedimentary bedrock. Hydraulic gradient analysis indicates that the alluvium mostly drains into the stream; hydrochemical and isotopic data also revealed a close connection between the alluvium and surface water in the Teviot Brook catchment (Duvert et al., 2015b). Borehole G1 is 13.9 m deep and it is screened from 12.3 m to its bottom, i.e. entirely within the alluvial stratum.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.470435,-28.179463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129502,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Teviot Brook (Ickkaybin), Queensland 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-4","true_coordinates":[152.5385032460705,-28.17946347953258],"spread_km":6.7},{"name":"Wilsons Peak Alert (rain gauge)","kind":"station","role":"precipitation data source for catchment","river_name":null,"geocode_query":"Wilsons Peak, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The stream gauging station corresponds to Teviot Brook at Croftby (145011A; operated by the Queensland Department of Natural Resources and Mines). The rainfall gauges correspond to Wilsons Peak Alert (040876), Carneys Creek The Ranch (040490) and Croftby Alert (040947), all run by the Bureau of Meteorology. ii. Can short-term variations in older (5–100 years) groundwater contributions be captured by 3H timeseries data? iii. How dissimilar are the RT of aquifers adjacent to streams (i.e. storage water) and the transit time of streamwater (i.e. exiting water)? 2 Study area 2.1 Physical setting The upper Teviot Brook catchment is located south-west of Brisbane (south-eastern Queensland, Australia), with its headwaters in the Great Dividing Range (Fig. 1). It covers an area of 95 km2 , and elevations range between 160 and 1375 m a.s.l..","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.4847222,-28.2486111],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677393,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wilsons Peak, The Head QLD 4373, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-5"},{"name":"Carneys Creek The Ranch (rain gauge)","kind":"station","role":"precipitation data source for catchment","river_name":null,"geocode_query":"Carneys Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The stream gauging station corresponds to Teviot Brook at Croftby (145011A; operated by the Queensland Department of Natural Resources and Mines). The rainfall gauges correspond to Wilsons Peak Alert (040876), Carneys Creek The Ranch (040490) and Croftby Alert (040947), all run by the Bureau of Meteorology. ii. Can short-term variations in older (5–100 years) groundwater contributions be captured by 3H timeseries data? iii. How dissimilar are the RT of aquifers adjacent to streams (i.e. storage water) and the transit time of streamwater (i.e. exiting water)? 2 Study area 2.1 Physical setting The upper Teviot Brook catchment is located south-west of Brisbane (south-eastern Queensland, Australia), with its headwaters in the Great Dividing Range (Fig. 1). It covers an area of 95 km2 , and elevations range between 160 and 1375 m a.s.l..","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.5463664,-28.1825999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129502,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Carneys Creek QLD 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-6"},{"name":"Croftby Alert (rain gauge)","kind":"station","role":"precipitation data source for catchment","river_name":null,"geocode_query":"Croftby, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The stream gauging station corresponds to Teviot Brook at Croftby (145011A; operated by the Queensland Department of Natural Resources and Mines). The rainfall gauges correspond to Wilsons Peak Alert (040876), Carneys Creek The Ranch (040490) and Croftby Alert (040947), all run by the Bureau of Meteorology. ii. Can short-term variations in older (5–100 years) groundwater contributions be captured by 3H timeseries data? iii. How dissimilar are the RT of aquifers adjacent to streams (i.e. storage water) and the transit time of streamwater (i.e. exiting water)? 2 Study area 2.1 Physical setting The upper Teviot Brook catchment is located south-west of Brisbane (south-eastern Queensland, Australia), with its headwaters in the Great Dividing Range (Fig. 1). It covers an area of 95 km2 , and elevations range between 160 and 1375 m a.s.l..","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.583297,-28.07723],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129587,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Croftby QLD 4310, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-7","true_coordinates":[152.5832966,-28.1372299],"spread_km":6.7},{"name":"Brisbane Aero","kind":"station","role":"3H precipitation input data used for input function estimation","river_name":null,"geocode_query":"Brisbane Airport, Brisbane, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"These variations can be significant and have to be considered for achieving reliable estimates of TT distributions. Monthly 3H precipitation data measured by ANSTO from bulk samples collected at Brisbane Aero were used to estimate the 3H input function for the Teviot Brook catchment. Because Brisbane Aero is ca. 100 km north-east of Teviot Brook, the rainfall 3H concentrations are likely to be significantly different between these two locations due to oceanic and altitudinal effects. According to Tadros et al. (2014), 3H values for Toowoomba (i.e. located in the Great Dividing Range near Teviot Brook) were about 0.4 TU above those for Brisbane Aero for the period 2005–2011. Based on this work, an increment of +0.4 TU was applied to values measured at Brisbane Aero in order to obtain a first estimate of rainfall 3H concentrations for Teviot Brook (input series A2 in Table 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1236896,-27.3946213],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130614,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane Airport (BNE), Airport Dr, Brisbane Airport QLD 4008, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-3cb45846a7566b3dd94619f4-site-8"}],"limitations":["Limited spatial coordinates for sampling sites.","Stationarity assumption in lumped-parameter models unlikely valid in highly variable headwater catchment.","Evapotranspiration effects complicate stable isotope and chloride tracer interpretation downstream.","3H input functions estimated from stations up to 100 km away, introducing uncertainty.","Potential underestimation or omission of very old (>100 years) groundwater contributions due to 3H tracer limitations.","Possible aggregation bias due to catchment heterogeneity not fully addressed.","Limited temporal resolution and duration of tracer records restrict dynamic transit time distribution assessment.","No direct tritium measurements at upstream site S1, limiting understanding of upstream old water contributions."],"river_ids":[],"site_ids":["doc-3cb45846a7566b3dd94619f4-site-0","doc-3cb45846a7566b3dd94619f4-site-1","doc-3cb45846a7566b3dd94619f4-site-2","doc-3cb45846a7566b3dd94619f4-site-3","doc-3cb45846a7566b3dd94619f4-site-4","doc-3cb45846a7566b3dd94619f4-site-5","doc-3cb45846a7566b3dd94619f4-site-6","doc-3cb45846a7566b3dd94619f4-site-7","doc-3cb45846a7566b3dd94619f4-site-8"],"anchor":[152.583297,-28.07723],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040069970,5040511570],"main_basin_ids":[5040069970,5040073410]},{"id":"doc-457bdfa6d551bbf966305b71","title":"Contrasting responses of water use efficiency to drought across global terrestrial ecosystems","display_title":"Global Contrasts in Ecosystem Water Use Efficiency","doi":"https://doi.org/10.1038/srep23284","theme":"Land & ecosystems","color":"#c8df98","year":2016,"publication_date":"2016-03-17","authors":["Yuting Yang","Huade Guan","Okke Batelaan","Tim R. McVicar","Di Long","Shilong Piao","Wei Liang","Bing Liu","Zhao Jin","Craig T. Simmons"],"summary":"This global study synthesizes how drought impacts ecosystem water use efficiency (WUE), the ratio of carbon uptake (GPP) to water loss (ET), across terrestrial ecosystems. Using data from 95 eddy covariance sites worldwide and a global machine-learning model (MTE), it reveals contrasting WUE responses to drought between arid and semi-arid/sub-humid zones. Arid ecosystems show increased WUE during drought mainly due to physical evaporation controls, while semi-arid/sub-humid ecosystems show decreased WUE driven by biological process (assimilation) sensitivity. The study also identifies drought legacy (memory effects) influencing current-year WUE in specific regions.","finding":"Water use efficiency responds oppositely to drought in arid versus semi-arid/sub-humid regions: in arid regions, WUE increases during drought due to physical process dominance, whereas in semi-arid/sub-humid regions, WUE decreases as biological processes dominate.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Using two observational WUE datasets (i.e., eddy-covariance measurements at 95 sites (526 siteyears) and global gridded diagnostic modelling based on existing observation and a data-adaptive machine learning approach), we find a contrasting response of WUE to drought between arid (WUE increases with drought) and semi-arid/sub-humid ecosystems (WUE decreases with drought), which is attributed to different sensitivities of ecosystem processes to changes in hydro-climatic conditions. WUE variability in arid ecosystems is primarily controlled by physical processes (i.e., evaporation), whereas WUE variability in semi-arid/sub-humid regions is mostly regulated by biological processes (i.e., assimilation). We also find that shifts in hydro-climatic conditions over years would intensify the drought effect on WUE.","verified_in_page":true},"topics":["ecosystem water use efficiency","drought response","gross primary production","evapotranspiration","carbon cycle","hydro-climatic variability","biome sensitivity","drought memory effect"],"study_period":"1982–2011","locations":[{"name":"Central and western Asia","kind":"region","role":"region with significant drought effects on arid ecosystem WUE","river_name":null,"geocode_query":"Central Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.831901,45.390687],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Asia","basin_id":4040050300,"main_basin_id":4040050300,"basin_region":"Central and South-East Asia","basin_area_km2":241047.3,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-0","true_coordinates":[68.8319005,45.4506875],"spread_km":6.7},{"name":"Southern Africa","kind":"region","role":"region with significant drought effects on arid ecosystem WUE","river_name":null,"geocode_query":"Southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.625749,-24.387077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-1","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7},{"name":"Central and western Australia","kind":"region","role":"region with significant drought effects on arid ecosystem WUE","river_name":null,"geocode_query":"Central Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.785153,-23.474134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006239,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Queensland, QLD, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-457bdfa6d551bbf966305b71-site-2","true_coordinates":[144.7851531,-23.4141339],"spread_km":6.7},{"name":"Great Plain of North America","kind":"region","role":"semi-arid/sub-humid ecosystem with positive WUE-drought relationship and drought memory effect","river_name":null,"geocode_query":"Great Plains, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Central Euro-Asia","kind":"region","role":"semi-arid/sub-humid ecosystem showing positive WUE-drought relationship and evidence of grassland desertification","river_name":null,"geocode_query":"Central Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"This is especially important for semi-arid/sub-humid ecosystems, as more severe droughts could result in a larger reduction in their WUE by reducing ecosystem productivity or considerable vegetation mortality5 . If drought continues, breakdown in ecosystem resilience may occur as ecosystem WUE reduces to a certain threshold, which, when crossed, can result in biome reorganization24. This phenomenon has already been observed during large-scale shrub encroachment in North America42 and grassland desertification in Central Euro-Asia43. Moreover, such processes may be further accelerated by the memory effect of drought on ecosystem processes if climate variability increases concurrently with droughts. Methods Data sets. Our analyses are based on two WUE data sets that are both ultimately derived from EC measurements. Firstly, GPP and ET observations at 95 flux sites (526 site-years) were obtained (Supplementary Table S1). Figure 4.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.913243,45.432146],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Asia","basin_id":4040050300,"main_basin_id":4040050300,"basin_region":"Central and South-East Asia","basin_area_km2":241047.3,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-4","true_coordinates":[68.8319005,45.4506875],"spread_km":6.7},{"name":"South-eastern Amazon Basin","kind":"region","role":"semi-arid/sub-humid ecosystem with positive WUE-drought relationship and drought memory effect","river_name":null,"geocode_query":"Amazon Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.617501,-1.686555],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon River","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-5","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Western Pampas Steppe","kind":"region","role":"region with semi-arid/sub-humid ecosystems showing positive WUE-drought relationship","river_name":null,"geocode_query":"Pampas, Argentina","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-64.2816443,-30.9344374],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"La Pampa, Córdoba Province, Argentina","basin_id":6040040460,"main_basin_id":6040040460,"basin_region":"South America","basin_area_km2":308235.5,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-6"},{"name":"Areas surrounding the Congo Basin, Africa","kind":"region","role":"semi-arid/sub-humid region showing positive WUE-drought relationship","river_name":null,"geocode_query":"Congo Basin, Central Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"For humid regions, no significant relationship between WUE and PDSI is observed (p< 0.05), suggesting that drought has limited effects on ecosystem WUE in non-water limited regions. The distinct response of WUE to drought between arid and semi-arid/sub-humid regions is reinforced when averaging the Pearson’s coefficient between WUE and WI (or PDSI) by biome types (Fig. 3c) and/or climate zones (Fig. 3a,b). The good agreement between climate zone and vegetation functional type demonstrates a predominant role of climate in shaping ecosystems18. Consistent with the results shown in Fig. 1, arid zone WUE (i.e., desert vegetation and shrubland) responds negatively with an increase in WI (or PDSI), with the negative correlation becoming more significant for areas of a drier climate (Fig. 3). In contrast, the WUE and WI (or PDSI) relationship is completely reversed to a positive relationship for biomes in semi-arid and sub-humid regions (i.e., grassland, croplands and savanna, Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.836605,-1.965431],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Congo River","basin_id":1041213640,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":894488.8,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-7","true_coordinates":[18.77950841330257,-1.946890029824763],"spread_km":6.7},{"name":"Eastern Amazon Basin","kind":"region","role":"humid region showing unique positive WUE-drought relationship in evergreen broadleaf forest biome","river_name":null,"geocode_query":"Eastern Amazon Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Interestingly, this positive relationship becomes weaker for areas of increased climate wetness and disappears in humid zones except for evergreen broad leaved forest located in the eastern Amazon Basin (Figs 1 and 3). Similar results are observed when the analyses are performed based on non-detrended and/or using WI instead of PDSI in the MTE-WUE data (see Supplementary Figs S3–S5). The fact that using the non-detrended time series results in the same conclusion (as using detrended variables) suggests that the long-term trends in both WUE and drought has not broken the interannual correlations between the two variables. As an integrated measure of the coupled water and carbon cycles, ecosystem WUE is determined by multiple, and often interacting physical and biological processes. The contrasting response of WUE to drought between arid and semi-arid/sub-humid biomes suggests a different sensitivity of ecosystem processes to drought among those biomes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.59992,-1.644128],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon River","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-8","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Tropical humid regions (Amazon, Congo, Southeast Asia)","kind":"region","role":"humid regions exhibiting ET-dominated WUE variability with limited drought effects","river_name":null,"geocode_query":"Amazon Basin; Congo Basin; Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Our analysis also reveals an ET-dominated WUE variability in tropical humid regions (i.e., Amazon, Congo and southeast Asia) (Fig. 4c), where variation of ecosystem functions are largely driven by energy supply rather than water availability32,33. Drought is often caused by less precipitation (and thus less cloud cover), which results in more incoming solar radiation and therefore accelerates ecosystem processes (e.g., GPP and ET)33–35. On the other hand, studies have shown that nutrient availability plays an important role in determining vegetation growth in the absence of water and temperature stresses25, but nutrient availability is less likely to directly affect the physical processes of ET (i.e., soil evaporation and evaporation from canopy interception). This may explain the overall lower sensitivity of GPP to drought than the sensitivity of ET to drought in these tropical rainforest regions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.71483,-2.24797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-457bdfa6d551bbf966305b71-site-9","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"Mediterranean region, Australia","kind":"region","role":"region with significant drought legacy (memory) effects on current-year WUE","river_name":null,"geocode_query":"Mediterranean climate region, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Many studies have suggested that drought legacy affects ecosystem productivity and water consumption36–39: known as “the memory effect”. Here we show that the memory effect of previous-year drought on current-year WUE is higher in three regions, i.e., the Great Plain, the Amazon Basin and the part of Australia experiencing a Mediterranean climate, but generally low in other areas (Fig. 5a, by conducting the hierarchical partitioning analysis40). Further support is given by the Akaike Information Criterion analysis41 on the WUE model by current-year PDSI and by two-year PDSIs (see Supplementary Fig. S8). Although the effect of hydro-climatic disturbance on WUE varies greatly among bio-climates, the memory effect shows a pattern where the impact of previous-year drought consistently shows an opposite sign than that of the current-year drought on WUE for all biomes (Fig. 5b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Some geographic regions are broadly defined due to lack of precise coordinates.","Coordinates for specific measurement sites were not provided in the text.","Site-level drought metric PDSI was not calculated due to short observation periods."],"river_ids":[],"site_ids":["doc-457bdfa6d551bbf966305b71-site-0","doc-457bdfa6d551bbf966305b71-site-1","doc-457bdfa6d551bbf966305b71-site-2","doc-457bdfa6d551bbf966305b71-site-4","doc-457bdfa6d551bbf966305b71-site-5","doc-457bdfa6d551bbf966305b71-site-6","doc-457bdfa6d551bbf966305b71-site-7","doc-457bdfa6d551bbf966305b71-site-8","doc-457bdfa6d551bbf966305b71-site-9"],"anchor":[18.836605,-1.965431],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[1041213640,1041624310,4040050300,5040015660,5040491510,6040040460,6040280400],"main_basin_ids":[1040015850,1040020040,4040050300,5040015660,5040087590,6040007000,6040040460]},{"id":"doc-530237e3868d61b362cc3e22","title":"A Method for comprehensively Assessing Economic Trade-Offs of New Irrigation Developments","display_title":"Assessing Irrigation Trade-Offs in Flinders, Australia","doi":"https://doi.org/10.1007/s11269-016-1443-2","theme":"Water management","color":"#eeabac","year":2016,"publication_date":"2016-08-01","authors":["Cuan Petheram","Justin Hughes","Lisa McKellar","S. Kim","Linda Holz","Perry Poulton","Michael Kehoe","Scott Podger","G Podger","David McJannet","John Hornbuckle"],"summary":"This study presents a method combining a river system model (Source) and an agricultural crop production model (APSIM) linked via a regression approach to efficiently evaluate economic trade-offs of greenfield irrigation in the Flinders catchment, northern Australia. The method explores various irrigation scheme sizes and farmer risk profiles, calculating impacts on crop yields, gross margins, and net present values using historical climate data and economic assumptions.","finding":"The analysis found that under the evaluated conditions, the proposed irrigation development based on a large dam near Cloncurry in the Flinders catchment is not economically sustainable, as the gross margins from crop production do not offset the high capital, operation, and maintenance costs over the investment period.","finding_evidence":{"passage_id":"p015-000","page":15,"quote":"and crop area decision of 8 ML/ha) (Fig. 7b). Negative gross margins occur under a small set of circumstances, when yields are too low to generate income sufficient to cover the variable costs. Median 30-year average scheme-scale gross margins increase with scheme area up to the size at which the scheme area no longer constrains the planted area. Gross margins remain the same for larger scheme areas. The largest scheme-scale crop gross margins occur at a crop area decision of between 3 and 4 ML/ha and at a scheme area of about 45,000 ha (Fig. 7b). 4.2 Financial Analysis The scheme-scale NPV is negative under all combinations of scheme area and crop area decision, because the revenue generated from the scheme (total crop gross margins) does not offset the capital, operation and maintenance costs of the scheme-scale and on-farm infrastructure over the life of the investment (Fig. 8). Losses are minimised by not undertaking an irrigation development.","verified_in_page":true},"topics":["economic trade-offs","hydrological modelling","agricultural production","risk analysis","irrigation schemes","crop yield","financial analysis","river system modelling"],"study_period":"1890-2011","locations":[{"name":"Flinders catchment","kind":"basin","role":"study catchment for hypothetical irrigation development assessment","river_name":null,"geocode_query":"Flinders catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The method is demonstrated in an assessment of a potential irrigation development in the Flinders catchment, northern Australia. 2 Hypothetical Scenario In this paper, a hypothetical potential irrigation development near Cloncurry, in the Flinders catchment, northern Australia (Fig. 1) was used to demonstrate the method. The scenario was based on local aspirations (e.g. NQ Register 2014; ABC Rural 2014) and recent economic development evaluations in the region (Meateng et al. 2012). Water for irrigation would be supplied from a 16 m high earth embankment dam built approximately 20 km upstream of Cloncurry (248 GL capacity), upstream of an area called Cave Hill. The cost of dam construction has been estimated to be about AUD$249 million, though there are considerable geological uncertainties for dam construction in the general area (Petheram et al. 2013).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.434214,-21.07843],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122860716,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040065600,"main_basin_id":5040065600,"basin_region":"Australasia","basin_area_km2":107785.3,"hydrology_source":"geofabric","site_id":"doc-530237e3868d61b362cc3e22-site-0","true_coordinates":[144.4342138,-21.0184301],"spread_km":6.7},{"name":"Cloncurry","kind":"settlement","role":"proximate town near irrigation development site and climate reference location","river_name":null,"geocode_query":"Cloncurry, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Downstream of Cloncurry over 70,000 ha of friable clay loam soils and cracking clay soils moderately suited to irrigated cereal crop production are located downstream of the dam site and these are described in Bartley et al. (2013). Before irrigation development, the area would require more intensive assessment of usable areas. The mean annual rainfall at Cloncurry is 477 mm (between 1890 and 2011) and is highly seasonal and variable between years. There are no climatic limitations to growing Sorghum (grain) during the dry season near Cloncurry, though there is a low frost risk between June and August. 3 Materials and Methods The materials and methods has been broken into four sections: 1. Key analysis tools. 2. Linking river system and crop production models. 3. Selecting the appropriate size of the irrigation development and farmer risk profile for a greenfield irrigation site. 4.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.5053246,-20.7068535],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":122859449,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cloncurry QLD 4824, Australia","basin_id":5040065600,"main_basin_id":5040065600,"basin_region":"Australasia","basin_area_km2":107785.3,"hydrology_source":"geofabric","site_id":"doc-530237e3868d61b362cc3e22-site-1"},{"name":"Cave Hill","kind":"other","role":"area downstream of proposed dam with soils moderately suited to irrigated cereal crop production","river_name":null,"geocode_query":"Cave Hill, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The method is demonstrated in an assessment of a potential irrigation development in the Flinders catchment, northern Australia. 2 Hypothetical Scenario In this paper, a hypothetical potential irrigation development near Cloncurry, in the Flinders catchment, northern Australia (Fig. 1) was used to demonstrate the method. The scenario was based on local aspirations (e.g. NQ Register 2014; ABC Rural 2014) and recent economic development evaluations in the region (Meateng et al. 2012). Water for irrigation would be supplied from a 16 m high earth embankment dam built approximately 20 km upstream of Cloncurry (248 GL capacity), upstream of an area called Cave Hill. The cost of dam construction has been estimated to be about AUD$249 million, though there are considerable geological uncertainties for dam construction in the general area (Petheram et al. 2013).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Flinders River","kind":"river","role":"river system modelled in the Source river system model to evaluate irrigation impacts","river_name":"Flinders River","geocode_query":"Flinders River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Irrigation demands in Source are traditionally generated using one of the inbuilt conceptual Irrigator Demand Models (Bethune and Podger 2013), which estimate crop water demand using the FAO56 method (Allen et al. 1998). The Flinders River model was modified to reflect the hypothetical development scenario outlined in Section 2. This involved incorporating and parameterising: & the dam, located at the nearest upstream node in the existing Source model (i.e. the location was conservatively selected so as not to overestimate inflows); & conveyance and field application efficiencies; & an extraction point at the re-regulation structure adjacent to the irrigation development; and & a ‘resource assessment’, which limits orders to the allocated water resource. An annual water resource accounting method was utilised. The model was re-configured so that existing water users downstream of the irrigation development did not experience a reduction in the reliability of their supply.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.680421,-19.293319],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":122859581,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Flinders River, Queensland, Australia","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-530237e3868d61b362cc3e22-site-3","true_coordinates":[142.6804208810083,-19.23331902699644],"spread_km":6.7}],"limitations":["No exact coordinates given for study sites, coordinates set to null","Study uses a hypothetical irrigation scheme rather than an existing development","Model inputs and parameters contain uncertainties, including hydrological and biophysical model inputs, and economic assumptions","Regressions used to link crop model to river model may introduce approximations","Economic analysis assumes constant prices and fixed discount rate, excluding price fluctuations or market dynamics"],"river_ids":[],"site_ids":["doc-530237e3868d61b362cc3e22-site-0","doc-530237e3868d61b362cc3e22-site-1","doc-530237e3868d61b362cc3e22-site-3"],"anchor":[142.680421,-19.293319],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040049720,5040065600],"main_basin_ids":[5040049720,5040065600]},{"id":"doc-67fab07bad961d1a3ff74ccf","title":"Multi-decadal trends in global terrestrial evapotranspiration and its components","display_title":"Global Multi-Decadal Trends in Terrestrial Evapotranspiration","doi":"https://doi.org/10.1038/srep19124","theme":"Land & ecosystems","color":"#c8df98","year":2016,"publication_date":"2016-01-11","authors":["Yongqiang Zhang","Jorge L. Peña‐Arancibia","Tim R. McVicar","Francis H. S. Chiew","Jai Vaze","Changming Liu","Xingjie Lu","Hongxing Zheng","Ying‐Ping Wang","Yi Liu","Diego G. Miralles","Ming Pan"],"summary":"This study uses the Penman-Monteith-Leuning (PML) diagnostic model driven by global meteorological and satellite data for 1981–2012 to estimate daily terrestrial evapotranspiration (ET) and its components: transpiration from vegetation (Et), soil evaporation (Es), and interception evaporation (Ei). The model is extensively validated against catchment water balances, flux towers, soil moisture, and field experiments worldwide. The study finds a significant global increase in ET primarily driven by increases in Et and Ei, partially counteracted by decreases in Es, with trends mostly explained by increasing leaf area index (LAI) due to global greening. The contrasting trends in ET components and the importance of vegetation dynamics highlight the need to incorporate realistic vegetation changes into Earth System Models.","finding":"Global terrestrial evapotranspiration increased significantly (p<0.01) from 1981 to 2012, driven by increases in vegetation transpiration (Et) and interception evaporation (Ei), partially offset by decreases in soil evaporation (Es); these opposing trends are primarily caused by increases in leaf area index due to vegetation greening.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei ). During this period, ET over land has increased significantly (p<0.01), caused by increases in Et and Ei , which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle. Terrestrial ET is a key component of the energy and water cycles over global land1 .","verified_in_page":true},"topics":["evapotranspiration","vegetation dynamics","remote sensing","climate variability","water cycle","energy balance","land surface modeling","leaf area index"],"study_period":"1981–2012","locations":[{"name":"Santa Rita Experimental Range","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Santa Rita Experimental Range, Arizona, USA","latitude":31.91,"longitude":-110.84,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-110.84,31.91],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040707810,"main_basin_id":7040008710,"basin_region":"North America and Caribbean","basin_area_km2":155895.6,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-0"},{"name":"Walnut Gulch Experimental Watershed","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Walnut Gulch Experimental Watershed, Arizona, USA","latitude":31.74,"longitude":-110.05,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-110.05,31.74],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040707810,"main_basin_id":7040008710,"basin_region":"North America and Caribbean","basin_area_km2":155895.6,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-1"},{"name":"Oak Ridge","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Oak Ridge, Tennessee, USA","latitude":35.96,"longitude":-84.29,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-84.29,35.96],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-2"},{"name":"Steinkreuz catchment","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Steinkreuz catchment, Bavaria, Germany","latitude":49.87,"longitude":10.47,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[10.47,49.87],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-3"},{"name":"Luan Cheng","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Luan Cheng, Hebei, China","latitude":37.88,"longitude":114.68,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.68,37.88],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040007410,"main_basin_id":4040007410,"basin_region":"Central and South-East Asia","basin_area_km2":215965.0,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-4"},{"name":"Oerst Forests","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Oerst Forests, New Zealand","latitude":-42.22,"longitude":172.25,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. (2011)47 1 Walnut Gulch Experimental Watershed (USA) 31.74 −110.05 53 54 Shrub Cavanaugh et al. (2011)47 1 Oak Ridge (USA) 35.96 −84.29 16 16 Forest Wilson (2001)48 3 Steinkreuz catchment (Germany) 49.87 10.47 10 20 Forest Kostner (2011)49 5 Luan Cheng (China) 37.88 114.68 30 28 Cropland Liu et al. (2002)50 5 Oerst Forests (New Zealand) −42.22 172.25 10 9 Forest Kelliher et al. (1992)51 1 Sultana Vineyard (Australia) −34.22 142.03 43 46 Vineyard Yunusa et al. (2004)52 2 Punjab Agricultural University (India) 30.93 75.87 39 23 Cropland Balwinder et al. (2011)53 2 Yang Ling (China) 34.33 108.40 30 21 Cropland Kang et al. (2003)54 10 lower coastal plain, North Carolina (USA) 35.80 −76.67 14 24 Forest Domec et al. (2012)55 3 Norunda Common (Sweden) 60.08 17.05 13 16 Forest Constaintin et al. (1999)56 2 Menglun Forest Reserve, Yunnan (China) 21.93 101.27 4 6 Tropical Forest Liu et al. (2006)57 2 Table 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[172.25,-42.22],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040082270,"main_basin_id":5040082270,"basin_region":"Australasia","basin_area_km2":152497.7,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-5"},{"name":"Sultana Vineyard","kind":"field_site","role":"Validation site for soil evaporation estimates","river_name":null,"geocode_query":"Sultana Vineyard, New South Wales, Australia","latitude":-34.22,"longitude":142.03,"evidence":{"passage_id":"p003-002","page":3,"quote":"Es/ET (Measured) Es/ET (Estimated) Land cover type Reference Year length Santa Rita Experimental Range (USA) 31.91 −110.84 58 42 Shrub Cavanaugh et al. 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(1997)68 3 Table 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.9,-10.1],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040285990,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":1368849.7,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-17"},{"name":"Cuieiras Biological Reservation","kind":"field_site","role":"Validation site for interception evaporation estimates","river_name":null,"geocode_query":"Cuieiras Biological Reservation, Amazonas, Brazil","latitude":-2.5,"longitude":-60.2,"evidence":{"passage_id":"p003-004","page":3,"quote":"Ei /P (Measured) Ei /P (Estimated) Land cover Type Reference Year length Tapajos National Forest south of Santarém (Brazil) −2.9 −54.9 11.6 9.89 Forest Czikowsky and Fitzjarrald (2009)58 3 Lambir Hills National Park, Sarawak (Malaysia) 4.3 114.0 8 9.79 Forest Kume et al. (2011)59 10 Central Kalimantan (Indonesia) −1.30 112.38 11.4 9.96 Forest Asdak et al. (1998)60 1 Pena Rojo (Colombia) −0.62 −70.72 12 9.93 Forest Martin et al. (2000)61 3 Reserva Florestal Ducke (Brazil) −2.95 −59.95 8.9 10.47 Forest Lloyd and Marques (1988)62 2 Abracos forests (Brazil) −10.1 −61.9 11.6 9.86 Forest Ubarana (1996)63 2 Cuieiras Biological Reservation (Brazil) −2.5 −60.2 13.3 10.18 Forest Cuartas et al. (2007)64 2 Central Amazonia (Brazil) −2.95 −59.95 9.1 10.47 Forest Shuttleworth (1988)65 2 Tai National Park (Ivory Coast) 5.85 −7.34 9.2 9.21 Forest Hutjes et al. (1990)66 1 Central Kalimantan (Indonesia) 0.1 113.9 13 9.40 Forest Vernimmen et al. (2007)67 1 South−east of Lisbon (Portugal) 38.63 −8.6 10.8 5.61 Forest Valente et al. (1997)68 3 Table 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-60.2,-2.5],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-18"},{"name":"Central Amazonia","kind":"field_site","role":"Validation site for interception evaporation estimates","river_name":null,"geocode_query":"Central Amazonia, Brazil","latitude":-2.95,"longitude":-59.95,"evidence":{"passage_id":"p003-004","page":3,"quote":"Ei /P (Measured) Ei /P (Estimated) Land cover Type Reference Year length Tapajos National Forest south of Santarém (Brazil) −2.9 −54.9 11.6 9.89 Forest Czikowsky and Fitzjarrald (2009)58 3 Lambir Hills National Park, Sarawak (Malaysia) 4.3 114.0 8 9.79 Forest Kume et al. (2011)59 10 Central Kalimantan (Indonesia) −1.30 112.38 11.4 9.96 Forest Asdak et al. (1998)60 1 Pena Rojo (Colombia) −0.62 −70.72 12 9.93 Forest Martin et al. (2000)61 3 Reserva Florestal Ducke (Brazil) −2.95 −59.95 8.9 10.47 Forest Lloyd and Marques (1988)62 2 Abracos forests (Brazil) −10.1 −61.9 11.6 9.86 Forest Ubarana (1996)63 2 Cuieiras Biological Reservation (Brazil) −2.5 −60.2 13.3 10.18 Forest Cuartas et al. (2007)64 2 Central Amazonia (Brazil) −2.95 −59.95 9.1 10.47 Forest Shuttleworth (1988)65 2 Tai National Park (Ivory Coast) 5.85 −7.34 9.2 9.21 Forest Hutjes et al. (1990)66 1 Central Kalimantan (Indonesia) 0.1 113.9 13 9.40 Forest Vernimmen et al. (2007)67 1 South−east of Lisbon (Portugal) 38.63 −8.6 10.8 5.61 Forest Valente et al. (1997)68 3 Table 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-59.95,-2.89],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-19","true_coordinates":[-59.95,-2.95],"spread_km":6.7},{"name":"Tai National Park","kind":"field_site","role":"Validation site for interception evaporation estimates","river_name":null,"geocode_query":"Tai National Park, Ivory Coast","latitude":5.85,"longitude":-7.34,"evidence":{"passage_id":"p003-004","page":3,"quote":"Ei /P (Measured) Ei /P (Estimated) Land cover Type Reference Year length Tapajos National Forest south of Santarém (Brazil) −2.9 −54.9 11.6 9.89 Forest Czikowsky and Fitzjarrald (2009)58 3 Lambir Hills National Park, Sarawak (Malaysia) 4.3 114.0 8 9.79 Forest Kume et al. (2011)59 10 Central Kalimantan (Indonesia) −1.30 112.38 11.4 9.96 Forest Asdak et al. (1998)60 1 Pena Rojo (Colombia) −0.62 −70.72 12 9.93 Forest Martin et al. (2000)61 3 Reserva Florestal Ducke (Brazil) −2.95 −59.95 8.9 10.47 Forest Lloyd and Marques (1988)62 2 Abracos forests (Brazil) −10.1 −61.9 11.6 9.86 Forest Ubarana (1996)63 2 Cuieiras Biological Reservation (Brazil) −2.5 −60.2 13.3 10.18 Forest Cuartas et al. (2007)64 2 Central Amazonia (Brazil) −2.95 −59.95 9.1 10.47 Forest Shuttleworth (1988)65 2 Tai National Park (Ivory Coast) 5.85 −7.34 9.2 9.21 Forest Hutjes et al. (1990)66 1 Central Kalimantan (Indonesia) 0.1 113.9 13 9.40 Forest Vernimmen et al. (2007)67 1 South−east of Lisbon (Portugal) 38.63 −8.6 10.8 5.61 Forest Valente et al. (1997)68 3 Table 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-7.34,5.85],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1040024080,"main_basin_id":1040024080,"basin_region":"Africa","basin_area_km2":358341.1,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-20"},{"name":"South-east of Lisbon","kind":"field_site","role":"Validation site for interception evaporation estimates","river_name":null,"geocode_query":"South-east of Lisbon, Portugal","latitude":38.63,"longitude":-8.6,"evidence":{"passage_id":"p003-004","page":3,"quote":"Ei /P (Measured) Ei /P (Estimated) Land cover Type Reference Year length Tapajos National Forest south of Santarém (Brazil) −2.9 −54.9 11.6 9.89 Forest Czikowsky and Fitzjarrald (2009)58 3 Lambir Hills National Park, Sarawak (Malaysia) 4.3 114.0 8 9.79 Forest Kume et al. (2011)59 10 Central Kalimantan (Indonesia) −1.30 112.38 11.4 9.96 Forest Asdak et al. (1998)60 1 Pena Rojo (Colombia) −0.62 −70.72 12 9.93 Forest Martin et al. (2000)61 3 Reserva Florestal Ducke (Brazil) −2.95 −59.95 8.9 10.47 Forest Lloyd and Marques (1988)62 2 Abracos forests (Brazil) −10.1 −61.9 11.6 9.86 Forest Ubarana (1996)63 2 Cuieiras Biological Reservation (Brazil) −2.5 −60.2 13.3 10.18 Forest Cuartas et al. (2007)64 2 Central Amazonia (Brazil) −2.95 −59.95 9.1 10.47 Forest Shuttleworth (1988)65 2 Tai National Park (Ivory Coast) 5.85 −7.34 9.2 9.21 Forest Hutjes et al. (1990)66 1 Central Kalimantan (Indonesia) 0.1 113.9 13 9.40 Forest Vernimmen et al. (2007)67 1 South−east of Lisbon (Portugal) 38.63 −8.6 10.8 5.61 Forest Valente et al. (1997)68 3 Table 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-8.6,38.63],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040018470,"main_basin_id":2040018470,"basin_region":"Europe and Middle East","basin_area_km2":22751.7,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-21"},{"name":"Sahara Desert","kind":"region","role":"Extreme climate area where soil evaporation dominates","river_name":null,"geocode_query":"Sahara Desert, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The measurements show that about 8–14% of annual P is partitioned to Ei for the selected forests, which are similar to the PML estimates that vary from 5% to 11%. Sixth, the ET partitioning can be qualitatively evaluated in extreme climates, such as deserts in Sahara, central Asia, Arabian Peninsula, and central Australia where mean annual potential ET is more than 10 times of mean annual P (Fig. 2a). In these areas, most of precipitation is used for Es, as estimated by the PML model (Figs 2c and 2d). Site Lat. Long.","verified_in_page":true},"resolution_status":"resolved","coordinates":[25.66283,23.356203],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sahara Desert","basin_id":1040040250,"main_basin_id":1040040250,"basin_region":"Africa","basin_area_km2":219482.9,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-22","true_coordinates":[25.66283,23.4162027],"spread_km":6.7},{"name":"Central Asia","kind":"region","role":"Extreme climate area where soil evaporation dominates","river_name":null,"geocode_query":"Central Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The measurements show that about 8–14% of annual P is partitioned to Ei for the selected forests, which are similar to the PML estimates that vary from 5% to 11%. Sixth, the ET partitioning can be qualitatively evaluated in extreme climates, such as deserts in Sahara, central Asia, Arabian Peninsula, and central Australia where mean annual potential ET is more than 10 times of mean annual P (Fig. 2a). In these areas, most of precipitation is used for Es, as estimated by the PML model (Figs 2c and 2d). Site Lat. Long.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.882173,45.499229],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Asia","basin_id":4040050300,"main_basin_id":4040050300,"basin_region":"Central and South-East Asia","basin_area_km2":241047.3,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-23","true_coordinates":[68.8319005,45.4506875],"spread_km":6.7},{"name":"Arabian Peninsula","kind":"region","role":"Extreme climate area where soil evaporation dominates","river_name":null,"geocode_query":"Arabian Peninsula","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The measurements show that about 8–14% of annual P is partitioned to Ei for the selected forests, which are similar to the PML estimates that vary from 5% to 11%. Sixth, the ET partitioning can be qualitatively evaluated in extreme climates, such as deserts in Sahara, central Asia, Arabian Peninsula, and central Australia where mean annual potential ET is more than 10 times of mean annual P (Fig. 2a). In these areas, most of precipitation is used for Es, as estimated by the PML model (Figs 2c and 2d). Site Lat. Long.","verified_in_page":true},"resolution_status":"resolved","coordinates":[47.44904,19.431411],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arabia","basin_id":2040085540,"main_basin_id":2040085540,"basin_region":"Europe and Middle East","basin_area_km2":171027.7,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-24","true_coordinates":[47.4490397,19.4914108],"spread_km":6.7},{"name":"Central Australia","kind":"region","role":"Extreme climate area where soil evaporation dominates","river_name":null,"geocode_query":"Central Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The measurements show that about 8–14% of annual P is partitioned to Ei for the selected forests, which are similar to the PML estimates that vary from 5% to 11%. Sixth, the ET partitioning can be qualitatively evaluated in extreme climates, such as deserts in Sahara, central Asia, Arabian Peninsula, and central Australia where mean annual potential ET is more than 10 times of mean annual P (Fig. 2a). In these areas, most of precipitation is used for Es, as estimated by the PML model (Figs 2c and 2d). Site Lat. Long.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.841777,-23.384134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006239,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Queensland, QLD, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-67fab07bad961d1a3ff74ccf-site-25","true_coordinates":[144.7851531,-23.4141339],"spread_km":6.7},{"name":"Northern and eastern Asia","kind":"region","role":"Region with strong positive ET trends mainly from increased transpiration (Et)","river_name":null,"geocode_query":"Northern and eastern Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[106.534838,38.734595],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Asia","basin_id":4040548370,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":248039.1,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-26","true_coordinates":[106.5348379,38.7945952],"spread_km":6.7},{"name":"India","kind":"region","role":"Region with strong positive ET and LAI increase","river_name":null,"geocode_query":"India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Eastern North America","kind":"region","role":"Region with strong positive ET trends mainly from increased transpiration (Et)","river_name":null,"geocode_query":"Eastern North America, USA/Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-82.324609,37.546105],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canada, KY 41519, USA","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-28","true_coordinates":[-82.3246094,37.6061055],"spread_km":6.7},{"name":"Europe","kind":"region","role":"Region with strong positive ET and LAI increase","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Northern Sub-Saharan Africa","kind":"region","role":"Region with strong positive ET driven by increased Et","river_name":null,"geocode_query":"Northern Sub-Saharan Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Northern and eastern Amazonia","kind":"region","role":"Region with strong positive ET trends mainly from increased transpiration (Et)","river_name":null,"geocode_query":"Northern and eastern Amazonia, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 5 P variability22,23. Compared to that, shallow soil water is a more immediate buffer for Es responding directly to the P variability. As a result, global variance in Es is comparable to that in Et, as Es is a larger component of ET in regions with a high inter-annual variability in P, like Australia or South Africa6 . The global multi-decadal trend (1981–2012) in ET is positive (p< 0.01), i.e. in the same direction as previous estimates1,6 (Fig. 3). This is caused by significant positive trends in Et (0.72± 0.23 mm year−2 ) and in Ei (0.14± 0.07 mm year−2 ), which are partly counter-balanced by a significant but smaller negative trend in Es (–0.32± 0.07mm year−2 ) (Fig. 4c). Strong positive ET trends are observed in northern and eastern Asia, India, eastern North America, Europe, northern Sub-Saharan Africa and northern and eastern Amazonia (Fig. 5a), mainly as a result of increased Et (Fig. 5b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Subtropical and temperate South America","kind":"region","role":"Region with negative ET trends mainly due to Es decreases","river_name":null,"geocode_query":"Subtropical and temperate South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Middle East","kind":"region","role":"Region with negative ET trends mainly due to Es decrease","river_name":null,"geocode_query":"Middle East","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"resolved","coordinates":[42.55096,29.238528],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Middle East","basin_id":2040085850,"main_basin_id":2040085850,"basin_region":"Europe and Middle East","basin_area_km2":78300.3,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-33","true_coordinates":[42.5509603,29.2985278],"spread_km":6.7},{"name":"Western United States","kind":"region","role":"Region with negative ET trends mainly due to Es decrease","river_name":null,"geocode_query":"Western United States, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.197897,40.333485],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western, NE 68464, USA","basin_id":7040569640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":1381472.0,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-34","true_coordinates":[-97.19789659999999,40.3934846],"spread_km":6.7},{"name":"Sahel","kind":"region","role":"Region with decreasing Es and increasing Et trends; greening effect","river_name":null,"geocode_query":"Sahel, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.543691,19.124317],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sahel","basin_id":1040041030,"main_basin_id":1040041030,"basin_region":"Africa","basin_area_km2":172561.6,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-35","true_coordinates":[13.5436913,19.0643174],"spread_km":6.7},{"name":"Indian Subcontinent","kind":"region","role":"Region with decreasing soil evaporation (Es) and increasing transpiration (Et)","river_name":null,"geocode_query":"Indian Subcontinent","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Southern China","kind":"region","role":"Region with decreasing Es and increasing Et","river_name":null,"geocode_query":"Southern China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Negative ET trends are observed over parts of subtropical and temperate South America, the Middle East and western United States, and are mainly explained by reductions in Es (Fig. 5a,c). Decreases in Es in the Sahel24, Indian Subcontinent and southern China are also accompanied by increases in Et (Fig. 5b,c). Causality analysis. The contrasting positive trend in Et and negative trend in Es is mostly explained by the increase in leaf area index (LAI) (Fig. 5d). This increasing trend in LAI has been attributed to CO2 fertilization25, global warming26, increased productivity in croplands27, afforestation and forest protection27,28. The increase in LAI also means more shading of the soil surface and less coupling between the atmosphere and the soil surface, and these are likely to be the main reasons for the decreasing trend in Es 29,30.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-80.5810569,35.3831543],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"591 Warren Coleman Blvd, Concord, NC 28025, USA","basin_id":7040043090,"main_basin_id":7040043090,"basin_region":"North America and Caribbean","basin_area_km2":40952.6,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-37"},{"name":"Northern Australia","kind":"region","role":"Region with increasing ET trend influenced by precipitation","river_name":null,"geocode_query":"Northern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 6 strong correlation between P and Es (and ET) (Fig. 5e) and the P trend influences Es and ET trends in the southern mid-latitudes, such as the increasing trend in northern Australia and southern Africa and the decreasing trend in southern South America (Fig. 5a,c,f). In these regions, the dynamics of the EI Niño/Southern Oscillation6,32 dominate the multi-decadal P and ET variability. When stratified using the P trend, the ET trend gradually rises with increasing P trend (see Supplementary Information Fig. S4). Furthermore, a decrease in Es trend and Ei trend occurs when the P trend decreases; a strong increase in Ei trend accompanies the strong increase in P trend. Figure 5.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southern Africa","kind":"region","role":"Region with increasing ET trend influenced by precipitation","river_name":null,"geocode_query":"Southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 6 strong correlation between P and Es (and ET) (Fig. 5e) and the P trend influences Es and ET trends in the southern mid-latitudes, such as the increasing trend in northern Australia and southern Africa and the decreasing trend in southern South America (Fig. 5a,c,f). In these regions, the dynamics of the EI Niño/Southern Oscillation6,32 dominate the multi-decadal P and ET variability. When stratified using the P trend, the ET trend gradually rises with increasing P trend (see Supplementary Information Fig. S4). Furthermore, a decrease in Es trend and Ei trend occurs when the P trend decreases; a strong increase in Ei trend accompanies the strong increase in P trend. Figure 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.625749,-24.327077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-67fab07bad961d1a3ff74ccf-site-39","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7},{"name":"Southern South America","kind":"region","role":"Region with decreasing ET trend influenced by precipitation","river_name":null,"geocode_query":"Southern South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"www.nature.com/scientificreports/ Scientific Reports | 6:19124 | DOI: 10.1038/srep19124 6 strong correlation between P and Es (and ET) (Fig. 5e) and the P trend influences Es and ET trends in the southern mid-latitudes, such as the increasing trend in northern Australia and southern Africa and the decreasing trend in southern South America (Fig. 5a,c,f). In these regions, the dynamics of the EI Niño/Southern Oscillation6,32 dominate the multi-decadal P and ET variability. When stratified using the P trend, the ET trend gradually rises with increasing P trend (see Supplementary Information Fig. S4). Furthermore, a decrease in Es trend and Ei trend occurs when the P trend decreases; a strong increase in Ei trend accompanies the strong increase in P trend. Figure 5.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"}],"limitations":["Leaf area index (LAI) data and meteorological forcing data used have finite spatial and temporal resolution and may contain uncertainties.","The PML model does not directly incorporate effects of increased CO2 on vegetation water use efficiency, possibly overestimating Et trends.","The CABLE land surface model simulation used to estimate CO2 fertilization effects requires assumptions and has its own uncertainties.","Validation data from catchments assumes negligible long-term soil water storage changes, which may not always hold.","The PML model uses a single free parameter for maximum stomatal conductance, estimated via trial-and-error, which may introduce parameter uncertainty.","Some validation sites have limited temporal coverage and represent ecosystem types unevenly globally.","Comparisons with CMIP5 models are limited by the number of models that archive ET components and possible inconsistencies in variable definitions.","Global maps and analyses exclude regions with dense tropical forest soil moisture retrievals due to remote sensing limitations."],"river_ids":[],"site_ids":["doc-67fab07bad961d1a3ff74ccf-site-0","doc-67fab07bad961d1a3ff74ccf-site-1","doc-67fab07bad961d1a3ff74ccf-site-2","doc-67fab07bad961d1a3ff74ccf-site-3","doc-67fab07bad961d1a3ff74ccf-site-4","doc-67fab07bad961d1a3ff74ccf-site-5","doc-67fab07bad961d1a3ff74ccf-site-6","doc-67fab07bad961d1a3ff74ccf-site-7","doc-67fab07bad961d1a3ff74ccf-site-8","doc-67fab07bad961d1a3ff74ccf-site-9","doc-67fab07bad961d1a3ff74ccf-site-10","doc-67fab07bad961d1a3ff74ccf-site-11","doc-67fab07bad961d1a3ff74ccf-site-12","doc-67fab07bad961d1a3ff74ccf-site-13","doc-67fab07bad961d1a3ff74ccf-site-14","doc-67fab07bad961d1a3ff74ccf-site-15","doc-67fab07bad961d1a3ff74ccf-site-16","doc-67fab07bad961d1a3ff74ccf-site-17","doc-67fab07bad961d1a3ff74ccf-site-18","doc-67fab07bad961d1a3ff74ccf-site-19","doc-67fab07bad961d1a3ff74ccf-site-20","doc-67fab07bad961d1a3ff74ccf-site-21","doc-67fab07bad961d1a3ff74ccf-site-22","doc-67fab07bad961d1a3ff74ccf-site-23","doc-67fab07bad961d1a3ff74ccf-site-24","doc-67fab07bad961d1a3ff74ccf-site-25","doc-67fab07bad961d1a3ff74ccf-site-26","doc-67fab07bad961d1a3ff74ccf-site-28","doc-67fab07bad961d1a3ff74ccf-site-33","doc-67fab07bad961d1a3ff74ccf-site-34","doc-67fab07bad961d1a3ff74ccf-site-35","doc-67fab07bad961d1a3ff74ccf-site-37","doc-67fab07bad961d1a3ff74ccf-site-39"],"anchor":[-7.34,5.85],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[1040024080,1040040250,1040041030,1041624310,2040018470,2040023010,2040031500,2040085540,2040085850,4040007410,4040050300,4040548370,4040627620,4040794730,4041043580,5040015830,5040017830,5040082270,5040491510,5040591050,6040239720,6040280400,6040280410,6040285990,7040042040,7040043090,7040569640,7040612640,7040707810],"main_basin_ids":[1040015850,1040024080,1040040250,1040041030,2040018470,2040023010,2040031500,2040085540,2040085850,4040007410,4040007850,4040017020,4040033640,4040050300,5040015830,5040017830,5040073410,5040082270,5040087590,6040007000,7040008710,7040042040,7040043090,7040047060]},{"id":"doc-a5163208542601520150c9fd","title":"Temporal and Spatial Variations of Drought in China: Reconstructed from Historical Memorials Archives during 1689-1911","display_title":"Historical Reconstruction of Droughts in Qing Dynasty China","doi":"https://doi.org/10.1371/journal.pone.0148072","theme":"Climate","color":"#98d4f1","year":2016,"publication_date":"2016-02-02","authors":["Jinhong Wan","Denghua Yan","Guobin Fu","Lu Hao","Yaojie Yue","Ruoxi Li","Yunpeng Li","Jiangang Liu","Jun Deng"],"summary":"This study reconstructs temporal and spatial drought variations in China from 1689 to 1911 by analyzing 2494 historical Memorials to the Throne (Zou Zhe), official communications by local officials to the Qing emperors. The research identifies drought frequency, distribution, and severity across China with county-level resolution over 223 years, revealing key drought-prone areas and their temporal evolution. The data provide important historical climate context useful for understanding drought impacts and mitigating agricultural risk.","finding":"Three main drought-prone areas were identified during 1689-1911: (1) Gansu and Ningxia in Northwest China, (2) Shandong, Hebei, Henan, and Tianjin in North China, and (3) Anhui and Jiangsu in the Jianghuai Region. Additionally, drought-prone areas expanded from North China to South China since the late 19th century.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"topics":["historical climate data","spatial analysis","temporal analysis","drought reconstruction","archival research","climate variability","agroclimatology","hydroclimatology"],"study_period":"1689-1911","locations":[{"name":"Gansu province","kind":"region","role":"identified drought-prone area in Northwest China","river_name":null,"geocode_query":"Gansu province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Ningxia Hui Autonomous Region","kind":"region","role":"identified drought-prone area in Northwest China","river_name":null,"geocode_query":"Ningxia Hui Autonomous Region, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shandong province","kind":"region","role":"identified drought-prone area in North China","river_name":null,"geocode_query":"Shandong province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Hebei province","kind":"region","role":"identified drought-prone area in North China","river_name":null,"geocode_query":"Hebei province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Henan province","kind":"region","role":"identified drought-prone area in North China","river_name":null,"geocode_query":"Henan province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tianjin","kind":"settlement","role":"identified drought-prone area in North China","river_name":null,"geocode_query":"Tianjin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Anhui province","kind":"region","role":"identified drought-prone area in Jianghuai region","river_name":null,"geocode_query":"Anhui province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Jiangsu province","kind":"region","role":"identified drought-prone area in Jianghuai region","river_name":null,"geocode_query":"Jiangsu province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shanxi province","kind":"region","role":"included in North China drought-prone region","river_name":null,"geocode_query":"Shanxi province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shaanxi province","kind":"region","role":"part of Northwest China drought area","river_name":null,"geocode_query":"Shaanxi province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Hubei province","kind":"region","role":"drought-prone area in south - recorded droughts mainly east of Yangtze River","river_name":null,"geocode_query":"Hubei province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Jiangxi province","kind":"region","role":"drought-prone area in south - recorded droughts mainly east of Yangtze River","river_name":null,"geocode_query":"Jiangxi province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Zhejiang province","kind":"region","role":"drought-prone area in south - recorded droughts mainly east of Yangtze River","river_name":null,"geocode_query":"Zhejiang province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"needs to be pointed out, this study has much better spatial resolution compared with NRGMND’s research. Spatial distribution in Qing Dynasty The Map of Drought Distribution in Qing Dynasty (Fig 4) indicates that the drought spatial distribution characteristic during 1689–1911 is scattered on the whole and concentration in certain small areas. Being scattered on the whole means that nearly all provinces of China have drought records. Concentration in certain small areas means that, while drought was widespread in China, there were three drought-prone areas. They were the drought-prone area of Northwest China, covering Gansu, Ningxia and Shaanxi provinces; the drought-prone area of Haihe River Basin, covering Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces; and the drought-prone area of Huaihe River—Yangtze River region, covering Anhui, Jiangsu, Hubei, Zhejiang and Jiangxi provinces. Not all regions have complete memorial archives on drought.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Taihu Lake Basin","kind":"region","role":"area with frequent summer droughts","river_name":null,"geocode_query":"Taihu Lake Basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"Fig 7 indicates that: (1) spring drought took place less than 25 times. The drought-prone area was mainly in Huang-Huai region where Beijing, Tianjin and Hebei were the worst-hit. In most areas, drought occurred once every nine years. (2) In the most extreme/frequent cases, summer drought occurred 46 times. This mainly happened in three areas, namely Shaanxi, Gansu, Ningxia of Northwest China, Beijing, Tianjin and Hebei of the Haihe River Basin, and Jiangsu, Henan, Anhui of the Huaihe River region. In most areas, drought occurred once every five years. In addition, summer drought was also very severe at the downstream of Hanjiang River, Poyang Lake Basin and Taihu Lake Basin. Most of these areas drought occurring were once every nine years. (3) Autumn drought occurred less than 30 times and was mainly at the downstream of the Yangtze River and Huaihe River Basin. The frequency in most areas was once every nine years.","verified_in_page":true},"resolution_status":"resolved","coordinates":[120.20511,31.197952],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Taihu Lake, China","basin_id":4040009890,"main_basin_id":4040009890,"basin_region":"Central and South-East Asia","basin_area_km2":212335.4,"hydrology_source":"hydrobasins","site_id":"doc-a5163208542601520150c9fd-site-13","true_coordinates":[120.2051096,31.2579515],"spread_km":6.7},{"name":"Shuntian Fu (historic, now Beijing, Tianjin, Hebei)","kind":"region","role":"location affected by 1876-1878 severe drought","river_name":null,"geocode_query":"Beijing, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"As rainfall materialized in autumn of 1878, the three-year drought was finally over. Spatial distribution characteristics of seasonal drought Agriculture is inherently sensitive to drought[24]. Annually, drought causes a large number of crops to fail, especially in crop growth period, seriously hindering agricultural production which is key to ensuring food security and welfare of 1.3 billion people in China [5, 25]. Therefore, a map of spatial distribution of drought in each season is drawn in this section to illustrate the seasonal characteristics of drought. Fig 7 shows drought distribution in different seasons between 1689 and 1911. Table 3. Examples of historical records on the 1876–1877 extreme drought episodes. Region 1876 1877 Shandong “The spring and summer were quite dry and the wheat was in failure harvest. The grain output was even lower than 1 grade.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.407395,39.904211],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Beijing, China","basin_id":4040007410,"main_basin_id":4040007410,"basin_region":"Central and South-East Asia","basin_area_km2":215965.0,"hydrology_source":"hydrobasins","site_id":"doc-a5163208542601520150c9fd-site-14"},{"name":"Henan province","kind":"region","role":"location affected by 1876-1878 severe drought","river_name":null,"geocode_query":"Henan province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"As rainfall materialized in autumn of 1878, the three-year drought was finally over. Spatial distribution characteristics of seasonal drought Agriculture is inherently sensitive to drought[24]. Annually, drought causes a large number of crops to fail, especially in crop growth period, seriously hindering agricultural production which is key to ensuring food security and welfare of 1.3 billion people in China [5, 25]. Therefore, a map of spatial distribution of drought in each season is drawn in this section to illustrate the seasonal characteristics of drought. Fig 7 shows drought distribution in different seasons between 1689 and 1911. Table 3. Examples of historical records on the 1876–1877 extreme drought episodes. Region 1876 1877 Shandong “The spring and summer were quite dry and the wheat was in failure harvest. The grain output was even lower than 1 grade.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shandong province","kind":"region","role":"location affected by 1876-1878 severe drought","river_name":null,"geocode_query":"Shandong province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"As rainfall materialized in autumn of 1878, the three-year drought was finally over. Spatial distribution characteristics of seasonal drought Agriculture is inherently sensitive to drought[24]. Annually, drought causes a large number of crops to fail, especially in crop growth period, seriously hindering agricultural production which is key to ensuring food security and welfare of 1.3 billion people in China [5, 25]. Therefore, a map of spatial distribution of drought in each season is drawn in this section to illustrate the seasonal characteristics of drought. Fig 7 shows drought distribution in different seasons between 1689 and 1911. Table 3. Examples of historical records on the 1876–1877 extreme drought episodes. Region 1876 1877 Shandong “The spring and summer were quite dry and the wheat was in failure harvest. The grain output was even lower than 1 grade.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shaanxi province","kind":"region","role":"location affected by 1876-1878 severe drought","river_name":null,"geocode_query":"Shaanxi province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"As rainfall materialized in autumn of 1878, the three-year drought was finally over. Spatial distribution characteristics of seasonal drought Agriculture is inherently sensitive to drought[24]. Annually, drought causes a large number of crops to fail, especially in crop growth period, seriously hindering agricultural production which is key to ensuring food security and welfare of 1.3 billion people in China [5, 25]. Therefore, a map of spatial distribution of drought in each season is drawn in this section to illustrate the seasonal characteristics of drought. Fig 7 shows drought distribution in different seasons between 1689 and 1911. Table 3. Examples of historical records on the 1876–1877 extreme drought episodes. Region 1876 1877 Shandong “The spring and summer were quite dry and the wheat was in failure harvest. The grain output was even lower than 1 grade.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Jiangsu province","kind":"region","role":"location affected by 1876-1878 severe drought","river_name":null,"geocode_query":"Jiangsu province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"As rainfall materialized in autumn of 1878, the three-year drought was finally over. Spatial distribution characteristics of seasonal drought Agriculture is inherently sensitive to drought[24]. Annually, drought causes a large number of crops to fail, especially in crop growth period, seriously hindering agricultural production which is key to ensuring food security and welfare of 1.3 billion people in China [5, 25]. Therefore, a map of spatial distribution of drought in each season is drawn in this section to illustrate the seasonal characteristics of drought. Fig 7 shows drought distribution in different seasons between 1689 and 1911. Table 3. Examples of historical records on the 1876–1877 extreme drought episodes. Region 1876 1877 Shandong “The spring and summer were quite dry and the wheat was in failure harvest. The grain output was even lower than 1 grade.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Lack of drought data in early Qing dynasty before 1689 due to wars and instability","Reduced drought records during the Taiping Rebellion (1851-1865) in Yangtze River provinces due to war","Sparse or no data from regions less politically or economically developed during Qing Dynasty such as Tibet, Qinghai, Xinjiang, and Northeast China","Systematic reporting limitations related to administrative and socio-political factors in some provinces","No explicit latitude/longitude coordinates provided, only regional administrative levels included"],"river_ids":[],"site_ids":["doc-a5163208542601520150c9fd-site-13","doc-a5163208542601520150c9fd-site-14"],"anchor":[120.20511,31.197952],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040007410,4040009890],"main_basin_ids":[4040007410,4040009890]},{"id":"doc-ad8c6ef25ae1f83c4221e72e","title":"Advantages of Using Microwave Satellite Soil Moisture over Gridded Precipitation Products and Land Surface Model Output in Assessing Regional Vegetation Water Availability and Growth Dynamics for a Lateral Inflow Receiving Landscape","display_title":"Microwave Soil Moisture Better Predicts Vegetation","doi":"https://doi.org/10.3390/rs8050428","theme":"Land & ecosystems","color":"#c8df98","year":2016,"publication_date":"2016-05-20","authors":["Tiexi Chen","Tim McVicar","Guojie Wang","Xing Chen","Richard De Jeu","Yi Liu","Hong Shen","Fangmin Zhang","Albertus Dolman"],"summary":"This study compares satellite microwave soil moisture, gridded precipitation products, and land surface model outputs in predicting vegetation water availability and growth in northeastern Australia's lateral inflow landscapes. Findings reveal that satellite soil moisture better captures vegetation dynamics due to its longer memory and ability to reflect lateral soil water inflow, especially in clay-rich soils, compared to precipitation and model outputs which often underestimate soil moisture persistence and neglect flooding processes.","finding":"Satellite remotely sensed soil moisture has a longer memory and better predicts vegetation dynamics than precipitation or land surface models in lateral inflow receiving areas with clay-rich soils in northeastern Australia.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"Thenkabail Received: 6 March 2016; Accepted: 12 May 2016; Published: 20 May 2016 Abstract: To improve the understanding of water–vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive soil moisture anomalies. The precipitation anomalies disappeared within a month. In contrast, the soil moisture anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months.","verified_in_page":true},"topics":["soil moisture","remote sensing","vegetation dynamics","precipitation","land surface models","eco-hydrology","lateral inflow","soil properties"],"study_period":"December 2008 to May 2009","locations":[{"name":"northeastern Australia","kind":"region","role":"study region with lateral inflow receiving landscapes to assess vegetation-water relations using satellite and model data","river_name":null,"geocode_query":"northeastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Thenkabail Received: 6 March 2016; Accepted: 12 May 2016; Published: 20 May 2016 Abstract: To improve the understanding of water–vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive soil moisture anomalies. The precipitation anomalies disappeared within a month. In contrast, the soil moisture anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"REC sub-region, northeastern Australia","kind":"region","role":"rectangular sub-region selected for detailed analysis of soil moisture, precipitation, and vegetation dynamics","river_name":null,"geocode_query":"S19.25°–S22.75°; E137.5°–E144.5°, northeastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The selected rectangular sub-region (REC, (S19.25°–S22.75°; E137.5°–E144.5°) covering about 28,340 km2) and square sub-region (SQU, (S23.5°–S27.5°; E137.5°– E141.5°) being about 17,892 km2) are illustrated. In (a) the DEM of GTOPO30 data is used, and statistical characteristics of elevation in REC and SQU sub-regions, including minimum, maximum, mean, and standard deviation, are (87, 1136, 273, 126) and (44, 202, 87, 25) meters above mean sealevel, respectively. In (b) land cover is based on GlobCover2009 data from European Space Agency GlobCover Portal. More detail of the legend is listed in Table 1. 2.2. Datasets The satellite soil moisture product (ESA CCI soil moisture v. 0.20) was obtained from the European Space Agency Climate Change Initiative data portal (see http://www.esa-soilmoisture-cci.org/) [22,32,33], and was generated by merging active and passive microwave observations [34,35].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"SQU sub-region, northeastern Australia","kind":"region","role":"square sub-region located in Australia's Channel Country chosen for detailed study of lateral inflow effects on soil moisture and vegetation","river_name":null,"geocode_query":"S23.5°–S27.5°; E137.5°–E141.5°, northeastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The selected rectangular sub-region (REC, (S19.25°–S22.75°; E137.5°–E144.5°) covering about 28,340 km2) and square sub-region (SQU, (S23.5°–S27.5°; E137.5°– E141.5°) being about 17,892 km2) are illustrated. In (a) the DEM of GTOPO30 data is used, and statistical characteristics of elevation in REC and SQU sub-regions, including minimum, maximum, mean, and standard deviation, are (87, 1136, 273, 126) and (44, 202, 87, 25) meters above mean sealevel, respectively. In (b) land cover is based on GlobCover2009 data from European Space Agency GlobCover Portal. More detail of the legend is listed in Table 1. 2.2. Datasets The satellite soil moisture product (ESA CCI soil moisture v. 0.20) was obtained from the European Space Agency Climate Change Initiative data portal (see http://www.esa-soilmoisture-cci.org/) [22,32,33], and was generated by merging active and passive microwave observations [34,35].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Lake Eyre Basin","kind":"region","role":"hydrologic basin boundary illustrated to contextualize precipitation and soil moisture anomalies","river_name":null,"geocode_query":"Lake Eyre Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Figure 2. (a1–a6) Monthly anomalies of gridded precipitation (left column of panels, mm·month−1), (b1–b6) satellite soil moisture (middle column of panels, m3·m−3) and (c1–c6) NDVI (right column of panels, unitless) from December 2008 to May 2009. All are at 0.25° spatial resolution. The Lake Eyre Basin boundary is illustrated in subplot a1 (orange line). The detailed spatial-temporal patterns in sub-region SQU further demonstrate the different impacts of precipitation and soil moisture on NDVI (Figure 2). The flood extent was identified using MODIS OWL data (Figure 3). Surface water in this area mainly occurred after 17 January 2009 (Figure 3). There were no distinct precipitation events during February–May 2009 (Figure 2(a3–a6)). In Figure 2. (a1–a6) Monthly anomalies of gridded precipitation (left column of panels, mm ̈ month ́1 ), (b1–b6) satellite soil moisture (middle column of panels, m3 ̈ m ́3 ) and (c1–c6) NDVI (right column of panels, unitless) from December 2008 to May 2009.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.292389,-28.23501],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119370154,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Eyre, South Australia 5733, Australia","basin_id":5040491380,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":19208.6,"hydrology_source":"geofabric","site_id":"doc-ad8c6ef25ae1f83c4221e72e-site-3","true_coordinates":[137.2923893,-28.1750101],"spread_km":6.7},{"name":"Darwin, Northern Territory, Australia","kind":"settlement","role":"reference city location appearing on figures for spatial orientation","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Remote Sens. 2016, 8, 428 3 of 13 growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Remote Sens. 2016, 8, 428 3 of 13 a complex anastomosing river system with wide floodplain inundation (and subsequent vegetation growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Figure 1. Topography (a) and land cover (b) over northeastern Australia (S10°–S30°; E130°–E150°). Letters D, T, and A indicate the cities of Darwin, Townsville, and Alice Springs, respectively, which are identical in the following figures.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.844445,-12.523733],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-ad8c6ef25ae1f83c4221e72e-site-4","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Townsville, Queensland, Australia","kind":"settlement","role":"reference city location appearing on figures for spatial orientation","river_name":null,"geocode_query":"Townsville, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Remote Sens. 2016, 8, 428 3 of 13 growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Remote Sens. 2016, 8, 428 3 of 13 a complex anastomosing river system with wide floodplain inundation (and subsequent vegetation growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Figure 1. Topography (a) and land cover (b) over northeastern Australia (S10°–S30°; E130°–E150°). Letters D, T, and A indicate the cities of Darwin, Townsville, and Alice Springs, respectively, which are identical in the following figures.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.81366,-19.319878],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652811,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Townsville City QLD 4810, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-ad8c6ef25ae1f83c4221e72e-site-5","true_coordinates":[146.8136596,-19.2598781],"spread_km":6.7},{"name":"Alice Springs, Northern Territory, Australia","kind":"settlement","role":"reference city location appearing on figures for spatial orientation","river_name":null,"geocode_query":"Alice Springs, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Remote Sens. 2016, 8, 428 3 of 13 growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Remote Sens. 2016, 8, 428 3 of 13 a complex anastomosing river system with wide floodplain inundation (and subsequent vegetation growth) caused by lateral inflow following pluvial events in the headwaters (see Jarihani et al. [29,30] and the references therein). The topography is illustrated in Figure 1a using Global 30 Arc-Second Elevation (GTOPO30) [31]. Figure 1. Topography (a) and land cover (b) over northeastern Australia (S10°–S30°; E130°–E150°). Letters D, T, and A indicate the cities of Darwin, Townsville, and Alice Springs, respectively, which are identical in the following figures.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.880747,-23.758042],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-ad8c6ef25ae1f83c4221e72e-site-6","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Channel Country, Queensland, Australia","kind":"region","role":"hydrologically complex region with anastomosing river system and floodplains producing lateral inflow effects on soil moisture and vegetation","river_name":null,"geocode_query":"Channel Country, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Hence, in this paper, we provide direct evidence to answer the question “which of (i) remotely sensed soil moisture; (ii) gridded precipitation products; or (iii) land surface model outputs provides the optimal description of observed NDVI-based vegetation dynamics for a large regional inflow receiving landscape in northeastern Australia?” 2. Materials and Methods 2.1. Study Area The study region is northeastern Australia (Figure 1), in which two sub-regions (denoted as REC and SQU) were selected for detailed investigation. To maximize the difference between the precipitation and soil moisture, the southern sub-region (SQU) lies in Australia’s “channel country”, a complex anastomosing river system with wide floodplain inundation (and subsequent vegetation","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Lake Eyre floodplain, Queensland, Australia","kind":"region","role":"area of floodplain inundation documented via MODIS OWL data during study period relevant for lateral inflow and soil moisture persistence","river_name":null,"geocode_query":"Lake Eyre floodplain, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Mapping of surface water using the MODIS Open Water Likelihood (OWL) product from 26 December 2008 to 2 June 2009 using a threshold of 6%. Data in 18 February 2009 is not available due to cloud contamination. The seasonal patterns of the REC and SQU sub-regions are illustrated in Figure 4. Temporal patterns of precipitation over both sub-regions are similar, with clear rainy and dry seasons. Precipitation peaks in January, although the amounts received in these two sub-regions are quite Figure 3. Mapping of surface water using the MODIS Open Water Likelihood (OWL) product from 26 December 2008 to 2 June 2009 using a threshold of 6%. Data in 18 February 2009 is not available due to cloud contamination. The seasonal patterns of the REC and SQU sub-regions are illustrated in Figure 4. Temporal patterns of precipitation over both sub-regions are similar, with clear rainy and dry seasons.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Vertisol soil regions in northeastern Australia","kind":"landscape","role":"areas covered by clay-rich vertisols demonstrating high water holding capacity influencing soil moisture persistence and vegetation response","river_name":null,"geocode_query":"northeastern Australia vertisols, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-005","page":9,"quote":"Location of vertisols for the study region based on Harmonized World Soil Database. Vertisols cover 50.2% and 37.3% of the REC and SQU sub-regions, respectively. Four of the five selected land surface models underestimated the memory length of soil moisture, and all five models failed to represent the soil moisture anomalies over the lateral inflow receiving SQU sub-region (Figure 6). A potential reason is their lack of water transport process in the models. Figure 7. Location of vertisols for the study region based on Harmonized World Soil Database. Vertisols cover 50.2% and 37.3% of the REC and SQU sub-regions, respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Exact coordinates for sub-regions REC and SQU were derived from bounding box coordinates but no precise central points were given.","Study region scale is broad and lacks finer site-specific hydrological measurements.","Only satellite and model data products were used; local ground soil moisture point validations are referenced but not detailed in this text.","The temporal resolution is monthly with a focus on a six-month case study period in 2008–2009 limiting seasonal variability assessment."],"river_ids":[],"site_ids":["doc-ad8c6ef25ae1f83c4221e72e-site-3","doc-ad8c6ef25ae1f83c4221e72e-site-4","doc-ad8c6ef25ae1f83c4221e72e-site-5","doc-ad8c6ef25ae1f83c4221e72e-site-6"],"anchor":[133.880747,-23.758042],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040061670,5040067860,5040491370,5040491380],"main_basin_ids":[5040061670,5040067860,5040087590]},{"id":"doc-09a6878c9a1c48653bada820","title":"Wind speed variability over the Canary Islands, 1948–2014: focusing on trend differences at the land–ocean interface and below–above the trade-wind inversion layer","display_title":"Wind Speed Variability in Canary Islands","doi":"https://doi.org/10.1007/s00382-017-3861-0","theme":"Climate","color":"#98d4f1","year":2017,"publication_date":"2017-08-21","authors":["César Azorín-Molina","Melisa Menéndez","Tim R. McVicar","Adrián Acevedo","Sergio M. Vicente‐Serrano","Emilio Cuevas","Lorenzo Minola","Deliang Chen"],"summary":"This study analyzes wind speed trends and variability over the Canary Islands archipelago and the surrounding Eastern North Atlantic Ocean from 1948 to 2014, using homogenized land-based observations and high-resolution atmospheric hindcasts. It assesses differences in wind speed trends at the land–ocean interface and below and above the trade-wind inversion layer (TWIL). The research reveals significant decadal changes in wind speeds, a distinct seasonal pattern of strengthening and weakening trade winds below the TWIL, and opposing trends above the TWIL. Large-scale atmospheric circulation indices explain much of the wind speed variability observed.","finding":"The study found a widespread, significant decline in oceanic trade-wind speeds from 1948 to 2014, but no significant long-term trends from 1989 to 2014 at the land–ocean interface below the TWIL. Instead, a distinct seasonal pattern emerged, with strengthening trade winds in late spring and summer (significant in May and August) and weakening in winter, spring, and autumn (significant in April and September). Above the TWIL at the high-elevation Izaña Observatory, wind speeds exhibited opposite and significant increasing trends.","finding_evidence":{"passage_id":"p019-006","page":19,"quote":"For the common 1989–2014 period all SeaWind I, SeaWind II and the eight land stations below the trade-wind inversion layer showed no significant trends annually, but displayed a distinct seasonal pattern with a strengthening (late spring and summer; significant in May and August) and weakening (winter–spring– autumn; significant in April and September) of tradewinds. Above the trade-wind inversion layer in Izaña, free troposphere winds tended to significantly increase for almost all time-scales analysed. 2. Land observations resembled the same multi-decadal variability simulated over ocean, with a statistically significant agreement of the wind speed trends at the land–ocean interface below the trade-wind inversion layer. Above this inversion layer in Izaña, a decoupled variability and significant opposite trends of winds","verified_in_page":true},"topics":["atmospheric-circulation","wind-speed-trends","trade-wind-inversion-layer","climate-variability","land-ocean-interface","data-homogenization","seasonal-variability","topographic-effects"],"study_period":"1948–2014","locations":[{"name":"Canary Islands archipelago","kind":"region","role":"primary study region","river_name":null,"geocode_query":"Canary Islands, Eastern North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Hence our objectives are to: (1) report for the first time wind speed variability over the Canary Island and surrounding Eastern North Atlantic Ocean for 1948–2014; (2) simultaneously assess wind speed trend differences across the land–ocean interface, and below–above the TWIL; and (3) investigate the role played by changes in the large-scale atmospheric circulation on the spatio-temporal variability of wind speed, with focus on trade-winds. 2 Study area and datasets 2.1 Study area The Canary Islands are an archipelago located between 27.6°–29.4°N, and 13.3°–18.2°W, in the subtropical Eastern North Atlantic region (Macaronesia) greatly affected by the Azores high pressure system and associated trade-winds (Cropper and Hanna 2014), approximately 100-km west from the African continent (Fig. 1). All seven islands are volcanic in origin with very complex steep terrain (high surface roughness).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Izaña Atmospheric Observatory, Tenerife, Canary Islands, Spain","kind":"station","role":"high mountain weather station above trade-wind inversion layer, providing data above TWIL","river_name":null,"geocode_query":"Izaña Atmospheric Observatory, Tenerife, Canary Islands, Spain","latitude":28.31,"longitude":-16.5,"evidence":{"passage_id":"p003-001","page":3,"quote":"The observational land-based station network observes two key tropospheric layers, with: (1) eight lowelevation land-stations located near the coast and below the TWIL where trade-winds dominate; and (2) one mountain weather station above a quasi-permanent marine boundary layer in the Izaña Atmospheric Observatory at 2373 m a.s.l (http://izana.aemet.es/index.php?lang=en; last accessed 1 June 2017) where free-troposphere winds (almost permanent north-westerly flows above the TWIL) prevail (Cuevas et al. 2013). Therefore, wind speed within and above TWIL have been homogenized and analyzed separately. Observed wind speed datasets were subjected to a quality control and homogenization protocol to remove systematic errors (i.e., inhomogeneities) due to multiple causes (Pryor et al. 2009) and uncertainties due to the lack of metadata.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-16.4994264,28.3089862],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"38300, Santa Cruz de Tenerife, Spain","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-1"},{"name":"Eastern North Atlantic Ocean surrounding Canary Islands","kind":"other","role":"study area for oceanic wind speed hindcasts assessing marine wind speed trends","river_name":null,"geocode_query":"Eastern North Atlantic Ocean, near Canary Islands","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Thus, the observed wind speed dataset consists of nine homogenized series across the Canary Island archipelago (see Fig. 1), which is a reasonable number in terms of spatial data density for terrestrial stilling studies (1 station per 833 km2 ) compared to other studies (e.g., 1 station per 7384 km2 for Spain and Portugal in Azorin-Molina et al. 2016, and 1 station per 48,125 km2 for Australia in; McVicar et al. 2008). Azorin-Molina et al. (2016) concluded that few stations (i.e., ~5–10) are enough to capture the decadal variability and trends of wind speed.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"La Palma, Canary Islands, Spain","kind":"region","role":"island within Canary Islands archipelago with wind speed data stations and local wind trend analysis","river_name":null,"geocode_query":"La Palma, Canary Islands, Spain","latitude":28.63,"longitude":-17.76,"evidence":{"passage_id":"p004-005","page":4,"quote":"Table 1 Description of the land based stations across the Canary Islands archipelago (for locations see numbers in Fig. 1) and SeaWind I and II hindcast products For SeaWind products, elevation refers to the vertical distance above the ocean surface that wind speed is simulated # Id Station name/# grid points Latitude/ domain (decimalo) Longitude/ domain (decimalo) Elevation a.s.l. (m) Location Start date (mm/yyy) 1 C139E La Palma 28.63 −17.76 33 Coast 04/1970 2 C929I El Hierro 27.82 −17.89 32 Coast 12/1973 3 C429I Tenerife S 28.05 −16.56 64 Coast 07/1980 4 C447A Tenerife N 28.48 −16.33 632 Inland 01/1961 5 C449C SC Tenerife 28.46 −16.26 35 Coast 01/1943 6 C649I Gran Canaria 27.92 −15.39 24 Coast 01/1961 7 C249I Fuerteventura 28.44 −13.86 25 Coast 10/1969 8 C029O Lanzarote 28.95 −13.60 14 Coast 11/1972 9 C430E Izaña 28.31 −16.50 2373 Mountain 01/1916 – SeaWind I 700 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1948 – SeaWind II 2408 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1989","verified_in_page":true},"resolution_status":"resolved","coordinates":[-17.76,28.63],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-3"},{"name":"El Hierro, Canary Islands, Spain","kind":"region","role":"island within Canary Islands archipelago with wind speed data stations and local wind trend analysis","river_name":null,"geocode_query":"El Hierro, Canary Islands, Spain","latitude":27.82,"longitude":-17.89,"evidence":{"passage_id":"p004-005","page":4,"quote":"Table 1 Description of the land based stations across the Canary Islands archipelago (for locations see numbers in Fig. 1) and SeaWind I and II hindcast products For SeaWind products, elevation refers to the vertical distance above the ocean surface that wind speed is simulated # Id Station name/# grid points Latitude/ domain (decimalo) Longitude/ domain (decimalo) Elevation a.s.l. (m) Location Start date (mm/yyy) 1 C139E La Palma 28.63 −17.76 33 Coast 04/1970 2 C929I El Hierro 27.82 −17.89 32 Coast 12/1973 3 C429I Tenerife S 28.05 −16.56 64 Coast 07/1980 4 C447A Tenerife N 28.48 −16.33 632 Inland 01/1961 5 C449C SC Tenerife 28.46 −16.26 35 Coast 01/1943 6 C649I Gran Canaria 27.92 −15.39 24 Coast 01/1961 7 C249I Fuerteventura 28.44 −13.86 25 Coast 10/1969 8 C029O Lanzarote 28.95 −13.60 14 Coast 11/1972 9 C430E Izaña 28.31 −16.50 2373 Mountain 01/1916 – SeaWind I 700 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1948 – SeaWind II 2408 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1989","verified_in_page":true},"resolution_status":"resolved","coordinates":[-17.89,27.82],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-4"},{"name":"Tenerife, Canary Islands, Spain","kind":"region","role":"largest Canary Island with multiple land-based wind stations including Izaña Observatory","river_name":null,"geocode_query":"Tenerife, Canary Islands, Spain","latitude":28.31,"longitude":-16.5,"evidence":{"passage_id":"p007-001","page":7,"quote":"Therefore, Fig. 5 only looks at seasonal wind climatology for the SeaWind II product, also including mean wind statistics from land observations; Supplementary Figure S2 shows monthly wind climatologies. Annually (Fig. 4), mean wind speed ranges from ~6 to 9 m s−1 over the ocean, whereas over land (eight stations below the TWIL) complex surface roughness among other factors weakens wind speed to ~4–5 m s−1. Seasonally (Fig. 5), north-easterly trade-winds are higher in summer exceeding>10 m s−1 over oceans (~5–8 m s−1 over land), and weaker in winter-autumn oscillating around ~5–8 m s−1 for ocean (3–5 m s−1 for land).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-16.6291304,28.2915637],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tenerife, Santa Cruz de Tenerife, Spain","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-5"},{"name":"Gran Canaria, Canary Islands, Spain","kind":"region","role":"island in Canary Islands archipelago with wind speed data and orographic influence studied","river_name":null,"geocode_query":"Gran Canaria, Canary Islands, Spain","latitude":27.92,"longitude":-15.39,"evidence":{"passage_id":"p011-001","page":11,"quote":"This predominance of wind speed declines over ocean is not observed at the land stations annually, where half of the stations below the TWIL had opposite increasing trends. It is noticeable the differences observed on Tenerife island where all three stations below the TWIL reported negative trends whereas Izaña (located above the TWIL in the free troposphere) had a statistically significant positive trend. Seasonally (Fig. 9), a distinct pattern with a dominance of grid-cells showing negative trends for winter, spring and autumn is found, with more declining wind speed trends being statistically significant for the latter season. A major finding when compared to the 1948–2014 extent shown in Fig. 8, is the recent and widespread increase of Fig. 7 Annual and seasonal spatial distribution of the sign, magnitude and statistical significance of wind speed trends for SeaWind I (1948–2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-15.5474373,27.9202202],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gran Canaria, Las Palmas, Spain","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-6"},{"name":"Fuerteventura, Canary Islands, Spain","kind":"region","role":"island in Canary Islands archipelago with wind speed observations","river_name":null,"geocode_query":"Fuerteventura, Canary Islands, Spain","latitude":28.44,"longitude":-13.86,"evidence":{"passage_id":"p004-005","page":4,"quote":"Table 1 Description of the land based stations across the Canary Islands archipelago (for locations see numbers in Fig. 1) and SeaWind I and II hindcast products For SeaWind products, elevation refers to the vertical distance above the ocean surface that wind speed is simulated # Id Station name/# grid points Latitude/ domain (decimalo) Longitude/ domain (decimalo) Elevation a.s.l. (m) Location Start date (mm/yyy) 1 C139E La Palma 28.63 −17.76 33 Coast 04/1970 2 C929I El Hierro 27.82 −17.89 32 Coast 12/1973 3 C429I Tenerife S 28.05 −16.56 64 Coast 07/1980 4 C447A Tenerife N 28.48 −16.33 632 Inland 01/1961 5 C449C SC Tenerife 28.46 −16.26 35 Coast 01/1943 6 C649I Gran Canaria 27.92 −15.39 24 Coast 01/1961 7 C249I Fuerteventura 28.44 −13.86 25 Coast 10/1969 8 C029O Lanzarote 28.95 −13.60 14 Coast 11/1972 9 C430E Izaña 28.31 −16.50 2373 Mountain 01/1916 – SeaWind I 700 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1948 – SeaWind II 2408 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1989","verified_in_page":true},"resolution_status":"resolved","coordinates":[-13.86,28.44],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-7"},{"name":"Lanzarote, Canary Islands, Spain","kind":"region","role":"easternmost Canary Island with wind speed data stations","river_name":null,"geocode_query":"Lanzarote, Canary Islands, Spain","latitude":28.95,"longitude":-13.6,"evidence":{"passage_id":"p004-005","page":4,"quote":"Table 1 Description of the land based stations across the Canary Islands archipelago (for locations see numbers in Fig. 1) and SeaWind I and II hindcast products For SeaWind products, elevation refers to the vertical distance above the ocean surface that wind speed is simulated # Id Station name/# grid points Latitude/ domain (decimalo) Longitude/ domain (decimalo) Elevation a.s.l. (m) Location Start date (mm/yyy) 1 C139E La Palma 28.63 −17.76 33 Coast 04/1970 2 C929I El Hierro 27.82 −17.89 32 Coast 12/1973 3 C429I Tenerife S 28.05 −16.56 64 Coast 07/1980 4 C447A Tenerife N 28.48 −16.33 632 Inland 01/1961 5 C449C SC Tenerife 28.46 −16.26 35 Coast 01/1943 6 C649I Gran Canaria 27.92 −15.39 24 Coast 01/1961 7 C249I Fuerteventura 28.44 −13.86 25 Coast 10/1969 8 C029O Lanzarote 28.95 −13.60 14 Coast 11/1972 9 C430E Izaña 28.31 −16.50 2373 Mountain 01/1916 – SeaWind I 700 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1948 – SeaWind II 2408 [26.0, 31.0N] [12.0, 20.0W] 10 Ocean 01/1989","verified_in_page":true},"resolution_status":"resolved","coordinates":[-13.6,28.95],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1040034610,"main_basin_id":1040034610,"basin_region":"Africa","basin_area_km2":8752.8,"hydrology_source":"hydrobasins","site_id":"doc-09a6878c9a1c48653bada820-site-8"}],"limitations":["Incomplete specific coordinates for some study sites.","Data gaps and spatial heterogeneity in land wind speed station records.","Challenges in homogenizing historical wind speed datasets due to limited metadata.","Potential positive bias in older reanalysis boundary conditions used for hindcasts."],"river_ids":[],"site_ids":["doc-09a6878c9a1c48653bada820-site-1","doc-09a6878c9a1c48653bada820-site-3","doc-09a6878c9a1c48653bada820-site-4","doc-09a6878c9a1c48653bada820-site-5","doc-09a6878c9a1c48653bada820-site-6","doc-09a6878c9a1c48653bada820-site-7","doc-09a6878c9a1c48653bada820-site-8"],"anchor":[-13.86,28.44],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[1040034610],"main_basin_ids":[1040034610]},{"id":"doc-2984cf3bb48abf46cd0b316c","title":"Aquatic live animal radiotracing studies for ecotoxicological applications: Addressing fundamental methodological deficiencies","display_title":"Methods in Live Animal Aquatic Radiotracing","doi":"https://doi.org/10.1016/j.jenvrad.2017.05.017","theme":"Other science","color":"#d1b5e7","year":2017,"publication_date":"2017-06-17","authors":["Tom Cresswell","Marc Métian","Lisa A. Golding","Mike D. Wood"],"summary":"This study evaluates key methodological deficiencies in live aquatic animal radiotracing techniques used in ecotoxicology. Using gamma-emitting isotopes and live counting methods, the authors focus on factors such as animal rinsing to remove adsorbed contamination, reducing errors from animal movement during radioanalysis, and proper geometry corrections for converting counts to activity. Their findings stem from experimental radiotracer studies on six aquatic species including freshwater snails, prawns, shore crabs, oysters, turbots, and zebrafish. The paper underscores the need for improved and standardized protocols to ensure data accuracy, replicability, and quality control in live animal radiotracing studies, with implications for both laboratory and future field monitoring.","finding":"Three rinse cycles (15 rinses) of Milli-Q water are sufficient to remove ionic and nanoparticulate radiotracers adsorbed on the cuticle of live freshwater snails, enabling accurate quantification of internal bioaccumulated activity.","finding_evidence":{"passage_id":"p004-005","page":4,"quote":"Fig. 2 shows the reduction of radioactive nanoparticles after successive rinse cycles for five live snails relative to controls that were not rinsed. The non-rinsed snails did not lose any radioactive nanoparticles suggesting minimal desorption or egestion of any ingested nanoparticles while out of water. The rinsed snails continued to lose radioactivity up to three rinse cycles (i.e. 15 cumulative rinses), after which, there was no significant reduction in 141Ce radioactivity up to six consecutive rinse cycles (Student's ttest; p 1⁄4 0.45). This suggests that three rinse cycles was sufficient to remove the nanoparticles adsorbed to the cuticle. Interestingly, six rinse cycles did not remove the same proportion of radioactivity associated with nanoparticles as for the ionic cerium trials conducted with empty snail shells. This was likely due to the ingestion of 141CeO2 nanoparticles by the live snails.","verified_in_page":true},"topics":["radioecology","bioaccumulation","methodology","radiotracing"," aquatic ecotoxicology","animal rinsing","gamma spectroscopy","geometry correction"],"study_period":null,"locations":[{"name":"Kirrawee DC, New South Wales","kind":"facility","role":"Location of Australian Nuclear Science and Technology Organisation (ANSTO) used for production of radioisotopes","river_name":null,"geocode_query":"Kirrawee DC, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Wood d a Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia b International Atomic Energy Agency Environment Laboratories (IAEA-EL), Radioecology Laboratory, 4a Quai Antoine Ier, MC-98000, Principality of Monaco, Monaco c Centre for Environmental Contaminants Research, CSIRO Land and Water, Lucas Heights, NSW, 2234, Australia d Ecosystems & Environment Research Centre, School of Environment & Life Sciences, University of Salford, Salford M5 4WT, United Kingdom article info Article history: Received 18 December 2016 Received in revised form 8 May 2017 Accepted 31 May 2017 Available online xxx Keywords: Metal Method Nanomaterial Radiotracing Invertebrate Vertebrate Live-counting abstract The use of live animal gamma radioisotope tracer techniques in the field of ecotoxicology allows laboratory studies to accurately monitor contaminant biokinetics in real time for an individual organism.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Principality of Monaco","kind":"region","role":"Location of International Atomic Energy Agency Environment Laboratories involved in radiotracer studies","river_name":null,"geocode_query":"Principality of Monaco","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Wood d a Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia b International Atomic Energy Agency Environment Laboratories (IAEA-EL), Radioecology Laboratory, 4a Quai Antoine Ier, MC-98000, Principality of Monaco, Monaco c Centre for Environmental Contaminants Research, CSIRO Land and Water, Lucas Heights, NSW, 2234, Australia d Ecosystems & Environment Research Centre, School of Environment & Life Sciences, University of Salford, Salford M5 4WT, United Kingdom article info Article history: Received 18 December 2016 Received in revised form 8 May 2017 Accepted 31 May 2017 Available online xxx Keywords: Metal Method Nanomaterial Radiotracing Invertebrate Vertebrate Live-counting abstract The use of live animal gamma radioisotope tracer techniques in the field of ecotoxicology allows laboratory studies to accurately monitor contaminant biokinetics in real time for an individual organism.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Lucas Heights, New South Wales","kind":"region","role":"Location of CSIRO Land and Water centre involved in aquatic radiotracer studies","river_name":null,"geocode_query":"Lucas Heights, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Wood d a Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia b International Atomic Energy Agency Environment Laboratories (IAEA-EL), Radioecology Laboratory, 4a Quai Antoine Ier, MC-98000, Principality of Monaco, Monaco c Centre for Environmental Contaminants Research, CSIRO Land and Water, Lucas Heights, NSW, 2234, Australia d Ecosystems & Environment Research Centre, School of Environment & Life Sciences, University of Salford, Salford M5 4WT, United Kingdom article info Article history: Received 18 December 2016 Received in revised form 8 May 2017 Accepted 31 May 2017 Available online xxx Keywords: Metal Method Nanomaterial Radiotracing Invertebrate Vertebrate Live-counting abstract The use of live animal gamma radioisotope tracer techniques in the field of ecotoxicology allows laboratory studies to accurately monitor contaminant biokinetics in real time for an individual organism.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.987244,-34.11064],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054723,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lucas Heights NSW 2234, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2984cf3bb48abf46cd0b316c-site-2","true_coordinates":[150.9872444,-34.0506403],"spread_km":6.7},{"name":"Salford, United Kingdom","kind":"region","role":"Location of University of Salford involved in aquatic radiotracer studies","river_name":null,"geocode_query":"Salford, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Wood d a Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia b International Atomic Energy Agency Environment Laboratories (IAEA-EL), Radioecology Laboratory, 4a Quai Antoine Ier, MC-98000, Principality of Monaco, Monaco c Centre for Environmental Contaminants Research, CSIRO Land and Water, Lucas Heights, NSW, 2234, Australia d Ecosystems & Environment Research Centre, School of Environment & Life Sciences, University of Salford, Salford M5 4WT, United Kingdom article info Article history: Received 18 December 2016 Received in revised form 8 May 2017 Accepted 31 May 2017 Available online xxx Keywords: Metal Method Nanomaterial Radiotracing Invertebrate Vertebrate Live-counting abstract The use of live animal gamma radioisotope tracer techniques in the field of ecotoxicology allows laboratory studies to accurately monitor contaminant biokinetics in real time for an individual organism.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.2900071,53.4872927],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines 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region; Global experimental (common model in ecotoxicological studies on fish) Sydney Rock Oyster Saccostrea glomerata 78 53 23 55z Australia and New Zealand Turbot Scophthalmus maximus 35 5 7.5 0.294z Western Atlantic and Mediterranean Sea ydry weight; z fresh weight. 2 T.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Eastern Australia","kind":"region","role":"Distribution area of freshwater prawn Macrobrachium australiense used in radiotracer studies","river_name":null,"geocode_query":"Eastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Common name Genus and Species Length (mm) Width (mm) Depth (mm) Mass 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Australia, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-2984cf3bb48abf46cd0b316c-site-5","true_coordinates":[148.3716902,-27.6728168],"spread_km":6.7},{"name":"Temperate eastern Australia","kind":"region","role":"Distribution area of spotted smooth shore crab Paragrapsus laevis studied by radiotracing","river_name":null,"geocode_query":"Temperate eastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Common name Genus and Species Length (mm) Width (mm) Depth (mm) Mass (g) Distribution New Zealand Mud Snail Potamopyrgus antipodarum 5 1.5 1.5 0.0042y Native to New Zealand; invasive in Asia, Europe, North America Freshwater Prawn Macrobrachium australiense 43 7.5 7.5 0.72z Eastern Australia Spotted Smooth Shore Crab Paragrapsus laevis 17 21 15 6.6z Temperate eastern Australia Zebrafish Danio rerio. 75 55 8 12z Native to Himalayan region; Global experimental (common model in ecotoxicological studies on fish) Sydney Rock Oyster Saccostrea glomerata 78 53 23 55z Australia and New Zealand Turbot Scophthalmus maximus 35 5 7.5 0.294z Western Atlantic and Mediterranean Sea ydry weight; z fresh weight. 2 T.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.303941,-27.672817],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677010,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eastern states of Australia, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-2984cf3bb48abf46cd0b316c-site-6","true_coordinates":[148.3716902,-27.6728168],"spread_km":6.7},{"name":"Himalayan region","kind":"region","role":"Native region of zebrafish 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Zealand; invasive in Asia, Europe, North America Freshwater Prawn Macrobrachium australiense 43 7.5 7.5 0.72z Eastern Australia Spotted Smooth Shore Crab Paragrapsus laevis 17 21 15 6.6z Temperate eastern Australia Zebrafish Danio rerio. 75 55 8 12z Native to Himalayan region; Global experimental (common model in ecotoxicological studies on fish) Sydney Rock Oyster Saccostrea glomerata 78 53 23 55z Australia and New Zealand Turbot Scophthalmus maximus 35 5 7.5 0.294z Western Atlantic and Mediterranean Sea ydry weight; z fresh weight. 2 T.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Atlantic and Mediterranean Sea","kind":"region","role":"Distribution area of turbot Scophthalmus maximus used in aquatic 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These exposures were conducted in synthetic, medium hardness water based on a USEPA recipe (USEPA, 2002) and in filtered (0.45 mm) local river water. Both exposure media were at comparable pH, hardness, total dissolved solids and alkalinity. To determine the cuticle-only adsorption of ionic cerium, nine empty snail shells (to ensure no bioaccumulation) were exposed to 82 mg Ce(III) in 10 mL of each media (i.e. synthetic water and river water; n 1⁄4 9 in each) for 2 h. The shells were then removed from the exposure solutions, individually transferred to 6 mL polycarbonate vials and radioanalysed. The shells were subsequently rinsed with one of the following rinse media (representing 1 rinse cycle) using wash bottles: 1) Milli-Q rinse, which consisted of 5 2.5 mL flushings of Milli-Q water; 2) 50 mM EDTA rinse, which consisted of 3 1.5 mL flushings of 50 mM EDTA and 2 1.5 mL flushing of Milli-Q water; 3) 100 mM EDTA rinse conducted as per the 50 mM EDTA rinse.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sydney Basin, New South Wales, Australia","kind":"region","role":"Region of origin for synthetic freshwater exposure medium used in experiments simulating natural conditions","river_name":null,"geocode_query":"Sydney Basin, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"neutron bombardment. These exposures were conducted in synthetic, medium hardness water based on a USEPA recipe (USEPA, 2002) and in filtered (0.45 mm) local river water. Both exposure media were at comparable pH, hardness, total dissolved solids and alkalinity. To determine the cuticle-only adsorption of ionic cerium, nine empty snail shells (to ensure no bioaccumulation) were exposed to 82 mg Ce(III) in 10 mL of each media (i.e. synthetic water and river water; n 1⁄4 9 in each) for 2 h. The shells were then removed from the exposure solutions, individually transferred to 6 mL polycarbonate vials and radioanalysed. The shells were subsequently rinsed with one of the following rinse media (representing 1 rinse cycle) using wash bottles: 1) Milli-Q rinse, which consisted of 5 2.5 mL flushings of Milli-Q water; 2) 50 mM EDTA rinse, which consisted of 3 1.5 mL flushings of 50 mM EDTA and 2 1.5 mL flushing of Milli-Q water; 3) 100 mM EDTA rinse conducted as per the 50 mM EDTA rinse.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.207684,-33.92225],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2984cf3bb48abf46cd0b316c-site-11","true_coordinates":[151.207684,-33.8622503],"spread_km":6.7}],"limitations":["Exact coordinates for study locations and species native ranges were not provided in the source document.","Broader regional geographies used where specific sites were unspecified.","Some facility locations are given at city or institutional level without precise GPS coordinates."],"river_ids":[],"site_ids":["doc-2984cf3bb48abf46cd0b316c-site-2","doc-2984cf3bb48abf46cd0b316c-site-3","doc-2984cf3bb48abf46cd0b316c-site-5","doc-2984cf3bb48abf46cd0b316c-site-6","doc-2984cf3bb48abf46cd0b316c-site-7","doc-2984cf3bb48abf46cd0b316c-site-9","doc-2984cf3bb48abf46cd0b316c-site-11"],"anchor":[83.9310039,28.5983159],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040048790,4040960480,5040070710,5040511570],"main_basin_ids":[2040048790,4040025450,5040070710,5040073410]},{"id":"doc-4284636f963c21c5c0d559dc","title":"Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer","display_title":"Model-based CPT data for aquifer mapping in Belgium","doi":"https://doi.org/10.1371/journal.pone.0176656","theme":"Groundwater","color":"#a8b9ff","year":2017,"publication_date":"2017-05-03","authors":["Bart Rogiers","Dirk Mallants","Okke Batelaan","Matej Gedeon","Marijke Huysmans","Alain Dassargues"],"summary":"This study developed and applied a model-based clustering methodology to classify cone penetration test (CPT) data, enabling automated lithostratigraphic mapping of a heterogeneous sedimentary aquifer system in northern Belgium. Using a large CPT dataset (~60 km2) and borehole sediment properties from the Nete basin, the authors compared traditional empirical soil behaviour type (SBT) classifications with site-specific model-based and x-means clustering approaches. Model-based clustering incorporating stratigraphic depth (zstrat) yielded superior and more detailed identification of lithostratigraphic units, especially the critical Kasterlee Clay aquitard, compared to conventional or x-means methods. Automated mapping of the aquitard top boundary closely matched manual interpretations, offering a more objective and efficient approach for regional geomapping and hydrogeological characterization.","finding":"Model-based clustering of CPT data, especially when including stratigraphic depth, provides a more accurate, detailed, and spatially consistent classification of lithostratigraphic units—including key aquitard layers—in heterogeneous sedimentary aquifers than traditional classification charts or x-means clustering.","finding_evidence":{"passage_id":"p019-001","page":19,"quote":"On the other hand, the automatic approach is more objective than the manual approach, and consistently always uses the same criterion for detecting the lithostratigraphic boundary. More detailed investigations, e.g. cored boreholes, are needed to discriminate between both approaches in the areas with the largest misfit. Conclusion We have shown that model-based SBT classifications of CPT data can be useful for regional lithostratigraphic mapping. The obtained SBT classes provide more detailed information than those obtained with frequently used deterministic unsupervised clustering algorithms like kand x-means clustering. Moreover, the obtained classification better honours the intrinsic classes within the data, in contrast to the classical literature SBT classification charts.","verified_in_page":true},"topics":["soil-behaviour-type-classification","sedimentary-aquifers","cone-penetration-testing","automated-lithostratigraphic-mapping","model-based-clustering","hydrogeological-characterization","spatial-variability","geophysical-data-analysis"],"study_period":null,"locations":[{"name":"Nete basin, northern Belgium","kind":"basin","role":"primary study groundwater basin characterized by CPT data and borehole cores","river_name":null,"geocode_query":"Nete basin, Flanders, Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Fissile Material), reaching depths of up to 40 to 50 m has been carried out in 2008–2010 within the Nete basin, north Belgium (Fig 1A and 1B) [34]. A large amount of hydrogeological information has been collected in an area of about 60 km2 (and permission was granted by ONDRAF/NIRAS for its use in this work), including nearly 400 m of continuous borehole cores, wireline logs from boreholes including natural gamma radiation and electrical resistivity, about 200 CPTs on a quasi-regular 600x600 m sampling grid and about 90 on a finer 30x30 m grid, and various hydrogeological measurements on undisturbed cores (Fig 1C and 1D) [34]. Since most of the data originate from standard CPTs while trial corrections for the small number of CPTu (with pore pressure registration) tests proved to be insignificant (mainly due to the shallow depths involved), the corrected cone tip resistance formulation using pore water pressure (Eq 2) is not applied in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[4.423302571415447,51.07362054891473],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-4284636f963c21c5c0d559dc-site-0"},{"name":"Mol-Dessel area, Flanders, Belgium","kind":"landscape","role":"hydrogeological characterization site including CPT and extensive borehole data","river_name":null,"geocode_query":"Mol-Dessel area, Flanders, Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Fissile Material), reaching depths of up to 40 to 50 m has been carried out in 2008–2010 within the Nete basin, north Belgium (Fig 1A and 1B) [34]. A large amount of hydrogeological information has been collected in an area of about 60 km2 (and permission was granted by ONDRAF/NIRAS for its use in this work), including nearly 400 m of continuous borehole cores, wireline logs from boreholes including natural gamma radiation and electrical resistivity, about 200 CPTs on a quasi-regular 600x600 m sampling grid and about 90 on a finer 30x30 m grid, and various hydrogeological measurements on undisturbed cores (Fig 1C and 1D) [34]. Since most of the data originate from standard CPTs while trial corrections for the small number of CPTu (with pore pressure registration) tests proved to be insignificant (mainly due to the shallow depths involved), the corrected cone tip resistance formulation using pore water pressure (Eq 2) is not applied in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[5.029925,51.269217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2480 Dessel, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-4284636f963c21c5c0d559dc-site-1","true_coordinates":[5.1129213,51.2392168],"spread_km":6.7},{"name":"Quaternary and Neogene Formations, northern Belgium","kind":"region","role":"lithostratigraphic units studied via CPT and borehole sediment analysis","river_name":null,"geocode_query":"Northern Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The latter allows comparison between the CPT data and SBT classifications to the sediment properties obtained from the borehole cores and wireline logs. The local lithostratigraphic succession consists of, from top to bottom, various Quaternary deposits, the Mol Upper Sands, the Mol Lower Sands, the Kasterlee Sands, the Kasterlee Clay, the Diest Clayey Top, and the Diest Sands (Figs 2A, 3, and 4A). The Quaternary sediments mainly consist of different phases of eolian deposits and to a lesser degree alluvial deposits from the Nete rivers [40, 41]. The Pliocene Mol Formation consists of white, coarse and medium fine sands. It is sometimes lignitic and can contain some lenses of micaceous clay [42]; only the latter has been reported in the current study area. The bottom part of the ~20 m thick formation has low levels Fig 1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Kasterlee Clay aquitard, northern Belgium","kind":"landscape","role":"key aquitard layer distinguished and mapped by CPT classification in the study area","river_name":null,"geocode_query":"Kasterlee Clay, Northern Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"(from Quaternary sands to Kasterlee Sands) and lower aquifer sediments (from Diest Clayey Top down), separated by the Kasterlee Clay aquitard which has the overall lowest Qt values (thin blue layer in Fig 4B). On the basis of the Fr values alone the most clay-rich layers, i.e. Kasterlee Clay and the underlying Diest Clayey Top, cannot be distinguished easily. Conversely, visual separation of Quaternary sands and Mol Upper Sands on the basis of Fr values becomes more apparent in the NE section of the data panel where the lowest recorded Fr values occur. Previously Schiltz [17, 18] manually delineated all the lithostratigraphic boundaries except those for Quaternary—Mol Upper and Mol Lower—Kasterlee Sands horizons, using this CPT data set and the measured (qc and fs) and derived (Rf, see Eq 1) parameters combined with the SBT classification of Robertson et al. [4]. Continuous 2D maps of these derived lithostratigraphic boundaries were obtained by universal kriging [16].","verified_in_page":true},"resolution_status":"resolved","coordinates":[4.9664674,51.2408811],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2460 Kasterlee, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-4284636f963c21c5c0d559dc-site-3"},{"name":"Mol Upper and Lower Sands, northern Belgium","kind":"landscape","role":"major sandy lithostratigraphic units characterized and discriminated in CPT dataset","river_name":null,"geocode_query":"Mol Formation, Flanders, Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"of glauconite (< 2%) [34]. In the current study area, the Pliocene Mol Formation is divided into the Mol Upper and Mol Lower Sands [34]. The latter are very well sorted, finer and darker in colour, while the former are moderately to well sorted medium sands with a basal gravel layer. Because of the high siliceous content of this sand (99% SiO2) [43], it is being mined for various industrial uses [44]. The Miocene Kasterlee Formation consists of a relatively homogeneous fine, micaceous, slightly glauconitic, sandy upper part [39], and a very heterogeneous alternation of clay lenses (clay contents of up to 40%) and sand banks in the lower 5–7 m [34, 45, 38]. The more homogeneous upper part (~ 1.5–6 m thick) is referred to as Kasterlee Sands while the heterogeneous clay-rich lower part is named Kasterlee Clay. The Diest Formation consists of grey-green to brownish, mostly coarse and locally clayey glauconiferous sand, often with sandstone layers [39].","verified_in_page":true},"resolution_status":"resolved","coordinates":[5.1151634,51.1839796],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2400 Mol, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-4284636f963c21c5c0d559dc-site-4"},{"name":"Diest Clayey Top and Diest Sands, northern Belgium","kind":"landscape","role":"lower aquifer lithostratigraphic units analyzed and classified by CPT data","river_name":null,"geocode_query":"Diest Formation, Northern Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"of glauconite (< 2%) [34]. In the current study area, the Pliocene Mol Formation is divided into the Mol Upper and Mol Lower Sands [34]. The latter are very well sorted, finer and darker in colour, while the former are moderately to well sorted medium sands with a basal gravel layer. Because of the high siliceous content of this sand (99% SiO2) [43], it is being mined for various industrial uses [44]. The Miocene Kasterlee Formation consists of a relatively homogeneous fine, micaceous, slightly glauconitic, sandy upper part [39], and a very heterogeneous alternation of clay lenses (clay contents of up to 40%) and sand banks in the lower 5–7 m [34, 45, 38]. The more homogeneous upper part (~ 1.5–6 m thick) is referred to as Kasterlee Sands while the heterogeneous clay-rich lower part is named Kasterlee Clay. The Diest Formation consists of grey-green to brownish, mostly coarse and locally clayey glauconiferous sand, often with sandstone layers [39].","verified_in_page":true},"resolution_status":"resolved","coordinates":[5.055685599999999,50.9794615],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Diest, Belgium","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-4284636f963c21c5c0d559dc-site-5"}],"limitations":["Exact coordinates of study area and CPT sites are not provided in text.","Study limited to unconsolidated sandy sediments in a regional Belgian basin; findings may not generalize to other lithologies or regions.","Automated lithostratigraphic mapping tested on a single boundary (top of aquitard); multi-boundary mapping untested.","Data density sensitivity of model-based clustering required uniform stratigraphic depth sampling to avoid bias.","Manual interpretations used as reference for mapping; actual ground truth validation is indirect through borehole data."],"river_ids":[],"site_ids":["doc-4284636f963c21c5c0d559dc-site-0","doc-4284636f963c21c5c0d559dc-site-1","doc-4284636f963c21c5c0d559dc-site-3","doc-4284636f963c21c5c0d559dc-site-4","doc-4284636f963c21c5c0d559dc-site-5"],"anchor":[5.1151634,51.1839796],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040022160],"main_basin_ids":[2040022160]},{"id":"doc-537767ce17cf2a354c190be6","title":"Assessment of an ensemble seasonal streamflow forecasting system for Australia","display_title":"Continent-wide Seasonal Streamflow Forecasting in Australia","doi":"https://doi.org/10.5194/hess-21-6007-2017","theme":"Flow & ecology","color":"#80edc2","year":2017,"publication_date":"2017-11-30","authors":["James C. Bennett","Quan J. Wang","David E. Robertson","Andrew Schepen","Ming Li","Kelvin Michael"],"summary":"This study assesses a new streamflow forecasting approach called forecast guided stochastic scenarios (FoGSS) across 63 Australian catchments, including 21 ephemeral rivers. FoGSS uses calibrated seasonal climate forecasts to drive rainfall-runoff models combined with a multi-stage hydrological error model to generate reliable, unbiased forecasts out to 12 months. The system generally provides skilful and reliable forecasts in all but the driest catchments, where the method underestimates zero-flow occurrences, leading to negative skill and bias. The study experiments with alternative rainfall forcings, hydrological models, and Bayesian priors to improve performance, finding that while including forecast rainfall adds skill especially for longer accumulations, the use of a Bayesian prior to nudge forecasts toward climatology reduces bias in dry catchments.","finding":"FoGSS is a viable, skilful seasonal streamflow forecasting system for Australia that outperforms traditional historical inflow scenarios in most catchments except the very dry ones, where forecast reliability suffers due to underestimation of zero-flow events in the error model.","finding_evidence":{"passage_id":"p009-002","page":9,"quote":"We will see in the following experiments that this can have a particularly strong influence on bias. www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst. Sci., 21, 6007–6030, 2017","verified_in_page":true},"topics":["ensemble streamflow forecasting","forecast skill","forecast reliability","hydrological modelling","climate forecast calibration","ephemeral rivers","Bayesian prior estimation","rainfall-runoff modelling"],"study_period":"1982–2010","locations":[{"name":"Australia","kind":"region","role":"continent-wide study area encompassing diverse climates and catchments","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"The alpha index essentially reflects the divergence of PIT values from the 1–1 line in PIT plots, ranging from 1 (perfectly reliable) to 0 (worst reliability). 3.3 Catchments and data We assess FoGSS forecasts on 63 Australian catchments ranging in size from < 100 km2 to > 200 000 km2 (Appendix A). Catchments are distributed across the continent, encompassing temperate, desert, subtropical and tropical climates. Rainfall and potential evaporation data are taken from the gridded AWAP dataset (http://www.bom.gov.au/jsp/ awap), which interpolates gauged observations with a Barnes successive correction analysis (Jones et al., 2009; Raupach et al., 2008). Streamflow data are mainly from gauges, but we have also included several “inflow sites”, which are not directly gauged. The inflow site records give total inflow to storages, and are calculated from a combination of streamflow gauge records, storage levels, and discharge from storages.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Fitzroy River, Western Australia, Australia","kind":"river","role":"case study catchment—ephemeral river with intermittent flow tested for forecast performance","river_name":"Fitzroy River","geocode_query":"Fitzroy River, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.138927,-18.541948],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":102217189,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-1","true_coordinates":[125.138927,-18.481948],"spread_km":6.7},{"name":"Ranken River, Northern Territory, Australia","kind":"river","role":"case study catchment—very dry ephemeral river with frequent zero flows, highlighting forecasting challenges","river_name":"Ranken River","geocode_query":"Ranken River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.6801276,-20.6009574],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115717405,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"AfonRanken, Ranken NT 4825, Australia","basin_id":5040324520,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":51614.6,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-2"},{"name":"Herbert River, Queensland, Australia","kind":"river","role":"case study catchment—perennial river used to exemplify successful forecasting by FoGSS","river_name":"Herbert River","geocode_query":"Herbert River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.2556205,-17.6925324],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Herbert River Rd, Innot Hot Springs QLD 4872, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-3"},{"name":"Lake Eppalock inflows (Campaspe River), Victoria, Australia","kind":"other","role":"case study inflow site representing seasonal ephemeral inflow data for forecast validation","river_name":null,"geocode_query":"Lake Eppalock, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.51633,-36.8671619],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43663345,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Eppalock VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-4"},{"name":"Goobarragandra River, New South Wales, Australia","kind":"river","role":"case study catchment—perennial river with strong catchment memory, used to demonstrate FoGSS skill","river_name":"Goobarragandra River","geocode_query":"Goobarragandra River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.46694456000012,-35.44138884399996],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43669820,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-5"},{"name":"Ringarooma River, Tasmania, Australia","kind":"river","role":"case study catchment—alpine, temperate perennial river used to illustrate forecast skill and catchment memory effects","river_name":"Ringarooma River","geocode_query":"Ringarooma River, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Like all northern, tropical regions in Australia, the Fitzroy receives most rainfall in the monsoon period (November–March), and very little rainfall at other times of the year. – Ranken River (Northern Territory): an extremely dry catchment that ceases to flow for long periods, flowing regularly only in March. The Ranken River can record zero flows at any time of year, and is usually dry from April to December. Over the period 1982–2009, the river never flowed in September. – Herbert River (Queensland): perennial river that receives the bulk of its rainfall in the monsoon period (November–March). – Lake Eppalock inflows (Victoria): Lake Eppalock receives inflow from the temperate and seasonally ephemeral Campaspe River, largely during July– November. This is a high-quality inflow series synthesized from stream gauge and storage level records, and often receives zero inflow in late summer to early autumn (January–April). www.hydrol-earth-syst-sci.net/21/6007/2017/ Hydrol. Earth Syst.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.87615691576,-41.08431405307187],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31292824,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ringarooma River, Tasmania, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-537767ce17cf2a354c190be6-site-6"}],"limitations":["Coordinates for catchments and gauges are not provided and thus latitude and longitude are set to null.","The study uses continental and catchment-scale regions which may mask local hydrological complexities.","The forecasting system underestimates zero-flow events in very dry catchments, limiting reliability there.","The study period for climate and streamflow data is limited to 1982–2010, restricting longer-term validation.","Some catchments include composite inflow sites rather than direct gauges, potentially affecting data homogeneity."],"river_ids":["43640281","43642059","43642266","43642779","43643648","43644594","43644625","43644649","43644773","43644909","43644980","43645634","43645783","43645883","43646211","43646323","43652598","ne-491"],"site_ids":["doc-537767ce17cf2a354c190be6-site-1","doc-537767ce17cf2a354c190be6-site-2","doc-537767ce17cf2a354c190be6-site-3","doc-537767ce17cf2a354c190be6-site-4","doc-537767ce17cf2a354c190be6-site-5","doc-537767ce17cf2a354c190be6-site-6"],"anchor":[144.51633,-36.8671619],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040059440,5040067860,5040077640,5040324520,5040591050],"main_basin_ids":[5040059440,5040067860,5040073410,5040077640,5040087590]},{"id":"doc-61536a49907deb2ec04ccfc6","title":"A systematic review of rodent pest research in Afro-Malagasy small-holder farming systems: Are we asking the right questions?","display_title":"Rodent Pest Research in Afro-Malagasy Farming","doi":"https://doi.org/10.1371/journal.pone.0174554","theme":"Land & ecosystems","color":"#c8df98","year":2017,"publication_date":"2017-03-30","authors":["Lourens H. Swanepoel","Corrie M. Swanepoel","Peter R. Brown","Seth J. Eiseb","Steven M. Goodman","Mark Keith","Frikkie Kirsten","Herwig Leirs","Themb’alilahlwa A. M. Mahlaba","Rhodes H. Makundi","Phanuel Malebane","Emil F. von Maltitz","Apia W. Massawe","Ara Monadjem","Loth S. Mulungu","Grant R. Singleton","Peter J. Taylor","Voahangy Soarimalala","Steven R. Belmain"],"summary":"This systematic review examined rodent pest research affecting small-holder farming systems across Africa and Madagascar from 1910 to 2015. It analyzed 162 publications, highlighting that rodent pests significantly impact agriculture, causing median crop losses up to 46% during seedling stages and about 15% at maturity. Research is concentrated in few countries, especially Tanzania which emphasizes applied research. The review found scarce empirical data on effectiveness of rodent control methods, indicating a need for more sustainable management studies focused on reducing rodent damage to improve food security.","finding":"Rodent pests cause substantial crop damage to Afro-Malagasy small-holder farms, with median losses of 46% at seedling stages and 15% during mature crop stages, but there are few rigorous studies on the effectiveness of control interventions.","finding_evidence":{"passage_id":"p008-000","page":8,"quote":"(Table 1) of which Mastomys spp. (57% of studies), Arvicanthis spp. (34%) and Rattus spp. (31%; Table 1) were the most important species. Crop impact Extracting meaningful information on crop losses caused by pest rodents proved to be challenging. Nonetheless, 34% of studies provided some data on crop damage. From these studies, we excluded the review and interview studies since these did not always describe their method of damage estimation, which included several interview studies where damage were quantified by severity, rather than giving damage estimates. There were large variations in crop loss estimates between the different growth stages and storage, as well as between losses reported as range values or point estimates (Fig 5A & 5B). Median crop losses were significantly different between crop stages and storage (H = 23.25, df = 2, p<0.0001) and the highest losses were recorded during the seedling stage (Fig 5B).","verified_in_page":true},"topics":["rodent pest ecology","crop damage assessment","pest management","small-holder agriculture","agroecology","food security","ecological control","systematic review"],"study_period":"1910-2015","locations":[{"name":"Tanzania","kind":"region","role":"Primary location of rodent pest applied research and long-term study site","river_name":null,"geocode_query":"Tanzania","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"but small increase in publication rate over the time period (Mann-Kendall test: tau = 0.369, p < 0.0001; Fig 2). Whilst rodent pest research has been conducted across much of the African continent (32 out of 48, countries (including Madagascar) registered at least one relevant study), there was a disparity in the number of studies per country (χ2 18 = 205.72, p < 0.0001; Fig 3). Research was focused in nodes in East and West Africa. Specifically four countries; Tanzania (24.69%), Ethiopia (8.64%), Nigeria (8.64%), and Kenya (8.02%), accounted for 50% of all rodent pest research on the continent (Fig 3). The high research intensity in Tanzania is due to the Pest Management Centre at the Sokoine University of Agriculture, with a strong focus on rodent pests in agricultural systems. Similarly, individual studies per country clustered around established research sites (Fig 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Nigeria","kind":"region","role":"Key country with notable rodent pest research","river_name":null,"geocode_query":"Nigeria","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"In parts of South America, native rodents cause crop damage varying between 5 and 90% of total production [17]. Post-harvest losses caused by rodents in grain stores add around 5–14% each year to the losses of small-holder families [18, 19]. Meerburg et al. [20] conservatively estimated that if rodent losses to rice production could be reduced by 5% this would save 70 million tonnes of rice, which is sufficient to provide the annual food consumption for almost 280 million people in developing countries, i.e. enough to feed 34% of the total undernourished people in the world. Thus, controlling rodent numbers to prevent subsequent losses remains one of the key strategies to secure long-term food security, agro-ecological sustainability and economic development, especially among small-holder farmers. Rodents are also an important public health issue [20, 21], but this aspect is not covered in this review.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Ethiopia","kind":"region","role":"Important location with studies on rodent crop damage and ecology","river_name":null,"geocode_query":"Ethiopia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"but small increase in publication rate over the time period (Mann-Kendall test: tau = 0.369, p < 0.0001; Fig 2). Whilst rodent pest research has been conducted across much of the African continent (32 out of 48, countries (including Madagascar) registered at least one relevant study), there was a disparity in the number of studies per country (χ2 18 = 205.72, p < 0.0001; Fig 3). Research was focused in nodes in East and West Africa. Specifically four countries; Tanzania (24.69%), Ethiopia (8.64%), Nigeria (8.64%), and Kenya (8.02%), accounted for 50% of all rodent pest research on the continent (Fig 3). The high research intensity in Tanzania is due to the Pest Management Centre at the Sokoine University of Agriculture, with a strong focus on rodent pests in agricultural systems. Similarly, individual studies per country clustered around established research sites (Fig 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Kenya","kind":"region","role":"Location with rodent pest outbreak and damage studies","river_name":null,"geocode_query":"Kenya","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Rodent pests are problematic in terms of agriculture and public health since they can inflict considerable economic damage [9, 10], because of their abundance, diversity, generalist feeding habits and their high reproductive output [11]. In Asia, under traditional rice farming systems, rodents typically cause chronic losses to rice in the order of 5–10% per annum [12], while episodic population outbreaks cause severe losses that place at risk the food security of entire communities [13]. In Tanzania, chronic preharvest losses to maize are around 15%, while damage at sowing and to seedlings can exceed 40% [14]. Population irruptions of Mastomys natalensis have caused yield losses up to 48%, and during acute outbreaks, damage has reached 80–100% of sowing and seedling stages in maize [14, 15]. In Kenya, rodents have caused losses of 20–30% to maize crops, with 34–100% during rodent outbreaks [16].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Madagascar","kind":"region","role":"Included in focal Afro-Malagasy region for rodent pest impact studies","river_name":null,"geocode_query":"Madagascar","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"To quantify rodent pest impacts and identify trends in rodent pest research impacting on small-holder agriculture in the AfroMalagasy region we did a systematic review of research outputs from 1910 to 2015, by developing an a priori defined set of criteria to allow for replication of the review process. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We reviewed 162 publications, and while rodent pest research was spatially distributed across Africa (32 countries, including Madagascar), there was a disparity in number of studies per country with research biased towards four countries (Tanzania [25%], Nigeria [9%], Ethiopia [9%], Kenya [8%]) accounting for 51% of all rodent pest research in the AfroMalagasy region.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Morogoro, Tanzania","kind":"field_site","role":"Site of Pest Management Centre focusing on rodent pest research","river_name":null,"geocode_query":"Morogoro, Tanzania","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"but small increase in publication rate over the time period (Mann-Kendall test: tau = 0.369, p < 0.0001; Fig 2). Whilst rodent pest research has been conducted across much of the African continent (32 out of 48, countries (including Madagascar) registered at least one relevant study), there was a disparity in the number of studies per country (χ2 18 = 205.72, p < 0.0001; Fig 3). Research was focused in nodes in East and West Africa. Specifically four countries; Tanzania (24.69%), Ethiopia (8.64%), Nigeria (8.64%), and Kenya (8.02%), accounted for 50% of all rodent pest research on the continent (Fig 3). The high research intensity in Tanzania is due to the Pest Management Centre at the Sokoine University of Agriculture, with a strong focus on rodent pests in agricultural systems. Similarly, individual studies per country clustered around established research sites (Fig 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[37.6591144,-6.8277556],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Morogoro, Tanzania","basin_id":1040008480,"main_basin_id":1040008480,"basin_region":"Africa","basin_area_km2":211436.0,"hydrology_source":"hydrobasins","site_id":"doc-61536a49907deb2ec04ccfc6-site-5"}],"limitations":["Many studies lacked full-text availability reducing comprehensive analysis","Sparse replication and control in intervention studies limit inference about management effectiveness","Spatial bias with research concentrated in few countries limits continental generalization","Lack of standardized crop damage assessment methods across studies reduces comparability"],"river_ids":[],"site_ids":["doc-61536a49907deb2ec04ccfc6-site-5"],"anchor":[37.6591144,-6.8277556],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[1040008480],"main_basin_ids":[1040008480]},{"id":"doc-6686de9de1d6d92f695608b9","title":"Detecting inter-aquifer leakage in areas with limited data using hydraulics and multiple environmental tracers, including 4He, 36Cl/Cl, 14C and 87Sr/86Sr","display_title":"Inter-aquifer Leakage in Great Artesian Basin","doi":"https://doi.org/10.1007/s10040-017-1609-x","theme":"Groundwater","color":"#a8b9ff","year":2017,"publication_date":"2017-06-16","authors":["Stacey C. Priestley","Daniel Wohling","Mark Keppel","Vincent Post","Andrew J. Love","Paul Shand","Lina Tyroller","Rolf Kipfer"],"summary":"This study investigates the hydraulic connectivity and inter-aquifer leakage between the Great Artesian Basin (GAB) and the underlying Arckaringa Basin in central Australia, a region with limited groundwater data. Using geology, hydraulics, and multiple environmental tracers—including He-4, Cl-36/Cl, C-14 and Sr-87/Sr-86 isotopes—the research identifies areas of enhanced inter-aquifer leakage, recharge zones from ephemeral rivers, and establishes a methodology to study large groundwater basins with sparse well data. Key findings include diffuse inter-aquifer leakage rates less than 1 mm/year where an aquitard is present, increased leakage where the aquitard is thin or absent, and mixing signatures indicating connectivity near certain well locations.","finding":"Enhanced inter-aquifer leakage from the upper Great Artesian Basin to the lower Arckaringa Basin aquifer occurs notably where the lower aquitard is thin or absent, demonstrated by hydraulic head similarities, tracer overlap, and drawdown response to pumping at well couplet 10.","finding_evidence":{"passage_id":"p012-005","page":12,"quote":"This seems to be the case at well couplet 10, and possibly at well couplets 7 and 9, because the age tracers indicate the addition of younger water to the lower aquifer and basement at L10 and L9 (indicated by 36Cl/Cl and 4 He) and B7 (indicated by 36Cl/ Cl and 14C). The 87Sr/86Sr ratios are less radiogenic and more similar to the upper aquifer (L9, L10, B7 and B9), and furthermore, there is overlap in chloride concentration, δ2 H and δ18O in the upper and lower aquifers at well couplet 10. At well couplets 7 and 9, the freshwater head difference is similar to most of the other couplets, but at site 10, the difference is less than 5 m. Moreover, a hydraulic connection between the aquifers at well couplet 10 may be indicated by a slight drawdown at U10b, which was recorded during the pumping of groundwater from the lower aquifer (Fig. 5). This could also explain why the pre-pumping head difference was small at this location.","verified_in_page":true},"topics":["groundwater flow","environmental tracers","hydraulic connectivity","aquifer leakage","isotope hydrology","regional groundwater recharge","hydrogeology","age dating"],"study_period":"Not specified","locations":[{"name":"Great Artesian Basin","kind":"basin","role":"primary upper aquifer system studied for groundwater connectivity","river_name":null,"geocode_query":"Great Artesian Basin, Central Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"REPORT Detecting inter-aquifer leakage in areas with limited data using hydraulics and multiple environmental tracers, including 4 He, 36Cl/Cl, 14C and 87Sr/86Sr Stacey C. Priestley1 & Daniel L. Wohling2 & Mark N. Keppel2 & Vincent E. A. Post1 & Andrew J. Love 1 & Paul Shand1,3 & Lina Tyroller4 & Rolf Kipfer4,5 Received: 6 December 2016 /Accepted: 19 May 2017 /Published online: 16 June 2017 # The Author(s) 2017. This article is an open access publication Abstract The investigation of regionally extensive groundwater systems in remote areas is hindered by a shortage of data due to a sparse observation network, which limits our understanding of the hydrogeological processes in arid regions. The study used a multidisciplinary approach to determine hydraulic connectivity between the Great Artesian Basin (GAB) and the underlying Arckaringa Basin in the desert region of Central Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.922866,-23.744005],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-0","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Arckaringa Basin","kind":"basin","role":"primary lower aquifer system underlying the GAB investigated for inter-aquifer leakage","river_name":null,"geocode_query":"Arckaringa Basin, Central Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"REPORT Detecting inter-aquifer leakage in areas with limited data using hydraulics and multiple environmental tracers, including 4 He, 36Cl/Cl, 14C and 87Sr/86Sr Stacey C. Priestley1 & Daniel L. Wohling2 & Mark N. Keppel2 & Vincent E. A. Post1 & Andrew J. Love 1 & Paul Shand1,3 & Lina Tyroller4 & Rolf Kipfer4,5 Received: 6 December 2016 /Accepted: 19 May 2017 /Published online: 16 June 2017 # The Author(s) 2017. This article is an open access publication Abstract The investigation of regionally extensive groundwater systems in remote areas is hindered by a shortage of data due to a sparse observation network, which limits our understanding of the hydrogeological processes in arid regions. The study used a multidisciplinary approach to determine hydraulic connectivity between the Great Artesian Basin (GAB) and the underlying Arckaringa Basin in the desert region of Central Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.945277,-23.708461],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-1","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Southwestern Great Artesian Basin near Prominent Hill mine","kind":"region","role":"study area focus with most well couplets, groundwater abstraction site enforcing study of leakage","river_name":null,"geocode_query":"Prominent Hill mine, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"Given the lack of wells completed in the lower aquifer, this study focused on the area with the most wells available (Fig. 1a). The Prominent Hill mine has been extracting groundwater from the lower aquifer from these wells since 2006 (SKM 2010). Methods Published geological information, including interpreted elevation top of aquifers and aquitards, as well as aquifer isopachs (Sampson et al. 2012a, b, 2015) and fault locations (Ambrose and Flint 1980; Cowley and Martin 1991; Drexel and Preiss 1995), were used to identify zones that may potentially be associated with enhanced vertical permeability. Where the thickness of the aquitard is lower, resistance to vertical flow Fig. 2 Hydrogeological transect following the generalised groundwater flow path in the lower aquifer (Fig. 1a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[135.5646667,-29.7297134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119369991,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Unnamed Rd,, Glendambo SA 5723, Australia","basin_id":5040087590,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":55047.8,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-2"},{"name":"Ephemeral Rivers and Waterholes in Study Area","kind":"other","role":"locations of recharge to upper aquifer supporting younger groundwater signatures","river_name":null,"geocode_query":"Central Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"The inconsistency between 14C, radiogenic 4 He and 36Cl/Cl is possibly due to mixing and diffusion; however, the general pattern of increasing groundwater residence times is consistent with the west– east directed groundwater flow in the upper aquifer, as inferred from the hydraulic head distribution (Fig. 1a). The lower aquifer and basement groundwater residence times are generally older (several 10’s to 100’s ka) than the upper aquifer, with the highest radiogenic 4 He concentrations (2 × 10−4 ccSTP/g), and, hence oldest groundwater, toward the end of the inferred groundwater flow path at L3 (Fig. 1a). Recharge Love et al. (2013) contended that recharge to the upper aquifer would occur at ephemeral rivers where the aquifer sub-crops or the upper aquitard is absent. Groundwater samples in the upper aquifer close to ephemeral rivers (samples U3, U4 and U9) and waterholes (samples also U3 and U7) with 14CDIC residence times of <6 ka support this interpretation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.937494,-23.668042],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Australia NT, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-3","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Millers Creek Station","kind":"settlement","role":"local pastoral station supported by groundwater from the Great Artesian Basin","river_name":null,"geocode_query":"Millers Creek Station, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-006","page":15,"quote":"Acknowledgements This study and paper were funded by the Government of South Australia’s Department of Environment, Water and Natural Resources (DEWNR), Australia under its Bioregional Assessment Program on the Arckaringa Basin. The authors would also like to thank AINSE Ltd for providing financial assistance (Award - PGRA) to Stacey Priestley. We thank Peter Kretschmer for assistance in the field. Noble gases analysis was completed at the noble gas laboratory of the Swiss Federal Institute of Technology (ETH) and we thank Matthias Brennwald for help with analysis. We thank the traditional owners and custodians (past and present) of the South Australian spring country, particularly the Antakarinja peoples. We also thank the pastoralist and graziers of the south-western GAB, in particular Millers Creek Station, Billa Kalina Station and Anna Creek Station. Finally, we thank OZ Minerals and BHP Billiton for access to their mine water supply wells. 2044 Hydrogeol J (2017) 25:2031–2047","verified_in_page":true},"resolution_status":"resolved","coordinates":[135.9800848,-29.97916249999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119369936,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Millers Creek SA 5723, Australia","basin_id":5040087760,"main_basin_id":5040087760,"basin_region":"Australasia","basin_area_km2":146408.6,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-4"},{"name":"Billa Kalina Station","kind":"settlement","role":"local pastoral station supported by groundwater from the Great Artesian Basin","river_name":null,"geocode_query":"Billa Kalina Station, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-006","page":15,"quote":"Acknowledgements This study and paper were funded by the Government of South Australia’s Department of Environment, Water and Natural Resources (DEWNR), Australia under its Bioregional Assessment Program on the Arckaringa Basin. The authors would also like to thank AINSE Ltd for providing financial assistance (Award - PGRA) to Stacey Priestley. We thank Peter Kretschmer for assistance in the field. Noble gases analysis was completed at the noble gas laboratory of the Swiss Federal Institute of Technology (ETH) and we thank Matthias Brennwald for help with analysis. We thank the traditional owners and custodians (past and present) of the South Australian spring country, particularly the Antakarinja peoples. We also thank the pastoralist and graziers of the south-western GAB, in particular Millers Creek Station, Billa Kalina Station and Anna Creek Station. Finally, we thank OZ Minerals and BHP Billiton for access to their mine water supply wells. 2044 Hydrogeol J (2017) 25:2031–2047","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.3624686,-29.9145478],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119369975,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Billa Kalina SA 5720, Australia","basin_id":5040087760,"main_basin_id":5040087760,"basin_region":"Australasia","basin_area_km2":146408.6,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-5"},{"name":"Anna Creek Station","kind":"settlement","role":"local pastoral station supported by groundwater from the Great Artesian Basin","river_name":null,"geocode_query":"Anna Creek Station, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-006","page":15,"quote":"Acknowledgements This study and paper were funded by the Government of South Australia’s Department of Environment, Water and Natural Resources (DEWNR), Australia under its Bioregional Assessment Program on the Arckaringa Basin. The authors would also like to thank AINSE Ltd for providing financial assistance (Award - PGRA) to Stacey Priestley. We thank Peter Kretschmer for assistance in the field. Noble gases analysis was completed at the noble gas laboratory of the Swiss Federal Institute of Technology (ETH) and we thank Matthias Brennwald for help with analysis. We thank the traditional owners and custodians (past and present) of the South Australian spring country, particularly the Antakarinja peoples. We also thank the pastoralist and graziers of the south-western GAB, in particular Millers Creek Station, Billa Kalina Station and Anna Creek Station. Finally, we thank OZ Minerals and BHP Billiton for access to their mine water supply wells. 2044 Hydrogeol J (2017) 25:2031–2047","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.1693952,-28.8937546],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119369986,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Oodnadatta Track, William Creek SA 5710, Australia","basin_id":5040087590,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":55047.8,"hydrology_source":"geofabric","site_id":"doc-6686de9de1d6d92f695608b9-site-6"}],"limitations":["Sparse observation well network limits spatial resolution of leakage detection","Lack of multi-level observation wells restricts vertical flow measurement detail","Difficulty in selecting end-member values for isotope mixing calculations","Inconsistent groundwater age tracer results due to mixing and diffusion processes in low-flow environment"],"river_ids":[],"site_ids":["doc-6686de9de1d6d92f695608b9-site-0","doc-6686de9de1d6d92f695608b9-site-1","doc-6686de9de1d6d92f695608b9-site-2","doc-6686de9de1d6d92f695608b9-site-3","doc-6686de9de1d6d92f695608b9-site-4","doc-6686de9de1d6d92f695608b9-site-5","doc-6686de9de1d6d92f695608b9-site-6"],"anchor":[136.1693952,-28.8937546],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040087590,5040087760,5040491370],"main_basin_ids":[5040087590,5040087760]},{"id":"doc-6bf0a34899c3d8c66d6d32d7","title":"Contrasting runoff trends between dry and wet parts of eastern Tibetan Plateau","display_title":"Runoff Trends in Eastern Tibetan Plateau","doi":"https://doi.org/10.1038/s41598-017-15678-x","theme":"Flow & ecology","color":"#80edc2","year":2017,"publication_date":"2017-11-07","authors":["Yuanyuan Wang","Yongqiang Zhang","Francis H. S. Chiew","Tim R. McVicar","Lu Zhang","Hongxia Li","Guanghua Qin"],"summary":"This study used satellite-based products of precipitation, evapotranspiration, and total water storage changes to analyze spatial and temporal variations in annual runoff across the eastern Tibetan Plateau (2003–2014). Findings showed increased runoff trends in the dry northern region driven mainly by precipitation increases, and decreased runoff trends in the wet southeastern region, influenced by precipitation decreases and increased evapotranspiration. Cryosphere dynamics, including permafrost degradation in the north and glacier melt in the southwest, also significantly affected total water storage changes and runoff variability.","finding":"Runoff increased in the dry northern part of eastern Tibetan Plateau due to precipitation increase, but decreased in the wet southeastern part owing to precipitation decrease and amplified by evapotranspiration increases.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Annual runoff increased strongly in dry part because of increases in precipitation, but decreased in wet part because of decreases in precipitation, aggravated by noticeable increases in evapotranspiration in the north of wet part. Although precipitation primarily governed temporal-spatial pattern of runoff, total water storage change contributed greatly to runoff variation in regions with wide-spread permanent snow/ice or permafrost. Our study indicates that the contrasting runoff trends between the dry and wet parts of eastern TP requires a change in water security strategy, and attention should be paid to the negative water resources impacts detected for southwestern part which has undergone vast glacier retreat and decreasing precipitation. As the highest plateau in the world, the TP plays an important role in the climate and hydrology of eastern and southern Asia1–3 .","verified_in_page":true},"topics":["runoff variability","precipitation trends","evapotranspiration","total water storage","cryosphere hydrology","permafrost degradation","glacier retreat","water balance modeling"],"study_period":"2003–2014","locations":[{"name":"eastern Tibetan Plateau","kind":"region","role":"study region for runoff trend analysis","river_name":null,"geocode_query":"eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"SCiENtifiC Reports | 7: 15458 | DOI:10.1038/s41598-017-15678-x 1 www.nature.com/scientificreports Contrasting runoff trends between dry and wet parts of eastern Tibetan Plateau Yuanyuan Wang1,2, Yongqiang Zhang 2, Francis H. S. Chiew2, Tim R. McVicar2,3, Lu Zhang 2, Hongxia Li4 & Guanghua Qin4 As the “Asian Water Tower”, the Tibetan Plateau (TP) provides water resources for more than 1.4 billion people, but suffers from climatic and environmental changes, followed by the changes in water balance components. We used state-of-the-art satellite-based products to estimate spatial and temporal variations and trends in annual precipitation, evapotranspiration and total water storage change across eastern TP, which were then used to reconstruct an annual runoff variability series for 2003–2014. The basin-scale reconstructed streamflow variability matched well with gauge observations for five large rivers.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yangtze River basin, eastern Tibetan Plateau, China","kind":"basin","role":"study basin with gauge data validating runoff reconstruction","river_name":"Yangtze","geocode_query":"Yangtze River basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Two gauges are for the Yangtze River (purple represents the Yangtze River basin) and one gauge is for each of the other three rivers (blue denotes the Yellow river basin, yellow represents the Mekong river basin, and orange the Salween river basin). Coloured area is the extent of eastern TP in this study. The map was generated using Google Earth (v 7.1.7.2606, https://www. google.com/earth/), Image Landsat/Copernicus.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yellow River basin, eastern Tibetan Plateau, China","kind":"basin","role":"study basin with gauge data validating runoff reconstruction","river_name":"Yellow","geocode_query":"Yellow River basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Two gauges are for the Yangtze River (purple represents the Yangtze River basin) and one gauge is for each of the other three rivers (blue denotes the Yellow river basin, yellow represents the Mekong river basin, and orange the Salween river basin). Coloured area is the extent of eastern TP in this study. The map was generated using Google Earth (v 7.1.7.2606, https://www. google.com/earth/), Image Landsat/Copernicus.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Mekong River basin, eastern Tibetan Plateau, China","kind":"basin","role":"study basin with gauge data validating runoff reconstruction","river_name":"Mekong","geocode_query":"Mekong River basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Two gauges are for the Yangtze River (purple represents the Yangtze River basin) and one gauge is for each of the other three rivers (blue denotes the Yellow river basin, yellow represents the Mekong river basin, and orange the Salween river basin). Coloured area is the extent of eastern TP in this study. The map was generated using Google Earth (v 7.1.7.2606, https://www. google.com/earth/), Image Landsat/Copernicus.","verified_in_page":true},"resolution_status":"resolved","coordinates":[91.605121,31.035414],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040023060,"main_basin_id":4040023060,"basin_region":"Central and South-East Asia","basin_area_km2":266292.4,"hydrology_source":"hydrobasins","site_id":"doc-6bf0a34899c3d8c66d6d32d7-site-3"},{"name":"Salween River basin, eastern Tibetan Plateau, China","kind":"basin","role":"study basin with gauge data validating runoff reconstruction","river_name":"Salween","geocode_query":"Salween River basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Two gauges are for the Yangtze River (purple represents the Yangtze River basin) and one gauge is for each of the other three rivers (blue denotes the Yellow river basin, yellow represents the Mekong river basin, and orange the Salween river basin). Coloured area is the extent of eastern TP in this study. The map was generated using Google Earth (v 7.1.7.2606, https://www. google.com/earth/), Image Landsat/Copernicus.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Zhimenda basin, northern eastern Tibetan Plateau, China","kind":"basin","role":"example region illustrating permafrost degradation effects on water storage and runoff","river_name":null,"geocode_query":"Zhimenda basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"In wet southeast, overall decreased trends in Q were similar to those in P trends, but in northern part of southeast, noticeable increased trends in ET (+4–+7mm year−2 ) aggravated the decreased trends in Q. As a result, trend in aggregated Q from regions with P above 650mm year−1 was −2.17mm year−2 , resultant from the trends in P, ET and ΔTWS being +0.58mm year−2 , +2.43mm year−2 , and +0.32mm year−2 , respectively. Contribution of ΔTWS. As expected, ΔTWS responds mainly to P, especially in dry regions (see Supplementary Information, Fig. S2). The reason that ΔTWS contributed greatly in explaining the inter-annual variation of Q in north and south-west is probably because ΔTWS can reflect cryosphere hydrological dynamics. In northern part, trends in both TWS and ΔTWS were clearly positive; respectively these indicate that total water storage increased and the rate of change increased too. To be more specific, we took Zhimenda basin located in north as an example.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Jiayuqiao basin, southwestern eastern Tibetan Plateau, China","kind":"basin","role":"example region illustrating glacier retreat effects on water storage and runoff","river_name":null,"geocode_query":"Jiayuqiao basin, eastern Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Trend in Q was largely determined by trend in P-ΔTWS. In southwestern part, TWS trend was negative, contrasted by positive trend in ΔTWS, which means total water storage decreased but the rate has slowed or even reversed. This region has a high percentage of permanent snow/ice, and change in the mass of snow/ice can be reflected by ΔTWS9,32 (see Supplementary Information, Fig. S3). Taking Jiayuqiao basin located in this area as an example, substantial decrease in TWS occurred over 2003–2006 when mean annual ΔTWS and P were −23.2mm year−1 and 671mm year−1 , respectively. Over 2007– 2014 mean annual ΔTWS and P were +0.31 mm year−1 and 677 mm year−1 , respectively. It means that there existed previously a huge loss in snow/ice mass caused by warming (P hardly changed), and meltwater accounted for a large proportion of Q. Afterward, the remaining snow/ice didn’t change greatly (ΔTWS was approaching to 0), providing limited amount of meltwater.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Inability to assign precise latitude and longitude due to unavailable coordinates in source text.","Coarse spatial resolution of GRACE mascon TWS data (native resolution ~3 degrees) causing leakage errors and abrupt boundaries in ΔTWS data.","Uncertainties in evapotranspiration trend estimates are relatively high due to differences among ET satellite products and input data quality limitations over Tibetan Plateau.","Validation basins cover 59% of research domain, leaving some extrapolation uncertainty for uncovered areas."],"river_ids":["43643177","43645137","ne-230","ne-301"],"site_ids":["doc-6bf0a34899c3d8c66d6d32d7-site-3"],"anchor":[91.605121,31.035414],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[4040023060],"main_basin_ids":[4040023060]},{"id":"doc-a7ccbab7ffdcb9065d6f0c6d","title":"Recent increases in terrestrial carbon uptake at little cost to the water cycle","display_title":"Rising Carbon Uptake Efficiency Globally","doi":"https://doi.org/10.1038/s41467-017-00114-5","theme":"Climate","color":"#98d4f1","year":2017,"publication_date":"2017-07-17","authors":["Lei Cheng","Lu Zhang","Ying‐Ping Wang","Josep G. Canadell","Francis H. S. Chiew","Jason Beringer","Longhui Li","Diego G. Miralles","Shilong Piao","Yongqiang Zhang"],"summary":"This study develops a new analytical water use efficiency (WUE) model that upscales leaf-level photosynthesis processes to estimate global gross primary production (GPP) constrained by ecosystem evapotranspiration (E) measurements from 1982 to 2011. Using multiple global datasets, it finds that terrestrial carbon uptake has increased significantly over this period, largely driven (about 90%) by increased carbon uptake per unit of water used, i.e., enhanced WUE, due mainly to rising atmospheric CO2 concentrations and increased canopy leaf area index (L). Despite the increase in GPP, ecosystem water use (E) has shown little change globally, indicating a shift in the water-carbon coupling with minimal water cost for enhanced carbon uptake.","finding":"From 1982 to 2011, global terrestrial gross primary production increased significantly by 0.83 ± 0.26 Pg C per year², primarily driven by a roughly 90% contribution from increased water use efficiency (carbon uptake per unit water loss), while total evapotranspiration increased only marginally. This enhancement in water use efficiency is principally attributed to rising atmospheric CO2 concentration and increased canopy leaf area index, with vapor pressure deficit exerting a negative influence.","finding_evidence":{"passage_id":"p004-001","page":4,"quote":"The results suggest that estimated increase in global GPP under climate change and rising Ca conditions over the past 30 years is taking place at no cost of using proportionally more water, but it is largely driven by the increase in carbon uptake per unit of water use, i.e. WUE. Contributions of the four different variables (i.e. Ca, D, L and fEi ) to the estimated trend in WUE are further analysed (see Methods) and shown in Fig. 2d. Three of the four variables (except fEi ) have significant (p < 0.01) contributions to the estimated increasing trend in global WUE, of which Ca and L have positive contributions but D has a negative contribution to the estimated increase in WUE. The contributions of Ca, D, L and fEi to the estimated trend in global WUE are 9.9 ± 1.7, −3.4 ± 1.1, 7.4 ± 4.3 and 0.04 ± 0.4 mg C mm−1 H2O per year, or 77 ± 20%, −27 ± 11%, 49 ± 16% and 0.2 ± 3%, respectively. The contributions of four drivers of WUE (i.e.","verified_in_page":true},"topics":["global carbon cycle","ecosystem water use efficiency","gross primary production","leaf area index","vapour pressure deficit","evapotranspiration","carbon uptake","atmospheric CO2"],"study_period":"1982-2011","locations":[{"name":"Mauna Loa","kind":"station","role":"source of atmospheric CO2 concentration data","river_name":null,"geocode_query":"Mauna Loa, Hawaii, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The five variables are Ca, pa, D, L and fEi . Parameter k, the extinction coefficient of radiation, is set as a constant of 0.6 for all the vegetated land. Global map of parameter g1 (Supplementary Fig. 2) is generated by interpolating g1 values of different plant functional types (Supplementary Table 1) adopted from ref. 30 with a global vegetation classification map (i.e. SYNMAP57). The data for variable Ca are observed annual mean data at Mauna Loa site58 and accessed from NOAA’s Earth System Research Laboratory. Variable pa is a mean annual atmospheric pressure field estimated from the monthly climatology data set developed by Climatic Research Unit (CRU)59. Three different inputs for variable D are used, which are derived from three global climate forcing data sets including the CRU-NCEP data set59, the Princeton Global Forcing data60 and the WATCH Forcing Data ERA-Interim (WFDEI) meteorological forcing data61.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-155.5922066,19.4721389],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mauna Loa, Hawaii 96785, USA","hydrology_source":"none","site_id":"doc-a7ccbab7ffdcb9065d6f0c6d-site-0"},{"name":"boreal forest region","kind":"region","role":"major region exhibiting significant increases in both gross primary production and water use efficiency driven by rising CO2 and leaf area index","river_name":null,"geocode_query":"boreal forest, northern hemisphere","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Spatially, an estimated large increase in GPP can be found mainly in two regions, i.e. the boreal forest and tropical forest regions (Fig. 3a). The relative large GPP increase in the boreal forest region results from positive contributions from both E (Fig. 4a) and WUE (Fig. 4b). In turn, positive contribution of WUE in this region is mainly driven by rising Ca and enhanced canopy L (Fig. 4c, e). For the tropical forest region, the large GPP increase mainly results from the increase in WUE in this region, caused by rising Ca (Fig. 4c), which is consistent with increased WUE trend derived from tropical tree growth rings7. At the global scale, the most important driver for the increases in GPP and WUE from 1982 to 2011 is rising Ca. Global GPP increases at a rate of 17% per 10% increase in Ca.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"tropical forest region","kind":"region","role":"major region exhibiting significant increase in gross primary production mainly driven by rising atmospheric CO2","river_name":null,"geocode_query":"tropical forests","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Spatially, an estimated large increase in GPP can be found mainly in two regions, i.e. the boreal forest and tropical forest regions (Fig. 3a). The relative large GPP increase in the boreal forest region results from positive contributions from both E (Fig. 4a) and WUE (Fig. 4b). In turn, positive contribution of WUE in this region is mainly driven by rising Ca and enhanced canopy L (Fig. 4c, e). For the tropical forest region, the large GPP increase mainly results from the increase in WUE in this region, caused by rising Ca (Fig. 4c), which is consistent with increased WUE trend derived from tropical tree growth rings7. At the global scale, the most important driver for the increases in GPP and WUE from 1982 to 2011 is rising Ca. Global GPP increases at a rate of 17% per 10% increase in Ca.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"northeast China","kind":"region","role":"region showing negative trends in water use efficiency and gross primary production","river_name":null,"geocode_query":"northeast China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Central Africa experienced a decrease in GPP −15 −10 −5 0 5 10 15 20 Global GPP anomalies Pg C year−1 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 1980 1985 1990 1995 2000 2005 2010 Global WUE anomalies g C mm−1 H2O −0.3 0.0 0.3 0.6 0.9 1.2 Total E WUE Global GPP trends Pg C year−2 −5 0 5 10 15 20 Total Ca D LfEi Global WUE trends mg C mm−1 H2O year−1 Year a b cd Fig. 2 Estimated trends in global gross primary production (GPP) and water use efficiency (WUE) and their drivers over 1982–2011. a, b Annual mean anomalies and its standard deviation (shown as error bars) of global GPP in Pg C per year (n = 84) and global WUE in g C mm−1 H2O(n = 12) during 1982–2011, respectively. The green line in a and blue line in b represent the linear trend over the past three decades. c Contribution of E and WUE to total global trends in GPP (Total). d Contributions of four factors to the total increase in global WUE (Total).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mongolia","kind":"region","role":"region showing negative trends in water use efficiency and gross primary production","river_name":null,"geocode_query":"Mongolia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Central Africa experienced a decrease in GPP −15 −10 −5 0 5 10 15 20 Global GPP anomalies Pg C year−1 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 1980 1985 1990 1995 2000 2005 2010 Global WUE anomalies g C mm−1 H2O −0.3 0.0 0.3 0.6 0.9 1.2 Total E WUE Global GPP trends Pg C year−2 −5 0 5 10 15 20 Total Ca D LfEi Global WUE trends mg C mm−1 H2O year−1 Year a b cd Fig. 2 Estimated trends in global gross primary production (GPP) and water use efficiency (WUE) and their drivers over 1982–2011. a, b Annual mean anomalies and its standard deviation (shown as error bars) of global GPP in Pg C per year (n = 84) and global WUE in g C mm−1 H2O(n = 12) during 1982–2011, respectively. The green line in a and blue line in b represent the linear trend over the past three decades. c Contribution of E and WUE to total global trends in GPP (Total). d Contributions of four factors to the total increase in global WUE (Total).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"inland Australia","kind":"region","role":"area showing negative trends in water use efficiency and gross primary production","river_name":null,"geocode_query":"inland Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Central Africa experienced a decrease in GPP −15 −10 −5 0 5 10 15 20 Global GPP anomalies Pg C year−1 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 1980 1985 1990 1995 2000 2005 2010 Global WUE anomalies g C mm−1 H2O −0.3 0.0 0.3 0.6 0.9 1.2 Total E WUE Global GPP trends Pg C year−2 −5 0 5 10 15 20 Total Ca D LfEi Global WUE trends mg C mm−1 H2O year−1 Year a b cd Fig. 2 Estimated trends in global gross primary production (GPP) and water use efficiency (WUE) and their drivers over 1982–2011. a, b Annual mean anomalies and its standard deviation (shown as error bars) of global GPP in Pg C per year (n = 84) and global WUE in g C mm−1 H2O(n = 12) during 1982–2011, respectively. The green line in a and blue line in b represent the linear trend over the past three decades. c Contribution of E and WUE to total global trends in GPP (Total). d Contributions of four factors to the total increase in global WUE (Total).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"east of North America","kind":"region","role":"area where positive contribution of evapotranspiration leads to increasing gross primary production","river_name":null,"geocode_query":"eastern North America, USA/Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Positive contributions of WUE are found in most regions globally but negative contributions of WUE are limited to some small patches in the Northeast of China, Mongolia and inland of Australia (Fig. 4b). The contributions of four different variables to the estimated trend in WUE at the grid level are shown in Fig. 4c–f. The Ca has positive effects on ecosystem WUE globally as it is related linearly with WUE over all vegetated land and has increased monotonically over the study period. For the other three variables, their contributions vary spatially from negative to positive. Changes in D have negative effects on ecosystem WUE in about 69 ± 3% of the vegetated land area, especially in Europe, East Asia, Alaska and the eastern parts of South America, and have positive effects in the northwest of South America, the north of central Russia, and the south of Africa.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-82.25902,37.636105],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canada, KY 41519, USA","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-a7ccbab7ffdcb9065d6f0c6d-site-6","true_coordinates":[-82.3246094,37.6061055],"spread_km":6.7},{"name":"boreal forest regions in Europe and Asia","kind":"region","role":"areas with significant positive evapotranspiration contribution to gross primary production trends","river_name":null,"geocode_query":"boreal forest, Europe and Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Positive contributions of WUE are found in most regions globally but negative contributions of WUE are limited to some small patches in the Northeast of China, Mongolia and inland of Australia (Fig. 4b). The contributions of four different variables to the estimated trend in WUE at the grid level are shown in Fig. 4c–f. The Ca has positive effects on ecosystem WUE globally as it is related linearly with WUE over all vegetated land and has increased monotonically over the study period. For the other three variables, their contributions vary spatially from negative to positive. Changes in D have negative effects on ecosystem WUE in about 69 ± 3% of the vegetated land area, especially in Europe, East Asia, Alaska and the eastern parts of South America, and have positive effects in the northwest of South America, the north of central Russia, and the south of Africa.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"southeast China","kind":"region","role":"region showing positive contribution of evapotranspiration to gross primary production trends","river_name":null,"geocode_query":"southeast China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Positive contributions of WUE are found in most regions globally but negative contributions of WUE are limited to some small patches in the Northeast of China, Mongolia and inland of Australia (Fig. 4b). The contributions of four different variables to the estimated trend in WUE at the grid level are shown in Fig. 4c–f. The Ca has positive effects on ecosystem WUE globally as it is related linearly with WUE over all vegetated land and has increased monotonically over the study period. For the other three variables, their contributions vary spatially from negative to positive. Changes in D have negative effects on ecosystem WUE in about 69 ± 3% of the vegetated land area, especially in Europe, East Asia, Alaska and the eastern parts of South America, and have positive effects in the northwest of South America, the north of central Russia, and the south of Africa.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Europe","kind":"region","role":"area where increased vapor pressure deficit negatively affects water use efficiency","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"East Asia","kind":"region","role":"area where increased vapor pressure deficit negatively affects water use efficiency","river_name":null,"geocode_query":"East Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"resolved","coordinates":[106.61182,38.794595],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Asia","basin_id":4040548370,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":248039.1,"hydrology_source":"hydrobasins","site_id":"doc-a7ccbab7ffdcb9065d6f0c6d-site-10","true_coordinates":[106.5348379,38.7945952],"spread_km":6.7},{"name":"Alaska","kind":"region","role":"area where increased vapor pressure deficit negatively affects water use efficiency","river_name":null,"geocode_query":"Alaska, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"eastern parts of South America","kind":"region","role":"area where increased vapor pressure deficit negatively affects water use efficiency","river_name":null,"geocode_query":"eastern South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"northwest of South America","kind":"region","role":"area where vapor pressure deficit changes have positive effects on water use efficiency","river_name":null,"geocode_query":"northwest South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"north of central Russia","kind":"region","role":"area where vapor pressure deficit changes have positive effects on water use efficiency","river_name":null,"geocode_query":"north central Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"south of Africa","kind":"region","role":"area where vapor pressure deficit changes have positive effects on water use efficiency","river_name":null,"geocode_query":"southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"However, vapour pressure deficit (D) has significant (p < 0.05) negative effects on WUE only in about 37 ± 2% of the global vegetated area and significant (p < 0.05) positive effects only in about 10 ± 3% of the global vegetated area. Changes in L have significant (p < 0.05) positive effects on WUE in about 52 ± 17% of the vegetated areas, especially at northern high latitudes, and have significant negative effects only in a small fraction of vegetated land (~4 ± 1%). Changes in fEi have only marginal contribution to the global changes in WUE because the magnitude of the contribution is small (Fig. 2d) and extent of the significant contribution is very limited (~18%). Discussion The carbon uptake capacity of terrestrial ecosystems is both determined by availability of radiation (or light) and water27, 36, 37.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.56871,-24.297077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-a7ccbab7ffdcb9065d6f0c6d-site-15","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7}],"limitations":["Geographical coordinates mostly not provided in the text and not inferred due to PDF constraints.","Large regional-scale areas described without precise coordinates.","Model parameter g1 assigned from interpolated plant functional types rather than measured in situ globally.","Limited long-term eddy covariance observations for validation, especially in tropical and boreal forests.","Use of canopy leaf area index (L) and interception fraction (fEi) data products which have some inconsistencies and step changes around 2000.","Uncertainties in climate forcings such as vapor pressure deficit (D) and evapotranspiration (E) derived from multiple datasets causing variation in results."],"river_ids":[],"site_ids":["doc-a7ccbab7ffdcb9065d6f0c6d-site-0","doc-a7ccbab7ffdcb9065d6f0c6d-site-6","doc-a7ccbab7ffdcb9065d6f0c6d-site-10","doc-a7ccbab7ffdcb9065d6f0c6d-site-15"],"anchor":[-82.25902,37.636105],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[1041624310,4040548370,7040612640],"main_basin_ids":[1040015850,4040007850,7040047060]},{"id":"doc-dab04c20adc97745cb3e2ace","title":"Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors","display_title":"Multiscale Soil Moisture Mapping with Cosmic Rays","doi":"https://doi.org/10.5194/hess-21-6049-2017","theme":"Land & ecosystems","color":"#c8df98","year":2017,"publication_date":"2017-12-01","authors":["David McJannet","Aaron Hawdon","Brett Baker","Luigi Renzullo","Ross Searle"],"summary":"This study developed and tested techniques to estimate soil moisture at multiple spatial scales (broad 36×36 km and intensive 10×10 km) using static cosmic-ray neutron sensors and a mobile cosmic-ray 'rover' in the Mallee cropping region of Victoria, Australia. Calibrations accounted for soil and environmental variables, and rover data were validated against gravimetric soil moisture samples and modeled soil moisture from the AWRA-L hydrological model. The intensive surveys provided reliable 1 km resolution soil moisture maps and allowed property-scale time series estimations, while broad-scale surveys effectively represented soil moisture patterns at 9 km resolution. This multiscale approach bridges gaps between traditional point sensors and satellite or model scales, supporting validation and calibration of remote sensing and land surface models.","finding":"Rover surveys conducted at different scales within the Mallee, Victoria, Australia, produced reliable soil moisture estimates at resolutions matching model and satellite retrievals: intensive surveys at 1 km and broad surveys at 9 km resolution, validated by independent gravimetric samples and model data.","finding_evidence":{"passage_id":"p009-001","page":9,"quote":"The range is important as it is related to the spatial scale of the variability in neutron intensity. The lack of a sill for the broad-scale survey reflects differences in variability in neutron observations on this larger scale. The variogram model for the intensive surveys showed more cyclicity (or the “hole effect”) which could be related to underlying geological periodicity (Yang and Kaleita, 2007). The empirical variograms were well described by the exponential models, giving confidence in interpolated rover counts across the respective survey areas. 3.5 Intensive-scale rover surveys Interpolated counts and derived volumetric soil moisture content for each of the three intensive-scale surveys are shown in Fig. 7. A large range in soil moisture content was observed over the three surveys, with values ranging between 0.01 m3 m−3 in April 2016 through to 0.30 m3 m−3 in June 2016.","verified_in_page":true},"topics":["soil moisture estimation","cosmic-ray neutron sensing","soil hydrology","spatial interpolation","soil properties","remote sensing validation","land surface modelling","agricultural land management"],"study_period":null,"locations":[{"name":"Mallee region, Victoria","kind":"region","role":"Study region for multiscale soil moisture measurements","river_name":null,"geocode_query":"Mallee region, Victoria, Australia","latitude":-35.684,"longitude":142.858,"evidence":{"passage_id":"p002-004","page":2,"quote":"We also use our observations on different scales to test the reliability of our experimental design. 2 Methods 2.1 Site description The study area is located in the Shire of Buloke in the Mallee region of Victoria, Australia (Fig. 1). The measurement campaign took place across a 36 × 36 km region centred on −35.684◦ S, 142.858◦ E, which lies between the towns of Birchip to the south and Sea Lake to the north. The Mallee is a rain-fed agricultural region with wheat and barley being widely grown. Much of the native vegetation has been removed since European settlement. In the region of interest the landscape is flat with an elevation ranging between 50 to 120 m a.s.l.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.858,-35.684],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43661622,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-0"},{"name":"Birchip, Victoria","kind":"settlement","role":"Southern boundary of study area","river_name":null,"geocode_query":"Birchip, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We also use our observations on different scales to test the reliability of our experimental design. 2 Methods 2.1 Site description The study area is located in the Shire of Buloke in the Mallee region of Victoria, Australia (Fig. 1). The measurement campaign took place across a 36 × 36 km region centred on −35.684◦ S, 142.858◦ E, which lies between the towns of Birchip to the south and Sea Lake to the north. The Mallee is a rain-fed agricultural region with wheat and barley being widely grown. Much of the native vegetation has been removed since European settlement. In the region of interest the landscape is flat with an elevation ranging between 50 to 120 m a.s.l.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.9171184,-35.9840231],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661622,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Birchip VIC 3483, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-1"},{"name":"Sea Lake, Victoria","kind":"settlement","role":"Northern boundary of study area","river_name":null,"geocode_query":"Sea Lake, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We also use our observations on different scales to test the reliability of our experimental design. 2 Methods 2.1 Site description The study area is located in the Shire of Buloke in the Mallee region of Victoria, Australia (Fig. 1). The measurement campaign took place across a 36 × 36 km region centred on −35.684◦ S, 142.858◦ E, which lies between the towns of Birchip to the south and Sea Lake to the north. The Mallee is a rain-fed agricultural region with wheat and barley being widely grown. Much of the native vegetation has been removed since European settlement. In the region of interest the landscape is flat with an elevation ranging between 50 to 120 m a.s.l.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.8495524,-35.5041988],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661622,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sea Lake VIC 3533, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-2"},{"name":"Bishes site, Mallee region, Victoria","kind":"field_site","role":"Location of static cosmic-ray neutron sensor used for calibration and continuous measurements","river_name":null,"geocode_query":"Bishes, Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The climate of the area is classified as semi-arid with an average annual rainfall of 368 mm, an average daily minimum temperature in July of 3.6 ◦C and an average daily maximum temperature in January of 30.7 ◦C (Anwar et al., 2007). 2.2 Static cosmic-ray neutron sensors Cosmic-ray neutron sensors were installed at two locations in the designated field survey area (Fig. 1). These two locations are named Bishes (northern probe) and Bennetts (southern probe). Each of these sensors included a single polyethylene shielded cosmic-ray probe (CRP-1000B, Hydroinnova, Albuquerque, USA), which monitors neutron intensity in the epithermal to fast neutron energy range. Each system also measured barometric pressure, temperature and relative humidity, which are required for measurement correction procedures.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Bennetts site, Mallee region, Victoria","kind":"field_site","role":"Location of static cosmic-ray neutron sensor used for calibration and continuous measurements","river_name":null,"geocode_query":"Bennetts, Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The climate of the area is classified as semi-arid with an average annual rainfall of 368 mm, an average daily minimum temperature in July of 3.6 ◦C and an average daily maximum temperature in January of 30.7 ◦C (Anwar et al., 2007). 2.2 Static cosmic-ray neutron sensors Cosmic-ray neutron sensors were installed at two locations in the designated field survey area (Fig. 1). These two locations are named Bishes (northern probe) and Bennetts (southern probe). Each of these sensors included a single polyethylene shielded cosmic-ray probe (CRP-1000B, Hydroinnova, Albuquerque, USA), which monitors neutron intensity in the epithermal to fast neutron energy range. Each system also measured barometric pressure, temperature and relative humidity, which are required for measurement correction procedures.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Broad-scale survey area, Mallee region, Victoria","kind":"region","role":"36×36 km rover survey area designed to match satellite pixel scale","river_name":null,"geocode_query":"Mallee region, Victoria, Australia","latitude":-35.684,"longitude":142.858,"evidence":{"passage_id":"p004-003","page":4,"quote":"The broad-scale design included a survey over an area with dimensions of approximately 36 × 36 km which encapsulated a single Soil Moisture Active Passive (SMAP) satellite pixel. Using typical counting rates for this area and by targeting an output resolution for soil moisture of 9 × 9 km, we calculated that the maximum driving speed for this survey was 90 km h−1 . This provided a good density of measurement points for interpolation purposes. The survey area and measurement points from the driving track are shown in Fig. 2. The broad-scale surveys typically took 10 h to complete, involved over 600 measurements and the average speed travelled was around 60 km h−1 . The intensive-scale survey covered an area of approximately 10 × 10 km and was located in the south-eastern corner of the broad-scale survey (Fig. 2). In this survey a target resolution for soil moisture of 1 × 1 km was used for which we calculated that the maximum driving speed should not exceed 30 km h−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.322633,-34.71525],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Mallee, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-5","true_coordinates":[142.2797032,-34.7637906],"spread_km":6.7},{"name":"Intensive-scale survey area, SE corner of broad scale, Mallee region, Victoria","kind":"region","role":"10×10 km rover survey area for finer scale soil moisture mapping at farm property scale","river_name":null,"geocode_query":"Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The broad-scale design included a survey over an area with dimensions of approximately 36 × 36 km which encapsulated a single Soil Moisture Active Passive (SMAP) satellite pixel. Using typical counting rates for this area and by targeting an output resolution for soil moisture of 9 × 9 km, we calculated that the maximum driving speed for this survey was 90 km h−1 . This provided a good density of measurement points for interpolation purposes. The survey area and measurement points from the driving track are shown in Fig. 2. The broad-scale surveys typically took 10 h to complete, involved over 600 measurements and the average speed travelled was around 60 km h−1 . The intensive-scale survey covered an area of approximately 10 × 10 km and was located in the south-eastern corner of the broad-scale survey (Fig. 2). In this survey a target resolution for soil moisture of 1 × 1 km was used for which we calculated that the maximum driving speed should not exceed 30 km h−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.236774,-34.71525],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Mallee, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-6","true_coordinates":[142.2797032,-34.7637906],"spread_km":6.7},{"name":"Sandhill area, Mallee region, Victoria","kind":"landscape","role":"Locally drier sandy ridge affecting soil moisture variability and sensor interpolation","river_name":null,"geocode_query":"Sandhill, Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"Higher than average counting rates and, hence, lower soil moisture were consistently observed in the central northern region of the survey area. This area is characterised by a ridge of sandy soil with rock fragments and is known locally as “Sandhill”. Wetter soil moisture conditions were observed through the central and southern parts of the survey area. Comparison of intensive rover survey soil moisture estimates for the CRNS locations at the three different survey dates shows excellent agreement between the two measurement methods (Fig. 8). The rover survey estimate is taken www.hydrol-earth-syst-sci.net/21/6049/2017/ Hydrol. Earth Syst. Sci., 21, 6049–6067, 2017","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.1357069,-34.217249],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661231,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sandhill Ct, Mildura VIC 3500, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-7"},{"name":"Selected farm properties in intensive survey area, Mallee region, Victoria","kind":"landscape","role":"Farm properties used for property-scale soil moisture estimation and regression modelling","river_name":null,"geocode_query":"Mallee region, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"Comparison of these two independent soil moisture products with the rover surveys increases confidence in rover survey results on the intensive scale. The rover surveys on the intensive scale also offer the opportunity to estimate soil moisture on the farm property scale. A number of properties in the intensive-scale zone are identified in Fig. 10 and the intensive-scale rover survey from March 2017 has been used to derive property-average soil moisture conditions in this figure. The average size of the identified properties is approximately 1 km2 . Point-to-area linear regression modelling based on continuous CRNS measurements from the Bennetts sensor and three intensive rover surveys was applied to 50 properties identified in the intensive survey area and very strong linear relationships were derived with an average R 2 value of 0.97 (range = 0.87–1.00, see Table A1 in Appendix A for full results).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.210242,-34.782332],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Mallee, VIC, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dab04c20adc97745cb3e2ace-site-8","true_coordinates":[142.2797032,-34.7637906],"spread_km":6.7}],"limitations":["Lack of explicit latitude/longitude coordinates for some sites.","Point soil moisture samples provide only approximate validation due to scale mismatch with rover observations.","Impact of roads on rover neutron counts not yet accounted for and may introduce bias.","Property-scale regression models based on limited survey occasions (three) requiring further validation."],"river_ids":[],"site_ids":["doc-dab04c20adc97745cb3e2ace-site-0","doc-dab04c20adc97745cb3e2ace-site-1","doc-dab04c20adc97745cb3e2ace-site-2","doc-dab04c20adc97745cb3e2ace-site-5","doc-dab04c20adc97745cb3e2ace-site-6","doc-dab04c20adc97745cb3e2ace-site-7","doc-dab04c20adc97745cb3e2ace-site-8"],"anchor":[142.858,-35.684],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-ecff0f4242e975a3a0cec107","title":"Complex relationship between seasonal streamflow forecast skill and value in reservoir operations","display_title":"Seasonal Streamflow Forecasts in Australian Reservoirs","doi":"https://doi.org/10.5194/hess-21-4841-2017","theme":"Water management","color":"#eeabac","year":2017,"publication_date":"2017-09-28","authors":["Sean W. D. Turner","James C. Bennett","David E. Robertson","Stefano Galelli"],"summary":"This study investigates how the skill of seasonal streamflow forecasts translates into operational value for reservoir operations in Australia. Using synthetic and actual forecast systems applied to fabricated reservoir designs with inflows from four Australian catchments, the authors explore two operating objectives: maintaining a target storage level (level objective) and meeting a target water demand (supply objective). The results reveal a strong positive relationship between forecast skill and operational value under the level objective but a weak, inconsistent relationship for the supply objective. This discrepancy arises because storage buffers inflow variability under the supply objective, making forecasts valuable only during critical drought periods. The inconsistency in forecast skill during these critical periods determines forecast usefulness for supply-focused reservoirs. The study thus highlights the need to assess forecast skill consistency and the timing of critical decisions when evaluating forecast value for reservoir operations.","finding":"Forecast skill consistently predicts operational value for reservoirs managed to maintain a target storage level (level objective), but not for those managed to meet a target water supply demand (supply objective), where storage buffers inflow variability and forecast value depends on forecast accuracy during critical drought periods.","finding_evidence":{"passage_id":"p009-003","page":9,"quote":"We then assess the performance of the forecast-informed operating scheme against the forecast error injected by the MMFE. 3.2 Results for experiment 1 Figure 5 shows the value of the forecast-informed scheme for each reservoir. The value of forecasts is presented as the reduction in cost relative to control rules (%). A positive cost reduction indicates that the forecast-informed scheme outperforms control rules, and a negative cost reduction indicates that control rules outperform the forecast-informed scheme. Forecasts with zero error (i.e. perfect forecasts) outperform control rules in all cases, regardless of the objective. Interestingly, when operated with a perfect forecast, the reservoirs operated to meet the supply objective enjoy a significantly larger percentage increase in performance (40– 60 %) compared with the reservoirs operated to the level objective (20–40 %).","verified_in_page":true},"topics":["seasonal forecast skill","reservoir operation","forecast value","reservoir storage control","water supply management","forecast uncertainty","hydrological drought","adaptive control"],"study_period":"1982–2010","locations":[{"name":"Burrinjuck Dam","kind":"facility","role":"study reservoir location and inflow source","river_name":null,"geocode_query":"Burrinjuck Dam, New South Wales, Australia","latitude":-35.0,"longitude":148.58,"evidence":{"passage_id":"p003-001","page":3,"quote":"State (Mm3 ) (km2 ) Burrinjuck Perennial 1252.1 0.90 1631 1900–2014 −35.00 148.58 NSW Lake Eppalock Ephemeral 166.8 0.82 1749 1900–2014 −36.88 144.56 VIC Serpentine Intermittent 58.4 0.69 664 1912–2014 −32.40 116.10 WA Upper Yarra Perennial 153.3 0.43 337 1913–2014 −37.68 145.92 VIC the RMSE divided by the standard deviation of observations. Since zero error corresponds to the perfect forecast, all lead times have nRMSE of 0 when no error is injected. As the injected error increases, the performance gap between short and longer lead time forecasts widens, reflecting a deterioration of forecast performance that one would expect with a weaker forecasting system. 2.1.2 Actual forecasts: forecast guided stochastic scenarios (FoGSS) In our second experiment, we apply the forecast guided stochastic scenarios (FoGSS) experimental streamflow forecast system (Bennett et al., 2016, 2017, this issue).","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.58,-35.0],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43670061,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ecff0f4242e975a3a0cec107-site-0"},{"name":"Lake Eppalock","kind":"facility","role":"study reservoir location and inflow source","river_name":null,"geocode_query":"Lake Eppalock, Victoria, Australia","latitude":-36.88,"longitude":144.56,"evidence":{"passage_id":"p003-001","page":3,"quote":"State (Mm3 ) (km2 ) Burrinjuck Perennial 1252.1 0.90 1631 1900–2014 −35.00 148.58 NSW Lake Eppalock Ephemeral 166.8 0.82 1749 1900–2014 −36.88 144.56 VIC Serpentine Intermittent 58.4 0.69 664 1912–2014 −32.40 116.10 WA Upper Yarra Perennial 153.3 0.43 337 1913–2014 −37.68 145.92 VIC the RMSE divided by the standard deviation of observations. Since zero error corresponds to the perfect forecast, all lead times have nRMSE of 0 when no error is injected. As the injected error increases, the performance gap between short and longer lead time forecasts widens, reflecting a deterioration of forecast performance that one would expect with a weaker forecasting system. 2.1.2 Actual forecasts: forecast guided stochastic scenarios (FoGSS) In our second experiment, we apply the forecast guided stochastic scenarios (FoGSS) experimental streamflow forecast system (Bennett et al., 2016, 2017, this issue).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.56,-36.88],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43663346,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ecff0f4242e975a3a0cec107-site-1"},{"name":"Serpentine Reservoir","kind":"facility","role":"study reservoir location and inflow source","river_name":null,"geocode_query":"Serpentine Reservoir, Western Australia, Australia","latitude":-32.4,"longitude":116.1,"evidence":{"passage_id":"p003-001","page":3,"quote":"State (Mm3 ) (km2 ) Burrinjuck Perennial 1252.1 0.90 1631 1900–2014 −35.00 148.58 NSW Lake Eppalock Ephemeral 166.8 0.82 1749 1900–2014 −36.88 144.56 VIC Serpentine Intermittent 58.4 0.69 664 1912–2014 −32.40 116.10 WA Upper Yarra Perennial 153.3 0.43 337 1913–2014 −37.68 145.92 VIC the RMSE divided by the standard deviation of observations. Since zero error corresponds to the perfect forecast, all lead times have nRMSE of 0 when no error is injected. As the injected error increases, the performance gap between short and longer lead time forecasts widens, reflecting a deterioration of forecast performance that one would expect with a weaker forecasting system. 2.1.2 Actual forecasts: forecast guided stochastic scenarios (FoGSS) In our second experiment, we apply the forecast guided stochastic scenarios (FoGSS) experimental streamflow forecast system (Bennett et al., 2016, 2017, this issue).","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.1,-32.4],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70413126,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-ecff0f4242e975a3a0cec107-site-2"},{"name":"Upper Yarra Reservoir","kind":"facility","role":"study reservoir location and inflow source","river_name":null,"geocode_query":"Upper Yarra Reservoir, Victoria, Australia","latitude":-37.68,"longitude":145.92,"evidence":{"passage_id":"p003-001","page":3,"quote":"State (Mm3 ) (km2 ) Burrinjuck Perennial 1252.1 0.90 1631 1900–2014 −35.00 148.58 NSW Lake Eppalock Ephemeral 166.8 0.82 1749 1900–2014 −36.88 144.56 VIC Serpentine Intermittent 58.4 0.69 664 1912–2014 −32.40 116.10 WA Upper Yarra Perennial 153.3 0.43 337 1913–2014 −37.68 145.92 VIC the RMSE divided by the standard deviation of observations. Since zero error corresponds to the perfect forecast, all lead times have nRMSE of 0 when no error is injected. As the injected error increases, the performance gap between short and longer lead time forecasts widens, reflecting a deterioration of forecast performance that one would expect with a weaker forecasting system. 2.1.2 Actual forecasts: forecast guided stochastic scenarios (FoGSS) In our second experiment, we apply the forecast guided stochastic scenarios (FoGSS) experimental streamflow forecast system (Bennett et al., 2016, 2017, this issue).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.92,-37.68],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21559582,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-ecff0f4242e975a3a0cec107-site-3"}],"limitations":["reservoir inflow data from four Australian catchments only","fabricated reservoirs rather than actual reservoir operational parameters","forecast skill evaluated mostly over 1982–2010 data period","synthetic forecasts used with an injected error model that may not reflect all real-world uncertainties"],"river_ids":[],"site_ids":["doc-ecff0f4242e975a3a0cec107-site-0","doc-ecff0f4242e975a3a0cec107-site-1","doc-ecff0f4242e975a3a0cec107-site-2","doc-ecff0f4242e975a3a0cec107-site-3"],"anchor":[144.56,-36.88],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040055960,5040070710,5040591050],"main_basin_ids":[5040055960,5040070710,5040073410]},{"id":"doc-fa97b2ebcebda976fb4c38fc","title":"Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods","display_title":"Flow Connectivity Effects on Thailand Flood Model","doi":"https://doi.org/10.5194/hess-21-5143-2017","theme":"Floodplains","color":"#e9bd77","year":2017,"publication_date":"2017-10-12","authors":["Cherry May R. Mateo","Dai Yamazaki","Hyungjun Kim","Adisorn Champathong","Jai Vaze","Taikan Oki"],"summary":"This study evaluates how spatial resolution and flow connectivity representation influence the ability of the CaMa-Flood global river model (GRM) to simulate the 2011 extreme flood in Thailand's Chao Phraya River Basin. It demonstrates that while finer spatial resolution slightly improves model predictions, this is only true when multiple downstream connectivity (MDC) is represented, allowing for realistic bifurcating flows within floodplains and deltas. Single downstream connectivity (SDC) assumptions lead to poorer performance at fine resolutions due to unrealistic flow constraints causing backflows and inaccurate flood extents. The study highlights the importance of MDC representation in large-scale flood inundation modeling, especially for flat and complex river basin landscapes.","finding":"Representing multiple downstream flow connectivity (MDC) is critical for realistic simulation of flood inundation in large, flat floodplains such as the Chao Phraya River Basin, particularly at finer spatial resolutions. MDC pathways maintain realistic flow connectivity and flow capacity between floodplain grids, preventing the severe backflows and flood extent inaccuracies seen under single downstream connectivity (SDC) assumptions at fine resolutions.","finding_evidence":{"passage_id":"p007-005","page":7,"quote":"The changes in model efficiency are more evident in SDC simulations: between simulation at 10 and 1 km resolutions (Fig. 5a–e), NSE and correlation of discharge drastically declined by as much as 50 and 10 %, respectively, while RMSE, PBIAS and difference in discharge peak timing drastically increased by as much as 90, 35, and 70 %, respectively. On the other hand, model efficiency incrementally increased and errors marginally decreased with finer resolutions in MDC simulations (below 3 % change between simulations in 10 and 1 km). Although statistically insignificant (less than 5 % between simulations in 10 and 1 km), both MDC and SDC simulations show increasing trends in the fit of flood extent (F shown in Fig. 5f) with finer resolution.","verified_in_page":true},"topics":["hydrological-modeling","flow-connectivity","spatial-resolution","river-network","flood-inundation","model-calibration","river-basin","hydrodynamics"],"study_period":"2010–2011","locations":[{"name":"Chao Phraya River Basin, Thailand","kind":"basin","role":"study basin for flood simulation","river_name":null,"geocode_query":"Chao Phraya River Basin, Thailand","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Quantitative and qualitative assessments of the results are presented in Sect. 5. Lessons learned with regards to large-scale flood inundation modeling, and the caveats of the study are discussed in Sect. 6. The paper concludes with a summary of the findings and insights to the necessary development in GRMs towards finer resolution modeling of largeto global-scale floods. 2 Test basin: Chao Phraya River Basin, Thailand In this paper we assess the capability of CaMa-Flood to simulate a large-scale flood in the Chao Phraya River Basin in Thailand. The basin was chosen because of the complexity of its river network, and the recent occurrence of a large-scale flood. With a catchment area of approximately 158 000 km2 , the Chao Phraya River Basin (shown in Fig. 1) is the largest and most important geographical unit in Thailand (Sripong et al., 2000).","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.3278107014095,14.61909777289625],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040017030,"main_basin_id":4040017030,"basin_region":"Central and South-East Asia","basin_area_km2":235439.1,"hydrology_source":"hydrobasins","site_id":"doc-fa97b2ebcebda976fb4c38fc-site-0"},{"name":"Bangkok, Thailand","kind":"settlement","role":"capital city located in study basin","river_name":null,"geocode_query":"Bangkok, Thailand","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The northern region of the basin consists of mountainous areas; its middle region is a floodplain with a gentle slope of approximately 1/15 000, and its lowermost region is a delta. These geographic features make the region highly prone to flood inundation. In 2011, enormous economic losses estimated at USD 45 billion (World Bank, 2012) were incurred due to the Figure 1. The Chao Phraya River Basin. Model domain, elevation map, river network, and validation stations. The orange dot marks the location of the station used for calibration. White dots mark the stations used for validation. The red dot marks the capital of Thailand, Bangkok. Areas in orange squares are zoomed in for analysis of results. extensive flooding of Thailand’s industrial zones. The total rainfall during the rainy season was 143 % (∼ 1439 mm) of that of the average rainy season from 1982 to 2002 (Komori et al., 2012); the flood peaks have exceeded the estimated 100-year return period peak discharges (DHI, 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.5017651,13.7563309],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bangkok, Thailand","basin_id":4040017030,"main_basin_id":4040017030,"basin_region":"Central and South-East Asia","basin_area_km2":235439.1,"hydrology_source":"hydrobasins","site_id":"doc-fa97b2ebcebda976fb4c38fc-site-1"}],"limitations":["Calibration performed primarily at coarse spatial resolution and transferred without full recalibration to finer resolutions; limited validation of MDC scheme outside three basins including this study basin","Uncertainties remain in representation of small MDC pathways due to DEM resolution limits and lack of exact mapping of artificial canals or small channels","Satellite inundation data used for validation is coarse (~20 km resolution), limiting detailed flood extent validation","Runoff forcing data is at coarser resolution (5 arcmin), possibly limiting benefits of finer CaMa-Flood grid resolutions","Natural and anthropogenic changes to river channel networks and artificial waterway operations are not represented in model setup"],"river_ids":[],"site_ids":["doc-fa97b2ebcebda976fb4c38fc-site-0","doc-fa97b2ebcebda976fb4c38fc-site-1"],"anchor":[100.5017651,13.7563309],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[4040017030],"main_basin_ids":[4040017030]},{"id":"doc-0572f800219d2f20ebff8202","title":"Future climate and runoff projections across South Asia from CMIP5 global climate models and hydrological modelling","display_title":"Projected Climate and Runoff Changes in South Asia","doi":"https://doi.org/10.1016/j.ejrh.2018.06.004","theme":"Climate","color":"#98d4f1","year":2018,"publication_date":"2018-07-02","authors":["Hongxing Zheng","Francis H. S. Chiew","Steve Charles","Geoff Podger"],"summary":"This study presents projections of future climate variables (temperature, precipitation, potential evaporation) and resulting runoff changes across South Asia during 2046–2075 relative to 1976–2005 under the RCP8.5 scenario. It uses empirical downscaling of 42 CMIP5 global climate models and hydrological models including the H08 hydrological model and Budyko relationship. The region is divided into nine freshwater ecoregion sub-regions for detailed analysis. The study discusses runoff sensitivities to climatic variables, uncertainties in projections, and impacts on water supply reliability in specific basins.","finding":"The median projection indicates mean annual runoff will increase by 20–30% across most of the Indian sub-continent except in the far north-east and far north-west, mainly driven by precipitation increase and moderated or intensified by changes in temperature and potential evaporation. There remains large uncertainty due primarily to GCM precipitation variability.","finding_evidence":{"passage_id":"p016-000","page":16,"quote":"GCMs to provide an indication of the full range of uncertainty. 5. Summary and conclusion The paper presents future climate and runoff projections, estimated using a consistent method, across South Asia. The future runoff is modelled using the Budyko relationship and H08 global hydrological model, with future climate inputs informed by projections from the 42 CMIP5 GCMs. The modelling results indicate that future runoff will increase throughout most of the region (Indian sub-continent) except in the far north-east (southern part of the Tibetan Plateau) and the arid far north-west region. The median projection shows increases in mean annual runoff of 20–30% in the Indian sub-continent for 2046–2075 relative to 1976–2005 for RCP8.5 greenhouse gas concentration pathway. However, there is considerable uncertainty in the projections, with a 10th and 90th percentile range of more than 50%.","verified_in_page":true},"topics":["climate-change-projections","downscaling","runoff-projections","hydrological-modeling","precipitation-change","temperature-change","water-security","climate-uncertainty"],"study_period":"1976–2005 baseline; 2046–2075 future projection","locations":[{"name":"South Asia","kind":"region","role":"study region encompassing entire analysis domain","river_name":null,"geocode_query":"South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Contents lists available at ScienceDirect Journal of Hydrology: Regional Studies journal homepage: www.elsevier.com/locate/ejrh Future climate and runoff projections across South Asia from CMIP5 global climate models and hydrological modelling Hongxing Zhenga,⁎ , Francis H.S. Chiewa , Steve Charlesb , Geoff Podgera aCSIRO Land and Water, GPO BOX 1666, Canberra ACT 2601, Australia bCSIRO Land and Water, Perth, WA, Australia ARTICLE INFO Keywords: Climate change Projections Runoff Precipitation Water security South Asia ABSTRACT Study region: The South Asia. Study focus: This paper presents future climate and runoff projections for the South Asia region under the RCP8.5 scenario with climate change informed by 42 CMIP5 GCMs. Runoff is projected for 0.5° grids using hydrological models with future climate inputs obtained by empirically scaling the historical climate series.","verified_in_page":true},"resolution_status":"resolved","coordinates":[76.456309,24.97764],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Asia","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-0","true_coordinates":[76.4563087,25.03764],"spread_km":6.7},{"name":"North-West region (NW), South Asia","kind":"region","role":"major sub-region for runoff and climate change analysis","river_name":null,"geocode_query":"North-West region, South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[77.0684107,28.7196003],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North West Delhi, Delhi, India","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-1"},{"name":"Tibetan Plateau (TP), South Asia","kind":"region","role":"major sub-region partly studied for runoff and climate projections","river_name":null,"geocode_query":"Tibetan Plateau, South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Indus River Basin (IND), South Asia","kind":"basin","role":"key basin with modeled climate and runoff projections","river_name":"Indus","geocode_query":"Indus River Basin, South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.46428,29.844518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-3","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Ganges-Brahmaputra River Basin (GA), South Asia","kind":"basin","role":"major basin analyzed for runoff and climate change responses","river_name":"Ganges-Brahmaputra","geocode_query":"Ganges-Brahmaputra River Basin, South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.13545859999999,26.0708776],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-4"},{"name":"Arakan Coast (AC), South Asia","kind":"region","role":"sub-region included in climate and runoff assessments","river_name":null,"geocode_query":"Arakan Coast, South Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[94,19],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arakan Coast, Myanmar (Burma)","hydrology_source":"none","site_id":"doc-0572f800219d2f20ebff8202-site-5"},{"name":"Narmad-Tapti River Basin (NT), South Asia","kind":"basin","role":"sub-region showing substantial runoff increase in projections","river_name":null,"geocode_query":"Narmad-Tapti River Basin, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.13447156879938,21.22264228149762],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040029530,"main_basin_id":4040029530,"basin_region":"Central and South-East Asia","basin_area_km2":316845.9,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-6"},{"name":"Deccan Plateau (DP), India","kind":"region","role":"region analyzed for climate and runoff changes","river_name":null,"geocode_query":"Deccan Plateau, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[77,14],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Deccan, Hemavathi, Andhra Pradesh 515286","basin_id":4040027940,"main_basin_id":4040027940,"basin_region":"Central and South-East Asia","basin_area_km2":258784.2,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-7"},{"name":"Ghats Coast (GH), India","kind":"region","role":"sub-region included in hydrological projections","river_name":null,"geocode_query":"Ghats Coast, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sri Lanka Island (SL)","kind":"region","role":"region analyzed for runoff and climate impacts","river_name":null,"geocode_query":"Sri Lanka","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The structure of the paper is as follows – Section 2 outlines the data and methods used; Section 3 presents and discusses the projected changes in the climate variables across South Asia; Section 4 presents the climate change impacts on runoff and water supply security, and also discusses results and implications from different modelling considerations; Section 5 presents the summary and conclusions. 2. Data and methods In this study, the South Asia region refers to the domain between 5.25–38.00 °N and 65.25–93.75 °E. To facilitate presentation and discussion of results, nine sub-regions based on the freshwater ecoregion map of Abell et al. (2008) are used. The nine sub-regions, as shown in Fig. 1, are North-West region (NW), part of the Tibetan Plateau (TP), Indus River Basin (IND), Ganges-Brahmaputra River Basin (GA), part of the Arakan Coast (AC), Narmad-Tapti River Basin (NT), Deccan Plateau (DP), Ghats Coast (GH), and Sri Lanka Island (SL). 2.1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Koshi basin, Himalaya, Nepal/India","kind":"basin","role":"location for detailed storage reliability and runoff projection analysis","river_name":null,"geocode_query":"Koshi River Basin, Nepal","latitude":27.25,"longitude":86.25,"evidence":{"passage_id":"p013-001","page":13,"quote":"Grid Brahmani-Baitarni grid (21.25 °N, 85.75 °E) Koshi grid (27.25 °N, 86.25 °E) Scaling method Annual scaling Seasonal scaling Daily scaling Bias correction Annual scaling Seasonal scaling Daily scaling Bias correction Percentage change in storage reliability 10th percentile −7.7 −6.4 −8.4 −16.2 −9.0 −6.4 −5.5 −27.2 Median 0.0 0.6 −0.7 −4.1 0.8 1.5 1.8 3.4 90th percentile 2.3 2.3 2.3 3.6 3.7 4.6 4.6 7.9 H. Zheng et al. Journal of Hydrology: Regional Studies 18 (2018) 92–109 104","verified_in_page":true},"resolution_status":"resolved","coordinates":[86.25,27.25],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-10"},{"name":"Brahmani-Baitarni basin, India","kind":"basin","role":"location for detailed runoff and water supply security analysis","river_name":null,"geocode_query":"Brahmani-Baitarni River Basin, India","latitude":21.25,"longitude":85.75,"evidence":{"passage_id":"p013-001","page":13,"quote":"Grid Brahmani-Baitarni grid (21.25 °N, 85.75 °E) Koshi grid (27.25 °N, 86.25 °E) Scaling method Annual scaling Seasonal scaling Daily scaling Bias correction Annual scaling Seasonal scaling Daily scaling Bias correction Percentage change in storage reliability 10th percentile −7.7 −6.4 −8.4 −16.2 −9.0 −6.4 −5.5 −27.2 Median 0.0 0.6 −0.7 −4.1 0.8 1.5 1.8 3.4 90th percentile 2.3 2.3 2.3 3.6 3.7 4.6 4.6 7.9 H. Zheng et al. Journal of Hydrology: Regional Studies 18 (2018) 92–109 104","verified_in_page":true},"resolution_status":"resolved","coordinates":[85.75,21.25],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-0572f800219d2f20ebff8202-site-11"}],"limitations":["Uncertainty from GCM precipitation projections is very large and dominates runoff projection uncertainty.","The hydrological model H08, while useful for broad-scale projection, lacks detailed local calibration or validation and cannot replace detailed basin-specific studies.","Some climate downscaling methods (e.g. quantile mapping) introduce additional uncertainties and biases, especially in precipitation distributions.","Spatial coordinates for many regions and basins are not provided or precisely defined in the source.","The study uses 0.5° grid resolution which may not capture fine spatial variability in mountainous or heterogeneous terrain."],"river_ids":[],"site_ids":["doc-0572f800219d2f20ebff8202-site-0","doc-0572f800219d2f20ebff8202-site-1","doc-0572f800219d2f20ebff8202-site-3","doc-0572f800219d2f20ebff8202-site-4","doc-0572f800219d2f20ebff8202-site-5","doc-0572f800219d2f20ebff8202-site-6","doc-0572f800219d2f20ebff8202-site-7","doc-0572f800219d2f20ebff8202-site-10","doc-0572f800219d2f20ebff8202-site-11"],"anchor":[76.456309,24.97764],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040027940,4040029530,4040794730,4040960200,4040960480],"main_basin_ids":[4040025450,4040025460,4040027940,4040029530,4040033640]},{"id":"doc-2699d2799a0c9aa3c95fe69e","title":"Addressing RFID Misreadings to Better Infer Bee Hive Activity","display_title":"RFID Analysis of Honey Bee Hive Activity","doi":"https://doi.org/10.1109/access.2018.2844181","theme":"Land & ecosystems","color":"#c8df98","year":2018,"publication_date":"2018-01-01","authors":["Ferry Susanto","Thomas L. Gillard","Paulo de Souza","Benita Vincent","Setia Budi","Auro C. Almeida","Gustavo Pessin","Helder Arruda","Raymond Williams","Ulrich Engelke","Peter Marendy","Pascal Hirsch","Jing He"],"summary":"This study develops a method to address the issue of missing RFID tag readings when monitoring honey bee (Apis mellifera) activity at apiaries. Using a six-month field experiment in Tasmania, Australia, involving RFID-tagged bees in four hives, the method applies curve fitting via genetic algorithms to model bee behaviors—namely 'by the entry', 'short mission', and 'foraging'. This approach allows improved inference of colony-level activity despite incomplete data, providing temporal behavioral distributions and behavioral estimates over time.","finding":"The RFID data for tagged honey bees, despite substantial missed readings, can be effectively modeled as a mixture of Gaussian distributions representing different behavioral categories. This modeling allows estimation of the number of bees engaged in behaviors like foraging at specific times or periods, improving the quality and interpretation of behavioral data from RFID monitoring despite sensor limitations.","finding_evidence":{"passage_id":"p009-001","page":9,"quote":"It suggests that bees start foraging at approximately 7 am and finish at 8 pm. Around noon (between approximately 12 pm and 1 pm), most bees are involved in foraging role (e.g. exploiter, recruit, scout, water carrier); followed by bythe-entry activities (e.g. hive defense, temperature control); and lastly, on short missions (e.g. orientation flights, wandering around the nest). The proportion of bees involved in different behaviors varies relative to time of day. Therefore, a normalisation of the curve fitted Gaussian PDF (Figure 6) is depicted in Figure 7. The normalized curve reveals that approximately 80% of the workers within the colony are engaged in BTE and ≈ 20% are in SM during early morning (before sunrise) and late at night (after sunset). C.","verified_in_page":true},"topics":["rfid technology","bee behavior classification","genetic algorithm optimization","data quality improvement","animal tracking","field experiment","curve fitting","behavioral ecology"],"study_period":"April to November 2014","locations":[{"name":"Geeveston, Tasmania","kind":"settlement","role":"site of the RFID-tagged honey bee field experiment","river_name":null,"geocode_query":"Geeveston, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"F. Susanto et al.: Addressing RFID Misreadings to Better Infer Bee Hive Activity FIGURE 1. The bee experiment conducted at Geeveston, Tasmania: (a) hive entrance; and (b) RFID reader installed underneath the entrance that detects the passage of tagged bees. was actually doing. The assessment of what behavior a bee was exhibiting usually needs to take into account several successive RFID detections. The daily activity of bees is greatly affected by external factors, especially the weather, and in particular temperature, precipitation, solar radiation, and wind speed [28]. If, for example, the hive becomes too hot, bees can leave the hive and ‘‘beard’’ at the entrance, or use their wings to ventilate at the entry of the hive. If the temperature is too low (i.e. typically below 10◦C) bees will not leave the hive, and instead form a cluster on the comb of the brood nest to maintain the optimal brood temperature of 34.5 ◦C. Bees do not fly during storms or during high wind speeds.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.9289332,-43.16404199999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31291658,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Geeveston TAS 7116, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-2699d2799a0c9aa3c95fe69e-site-0"},{"name":"Tasmania","kind":"region","role":"regional context and meteorological influence on bee behavior","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-002","page":11,"quote":"An increased probability of bees undertaking short missions and remaining by the entry is seen during the hours when bees are not actively engaged in foraging activities. This is likely due to forager role plasticity resulting in the reallocation of foragers to defensive or hygienic roles, or simply a matter of proportions altering as numbers of bees engaged in BTE and SM increases relative to FG (Figure 7). Not only was activity variable over the daily cycle, but our six months of data include a distinct shift in behavior over the long term (Figure 8). The bee tagging period was commenced in April and terminated in October. In April and November tagged bee numbers within the hives were much lower than in the intermediate months as a result of tagged populations becoming established and dying out respectively (Figure 3). As a result of this, only data from May to October was included in the analysis.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Coordinates for sites in Tasmania are not specified in the text, set as null.","Small number of hives (four) limits replicability and representativeness.","Monthly data for April and November excluded due to insufficient data for curve fitting.","No independent calibration method (e.g., video) was included to validate RFID-inferred behaviors."],"river_ids":[],"site_ids":["doc-2699d2799a0c9aa3c95fe69e-site-0"],"anchor":[146.9289332,-43.16404199999999],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040077640],"main_basin_ids":[5040077640]},{"id":"doc-28e8acce5a55b0138209893d","title":"Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment","display_title":"Postprocessed Ensemble Precipitation Forecasts in Alberta","doi":"https://doi.org/10.5194/hess-22-1957-2018","theme":"Flow & ecology","color":"#80edc2","year":2018,"publication_date":"2018-03-23","authors":["Sanjeev K. Jha","Durga L. Shrestha","Tricia A. Stadnyk","Paulin Coulibaly"],"summary":"This study evaluates the performance of the rainfall post-processing (RPP) Bayesian approach to improve precipitation forecasts in the cold, mountainous catchments of southern Alberta, Canada. Using precipitation forecasts from the Global Ensemble Forecasting System (GEFS) and the Global Deterministic Forecast System (GDPS) for the years 2013 to 2015, including the major 2013 flood event, the RPP was applied at subcatchment level across 15 subcatchments of the Bow, Oldman, and Red Deer river basins. Results demonstrate that RPP effectively reduces bias and error in raw forecasts, generates reliable ensembles capturing forecast uncertainty, and improves discrimination of precipitation events, especially for short lead times and extreme flooding events.","finding":"The rainfall post-processing (RPP) approach reliably removes bias and reduces errors in raw precipitation forecasts from GEFS and GDPS models across diverse subcatchments in Alberta, Canada, and generates ensembles that capture peak precipitation events such as the major 2013 flood, thereby improving uncertainty quantification compared to deterministic forecasts.","finding_evidence":{"passage_id":"p009-001","page":9,"quote":"For day 1, the deviation of the points from the diagonal (1 : 1 line) is higher, indicating a larger bias for day 1 compared to days 3 and 5. To explore this further, we calculated the frequency of observed data lying within the 10–90 % confidence boundary of calibrated QPFs. Figure 6 shows that in the case of calibrated GEFS, the calculated frequency of observed data for a lead time of 1 to 5 days varies between 0.78 and 0.88. However, for calibrated GDPS, the frequency lies between 0.87 and 0.9. Figure 6 demonstrates that as the lead time increases, the frequency of observed data lying within the [0.1–0.9] confidence boundary is higher. 4.2 Performance of calibrated QPFs during an extreme event As mentioned in Sect. 2, a severe flood event occurred from 20 to 24 June 2013 in Calgary (located near the outlet of subcatchment 7; see Fig. 1b). Therefore we examine subcatchment-averaged precipitation obtained from raw and calibrated QPFs against observed data.","verified_in_page":true},"topics":["ensemble forecasting","precipitation","statistical post-processing","forecast verification","bias correction","uncertainty quantification","extreme events","hydrological modeling"],"study_period":"2013–2015","locations":[{"name":"southern Alberta","kind":"region","role":"study area encompassing study catchments with cold, mountainous climate","river_name":null,"geocode_query":"southern Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"To the best of our knowledge, this is the first time an approach for generating ensembles from a deterministic QPF is tested in any Canadian catchment explicitly for the benefit of forecast centers. As part of the National Sciences and Engineering Research Council Canadian Floodnet 3.1 project, the first author visited flood forecast centers in western Canada. One of the common concerns raised by forecasters was the need for generating ensembles in order to assess risk associated with streamflow forecasts provided to the public. The conclusions from the present study will be highly relevant and beneficial for flood forecast centers in Canada. 2 Study area and datasets The selected study area is southern Alberta, located in western Canada (Fig. 1a). The Rocky Mountains are located at the southern boundary with the United States and the western boundary with British Columbia, with the Canadian Prairies region extending toward the southeastern portion of the province.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-111.738566,49.9546196],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Alberta, AB, Canada","basin_id":7040282770,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":28557.8,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-0"},{"name":"Bow River basin","kind":"basin","role":"major river basin in the study area where extreme precipitation and flooding occurred","river_name":"Bow","geocode_query":"Bow River basin, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Topography plays a major role in generating cyclonic weather systems common to Alberta. The Oldman, Bow, and Red Deer River basins, all located at the foothills of the Canadian Rocky Mountain range, are subjected to extreme precipitation events. In June 2013 a major flood occurred in this region, resulting from the combined effect of heavy rainfall during mountain snowpack melt over partially frozen ground (Pomeroy et al., 2016; Teufel et al., 2016). Some river basins received 1.5 × 1 : 100-year rainfall, estimated to be 250 mm rain in 24 h. The 2013 flood affected most of southern Alberta from Canmore to Calgary and beyond, causing evacuation of around 100 000 people and a reported cost of damage of infrastructure exceeding CAD 6 billion (Milrad et al., 2015). The spatial distribution of convective precipitation and orography make it difficult for any NWP model to successfully predict the summertime convective precipitation in Alberta.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-113.9769169173594,50.80014659392783],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040282720,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":24883.9,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-1"},{"name":"Oldman River basin","kind":"basin","role":"major river basin in the study area subject to extreme precipitation events","river_name":"Oldman","geocode_query":"Oldman River basin, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Topography plays a major role in generating cyclonic weather systems common to Alberta. The Oldman, Bow, and Red Deer River basins, all located at the foothills of the Canadian Rocky Mountain range, are subjected to extreme precipitation events. In June 2013 a major flood occurred in this region, resulting from the combined effect of heavy rainfall during mountain snowpack melt over partially frozen ground (Pomeroy et al., 2016; Teufel et al., 2016). Some river basins received 1.5 × 1 : 100-year rainfall, estimated to be 250 mm rain in 24 h. The 2013 flood affected most of southern Alberta from Canmore to Calgary and beyond, causing evacuation of around 100 000 people and a reported cost of damage of infrastructure exceeding CAD 6 billion (Milrad et al., 2015). The spatial distribution of convective precipitation and orography make it difficult for any NWP model to successfully predict the summertime convective precipitation in Alberta.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-113.1484011421511,49.81284393564352],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040282770,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":28557.8,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-2"},{"name":"Red Deer River basin","kind":"basin","role":"major river basin in the study area subject to extreme precipitation events","river_name":"Red Deer","geocode_query":"Red Deer River basin, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Topography plays a major role in generating cyclonic weather systems common to Alberta. The Oldman, Bow, and Red Deer River basins, all located at the foothills of the Canadian Rocky Mountain range, are subjected to extreme precipitation events. In June 2013 a major flood occurred in this region, resulting from the combined effect of heavy rainfall during mountain snowpack melt over partially frozen ground (Pomeroy et al., 2016; Teufel et al., 2016). Some river basins received 1.5 × 1 : 100-year rainfall, estimated to be 250 mm rain in 24 h. The 2013 flood affected most of southern Alberta from Canmore to Calgary and beyond, causing evacuation of around 100 000 people and a reported cost of damage of infrastructure exceeding CAD 6 billion (Milrad et al., 2015). The spatial distribution of convective precipitation and orography make it difficult for any NWP model to successfully predict the summertime convective precipitation in Alberta.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-113.8128207,52.278895],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040256540,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":50559.8,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-3"},{"name":"Calgary","kind":"settlement","role":"urban center near the outlet of subcatchment 7 affected by the 2013 flood event","river_name":null,"geocode_query":"Calgary, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"For day 1, the deviation of the points from the diagonal (1 : 1 line) is higher, indicating a larger bias for day 1 compared to days 3 and 5. To explore this further, we calculated the frequency of observed data lying within the 10–90 % confidence boundary of calibrated QPFs. Figure 6 shows that in the case of calibrated GEFS, the calculated frequency of observed data for a lead time of 1 to 5 days varies between 0.78 and 0.88. However, for calibrated GDPS, the frequency lies between 0.87 and 0.9. Figure 6 demonstrates that as the lead time increases, the frequency of observed data lying within the [0.1–0.9] confidence boundary is higher. 4.2 Performance of calibrated QPFs during an extreme event As mentioned in Sect. 2, a severe flood event occurred from 20 to 24 June 2013 in Calgary (located near the outlet of subcatchment 7; see Fig. 1b). Therefore we examine subcatchment-averaged precipitation obtained from raw and calibrated QPFs against observed data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-114.0718831,51.04473309999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Calgary, AB, Canada","basin_id":7040282720,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":24883.9,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-4"},{"name":"subcatchment 7 (Low Bow Local Bearspaw)","kind":"reach","role":"study subcatchment including area near Calgary, focus for flood event analysis","river_name":null,"geocode_query":"Low Bow Local Bearspaw, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"For day 1, the deviation of the points from the diagonal (1 : 1 line) is higher, indicating a larger bias for day 1 compared to days 3 and 5. To explore this further, we calculated the frequency of observed data lying within the 10–90 % confidence boundary of calibrated QPFs. Figure 6 shows that in the case of calibrated GEFS, the calculated frequency of observed data for a lead time of 1 to 5 days varies between 0.78 and 0.88. However, for calibrated GDPS, the frequency lies between 0.87 and 0.9. Figure 6 demonstrates that as the lead time increases, the frequency of observed data lying within the [0.1–0.9] confidence boundary is higher. 4.2 Performance of calibrated QPFs during an extreme event As mentioned in Sect. 2, a severe flood event occurred from 20 to 24 June 2013 in Calgary (located near the outlet of subcatchment 7; see Fig. 1b). Therefore we examine subcatchment-averaged precipitation obtained from raw and calibrated QPFs against observed data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-114.311164,51.138655],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bearspaw, AB T3Z 1C7, Canada","basin_id":7040282720,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":24883.9,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-5"},{"name":"subcatchment 10 (Canmore Ghost Waiparous)","kind":"reach","role":"mountainous subcatchment with peak precipitation during flooding event","river_name":null,"geocode_query":"Canmore Ghost Waiparous, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"For day 1, the deviation of the points from the diagonal (1 : 1 line) is higher, indicating a larger bias for day 1 compared to days 3 and 5. To explore this further, we calculated the frequency of observed data lying within the 10–90 % confidence boundary of calibrated QPFs. Figure 6 shows that in the case of calibrated GEFS, the calculated frequency of observed data for a lead time of 1 to 5 days varies between 0.78 and 0.88. However, for calibrated GDPS, the frequency lies between 0.87 and 0.9. Figure 6 demonstrates that as the lead time increases, the frequency of observed data lying within the [0.1–0.9] confidence boundary is higher. 4.2 Performance of calibrated QPFs during an extreme event As mentioned in Sect. 2, a severe flood event occurred from 20 to 24 June 2013 in Calgary (located near the outlet of subcatchment 7; see Fig. 1b). Therefore we examine subcatchment-averaged precipitation obtained from raw and calibrated QPFs against observed data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-115.3479101,51.08836660000001],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canmore, AB, Canada","basin_id":7040282720,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":24883.9,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-6"},{"name":"subcatchment 11 (Spray Kananaskis)","kind":"reach","role":"mountainous subcatchment with peak precipitation during flooding event","river_name":null,"geocode_query":"Spray Kananaskis, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"For day 1, the deviation of the points from the diagonal (1 : 1 line) is higher, indicating a larger bias for day 1 compared to days 3 and 5. To explore this further, we calculated the frequency of observed data lying within the 10–90 % confidence boundary of calibrated QPFs. Figure 6 shows that in the case of calibrated GEFS, the calculated frequency of observed data for a lead time of 1 to 5 days varies between 0.78 and 0.88. However, for calibrated GDPS, the frequency lies between 0.87 and 0.9. Figure 6 demonstrates that as the lead time increases, the frequency of observed data lying within the [0.1–0.9] confidence boundary is higher. 4.2 Performance of calibrated QPFs during an extreme event As mentioned in Sect. 2, a severe flood event occurred from 20 to 24 June 2013 in Calgary (located near the outlet of subcatchment 7; see Fig. 1b). Therefore we examine subcatchment-averaged precipitation obtained from raw and calibrated QPFs against observed data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-115.3310519,50.9047121],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Smith Dorrien Trail, Kananaskis, AB, Canada","basin_id":7040282720,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":24883.9,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-7"},{"name":"South Saskatchewan River basin","kind":"basin","role":"major combined basin containing study rivers flowing eastward into the Canadian Prairies","river_name":"South Saskatchewan River","geocode_query":"South Saskatchewan River basin, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Based on the world map of Peel et al. (2007), the study area is classified as having a warm summer, humid continental climate. The Köppen–Geiger classification system presented for Canada in Delavau et al. (2015) shows that our study area falls within the KPN42 (Dfb – snow, fully humid precipitation, warm summer), KPN43 (Dfc – snow, fully humid precipitation, cool summer), and KPN 62 (ET polar tundra). All of the three river basins are part of the South Saskatchewan River basin, which flows eastward towards the Canadian Prairies. The combined basin area is approximately 101 720 km2 (AEP, 2017). For the purpose of hydrological prediction, the River Forecast Centre in Alberta uses 15 subcatchments (marked with numbers 1 to 15 in Fig. 1b) to delineate the study area, with drainage areas as indicated in Table 2. The distribution of precipitation gauges and forecast locations is uneven in the various subcatchments (Fig. 1b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-108.3792670524123,51.55802186410026],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040194860,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":153257.2,"hydrology_source":"hydrobasins","site_id":"doc-28e8acce5a55b0138209893d-site-8"}],"limitations":["Study uses only 3 years of data, which may limit inference of extremes and spatial variations.","Spatial nonstationarity and topography effects were not directly modeled in the Bayesian post-processing approach.","Coordinates for subcatchments and other geographic entities were not provided in the text; exact locations are unspecified.","Observational gauge distribution is uneven, which may affect subcatchment precipitation averaging and verification."],"river_ids":[],"site_ids":["doc-28e8acce5a55b0138209893d-site-0","doc-28e8acce5a55b0138209893d-site-1","doc-28e8acce5a55b0138209893d-site-2","doc-28e8acce5a55b0138209893d-site-3","doc-28e8acce5a55b0138209893d-site-4","doc-28e8acce5a55b0138209893d-site-5","doc-28e8acce5a55b0138209893d-site-6","doc-28e8acce5a55b0138209893d-site-7","doc-28e8acce5a55b0138209893d-site-8"],"anchor":[-114.0718831,51.04473309999999],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[7040194860,7040256540,7040282720,7040282770],"main_basin_ids":[7040022240]},{"id":"doc-3d2f0b3b4460a15ce5a89256","title":"Recent trends in wind speed across Saudi Arabia, 1978–2013: a break in the stilling","display_title":"Wind Speed Trends in Saudi Arabia 1978–2013","doi":"https://doi.org/10.1002/joc.5423","theme":"Climate","color":"#98d4f1","year":2018,"publication_date":"2018-01-29","authors":["César Azorín-Molina","Shafiqur Rehman","José A. Guijarro","Tim R. McVicar","Lorenzo Minola","Deliang Chen","Sergio M. Vicente‐Serrano"],"summary":"This study analyzes trends in near-surface wind speed across Saudi Arabia from 1978 to 2013 using 19 homogenized meteorological stations. It identifies a significant overall decline in wind speed, particularly in winter and spring, but reveals a recent break in this trend since 2002, including some recovery in summer and autumn. Wind speed changes differ by geography and elevation, with coastal and inland stations showing declines and mountain stations showing increases during summer. Atmospheric circulation patterns, notably the North Atlantic Oscillation, Southern Oscillation, and East Atlantic indices, significantly influence these wind speed trends.","finding":"A significant decline in near-surface wind speed of −0.058 m s⁻¹ per decade was observed across Saudi Arabia from 1978-2013, with a break in this stilling trend occurring around 2002, after which wind speeds showed non-significant increases especially in summer and autumn.","finding_evidence":{"passage_id":"p008-002","page":8,"quote":"Also, we detected a clear intra-annual cycle, with declines for all months, but being more negative and statistically significant (p<0.05) between November and March, and a weak and non-significant decline between April and September; with the exception of a statistically significant slowdown in August. This is the same trend pattern for the coast and inland series, whereas the mountain series displayed an opposite positive sign between April and September. These increasing trends are statistically significant (p<0.05) in June and August. Figures 4 and 5 represent the temporal variability of annual/seasonal and monthly wind speed series, respectively, for the four series. Annually, the 10-year Gaussian low-pass filter uncovers three phases in the multidecadal variability of wind speed across SA: (1) a non-trending phase from 1978 until around 1990, (2) a marked reduction phase from 1991 until 2001 and (3) a recovery of wind speed phase since 2002 until present.","verified_in_page":true},"topics":["wind speed variability","climate variability","teleconnection indices","surface atmospheric circulation","data homogenization","seasonal trends","elevation effects","atmospheric circulation"],"study_period":"1978–2013","locations":[{"name":"Saudi Arabia","kind":"region","role":"study area","river_name":null,"geocode_query":"Saudi Arabia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Our study extends and improves Rehman’s (2013) initial study in several ways: the key step (1) above on quality control and homogenization of wind speed data was previously omitted, (2) including more recent 2006–2013 data thus evaluating more recent changes in wind speed in the last decade than was previously performed and (3) not only assessing annual scales as done previously. Our secondary objective is to better understand the role played by atmospheric circulation analysing the influence of teleconnection indices on the observed wind speed changes. 2. Data and methods 2.1. Study area Figure 1 shows the geographical setting and topographic features of SA, occupying an area between 15∘ and 33∘N, and 34∘ and 56∘E. This Middle East country represents the largest territory of the Arabian Peninsula, covering 2.15 million km2, and is located between the Red Sea in the west and the Persian Gulf to the east. It is dominated by desert, semi-desert and shrub lands.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Jabal al-Hejaz Mountains, Saudi Arabia","kind":"landscape","role":"mountain study area with highest peak influencing wind trends","river_name":null,"geocode_query":"Jabal al-Hejaz Mountains, Saudi Arabia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The territory can be divided into three regions: (1) the central plateau rising abruptly from the Red Sea, with a narrow coastal plain, and highlands, which descend gradually towards the Persian Gulf, (2) the Jabal al-Hejaz Mountains in the southwest, with the highest peak reaching 3133 m a.s.l. in Jabal Sawda, and (3) the southeast sand desert known as the ‘Empty Quarter’ (647 500 km2), an inhabited land where no wind speed observations are available. Because it is tropical and sub-tropical latitudinal location and the role of the Saudi Arabian high pressure, the climate is ‘desert climate’ according to Köppen and Geiger, except for the mountain areas. This means that high day time air temperatures, particularly in summer with extremely high values (average of 45 ∘C), cold nights and large diurnal range air temperatures prevail.","verified_in_page":true},"resolution_status":"resolved","coordinates":[40.9995327,23.00018],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"جبال الحجاز 44435, Saudi Arabia","basin_id":2040086170,"main_basin_id":2040086170,"basin_region":"Europe and Middle East","basin_area_km2":55971.1,"hydrology_source":"hydrobasins","site_id":"doc-3d2f0b3b4460a15ce5a89256-site-1"},{"name":"Turaif, Saudi Arabia","kind":"station","role":"example station with multiple homogenization breakpoints","river_name":null,"geocode_query":"Turaif, Saudi Arabia","latitude":31.7,"longitude":38.7,"evidence":{"passage_id":"p004-001","page":4,"quote":"Figure 2(b) displays an example of break-point detection on the series of anomalies, namely in the Turaif series (id. 27 in Table 1), where the highest SNHT reaches a value of 85 in 1992. Other statistically significant break-points were detected in successive iterations within the program run. Finally, Figure 2(c), top, presents the reconstruction of the series of this station after having split it at the four break-points with SNHT>25. Original data are plotted in black, and the five homogeneous sub-periods generate complete adjusted series, which are plotted in different colours. The lower part of the figure shows the correction factors applied to the data in each of the adjusted series. After completing this robust quality control and homogenization protocol using all available 29 raw series, 19 homogeneous wind speed series are used in this study; a description of them is given in Table 1 and its spatial distribution in Figure 1. 2.4.","verified_in_page":true},"resolution_status":"resolved","coordinates":[38.6634693,31.6660775],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Turaif Saudi Arabia","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3d2f0b3b4460a15ce5a89256-site-2"},{"name":"Yanbo, Saudi Arabia","kind":"station","role":"coastal measurement site used in preliminary wind variability studies","river_name":null,"geocode_query":"Yanbo, Saudi Arabia","latitude":24.2,"longitude":38.1,"evidence":{"passage_id":"p002-001","page":2,"quote":"For Saudi Arabia (hereafter SA), Rehman (2004) and Rehman et al. (2007) conducted two preliminary studies on the variability of winds for site-specific stations (i.e. Yanbo 24∘N, 38∘E; and Rafha 30∘N, 44∘E) and over different time-spans (i.e. 1970–1983; and 1970–2004), finding a decline of −2.222 m s−1 dec−1 and an increase of +0.040 m s−1 dec−1, respectively. Recently, Rehman (2013) assessed annual wind speed trends from 1970 to 2006 using 28 stations and found that 18 show declines and the remaining 10 stations increases, with an overall trend of −0.185 m s−1 dec−1. No other studies have examined wind speed changes across SA, with the majority of the published works performing wind data analysis for wind power potential assessment (e.g. Rehman, 2004; Al-Abbadi, 2005).","verified_in_page":true},"resolution_status":"resolved","coordinates":[38.0618346,24.0895265],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yanbu Saudi Arabia","basin_id":2040081260,"main_basin_id":2040081260,"basin_region":"Europe and Middle East","basin_area_km2":128548.0,"hydrology_source":"hydrobasins","site_id":"doc-3d2f0b3b4460a15ce5a89256-site-3"},{"name":"Rafha, Saudi Arabia","kind":"station","role":"inland measurement site used in preliminary wind variability studies","river_name":null,"geocode_query":"Rafha, Saudi Arabia","latitude":29.6,"longitude":43.5,"evidence":{"passage_id":"p002-001","page":2,"quote":"For Saudi Arabia (hereafter SA), Rehman (2004) and Rehman et al. (2007) conducted two preliminary studies on the variability of winds for site-specific stations (i.e. Yanbo 24∘N, 38∘E; and Rafha 30∘N, 44∘E) and over different time-spans (i.e. 1970–1983; and 1970–2004), finding a decline of −2.222 m s−1 dec−1 and an increase of +0.040 m s−1 dec−1, respectively. Recently, Rehman (2013) assessed annual wind speed trends from 1970 to 2006 using 28 stations and found that 18 show declines and the remaining 10 stations increases, with an overall trend of −0.185 m s−1 dec−1. No other studies have examined wind speed changes across SA, with the majority of the published works performing wind data analysis for wind power potential assessment (e.g. Rehman, 2004; Al-Abbadi, 2005).","verified_in_page":true},"resolution_status":"resolved","coordinates":[43.5192704,29.62728449999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rafha Saudi Arabia","basin_id":2040085850,"main_basin_id":2040085850,"basin_region":"Europe and Middle East","basin_area_km2":78300.3,"hydrology_source":"hydrobasins","site_id":"doc-3d2f0b3b4460a15ce5a89256-site-4"},{"name":"Red Sea, Arabian Peninsula","kind":"region","role":"geographical feature influencing coastal wind stations","river_name":null,"geocode_query":"Red Sea, Arabian Peninsula","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Our study extends and improves Rehman’s (2013) initial study in several ways: the key step (1) above on quality control and homogenization of wind speed data was previously omitted, (2) including more recent 2006–2013 data thus evaluating more recent changes in wind speed in the last decade than was previously performed and (3) not only assessing annual scales as done previously. Our secondary objective is to better understand the role played by atmospheric circulation analysing the influence of teleconnection indices on the observed wind speed changes. 2. Data and methods 2.1. Study area Figure 1 shows the geographical setting and topographic features of SA, occupying an area between 15∘ and 33∘N, and 34∘ and 56∘E. This Middle East country represents the largest territory of the Arabian Peninsula, covering 2.15 million km2, and is located between the Red Sea in the west and the Persian Gulf to the east. It is dominated by desert, semi-desert and shrub lands.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Persian Gulf, Arabian Peninsula","kind":"region","role":"geographical feature influencing coastal wind stations","river_name":null,"geocode_query":"Persian Gulf, Arabian Peninsula","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Our study extends and improves Rehman’s (2013) initial study in several ways: the key step (1) above on quality control and homogenization of wind speed data was previously omitted, (2) including more recent 2006–2013 data thus evaluating more recent changes in wind speed in the last decade than was previously performed and (3) not only assessing annual scales as done previously. Our secondary objective is to better understand the role played by atmospheric circulation analysing the influence of teleconnection indices on the observed wind speed changes. 2. Data and methods 2.1. Study area Figure 1 shows the geographical setting and topographic features of SA, occupying an area between 15∘ and 33∘N, and 34∘ and 56∘E. This Middle East country represents the largest territory of the Arabian Peninsula, covering 2.15 million km2, and is located between the Red Sea in the west and the Persian Gulf to the east. It is dominated by desert, semi-desert and shrub lands.","verified_in_page":true},"resolution_status":"resolved","coordinates":[47.44904,19.551411],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arabia","basin_id":2040085540,"main_basin_id":2040085540,"basin_region":"Europe and Middle East","basin_area_km2":171027.7,"hydrology_source":"hydrobasins","site_id":"doc-3d2f0b3b4460a15ce5a89256-site-6","true_coordinates":[47.4490397,19.4914108],"spread_km":6.7}],"limitations":["Incomplete or missing latitude/longitude coordinates for many sites","Broad regional geography limits specificity of spatial analysis","Some uncertainty due to potential unaccounted for local factors influencing wind speed trends","Lack of metadata for some stations complicates attribution of breakpoints and inhomogeneities"],"river_ids":[],"site_ids":["doc-3d2f0b3b4460a15ce5a89256-site-1","doc-3d2f0b3b4460a15ce5a89256-site-2","doc-3d2f0b3b4460a15ce5a89256-site-3","doc-3d2f0b3b4460a15ce5a89256-site-4","doc-3d2f0b3b4460a15ce5a89256-site-6"],"anchor":[40.9995327,23.00018],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[2040081260,2040085540,2040085720,2040085850,2040086170],"main_basin_ids":[2040081260,2040085540,2040085720,2040085850,2040086170]},{"id":"doc-692ffdcfc2195f6a5794c16b","title":"Southern Hemisphere westerlies as a driver of the early deglacial atmospheric CO2 rise","display_title":"Southern Hemisphere Westerlies and Early Deglacial CO2 Rise","doi":"https://doi.org/10.1038/s41467-018-04876-4","theme":"Climate","color":"#98d4f1","year":2018,"publication_date":"2018-06-21","authors":["Laurie Menviel","Paul Spence","Jimin Yu","Matthew A. Chamberlain","Richard J. Matear","Katrin J. Meißner","Matthew H. England"],"summary":"This study investigates the mechanisms driving the early deglacial atmospheric CO2 rise during Heinrich Stadial 1 (~17.6–14.7 ka). Using transient simulations with the isotope-enabled Earth System Model LOVECLIM and a global eddy-permitting ocean-sea ice-carbon cycle model, the research finds that intensified Southern Hemisphere (SH) westerlies enhanced Southern Ocean convection and upwelling of carbon-rich Circumpolar Deep Water, leading to atmospheric CO2 increases, δ13CO2 and Δ14C decreases consistent with paleorecords. The work highlights a two-phase CO2 rise driven by ocean circulation changes linked to SH westerlies shifts and Antarctic Bottom Water (AABW) and Antarctic Intermediate Water (AAIW) ventilation.","finding":"Intensified Southern Hemisphere westerlies during Heinrich Stadial 1 enhanced Southern Ocean upwelling and convection, causing a significant and rapid increase in atmospheric CO2 due to outgassing of carbon-rich deep waters, supported by model simulations matching paleoclimate records.","finding_evidence":{"passage_id":"p004-001","page":4,"quote":"A further intensification of the SH westerlies and reduced Southern Ocean freshwater flux at 16.2 ka enhances Southern Ocean convection in our simulation, resulting in stronger Antarctic Intermediate Water (AAIW) and AABW formation during HS1 (Fig. 3d, e), consistent with reduced ventilation ages in the South Atlantic6 and the Pacific38 (Fig. 1h, i) as well as a peak in the Southern Ocean opal flux9. Enhanced formation of AAIW and AABW and the associated upwelling of Circumpolar Deep Water decrease the oceanic carbon content below ~2000 m depth and particularly in the deep South Pacific, leading to CO2 outgassing in the Southern Ocean (Fig. 2). As a result, the simulated atmospheric CO2 increases in close agreement with high-resolution Antarctic ice core records (Fig. 1d)7, with a simulated 19 ppm pCO2 increase between 17.2 and 16.2 ka and an abrupt 9 ppm rise at 16.2 ka.","verified_in_page":true},"topics":["antarctic bottom water","antarctic intermediate water","carbon isotopes","carbon cycle","climate variability","deglaciation","southern hemisphere westerlies","southern ocean ventilation"],"study_period":"~17.6–14.7 ka (Heinrich Stadial 1)","locations":[{"name":"Southern Ocean","kind":"region","role":"Primary study region where intensified SH westerlies increase convection and upwelling, driving CO2 outgassing","river_name":null,"geocode_query":"Southern Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"A further intensification of the SH westerlies and reduced Southern Ocean freshwater flux at 16.2 ka enhances Southern Ocean convection in our simulation, resulting in stronger Antarctic Intermediate Water (AAIW) and AABW formation during HS1 (Fig. 3d, e), consistent with reduced ventilation ages in the South Atlantic6 and the Pacific38 (Fig. 1h, i) as well as a peak in the Southern Ocean opal flux9. Enhanced formation of AAIW and AABW and the associated upwelling of Circumpolar Deep Water decrease the oceanic carbon content below ~2000 m depth and particularly in the deep South Pacific, leading to CO2 outgassing in the Southern Ocean (Fig. 2). As a result, the simulated atmospheric CO2 increases in close agreement with high-resolution Antarctic ice core records (Fig. 1d)7, with a simulated 19 ppm pCO2 increase between 17.2 and 16.2 ka and an abrupt 9 ppm rise at 16.2 ka.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-160.234046,-68.498013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Ocean","hydrology_source":"none","site_id":"doc-692ffdcfc2195f6a5794c16b-site-0","true_coordinates":[-160.2340459,-68.4380135],"spread_km":6.7},{"name":"Weddell Sea","kind":"region","role":"Location of polynya and enhanced deep convection leading to increased Antarctic Bottom Water formation in ocean model experiment","river_name":null,"geocode_query":"Weddell Sea, Southern Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"To test whether the Southern Ocean CO2 outgassing response to a strengthening of SH westerlies shown above is a robust feature, we perform an experiment under fixed modernday forcing with a global high-resolution ocean sea–ice carbon cycle model that resolves most of the ocean mesoscale energy (Methods). The model is perturbed with a poleward intensifying Southern Ocean wind scenario based on observed and projected 21st century wind trends (Supplementary Fig. 10). The model’s response is dominated by a large polynya in the Weddell Sea, that is similar in scale to an observed 1970’s Weddell Sea polynya47 and lasts for ~6 years (Supplementary Fig. 10). The polynya intensifies deep convection in the Weddell Sea, thus leading to a strengthening of AABW from 19 to 30 Sv and a CO2 outgassing in the Southern Ocean (Fig. 6a, b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North Atlantic Ocean","kind":"region","role":"Region where North Atlantic Deep Water (NADW) formation weakened during HS1, triggering teleconnections affecting SH westerlies and Southern Ocean ventilation","river_name":null,"geocode_query":"North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"A detailed study of the deglaciation would provide a more direct link between changes in the climate and carbon cycle and would allow a direct model-data comparison, thus further constraining the processes responsible for atmospheric CO2 changes. HS1 (~17.6–14.7 ka), at the beginning of the last deglaciation, is an important period to understand as it represents a major phase of atmospheric CO2 rise and the transition out of the glacial period. Paleoproxy records suggest that North Atlantic Deep Water (NADW) formation weakened significantly during HS113, effectively reducing the meridional heat transport to the North Atlantic and leading to cold and dry conditions over Greenland14 and the North Atlantic15. In contrast, paleoproxies indicate that Antarctic surface air temperature and Southern Ocean surface waters experienced a warming of ~5 °C and ~3 °C16,17, respectively.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-39.462891,35.686512],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Atlantic Ocean","hydrology_source":"none","site_id":"doc-692ffdcfc2195f6a5794c16b-site-2","true_coordinates":[-39.462891,35.746512],"spread_km":6.7},{"name":"Antarctica","kind":"region","role":"Site of Antarctic Bottom Water formation and air temperature changes affected by Southern Ocean ventilation and westerly shifts","river_name":null,"geocode_query":"Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"A further intensification of the SH westerlies and reduced Southern Ocean freshwater flux at 16.2 ka enhances Southern Ocean convection in our simulation, resulting in stronger Antarctic Intermediate Water (AAIW) and AABW formation during HS1 (Fig. 3d, e), consistent with reduced ventilation ages in the South Atlantic6 and the Pacific38 (Fig. 1h, i) as well as a peak in the Southern Ocean opal flux9. Enhanced formation of AAIW and AABW and the associated upwelling of Circumpolar Deep Water decrease the oceanic carbon content below ~2000 m depth and particularly in the deep South Pacific, leading to CO2 outgassing in the Southern Ocean (Fig. 2). As a result, the simulated atmospheric CO2 increases in close agreement with high-resolution Antarctic ice core records (Fig. 1d)7, with a simulated 19 ppm pCO2 increase between 17.2 and 16.2 ka and an abrupt 9 ppm rise at 16.2 ka.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"South Pacific Ocean","kind":"region","role":"Region of deep and intermediate oceanic δ13C and ventilation age changes due to enhanced Southern Ocean overturning and upwelling","river_name":null,"geocode_query":"South Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Time evolution of selected paleo records across HS1 (black) compared to the results of a transient simulation (red, LH1–SO–SHW). a North Atlantic Pa/Th81 and simulated NADW transport; b AABW transport; c Antarctic air temperature16; d Atmospheric pCO2 7 on the WD2014 chronology82; e δ13CO2 19 on the WD2014 chronology82; f Δ14CO2 20; g δ13C anomalies averaged over the intermediate South Pacific (170–180°E, 40–30°S, 1007–1443 m) and compared to a benthic δ13C record55; Ventilation age anomalies from h the intermediate South Pacific 75°W–50°W, 44°S–48°S, 1443–1992 m38, i the deep South Atlantic 14°W–8°W, 40°S–48°S, 3300–4020 m6 and j the deep North Pacific 152°W–147°W, 51°N–57°N, 3300–4020 m33. EDC and WDC, respectively, stand for EPICA Dome C and West Antarctic Ice Sheet Divide ice cores. 5–21 years moving average are shown for all the simulated variables except for Δ14CO2 and the oceanic ventilation ages to filter the high-frequency variability.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.508523,-8.843195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-692ffdcfc2195f6a5794c16b-site-4","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"South Atlantic Ocean","kind":"region","role":"Area of reduced carbon content and ventilation age declines reflecting enhanced Southern Ocean ventilation and AABW formation","river_name":null,"geocode_query":"South Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"A further intensification of the SH westerlies and reduced Southern Ocean freshwater flux at 16.2 ka enhances Southern Ocean convection in our simulation, resulting in stronger Antarctic Intermediate Water (AAIW) and AABW formation during HS1 (Fig. 3d, e), consistent with reduced ventilation ages in the South Atlantic6 and the Pacific38 (Fig. 1h, i) as well as a peak in the Southern Ocean opal flux9. Enhanced formation of AAIW and AABW and the associated upwelling of Circumpolar Deep Water decrease the oceanic carbon content below ~2000 m depth and particularly in the deep South Pacific, leading to CO2 outgassing in the Southern Ocean (Fig. 2). As a result, the simulated atmospheric CO2 increases in close agreement with high-resolution Antarctic ice core records (Fig. 1d)7, with a simulated 19 ppm pCO2 increase between 17.2 and 16.2 ka and an abrupt 9 ppm rise at 16.2 ka.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-15.996094,-33.72434],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Atlantic Ocean","hydrology_source":"none","site_id":"doc-692ffdcfc2195f6a5794c16b-site-5"},{"name":"North Pacific Ocean","kind":"region","role":"Region where intermediate water formation strengthened affecting the global carbon cycle during HS1","river_name":null,"geocode_query":"North Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-008","page":2,"quote":"The transient simulation is initialised from a Last Glacial Maximum (LGM) state constrained by oceanic δ13C and ventilation age distributions31. The LGM ocean circulation is characterised by shallow NADW, relatively weak North Pacific Intermediate Water (NPIW) and very weak Antarctic Bottom Water (AABW), obtained by adding a meltwater flux into the Southern Ocean and weakening the SH windstress by 20%31. By forcing the model with changes in orbital parameters, Northern Hemisphere icesheet extent and albedo as well as freshwater input in the North Atlantic (Methods), the deep ocean convection in the Norwegian Sea is suppressed, thus resulting in very weak NADW formation during HS1 (Fig. 1a). Reduced moisture transport from the Atlantic to the Pacific and a deepening of the Aleutian low due to NADW cessation leads to stronger NPIW formation in agreement with paleoproxy records32,33 (Fig. 1j).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.478167,-8.835157],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-692ffdcfc2195f6a5794c16b-site-6","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Bermuda Rise","kind":"other","role":"Location of 231Pa/230Th record indicating NADW collapse timing used to align model forcing","river_name":null,"geocode_query":"Bermuda Rise, North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"eddy-driven jet poleward and strengthen the SH westerlies by ~25%. To include this atmospheric teleconnection between the tropics and the high southern latitudes, which is not well represented in our coarse resolution atmospheric model, the SH westerlies are artificially strengthened from their LGM state commencing at 17.2 ka (Methods, simulation LH1–SO–SHW). This timing of 17.2 ka corresponds, within dating uncertainties, to the Bermuda Rise 231Pa/230Th record reaching maximum values13, thus indicating very weak NADW transport. However, another phase of NADW weakening36, probably associated with the disintegration of the Laurentide ice-sheet and Heinrich event 1, occurred at ~16.2 ka37.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Coordinates for Southern Ocean and linked regions are broad regional geographies and not precisely located due to lack of explicit coordinates.","The study uses model simulations which rely on boundary conditions and assumptions about wind shifts and meltwater fluxes that carry inherent uncertainties.","The high-resolution ocean model uses modern forcing conditions which may differ from past glacial boundary states, limiting direct paleoclimate comparability."],"river_ids":[],"site_ids":["doc-692ffdcfc2195f6a5794c16b-site-0","doc-692ffdcfc2195f6a5794c16b-site-2","doc-692ffdcfc2195f6a5794c16b-site-4","doc-692ffdcfc2195f6a5794c16b-site-5","doc-692ffdcfc2195f6a5794c16b-site-6"],"anchor":[-124.478167,-8.835157],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[],"main_basin_ids":[]},{"id":"doc-9a07adada1a63c79754c0cc6","title":"An approach to homogenize daily peak wind gusts: An application to the Australian series","display_title":"Homogenizing Australian Daily Peak Wind Gusts","doi":"https://doi.org/10.1002/joc.5949","theme":"Climate","color":"#98d4f1","year":2018,"publication_date":"2018-11-27","authors":["César Azorín-Molina","José A. Guijarro","Tim R. McVicar","Blair Trewin","Andrew Frost","Deliang Chen"],"summary":"This study develops and applies a novel homogenization approach for daily peak wind gust (DPWG) data using 548 Australian stations spanning 1941–2016. The approach leverages break points detected at monthly scale to split and homogenize daily series without relying on monthly corrections, implemented via Climatol version 3.1. The method improves quality control, addresses observational inhomogeneities especially related to instrument changes, and infills missing data. The study finds that homogenization reduces site trend variability and reveals a stronger negative regional trend in DPWG compared to raw data, with limited added value from using NCEP/NCAR reanalysis data as reference.","finding":"The homogenization procedure detected 353 break points and increased the number of stations showing significant negative trends; regional DPWG trend magnitude was larger after homogenization (−0.073 m/s per decade) than before (−0.005 m/s per decade), indicating more reliable trend estimates.","finding_evidence":{"passage_id":"p015-006","page":15,"quote":"In spite of the low intersite correlations, the homogenization procedure implemented using Climatol Version 3.1 detected 353 break points in the 548 Australian series with DPWG data for 1941–2016. This resulted in an increase in the number of stations showing significant negative trends, with the regional DPWG trend being larger in magnitude after (−0.073 m s−1 dec−1 ) than before (−0.005 m s−1 dec−1 ) the homogenization; and AZORIN-MOLINA ET AL. 15","verified_in_page":true},"topics":["wind speed","climate variability","data homogenization","time series analysis","trend analysis","extreme weather","meteorological instrumentation","statistical climatology"],"study_period":"1941–2016","locations":[{"name":"Australia","kind":"region","role":"study region for DPWG homogenization","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"This homogenization approach is described in detail and demonstrated through application to a large DPWG dataset (548 stations) covering Australia for 1941 –2016. Our secondary objectives are: (b) investigating the ability of reanalysed mean wind-speed data (from reanalysis) to aid the detection of break points when used as reference series in the absence of neighbouring stations; and finally (c) creating a long-term quality-controlled and homogenized DPWG dataset for the assessment and attribution of the variability of extremes across Australia for future studies. 2 | DATA AND METHODS 2.1 | Observed DPWGs and metadata The original dataset contains 706 DPWG series across Australia spanning from 1 April 1939 until 31 December 2016.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Esperance, Western Australia, Australia","kind":"station","role":"example station for break point detection and series reconstruction","river_name":null,"geocode_query":"Esperance, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"expressed in metres per second per decade (m s−1 dec−1 ) and the nonparametric correlation coefficient of Mann-Kendall ́s tau-b (Kendall and Gibbons, 1990) was used to assess the statistical significance of linear trends at p < 0.05. The assessment of trends between the raw and the homogenized datasets was conducted for two periods: (a) 1948–2016; and (b) 1971–2016, to quantify the impact of the density of DPWG observations during the first decades on trends. The comparison is described in Section 4.4. Finally, comparison of all these results conducted to choosing the most suitable lists of break points to use as input to a last application of Climatol, this time to the original DPWG series, to split them at the shift dates and proceed to the reconstruction of all homogeneous subperiods. Homogenized and complete DPWG were obtained by applying the infilling method implemented in Climatol.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.8912724,-33.8613953],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61119883,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Esperance WA 6450, Australia","basin_id":5040076530,"main_basin_id":5040076530,"basin_region":"Australasia","basin_area_km2":23590.0,"hydrology_source":"geofabric","site_id":"doc-9a07adada1a63c79754c0cc6-site-1"},{"name":"Canberra, Australian Capital Territory, Australia","kind":"facility","role":"location of CSIRO Land and Water and some authors affiliations related to data and methods","river_name":null,"geocode_query":"Canberra, Australian Capital Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"RESEARCH ARTICLE An approach to homogenize daily peak wind gusts: An application to the Australian series Cesar Azorin-Molina1 | Jose A. Guijarro2 | Tim R. McVicar3,4 | Blair C. Trewin5 | Andrew J. Frost6 | Deliang Chen1 1 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden 2 State Meteorological Agency (AEMET), Balearic Islands Office, Palma de Mallorca, Spain 3 CSIRO Land and Water, Canberra, Australian Capital Territory, Australia 4 Australian Research Council Centre of Excellence for Climate System Science, Sydney, New South Wales, Australia 5 Australian Bureau of Meteorology, Melbourne, Victoria, Australia 6 Australian Bureau of Meteorology, Sydney, New South Wales, Australia Correspondence Cesar Azorin-Molina, Department of Earth Sciences, University of Gothenburg, Box 460, 405 30 Gothenburg, Sweden.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Melbourne, Victoria, Australia","kind":"facility","role":"location of Australian Bureau of Meteorology office involved in data provision and study","river_name":null,"geocode_query":"Melbourne, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"RESEARCH ARTICLE An approach to homogenize daily peak wind gusts: An application to the Australian series Cesar Azorin-Molina1 | Jose A. Guijarro2 | Tim R. McVicar3,4 | Blair C. Trewin5 | Andrew J. Frost6 | Deliang Chen1 1 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden 2 State Meteorological Agency (AEMET), Balearic Islands Office, Palma de Mallorca, Spain 3 CSIRO Land and Water, Canberra, Australian Capital Territory, Australia 4 Australian Research Council Centre of Excellence for Climate System Science, Sydney, New South Wales, Australia 5 Australian Bureau of Meteorology, Melbourne, Victoria, Australia 6 Australian Bureau of Meteorology, Sydney, New South Wales, Australia Correspondence Cesar Azorin-Molina, Department of Earth Sciences, University of Gothenburg, Box 460, 405 30 Gothenburg, Sweden.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.035289,-37.832169],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Melbourne VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-9a07adada1a63c79754c0cc6-site-3","true_coordinates":[144.9630576,-37.8136276],"spread_km":6.7},{"name":"Sydney, New South Wales, Australia","kind":"facility","role":"location of Australian Bureau of Meteorology and Australian Research Council Centre of Excellence involved in study","river_name":null,"geocode_query":"Sydney, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"RESEARCH ARTICLE An approach to homogenize daily peak wind gusts: An application to the Australian series Cesar Azorin-Molina1 | Jose A. Guijarro2 | Tim R. McVicar3,4 | Blair C. Trewin5 | Andrew J. Frost6 | Deliang Chen1 1 Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden 2 State Meteorological Agency (AEMET), Balearic Islands Office, Palma de Mallorca, Spain 3 CSIRO Land and Water, Canberra, Australian Capital Territory, Australia 4 Australian Research Council Centre of Excellence for Climate System Science, Sydney, New South Wales, Australia 5 Australian Bureau of Meteorology, Melbourne, Victoria, Australia 6 Australian Bureau of Meteorology, Sydney, New South Wales, Australia Correspondence Cesar Azorin-Molina, Department of Earth Sciences, University of Gothenburg, Box 460, 405 30 Gothenburg, Sweden.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.276404,-33.880791],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-9a07adada1a63c79754c0cc6-site-4","true_coordinates":[151.207684,-33.8622503],"spread_km":6.7},{"name":"Bureau of Meteorology Observatories (35 selected stations) across Australia","kind":"station","role":"set of 35 stations with detailed metadata used to compare detected break points and evaluate homogenization","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-003","page":9,"quote":"Darwin (14015) ceased operations on 24 December 1974 (due to cyclone Tracy) and recommenced in 1976 (after the station was re-built), Gove (14508) commenced records in 1986 and Weipa (27045) in 1994 [Colour figure can be viewed at wileyonlinelibrary.com] AZORIN-MOLINA ET AL. 9","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"NCEP/NCAR reanalysis grid-cells over Australia","kind":"region","role":"grid-cell references trialled in homogenization for break point detection to support sparse station network","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-006","page":5,"quote":"The geographical distribution of the stations and centre points of the NCEP/NCAR reanalysis grid-cells across Australia are shown in Figure 1b. 3 | AN APPROACH TO HOMOGENIZE DPWG SERIES 3.1 | The Climatol package and overview of the homogenization approach Climatol (Guijarro, 2018) is an R (R Core Team, 2015) package for homogenizing climate series and producing automated products (e.g., homogenized time series) derived from them. This package can be freely downloaded from https://CRAN.R-project.org/package=climatol (last accessed 1 December 2018) and further information about the documentation and improvements of the most recent version can be found in http://www.climatol.eu/ (last accessed AZORIN-MOLINA ET AL. 5","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Metadata are incomplete and not all measured instrumental changes produce detectable break points, limiting metadata-based validation.","Low spatial correlations between wind stations limit reliability of reference data for homogenization.","Coordinates are not provided for stations, limiting geospatial precision for mapping."],"river_ids":[],"site_ids":["doc-9a07adada1a63c79754c0cc6-site-1","doc-9a07adada1a63c79754c0cc6-site-3","doc-9a07adada1a63c79754c0cc6-site-4"],"anchor":[145.035289,-37.832169],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070710,5040076530],"main_basin_ids":[5040070710,5040076530]},{"id":"doc-b69ec50312ace258cdbd4830","title":"How good are hydrological models for gap-filling streamflow data?","display_title":"Hydrological Models for Gap-filling Australian Streamflow","doi":"https://doi.org/10.5194/hess-22-4593-2018","theme":"Flow & ecology","color":"#80edc2","year":2018,"publication_date":"2018-08-30","authors":["Yongqiang Zhang","David Post"],"summary":"This study quantitatively evaluates the reliability of using hydrological models (GR4J and SIMHYD) to fill missing daily streamflow data in 217 unregulated Australian catchments, assessing impacts on annual streamflow and trend estimations across different missing data rates (5%, 10%, 20%). Findings indicate that gap-filling with calibrated hydrological models yields almost identical results to nearly complete data (<1% missing) for annual trends, with minimal bias especially when missing data is below 10%. The accuracy is robust across most catchments and conditions, with slight underestimation of very high flows. The study provides a data-driven threshold for acceptable missing data rates in hydrological analyses.","finding":"Gap-filling missing streamflow data using calibrated hydrological models (GR4J and SIMHYD) mostly preserves annual streamflow trend accuracy, with less than 1% error in catchments with under 10% missing data rate, supporting the extension of catchment inclusion thresholds in hydrological analyses.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends. 1 Introduction Streamflow is channel runoff, i.e. the flow of water in streams and rivers and water accumulated from surface runoff from the land surface and groundwater recharge.","verified_in_page":true},"topics":["streamflow measurement","hydrological modelling","data gap-filling","trend analysis","model calibration","missing data impact","catchment hydrology","aridity index"],"study_period":"1981-2010","locations":[{"name":"Australia","kind":"region","role":"study area","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"4594 Y. Zhang and D. Post: Comparing hydrological models for gap-filling streamflow data there are no studies in the literature to comprehensively evaluate the reliability and accuracy of the gap-filled data that are influenced by different thresholds and by patterns of missing data. Our study aims to provide a framework to evaluate the annual trends and annual variables obtained from gap-filled streamflow data using two hydrological models (GR4J and SIMHYD), together with a large streamflow dataset available across the Australian continent (Zhang et al., 2013). This can guide researchers to more sensibly define a threshold for catchment selection and hydrological analysis. 2 Data and methods 2.1 Data We obtained a daily streamflow dataset from 780 unregulated catchments widely spread across Australia (Zhang et al., 2013).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"217 unregulated catchments across Australia","kind":"basin","role":"field study sites","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"4594 Y. Zhang and D. Post: Comparing hydrological models for gap-filling streamflow data there are no studies in the literature to comprehensively evaluate the reliability and accuracy of the gap-filled data that are influenced by different thresholds and by patterns of missing data. Our study aims to provide a framework to evaluate the annual trends and annual variables obtained from gap-filled streamflow data using two hydrological models (GR4J and SIMHYD), together with a large streamflow dataset available across the Australian continent (Zhang et al., 2013). This can guide researchers to more sensibly define a threshold for catchment selection and hydrological analysis. 2 Data and methods 2.1 Data We obtained a daily streamflow dataset from 780 unregulated catchments widely spread across Australia (Zhang et al., 2013).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Study excludes gaps longer than one year, limiting applicability to long-duration missing data cases.","No specific coordinates for individual catchments were provided.","The study focuses on annual trends and streamflow; low flow metrics or ecological impacts were not evaluated extensively.","Results based on Australian datasets and may not directly transfer to regions with different missing data patterns or hydrology."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[],"main_basin_ids":[]},{"id":"doc-d8bd15eb1950fb07642c26f8","title":"Determining the initial spatial extent of an environmental impact assessment with a probabilistic screening methodology","display_title":"Probabilistic Assessment of Coal Impacts in Gloucester Basin","doi":"https://doi.org/10.1016/j.envsoft.2018.08.020","theme":"Groundwater","color":"#a8b9ff","year":2018,"publication_date":"2018-09-01","authors":["Luk Peeters","Dan Pagendam","Russell S. Crosbie","Praveen K. Rachakonda","Warrick Dawes","Lei Gao","Steve Marvanek","Yongqiang Zhang","Tim R. McVicar"],"summary":"This study develops a probabilistic screening methodology to determine the initial spatial extent for environmental impact assessments related to groundwater drawdown from coal mining and coal seam gas extraction. It integrates quantitative uncertainty analyses, qualitative uncertainty assessments, sensitivity analyses, and emulation techniques to provide a robust, transparent delineation of zones where hydrological impacts are likely, focusing on the Gloucester Basin in New South Wales, Australia.","finding":"The zone in which there is more than 5% probability of exceeding 0.2 m groundwater drawdown due to coal development in the Gloucester Basin does not extend more than 5 km beyond the footprint of the open-cut mines.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"element model. The maximum drawdown values are simulated at 1000 model node locations. The results are summarised in a map of the probability of exceeding 0.2 m (Fig. 6). Fig. 6 shows that the zone of potential hydrological change, the contour of 0.05 probability of exceeding 0.2 m drawdown, extends about 5 km from the edge of the coal mines. Fig. 5. Histograms of the posterior marginal parameter distributions. Log indicates natural log. Note that lower thresholds of 1E-7 m/d and 1E-6 1/m are implemented in the model for hydraulic conductivity and specific storage respectively. L.J.M. Peeters et al. Environmental Modelling and Software 109 (2018) 353–367 362","verified_in_page":true},"topics":["environmental impact assessment","uncertainty analysis","groundwater drawdown","probabilistic modeling","sensitivity analysis","coal mining impacts","hydrogeology","model emulation"],"study_period":null,"locations":[{"name":"Gloucester Basin","kind":"basin","role":"study area and model domain","river_name":null,"geocode_query":"Gloucester Basin, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"This qualitative uncertainty analysis can then be used as a starting point to identify knowledge and data gaps, guide future data collection and monitoring strategies as well as focussing subsequent numerical modelling. Referring to the initial spatial extent in explicit probabilistic terms as well as highlighting the underpinning assumptions, emphasise that this is a first step in the analysis and that impacts outside of this zone, although unlikely, cannot be fully excluded. 3. Study area The 348 km2 Gloucester geological basin is situated about 250 km north east of Sydney, Australia (Fig. 1). The region has a sub-tropical climate with summer dominant precipitation. The average rainfall between 1982 and 2012 was 1095 mm/ yr and potential evapotranspiration 1587 mm/yr (Jones et al., 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.953401,-32.084457],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26971272,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-d8bd15eb1950fb07642c26f8-site-0","true_coordinates":[151.9534009,-32.0244571],"spread_km":6.7},{"name":"Gloucester Basin coal mines","kind":"region","role":"location of coal mining and coal seam gas development impacting groundwater","river_name":null,"geocode_query":"Gloucester Basin, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"This qualitative uncertainty analysis can then be used as a starting point to identify knowledge and data gaps, guide future data collection and monitoring strategies as well as focussing subsequent numerical modelling. Referring to the initial spatial extent in explicit probabilistic terms as well as highlighting the underpinning assumptions, emphasise that this is a first step in the analysis and that impacts outside of this zone, although unlikely, cannot be fully excluded. 3. Study area The 348 km2 Gloucester geological basin is situated about 250 km north east of Sydney, Australia (Fig. 1). The region has a sub-tropical climate with summer dominant precipitation. The average rainfall between 1982 and 2012 was 1095 mm/ yr and potential evapotranspiration 1587 mm/yr (Jones et al., 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.953401,-31.964457],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26971272,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gloucester NSW 2422, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-d8bd15eb1950fb07642c26f8-site-1","true_coordinates":[151.9534009,-32.0244571],"spread_km":6.7},{"name":"New South Wales","kind":"region","role":"regional context and legislative framework for groundwater protection","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Synthesis and communication. 2.1. Objectives, model outcomes and conceptualization In this study we focus on defining the hydrological change, the acceptable change threshold and exceedance probability in the context of coal mining and coal seam gas extraction (also known as coal bed methane extraction). Based on a formal process of defining the most likely coal resource development pathway (Lewis, 2014), identifying hazards through impact modes and effect analysis (Ford et al., 2016) and developing causal pathways (Henderson et al., 2016) linking the coal development to potential impact on water related assets (Mount et al., 2017), it is established that the most relevant hydrological change for groundwater is the maximum drawdown (i.e. change in groundwater level) in time at the location of the asset, in the aquifer hosting the water table (Holland et al., 2017).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Sydney","kind":"settlement","role":"reference city near Gloucester Basin, providing geographic context","river_name":null,"geocode_query":"Sydney, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"This qualitative uncertainty analysis can then be used as a starting point to identify knowledge and data gaps, guide future data collection and monitoring strategies as well as focussing subsequent numerical modelling. Referring to the initial spatial extent in explicit probabilistic terms as well as highlighting the underpinning assumptions, emphasise that this is a first step in the analysis and that impacts outside of this zone, although unlikely, cannot be fully excluded. 3. Study area The 348 km2 Gloucester geological basin is situated about 250 km north east of Sydney, Australia (Fig. 1). The region has a sub-tropical climate with summer dominant precipitation. The average rainfall between 1982 and 2012 was 1095 mm/ yr and potential evapotranspiration 1587 mm/yr (Jones et al., 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.250155,-33.813709],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-d8bd15eb1950fb07642c26f8-site-3","true_coordinates":[151.207684,-33.8622503],"spread_km":6.7}],"limitations":["Geographic coordinates for study locations were not provided in the text and could not be included.","Quantitative assessments depend on model assumptions and specified thresholds which are subjective.","The qualitative uncertainty analysis scoring is subjective and depends on expert judgment.","Regional scale of study area may obscure localized variability in hydrogeology."],"river_ids":[],"site_ids":["doc-d8bd15eb1950fb07642c26f8-site-0","doc-d8bd15eb1950fb07642c26f8-site-1","doc-d8bd15eb1950fb07642c26f8-site-3"],"anchor":[151.953401,-31.964457],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-e1505daf60eb55c434595a0f","title":"Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods","display_title":"Seasonal Streamflow Forecasting in Upper Indus Basin","doi":"https://doi.org/10.5194/hess-22-3533-2018","theme":"Flow & ecology","color":"#80edc2","year":2018,"publication_date":"2018-06-29","authors":["Stephen P. Charles","Quan J. Wang","Mobin-ud-Din Ahmad","Danial Hashmi","Andrew Schepen","Geoff Podger","David E. Robertson"],"summary":"This study assesses seasonal streamflow forecasting methods for the upper Indus Basin (UIB) of Pakistan, focusing on inflows to Tarbela Dam on the Indus River and Mangla Dam on the Jhelum River. It compares a statistical Bayesian joint probability (BJP) model using antecedent flow and climate indices, a snowmelt runoff model (SRM) using ensemble streamflow prediction (ESP), and a hybrid approach combining both. The Bayesian joint probability approach demonstrated reliable and skilful probabilistic forecasts for Kharif season flows, especially using March flow and ENSO indices as predictors, while the SRM approach showed bias and overconfidence in uncertainty spread.","finding":"The Bayesian joint probability model using antecedent streamflow (March flow) and ENSO-based climate indices as predictors provides reliable, unbiased, and skillful probabilistic seasonal streamflow forecasts for the Kharif season inflows to both Mangla and Tarbela dams; in contrast, the snowmelt runoff model (SRM) ensemble streamflow prediction forecasts are biased and underestimate forecast uncertainty, limiting their usefulness.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Comparison of Jhelum at Mangla BJP and SRM–ESP forecasts, for the shorter 2001–2015 period for which SRM results are available, show a contrast between reliable BJP forecasts (Fig. 6a) and biased SRM forecasts, including five values of 0 or 1 indicating that the SRM forecast distribution is too narrow (Fig. 7a). A feature of robust forecasts is their stability across the full period of record and range of flow magnitudes. Figures 4b and 5b show a uniform spread of PIT values and hence stability across the full period of record, for Jhelum at Mangla and Indus at Tarbela BJP forecasts respectively. Similarly, forecast stability across the range of flow magnitudes is verified by the uniformity of PIT values against forecast median, as shown in Figs. 4c and 5c for Jhelum at Mangla and Indus at Tarbela respectively. For the Jhelum at Mangla BJP and SRM–ESP comparison, stability across time and flow magnitude are harder to assess given the short 15-year sample size.","verified_in_page":true},"topics":["seasonal streamflow forecasting","statistical hydrology","hydrological modelling","Bayesian models","ENSO influence","hydrological predictability","snowmelt runoff","forecast verification"],"study_period":"1975–2015","locations":[{"name":"Tarbela Dam","kind":"facility","role":"Major water supply dam, study target for seasonal streamflow forecasting","river_name":"Indus","geocode_query":"Tarbela Dam, Khyber Pakhtunkhwa, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April– September) streamflow forecasts for inflow to Pakistan’s two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor.","verified_in_page":true},"resolution_status":"resolved","coordinates":[72.698952,34.027543],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pakistan","basin_id":4040648320,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":69087.2,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-0","true_coordinates":[72.6989522,34.0875432],"spread_km":6.7},{"name":"Mangla Dam","kind":"facility","role":"Major water supply dam, study target for seasonal streamflow forecasting","river_name":"Jhelum","geocode_query":"Mangla Dam, Azad Kashmir, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April– September) streamflow forecasts for inflow to Pakistan’s two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.736018,33.156738],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mangla Dam Lake, Pakistan","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-1","true_coordinates":[73.7360175,33.2167383],"spread_km":6.7},{"name":"Upper Indus Basin","kind":"basin","role":"Study region encompassing headwater catchments feeding Tarbela and Mangla dams","river_name":null,"geocode_query":"Upper Indus Basin, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The glaciated and snow covered sub-basins of the UIB, encompassing glaciated headwater catchments within the northern Hindu Kush, Karakoram and western Himalayan mountain ranges, dominate water generation within the Indus Basin (Alford et al., 2014). The UIB’s tributaries include the Indus at Kharmong: Shigar, Shyok and Astore in the Karakoram Himalaya; the Jhelum, Chenab, Ravi and Sutlej in the western Himalaya; the Hunza, Gilgit, Kabul, Swat and Chitral in the Hindu Kush mountains (Fig. 1). These basins can be classified as having a flow regime that is either glacier-melt dominated (Hunza, Shigar and Shyok) or snowmelt dominated (Jhelum, Kabul, Gilgit, Astore and Swat; Hasson et al., 2014). The predominant source of flow in the UIB is snowmelt, with glacier melt a secondary source, Hydrol. Earth Syst. Sci., 22, 3533–3549, 2018 www.hydrol-earth-syst-sci.net/22/3533/2018/","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.524222,29.874518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-2","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Indus River","kind":"river","role":"Main river system studied for inflows to Tarbela Dam","river_name":"Indus","geocode_query":"Indus River, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April– September) streamflow forecasts for inflow to Pakistan’s two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.3213631,25.4201198],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indus River Rd, Hyderabad, Pakistan","basin_id":4040033640,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":17092.8,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-3"},{"name":"Jhelum River","kind":"river","role":"Tributary of Indus studied for inflows to Mangla Dam","river_name":"Jhelum","geocode_query":"Jhelum River, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April– September) streamflow forecasts for inflow to Pakistan’s two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.62558,33.170333],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-4","true_coordinates":[73.62558,33.230333],"spread_km":6.7},{"name":"Hindu Kush Mountains","kind":"landscape","role":"Source area of tributaries feeding the UIB","river_name":null,"geocode_query":"Hindu Kush, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The glaciated and snow covered sub-basins of the UIB, encompassing glaciated headwater catchments within the northern Hindu Kush, Karakoram and western Himalayan mountain ranges, dominate water generation within the Indus Basin (Alford et al., 2014). The UIB’s tributaries include the Indus at Kharmong: Shigar, Shyok and Astore in the Karakoram Himalaya; the Jhelum, Chenab, Ravi and Sutlej in the western Himalaya; the Hunza, Gilgit, Kabul, Swat and Chitral in the Hindu Kush mountains (Fig. 1). These basins can be classified as having a flow regime that is either glacier-melt dominated (Hunza, Shigar and Shyok) or snowmelt dominated (Jhelum, Kabul, Gilgit, Astore and Swat; Hasson et al., 2014). The predominant source of flow in the UIB is snowmelt, with glacier melt a secondary source, Hydrol. Earth Syst. Sci., 22, 3533–3549, 2018 www.hydrol-earth-syst-sci.net/22/3533/2018/","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.263333,36.832222],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hindu Kush","basin_id":4040545690,"main_basin_id":4040050220,"basin_region":"Central and South-East Asia","basin_area_km2":121256.0,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-5","true_coordinates":[73.2633333,36.8922222],"spread_km":6.7},{"name":"Karakoram Mountains","kind":"landscape","role":"Mountain range with glaciated headwater catchments in UIB","river_name":null,"geocode_query":"Karakoram, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The glaciated and snow covered sub-basins of the UIB, encompassing glaciated headwater catchments within the northern Hindu Kush, Karakoram and western Himalayan mountain ranges, dominate water generation within the Indus Basin (Alford et al., 2014). The UIB’s tributaries include the Indus at Kharmong: Shigar, Shyok and Astore in the Karakoram Himalaya; the Jhelum, Chenab, Ravi and Sutlej in the western Himalaya; the Hunza, Gilgit, Kabul, Swat and Chitral in the Hindu Kush mountains (Fig. 1). These basins can be classified as having a flow regime that is either glacier-melt dominated (Hunza, Shigar and Shyok) or snowmelt dominated (Jhelum, Kabul, Gilgit, Astore and Swat; Hasson et al., 2014). The predominant source of flow in the UIB is snowmelt, with glacier melt a secondary source, Hydrol. Earth Syst. Sci., 22, 3533–3549, 2018 www.hydrol-earth-syst-sci.net/22/3533/2018/","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.0444174,35.4202093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"N-35, Pakistan","basin_id":4040648320,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":69087.2,"hydrology_source":"hydrobasins","site_id":"doc-e1505daf60eb55c434595a0f-site-6"},{"name":"Western Himalaya","kind":"landscape","role":"Mountain range with snowmelt dominated sub-basins in UIB","river_name":null,"geocode_query":"Western Himalaya, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The glaciated and snow covered sub-basins of the UIB, encompassing glaciated headwater catchments within the northern Hindu Kush, Karakoram and western Himalayan mountain ranges, dominate water generation within the Indus Basin (Alford et al., 2014). The UIB’s tributaries include the Indus at Kharmong: Shigar, Shyok and Astore in the Karakoram Himalaya; the Jhelum, Chenab, Ravi and Sutlej in the western Himalaya; the Hunza, Gilgit, Kabul, Swat and Chitral in the Hindu Kush mountains (Fig. 1). These basins can be classified as having a flow regime that is either glacier-melt dominated (Hunza, Shigar and Shyok) or snowmelt dominated (Jhelum, Kabul, Gilgit, Astore and Swat; Hasson et al., 2014). The predominant source of flow in the UIB is snowmelt, with glacier melt a secondary source, Hydrol. Earth Syst. Sci., 22, 3533–3549, 2018 www.hydrol-earth-syst-sci.net/22/3533/2018/","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North Atlantic","kind":"region","role":"Climate influence region connected to NAO index affecting UIB precipitation and streamflow variability","river_name":null,"geocode_query":"North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"It has a direct influence on the interannual variability of the westerly winds (westerly disturbances) and their water content traversing Europe, the Mediterranean and the Middle East region into the mountains of the UIB (Yadav et al., 2009a; Syed et al., 2010; Filippi et al., 2014). Indices of the NAO have been linked with UIB weather station winter precipitation (Archer and Fowler, 2004; Afzal et al., 2013; Filippi et al., 2014), western Indus Basin’s winter snow cover and weather station precipitation (Hasson et al., 2014), Pakistan weather station temperature (del Río et al., 2013) and winter precipitation in north-west India (Kar and Rana, 2014). ENSO is a dominant pattern of multi-year variability driven by ocean–atmosphere interactions in the tropical Pacific (Wolter and Timlin, 2011), influencing climate globally including the variability of both western disturbances and monsoon processes experienced by the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-39.388964,35.746512],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Atlantic Ocean","hydrology_source":"none","site_id":"doc-e1505daf60eb55c434595a0f-site-8","true_coordinates":[-39.462891,35.746512],"spread_km":6.7},{"name":"Tropical Pacific Ocean","kind":"region","role":"Ocean region driving ENSO climate variability influencing UIB precipitation and snow accumulation","river_name":null,"geocode_query":"Tropical Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The commonly used SOI (Southern Oscillation Index) has been linked with winter Hindu Kush Himalayan region precipitation (Afzal et al., 2013), Indian Summer Monsoon precipitation (Ashok et al., 2004; Ashok and Saji, 2007), central southwest Asian winter precipitation (Syed et al., 2006), Pakistan weather station temperature (del Río et al., 2013) and north-west India winter precipitation (Kar and Rana, 2014). Stronger links have been reported between ENSO, western disturbances and interannual winter precipitation variability in recent decades (Yadav et al., 2009a, b). 3 Methods 3.1 BJP forecasting models The statistical seasonal forecasting model used is the Bayesian joint probability (BJP) approach of Wang et al. (2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.455945,-8.813195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-e1505daf60eb55c434595a0f-site-9","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7}],"limitations":["Lack of access to snowmelt runoff model (SRM) for Tarbela limits direct model comparison there","Short record lengths (e.g., for MODIS and GLDAS snow products) limit robustness of some predictors","Potential for spurious correlation in lagged climate predictors such as MEIMayJun","SRM calibrated over short period (2003-2010) without full cross-validation can lead to overfitting and bias","Exact coordinates of study locations are not provided in the text, latitudes and longitudes remain unspecified"],"river_ids":["ne-536","ne-537","ne-810"],"site_ids":["doc-e1505daf60eb55c434595a0f-site-0","doc-e1505daf60eb55c434595a0f-site-1","doc-e1505daf60eb55c434595a0f-site-2","doc-e1505daf60eb55c434595a0f-site-3","doc-e1505daf60eb55c434595a0f-site-4","doc-e1505daf60eb55c434595a0f-site-5","doc-e1505daf60eb55c434595a0f-site-6","doc-e1505daf60eb55c434595a0f-site-8","doc-e1505daf60eb55c434595a0f-site-9"],"anchor":[68.3213631,25.4201198],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[4040033640,4040545690,4040648320,4040794730],"main_basin_ids":[4040033640,4040050220]},{"id":"doc-eb046c90a1082e3779bb116e","title":"Development and analysis of the Soil Water Infiltration Global database","display_title":"Global Soil Water Infiltration Database","doi":"https://doi.org/10.5194/essd-10-1237-2018","theme":"Land & ecosystems","color":"#c8df98","year":2018,"publication_date":"2018-07-10","authors":["Mehdi Rahmati","Lutz Weihermüller","Jan Vanderborght","Yakov A. Pachepsky","Lili Mao","Seyed Hamidreza Sadeghi","Niloofar Moosavi","Hossein Kheirfam","Carsten Montzka","Kris Van Looy","Brigitta Toth","Zeinab Hazbavi","Wafa Al Yamani","Ammar A. Albalasmeh","Ma'in Z. Alghzawi","Rafael Angulo-Jaramillo","Antônio Celso Dantas Antonino","George Arampatzis","Robson André Armindo","Hossein Asadi","Yazidhi Bamutaze","Jordi Batlle-Aguilar","Béatrice Béchet","Fabian Becker","Günter Blöschl","Klaus Bohne","Isabelle Braud","Clara Castellano","Artemi Cerdà","Maha Chalhoub","Rogerio Cichota","Milena Císlerová","Brent Clothier","Yves Coquet","Wim Cornelis","Corrado Corradini","Artur Paiva Coutinho","Muriel Bastista de Oliveira","José Ronaldo de Macedo","Matheus Fonseca Durães","Hojat Emami","Iraj Eskandari","Asghar Farajnia","Alessia Flammini","Nándor Fodor","Mamoun Gharaibeh","Mohamad Hossein Ghavimipanah","Teamrat A. Ghezzehei","Simone Giertz","Evangelos G. Hatzigiannakis","Rainer Horn","Juan José Jiménez","Diederik Jacques","Saskia Deborah Keesstra","Hamid Kelishadi","Mahboobeh Kiani-Harchegani","Mehdi Kouselou","Madan Kumar Jha","Laurent Lassabatere","Xiaoyan Li","Mark A. Liebig","Lubomír Lichner","María Victoria López","Deepesh Machiwal","Dirk Mallants","Micael Stolben Mallmann","Jean Dalmo de Oliveira Marques","Miles R. Marshall","Jan Mertens","Félicien Meunier","Mohammad Hossein Mohammadi","Binayak P. Mohanty","Mansonia Pulido-Moncada","Suzana Montenegro","Renato Morbidelli","David Moret-Fernández","Ali Akbar Moosavi","Mohammad Reza Mosaddeghi","Seyed Bahman Mousavi","Hasan Mozaffari","Kamal Nabiollahi","Mohammad Reza Neyshabouri","Marta Vasconcelos Ottoni","Theophilo Benedicto Ottoni Filho","Mohammad Reza Pahlavan-Rad","Andreas Panagopoulos","Stephan Peth","Pierre-Emmanuel Peyneau","Tommaso Picciafuoco","Jean Poesen","Manuel Pulido","Dalvan José Reinert","Sabine Reinsch","Meisam Rezaei","Francis Parry Roberts","David Robinson","Jesús Rodrigo-Comino","Otto Corrêa Rotunno Filho","Tadaomi Saito","Hideki Suganuma","Carla Saltalippi","Renáta Sándor","Brigitta Schütt","Manuel Seeger","Nasrollah Sepehrnia","Ehsan Sharifi Moghaddam","Manoj Shukla","Shiraki Shutaro","Ricardo Sorando","Ajayi Asishana Stanley","Peter Strauss","Zhongbo Su","Ruhollah Taghizadeh-Mehrjardi","Encarnación Taguas","Wenceslau Geraldes Teixeira","Ali Reza Vaezi","Mehdi Vafakhah","Tomas Vogel","Iris Vogeler","Jana Votrubova","Steffen Werner","Thierry Winarski","Deniz Yilmaz","Michael H. Young","Steffen Zacharias","Yijian Zeng","Ying Zhao","Hong Zhao","Harry Vereecken"],"summary":"This study presents the Soil Water Infiltration Global (SWIG) database, comprising 5023 soil water infiltration measurements collected globally from 54 countries spanning 1976-2017. The database includes measurements of infiltration curves, soil properties, land use, and experimental metadata useful for better understanding soil hydraulic properties, testing infiltration models, and developing pedotransfer functions. The geographic coverage includes diverse climates and soil types, with major data contributions from Iran, China, and the USA. The data are freely available online.","finding":"Soil saturated hydraulic conductivity (Ksat) exhibits far greater variability and is less tightly controlled by soil texture than traditionally assumed, with field measurements showing that land use and soil structure significantly influence infiltration properties beyond texture alone.","finding_evidence":{"passage_id":"p015-003","page":15,"quote":"It was observed that the standard deviation of Ksat in the SWIG database is typically larger than the standard deviations obtained from the databases in the literature. This indicates that texture is apparently not the most important control on Ksat values. However, one would also pose that much of the lack of correlation between soil texture and predicted Ksat from the SWIG database is related to the lack of soil structural information, such as macro porosity quantification or other possible soil attributes. Indeed, many of the datasets presented in our paper on saturated and near-saturated flow can be used to infer the state of the soil’s structure, namely its macroporosity, by using the slope of the near-saturated conductivity curve, via Philip’s “flow-weighted mean poresize” analysis. White and Sully (1987) have discussed this in a great detail.","verified_in_page":true},"topics":["soil hydrology","infiltration","soil texture","soil hydraulic conductivity","pedotransfer functions","soil structure","land use","soil measurement methods"],"study_period":"1976-2017","locations":[{"name":"Iran","kind":"region","role":"major contributor of infiltration data","river_name":null,"geocode_query":"Iran","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Data were derived from 54 countries (Table 4). The largest number of data sources were provided by scientists in Iran (n = 38), China (n = 23), and the USA (n = 15), whereby one data source might contain several water infiltration measurements. The SWIG database covers measurements from 1976 to 2017. A sparse coverage was obtained for the higher latitudes of the Northern Hemisphere (above 60◦ ) including Norway, Finland, Sweden, Iceland, Greenland, and Russia. The lack of reports with infiltration data from most countries of the former Soviet Union as well as the Sahelian and Saharan countries is also notable, as well as the small number of infiltration data from Australia. Figure 3 shows the number of samples by climatic zone (Rubel et al., 2017; Kottek et al., 2006).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"China","kind":"region","role":"major contributor of infiltration data","river_name":null,"geocode_query":"China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Data were derived from 54 countries (Table 4). The largest number of data sources were provided by scientists in Iran (n = 38), China (n = 23), and the USA (n = 15), whereby one data source might contain several water infiltration measurements. The SWIG database covers measurements from 1976 to 2017. A sparse coverage was obtained for the higher latitudes of the Northern Hemisphere (above 60◦ ) including Norway, Finland, Sweden, Iceland, Greenland, and Russia. The lack of reports with infiltration data from most countries of the former Soviet Union as well as the Sahelian and Saharan countries is also notable, as well as the small number of infiltration data from Australia. Figure 3 shows the number of samples by climatic zone (Rubel et al., 2017; Kottek et al., 2006).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States of America","kind":"region","role":"major contributor of infiltration data","river_name":null,"geocode_query":"United States of America","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Data were derived from 54 countries (Table 4). The largest number of data sources were provided by scientists in Iran (n = 38), China (n = 23), and the USA (n = 15), whereby one data source might contain several water infiltration measurements. The SWIG database covers measurements from 1976 to 2017. A sparse coverage was obtained for the higher latitudes of the Northern Hemisphere (above 60◦ ) including Norway, Finland, Sweden, Iceland, Greenland, and Russia. The lack of reports with infiltration data from most countries of the former Soviet Union as well as the Sahelian and Saharan countries is also notable, as well as the small number of infiltration data from Australia. Figure 3 shows the number of samples by climatic zone (Rubel et al., 2017; Kottek et al., 2006).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Slovakia","kind":"region","role":"contributing data from infiltration studies","river_name":null,"geocode_query":"Slovakia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 1239 44Enginereed and Geosystems Analysis Unit, Belgian Nuclear Research Centre, Mol, Belgium 45Soil, Water and Land Use Team, Wageningen Environmental Research, Wageningen UR, 6708PB Wageningen, the Netherlands 46Civil, Surveying and Environmental Engineering, the University of Newcastle, Callaghan 2308, Australia 47Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran 48Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur – 721302, West Bengal, India 49State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 50Research Soil Scientist, USDA Agricultural Research Service, Mandan, ND, USA 51Institute of Hydrology, Slovak Academy of Sciences, Bratislava, Slovakia 52Departamento de Suelo y Agua, Estación Experimental de Aula Dei (EEAD), Consejo Superior de Investigaciones Científicas (CSIC), P.O.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Australia","kind":"region","role":"studied region for infiltration data including South Australia","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Box 7062, Kampala, Uganda 17UMR 1402 INRA AgroParisTech Functional Ecology and Ecotoxicology of Agroecosystems, Institut National de la Recherche Agronomique, AgroParisTech B.P. 01, 78850 Thiverval-Grignon, France 18UMR 8148 IDES CNRS/Université Paris-Sud, XI Bât. 504, Faculté des Sciences 91405, Orsay CEDEX, France 19Innovative Groundwater Solutions (IGS), Victor Harbor, 5211, South Australia, Australia 20IFSTTAR, GERS, EE, 44344 Bouguenais, France 21Freie Universität Berlin, Department of Earth Sciences, Institute of Geographical Sciences, Malteserstr. 74-100, Lankwitz, 12249, Berlin, Germany 22Centre for Water Resource Systems, TU Wien, Karlsplatz 13, 1040 Vienna, Austria 23Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Karlsplatz 13/222, 1040 Vienna, Austria 24Faculty of Agricultural and Environmental Sciences, University of Rostock, Germany 25Irstea, UE RiverLy, Lyon-Villeurbanne Center, 69625 Villeurbanne, France 26Pyrenean Institute of Ecology-CSIC, AV.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia","kind":"region","role":"location of groundwater study cited for infiltration research","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Box 7062, Kampala, Uganda 17UMR 1402 INRA AgroParisTech Functional Ecology and Ecotoxicology of Agroecosystems, Institut National de la Recherche Agronomique, AgroParisTech B.P. 01, 78850 Thiverval-Grignon, France 18UMR 8148 IDES CNRS/Université Paris-Sud, XI Bât. 504, Faculté des Sciences 91405, Orsay CEDEX, France 19Innovative Groundwater Solutions (IGS), Victor Harbor, 5211, South Australia, Australia 20IFSTTAR, GERS, EE, 44344 Bouguenais, France 21Freie Universität Berlin, Department of Earth Sciences, Institute of Geographical Sciences, Malteserstr. 74-100, Lankwitz, 12249, Berlin, Germany 22Centre for Water Resource Systems, TU Wien, Karlsplatz 13, 1040 Vienna, Austria 23Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Karlsplatz 13/222, 1040 Vienna, Austria 24Faculty of Agricultural and Environmental Sciences, University of Rostock, Germany 25Irstea, UE RiverLy, Lyon-Villeurbanne Center, 69625 Villeurbanne, France 26Pyrenean Institute of Ecology-CSIC, AV.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"France","kind":"region","role":"source of infiltration data and study location","river_name":null,"geocode_query":"France","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Box 3037, CEP 37200-000, Lavras, Brazil 15Department of Soil Science, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran 16Department of Geography, Geo-Informatics and Climatic Sciences, Makerere University, P.O.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Spain","kind":"region","role":"location of infiltration studies and soil hydraulic research","river_name":null,"geocode_query":"Spain","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Montañana 1005, Av. Victoria s/n. 50059 Zaragoza, 22700 Jaca, Huesca, Spain 27Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, Valencia, Spain 28Plant and Food Research, Mount Albert Research Station, Auckland, New Zealand 29Czech Technical University in Prague, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6, Czech Republic 30Plant and Food Research, Palmerston North, New Zealand 31ISTO UMR 7327 Université d’Orléans, CNRS, BRGM, 45071 Orléans, France 32Department of Soil Management, UNESCO Chair on Eremology, Ghent University, Ghent, Belgium 33Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy 34UniRedentor University Center.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United Kingdom","kind":"region","role":"location with infiltration data contributions","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Montañana 1005, Av. Victoria s/n. 50059 Zaragoza, 22700 Jaca, Huesca, Spain 27Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, Valencia, Spain 28Plant and Food Research, Mount Albert Research Station, Auckland, New Zealand 29Czech Technical University in Prague, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6, Czech Republic 30Plant and Food Research, Palmerston North, New Zealand 31ISTO UMR 7327 Université d’Orléans, CNRS, BRGM, 45071 Orléans, France 32Department of Soil Management, UNESCO Chair on Eremology, Ghent University, Ghent, Belgium 33Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy 34UniRedentor University Center.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Brazil","kind":"region","role":"study area for infiltration data collection","river_name":null,"geocode_query":"Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Box 3037, CEP 37200-000, Lavras, Brazil 15Department of Soil Science, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran 16Department of Geography, Geo-Informatics and Climatic Sciences, Makerere University, P.O.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"New Zealand","kind":"region","role":"location of infiltration studies","river_name":null,"geocode_query":"New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Montañana 1005, Av. Victoria s/n. 50059 Zaragoza, 22700 Jaca, Huesca, Spain 27Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, Valencia, Spain 28Plant and Food Research, Mount Albert Research Station, Auckland, New Zealand 29Czech Technical University in Prague, Faculty of Civil Engineering, Thákurova 7, 166 29 Prague 6, Czech Republic 30Plant and Food Research, Palmerston North, New Zealand 31ISTO UMR 7327 Université d’Orléans, CNRS, BRGM, 45071 Orléans, France 32Department of Soil Management, UNESCO Chair on Eremology, Ghent University, Ghent, Belgium 33Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy 34UniRedentor University Center.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Hubei Province, China","kind":"region","role":"site of rainfall infiltration study","river_name":null,"geocode_query":"Hubei Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p022-003","page":22,"quote":"Jacka, L., Pavlásek, J., Pech, P., and Kuráž, V.: Assess- ˇ ment of evaluation methods using infiltration data measured in heterogeneous mountain soils, Geoderma, 276, 74–83, https://doi.org/10.1016/j.geoderma.2016.04.023, 2016. Jacques, D.: Analysis of water flow and solute transport at the field scale, PhD dissertation, Catholique University Leuven, Leuven, Belgium, 2000. Jacques, D., Mohanty, B. P., and Feyen, J.: Comparison of alternative methods for deriving hydraulic properties and scaling factors from single-disc tension infiltrometer measurements, Water Resour. Res., 38, 25-1–25-14, https://doi.org/10.1029/2001WR000595, 2002. Jemsi, S., Sayyad, G., Jafarnezhadi, A., and KashefiPour, S. M.: Development of infiltration rate pedotransfer functions using artificial neural networks and multiple linear regressions for Khuzestan province in south of Iran, International Journal of Agriculture: Research and Review, 3, 766–770, 2013.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Qinghai Lake Watershed, Qinghai-Tibet Plateau, China","kind":"region","role":"site of soil macropore quantification using computed tomography","river_name":null,"geocode_query":"Qinghai Lake Watershed, Qinghai-Tibet Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p022-001","page":22,"quote":"Y., and Assouline, S.: Infiltration, in: The handbook of groundwater engineering, edited by: Delleur, J. W., CRC Press, Taylor & Francis Group, Boca Raton, New York, USA, 2006. Hu, X., Li, Z.-C., Li, X.-Y., and Liu, L.-y.: Quantification of soil macropores under alpine vegetation using computed tomography in the Qinghai Lake Watershed, NE Qinghai–Tibet Plateau, Geoderma, 264, 244–251, https://doi.org/10.1016/j.geoderma.2015.11.001, 2016. Huang, L., Zhang, P., Hu, Y., and Zhao, Y.: Vegetation succession and soil infiltration characteristics under different aged refuse dumps at the Heidaigou opencast coal mine, Global Ecology and Conservation, 4, 255–263, https://doi.org/10.1016/j.gecco.2015.07.006, 2015. Huang, M., Barbour, S. L., Elshorbagy, A., Zettl, J. D., and Cheng Si, B.: Infiltration and drainage processes in multi-layered coarse soils, Can. J. Soil Sci., 91, 169–183, https://doi.org/10.4141/cjss09118, 2011.","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.349789,36.699903],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qinghai Lake, China","basin_id":4040578640,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":62487.2,"hydrology_source":"hydrobasins","site_id":"doc-eb046c90a1082e3779bb116e-site-12","true_coordinates":[100.3497895,36.75990290000001],"spread_km":6.7},{"name":"Tottori University, Tottori, Japan","kind":"facility","role":"location affiliated with soil infiltration studies","river_name":null,"geocode_query":"Tottori University, Tottori, Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 76Faculty of Agriculture, Tottori University, 4-101 Koyama-Minami, Tottori 680-8553, Japan 77Department of Materials and Life Science, Seikei University, 3-3-1, Kichijoji-kitamachi, Musashino, Tokyo 180-8633, Japan 78Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran 79Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico, USA 80Japan International Research Center for Agricultural Science, Rural Development Division, Tsukuba, Japan 81Department of Agricultural and Bio-Resources Engineering, Faculty of Engineering, Ahmadu Bello University Zaria, Nigeria 82Institute for Land and Water Management Research, Federal Agency for Water Management, Pollnbergstraße 1, 3252 Petzenkirchen, Austria 83Department of Water Resources, ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands 84Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Yazd Province, Iran 85University of Córdoba, Department of Rural Engineering, 14071 Córdoba, Spain 86Soil Physics, Embrapa Soils, Rua Jardim Botânico, 1026, 22460-00 Rio de Janeiro, RJ, Brazil 87Department of Soil Science, Agriculture Faculty, University of Zanjan, Zanjan, Iran 88Department of Geography, Ruhr University Bochum, 44799 Bochum, Germany 89Engineering Faculty, Civil Engineering Department, Munzur University, Tunceli, Turkey 90Bureau of Economic Geology, John A. and Katherine G.","verified_in_page":true},"resolution_status":"resolved","coordinates":[134.1733553,35.5152072],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"4 Chome-101 南 湖山町 Tottori, 680-0945, Japan","basin_id":4040039450,"main_basin_id":4040039450,"basin_region":"Central and South-East Asia","basin_area_km2":295817.7,"hydrology_source":"hydrobasins","site_id":"doc-eb046c90a1082e3779bb116e-site-13"},{"name":"Ruhr University Bochum, Germany","kind":"facility","role":"institution affiliated with infiltration data contributors","river_name":null,"geocode_query":"Ruhr University Bochum, Bochum, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Rahmati et al.: Development and analysis of the Soil Water Infiltration Global database 76Faculty of Agriculture, Tottori University, 4-101 Koyama-Minami, Tottori 680-8553, Japan 77Department of Materials and Life Science, Seikei University, 3-3-1, Kichijoji-kitamachi, Musashino, Tokyo 180-8633, Japan 78Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran 79Plant and Environmental Sciences, New Mexico State University, Las Cruces, New Mexico, USA 80Japan International Research Center for Agricultural Science, Rural Development Division, Tsukuba, Japan 81Department of Agricultural and Bio-Resources Engineering, Faculty of Engineering, Ahmadu Bello University Zaria, Nigeria 82Institute for Land and Water Management Research, Federal Agency for Water Management, Pollnbergstraße 1, 3252 Petzenkirchen, Austria 83Department of Water Resources, ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands 84Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Yazd Province, Iran 85University of Córdoba, Department of Rural Engineering, 14071 Córdoba, Spain 86Soil Physics, Embrapa Soils, Rua Jardim Botânico, 1026, 22460-00 Rio de Janeiro, RJ, Brazil 87Department of Soil Science, Agriculture Faculty, University of Zanjan, Zanjan, Iran 88Department of Geography, Ruhr University Bochum, 44799 Bochum, Germany 89Engineering Faculty, Civil Engineering Department, Munzur University, Tunceli, Turkey 90Bureau of Economic Geology, John A. and Katherine G.","verified_in_page":true},"resolution_status":"resolved","coordinates":[7.2613027,51.444419],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Universitätsstraße 150, 44801 Bochum, Germany","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-eb046c90a1082e3779bb116e-site-14"},{"name":"Institute of Bio- and Geosciences (IBG-3), Forschungszentrum Jülich, Germany","kind":"facility","role":"key scientific institution coordinating data collection and analysis for SWIG database","river_name":null,"geocode_query":"Forschungszentrum Jülich GmbH, Jülich, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"Young90, Steffen Zacharias91, Yijian Zeng83 , Ying Zhao92, Hong Zhao83, and Harry Vereecken2,3 1Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran 2Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany 3 ISMC International Soil Modeling Consortium, Institute of Bio and Geosciences Forschungszentrum Jülich, 52425 Jülich, Germany 4USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA 5Key Laboratory of Dryland Agriculture, Ministry of Agriculture, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China 6Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran 7Department of Environmental Sciences, Urmia Lake Research Institute, Urmia University, Urmia, Iran Published by Copernicus Publications.","verified_in_page":true},"resolution_status":"resolved","coordinates":[6.4041378,50.9049698],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wilhelm-Johnen-Straße, 52428 Jülich, Germany","basin_id":2040022160,"main_basin_id":2040022160,"basin_region":"Europe and Middle East","basin_area_km2":76120.6,"hydrology_source":"hydrobasins","site_id":"doc-eb046c90a1082e3779bb116e-site-15"},{"name":"University of Maragheh, Iran","kind":"facility","role":"affiliation of first author and contributor of data","river_name":null,"geocode_query":"University of Maragheh, Maragheh, Iran","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"Young90, Steffen Zacharias91, Yijian Zeng83 , Ying Zhao92, Hong Zhao83, and Harry Vereecken2,3 1Department of Soil Science and Engineering, Faculty of Agriculture, University of Maragheh, Maragheh, Iran 2Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, Germany 3 ISMC International Soil Modeling Consortium, Institute of Bio and Geosciences Forschungszentrum Jülich, 52425 Jülich, Germany 4USDA-ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA 5Key Laboratory of Dryland Agriculture, Ministry of Agriculture, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China 6Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran 7Department of Environmental Sciences, Urmia Lake Research Institute, Urmia University, Urmia, Iran Published by Copernicus Publications.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Absence of precise coordinates for most study locations","Regional scale of many data sources limits local specificity","Some soil property measurements lack methodological standardization","Potential transcription errors despite thorough checking","Variability due to differing measurement methodologies and personnel experience","Approximate latitude/longitude values available, not exact site coordinates"],"river_ids":[],"site_ids":["doc-eb046c90a1082e3779bb116e-site-12","doc-eb046c90a1082e3779bb116e-site-13","doc-eb046c90a1082e3779bb116e-site-14","doc-eb046c90a1082e3779bb116e-site-15"],"anchor":[100.349789,36.699903],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040022160,2040023010,4040039450,4040578640],"main_basin_ids":[2040022160,2040023010,4040007850,4040039450]},{"id":"doc-f39e1a3abb674f77f1924362","title":"Present and future Köppen-Geiger climate classification maps at 1-km resolution","display_title":"Global Köppen-Geiger Climate Maps at 1-km","doi":"https://doi.org/10.1038/sdata.2018.214","theme":"Climate","color":"#98d4f1","year":2018,"publication_date":"2018-10-30","authors":["Hylke E. Beck","Niklaus E. Zimmermann","Tim R. McVicar","Noemi Vergopolan","Alexis Berg","Eric F. Wood"],"summary":"This study produces new global Köppen-Geiger climate classification maps at an unprecedented 1-km spatial resolution for present-day (1980–2016) and future (2071–2100) under climate change scenario RCP8.5. The present maps are based on an ensemble of four topographically corrected datasets, while the future maps utilize climate anomaly methods applied to 32 CMIP5 climate model projections. Confidence levels associated with classifications are also provided, indicating uncertainty especially near climate zone boundaries and at high latitudes. The new maps show improved accuracy and detail compared to previous datasets and have applications in ecological and vegetation distribution modeling.","finding":"The 1-km resolution present-day Köppen-Geiger climate classification map exhibits substantially higher accuracy (80.0%) than previous global maps (66.1–73.4%), due to use of multiple high-resolution, topographically corrected climate datasets and an ensemble approach, with confidence maps effectively indicating classification reliability.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"The newly derived high-resolution present-day Köppen-Geiger classification (Fig. 1a) exhibited a classification accuracy of 80.0%, while the maps of Kottek et al.20, Peel et al.21, and Kriticos et al.22 exhibited classification accuracies of 66.1, 70.9, and 73.4%, respectively. These results confirm that the new map is more accurate, which is primarily due to its high (1 km) resolution and use of an ensemble of topographically-corrected climatic datasets. The map of Kottek et al.20 showed the lowest classification accuracy, due to its low (0.5°) resolution. The map of Peel et al.21 also performed less well, due to a lack of topographic corrections and the use of a relatively small number of stations. We also tested the usefulness of the confidence map associated with the new present-day classification (Fig. 2a) using station observations. We obtained a mean confidence level of 92.6% for the correctly classified stations (n=17,667) and 77.4% for the misclassified stations (n =4411).","verified_in_page":true},"topics":["climate-classification","climate-change-projection","spatial-resolution","climate-modeling","topographic-correction","uncertainty-assessment","vegetation-distribution","climatology"],"study_period":"1980-2016 (present); 2071-2100 (future)","locations":[{"name":"Alps, Europe","kind":"region","role":"validation and illustrative site showing classification detail and confidence maps","river_name":null,"geocode_query":"Alps, Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The mean confidence level was thus substantially lower for the misclassified stations, confirming that the confidence map provides a useful indication of the classification accuracy. Figures 4 and 5 show historic Köppen-Geiger classification maps from all three previous studies and our present-day map for the Alps (Europe) and the central Rocky Mountains (North America), respectively, illustrating the enhanced detail in our map. The other maps sometimes fail to depict important topographic features; the map of Peel et al.21, for example, does not represent the Apennine mountains (Italy), due to a lack of topographic corrections (Fig. 4e). The new map (Figs. 4a and 5a) also exhibits better agreement with a Landsat-based forest cover map36 (30-m resolution; Figs. 4c and 5c). The spatial extent of the polar (E) climate, for example, corresponds closely with treelines in the forest cover maps.","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.656782,46.827398],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alps","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-f39e1a3abb674f77f1924362-site-0","true_coordinates":[9.6567822,46.8873983],"spread_km":6.7},{"name":"Central Rocky Mountains, North America","kind":"region","role":"validation and illustrative site showing classification detail and confidence maps","river_name":null,"geocode_query":"Central Rocky Mountains, North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The mean confidence level was thus substantially lower for the misclassified stations, confirming that the confidence map provides a useful indication of the classification accuracy. Figures 4 and 5 show historic Köppen-Geiger classification maps from all three previous studies and our present-day map for the Alps (Europe) and the central Rocky Mountains (North America), respectively, illustrating the enhanced detail in our map. The other maps sometimes fail to depict important topographic features; the map of Peel et al.21, for example, does not represent the Apennine mountains (Italy), due to a lack of topographic corrections (Fig. 4e). The new map (Figs. 4a and 5a) also exhibits better agreement with a Landsat-based forest cover map36 (30-m resolution; Figs. 4c and 5c). The spatial extent of the polar (E) climate, for example, corresponds closely with treelines in the forest cover maps.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.5141639,39.8019322],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central City, CO, USA","basin_id":7040569640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":1381472.0,"hydrology_source":"hydrobasins","site_id":"doc-f39e1a3abb674f77f1924362-site-1"}],"limitations":["Future climate projections use anomaly method which may not fully correct spatial biases between modeled and observed climatologies.","Coarser resolution (1–2°) of climate model outputs limits representation of sub-grid scale climate heterogeneities in future maps.","Future vegetation changes may lag climate zone shifts and are influenced by factors not included in the Köppen-Geiger classification (e.g., CO2 effects).","Topographic effects improve accuracy but some regional fine-scale interactions remain uncertain."],"river_ids":[],"site_ids":["doc-f39e1a3abb674f77f1924362-site-0","doc-f39e1a3abb674f77f1924362-site-1"],"anchor":[-105.5141639,39.8019322],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[2040023010,7040569640],"main_basin_ids":[2040023010,7040047060]},{"id":"doc-12d49e1240fa34e200403175","title":"Applying Multi-Layer Artificial Neural Network and Mutual Information to the Prediction of Trends in Dissolved Oxygen","display_title":"ML Neural Network Predicts DO Trends at Baffle Creek","doi":"https://doi.org/10.3389/fenvs.2019.00046","theme":"Flow & ecology","color":"#80edc2","year":2019,"publication_date":"2019-04-16","authors":["Yifan Zhang","Peter Fitch","Maria P. Vilas","Peter J. Thorburn"],"summary":"This study developed a novel water quality predictive model combining multi-layer artificial neural networks (MANN) and mutual information (MI) feature selection to accurately predict short-term dissolved oxygen (DO) trends. Using in situ sensor data from Baffle Creek estuary, Queensland, Australia, the model skillfully captured nonlinear dynamics, outperforming other models like single-layer ANN, support vector regression, and linear regression. The MANN with dropout prevented overfitting, and MI-based feature selection included key variables such as pH, chlorophyll-a, and water temperature to enhance prediction accuracy. Accurate short-term DO forecasts support aquatic ecosystem management and timely mitigation of low oxygen events.","finding":"The MANN model with mutual information feature selection and dropout achieved high accuracy in predicting 90- and 120-minute ahead DO concentrations with R² scores up to 0.95 in the wet season, significantly outperforming other models and parameter selection methods.","finding_evidence":{"passage_id":"p007-002","page":7,"quote":"They are defined as: MAE = 1 n Xn i=1 |fi − ˆ fi | (4) R 2 = 1 − Pn i=1 (fi − ˆ fi) 2 Pn i=1 (fi − fi) 2 (5) RMSE = vuut 1 n Xn i=1 (|fi − ˆ fi |) 2 (6) where fi is the observed value, ˆ fi is the predicted value and fi = 1 n Pn i=1 fi . 3.3. Prediction Results Figure 7 shows the prediction of DO in both wet and dry seasons. It is clear that the concentration of DO has stronger variation in the wet season (Figure 7A) than it is in the dry season (Figure 7B). In the wet season, the concentration of DO increases from around 4.0 mg L −1 to nearly 7.5 mg L−1 within the first month and thereafter fluctuates between 5.5 and 7.0 mg L−1 . During this period, our MANN model is effective in predicting the diurnal variation pattern as well as the long term variability. Also, the MANN model has a quick response when DO concentrations start to increase or decrease. This means the MANN model is very sensitive when the trend of DO begins to change.","verified_in_page":true},"topics":["dissolved oxygen prediction","artificial neural networks","mutual information feature selection","water quality modeling","aquatic ecosystem health","machine learning in environmental science","environmental monitoring","nonlinear time series forecasting"],"study_period":"2013-11-01 to 2014-10-31","locations":[{"name":"Baffle Creek estuary system","kind":"station","role":"study site for water quality data collection","river_name":null,"geocode_query":"Baffle Creek estuary, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"In our predictive model, a mutual information based feature selection strategy is designed to evaluate the association between water quality variables. Moreover, a MANN is used to thoroughly learn levels of representations and approximate complex non-linear relationships within multivariate time series data. 2. MATERIALS AND METHODS 2.1. Study Area and Water Quality Data The water quality of Baffle Creek is studied as an example. Baffle Creek estuary system is one of the largest estuaries in Queensland, Australia and has a catchment area of approximately 3,000 km2 . The time series of water quality data has been collected by the in situ near real-time monitoring sensors (YSI, Model 6600) deployed 16 km from the mouth of Baffle Creek (Figure 1). Readings of water temperature, electric conductivity (EC), pH, dissolved oxygen (DO), turbidity, and chlorophyll-a (Chl-a) were recorded every half hour at 0.2 m below water surface1 .","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Baffle Creek catchment area","kind":"basin","role":"catchment of the estuary system under study","river_name":"Baffle Creek","geocode_query":"Baffle Creek catchment area Queensland Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Zhang et al. Machine Learning-Based Water Quality Prediction FIGURE 1 | Baffle Creek catchment area and the water quality monitoring station. TABLE 1 | Details of water quality data. Variables Unit Min Max Mean Std dev Season Water temperature ◦C 23.84 32.73 27.61 1.76 Wet 15.73 29.88 21.57 2.73 Dry EC μS cm−1 13 55,600 37719.06 17070.59 Wet 2,651 52,350 34658.05 10481.41 Dry pH u. of pH 6.48 8.47 7.8 0.33 Wet 7.45 8.39 7.99 0.12 Dry DO mg L−1 3.72 8.06 6.12 0.59 Wet 5.95 9.13 7.33 0.56 Dry Turbidity NTU 1.2 1,289 11.74 49.79 Wet 0.8 80 4.29 2.75 Dry Chl-a μg L−1 0.1 31.1 4.57 2.55 Wet 0.5 415.4 5.55 9.74 Dry Frontiers in Environmental Science | www.frontiersin.org 3 April 2019 | Volume 7 | Article 46","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.8984888,-24.4571204],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":12128344,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069600,"main_basin_id":5040069600,"basin_region":"Australasia","basin_area_km2":14751.2,"hydrology_source":"geofabric","site_id":"doc-12d49e1240fa34e200403175-site-1"}],"limitations":["Exact coordinates of the Baffle Creek monitoring station and catchment are not provided, set as null.","The study area is limited to a single estuary catchment in Queensland, Australia, which may limit generalizability.","The text does not specify finer spatial resolution study sites within the Baffle Creek catchment."],"river_ids":[],"site_ids":["doc-12d49e1240fa34e200403175-site-1"],"anchor":[151.8984888,-24.4571204],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040069600],"main_basin_ids":[5040069600]},{"id":"doc-20821f2c8889b1092f1eb294","title":"Methods of mathematical modelling for calculating flow characteristics of ungauged rivers in engineering design tasks (by the example of the Khemchik River, Tyva Republic, Russia)","display_title":"Hydrological Modelling of Khemchik River, Tyva","doi":"https://doi.org/10.1088/1755-1315/381/1/012068","theme":"Flow & ecology","color":"#80edc2","year":2019,"publication_date":"2019-11-01","authors":["Nataliia Nesterova","Olga Makarieva","D. A. Post"],"summary":"This study applied the deterministic Hydrograph hydrological model to calculate maximum discharge frequency curves for the ungauged upstream Khemchik River catchment in the Tyva Republic, Russia (area 1750 km2). Model parameters were evaluated for three main landscape types and verified against data from two gauged catchments. Simulations used meteorological data from the Teeli station for 1966–2012. Results showed that modeled maximum discharges exceed standard method estimates by 1.3 to 5 times, consistent with observed flooding reported by local authorities, indicating standard methods underestimate flood risks in this mountainous region.","finding":"Modeled maximum discharge values for the Khemchik River catchment are 1.3 to 5 times higher than standard flood frequency analysis estimates, with local flood records supporting the plausibility of the model predictions.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"The maximum discharges in the frequency interval from 99 to 0.7 % obtained by standard methods [1]","verified_in_page":true},"topics":["hydrological modelling","flood frequency analysis","ungauged catchments","mountain hydrology","river discharge","hydrological parameters","meteorological data interpolation","flood risk assessment"],"study_period":"1966-2012","locations":[{"name":"Khemchik River catchment","kind":"basin","role":"primary study catchment for hydrological modeling","river_name":"Khemchik River","geocode_query":"Khemchik River, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The aim of this study is to assess the maximum discharge values of different recurrence frequency at the ungauged catchment of the Khemchik River (Khemchik village, Tyva Republic) with the catchment area of 1750 km2 using distributed hydrological modelling. The relevance of the research is due to the regular flooding of settlements in the valley of the Khemchik River basin and the need for engineering solutions to prevent dangerous hydrological phenomena. 2. Objects, methods and data The Khemchik River basin, a left tributary of the Yenisei River, originates at the Eastern slopes of the Shapshal ridge with an elevation of more than 3600 m. The streamflow of the Khemchik River is sourced from rain and snow with a significant underground contribution. Summer-autumn peaks formed by rains can be two-five times higher than the spring snow floods. The climate is dry with snow-poor winters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[91.72769505000805,51.63003361219238],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040483980,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":364437.2,"hydrology_source":"hydrobasins","site_id":"doc-20821f2c8889b1092f1eb294-site-0"},{"name":"Khemchik village","kind":"settlement","role":"study focus and reference location in the Khemchik River basin","river_name":"Khemchik River","geocode_query":"Khemchik, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"In this study, we assess the results of the application of the deterministic hydrological model Hydrograph for calculating the maximum discharge of various recurrence frequencies at the ungauged mountain catchment of the Khemchik River – Khemchik village (the Tyva Republic, Russia, area 1750 km2 ). We have assessed the parameters of the model for three main landscapes of the study area. Based on streamflow modelling at two gauged catchments, we have verified the parameters and algorithms of the hydrological mode land the results of modelling are satisfactory. The simulations of streamflow with daily time step for the Khemchik River – Khemchik village was conducted for the period between 1966 and 2012 using meteorological observational data from the Teeli meteorological station.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.1889645,51.0182435],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Khemchik, Tuva Republic, Russia, 668010","basin_id":3040483980,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":364437.2,"hydrology_source":"hydrobasins","site_id":"doc-20821f2c8889b1092f1eb294-site-1"},{"name":"Teeli meteorological station","kind":"station","role":"source of meteorological data used for hydrological modeling","river_name":null,"geocode_query":"Teeli meteorological station, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"In this study, we assess the results of the application of the deterministic hydrological model Hydrograph for calculating the maximum discharge of various recurrence frequencies at the ungauged mountain catchment of the Khemchik River – Khemchik village (the Tyva Republic, Russia, area 1750 km2 ). We have assessed the parameters of the model for three main landscapes of the study area. Based on streamflow modelling at two gauged catchments, we have verified the parameters and algorithms of the hydrological mode land the results of modelling are satisfactory. The simulations of streamflow with daily time step for the Khemchik River – Khemchik village was conducted for the period between 1966 and 2012 using meteorological observational data from the Teeli meteorological station.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Kara-Khol’ (Tapsy River basin)","kind":"basin","role":"gauged catchment for modeling parameter verification","river_name":"Tapsy River","geocode_query":"Kara-Khol’, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Geographical Foundations IOP Conf. Series: Earth and Environmental Science 381 (2019) 012068 IOP Publishing doi:10.1088/1755-1315/381/1/012068 4 talus landscape. At the same time, the amount of precipitation in the forest zone can be two-three times higher than in the steppe [21, 22]. The runoff coefficient in the forest is 0.50. The input information for the simulation was daily data on air temperature, humidity and precipitation at meteorological stations. Meteorological elements were interpolated from ground stations into RPs, accounting for change with elevation via a gradient. 3. Results of the maximum water discharge calculation Daily simulation of streamflow hydrographs in two watersheds of different scales was carried out to verify the proposed conceptual scheme of runoff formation processes (the Tapsy River basin – KaraKhol', 302 km2 ; the Khemchik River basin – Ime, 25500 km2 ) from 1975 to 1993.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.76342,51.283417],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040483980,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":364437.2,"hydrology_source":"hydrobasins","site_id":"doc-20821f2c8889b1092f1eb294-site-3"},{"name":"Ime gauging station (Khemchik River basin)","kind":"station","role":"gauging site used to verify hydrological model parameters","river_name":"Khemchik River","geocode_query":"Ime, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Geographical Foundations IOP Conf. Series: Earth and Environmental Science 381 (2019) 012068 IOP Publishing doi:10.1088/1755-1315/381/1/012068 4 talus landscape. At the same time, the amount of precipitation in the forest zone can be two-three times higher than in the steppe [21, 22]. The runoff coefficient in the forest is 0.50. The input information for the simulation was daily data on air temperature, humidity and precipitation at meteorological stations. Meteorological elements were interpolated from ground stations into RPs, accounting for change with elevation via a gradient. 3. Results of the maximum water discharge calculation Daily simulation of streamflow hydrographs in two watersheds of different scales was carried out to verify the proposed conceptual scheme of runoff formation processes (the Tapsy River basin – KaraKhol', 302 km2 ; the Khemchik River basin – Ime, 25500 km2 ) from 1975 to 1993.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Elegest River basin","kind":"basin","role":"analog basin used in standard flood frequency analysis comparison","river_name":"Elegest River","geocode_query":"Elegest River, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"The runoff formation conditions are significantly different at the Khemchik and Elegest Rivers despite their satisfying the rivers-analogues requirements [1]. For example, the average and the maximum elevation of the Elegest River catchment is lower by almost 1000 m than in the Khemchik River basin. In addition, the distinctive feature of the Khemchik River is the presence of glaciers, untypical of the catchment of the Elegest River. 4. Conclusions The study presents the possibility of using the distributed hydrological Hydrograph model for the assessment of maximum discharge frequency curve using the example of ungauged mountain catchment of the Khemchik River – Khemchik village (the area 1750 km2 , the Republic of Tyva). We have constructed the frequency curve based on the simulated maximum water discharge and compared the simulated values with those obtained by flood frequency analysis [1] using an analogous basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[94.06806347329982,51.45645471214638],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040483980,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":364437.2,"hydrology_source":"hydrobasins","site_id":"doc-20821f2c8889b1092f1eb294-site-5"},{"name":"Shapshal ridge","kind":"landscape","role":"mountain origin area of the Khemchik River basin","river_name":null,"geocode_query":"Shapshal ridge, Tyva Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The aim of this study is to assess the maximum discharge values of different recurrence frequency at the ungauged catchment of the Khemchik River (Khemchik village, Tyva Republic) with the catchment area of 1750 km2 using distributed hydrological modelling. The relevance of the research is due to the regular flooding of settlements in the valley of the Khemchik River basin and the need for engineering solutions to prevent dangerous hydrological phenomena. 2. Objects, methods and data The Khemchik River basin, a left tributary of the Yenisei River, originates at the Eastern slopes of the Shapshal ridge with an elevation of more than 3600 m. The streamflow of the Khemchik River is sourced from rain and snow with a significant underground contribution. Summer-autumn peaks formed by rains can be two-five times higher than the spring snow floods. The climate is dry with snow-poor winters.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Altai-Sayan region","kind":"region","role":"region of previous field study data used to inform modeling parameterization","river_name":null,"geocode_query":"Altai-Sayan region, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Geographical Foundations IOP Conf. Series: Earth and Environmental Science 381 (2019) 012068 IOP Publishing doi:10.1088/1755-1315/381/1/012068 3 zone [18], the parameter assessment was based on the data from the special observations of experimental hydrological site Mogot of State Hydrological Institute. The data on field studies, including a set of hydrometeorological observations, landscape descriptions, assessment of waterphysical properties of soils [19], conducted during the complex expeditions of the Faculty of Geography of St. Petersburg State University in the Altai-Sayan region in 2010-2013, were used for model calculations in the mountains of Western Sayan [20]. The authors of the study could not find any special observations data on the Khemchik River basin.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["No direct meteorological data within the Khemchik catchment; Teeli station data used despite its considerable distance and location beyond a mountain range, impacting data representativeness.","Model parameter estimation relied on expert assessments and analogues due to lack of local hydrological measurements.","Coordinates for study sites are not provided in the source, limiting spatial precision in geographic referencing.","Standard flood frequency analysis comparison is limited because the analog basin (Elegest River) differs markedly in elevation and glacial presence."],"river_ids":[],"site_ids":["doc-20821f2c8889b1092f1eb294-site-0","doc-20821f2c8889b1092f1eb294-site-1","doc-20821f2c8889b1092f1eb294-site-3","doc-20821f2c8889b1092f1eb294-site-5"],"anchor":[90.1889645,51.0182435],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[3040483980],"main_basin_ids":[3040004740]},{"id":"doc-23cc62c5096adb48587cfc55","title":"Towards monitoring groundwater‐dependent ecosystems using synthetic aperture radar imagery","display_title":"Monitoring Australian Groundwater-Dependent Ecosystems with SAR","doi":"https://doi.org/10.1002/hyp.13570","theme":"Groundwater","color":"#a8b9ff","year":2019,"publication_date":"2019-08-15","authors":["Pascal Castellazzi","Tanya M. Doody","Luk Peeters"],"summary":"This study develops a synthetic aperture radar (SAR)-derived index, SARGDE, to identify and monitor groundwater-dependent ecosystems (GDEs) using a year-long time series of Sentinel-1 SAR data across two contrasting Australian sites. By analyzing backscatter intensity and interferometric coherence, the index captures vegetation stability indicating groundwater reliance. The results show high similarity (~90%) with the existing Australian GDE Atlas, suggesting SARGDE offers a valuable, cloud-insensitive, and high-resolution remote sensing tool for large-scale GDE monitoring.","finding":"The SAR-derived SARGDEv1 index effectively discriminates groundwater-dependent ecosystems from non-GDEs by combining normalized standard deviations of InSAR coherence and VH intensity, and mean InSAR coherence, yielding a ~90% match with the Australian GDE Atlas.","finding_evidence":{"passage_id":"p009-002","page":9,"quote":"An approach is proposed to observe and concentrate this information into an operationally deployable deterministic index: SARGDEv1. The index was tested over two contrasting environments in Australia and compared with the current state of knowledge of large-scale GDE mapping in Australia (GDE Atlas). SARGDEv1 is ~90% similar to the GDE Atlas (considering true positives and true negatives). Little is known about the discrepancies between the two methods, and further work comparing them with in situ measurements will help in this regard. The technique shows promise to fill important gaps in GDE research. SARGDEv1 offers unprecedented monitoring capabilities, offering a yearly monitoring frequency, with high resolution, and even under cloud-covered environments. The technique has the advantage of being simple and relatively user-independent, as it requires only routine SAR processing.","verified_in_page":true},"topics":["radar remote sensing","groundwater-dependent ecosystems","vegetation monitoring","synthetic aperture radar","interferometric coherence","environmental mapping","machine learning dimensionality reduction","ecosystem hydrology"],"study_period":"2017","locations":[{"name":"Mount Gambier area","kind":"region","role":"study site representing karstic aquifer system with high permeability and GDE presence","river_name":null,"geocode_query":"Mount Gambier, Victoria and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"increases as leaf and branch density decreases during leaf abscission caused by tree stress or deciduous periods. 3 | DATA AND METHODS 3.1 | SAR imagery A total of 90 Sentinel-1A interferometric wide images in Single Look Complex (SLC) format were downloaded via the Alaskan Satellite Facility Web portal (https://vertex.daac.asf.alaska.edu). The granule name of each image is provided in Supporting Information (section 3). They correspond to 30 temporally consecutive SAR acquisitions (one image every 12 days) over three scenes in Australia (Figure 3): one on the southern Victoria and South Australia border and two merged scenes in the Northern Territory. Mount Gambier area (Site 1) corresponds to a karstic aquifer system, with high surface permeability, hydraulic conductivity, and porosity. Wildman-Kakadu area (Site 2) corresponds to a monsoonal floodplain environment along the coast, transitioning into a semi-arid grassland environment going inland.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Wildman-Kakadu area","kind":"region","role":"study site representing monsoonal floodplain environment with contrasting GDE types along ephemeral streams and floodplains","river_name":null,"geocode_query":"Wildman-Kakadu, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"increases as leaf and branch density decreases during leaf abscission caused by tree stress or deciduous periods. 3 | DATA AND METHODS 3.1 | SAR imagery A total of 90 Sentinel-1A interferometric wide images in Single Look Complex (SLC) format were downloaded via the Alaskan Satellite Facility Web portal (https://vertex.daac.asf.alaska.edu). The granule name of each image is provided in Supporting Information (section 3). They correspond to 30 temporally consecutive SAR acquisitions (one image every 12 days) over three scenes in Australia (Figure 3): one on the southern Victoria and South Australia border and two merged scenes in the Northern Territory. Mount Gambier area (Site 1) corresponds to a karstic aquifer system, with high surface permeability, hydraulic conductivity, and porosity. Wildman-Kakadu area (Site 2) corresponds to a monsoonal floodplain environment along the coast, transitioning into a semi-arid grassland environment going inland.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.127007,-12.445533],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214757,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wildman River, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-23cc62c5096adb48587cfc55-site-1","true_coordinates":[132.1270069162383,-12.38553285806452],"spread_km":6.7},{"name":"Bool Lagoon","kind":"region","role":"subset within study area 1 used for evaluating index performance and comparison with GDE Atlas","river_name":null,"geocode_query":"Bool Lagoon, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Second, the frequent cloud-cover occurring during monsoon season in WK site limits the accuracy of NDVI and evapotranspiration models on which the GDE Atlas relies. 6 | INTERPRETING SAR-DERIVED LARGESCALE GDE MAPS Observing GDEs over large scales provides insights over hydrogeologic, climatic, geomorphologic, and lithologic features (Figure 8). Lithological boundaries related to coastal sediment deposition episodes provide a strong spatial control over aquifer transmissivity, depth to groundwater static levels, and as a consequence, over GDE occurrence (Arrow 1 in Figure 8). Lakes formed by collapse of ground voids (sinkholes) created by hydro-chemical erosion of the FIGURE 5 Statistical parameters proposed for GDE discrimination over two subsets of SUB2 (a,c,e) and SUB1 (Bool Lagoon; b,d,f) shown on Figure 3. (a,b) Temporal average of InSAR coherence, (c,d) standard deviation of InSAR coherence, and (e,f) standard deviation of VH intensity data-cube.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.7147208,-37.1437173],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556959,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bool Lagoon SA 5271, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-23cc62c5096adb48587cfc55-site-2"},{"name":"Site 1 (Mount Gambier area karstic aquifer aquifer zone)","kind":"region","role":"hydrogeological feature providing groundwater to GDEs, influencing SARGDE values","river_name":null,"geocode_query":"Mount Gambier, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"unconfined/surficial calcareous aquifer occur throughout Site 1 (Figure 8a). These surface and subsurface karstic features provide easier access to groundwater for vegetation (Doody & Benyon, 2011a) and, as such, are often surrounded by GDEs (Arrow 2). When groundwater levels are close to the ground surface and groundwater supply is sufficient, GDEs can be part of a wetland system with strong groundwater/surface-water interactions (Arrow 3). Particularly sharp contrasts can be noted in Figure 8a (e.g., Arrow 4) as GDEs often occur in agricultural areas. The latter have a lower SARGDEv1 value due to high seasonal variability of the SAR response in all source data-cubes. Site 2 shows contrasting spatial features. GDEs mainly occur along floodplains (Arrows 5 and 6) or ephemeral streams (Arrows 6, 7, and 8). An interesting observation can be drawn from the other end of the SARGDEv1 value spectrum (from 8 to 10).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.780676,-37.888441],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556987,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Gambier SA 5290, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-23cc62c5096adb48587cfc55-site-3","true_coordinates":[140.7806761,-37.8284406],"spread_km":6.7},{"name":"Site 2 (Wildman-Kakadu area floodplain and ephemeral stream system)","kind":"region","role":"hydrogeological and ecological region illustrating GDE confinement along floodplains and ephemeral streams","river_name":null,"geocode_query":"Wildman-Kakadu, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"unconfined/surficial calcareous aquifer occur throughout Site 1 (Figure 8a). These surface and subsurface karstic features provide easier access to groundwater for vegetation (Doody & Benyon, 2011a) and, as such, are often surrounded by GDEs (Arrow 2). When groundwater levels are close to the ground surface and groundwater supply is sufficient, GDEs can be part of a wetland system with strong groundwater/surface-water interactions (Arrow 3). Particularly sharp contrasts can be noted in Figure 8a (e.g., Arrow 4) as GDEs often occur in agricultural areas. The latter have a lower SARGDEv1 value due to high seasonal variability of the SAR response in all source data-cubes. Site 2 shows contrasting spatial features. GDEs mainly occur along floodplains (Arrows 5 and 6) or ephemeral streams (Arrows 6, 7, and 8). An interesting observation can be drawn from the other end of the SARGDEv1 value spectrum (from 8 to 10).","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.188437,-12.385533],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214757,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wildman River, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-23cc62c5096adb48587cfc55-site-4","true_coordinates":[132.1270069162383,-12.38553285806452],"spread_km":6.7}],"limitations":["Exact latitude and longitude coordinates for study locations were not provided in the text, set to null.","The index SARGDEv1 is developed and tested only on Australian vegetation, mainly evergreen, and may not apply directly to deciduous or non-Australian vegetation types.","No direct in situ verification of SARGDEv1 was performed; comparison relies on a national-scale GDE Atlas model.","The temporal frequency needed for reliable index performance is not fully established; only a single year (2017) was studied.","Spatial resolution limitations may affect detection of narrow or sinuous GDE features, especially in heterogeneous floodplain environments."],"river_ids":[],"site_ids":["doc-23cc62c5096adb48587cfc55-site-1","doc-23cc62c5096adb48587cfc55-site-2","doc-23cc62c5096adb48587cfc55-site-3","doc-23cc62c5096adb48587cfc55-site-4"],"anchor":[140.7147208,-37.1437173],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040061670,5040070710],"main_basin_ids":[5040061670,5040070710]},{"id":"doc-33b95e1dd6ccfb90f1ec70f1","title":"High-resolution paleovalley classification from airborne electromagnetic imaging and deep neural network training using digital elevation model data","display_title":"Deep Learning Maps Paleovalleys in South Australia","doi":"https://doi.org/10.5194/hess-23-2561-2019","theme":"Groundwater","color":"#a8b9ff","year":2019,"publication_date":"2019-06-13","authors":["Zhenjiao Jiang","Dirk Mallants","Luk Peeters","Lei Gao","Camilla Soerensen","Gregoire Mariethoz"],"summary":"This study develops a novel methodology using super-resolution convolutional neural networks (SRCNN) combined with airborne electromagnetic (AEM) data and digital elevation models (DEM) to produce high-resolution binary maps of paleovalleys. Applied to the arid Anangu Pitjantjatjara Yankunytjatjara (APY) lands in northern South Australia, the method successfully delineates the extent and connectivity of buried paleovalley aquifers up to 100 m depth. Training used synthetic paleovalley networks derived from DEMs as analogs and Archie’s law to translate lithology to electrical conductivity, accounting for noise inherent in AEM surveys. The approach improves upon conventional methods by automating paleovalley identification and providing 3D structural insights useful for groundwater prospecting.","finding":"SRCNN applied to low-resolution AEM electrical conductivity data can remove noise and accurately classify paleovalley and non-paleovalley zones, producing high-resolution binary maps with preserved spatial connectivity that align well with borehole lithology and valley bottom flatness indices in South Australia's APY lands.","finding_evidence":{"passage_id":"p008-004","page":8,"quote":"After calibration of the SRCNN, a paleovalley index map is obtained (Fig. 5d). However, the histogram of the resultant paleovalley index displays a bimodal behavior, with peaks centered at 0 and 1 (Fig. 5e). By selecting a threshold paleovalley index value of 0.5, the paleovalley and the non-paleovalley data can be differentiated and converted to a binary paleovalley map (Fig. 5f). The resultant paleovalleys compare well with the reference (i.e., synthetic) paleovalleys in Fig. 5a. The selection of a threshold paleovalley index in the range of 0.2 to 0.8 does not have a significant influence on the resultant binary paleovalley pattern. Moreover, the resultant paleovalley index is less noisy in both paleovalley and non-paleovalley parts (Fig. 5d). The SRCNN is able to create connected paleovalley networks from the poorly connected EC values generated by bicubic interpolation (Fig. 5b), which is one of the most challenging features in geostatistics.","verified_in_page":true},"topics":["deep learning neural networks","airborne electromagnetic surveys","paleovalley aquifers","electrical conductivity mapping","hydrogeology","image classification","geophysical inversion","aquifer characterization"],"study_period":null,"locations":[{"name":"Anangu Pitjantjatjara Yankunytjatjara (APY) lands, northern South Australia","kind":"region","role":"primary study area for paleovalley mapping using airborne electromagnetic data and SRCNN","river_name":null,"geocode_query":"Anangu Pitjantjatjara Yankunytjatjara lands, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The method is applied to an arid region of South Australia to identify paleovalleys at depths up to 100 m, i.e., the depth up to which the AEM-derived EC has a sufficient signal-to-noise ratio. The paper is organized as follows; Sect. 2 presents the data availability in our study area. Section 3 introduces the methodology, which is followed by performance analyses in Sect. 4. Section 5 concludes the major findings. 2 Study area and dataset Australian landscapes are ancient, featuring the product of subdued tectonics, long-term subaerial exposure and an extremely limited extent of Quaternary glaciation. This often manifests itself in an extensive paleovalley network with deep weathering profiles and thick accumulation of unconsolidated alluvium and colluvium. The widespread paleovalley networks in today’s arid landscape are remnants of the Early Cenozoic inset valleys with Tertiary sedimentary infill and a thin and variable Quaternary cover (Magee, 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.634685,-26.988572],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61125590,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Anangu Pitjantjatjara Yankunytjatjara, SA 0872, Australia","basin_id":5040088010,"main_basin_id":5040088010,"basin_region":"Australasia","basin_area_km2":50967.7,"hydrology_source":"geofabric","site_id":"doc-33b95e1dd6ccfb90f1ec70f1-site-0","true_coordinates":[130.6346851,-26.928572],"spread_km":6.7},{"name":"Musgrave Province, northern South Australia","kind":"region","role":"geological province encompassing the study area with extensive paleovalley systems","river_name":null,"geocode_query":"Musgrave Province, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The method is applied to an arid region of South Australia to identify paleovalleys at depths up to 100 m, i.e., the depth up to which the AEM-derived EC has a sufficient signal-to-noise ratio. The paper is organized as follows; Sect. 2 presents the data availability in our study area. Section 3 introduces the methodology, which is followed by performance analyses in Sect. 4. Section 5 concludes the major findings. 2 Study area and dataset Australian landscapes are ancient, featuring the product of subdued tectonics, long-term subaerial exposure and an extremely limited extent of Quaternary glaciation. This often manifests itself in an extensive paleovalley network with deep weathering profiles and thick accumulation of unconsolidated alluvium and colluvium. The widespread paleovalley networks in today’s arid landscape are remnants of the Early Cenozoic inset valleys with Tertiary sedimentary infill and a thin and variable Quaternary cover (Magee, 2009).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Central-east section of APY lands, South Australia","kind":"region","role":"specific 80 km x 80 km test area for SRCNN paleovalley mapping","river_name":null,"geocode_query":"Central-east Anangu Pitjantjatjara Yankunytjatjara lands, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Two AEM surveys were flown in the APY lands in 2016, covering a total area of 33 500 km2 and featuring a line spacing of 2 km in the north–south direction (Soerensen et al., 2016). An area 80 km by 80 km in a central-east section of the APY lands is selected and used to test mapping paleovalleys based on a SRCNN analysis of electrical conductivity (EC) generated from the AEM survey (Fig. 1). In this area, the AEM survey was undertaken using the helicopter-borne SkyTEM312FAST system (Soerensen et al., 2016). The averaging trapezoidal filter was used to reduce the noise in lowand high-moment amplitude response data. Aarhus Workbench software was used to invert AEM data to obtain EC (Auken et al., 2009, 2014). In a final step, ordinary kriging was used to interpolate EC values to a spatial resolution of 400 m × 400 m in the horizontal plane and 10 m in the vertical cross section (Ley-Cooper and Munday, 2013; Soerensen et al., 2016).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.701982,-26.928572],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61125590,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Anangu Pitjantjatjara Yankunytjatjara, SA 0872, Australia","basin_id":5040088010,"main_basin_id":5040088010,"basin_region":"Australasia","basin_area_km2":50967.7,"hydrology_source":"geofabric","site_id":"doc-33b95e1dd6ccfb90f1ec70f1-site-2","true_coordinates":[130.6346851,-26.928572],"spread_km":6.7},{"name":"60 km southwest of APY lands study area, South Australia","kind":"region","role":"location of synthetic paleovalley examples at various pixel resolutions demonstrating SRCNN performance","river_name":null,"geocode_query":"Area 60 km southwest of Anangu Pitjantjatjara Yankunytjatjara lands, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Jiang et al.: High-resolution paleovalley classification by deep learning 2569 Figure 5. (a) DEM-generated synthetic paleovalley used as reference image in testing the SRCNN; (b) normalized electrical conductivities corresponding to the paleovalleys following (c) a skewed distribution (based on 4000 images in the test dataset). (d) Paleovalley index generated through processing EC images via the SRCNN; (e) bimodal distribution of paleovalley index (based on 4000 images in the test dataset); (f) the paleovalley index map converted into a binary paleovalley map by arbitrarily selecting the paleovalley index threshold as 0.5. and (3) it classifies the paleovalley and non-paleovalley components, which allows the selection of a threshold index to define paleovalley and non-paleovalley zones. 4.3 SRCNN performance under different image resolutions The next synthetic example considers 400 m wide synthetic paleovalleys generated in ArcGIS from the DEM in the zone about 60 km southwest of the study area in Fig. 1a.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.634685,-26.868572],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61125590,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Anangu Pitjantjatjara Yankunytjatjara, SA 0872, Australia","basin_id":5040088010,"main_basin_id":5040088010,"basin_region":"Australasia","basin_area_km2":50967.7,"hydrology_source":"geofabric","site_id":"doc-33b95e1dd6ccfb90f1ec70f1-site-3","true_coordinates":[130.6346851,-26.928572],"spread_km":6.7}],"limitations":["Training dataset was generated from 2-D DEM-derived present-day valley analogs, limiting vertical correlation modeling in SRCNN.","Only electrical conductivity from AEM data was used as input; areas with similar conductivity in paleovalley and non-paleovalley zones may require additional geophysical data to improve classification.","Model training assumes overlap in conductivity values reflecting noise; large overlap may reduce connectivity accuracy.","The SRCNN methodology may require retraining with updated parameters to adapt to different study areas with differing hydrogeology and geophysical properties."],"river_ids":[],"site_ids":["doc-33b95e1dd6ccfb90f1ec70f1-site-0","doc-33b95e1dd6ccfb90f1ec70f1-site-2","doc-33b95e1dd6ccfb90f1ec70f1-site-3"],"anchor":[130.701982,-26.928572],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040088010],"main_basin_ids":[5040088010]},{"id":"doc-5d0ddd6760b745092d7c32c1","title":"Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager","display_title":"Cloud Cover Retrieval Over Australia Using Himawari-8","doi":"https://doi.org/10.3389/fenvs.2019.00020","theme":"Climate","color":"#98d4f1","year":2019,"publication_date":"2019-02-26","authors":["Yi Qin","Andrew D. L. Steven","Thomas Schroeder","Tim R. McVicar","Jing Huang","Martin Cope","Shangzhi Zhou"],"summary":"This study develops and validates a time-series-based algorithm for cloud masking, classification, and optical depth retrieval using the Advanced Himawari Imager (AHI) satellite data over the Australian region. Validation against CALIOP lidar data shows high accuracy, especially when accounting for sensor footprint differences. Retrieved cloud properties were used to compute surface solar fluxes which compared well to in-situ measurements across 11 Australian sites. The cloud climatology derived exhibits strong resemblance to ISCCP data. The products support various applications including remote sensing, solar energy forecasting, vegetation monitoring, and health impact studies of smoke aerosols.","finding":"The algorithm achieved a cloud mask hit rate of 87% against CALIOP data, increasing to about 98% when excluding partially cloudy pixels, demonstrating high accuracy in cloud detection even for thin clouds with optical depth less than 0.25.","finding_evidence":{"passage_id":"p007-002","page":7,"quote":"As a dedicated sensor for cloud and aerosol detection and profiling, and with its altitude resolving ability, this instrument is particularly sensitive to the presence of “clouds” without interference of the surface, and therefore provides a reliable reference against which cloud masking products from other sensors can be validated (Wang et al., 2016; Karlsson and Hakansson, 2018). Figure 5 shows the hit rate, defined as the proportion of pixels identified by both AHI and CALIOP as either clear or “cloudy,” as a function of confidence threshold, cmin(discussed above and in Figure 3). Shown also in Figure 5 are the miss rate and its components: AHI clear–CALIOP cloudy (clear-cloudy), AHI cloudy–CALIOP clear (cloudy-clear). Figure 5 shows that at cmin = −3 the clear-cloudy and cloudyclear rates reach an equality. This indicates that the current cloud mask threshold, 1Ic,min in Eq (2), is too high that some cloudy pixels are misclassified as clear.","verified_in_page":true},"topics":["cloud masking","cloud classification","optical depth","satellite remote sensing","radiative transfer modeling","cloud climatology","solar radiation","aerosol detection"],"study_period":"June 2015 to November 2017","locations":[{"name":"Australian continent","kind":"region","role":"primary study region for cloud detection and classification","river_name":null,"geocode_query":"Australian continent, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Sci. 7:20. doi: 10.3389/fenvs.2019.00020 Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager Yi Qin1 *, Andrew D. L. Steven2 , Thomas Schroeder 2 , Tim R. McVicar 3 , Jing Huang1 , Martin Cope4 and Shangzhi Zhou5 1 CSIRO Oceans and Atmosphere, Canberra, ACT, Australia, 2 CSIRO Oceans and Atmosphere, Brisbane, QLD, Australia, 3 CSIRO Land and Water, Canberra, ACT, Australia, 4 CSIRO Oceans and Atmosphere, Melbourne, VIC, Australia, 5 China Meteorological Administration, Chongqing, China This paper presents a cloud masking, cloud classification and optical depth retrieval algorithm and its application to the Advanced Himawari Imager (AHI) on the Himawari-8/9 satellites using visible, near infrared and thermal infrared bands.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.5509603,-24.4041807],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115714671,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-0"},{"name":"Southeast Asia","kind":"region","role":"adjacent region covered for cloud climatology comparison","river_name":null,"geocode_query":"Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Figure 13b shows significant improvements over the flux range, and the accuaracy of the monthly flux calculated from the derived cloud properties is high. In this sense, this analysis shows that the quality of current classification and optical depth retrieval should be at least comparable to that of the ISCCP products. Regional Cloud Frequencies and Comparison With ISCCP The geographic distribution of clouds is of great relevance to many areas such as climate and solar energy industry. Figure 14 shows, for Australia and part of Southeast Asia, the frequency of all clouds and the broad cloud types, seasonally and yearly. The statistics were derived from about two years of data from 1 Jan 2016 to 30 Nov 2017 for the daylight hours from 22UTC to 8UTC (8–18 Australian east standard time). Characteristically cloud coverage over the continent is lower than the surrounding ocean with the majority of the continent having an average cloud frequency of about 30% annually.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.71483,-2.18797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-5d0ddd6760b745092d7c32c1-site-1","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"Northern Territory, Australia","kind":"region","role":"location of detected smoke plume from biomass burning","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-006","page":9,"quote":"Figure 11 shows an example of smoke plume detected in North Territory, Australia. Fine particles such as those originating from bush fires and hazard reduction burns have a range of impacts on human health including premature mortalities (Dennekamp et al., 2015; Haikerwal et al., 2015; O’Keeffe et al., 2016; Horsley et al., 2018). Smoke plumes quantified from this work could be useful in health impact analysis. Savanna biomass burning, as shown in Figure 11, contributes substantial amount of carbon emission (Hurst et al., 1994). Globally landscape and biomass fires contributes 2–4 Pg C year−1 CO2 emission (Bowman et al., 2009), or 20–40% the amount of carbon as that emitted from fossil-fuel combustion Frontiers in Environmental Science | www.frontiersin.org 9 February 2019 | Volume 7 | Article 20","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Lake Eyre Basin, Australia","kind":"region","role":"region showing unexpectedly high stratus cloud optical depth, indicating a need for further investigation","river_name":null,"geocode_query":"Lake Eyre Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-001","page":17,"quote":"It is used to represent all ice clouds including deep convection ice clouds which are often very thick and appears very bright. This is also shown in Figure 9 where it shows the cirrus reflectance can be as high as over 0.9 in the visible and near infrared bands. Another unexpected feature in Figure 16 is the relatively high stratus optical depth (last column) over the Lake Eyre Basin (located at southeast of central Australia), though the cloud frequency of the same area is low as shown in Figure 14. As the region is well known for being a major dust source region (Mitchell et al., 2017), this high optical depth may be an indication of misclassification by current algorithm. However, the optical depth peaks during winter and minimizes during summer is contradicting existing knowledge that the presence of dusts peaks in summer and minimizes in winter. It is therefore necessary to investigate further regarding the nature of the high stratus optical depth over the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.357123,-28.193551],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119370154,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Eyre, South Australia 5733, Australia","basin_id":5040491380,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":19208.6,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-3","true_coordinates":[137.2923893,-28.1750101],"spread_km":6.7},{"name":"Darwin, Northern Territory, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Darwin Northern Territory Australia","latitude":-12.42,"longitude":130.89,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.877666,-12.514209],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-4","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Broome, Western Australia, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Broome Western Australia Australia","latitude":-17.95,"longitude":122.24,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[122.236998,-18.02182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102208974,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Broome WA 6725, Australia","basin_id":5040059020,"main_basin_id":5040059020,"basin_region":"Australasia","basin_area_km2":23538.1,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-5","true_coordinates":[122.236998,-17.96182],"spread_km":6.7},{"name":"Townsville, Queensland, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Townsville Queensland Australia","latitude":-19.25,"longitude":146.77,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.874106,-19.278419],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652811,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Townsville City QLD 4810, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-6","true_coordinates":[146.8136596,-19.2598781],"spread_km":6.7},{"name":"Rockhampton, Queensland, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Rockhampton Queensland Australia","latitude":-23.38,"longitude":150.48,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.514763,-23.442346],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12126666,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rockhampton City QLD 4700, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-7","true_coordinates":[150.514763,-23.3823464],"spread_km":6.7},{"name":"Alice Springs, Northern Territory, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Alice Springs Northern Territory Australia","latitude":-23.8,"longitude":133.89,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.903158,-23.64166],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-8","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Geraldton, Western Australia, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Geraldton Western Australia Australia","latitude":-28.8,"longitude":114.7,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.611295,-28.836135],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":80910278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Geraldton WA 6530, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-9","true_coordinates":[114.611295,-28.7761346],"spread_km":6.7},{"name":"Kalgoorlie, Western Australia, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Kalgoorlie Western Australia Australia","latitude":-30.78,"longitude":121.45,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.466,-30.809],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61119362,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kalgoorlie - Boulder WA, Australia","basin_id":5040087690,"main_basin_id":5040087690,"basin_region":"Australasia","basin_area_km2":127703.4,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-10","true_coordinates":[121.466,-30.749],"spread_km":6.7},{"name":"Adelaide, South Australia, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Adelaide South Australia Australia","latitude":-34.95,"longitude":138.52,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.600746,-34.988499],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-11","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"Wagga Wagga, New South Wales, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Wagga Wagga New South Wales Australia","latitude":-35.16,"longitude":147.46,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.438849,-35.102598],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wagga Wagga NSW 2650, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-12","true_coordinates":[147.3655103,-35.1025977],"spread_km":6.7},{"name":"Melbourne, Victoria, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Melbourne Victoria Australia","latitude":-37.67,"longitude":144.83,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.007699,-37.765087],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Melbourne VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-13","true_coordinates":[144.9630576,-37.8136276],"spread_km":6.7},{"name":"Cape Grim, Tasmania, Australia","kind":"station","role":"site of in-situ solar flux measurement used for validation","river_name":null,"geocode_query":"Cape Grim Tasmania Australia","latitude":-40.68,"longitude":144.69,"evidence":{"passage_id":"p011-001","page":11,"quote":"FIGURE 10 | Error characterization of the Australian region from 6 Jun 2015 to 30 Jun 2018 for daylight hours (i.e., 22UTC to 8UTC or 8–18 Australian east standard time). Part (a) is the mean model error, defined by Equation (5), and (b) maximum error at the 95th percentile. (Beringer et al., 2015). The method developed here therefore provides a potential approach to account carbon emissions from bush fires. Validation by Surface-Level Solar Flux While very challenging to quantitatively validate the cloud class and optical depth directly, the downward flux at the surface can be calculated, using the retrieved cloud properties, and compared with in-situ measurements. This provides an indirect quantitative assessment of the quality of the retrieved cloud data. Three years (06 July 2015 to 30 June 2018) of solar flux data measured every 1 min at 11 sites across Australia (BoM, 2018) were collected and averaged to 10 min interval.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.69,-40.74],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":31289452,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-5d0ddd6760b745092d7c32c1-site-14","true_coordinates":[144.69,-40.68],"spread_km":6.7}],"limitations":["Some locations lack precise coordinates due to absence of explicit latitude/longitude data","Study region defined broadly as Australian continent and adjacent ocean; no finer geographic subdivisions with coordinates provided","Surface elevation assumed constant at sea level across Australia, potentially affecting accuracy at high elevation sites","Subpixel cloud fraction and cloud shadow effects currently not considered, potentially causing misclassification near cloud edges"],"river_ids":[],"site_ids":["doc-5d0ddd6760b745092d7c32c1-site-0","doc-5d0ddd6760b745092d7c32c1-site-1","doc-5d0ddd6760b745092d7c32c1-site-3","doc-5d0ddd6760b745092d7c32c1-site-4","doc-5d0ddd6760b745092d7c32c1-site-5","doc-5d0ddd6760b745092d7c32c1-site-6","doc-5d0ddd6760b745092d7c32c1-site-7","doc-5d0ddd6760b745092d7c32c1-site-8","doc-5d0ddd6760b745092d7c32c1-site-9","doc-5d0ddd6760b745092d7c32c1-site-10","doc-5d0ddd6760b745092d7c32c1-site-11","doc-5d0ddd6760b745092d7c32c1-site-12","doc-5d0ddd6760b745092d7c32c1-site-13","doc-5d0ddd6760b745092d7c32c1-site-14"],"anchor":[144.69,-40.74],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040015660,5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073420,5040077640,5040087690,5040491370,5040491380,5040591050],"main_basin_ids":[5040015660,5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073410,5040073420,5040077640,5040087590,5040087690]},{"id":"doc-652fa6db15ce74c42deb1483","title":"Building a stakeholder-led common vision increases the expected cost-effectiveness of biodiversity conservation","display_title":"Building a Common Vision for Biodiversity","doi":"https://doi.org/10.1371/journal.pone.0218093","theme":"Land & ecosystems","color":"#c8df98","year":2019,"publication_date":"2019-06-13","authors":["Rocío Ponce Reyes","Jennifer Firn","Sam Nicol","Iadine Chadès","Danial S. Stratford","Tara G. Martin","Stuart Whitten","Josie Carwardine"],"summary":"The study presents an approach to quantify the cost-effectiveness of investing in a stakeholder-led common vision alongside on-ground management strategies for biodiversity conservation. Using expert elicitation in the Queensland Brigalow Belt bioregion, an Australian biodiversity hotspot under multiple pressures, the study finds that a common vision improves feasibility and cost-effectiveness of conservation strategies, increasing expected species persistence by 9–52%. It provides a framework to prioritize resource allocation between social and ecological actions to maximize biodiversity outcomes.","finding":"Implementing a stakeholder-led common vision alongside conservation strategies significantly increases the expected persistence of threatened species by 9–52% and is likely a cost-effective investment, as its cost is relatively low compared to the increase in feasibility and benefits of on-ground management actions.","finding_evidence":{"passage_id":"p011-001","page":11,"quote":"The cost-effectiveness of a common vision and on-ground management strategies Strategies differed in terms of their relative cost-effectiveness in contributing to species conservation outcomes (Fig 3 and S3 Table). We concluded that managing fire and invasive plant species were the two most cost-effective on-ground strategies for improving the persistence of the Brigalow Belt’s threatened flora and fauna species over the next 50 years (Figs 3D and S1). The priority order of strategies was similar when considering the fauna species. Twenty-one of the 179 threatened species in the Brigalow Belt had greater than a 50% risk of being functionally lost from the region over the next 50 years if no threat management strategies were implemented only, flora species only and flora and fauna combined (S3 Table).","verified_in_page":true},"topics":["biodiversity conservation","stakeholder collaboration","cost-effectiveness","expert elicitation","prioritization","species persistence","landscape transformation","social-ecological integration"],"study_period":null,"locations":[{"name":"Queensland Brigalow Belt bioregion","kind":"region","role":"case study region of biodiversity conservation under multiple land uses","river_name":null,"geocode_query":"Brigalow Belt, Queensland, Australia","latitude":-21.98,"longitude":148.12,"evidence":{"passage_id":"p004-001","page":4,"quote":"Material and methods Case study region The Brigalow Belt () is a large multiple land-use region in central and north Queensland, Australia (- 21.98 ̊S, 148.12 ̊E). It has experienced one of the most rapid landscape transformations ever documented [30], where only a few pristine remnants persist and the majority of the native vegetation is degraded or has been cleared [31] (Fig 2). Since European settlement, a widespread conversion of forests and woodlands to pasture for livestock grazing and cropping [32] has reduced the extent of endemic brigalow forests and woodlands to only 8% (600 000 ha) of its original 7.3 million ha extent. The key threats to the Brigalow’s biodiversity are the cumulative impacts of the diverse regional land-uses: broad scale clearing, fragmentation and modification of native vegetation for agricultural development [33], urbanisation [34]; logging Fig 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.12,-21.98],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":12125129,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-652fa6db15ce74c42deb1483-site-0"},{"name":"Brisbane, Queensland, Australia","kind":"settlement","role":"location of CSIRO Land and Water and Queensland University of Technology involved in the study","river_name":null,"geocode_query":"Brisbane, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"RESEARCH ARTICLE Building a stakeholder-led common vision increases the expected cost-effectiveness of biodiversity conservation Rocı ́o Ponce ReyesID1 *, Jennifer Firn2 , Sam Nicol1 , Iadine Chadès1 , Danial S. Stratford3 , Tara G. Martin4 , Stuart Whitten3 , Josie Carwardine1 1 CSIRO Land and Water, EcoSciences Precinct, Brisbane, Queensland, Australia, 2 Queensland University of Technology, Gardens Point Campus, Brisbane, Queensland, Australia, 3 CSIRO Land and Water, Canberra, Australian Capital Territory, Australia, 4 Department of Forest & Conservation Sciences, University of British Columbia, Vancouver, Canada * Rocio.PonceReyes@csiro.au Abstract Uniting diverse stakeholders through communication, education or building a collaborative ‘common vision’ for biodiversity management is a recommended approach for enabling effective conservation in regions with multiple uses.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.093659,-27.470453],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-652fa6db15ce74c42deb1483-site-1","true_coordinates":[153.0260341,-27.4704528],"spread_km":6.7},{"name":"Canberra, Australian Capital Territory, Australia","kind":"settlement","role":"location of CSIRO Land and Water involved in the study","river_name":null,"geocode_query":"Canberra, Australian Capital Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"RESEARCH ARTICLE Building a stakeholder-led common vision increases the expected cost-effectiveness of biodiversity conservation Rocı ́o Ponce ReyesID1 *, Jennifer Firn2 , Sam Nicol1 , Iadine Chadès1 , Danial S. Stratford3 , Tara G. Martin4 , Stuart Whitten3 , Josie Carwardine1 1 CSIRO Land and Water, EcoSciences Precinct, Brisbane, Queensland, Australia, 2 Queensland University of Technology, Gardens Point Campus, Brisbane, Queensland, Australia, 3 CSIRO Land and Water, Canberra, Australian Capital Territory, Australia, 4 Department of Forest & Conservation Sciences, University of British Columbia, Vancouver, Canada * Rocio.PonceReyes@csiro.au Abstract Uniting diverse stakeholders through communication, education or building a collaborative ‘common vision’ for biodiversity management is a recommended approach for enabling effective conservation in regions with multiple uses.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.131032,-35.340185],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43671612,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canberra ACT, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-652fa6db15ce74c42deb1483-site-2","true_coordinates":[149.1310324,-35.2801846],"spread_km":6.7}],"limitations":["Cost and benefit estimates are based on expert elicitation and have inherent uncertainties.","Coordinates for some locations are not specified explicitly in the paper and thus are set as null.","Study context and findings are specific to the Brigalow Belt bioregion; transferability to other regions requires adaptation."],"river_ids":[],"site_ids":["doc-652fa6db15ce74c42deb1483-site-0","doc-652fa6db15ce74c42deb1483-site-1","doc-652fa6db15ce74c42deb1483-site-2"],"anchor":[148.12,-21.98],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040069590,5040069970,5040591050],"main_basin_ids":[5040069590,5040069970,5040073410]},{"id":"doc-b531cab6ca228f1ea627973b","title":"Dams, dam costs and damnable cost overruns","display_title":"Australian Dam Costs and Overruns Analysis","doi":"https://doi.org/10.1016/j.hydroa.2019.100026","theme":"Water management","color":"#eeabac","year":2019,"publication_date":"2019-02-12","authors":["Cuan Petheram","Thomas A. McMahon"],"summary":"This study analyses costs, cost overruns, and operation and maintenance costs for 98 Australian dams constructed since 1888. Multiple regression models relate dam costs and overruns to terrain, geologic, catchment, and climate variables, explaining 50% to 80% of variance. Cost overruns are common and often large, with sedimentary rock sites showing higher overruns. Key factors impacting overruns include construction length, geology, and optimistic assumptions during planning. Regional predictive models are developed for dam cost metrics and overruns, useful for planning and policy.","finding":"Australian dams commonly experience significant cost overruns, with a median overrun of 49% and mean of 120%, particularly for dams on sedimentary rock rather than hard rock, and longer construction periods strongly correlate with higher overruns.","finding_evidence":{"passage_id":"p002-000","page":2,"quote":"(96%) and nuclear power plants (207%). Odeck and Skjeseth (1995) observed that the uncertainties inherent in cost estimates are rarely brought to the attention of decision makers even as a sensitivity analysis. Information on dam costs and on dam cost overruns are not readily available in Australia. Major restructuring including commercialisation of the water authorities in Australia during the past several decades has resulted in the loss of much useful information, including infrastructure costs through being mislaid or deliberately disposed of. The aim in publishing this paper is to provide an Australian perspective on dam costs and dam cost overruns, to examine patterns of dam costs and cost overruns, and to develop a good predictor of costs and schedule overruns for the Australian situation.","verified_in_page":true},"topics":["dam construction costs","cost overrun analysis","operation and maintenance costs","infrastructure project delays","geotechnical risk","climate impact on infrastructure","multiple regression modeling","economic impact assessment"],"study_period":"1888-2012","locations":[{"name":"Australia","kind":"region","role":"study area covering all dams analysed","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Several cost metrics, dam costs and related data, were collated for 98 dams constructed across Australia since 1888. Dam costs, operation and maintenance (O&M) costs, and construction overrun costs were related to independent variables including dam site terrain characteristics and catchment and climate attributes. Each metric was related through multiple regression to mainly physical and climate attributes associated with the dam. Multiple correlation coefficients account for between 50% and 80% of the variance in the dependent variable. The exceedance range (defined as 75% exceedance to 25% exceedance) of final dam costs (2016 AUD) (98 dams) is from $393 per ML of reservoir storage to $2040 per ML. Annual O&M costs (33 dams), expressed as a % of final dam cost, varies from 0.14% to 0.35%, though these O&M costs do not include major upgrades.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Tasmania, Australia","kind":"region","role":"region hosting 71% of hydropower dams studied, mountainous headwater catchments","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"of irrigation reservoirs (median capacity of 312 GL, n = 32) the capacity of hydropower reservoirs is smaller (median capacity of 40 GL, n = 8). Furthermore due to the need for high dam walls, hydropower dams in Australia are mostly located in the mountainous headwater catchments of Tasmania (71% of the 41 hydropower dams) and the Australian Alps (22%) where the dams are sited on rivers with a lower inter-annual variability of streamflow than elsewhere in Australia and which continuously replenish the reservoir for longer periods each year (which means the yield can be higher than the storage capacity). In Table 3 the median unit costs of storage of concrete arch dams are tabulated as $2153/ML compared to $1064/ML for earth embankments, $977/ML for rock embankments and $624/ML for concrete gravity. The median costs per unit yield of earth embankment dams are listed as $1651/ML compared to rock embankments at $1506/ML.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Australian Alps, Australia","kind":"region","role":"region hosting 22% of hydropower dams studied, mountainous headwater catchments","river_name":null,"geocode_query":"Australian Alps, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"of irrigation reservoirs (median capacity of 312 GL, n = 32) the capacity of hydropower reservoirs is smaller (median capacity of 40 GL, n = 8). Furthermore due to the need for high dam walls, hydropower dams in Australia are mostly located in the mountainous headwater catchments of Tasmania (71% of the 41 hydropower dams) and the Australian Alps (22%) where the dams are sited on rivers with a lower inter-annual variability of streamflow than elsewhere in Australia and which continuously replenish the reservoir for longer periods each year (which means the yield can be higher than the storage capacity). In Table 3 the median unit costs of storage of concrete arch dams are tabulated as $2153/ML compared to $1064/ML for earth embankments, $977/ML for rock embankments and $624/ML for concrete gravity. The median costs per unit yield of earth embankment dams are listed as $1651/ML compared to rock embankments at $1506/ML.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148,-37],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21561244,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Australian Alps, Brumby VIC 3885, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-b531cab6ca228f1ea627973b-site-2"}],"limitations":["Incomplete geographic specificity beyond broad Australian regions","No precise coordinates for individual dam sites provided","Analysis reliant on historic and survey-based dam cost data","Limited post-construction public data on causes of cost overruns"],"river_ids":[],"site_ids":["doc-b531cab6ca228f1ea627973b-site-2"],"anchor":[148,-37],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-c902c16de9fd89f01611b39a","title":"Warming temperatures are impacting the hydrometeorological regime of Russian rivers in the zone of continuous permafrost","display_title":"Climate Warming Alters Russian Permafrost River Flow","doi":"https://doi.org/10.5194/tc-13-1635-2019","theme":"Flow & ecology","color":"#80edc2","year":2019,"publication_date":"2019-06-14","authors":["Olga Makarieva","Nataliia Nesterova","David Andrew Post","Artem Sherstyukov","Lyudmila Lebedeva"],"summary":"This study analyzed streamflow and meteorological data from 22 hydrological gauges across the Yana and Indigirka River basins in northeastern Siberia, Russia. These basins are entirely within continuous permafrost zones and span a range of watershed sizes and elevations. The research examined air temperature, precipitation, soil temperature, active layer thickness, and river flow over observational periods extending to 2015, some up to 79 years. Results show a significant warming trend of about +2.1 °C over the past 50 years, especially in autumn and spring, with a shift from snow to rain precipitation in key months. Despite some decreases in winter precipitation, increased streamflow trends were found predominantly in autumn-winter and spring freshet periods, along with an earlier spring freshet onset. These changes likely result from complex interactions among changing precipitation type, permafrost thaw, glacier and aufeis melt, and altered groundwater dynamics, affecting river hydrology and potentially Arctic Ocean freshwater input.","finding":"Warming temperatures have caused a significant shift from snow to rain precipitation in the Yana and Indigirka basins, driving increased autumn and winter streamflow despite declining winter precipitation totals.","finding_evidence":{"passage_id":"p016-004","page":16,"quote":"In the Indigirka River basin, the increase in streamflow can be attributed to the shift of precipitation type in September. Investigating the correlation between monthly streamflow and precipitation in September for four small watersheds (area < 100 km2 ) with meteorological stations located nearby, we found that the correlation coefficient varies from 0.16 to 0.59 for liquid precipitation and from 0.14 to 0.60 for total precipitation (Table 9, Fig. S12). We could not explain the reasons for low correlation at gauge ID 3433 (Table 9). It is also worth noting that for gauge ID 3501, the correlation coefficient increases from 0.27 to 0.46 for total and liquid precipitation, respectively. The shift of precipitation from solid to liquid occurs starting from 1993 and manifests in a considerable increase in rain fraction in comparison with snow at the stations located in the mountains.","verified_in_page":true},"topics":["hydrology","permafrost","climate change","streamflow","precipitation","river discharge","cryosphere dynamics","groundwater"],"study_period":"1936–2015","locations":[{"name":"Yana River basin","kind":"basin","role":"study area","river_name":null,"geocode_query":"Yana River basin, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Finally, some conclusions will be drawn in Sect. 6. 2 Study area 2.1 General description, relief The Yana and Indigirka rivers are two of the few large Arctic rivers whose basins are completely located in the zone of continuous permafrost (Fig. 1). The terrain is mainly mountainous. There is high elevation in the Verkhoyansk (Orulgan, 2389 m), Chersky (Pobeda, 3003 m), and Suntar– Khayata (Mus-Khaya, 2959 m) ranges, as well as wide river valleys. The average altitude of the research basins ranges from 320 to 1410 m and the elevations of the outlet gauges range from −1.55 to 833 m, respectively. The lower parts of the Yana and Indigirka basin area are represented by the Yano–Indigirskaya Lowland. The average height of the lowland is above 30–80 m, with some parts of the lowland raised up to more than 500 m. 2.2 Climate The study territory is the region where the Northern Hemisphere’s “pole of cold” is located.","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.3368091957941,71.41463936034775],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040009670,"main_basin_id":3040009670,"basin_region":"Siberia","basin_area_km2":233391.7,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-0"},{"name":"Indigirka River basin","kind":"basin","role":"study area","river_name":null,"geocode_query":"Indigirka River basin, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Finally, some conclusions will be drawn in Sect. 6. 2 Study area 2.1 General description, relief The Yana and Indigirka rivers are two of the few large Arctic rivers whose basins are completely located in the zone of continuous permafrost (Fig. 1). The terrain is mainly mountainous. There is high elevation in the Verkhoyansk (Orulgan, 2389 m), Chersky (Pobeda, 3003 m), and Suntar– Khayata (Mus-Khaya, 2959 m) ranges, as well as wide river valleys. The average altitude of the research basins ranges from 320 to 1410 m and the elevations of the outlet gauges range from −1.55 to 833 m, respectively. The lower parts of the Yana and Indigirka basin area are represented by the Yano–Indigirskaya Lowland. The average height of the lowland is above 30–80 m, with some parts of the lowland raised up to more than 500 m. 2.2 Climate The study territory is the region where the Northern Hemisphere’s “pole of cold” is located.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.0366518352452,65.27696085921617],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040010470,"main_basin_id":3040010470,"basin_region":"Siberia","basin_area_km2":341236.6,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-1"},{"name":"Verkhoyansk","kind":"settlement","role":"location of meteorological and hydrological stations","river_name":null,"geocode_query":"Verkhoyansk, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"O. Makarieva et al.: Warming changes streamflow in continuous permafrost (Russia) 1637 Figure 1. Meteorological stations and hydrological gauges within the study basins. ice with admixtures. Most of the research areas are covered by larch woodland with moss–lichen cover. In river valleys, grass–moss larch forest and swampy sparse growth forest are typical. Soil types at these landscapes are clayey podzol with partially decayed organic material underlain by frozen soil and bedrock. The updated map of permafrost landscapes (Fedorov et al., 2018) can be used for a more detailed description of the studied basins. 2.4 Permafrost In the studied river basins, permafrost thickness can reach over 450 m at watershed divides and up to 180 m in river valleys and depressions. There are highly dynamic cryogenic features such as retrogressive thaw slumps found in the Yana River basin that indicate ongoing permafrost degradation processes (Günther et al., 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.3993399,67.5505925],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Verkhoyansk, Sakha Republic, Russia, 678530","basin_id":3040009670,"main_basin_id":3040009670,"basin_region":"Siberia","basin_area_km2":233391.7,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-2"},{"name":"Oymyakon","kind":"settlement","role":"location of meteorological station recording extremely low temperatures and soil temperature measurement","river_name":null,"geocode_query":"Oymyakon, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The absolute minimum there has reached record levels for the Northern Hemisphere: down as far as −71 ◦C in Oymyakon and −68 ◦C in Verkhoyansk (Ivanova, 2006). The research region’s climate is distinctly continental. Long-time average annual air temperature ranges from −16.1 ◦C (Oymyakon, 726 m, 1930–2012) to −13.1 ◦C (Vostochnaya, 1288 m, 1942–2012). Minimum mean monthly temperatures are typically observed in January and can fall as low as −47.1 ◦C (Oymyakon) and −33.8 ◦C (Vostochnaya). Maximum average monthly temperatures are observed in July, averaging 15.7 and 11.8 ◦C for stations Yurty (590 m, 1957–2012) and Vostochnaya, respectively. Steady cold weather begins in the first week of October; spring starts in the second part of May or at the beginning of June, when seasonal snow cover starts to melt. Snow accumulation is relatively small and accounts for 25–30 cm of depth.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.773727,63.464138],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Oymyakon, Sakha Republic, Russia, 678750","basin_id":3040010470,"main_basin_id":3040010470,"basin_region":"Siberia","basin_area_km2":341236.6,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-3"},{"name":"Chersky Range","kind":"region","role":"mountain range in study basins affecting climate and glacier distribution","river_name":null,"geocode_query":"Chersky Range, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Finally, some conclusions will be drawn in Sect. 6. 2 Study area 2.1 General description, relief The Yana and Indigirka rivers are two of the few large Arctic rivers whose basins are completely located in the zone of continuous permafrost (Fig. 1). The terrain is mainly mountainous. There is high elevation in the Verkhoyansk (Orulgan, 2389 m), Chersky (Pobeda, 3003 m), and Suntar– Khayata (Mus-Khaya, 2959 m) ranges, as well as wide river valleys. The average altitude of the research basins ranges from 320 to 1410 m and the elevations of the outlet gauges range from −1.55 to 833 m, respectively. The lower parts of the Yana and Indigirka basin area are represented by the Yano–Indigirskaya Lowland. The average height of the lowland is above 30–80 m, with some parts of the lowland raised up to more than 500 m. 2.2 Climate The study territory is the region where the Northern Hemisphere’s “pole of cold” is located.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.966667,64.733333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Chersky Range, Sakha Republic, Russia, 678730","basin_id":3040010470,"main_basin_id":3040010470,"basin_region":"Siberia","basin_area_km2":341236.6,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-4"},{"name":"Suntar–Khayata Range","kind":"region","role":"mountain range in study basins housing glaciers and rock glaciers influencing hydrology","river_name":null,"geocode_query":"Suntar–Khayata Range, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Finally, some conclusions will be drawn in Sect. 6. 2 Study area 2.1 General description, relief The Yana and Indigirka rivers are two of the few large Arctic rivers whose basins are completely located in the zone of continuous permafrost (Fig. 1). The terrain is mainly mountainous. There is high elevation in the Verkhoyansk (Orulgan, 2389 m), Chersky (Pobeda, 3003 m), and Suntar– Khayata (Mus-Khaya, 2959 m) ranges, as well as wide river valleys. The average altitude of the research basins ranges from 320 to 1410 m and the elevations of the outlet gauges range from −1.55 to 833 m, respectively. The lower parts of the Yana and Indigirka basin area are represented by the Yano–Indigirskaya Lowland. The average height of the lowland is above 30–80 m, with some parts of the lowland raised up to more than 500 m. 2.2 Climate The study territory is the region where the Northern Hemisphere’s “pole of cold” is located.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.8886949,62.6029729],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Suntar-Khayata Range, Sakha Republic, Russia, 678750","basin_id":3040010470,"main_basin_id":3040010470,"basin_region":"Siberia","basin_area_km2":341236.6,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-5"},{"name":"Yana-Indigirskaya Lowland","kind":"region","role":"lowland region representing lower basin areas within study region","river_name":null,"geocode_query":"Yana-Indigirskaya Lowland, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Finally, some conclusions will be drawn in Sect. 6. 2 Study area 2.1 General description, relief The Yana and Indigirka rivers are two of the few large Arctic rivers whose basins are completely located in the zone of continuous permafrost (Fig. 1). The terrain is mainly mountainous. There is high elevation in the Verkhoyansk (Orulgan, 2389 m), Chersky (Pobeda, 3003 m), and Suntar– Khayata (Mus-Khaya, 2959 m) ranges, as well as wide river valleys. The average altitude of the research basins ranges from 320 to 1410 m and the elevations of the outlet gauges range from −1.55 to 833 m, respectively. The lower parts of the Yana and Indigirka basin area are represented by the Yano–Indigirskaya Lowland. The average height of the lowland is above 30–80 m, with some parts of the lowland raised up to more than 500 m. 2.2 Climate The study territory is the region where the Northern Hemisphere’s “pole of cold” is located.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Batagay-Alyta","kind":"settlement","role":"meteorological station providing climatic data","river_name":null,"geocode_query":"Batagay-Alyta, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"O. Makarieva et al.: Warming changes streamflow in continuous permafrost (Russia) 1641 Table 2. Characteristics of meteorological stations. Index Meteorological Latitude Longitude Elevation station (m) Yana River basin 21931 Kazachye 70.75 136.22 20 24261 Batagay-Alyta 67.80 130.38 494 24266 Verkhoyansk 67.55 133.38 137 24371 Ust’-Charky 66.80 136.68 273 Indigirka River basin 24382 Ust’-Moma 66.45 143.23 196 24585 Nera 64.55 143.12 523 24588 Yurty 64.05 141.88 590 24679 Vostochnaya 63.22 139.60 1288 24684 Agayakan 63.33 141.73 776 24688 Oymyakon 63.27 143.15 726 24691 Delyankir 63.83 145.60 802 24076 Deputatsky 69.33 139.67 275 21946 Chokurdakh 70.62 147.90 53 quate application can be found in Kundzewicz and Robson (2004).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.401779,67.79280899999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Batagai-Alyta, Sakha Republic, Russia, 678580","basin_id":3040009670,"main_basin_id":3040009670,"basin_region":"Siberia","basin_area_km2":233391.7,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-7"},{"name":"Vostochnaya","kind":"settlement","role":"high elevation meteorological station showing rain increase in autumn","river_name":null,"geocode_query":"Vostochnaya, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-000","page":10,"quote":"1644 O. Makarieva et al.: Warming changes streamflow in continuous permafrost (Russia) Table 4. Changes in monthly, seasonal, and annual precipitation (millimeters and percent), 1966–2015. Index Period 1 2 6 7 8 9 12 Cold (10-4) Warm The Yana River basin 21931 1966–2015 −26.0 −71 24261 1966–2012 −3.5 −67 24266 1966–2015 −19.3 −36 24371 1966–2015 −4.2 −23.6 −63 −46 The Indigirka River basin 24382 1966–2015 −6.7 −4.8 −16.0 ∗ −92 −71 −29 24585 1966–2015 −4.2 −58 24588 1966–2015 −4.3 −14.9 ∗ −68 −26 24679 1966–2015 −4.0 −3.3 20.7 −13.0 ∗ −90 −121 44 −30 24688 1966–2015 −4.4 −3.0 53 −60 −44 34 24691 1966–2015 −5.5 −62 24076 1966–2012 21.8 15.3 49 49 21946 1966–2012 −22.9 −48 −73 −60 Only the columns corresponding to the months in which the trends of precipitation were detected are shown in the table. Most of the values have statistical significance of p < 0.05. The asterisk (∗ ) indicates the significance 0.05 < p < 0.10.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Agayakan","kind":"region","role":"location with glaciers and meteorological data showing precipitation changes","river_name":null,"geocode_query":"Agayakan, Sakha Republic, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"O. Makarieva et al.: Warming changes streamflow in continuous permafrost (Russia) 1645 24679, 1288 m) exhibits the strongest changes: the mean share for the whole period is 0.43, with a trend value of 0.45. Oymyakon and Agayakan (IDs 24684, 24688) have a mean value of rain share equal to 0.79 and on average increase by 0.16 (Table 5). In September, the mean share of rain fluctuates from 0.56 at Vostochnaya (ID 24679, 1288 m) to 0.79 at Agayakan and Oymyakon (IDs 24684, 24688; 726 and 776 m). The trend value varies from 0.15 to 0.19 for increases in rain share for the whole period of observation at five stations (Table 5). In absolute values, the amount of rain increased on average by 60.7 %, or 12.2 mm in total, during the same period at six stations.","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.728668,63.33249999999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Agayakan, Sakha Republic, Russia, 678750","basin_id":3040010470,"main_basin_id":3040010470,"basin_region":"Siberia","basin_area_km2":341236.6,"hydrology_source":"hydrobasins","site_id":"doc-c902c16de9fd89f01611b39a-site-9"}],"limitations":["Incomplete precise geographic coordinates for study sites due to absence of explicit coordinates in source text","Broad regional geography with complex landscape heterogeneity not fully resolved by point data","Some soil temperature records contain instrument change artifacts affecting interpretation","Precipitation data uncorrected for gauge undercatch especially in winter, affecting accuracy of precipitation trends"],"river_ids":[],"site_ids":["doc-c902c16de9fd89f01611b39a-site-0","doc-c902c16de9fd89f01611b39a-site-1","doc-c902c16de9fd89f01611b39a-site-2","doc-c902c16de9fd89f01611b39a-site-3","doc-c902c16de9fd89f01611b39a-site-4","doc-c902c16de9fd89f01611b39a-site-5","doc-c902c16de9fd89f01611b39a-site-7","doc-c902c16de9fd89f01611b39a-site-9"],"anchor":[140.8886949,62.6029729],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[3040009670,3040010470],"main_basin_ids":[3040009670,3040010470]},{"id":"doc-e72e3035fa8760a92e57469e","title":"Mapping the multi-decadal mangrove dynamics of the Australian coastline","display_title":"Mapping Three Decades of Australian Mangroves","doi":"https://doi.org/10.1016/j.rse.2019.05.004","theme":"Land & ecosystems","color":"#c8df98","year":2019,"publication_date":"2019-05-18","authors":["Leo Lymburner","Peter Bunting","Richard Lucas","Peter Scarth","Imam Alam","Claire Phillips","Catherine Ticehurst","Alex Held"],"summary":"This study used Landsat 25 m resolution imagery from 1987 to 2016, integrated within Digital Earth Australia, combined with Global Mangrove Watch maps and LiDAR data to map and quantify annual changes in mangrove canopy cover across the Australian coastline. The approach related green vegetation fractional cover to planimetric canopy cover, allowing differentiation of woodland, open, and closed mangrove forests. Results showed notable expansion of mangroves until 2010 followed by declines, mainly in northern Australia influenced by natural drivers such as cyclones and sea level fluctuations, with Australia’s mangroves largely protected from direct human impacts.","finding":"The extent of Australia's mangroves increased from a minimum of approximately 10,715 ± 36 km² in 1992 to a peak of 11,388 ± 38 km² in 2010, then declined to 11,142 ± 57 km² by 2017, with dynamic changes concentrated mainly in northern coastal gulfs and sounds, reflecting natural influences such as sea level fluctuations and tropical cyclones.","finding_evidence":{"passage_id":"p010-002","page":10,"quote":"These losses were largely associated with the mass dieback event, causes of which included a rapid drop in sea level (Duke et al., 2017; Fig. 9b). This suggested that many of the mangrove areas experiencing dieback in 2015/ 16 (e.g., as reported by Duke et al., 2017 for the Gulf of Carpentaria and Lucas et al., 2017 for Kakadu National Park) were relatively recent colonists of the coastline. Furthermore, the Landsat time-series provides evidence that the extent of mangroves in the Gulf of Carpentaria started to decline as early as 2014 (Fig. 8). Of note is that, despite the dieback event, the overall area of mangroves was greater (by an estimated 427 km2 at the national level) in 2016 compared to the minimum in 1992. 4.4.3. Changes in mangrove area and canopy cover at a local scale The 25 m resolution of the Landsat series of sensors makes it possible to resolve where the mangrove expansion and contraction was occurring within specific estuaries (Figs. 11 and 12).","verified_in_page":true},"topics":["mangrove dynamics","remote sensing","land cover mapping","canopy cover","climate influences","cyclone impacts","coastal geomorphology","time series analysis"],"study_period":"1987-2016","locations":[{"name":"Australian coastline","kind":"region","role":"study area for mangrove mapping and dynamics","river_name":null,"geocode_query":"Australian coastline, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"In Australia, mangroves are protected by law and hence the natural drivers predominate. To determine annual national level changes in mangroves between 1987 and 2016, their extent (by canopy cover type) and dynamics were quantified using dense time-series (nominally every 16 days cloud permitting) of 25 m spatial resolution Landsat sensor data available within Digital Earth Australia (DEA). The potential area that mangroves occupied over this period was established as the union of mangrove maps generated for 1996, 2007–2010 and 2015/16 through the Global Mangrove Watch (GMW). Within this area, the green vegetation fractional cover (GVpc) was retrieved from each available cloud-masked Landsat scene through linear spectral unmixing. The 10th percentile (GVpc10) was then determined for each calendar year by comparing these data in a time-series.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Northern Australia","kind":"region","role":"major region of mangrove change and dieback events","river_name":null,"geocode_query":"Northern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"These losses were largely associated with the mass dieback event, causes of which included a rapid drop in sea level (Duke et al., 2017; Fig. 9b). This suggested that many of the mangrove areas experiencing dieback in 2015/ 16 (e.g., as reported by Duke et al., 2017 for the Gulf of Carpentaria and Lucas et al., 2017 for Kakadu National Park) were relatively recent colonists of the coastline. Furthermore, the Landsat time-series provides evidence that the extent of mangroves in the Gulf of Carpentaria started to decline as early as 2014 (Fig. 8). Of note is that, despite the dieback event, the overall area of mangroves was greater (by an estimated 427 km2 at the national level) in 2016 compared to the minimum in 1992. 4.4.3. Changes in mangrove area and canopy cover at a local scale The 25 m resolution of the Landsat series of sensors makes it possible to resolve where the mangrove expansion and contraction was occurring within specific estuaries (Figs. 11 and 12).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Gulf of Carpentaria","kind":"region","role":"key area of mangrove expansion and dieback; study site for mangrove change","river_name":null,"geocode_query":"Gulf of Carpentaria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"These losses were largely associated with the mass dieback event, causes of which included a rapid drop in sea level (Duke et al., 2017; Fig. 9b). This suggested that many of the mangrove areas experiencing dieback in 2015/ 16 (e.g., as reported by Duke et al., 2017 for the Gulf of Carpentaria and Lucas et al., 2017 for Kakadu National Park) were relatively recent colonists of the coastline. Furthermore, the Landsat time-series provides evidence that the extent of mangroves in the Gulf of Carpentaria started to decline as early as 2014 (Fig. 8). Of note is that, despite the dieback event, the overall area of mangroves was greater (by an estimated 427 km2 at the national level) in 2016 compared to the minimum in 1992. 4.4.3. Changes in mangrove area and canopy cover at a local scale The 25 m resolution of the Landsat series of sensors makes it possible to resolve where the mangrove expansion and contraction was occurring within specific estuaries (Figs. 11 and 12).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.1321981,-13.1840458],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gulf of Carpentaria","hydrology_source":"none","site_id":"doc-e72e3035fa8760a92e57469e-site-2"},{"name":"Roper River estuary, Northern Territory, Australia","kind":"field_site","role":"local scale site showing mangrove expansion and contraction dynamics","river_name":null,"geocode_query":"Roper River estuary, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"As an example, mangroves within the Roper River estuary in the western Gulf of Carpentaria (Northern Territory) and the coast-facing margin experienced a period of expansion in both a seaward and landward direction between 1987 and 2010 and an increase in canopy cover (Fig. 11). However, between 2010 and 2016, the mangrove canopy cover reduced or was lost through mass tree mortality (Duke et al., 2017). These areas of expansion and contraction are highlighted in Fig. 12, which also shows similar mapping for King Sound (mouth of the Fitzroy River, Western Australia) and the Gulf of Carpentaria (mouth of the Norman River, Queensland). In the latter case, a near complete dieback of mangroves occurred along the coastline north of the Norman River Fig. 8. Temporal trends in mangrove extent by cover class in Australia between 1987 and 2016. L. Lymburner, et al. Remote Sensing of Environment 238 (2020) 111185 10","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.237538,-15.006286],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Roper River, Elsey NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-e72e3035fa8760a92e57469e-site-3","true_coordinates":[133.2375381085312,-14.94628617529888],"spread_km":6.7},{"name":"King Sound (mouth of Fitzroy River), Western Australia, Australia","kind":"field_site","role":"local scale site with observed mangrove fluctuations","river_name":"Fitzroy River","geocode_query":"King Sound, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"As an example, mangroves within the Roper River estuary in the western Gulf of Carpentaria (Northern Territory) and the coast-facing margin experienced a period of expansion in both a seaward and landward direction between 1987 and 2010 and an increase in canopy cover (Fig. 11). However, between 2010 and 2016, the mangrove canopy cover reduced or was lost through mass tree mortality (Duke et al., 2017). These areas of expansion and contraction are highlighted in Fig. 12, which also shows similar mapping for King Sound (mouth of the Fitzroy River, Western Australia) and the Gulf of Carpentaria (mouth of the Norman River, Queensland). In the latter case, a near complete dieback of mangroves occurred along the coastline north of the Norman River Fig. 8. Temporal trends in mangrove extent by cover class in Australia between 1987 and 2016. L. Lymburner, et al. Remote Sensing of Environment 238 (2020) 111185 10","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Norman River estuary and proximal coastline, Queensland, Australia","kind":"field_site","role":"local scale site with near complete mangrove dieback observed","river_name":"Norman River","geocode_query":"Norman River estuary, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"As an example, mangroves within the Roper River estuary in the western Gulf of Carpentaria (Northern Territory) and the coast-facing margin experienced a period of expansion in both a seaward and landward direction between 1987 and 2010 and an increase in canopy cover (Fig. 11). However, between 2010 and 2016, the mangrove canopy cover reduced or was lost through mass tree mortality (Duke et al., 2017). These areas of expansion and contraction are highlighted in Fig. 12, which also shows similar mapping for King Sound (mouth of the Fitzroy River, Western Australia) and the Gulf of Carpentaria (mouth of the Norman River, Queensland). In the latter case, a near complete dieback of mangroves occurred along the coastline north of the Norman River Fig. 8. Temporal trends in mangrove extent by cover class in Australia between 1987 and 2016. L. Lymburner, et al. Remote Sensing of Environment 238 (2020) 111185 10","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.05494,-18.606346],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":122859900,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Norman River, Queensland, Australia","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-e72e3035fa8760a92e57469e-site-5","true_coordinates":[142.0549398791301,-18.54634593442701],"spread_km":6.7},{"name":"Junction Bay, Northern Territory, Australia","kind":"field_site","role":"site affected by Cyclone Monica resulting in mangrove loss and recovery","river_name":null,"geocode_query":"Junction Bay, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Within the Gulf of Carpentaria, a similar period of expansion was observed but the decline/loss of mangroves along the shoreline was much more prominent, leading to a sharp drop in extent in 2015/16. 4.4.4. Change in response to a severe tropical cyclone One of the few areas in Australia's north to undergo a decline in mangroves between 1994 and 2010 was Junction Bay in the Northern Territory, with this attributed to extensive damage caused by Cyclone Monica in April 2006. This Category 5 storm made landfall west of Maningrida in West Arnhem, Northern Territory. The mangroves were heavily impacted by the severe winds and associated storm surge associated (Staben and Evans, 2008) and resulted in a loss of extent and reduction in canopy density (Fig. 13).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Kakadu National Park, Northern Territory, Australia","kind":"region","role":"comparison site for mangrove change linked to environmental factors and dieback","river_name":null,"geocode_query":"Kakadu National Park, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"These losses were largely associated with the mass dieback event, causes of which included a rapid drop in sea level (Duke et al., 2017; Fig. 9b). This suggested that many of the mangrove areas experiencing dieback in 2015/ 16 (e.g., as reported by Duke et al., 2017 for the Gulf of Carpentaria and Lucas et al., 2017 for Kakadu National Park) were relatively recent colonists of the coastline. Furthermore, the Landsat time-series provides evidence that the extent of mangroves in the Gulf of Carpentaria started to decline as early as 2014 (Fig. 8). Of note is that, despite the dieback event, the overall area of mangroves was greater (by an estimated 427 km2 at the national level) in 2016 compared to the minimum in 1992. 4.4.3. Changes in mangrove area and canopy cover at a local scale The 25 m resolution of the Landsat series of sensors makes it possible to resolve where the mangrove expansion and contraction was occurring within specific estuaries (Figs. 11 and 12).","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.393766,-13.152293],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214982,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kakadu National Park, Kakadu Hwy, Jabiru NT 0886, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-e72e3035fa8760a92e57469e-site-7","true_coordinates":[132.3937658,-13.0922931],"spread_km":6.7},{"name":"Cambridge Gulf, Western Australia, Australia","kind":"region","role":"area affected by mangrove dieback and environmental changes","river_name":null,"geocode_query":"Cambridge Gulf, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"minimum extent in 1992, with this change taking place partly as a result of colonization of mangroves into new areas. Dieback also occurred beyond the Gulf of Carpentaria, as reported by Duke et al., (2017), with areas affected including the Cambridge Gulf in Western Australia and the Alligator Rivers Region (Kakadu National Park; Lucas et al., 2017). Changes in mangroves along the northern coastline also disproportionately influenced the national annual figures of mangrove extent by cover type. The dieback of mangroves is likely to be cyclical and dependent on sea level fluctuations (Asbridge and Lucas, 2016; Lovelock et al., 2017). The mangroves themselves can also migrate geographical positions over time, with this largely occurring because of changes in coastal geomorphology. However, there are areas of mangroves along the coastline which have remained in the same position and have not been adversely affected by changes in the coastal environment.","verified_in_page":true},"resolution_status":"resolved","coordinates":[128.6462286,-15.315877],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102210851,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cambridge Gulf WA 6743, Australia","basin_id":5040059450,"main_basin_id":5040059450,"basin_region":"Australasia","basin_area_km2":199214.0,"hydrology_source":"geofabric","site_id":"doc-e72e3035fa8760a92e57469e-site-8"},{"name":"Alligator Rivers region, Kakadu National Park, Northern Territory, Australia","kind":"region","role":"area affected by mangrove dieback","river_name":"Alligator Rivers","geocode_query":"Alligator Rivers, Kakadu National Park, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"minimum extent in 1992, with this change taking place partly as a result of colonization of mangroves into new areas. Dieback also occurred beyond the Gulf of Carpentaria, as reported by Duke et al., (2017), with areas affected including the Cambridge Gulf in Western Australia and the Alligator Rivers Region (Kakadu National Park; Lucas et al., 2017). Changes in mangroves along the northern coastline also disproportionately influenced the national annual figures of mangrove extent by cover type. The dieback of mangroves is likely to be cyclical and dependent on sea level fluctuations (Asbridge and Lucas, 2016; Lovelock et al., 2017). The mangroves themselves can also migrate geographical positions over time, with this largely occurring because of changes in coastal geomorphology. However, there are areas of mangroves along the coastline which have remained in the same position and have not been adversely affected by changes in the coastal environment.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.5057806,-12.6574444],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215033,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-e72e3035fa8760a92e57469e-site-9"}],"limitations":["Spatial resolution (25 m) limits detection of narrow or small mangrove patches, especially upper tidal creeks.","Time-series baseline limits detection of mangroves established prior to 1996 as mapping was unavailable before that year.","Potential inaccuracies in planimetric canopy cover in intertidal fringe areas where water co-occurs with canopy.","Use of 10th percentile green vegetation fraction may be compromised in regions with high cloud cover or less distinct mangrove-adjacent land covers."],"river_ids":["ne-491"],"site_ids":["doc-e72e3035fa8760a92e57469e-site-2","doc-e72e3035fa8760a92e57469e-site-3","doc-e72e3035fa8760a92e57469e-site-5","doc-e72e3035fa8760a92e57469e-site-7","doc-e72e3035fa8760a92e57469e-site-8","doc-e72e3035fa8760a92e57469e-site-9"],"anchor":[133.237538,-15.006286],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040049720,5040059450,5040061670,5040064130],"main_basin_ids":[5040049720,5040059450,5040061670,5040064130]},{"id":"doc-effc950441118b29a28e2ca8","title":"Software tools for management of conjunctive use of surface- and ground-water in the rural environment: integration of the Farm Process and the Crop Growth Module in the FREEWAT platform","display_title":"FREEWAT Platform for Conjunctive Water Use Management","doi":"https://doi.org/10.1016/j.agwat.2019.105717","theme":"Water management","color":"#eeabac","year":2019,"publication_date":"2019-08-01","authors":["Rudy Rossetto","Giovanna De Filippis","Federico Triana","Matteo Ghetta","Iacopo Borsi","Wolfgang Schmid"],"summary":"This study presents the integration of the Farm Process (FMP) and a Crop Growth Module (CGM) within the open-source GIS-integrated FREEWAT platform. The combined tool enables detailed spatially-distributed simulation and management of conjunctive use of surface water and groundwater, focusing on rural irrigation contexts. The approach is demonstrated in a Mediterranean study area in central Italy with irrigated sunflower, urban greens, and rainfed grasslands, showing groundwater contributed 47% of crop evapotranspiration and yielding crop productivity estimates consistent with observed values.","finding":"The integrated FMP-CGM within FREEWAT revealed that in the Mediterranean case study, groundwater supplied nearly half (47%) of the actual crop evapotranspiration, underscoring the significant role of natural groundwater uptake for crop water use.","finding_evidence":{"passage_id":"p013-000","page":13,"quote":"saturated and the capillary zone (1.63 Mm3 ). Fig. 9c presents the outflow components for the WDU1. While runoff accounts only for 3% (0.13 Mm3 ), 27% of the budget is water percolating to the aquifer (1.29 Mm3 ). The other outflow terms are related to evaporation from irrigation (Ei; 7%, 0.36 Mm3 ) and from groundwater (Egw), which in this case is a minor component (3%, 0.14 Mm3 ). The model allows to get also data on partitioning of transpiration: in this case transpiration from irrigation (Ti) was 1.28 Mm3 (26%), while transpiration from groundwater (Tgw) was 1.63 Mm3 (33%). Transpiration from precipitation (Tp) is a small amount (1%, 0.05 Mm3 ) due to the very dry year simulated. From Fig. 9c, we may derive the value of simulated crop consumptive use (ETc-act) as: ETc-act = Tgw + Tp + Ti + Egw + Ep + Ei This value accounts for a total of 373 mm during the whole growing season. Compared to the simulated value of ETc (396 mm), the observed value was lower by 23 mm.","verified_in_page":true},"topics":["conjunctive water use","hydrological modelling","crop growth modelling","groundwater irrigation","surface water irrigation","GIS integration","evapotranspiration","water resource management"],"study_period":"2017","locations":[{"name":"central Italy agricultural area","kind":"region","role":"study area with irrigated sunflower, urban green, rainfed grasslands","river_name":null,"geocode_query":"central Italy, Italy","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"3.3. Example application The coupling approach shown in Fig. 4 and so far described is demonstrated through a simple synthetic application. The example reported in Schmid et al. (2006) was adapted to set up a real world case study using climatic data from central Italy, from January 1st to December 31st, 2017. The CGM was then coupled to this FMP scenario to get sunflower yield at harvest under the specific water availability conditions. The study area, 115 km2 wide, is discretized through 460 square cells with 500 m side length. The simulated domain includes three WDUs (Fig. 5): - an irrigation district with sunflower crops (925 ha, WDU1); - an urban area with irrigated green spaces (amenity grasses, 775 ha, WDU2); - a rainfed grassland WDU is defined at the remaining cells (WDU3). Groundwater is hosted in a sandy aquifer represented through a single homogeneous, convertible model layer with thickness ranging from 277 m to 300 m. Groundwater flow occurs from west to east (Fig. 6a).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"southern Rincon Valley, New Mexico, USA","kind":"region","role":"previous application area of the Farm Process (FMP) model providing foundational modeling context","river_name":null,"geocode_query":"southern Rincon Valley, New Mexico, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-008","page":3,"quote":"A small-scale hydrological model of the southern Rincon Valley (New Mexico, USA) illustrated for the first time how FMP can simulate unmetered historic pumpage for real-world family farms driven crop consumptive use, impacts of surface- and ground-water abstraction on the Lower Rio Grande (LRG) stream and return flow, and scenarios of changing allotments influence deliveries and downstream stream gains/ losses (Schmid et al., 2009). A regional integrated hydrologic model using FMP was developed for the LRG as part of the US-Mexico Transboundary Aquifer Assessment Program of the USGS using MODFLOW-FMP (Hanson et al., 2013) and further developed to include nonagricultural land use, to extend into Mexico, and the model’s boundary conditions to be informed by a Transboundary Rio Grande Watershed Model (Rio Grande Transboundary Integrated Hydrologic Model using OWHM (RGTIHM, Hanson et al., 2018).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-107.064739,32.6728583],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rincon, NM 87940, USA","basin_id":7040049270,"main_basin_id":7040049270,"basin_region":"North America and Caribbean","basin_area_km2":571791.0,"hydrology_source":"hydrobasins","site_id":"doc-effc950441118b29a28e2ca8-site-1"},{"name":"Lower Rio Grande (LRG) stream","kind":"river","role":"location of prior regional integrated hydrologic modeling using FMP with MODFLOW","river_name":"Lower Rio Grande","geocode_query":"Lower Rio Grande, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-008","page":3,"quote":"A small-scale hydrological model of the southern Rincon Valley (New Mexico, USA) illustrated for the first time how FMP can simulate unmetered historic pumpage for real-world family farms driven crop consumptive use, impacts of surface- and ground-water abstraction on the Lower Rio Grande (LRG) stream and return flow, and scenarios of changing allotments influence deliveries and downstream stream gains/ losses (Schmid et al., 2009). A regional integrated hydrologic model using FMP was developed for the LRG as part of the US-Mexico Transboundary Aquifer Assessment Program of the USGS using MODFLOW-FMP (Hanson et al., 2013) and further developed to include nonagricultural land use, to extend into Mexico, and the model’s boundary conditions to be informed by a Transboundary Rio Grande Watershed Model (Rio Grande Transboundary Integrated Hydrologic Model using OWHM (RGTIHM, Hanson et al., 2018).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-74.8815594,39.0145573],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rio Grande, Middle Township, NJ 08242, USA","basin_id":7040039880,"main_basin_id":7040039880,"basin_region":"North America and Caribbean","basin_area_km2":59618.1,"hydrology_source":"hydrobasins","site_id":"doc-effc950441118b29a28e2ca8-site-2"},{"name":"Central Valley aquifer system, California, USA","kind":"aquifer","role":"example site of detailed groundwater availability assessment using MODFLOW-OWHM and FMP","river_name":null,"geocode_query":"Central Valley aquifer, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-009","page":3,"quote":"MODFLOW-OWHM and FMP have been also applied for a detailed assessment of groundwater availability of the Central Valley aquifer system, developing a hydrological model coupled with forecasts from global climate models and including efficient updates using remotely sensed data and GIS tools (Faunt et al., 2009). A regional hydrological flow model was also developed for the Cuyama Valley to quantify groundwater availability under varying cropping systems and climatic scenarios to inform regional stakeholders about potential constraints on water-supply availability (Hanson et al., 2015). For these models, the water demand and use of each WBS was driven by the potential crop evapotranspiration of ‘virtual crop types,’ i.e., groups of similar crops from land-use maps, calculated internally as products of associated virtual, composite crop coefficients and reference evapotranspiration.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-122.2011075,40.19987769999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Valley, California 96080, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-effc950441118b29a28e2ca8-site-3"},{"name":"Cuyama Valley, California, USA","kind":"region","role":"location for regional hydrological flow modeling with FMP informing water supply constraints","river_name":null,"geocode_query":"Cuyama Valley, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-009","page":3,"quote":"MODFLOW-OWHM and FMP have been also applied for a detailed assessment of groundwater availability of the Central Valley aquifer system, developing a hydrological model coupled with forecasts from global climate models and including efficient updates using remotely sensed data and GIS tools (Faunt et al., 2009). A regional hydrological flow model was also developed for the Cuyama Valley to quantify groundwater availability under varying cropping systems and climatic scenarios to inform regional stakeholders about potential constraints on water-supply availability (Hanson et al., 2015). For these models, the water demand and use of each WBS was driven by the potential crop evapotranspiration of ‘virtual crop types,’ i.e., groups of similar crops from land-use maps, calculated internally as products of associated virtual, composite crop coefficients and reference evapotranspiration.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-120.0831986,35.1096965],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cuyama Valley, California, USA","basin_id":7040008720,"main_basin_id":7040008720,"basin_region":"North America and Caribbean","basin_area_km2":177193.6,"hydrology_source":"hydrobasins","site_id":"doc-effc950441118b29a28e2ca8-site-4"}],"limitations":["Coordinates for study area and other locations are not provided in the paper.","The case study is a synthetic adaptation of a real area and thus may be less representative of complex real-world conditions.","Crop growth modeling was demonstrated only for one crop type (sunflower) and one year (2017)."],"river_ids":[],"site_ids":["doc-effc950441118b29a28e2ca8-site-1","doc-effc950441118b29a28e2ca8-site-2","doc-effc950441118b29a28e2ca8-site-3","doc-effc950441118b29a28e2ca8-site-4"],"anchor":[-107.064739,32.6728583],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[7040008720,7040013550,7040039880,7040049270],"main_basin_ids":[7040008720,7040013550,7040039880,7040049270]},{"id":"doc-165e354947f46a5da26add24","title":"Nutrient Transformation and Removal from Treated Wastewater Recycled Via Aquifer Storage and Recovery (ASR) in a Carbonate Aquifer","display_title":"Nutrient Removal in Aldinga ASR Scheme","doi":"https://doi.org/10.1007/s11270-020-4429-x","theme":"Groundwater","color":"#a8b9ff","year":2020,"publication_date":"2020-01-30","authors":["Joanne Vanderzalm","Declan Page","Rudi Regel","Greg Ingleton","Chibuikem Nwayo","Dennis Gonzalez"],"summary":"This study evaluates nutrient (nitrogen and phosphorus) transformation and removal during aquifer storage and recovery (ASR) of treated wastewater (TWW) in an anoxic carbonate aquifer at the Aldinga ASR scheme in South Australia. Over two ASR cycles from June 2014 to March 2016, monitoring data showed median removal of 40-60% for total nitrogen and nitrate, 70-95% for ammonia and total Kjeldahl nitrogen, and ~90% for total phosphorus primarily due to sorption. Mixing with ambient brackish groundwater increased salinity by ~40% in recovered water. The study compares MLE wastewater treatment combined with ASR to membrane bioreactor treatment, indicating complementary nutrient removal efficiencies and highlights operational considerations such as buffer zone creation to reduce salinity.","finding":"The study found that treated wastewater injected and recovered via ASR in an anoxic carbonate aquifer led to median removal of 40-60% for total nitrogen and nitrate, with higher removal rates for ammonia and total Kjeldahl nitrogen, and consistent ~90% removal of total phosphorus due to sorption, demonstrating effective natural nutrient attenuation during ASR storage cycles.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"4 Conclusion Treated wastewater injected, stored and recovered via an ASR scheme operated in an anoxic, carbonate aquifer reported a median removal of 40 to 60% for TN and NOx-N, the dominant N species over two ASR cycles, with higher removal of TKN (> 70%) and NH4-N (> 95%). The removal was attributed to nitrification of ammonium to form nitrate and subsequent denitrification to produce nitrogen gas. Median TP removal due to sorption was consistent at ~ 90% over the two ASR cycles. The efficiency of treatment for TP was more effective than previously observed for TWW ASR, presumably due to the stability or iron oxide sorption sites. The MLE process combined with ASR provides greater P removal than the MBR treatment step alone. However, MBR provides greater N removal. Therefore, the choice of treatment train must be informed by the requirements for protection of downstream environmental receptors.","verified_in_page":true},"topics":["nutrient removal","managed aquifer recharge","aquifer storage and recovery","nitrogen cycling","phosphorus sorption","water reuse","salinity","water quality monitoring"],"study_period":"June 2014 to March 2016","locations":[{"name":"Aldinga aquifer storage and recovery (ASR) scheme","kind":"facility","role":"site of full-scale ASR scheme using treated wastewater to assess nutrient transformation and removal","river_name":null,"geocode_query":"Aldinga, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The Aldinga ASR scheme was established to store TWW, which would normally be discharged to the Gulf of St Vincent (Fig. 1). The ASR scheme is located adjacent to the Aldinga WWTP and recycled water treatment plant (RWTP), approximately 18 km to the south of the Christies Beach WWTP. The TWW from the Christies Beach WWTP is injected into a confined limestone aquifer within the Tertiary Port Willunga Formation at a target interval of 30–70 m below ground during winter. The entire injected TWW volume is 65 Page 2 of 12 Water Air Soil Pollut (2020) 231: 65","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.483222,-35.327383],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289480,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Aldinga SA 5173, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-0","true_coordinates":[138.4832219,-35.2673827],"spread_km":6.7},{"name":"Christies Beach wastewater treatment plant (WWTP)","kind":"facility","role":"source of treated wastewater used for injection into the ASR scheme","river_name":null,"geocode_query":"Christies Beach, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The TWW used for irrigation is sourced from the Christies Beach wastewater treatment plant (WWTP). The Christies Beach WWTP receives ~ 30,000 m3 /d from mainly a residential urban sewage catchment (SA Water, 2013). The WWTP consists of two activated sludge treatment trains: A/B-plant uses the Modified Ludzak-Ettinger (MLE) process and C-plant uses the membrane bioreactor process (MBR) process. The A/Bplant consists of primary treatment, secondary treatment via MLE, clarification and chlorine disinfection. TWW from A/B-plant is approved for irrigation use and must meet the following regulatory values: biological oxygen demand (BOD) < 20 mg/L, total suspended solids (TSS) < 30 mg/L and Escherichia coli < 1000 colony forming units (cfu)/100 mL (annual median). The C-plant consists of 2 mm screening, MBR and UV disinfection and was commissioned in 2012 for improved nutrient removal prior to discharge to the marine environment, the Gulf of St Vincent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.4741924,-35.1392579],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289500,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Christies Beach SA 5165, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-1"},{"name":"Willunga Basin Water Recycling Scheme","kind":"region","role":"regional context of ASR water recycling scheme supporting irrigation with treated wastewater","river_name":null,"geocode_query":"Willunga Basin, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"For example, legislative requirements in some Australian jurisdictions require that all recharged TWW, volumetrically at least, be recovered annually. This leaves no opportunity for interseason water banking, freshening of naturally brackish aquifers and creates poorer quality recovered water when brackish groundwater is also extracted into the water reuse supply system. To facilitate the understanding of natural treatment processes, this study uses operational monitoring data from a full-scale ASR scheme using TWW to assess nutrient (N and P) transformation and fate.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.4736922,-35.1996125],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289480,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Willunga Basin, Moana SA 5169, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-2"},{"name":"Port Willunga Formation","kind":"aquifer","role":"confined limestone aquifer targeted for TWW injection and storage during ASR","river_name":null,"geocode_query":"Port Willunga Formation, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The Aldinga ASR scheme was established to store TWW, which would normally be discharged to the Gulf of St Vincent (Fig. 1). The ASR scheme is located adjacent to the Aldinga WWTP and recycled water treatment plant (RWTP), approximately 18 km to the south of the Christies Beach WWTP. The TWW from the Christies Beach WWTP is injected into a confined limestone aquifer within the Tertiary Port Willunga Formation at a target interval of 30–70 m below ground during winter. The entire injected TWW volume is 65 Page 2 of 12 Water Air Soil Pollut (2020) 231: 65","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.4618711,-35.2601634],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289480,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Port Willunga SA 5173, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-3"},{"name":"Gulf of St Vincent","kind":"region","role":"downstream marine environment receiving discharge from wastewater treatment plant, relevant for environmental protection goals","river_name":null,"geocode_query":"Gulf of St Vincent, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The TWW used for irrigation is sourced from the Christies Beach wastewater treatment plant (WWTP). The Christies Beach WWTP receives ~ 30,000 m3 /d from mainly a residential urban sewage catchment (SA Water, 2013). The WWTP consists of two activated sludge treatment trains: A/B-plant uses the Modified Ludzak-Ettinger (MLE) process and C-plant uses the membrane bioreactor process (MBR) process. The A/Bplant consists of primary treatment, secondary treatment via MLE, clarification and chlorine disinfection. TWW from A/B-plant is approved for irrigation use and must meet the following regulatory values: biological oxygen demand (BOD) < 20 mg/L, total suspended solids (TSS) < 30 mg/L and Escherichia coli < 1000 colony forming units (cfu)/100 mL (annual median). The C-plant consists of 2 mm screening, MBR and UV disinfection and was commissioned in 2012 for improved nutrient removal prior to discharge to the marine environment, the Gulf of St Vincent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.2393528,-35.1324002],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"St Vincent Gulf, Australia","hydrology_source":"none","site_id":"doc-165e354947f46a5da26add24-site-4"},{"name":"Observation well no. 6527–1052, T2 aquifer","kind":"station","role":"monitoring location providing ambient groundwater quality data before ASR operation","river_name":null,"geocode_query":"Aldinga, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Fig. 1 The Aldinga aquifer storage and recovery (ASR) scheme showing four ASR wells (6527–1770, 6527–1771, 6527–1772, 6527–1811). The ASR scheme receives treated wastewater (TWW) from Christies Beach wastewater treatment plant (WWTP) (not shown, ~ 18 km to the north) and is located adjacent to Aldinga WWTP and recycled water treatment plant (RWTP). Ambient groundwater quality is from observation well no. 6527– 1052, sampled July 2008 prior to commencement of ASR. See Table 1 for ASR well details Water Air Soil Pollut (2020) 231: 65 Page 3 of 12 65","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.546864,-35.237383],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289480,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Aldinga SA 5173, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-5","true_coordinates":[138.4832219,-35.2673827],"spread_km":6.7},{"name":"Aldinga wastewater treatment plant (WWTP) and recycled water treatment plant (RWTP)","kind":"facility","role":"adjacent facilities to Aldinga ASR scheme relevant to wastewater treatment and water recycling operations","river_name":null,"geocode_query":"Aldinga, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The Aldinga ASR scheme was established to store TWW, which would normally be discharged to the Gulf of St Vincent (Fig. 1). The ASR scheme is located adjacent to the Aldinga WWTP and recycled water treatment plant (RWTP), approximately 18 km to the south of the Christies Beach WWTP. The TWW from the Christies Beach WWTP is injected into a confined limestone aquifer within the Tertiary Port Willunga Formation at a target interval of 30–70 m below ground during winter. The entire injected TWW volume is 65 Page 2 of 12 Water Air Soil Pollut (2020) 231: 65","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.41958,-35.237383],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289480,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Aldinga SA 5173, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-165e354947f46a5da26add24-site-6","true_coordinates":[138.4832219,-35.2673827],"spread_km":6.7}],"limitations":["Lack of chloride data required substituting total dissolved solids (TDS) as conservative tracer for mixing estimations","Coordinates for specific wells and infrastructure not provided, latitude and longitude set to null","Dissolved organic carbon (DOC) and biodegradable DOC (BDOC) data incomplete limiting full explanation of denitrification dynamics","Study limited to anoxic carbonate aquifer in South Australia, results may not be generalizable to other aquifer types or climatic regions","Operational constraints (e.g. requirement to recover all injected water annually) influenced salinity increase but alternative operational scenarios were not tested"],"river_ids":[],"site_ids":["doc-165e354947f46a5da26add24-site-0","doc-165e354947f46a5da26add24-site-1","doc-165e354947f46a5da26add24-site-2","doc-165e354947f46a5da26add24-site-3","doc-165e354947f46a5da26add24-site-4","doc-165e354947f46a5da26add24-site-5","doc-165e354947f46a5da26add24-site-6"],"anchor":[138.41958,-35.237383],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040073420],"main_basin_ids":[5040073420]},{"id":"doc-1c5e0898b3175f769cba29ba","title":"Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections","display_title":"Bias in Downscaled Rainfall for Hydroclimate Projections","doi":"https://doi.org/10.5194/hess-24-2963-2020","theme":"Flow & ecology","color":"#80edc2","year":2020,"publication_date":"2020-06-08","authors":["Nicholas J. Potter","Francis H. S. Chiew","Stephen P. Charles","Guobin Fu","Hongxing Zheng","Lu Zhang"],"summary":"This study assesses biases in rainfall projections generated by the NARCliM regional climate modelling project for south-eastern Australia, focusing on Victoria. It evaluates how quantile–quantile mapping bias correction affects rainfall metrics and sequences important for runoff generation. Although bias correction effectively removes biases in rainfall amounts, significant residual biases remain in rainfall sequencing, particularly underestimating persistence of wet spells, leading to underestimation of runoff in hydrological models. Change signals in rainfall metrics are generally preserved after bias correction. The study highlights the need for improved bias-correction methods that correct rainfall sequencing to support water resource planning in Victorian catchments under climate change.","finding":"Quantile–quantile mapping bias correction reduces bias in daily rainfall amounts but cannot correct biases in rainfall sequencing, such as wet–wet transition probabilities; this residual bias causes underestimation of runoff in hydrological models for Victoria, Australia.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Figure 7 plots the percentage difference in bias-corrected ensemble-median mean annual runoff for each 0.1 ◦ × 0.1 ◦ cell compared to mean annual runoff modelled using AWAP-observed rainfall. The ensemble median of runoff across Victoria is underpredicted by 10–20 % across almost all of Victoria, which suggests that the residual bias in wet-spell occurrences and persistence is problematic for runoff modelling. Whereas the smallest percentage biases appear to be over the high-runoff-producing region (Fig. 7c), this region has the highest absolute biases with bias in runoff of more than −20 mm (Fig. 7b).","verified_in_page":true},"topics":["rainfall bias correction","dynamical downscaling","hydrological modelling","transition probabilities","climate projections","rainfall sequencing","runoff generation","quantile–quantile mapping"],"study_period":"1990–2009 historical and 2060–2079 future","locations":[{"name":"Victoria","kind":"region","role":"main regional study area for rainfall bias and hydroclimate projections","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The framework developed above is used in Sect. 3.3 as a novel way to represent the relationships between state transition probabilities and rainfall quantiles to investigate the effect of bias correction on transition probabilities. 3 Results 3.1 Assessment of regional performance of modelled rainfall Modelled rainfall is compared to observed (AWAP) rainfall in Fig. 2. ECHAM5-R1 was chosen as a representative GCM–RCM ensemble member here as it had the median historical regional rainfall across Victoria from the NARCliM model ensemble. The spatial rainfall fields downscaled from both the reanalysis and GCM both show reasonable agreement with the spatial pattern of the observed mean annual rainfall, with relatively larger rainfall across the mountain ranges to the east of the state and across the southern coast, and less rainfall to the more arid north-west region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.531259,-37.074704],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-1c5e0898b3175f769cba29ba-site-0","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7},{"name":"south-eastern Australia","kind":"region","role":"broader regional domain of NARCliM downscaled climate data covering study area and used for hydroclimatic projection","river_name":null,"geocode_query":"south-eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Unfortunately, it is not easy to tell exactly which characteristics of rainfall drive runoff generation, and in general the sensitivity will depend on catchment physical characteristics, storm type and intensity, as well as antecedent moisture and groundwater stores (Goodrich and Woolhiser, 1991; Bell and Moore, 2000; Beven, 2001). Spectral and multifractal approaches (e.g. Milly and Wetherald, 2002; Matsoukas et al., 2000; Tessier et al., 1996) show that rainfall variability at shorter timescales is by and large incorporated into soil moisture buffers, thus dampening runoff variability at these timescales. However, over timescales of several days and greater, variability in runoff matches variability in rainfall more and more closely.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"NSW and ACT Regional Climate Modelling Project (NARCliM) domain","kind":"region","role":"climate modelling domain providing dynamically downscaled rainfall data used for analysis","river_name":null,"geocode_query":"south-eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Specifically, we investigate whether key rainfall metrics contain biases in dynamically downscaled GCM hindcasts relative to observations. We then examine if bias correction acts to either enhance or moderate any such biases, and whether bias correction affects change signals (i.e. downscaled GCM future relative to downscaled GCM historical). Section 2 describes the data and methods used in this study, Sect. 3 presents results, and Sects. 4 and 5 contain a discussion and conclusions. 2 Data and methods 2.1 The NARCliM Project The NSW and ACT Regional Climate Modelling Project (NARCliM; https://climatechange.environment.nsw.gov. au/Climate-projections-for-NSW/About-NARCliM, last access: 28 April, 2020; Evans et al., 2014) is a regional climate change project to deliver high-resolution dynamically downscaled climate change projections. As noted previously, GCMs run at a spatial resolution that is unable to provide meaningful information for decision makers at catchment and basin scale.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"New South Wales (NSW), Australia","kind":"region","role":"region hosting NARCliM project providing downscaled climate projections; context for model evaluation","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Specifically, we investigate whether key rainfall metrics contain biases in dynamically downscaled GCM hindcasts relative to observations. We then examine if bias correction acts to either enhance or moderate any such biases, and whether bias correction affects change signals (i.e. downscaled GCM future relative to downscaled GCM historical). Section 2 describes the data and methods used in this study, Sect. 3 presents results, and Sects. 4 and 5 contain a discussion and conclusions. 2 Data and methods 2.1 The NARCliM Project The NSW and ACT Regional Climate Modelling Project (NARCliM; https://climatechange.environment.nsw.gov. au/Climate-projections-for-NSW/About-NARCliM, last access: 28 April, 2020; Evans et al., 2014) is a regional climate change project to deliver high-resolution dynamically downscaled climate change projections. As noted previously, GCMs run at a spatial resolution that is unable to provide meaningful information for decision makers at catchment and basin scale.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Australian Water Availability Project (AWAP)","kind":"facility","role":"source of high-quality gridded observed rainfall data for evaluation and bias correction","river_name":null,"geocode_query":"Bureau of Meteorology, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The historical (baseline) period for NARCliM projections is 1990–2009, and we relate NARCliM historical rainfall features to observations and NCEP/NCAR Reanalysis over the same period in this paper. Realisations of future rainfall follow the A2 scenario of the Special Report on Emissions Scenarios (SRES) (Nakicenovi ́ c et al., 2000) at 2060– ́ 2079. Change signals presented later are thus averages over 2060–2079 compared to 1990–2009. Observed rainfall data are obtained from the Australian Water Availability Project (AWAP; Jones et al., 2009). This is a 0.05◦ × 0.05◦ gridded dataset interpolating observations from point rainfall records from the Australian Bureau of Meteorology. The AWAP rainfall observations are projected over a regular latitude– longitude grid, hence the need for interpolation of NARCliM data to be aligned with this.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Newcastle, New South Wales, Australia","kind":"settlement","role":"site of a heavy rainfall event case studied to evaluate WRF model performance configurations","river_name":null,"geocode_query":"Newcastle, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"The full NARCliM model ensemble thus consists of 12 members (four selected GCMs, each downscaled by the three RCM configurations). Ji et al. (2016) evaluated model output against AWAP (see below) rainfall, demonstrating good representation of precipitation processes at a wide range of timescales, but conclude that bias correction is still needed before applying the data. For hydrological applications, the R2 configuration is recommended (Olson et al., 2016). Although we consider the entire modelling ensemble in this paper, in a related paper (Charles et al., 2020) we use only that specific RCM physics scheme for modelling runoff.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.843587,-32.898271],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26967317,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Newcastle NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-1c5e0898b3175f769cba29ba-site-5","true_coordinates":[151.7816802,-32.9282712],"spread_km":6.7},{"name":"Murray–Darling Basin, Australia","kind":"region","role":"comparison/reference region for prior hydrological and climate studies including dynamical downscaling evaluations","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-000","page":15,"quote":"N. J. Potter et al.: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections 2977 Sydney, Australia, available at: http://www.ccrc.unsw.edu.au/ sites/default/files/NARCliM/publications/TechNote2.pdf (last access: 8 July 2019), 2012b. Evans, J. P. and McCabe, M. F.: Regional climate simulation over Australia’s Murray-Darling basin: A multitemporal assessment, J. Geophys. Res.-Atmos., 115, D14114, https://doi.org/10.1029/2010JD013816, 2010. Evans, J. P., Ekström M., and Ji, F.: Evaluating the performance of a WRF physics ensemble over South-East Australia, Clim. Dynam., 39, 1241–1258, https://doi.org/10.1007/s00382-011-1244- 5, 2012. Evans, J. P., Ji, F., Abramowitz, G., and Ekström, M.: Optimally choosing small ensemble members to produce robust climate simulations, Environ. Res. Lett., 8, 044050, https://doi.org/10.1088/1748-9326/8/4/044050, 2013. Evans, J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.583252,-31.096543],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-1c5e0898b3175f769cba29ba-site-6","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"South-East Australia","kind":"region","role":"region where rainfall and climate dynamics modelling and bias correction analyses conducted","river_name":null,"geocode_query":"south-east Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-002","page":11,"quote":"In the context of the study area in the current paper, south-eastern Australia, the selection of GCMs representing important climate processes has been an ongoing research strand both for the CMIP3 climate model ensemble (Smith and Chandler, 2010; Kirono and Kent, 2011; Kent et al., 2013; CSIRO, 2012; Evans and Ji, 2012a; McMahon et al., 2015), which is used for the current NARCliM dataset, and the CMIP5 ensemble (see CSIRO and Bureau of Meteorology, 2015; Hope et al., 2016). As mentioned in the Introduction, the RCM component of NARCliM, WRF, has been tested extensively, including the general cold and wet bias over south-eastern Australia. It is suggested that the wet bias (Fig. 3a) is related to subgrid cloud cover representations (see, e.g., García-Díez et al., 2015; Di Virgilio et al., 2019), and correction of this is the subject of current, ongoing research (Di Virgilio et al., 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Exact latitude and longitude coordinates for regional locations are not provided in the source text; coordinates are set to null.","Broad regional geographies are used (e.g., south-eastern Australia) without finer subdivided locations based on provided data.","Bias correction is applied independently per grid cell ignoring spatial correlations, which is an acknowledged limitation.","The study uses current NARCliM dataset based on CMIP3 models, which are partially superseded by more recent CMIP5 datasets now in 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070710,5040591010,5040591050],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-20cf3b461a3dfb59b96757e7","title":"Bund removal to re-establish tidal flow, remove aquatic weeds and restore coastal wetland services—North Queensland, Australia","display_title":"Bund Removal Restores North Queensland Wetlands","doi":"https://doi.org/10.1371/journal.pone.0217531","theme":"Floodplains","color":"#e9bd77","year":2020,"publication_date":"2020-01-24","authors":["Brett N. Abbott","Jim Wallace","David M. Nicholas","Fazlul Karim","Nathan J. Waltham"],"summary":"This study evaluated the impact of removing an earth bund to reinstate tidal flows into the Boolgooroo lagoon of the Mungalla wetlands, North Queensland, Australia. Monitoring over one year before and four years after bund removal showed rapid ecological recovery including significant reductions in invasive freshwater weeds, re-establishment of native salt-tolerant vegetation, improved water quality parameters such as dissolved oxygen, and a tripling of fish species diversity. Tidal ingress was limited to high tides above 3.6 m and was modulated by seasonal rainfall and wetland water depth. The study highlights tidal reinstatement as a cost-effective, sustainable method for restoring wetland ecosystem services in coastal floodplains.","finding":"Removal of the bund re-established tidal connectivity leading to significant reduction of invasive freshwater weeds and restoration of native vegetation, improvement in water quality (higher dissolved oxygen, pH, and salinity oscillations), and increased fish biodiversity within 3 years.","finding_evidence":{"passage_id":"p027-002","page":27,"quote":"Surprisingly, gross changes towards a more natural system occurred within a relatively short timeframe, with the reappearance of native vegetation, Water lilies and Bulkuru, improvements in water quality and fish biodiversity taking less than 3 years. However, the weight of evidence presented here after 5 years of monitoring, shows that the abundance of native and invasive plant species appears to oscillate depending on seasonal rainfall which can induce hydrologic pressure to repress tidal water ingress, which in turn drives dissolved oxygen and temperature regimes (through vegetation and depth changes) within the wetland, affecting fish occupancy. These changes are modelled conceptually in Fig 19, describing what is happening on the Mungalla wetland post-bund removal, showing the oscillation or cycle between freshwater and saltwater tolerant plant species, associated water quality, and fish presence, associated with the preceding years’ weather conditions (primarily summer rainfall).","verified_in_page":true},"topics":["wetland-restoration","tidal-inundation","aquatic-invasive-weeds","water-quality","fish-biodiversity","coastal-ecology","hydrological-connection","remote-sensing"],"study_period":"2012–2017","locations":[{"name":"Boolgooroo lagoon, Mungalla wetlands","kind":"wetland","role":"study site","river_name":null,"geocode_query":"Boolgooroo lagoon, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Methods Ethics statement–“The fish biodiversity study was completed in accordance with the Queensland Animal Care and Protection Act 2001, and James Cook University animal ethics permit number A2178.” Location and climate Mungalla Station (146o 16’19”E, 18o 42’55”S), is a 830 ha property located in the lower part of the Herbert River catchment south east of Ingham, North Queensland (Fig 1). Mungalla station has been run as a cattle-grazing enterprise for over a century, and since 1999 by the Nywaigi Aboriginal people, who also manage the wetland area. Until bund removal on 6th October 2013 the wetland area above the bund was palustrine with saline estuarine wetland and saltmarsh below it. Typically saltmarsh would merge into intertidal grass-sedge wetlands dominated by Bulkuru (Eleocharis dulcis) [46]—the common name Bulkuru is a derivative of the indigenous term “Boolgooroo” for which this wetland is named, providing a clear indicator of the wetlands pre-European state.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.821984,-19.316756],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652762,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Queensland, QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-0","true_coordinates":[146.7615238,-19.2982152],"spread_km":6.7},{"name":"Mungalla station","kind":"facility","role":"research property and wetland management","river_name":null,"geocode_query":"Mungalla station, North Queensland, Australia","latitude":-18.715,"longitude":146.272,"evidence":{"passage_id":"p003-002","page":3,"quote":"Methods Ethics statement–“The fish biodiversity study was completed in accordance with the Queensland Animal Care and Protection Act 2001, and James Cook University animal ethics permit number A2178.” Location and climate Mungalla Station (146o 16’19”E, 18o 42’55”S), is a 830 ha property located in the lower part of the Herbert River catchment south east of Ingham, North Queensland (Fig 1). Mungalla station has been run as a cattle-grazing enterprise for over a century, and since 1999 by the Nywaigi Aboriginal people, who also manage the wetland area. Until bund removal on 6th October 2013 the wetland area above the bund was palustrine with saline estuarine wetland and saltmarsh below it. Typically saltmarsh would merge into intertidal grass-sedge wetlands dominated by Bulkuru (Eleocharis dulcis) [46]—the common name Bulkuru is a derivative of the indigenous term “Boolgooroo” for which this wetland is named, providing a clear indicator of the wetlands pre-European state.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.2620587,-18.706603],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652444,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Forrest Beach Rd via, Ingham QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-1"},{"name":"Herbert River catchment","kind":"region","role":"wider catchment context","river_name":"Herbert River","geocode_query":"Herbert River catchment, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Methods Ethics statement–“The fish biodiversity study was completed in accordance with the Queensland Animal Care and Protection Act 2001, and James Cook University animal ethics permit number A2178.” Location and climate Mungalla Station (146o 16’19”E, 18o 42’55”S), is a 830 ha property located in the lower part of the Herbert River catchment south east of Ingham, North Queensland (Fig 1). Mungalla station has been run as a cattle-grazing enterprise for over a century, and since 1999 by the Nywaigi Aboriginal people, who also manage the wetland area. Until bund removal on 6th October 2013 the wetland area above the bund was palustrine with saline estuarine wetland and saltmarsh below it. Typically saltmarsh would merge into intertidal grass-sedge wetlands dominated by Bulkuru (Eleocharis dulcis) [46]—the common name Bulkuru is a derivative of the indigenous term “Boolgooroo” for which this wetland is named, providing a clear indicator of the wetlands pre-European state.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.737339,-18.228774],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651528,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Herbert River, Queensland, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-2","true_coordinates":[145.7373393886557,-18.16877395870301],"spread_km":6.7},{"name":"Cassady Creek","kind":"river","role":"tributary connected to the wetland providing tidal inflow","river_name":"Cassady Creek","geocode_query":"Cassady Creek, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Because of the highly seasonal rainfall, freshwater only enters the wetland in the wet season as direct rainfall input, runoff from the surrounding sub-catchments and overbank flow from Palm Creek, which runs along the western boundary of Mungalla station. Once the bund was removed, the upstream wetland was again connected to the coastline, with the possibility of being fed tidally with seawater from a tributary of Palm Creek (just south of Forrest Beach) and Cassady Creek (Fig 1). Water depth and quality measurements Wetland water depth, temperature and electrical conductivity (EC) were monitored by loggers (CTD-Diver, Eijkelkamp Soil & Water, The Netherlands) located in five permanent positions in the wetland, beginning on 24th October 2012. The locations were 450 m, 250 m and 50 m above, and, 50 m and 250 m below the bund wall (Fig 1). The above bund logger locations Fig 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.2564551106286,-19.79072283511386],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16654446,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cassidy Creek, Kirknie QLD 4806, Australia","basin_id":5040068450,"main_basin_id":5040068450,"basin_region":"Australasia","basin_area_km2":130431.0,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-3"},{"name":"Palm Creek","kind":"river","role":"upstream freshwater source affecting nutrient input and weed spread","river_name":"Palm Creek","geocode_query":"Palm Creek, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Subsequently the two grasses have been recognized regionally as weeds, with olive hymenachne being declared a weed of national significance (WONS) 10 years after release [43]. Aside from the pasture introductions, two other nationally declared weeds are present in the wetlands, salvinia (Salvinia molesta), and water hyacinth (Eichhornia crassipes), both of which are spread by spore/seed or asexually through fragmentation. Weed growth and expansion into the wetland was exacerbated by Palm Creek carrying nutrients downstream from a large Bund removal to re-establish tidal flow, remove aquatic weeds and restore coastal wetland services PLOS ONE | https://doi.org/10.1371/journal.pone.0217531 January 24, 2020 2 / 33 analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.23,-18.76],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652411,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Palm Creek, Blackrock QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-4","true_coordinates":[146.23,-18.7],"spread_km":6.7},{"name":"Halifax Bay","kind":"region","role":"adjacent coastal bay to wetland and Great Barrier Reef lagoon","river_name":null,"geocode_query":"Halifax Bay, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The Boolgooroo wetland covers 60 ha of the total 160 ha Mungalla wetland complex (Fig 1), is bounded to the west by grazing lands and to the east by regrowth forest on coastal sand ridges. Mangroves and saltmarsh patches are present along the coast and to the south of the property, which is adjacent to Halifax Bay within the Great Barrier Reef lagoon. Inland, the surrounding catchment is dominated by sugar cane farms, with some areas of grazing. The area has a wet tropical climate with highly variable seasonal and annual rainfall. The long term mean rainfall at nearby Allingham is 2060 mm and is strongly seasonal with 85% falling in the six wettest months, November to April. Temperatures are highest in December (daily average 29.1 ̊C) and lowest in July (daily average 20.4 ̊C), with high humidity (~63– 77%) throughout the year.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.5,-18.8333333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Halifax Bay, Australia","hydrology_source":"none","site_id":"doc-20cf3b461a3dfb59b96757e7-site-5"},{"name":"Forrest Beach","kind":"settlement","role":"reference point east of wetland and for tidal height extrapolation","river_name":null,"geocode_query":"Forrest Beach, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"were all within the weed infested parts of the wetland. The loggers captured data from the bottom of the water column (10 cm above the soil surface) every 15 minutes and were downloaded as part of monthly routine maintenance visits. An additional water quality logger (Hydrolab, OTT Hydromet, Colorado, USA) was used above the bund at the 50 m location, adjacent to the Diver logger (Fig 1). The Hydrolab logger recorded values of EC, depth, pH and dissolved oxygen (DO) concentration every 30 minutes at 10 cm above the soil surface. To examine the effect of bund removal and location (above and below the bund) on depth, EC and temperature, Two-way analysis of variance (ANOVA) was carried out. Similarly, oneway ANOVA were run to examine the effects of bund removal on pH and DO (pH and DO were not measured at the below bund location). Data for both analyses were aggregated to daily averages from the 15 and 30-minute raw data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.294596,-18.767419],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652444,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Forrest Beach QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-6","true_coordinates":[146.2945964,-18.7074193],"spread_km":6.7},{"name":"Allingham (BOM station)","kind":"station","role":"meteorological data source for rainfall and temperature","river_name":null,"geocode_query":"Allingham, North Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Normality of data residuals was assessed through inspection of residual and Q-Q plots and although there was some deviation it is not problematic given sample sizes (n = 7445, CTD-Diver and n = 1218, Hydrolab), there also appeared to be some heteroscedasticity. As a result, analysis was run using a heteroscedasticity-corrected coefficient covariance matrix (hc3). ANOVA data presented are mean ± standard deviation, unless otherwise stated. Non-linear correlation was performed to assess the linear and non-linear strength of relationships between water quality measures. Time series analysis was used to detect diel variations and lags between variables. Ancillary data used in conjunction with the wetland monitoring data are daily meteorological data measured at Allingham (Australian Bureau of Meteorology (BOM) station No 032117) and tide data recorded at the Lucinda Jetty; which was then extrapolated to Forrest Beach; 1.5 km east of the wetland (4min offset).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.3013237,-18.6959902],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652444,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Allingham QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-7"},{"name":"Lucinda Jetty","kind":"facility","role":"tide data source used for extrapolating tidal heights at Forrest Beach","river_name":null,"geocode_query":"Lucinda Jetty, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Normality of data residuals was assessed through inspection of residual and Q-Q plots and although there was some deviation it is not problematic given sample sizes (n = 7445, CTD-Diver and n = 1218, Hydrolab), there also appeared to be some heteroscedasticity. As a result, analysis was run using a heteroscedasticity-corrected coefficient covariance matrix (hc3). ANOVA data presented are mean ± standard deviation, unless otherwise stated. Non-linear correlation was performed to assess the linear and non-linear strength of relationships between water quality measures. Time series analysis was used to detect diel variations and lags between variables. Ancillary data used in conjunction with the wetland monitoring data are daily meteorological data measured at Allingham (Australian Bureau of Meteorology (BOM) station No 032117) and tide data recorded at the Lucinda Jetty; which was then extrapolated to Forrest Beach; 1.5 km east of the wetland (4min offset).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.3311591,-18.5235412],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652455,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lucinda QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-20cf3b461a3dfb59b96757e7-site-8"}],"limitations":["Coordinates not provided for study locations, only approximate geocode queries possible","Some geographies described broadly (e.g. regional catchments) without precise boundaries or coordinates","Detailed local coordinates for wetland loggers only partially available (given relative distances)"],"river_ids":[],"site_ids":["doc-20cf3b461a3dfb59b96757e7-site-0","doc-20cf3b461a3dfb59b96757e7-site-1","doc-20cf3b461a3dfb59b96757e7-site-2","doc-20cf3b461a3dfb59b96757e7-site-3","doc-20cf3b461a3dfb59b96757e7-site-4","doc-20cf3b461a3dfb59b96757e7-site-5","doc-20cf3b461a3dfb59b96757e7-site-6","doc-20cf3b461a3dfb59b96757e7-site-7","doc-20cf3b461a3dfb59b96757e7-site-8"],"anchor":[146.5,-18.8333333],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040067860,5040068450],"main_basin_ids":[5040067860,5040068450]},{"id":"doc-22af997dc8df843c5aa927b0","title":"A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data","display_title":"Testing Groundwater Connection in Wildman River Sinkholes","doi":"https://doi.org/10.1029/2020wr027578","theme":"Groundwater","color":"#a8b9ff","year":2020,"publication_date":"2020-07-27","authors":["Trine Enemark","Luk Peeters","Dirk Mallants","B. A. Flinchum","Okke Batelaan"],"summary":"The study develops and applies a systematic hydrogeological conceptual model testing workflow integrating remote sensing and seismic refraction data to assess the connectivity of sinkhole-type depressions to groundwater in the Wildman River area, Northern Territory, Australia. Alternative conceptual models of the process (surface water and groundwater interactions) and physical structures (subsurface lithology) of sinkholes were tested using forward models and independent observational data, updating model probabilities through a Bayesian Popper-Bayes framework.","finding":"Three out of five depressions are conditionally confirmed to act as conduits for groundwater recharge, while for two depressions the data are inconclusive.","finding_evidence":{"passage_id":"p015-000","page":15,"quote":"Of the 16 different conceptual models, only two would allow for the depression to act as conduits of recharge, A‐II and B‐II. These conceptual models have been marked with a dashed box on Figure 8. In depressions S2, S3, and S4, conceptual model B‐II is outperforming the rest of the models, indicating consistency with the depressions being conduits of recharge. In depression S1 the observations plot on the edge of the prior range of realizations for all physical structures (Figure 7), though structure A and C are still classified as probable models. This indicates that the “real” physical structure still might be an unknown. For both the physical structure and the process structure, the model evidence is least decisive for depression S5. For this depression, more model testing would be required in order to discriminate between the conceptual models. 5.","verified_in_page":true},"topics":["groundwater connectivity","conceptual model testing","Bayesian model updating","remote sensing","seismic refraction","hydrogeology","sinkhole recharge","model uncertainty"],"study_period":null,"locations":[{"name":"Wildman River area","kind":"region","role":"primary study region for sinkhole-groundwater connectivity assessment","river_name":null,"geocode_query":"Wildman River area, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"We apply this approach to a simplified hydrogeological characterization of the Wildman River area (Northern Territory, Australia) and evaluate the connectivity of sinkhole‐type depressions to groundwater. Alternative models are developed representing the process structure (i.e., different fluxes representing interactions between surface water and groundwater) and physical structure (i.e., different lithologies underlying the depressions) of the conceptual model of the depressions. Remote sensing data are used to test the process structure, while geophysical data are used to test the physical structure. Both data types are used to remove inconsistent models from an ensemble of 16 models and to update the probability of the remaining alternative conceptual models. Three out of five depressions that are used as a test case are conditionally confirmed to act as conduits for recharge, while for the last two depressions, the data are inconclusive.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.127007,-12.325533],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214757,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wildman River, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-22af997dc8df843c5aa927b0-site-0","true_coordinates":[132.1270069162383,-12.38553285806452],"spread_km":6.7},{"name":"Northern Territory","kind":"region","role":"state-level administrative region containing the Wildman River area study site","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"We apply this approach to a simplified hydrogeological characterization of the Wildman River area (Northern Territory, Australia) and evaluate the connectivity of sinkhole‐type depressions to groundwater. Alternative models are developed representing the process structure (i.e., different fluxes representing interactions between surface water and groundwater) and physical structure (i.e., different lithologies underlying the depressions) of the conceptual model of the depressions. Remote sensing data are used to test the process structure, while geophysical data are used to test the physical structure. Both data types are used to remove inconsistent models from an ensemble of 16 models and to update the probability of the remaining alternative conceptual models. Three out of five depressions that are used as a test case are conditionally confirmed to act as conduits for recharge, while for the last two depressions, the data are inconclusive.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Wildman River area Sinkhole depressions S1 to S5","kind":"field_site","role":"selected sinkhole-type depressions investigated for groundwater connectivity","river_name":null,"geocode_query":"Wildman River area, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"A x‐ray diffraction analysis found that the dolostone mineralogical composition is mainly dolomite with fractions of muscovite and quartz (Turnadge et al., 2018) suggesting that dissolution and sinkhole formation may also have taken place. The locations of possible sinkhole features in the Wildman River area have been mapped by several sources (Easey et al., 2016; Mueller et al., 2016; Tickell & Zaar, 2017; Figure 2) and are described further in Supporting Information S1. At the end of the dry season (late October 2018), a fieldtrip was undertaken to investigate a subset of the mapped depressions in the Wildman River area. Five depressions (S1 to S5, Figure 2) were selected based on their accessibility and vicinity to existing boreholes and water level loggers. We prioritized the selection of sinkholes based on their difference in geometry and vegetation cover while making sure they covered different parts of the area.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.065577,-12.385533],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214757,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wildman River, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-22af997dc8df843c5aa927b0-site-2","true_coordinates":[132.1270069162383,-12.38553285806452],"spread_km":6.7},{"name":"Darwin Region","kind":"region","role":"regional context referenced for sinkhole water level variation and characteristics","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The geology consists of sand and clay sediments of Mesozoic‐Cenozoic origin ranging in thickness of less than 25 m to over 100 m and overlaying the basement geology of dolostone, siltstone, and sandstone (Tickell & Zaar, 2017). The area has in recent years been subject to two major hydrogeological investigations (Tickell & Zaar, 2017; Turnadge et al., 2018), but still several open questions remain regarding its hydrogeological conceptualization. Approximately 100 km west of the field site, sinkholes are known to have developed on top of the dolomitic bedrock (Tickell, 2013). These sinkholes are generally rounded, broad, shallow depressions, and often form closed water features that show phases of filling up with water in the wet season and drying out in the dry season (Schult & Welch, 2006). In the Wildman River area the dolostone is known to be less continuous but similar depressions are found.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.90034,-12.488658],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-22af997dc8df843c5aa927b0-site-3","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7}],"limitations":["Exact geographic coordinates for depressions S1 to S5 and precise seismic survey locations are not provided.","Study area geolocation is given at regional scale only without finer coordinates.","Prior model probabilities assignment is subjective and the model range is not fully collectively exhaustive.","Data testing is limited to dry season periods only due to data availability.","Seismic velocity model parameters use literature values leading to large uncertainty.","New data collection needed to refine conceptual model probabilities especially for inconclusive depressions."],"river_ids":[],"site_ids":["doc-22af997dc8df843c5aa927b0-site-0","doc-22af997dc8df843c5aa927b0-site-2","doc-22af997dc8df843c5aa927b0-site-3"],"anchor":[132.065577,-12.385533],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040061670],"main_basin_ids":[5040061670]},{"id":"doc-244fec960c3644b59958b311","title":"The use of fatty acids to identify food sources of secondary consumers in wetland mesocosms","display_title":"Fatty Acids Trace Food Sources in Wetland Fish","doi":"https://doi.org/10.1080/02705060.2020.1761463","theme":"Flow & ecology","color":"#80edc2","year":2020,"publication_date":"2020-01-01","authors":["Ivor Growns","Darren Ryder","Paul McInerney","Nick Bond","Galen Holt","Rebecca E. Lester","Ross M. Thompson"],"summary":"This study used fatty acid biomarkers in wetland mesocosms at the University of New England, Armidale, NSW, Australia, to investigate how different basal food sources—redgum leaf leachate, cyanobacteria, green algae, and biofilm—are incorporated into secondary consumers, namely Western carp gudgeons (Hypseleotris spp.). The mesocosms were established with soil from the Gingham watercourse wetland in NSW and stocked with invertebrates and fish collected locally. Fatty acid profiles and invertebrate assemblages were analyzed to understand trophic transfer mechanisms influenced by environmental flows in the Murray–Darling Basin (MDB).","finding":"Addition of different basal food sources to wetland mesocosms did not produce differing fatty acid profiles in substrates, invertebrates, or fish, indicating internally generated food resources within mesocosms outweighed treatment effects.","finding_evidence":{"passage_id":"p013-002","page":13,"quote":"It may be that the different taxa have different nutritional values but overall the planktonic invertebrates were more nutritious in terms of total PUFA. Experiments which involve the addition of different food sources to wetland mesocosms are rare. We are aware on only one other study, Williams et al. (2007) who added extra leaf material and algal cultures to pond mesocosms. However, similar to our results they found few differences in the invertebrates colonizing the different treatments. This may suggest that food sources generated within the mesocosms themselves may have outweighed any treatment effects. The addition of extra food sources did not generate differing FA profiles between treatments in the substratum fractions, invertebrates or fish. This is despite the FA profiles of the food sources added differing and being indicative of their different biomarkers; 14:0 for cyanobacteria, 18:3n-3 for green algae and 18:1n-9 for fungi in the biofilm.","verified_in_page":true},"topics":["fatty acid biomarkers","trophic transfer","environmental flows","freshwater ecology","food web dynamics","aquatic invertebrates","fish nutrition","wetland mesocosms"],"study_period":null,"locations":[{"name":"University of New England campus","kind":"facility","role":"Experimental site for wetland mesocosm study","river_name":null,"geocode_query":"University of New England, Armidale, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Methods Mesocosm design Mesocosm experiments were established at the University of New England (UNE) campus at Armidale, NSW. Soil was collected from a dry wetland (to a depth of approximately 10 mm) on the Gingham watercourse, New South Wales, Australia (29\u0001160 S 149\u0001210 E) in October 2017. Vegetation on the site is classified as water couch marsh grassland (Bowen and Simpson 2010; Southwell et al. 2015). Soil was transported to the UNE campus then passed through a 10-mm mechanical sieve for homogenization and to remove roots and vegetation. Fifteen 500-L vats with a basal surface area of 0.78 m2 were placed in an openended structure covered with white polyethylene which allowed enough ambient light for algae and vegetation growth. Vats were filled to a depth of 300 mm with potable water, allowed to stand for three days to dechlorinate and maintained at that depth for the JOURNAL OF FRESHWATER ECOLOGY 175","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Gingham watercourse wetland","kind":"field_site","role":"Source of wetland soil used in mesocosms","river_name":null,"geocode_query":"Gingham watercourse, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Methods Mesocosm design Mesocosm experiments were established at the University of New England (UNE) campus at Armidale, NSW. Soil was collected from a dry wetland (to a depth of approximately 10 mm) on the Gingham watercourse, New South Wales, Australia (29\u0001160 S 149\u0001210 E) in October 2017. Vegetation on the site is classified as water couch marsh grassland (Bowen and Simpson 2010; Southwell et al. 2015). Soil was transported to the UNE campus then passed through a 10-mm mechanical sieve for homogenization and to remove roots and vegetation. Fifteen 500-L vats with a basal surface area of 0.78 m2 were placed in an openended structure covered with white polyethylene which allowed enough ambient light for algae and vegetation growth. Vats were filled to a depth of 300 mm with potable water, allowed to stand for three days to dechlorinate and maintained at that depth for the JOURNAL OF FRESHWATER ECOLOGY 175","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.2602613062245,-29.30020861235285],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670583,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gingham Watercourse, New South Wales, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-244fec960c3644b59958b311-site-1"},{"name":"Tarban Creek","kind":"field_site","role":"Site of fish collection (Western carp gudgeons) for mesocosms","river_name":null,"geocode_query":"Tarban Creek, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"remainder of the experiment. Ten kilograms of wetland soil was added at the end of the three days resulting in a soil depth of approximately 10 mm. The vats were gently aerated with diffusers. The vats were allowed to stand for 28 days prior to the introduction of fish in order to produce enough invertebrate biomass from sediments to sustain juvenile fish. Individual shield shrimp (Triops australiensis) were removed from the vats when observed, since a pilot study and (Boulton and Lloyd 1992) had shown that either their activity within the soils or their feeding behaviour reduced the populations of other invertebrates of suitable prey size for small fish. Young Western carp gudgeons (Hypseleotris spp.) were collected in February 2018 from Tarban Creek (28\u0001580 2100S; 151\u0001540 100E), using bait traps (360 240 240 mm, 5 mm mesh) set overnight. Fish were transported to the mesocosms, where eight individuals were randomly selected and added to each vat.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.141048153808,-33.8346015638652],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054902,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tarban Creek, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-244fec960c3644b59958b311-site-2"},{"name":"Murray–Darling Basin","kind":"region","role":"Contextual region of ecological and environmental flow relevance and water management challenges","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"These include an understanding of the processes which link flow variability to fluxes of energy to enhance condition, fitness and recruitment of higher consumers such as fish and birds (Rolls et al. 2017). Australia, the world’s driest inhabited continent, has experienced profound water stress which has had substantive environmental, social and economic impacts. In south-eastern Australia the Murray–Darling Basin (MDB), Australia’s largest food growing region, has experienced acute ecological impacts of water resource development (Kingsford 2000; Jenkins et al. 2005; Murray-Darling Basin Authority 2011a). In the MDB, the high level of water use has proven to be unsustainable, both in meeting the needs of irrigators for agriculture and for the ecological health of rivers and wetlands (Kingsford 2000; Arthington and Pusey 2003; Murray-Darling Basin Authority 2011b; Docker and Robinson 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.573607,-31.088672],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-244fec960c3644b59958b311-site-3","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7}],"limitations":["No exact latitude/longitude coordinates available for key study sites; coordinates set to null.","Use of mesocosms may not fully replicate natural wetland conditions, limiting ecological realism.","Fatty acid biomarkers may not be entirely specific, with overlapping markers among algae, bacteria, and fungi groups.","Relatively short experiment duration may limit observed changes in fatty acid profiles and fish growth.","Study focused on a single fish species and several basal resource types, potentially limiting generality to other fauna or food 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040070710,5040524870,5040591010],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-3134e018d1525cb74a46b1a4","title":"Importance of Indigenous Peoples’ lands for the conservation of Intact Forest Landscapes","display_title":"Indigenous Lands Preserve Intact Forest Landscapes","doi":"https://doi.org/10.1002/fee.2148","theme":"Land & ecosystems","color":"#c8df98","year":2020,"publication_date":"2020-01-06","authors":["Julia E. Fa","James Watson","Ian Leiper","Peter Potapov","Tom Evans","Neil D. Burgess","Zsolt Molnár","Álvaro Fernández‐Llamazares","Tom Duncan","Stephanie Wang","Beau J. Austin","Harry Jonas","Cathy Robinson","Pernilla Malmer","Kerstin K. Zander","Micha V. Jackson","Erle C. Ellis","Eduardo S. Brondízio","Stephen T. Garnett"],"summary":"This study assesses the overlap between Indigenous Peoples' lands and Intact Forest Landscapes (IFLs) globally, revealing that at least 36% of IFLs occur within Indigenous territories. Using global geospatial data, the research highlights that Indigenous lands have higher proportions of IFLs and have experienced lower loss rates than other lands across multiple countries, biomes, and biogeographic realms. The findings emphasize Indigenous Peoples' crucial custodial role in forest conservation and climate change mitigation and advocate for legal recognition of their land rights and collaborative governance to maintain these critical ecosystems.","finding":"At least 36% of the world's Intact Forest Landscapes are located on Indigenous Peoples' lands, and these areas exhibit lower rates of forest loss compared to other lands.","finding_evidence":{"passage_id":"p003-003","page":3,"quote":"IFLs within Indigenous Peoples’ lands covered at least 4.2 million km2 or 36.2% of the world’s IFL area, which covers a total of 11.6 million km2 (Figure 2). The proportion of Indigenous Peoples’ lands mapped as IFLs was considerably higher (10.9%) than the proportion of other lands (defined here as all land outside Indigenous Peoples’ lands) mapped as IFLs (6.8%). In 36 of the 50 sampled countries, the ratio of IFL area to total forest zone area was higher on Indigenous Peoples' land than on other lands (Figure 3). For each country, the percentage of the forest zone occupied by IFLs in Indigenous Peoples’ lands was higher (20.8 ± 23.5%) than in other lands (13.4 ± 19.4%). In many countries Indigenous Peoples’ lands had a higher proportion of the countries’ IFLs than their proportion of the forest zone, in contrast to other lands (Figure 4). The global extent of IFLs has declined by 9.4% (1.2 million km2 ) since 2000.","verified_in_page":true},"topics":["intact forest landscapes","indigenous land tenure","forest conservation","climate change mitigation","biodiversity conservation","land governance","carbon storage","deforestation rates"],"study_period":null,"locations":[{"name":"Amazon Basin, Ecuador","kind":"region","role":"Example Indigenous Peoples' territory within an Intact Forest Landscape","river_name":null,"geocode_query":"Amazon Basin, Ecuador","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Examples of Indigenous Peoples living within IFLs worldwide include (b) three generations of women from the Cofán people in Chontapunta, Provincia de Napo, Ecuador, within the Amazon Basin; (c) Baka Pygmy women preparing dinner in a village near Mintom, southeastern Cameroon, an area within the forests of the Congo Basin, an important tropical moist forest in Africa; and (d) members of the Kwerba community in Papua, Indonesia, on the island of New Guinea, practicing with traditionally made hunting weapons. (a) (b) (c) (d) O Massana and A López-Baucells G Peña Gimeno A Ledeu/CIFOR ND Liswanti/CIFOR","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Congo Basin, Cameroon","kind":"region","role":"Example Indigenous Peoples' territory within an Intact Forest Landscape","river_name":null,"geocode_query":"Congo Basin, Cameroon","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Examples of Indigenous Peoples living within IFLs worldwide include (b) three generations of women from the Cofán people in Chontapunta, Provincia de Napo, Ecuador, within the Amazon Basin; (c) Baka Pygmy women preparing dinner in a village near Mintom, southeastern Cameroon, an area within the forests of the Congo Basin, an important tropical moist forest in Africa; and (d) members of the Kwerba community in Papua, Indonesia, on the island of New Guinea, practicing with traditionally made hunting weapons. (a) (b) (c) (d) O Massana and A López-Baucells G Peña Gimeno A Ledeu/CIFOR ND Liswanti/CIFOR","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Papua, Indonesia","kind":"region","role":"Example Indigenous Peoples' territory within an Intact Forest Landscape","river_name":null,"geocode_query":"Papua, Indonesia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Examples of Indigenous Peoples living within IFLs worldwide include (b) three generations of women from the Cofán people in Chontapunta, Provincia de Napo, Ecuador, within the Amazon Basin; (c) Baka Pygmy women preparing dinner in a village near Mintom, southeastern Cameroon, an area within the forests of the Congo Basin, an important tropical moist forest in Africa; and (d) members of the Kwerba community in Papua, Indonesia, on the island of New Guinea, practicing with traditionally made hunting weapons. (a) (b) (c) (d) O Massana and A López-Baucells G Peña Gimeno A Ledeu/CIFOR ND Liswanti/CIFOR","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Geospatial resolution and boundaries of Indigenous lands are imprecise and often disputed","Indigenous Peoples' land maps are conservative estimates, potentially underrepresenting overlap with IFLs","Limited mapped data available for some biogeographic realms such as Nearctic and Oceania","Coordinates for specific Indigenous communities are not provided, limiting precise spatial referencing"],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[],"main_basin_ids":[]},{"id":"doc-31e090556696d594e9f48a29","title":"COVID-19 induced economic loss and ensuring food security for vulnerable groups: Policy implications from Bangladesh","display_title":"COVID-19 Economic Loss and Food Security in Bangladesh","doi":"https://doi.org/10.1371/journal.pone.0240709","theme":"Other science","color":"#d1b5e7","year":2020,"publication_date":"2020-10-16","authors":["Khondoker Abdul Mottaleb","Mohammed Mainuddin","Tetsushi Sonobe"],"summary":"This study estimates the economic loss due to COVID-19 lockdown in Bangladesh and necessary compensation to ensure food security for daily wage workers. Using 2016–17 Household Income and Expenditure Survey data of over 50,000 respondents, it finds that complete lockdown results in approximately US$64.2 million daily wage loss among farm and nonfarm daily wage workers. The study estimates a minimum compensation of around US$1 per day per daily wage-based household to ensure minimum food security during lockdown, highlighting the vulnerability of these workers to income and food insecurity due to movement restrictions.","finding":"Complete lockdowns in Bangladesh cause a daily wage loss estimated at US$64.2 million among daily wage workers, and an additional compensation of about US$1 per day per daily wage-based household is necessary to ensure minimum food security during lockdown periods.","finding_evidence":{"passage_id":"p011-002","page":11,"quote":"Following Eq (2), multiplying the estimated daily wage earnings for farmworkers as BDT 272.2/day and for the nonfarm worker as BDT 361.5/day, under the assumption of a Table 3. Income, characteristics and food expenditure of the sampled respondents by their sector of employment.","verified_in_page":true},"topics":["covid-19","economic loss","daily wages","food security","household expenditure","income loss","poverty","lockdown effects"],"study_period":null,"locations":[{"name":"Bangladesh","kind":"region","role":"Study area and country analyzed for economic loss and food security due to COVID-19 lockdown","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"has been affecting high-and low-income countries, the aftermath of COVID-19 could have severe negative impacts on low-income developing countries. In Bangladesh, on March 23, 2020, in the wake of three deaths and 25 infections of COVID-19 [37], the Bangladesh government imposed a nationwide holiday and lockdown initially from March 26 to April 4, 2020 [38], and later extending the general holiday to April 11, and then to April 30, 2020 [39]. Finally, the government extended the general holiday and airport and road lockdown until May 30, 2020 [39]. Until April 17, 2020, out of 64 districts of Bangladesh, 38 districts were completely under lockdown and 17 were under partial lockdown [40] (Fig 1). As of September 14, 2020, Bangladesh had a total of 341,056 confirmed COVID19 cases, with 245,594 recovered and 4,802 deaths [37]. Fig 1. Lockdown status of the districts in Bangladesh and the share of the daily wage-based workers by districts.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Dhaka Division, Bangladesh","kind":"region","role":"Major nonfarm employment cluster including large garment industry; largest share of sampled respondents from here","river_name":null,"geocode_query":"Dhaka Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"A closer scrutiny of Table 2 reveals that, on average, the daily wage-based workers in the farm sector are statistically significantly older (40.1 years) with significantly fewer years of schooling (2.2 years) and was more likely to be from rural areas compared to others. In addition, the number of family members and earners were lower for households of daily wage workers in both farm and nonfarm sectors compared to the other group. Thus, daily wagebased workers in general, are relatively more resource poor compared to other workers. More than one-fifth of the sampled respondents were from Dhaka Division, followed by Chattogram (18.3%) and Khulna Divisions (15.2%) (Table 2). Dhaka and Chattogram are the largest PLOS ONE COVID-19 induced loss and food security PLOS ONE | https://doi.org/10.1371/journal.pone.0240709 October 16, 2020 9 / 20","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Chattogram Division, Bangladesh","kind":"region","role":"Major nonfarm employment cluster including large garment industry; second largest share of respondents","river_name":null,"geocode_query":"Chattogram Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"A closer scrutiny of Table 2 reveals that, on average, the daily wage-based workers in the farm sector are statistically significantly older (40.1 years) with significantly fewer years of schooling (2.2 years) and was more likely to be from rural areas compared to others. In addition, the number of family members and earners were lower for households of daily wage workers in both farm and nonfarm sectors compared to the other group. Thus, daily wagebased workers in general, are relatively more resource poor compared to other workers. More than one-fifth of the sampled respondents were from Dhaka Division, followed by Chattogram (18.3%) and Khulna Divisions (15.2%) (Table 2). Dhaka and Chattogram are the largest PLOS ONE COVID-19 induced loss and food security PLOS ONE | https://doi.org/10.1371/journal.pone.0240709 October 16, 2020 9 / 20","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Rangpur Division, Bangladesh","kind":"region","role":"High poverty region with a high proportion of daily wage farmworkers among sampled respondents","river_name":null,"geocode_query":"Rangpur Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"A closer scrutiny of Table 2 reveals that, on average, the daily wage-based workers in the farm sector are statistically significantly older (40.1 years) with significantly fewer years of schooling (2.2 years) and was more likely to be from rural areas compared to others. In addition, the number of family members and earners were lower for households of daily wage workers in both farm and nonfarm sectors compared to the other group. Thus, daily wagebased workers in general, are relatively more resource poor compared to other workers. More than one-fifth of the sampled respondents were from Dhaka Division, followed by Chattogram (18.3%) and Khulna Divisions (15.2%) (Table 2). Dhaka and Chattogram are the largest PLOS ONE COVID-19 induced loss and food security PLOS ONE | https://doi.org/10.1371/journal.pone.0240709 October 16, 2020 9 / 20","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Bagerhat District, Bangladesh","kind":"region","role":"Statistical base district for district dummies in models characterizing wage workers and wages","river_name":null,"geocode_query":"Bagerhat District, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"For example, in India, 34.5% of employed workers are casual labor, of which 21% are engaged in agriculture [60]. The daily average wage earnings per day by a casual labor ranged between India Rupee (Rs.) 253–282 [60]. A recent report stressed that in India, in April 2020, 12 million people became jobless, and the unemployment rate had increased from 8.8% Table 7. Estimated function explaining the occupation choice of the sampled respondents: Multinomial logit estimates with base category being salaried workers (= 0).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.7895478,22.6602436],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bagerhat District, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-31e090556696d594e9f48a29-site-4"}],"limitations":["Study based on 2016–17 pre-COVID survey data to estimate income loss during 2020 pandemic lockdown, implying assumptions about unchanged economic structure and behaviors.","Latitude and longitude coordinates unavailable for specific administrative divisions and districts mentioned; geographic specificity limited to administrative units.","Estimation of daily wage loss relies on assumptions about share of workers paid daily and fraction of income lost due to lockdown without current real-time wage data.","Findings on minimum compensation focus solely on food expenditures, not accounting for other household needs in long-term lockdowns.","District-level heterogeneity acknowledged but limits the ability to generalize across smaller or more granular geographic units."],"river_ids":[],"site_ids":["doc-31e090556696d594e9f48a29-site-4"],"anchor":[89.7895478,22.6602436],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-10","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-4064a8d8be83205140c55f50","title":"Comparing Palmer Drought Severity Index drought assessments using the traditional offline approach with direct climate model outputs","display_title":"Revised Drought Projections Using Coupled Models","doi":"https://doi.org/10.5194/hess-24-2921-2020","theme":"Climate","color":"#98d4f1","year":2020,"publication_date":"2020-06-03","authors":["Yuting Yang","Shulei Zhang","Michael L. Roderick","Tim R. McVicar","Dawen Yang","Wenbin Liu","Xiaoyan Li"],"summary":"This study compares traditional offline Palmer Drought Severity Index (PDSI) drought assessments with calculations using direct outputs from 16 CMIP5 climate models. It highlights that traditional offline methods overestimate drought severity, frequency, and extent under climate change due to neglecting vegetation responses to elevated atmospheric CO2 concentrations. Incorporating CO2 effects on evapotranspiration yields drought projections more consistent with coupled climate model outputs, indicating much smaller increases in drought globally.","finding":"Using direct hydrologic outputs from CMIP5 models to calculate PDSI, which accounts for vegetation response to elevated CO2, shows much smaller drought increases in drought severity, frequency, and extent than traditional offline methods that ignore these CO2 effects.","finding_evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"topics":["climate modelling","drought assessment","evapotranspiration","atmospheric CO2 effects","hydrological modelling","palmer drought severity index","vegetation response","climate projections"],"study_period":"1901–2100","locations":[{"name":"North America","kind":"region","role":"Region with widely reported increased drought severity by traditional offline PDSI methods","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.203424,54.474],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-4064a8d8be83205140c55f50-site-0","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"South America","kind":"region","role":"Region with traditional offline PDSI depicting drought increases not supported by coupled model outputs","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Europe","kind":"region","role":"Region where traditional offline PDSI shows drought increases but coupled model PDSI shows smaller changes","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Congo basin","kind":"region","role":"Area with apparent drought increase by offline PDSI but not by coupled PDSI calculations","river_name":null,"geocode_query":"Congo Basin, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.814796,-1.898349],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Congo River","basin_id":1041213640,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":894488.8,"hydrology_source":"hydrobasins","site_id":"doc-4064a8d8be83205140c55f50-site-3","true_coordinates":[18.77950841330257,-1.946890029824763],"spread_km":6.7},{"name":"Southern Africa","kind":"region","role":"Area with contrasting drought projections between traditional offline and coupled PDSI","river_name":null,"geocode_query":"Southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.511672,-24.327077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-4064a8d8be83205140c55f50-site-4","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7},{"name":"Southeastern China","kind":"region","role":"Region with traditional offline PDSI indicating drought increases inconsistent with coupled model outputs","river_name":null,"geocode_query":"Southeastern China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southern coastal areas of Australia","kind":"region","role":"Area with increased drought projected by traditional offline PDSI not supported by coupled model direct outputs","river_name":null,"geocode_query":"Southern coast Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Finally, t1.5 and t2 are, respectively, determined at the times when global mean Ta reached 0.9 and 1.4 ◦C above the 1986–2005 baseline, as this period was at least 0.6 ◦C warmer than the pre-industrial level (Hawkins et al., 2017; Schleussner et al., 2016). 3 Results 3.1 Predicted drought changes Figure 3 shows the global patterns of PDSI trends for the three PDSIs. Evident drought increases are depicted by PDSI_PM-RC across much of the North America, South America, central to southern Europe, Congo basin, southern Africa, southeastern China, and southern coastal areas of Australia (Fig. 3a), as widely reported previously (Dai, 2011, 2012; Dai et al., 2018; Cook et al., 2014; Lehner et al., 2018; Liu et al., 2018). However, those broad-scale trends are not identified by either PDSI_CMIP5 (Fig. 3b) or PDSI_PM[CO2] (Fig. 3c). Compared with PDSI-PM-RC, both PDSI_CMIP5 and PDSI_PM[CO2] show much smaller changes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.0937206,-34.0640771],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054826,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Sydney, NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-4064a8d8be83205140c55f50-site-6"}],"limitations":["Broad regional geographies without precise coordinates","Uncertainty in soil available water capacity input for PDSI calculation"],"river_ids":[],"site_ids":["doc-4064a8d8be83205140c55f50-site-0","doc-4064a8d8be83205140c55f50-site-3","doc-4064a8d8be83205140c55f50-site-4","doc-4064a8d8be83205140c55f50-site-6"],"anchor":[18.814796,-1.898349],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[1041213640,1041624310,5040070710,7040021740],"main_basin_ids":[1040015850,1040020040,5040070710,7040021740]},{"id":"doc-45524e7055cae63fe0e50227","title":"Application of Bioavailability Models to Derive Chronic Guideline Values for Nickel in Freshwaters of Australia and New Zealand","display_title":"Bioavailability Models for Nickel Guidelines","doi":"https://doi.org/10.1002/etc.4885","theme":"Water management","color":"#eeabac","year":2020,"publication_date":"2020-09-30","authors":["Jennifer L. Stauber","Lisa A. Golding","Adam Peters","Graham Merrington","Merrin S. Adams","Monique T. Binet","Graeme E. Batley","Francesca Gissi","Kitty McKnight","Emily Garman","Elizabeth Middleton","Jennifer Gadd","Chris Schlekat"],"summary":"This study evaluates bioavailability-based models to derive chronic guideline values for nickel in freshwaters in Australia and New Zealand. Four models including two biotic ligand models (BLMs) and two multiple linear regressions (MLRs) were compared for their accuracy in normalizing nickel ecotoxicity data. The trophic level-specific MLR model provided the best predictions for toxicity and was used to derive a protective concentration for 95% of species (PC95) of 3 µg Ni/L at an index water chemistry condition. The study shows that site-specific water chemistry significantly influences nickel bioavailability, stressing the need for bioavailability corrections in guideline values.","finding":"The trophic level-specific multiple linear regression (MLR) model outperformed the other bioavailability models, with 79% of predicted 10% effect concentrations within a factor of 2 of observed values, enabling derivation of an appropriate nickel guideline value adjusted for water chemistry.","finding_evidence":{"passage_id":"p007-000","page":7,"quote":"is, 3 μg Ni/L, if they were to be applied as guideline values for slightly‐to‐moderately disturbed systems. Model scores Figures 2 and 3 show the plots of the residual factors for each of the 4 models applied to all data: 1) trophic level‐specific MLRs, that is, algal MLR applied to algae in the ecotoxicity database, aquatic plant MLR applied to aquatic plants, invertebrate MLR applied to invertebrates, and fish MLR applied to fish (Figure 2A); 2) pooled MLR applied to all taxa (Figure 2B); 3) European Union BLM, all data (Figure 3A); and 4) softwater BLM, all data (Figure 3B). The trophic level‐specific MLR resulted in 78% of the normalized data being within a factor of 2 of the observed data, which was substantially better than the pooled MLR, which had 66% of the normalized data within a factor of 2 (Figure 2A and B).","verified_in_page":true},"topics":["metal bioavailability","chronic toxicity","nickel ecotoxicity","biotic ligand models","multiple linear regression models","species sensitivity distribution","water chemistry effects","environmental guidelines"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Study region relevant to freshwater nickel bioavailability and guideline development","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"© 2020 The Authors wileyonlinelibrary.com/ETC Environmental Toxicology and Chemistry—Volume 40, Number 1—pp. 100–112, 2021 Received: 16 June 2020 | Revised: 8 August 2020 | Accepted: 24 September 2020 100 Environmental Toxicology Application of Bioavailability Models to Derive Chronic Guideline Values for Nickel in Freshwaters of Australia and New Zealand Jenny Stauber,a, * Lisa Golding,a Adam Peters,b Graham Merrington,b Merrin Adams,a Monique Binet,a Graeme Batley,a Francesca Gissi,c Kitty McKnight,a Emily Garman,d Ellie Middleton,d Jennifer Gadd,e and Chris Schlekatd a Commonwealth Scientific and Industrial Research Organisation Land and Water, Lucas Heights, New South Wales, Australia b WCA Environment, Faringdon, Oxfordshire, United Kingdom c Commonwealth Scientific and Industrial Research Organisation Oceans and Atmosphere, Lucas Heights, New South Wales, Australia d NiPERA, Durham, North Carolina, USA e National Institute of Water and Atmospheric Research, Auckland, New Zealand Abstract: There has been an increased emphasis on incorporating bioavailability‐based approaches into freshwater guideline value derivations for metals in the Australian and New Zealand water quality guidelines.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New Zealand","kind":"region","role":"Study region relevant to freshwater nickel bioavailability and guideline development","river_name":null,"geocode_query":"New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"© 2020 The Authors wileyonlinelibrary.com/ETC Environmental Toxicology and Chemistry—Volume 40, Number 1—pp. 100–112, 2021 Received: 16 June 2020 | Revised: 8 August 2020 | Accepted: 24 September 2020 100 Environmental Toxicology Application of Bioavailability Models to Derive Chronic Guideline Values for Nickel in Freshwaters of Australia and New Zealand Jenny Stauber,a, * Lisa Golding,a Adam Peters,b Graham Merrington,b Merrin Adams,a Monique Binet,a Graeme Batley,a Francesca Gissi,c Kitty McKnight,a Emily Garman,d Ellie Middleton,d Jennifer Gadd,e and Chris Schlekatd a Commonwealth Scientific and Industrial Research Organisation Land and Water, Lucas Heights, New South Wales, Australia b WCA Environment, Faringdon, Oxfordshire, United Kingdom c Commonwealth Scientific and Industrial Research Organisation Oceans and Atmosphere, Lucas Heights, New South Wales, Australia d NiPERA, Durham, North Carolina, USA e National Institute of Water and Atmospheric Research, Auckland, New Zealand Abstract: There has been an increased emphasis on incorporating bioavailability‐based approaches into freshwater guideline value derivations for metals in the Australian and New Zealand water quality guidelines.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Appletree Creek, Australia","kind":"river","role":"Example natural water site used to assess bioavailability model predictions and site-specific guideline values","river_name":"Appletree Creek","geocode_query":"Appletree Creek, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.2310965,-25.2268472],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12128968,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Apple Tree Creek QLD 4660, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-2"},{"name":"Magela Creek, Australia","kind":"river","role":"Example natural water site used to assess bioavailability model predictions and site-specific guideline values","river_name":"Magela Creek","geocode_query":"Magela Creek, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.8753559,-12.5826134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217157,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arnhem Highway/Oenpelli Rd, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-3"},{"name":"Ovens River, Australia","kind":"river","role":"Natural water site modeled with two different pH and DOC conditions to evaluate guideline value variations","river_name":"Ovens River","geocode_query":"Ovens River, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.856389,-36.592358],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676905,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-4","true_coordinates":[146.85638899200012,-36.65235757999994],"spread_km":6.7},{"name":"Minamurra River, Australia","kind":"river","role":"Natural water site used for testing bioavailability models and site-specific guideline values","river_name":"Minamurra River","geocode_query":"Minamurra River, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.7861033530131,-34.63011562792195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054393,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Minnamurra River, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-5"},{"name":"Tea Tree, Australia","kind":"river","role":"Natural water site used for model evaluation in guideline value derivation","river_name":"Tea Tree","geocode_query":"Tea Tree, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.3159312,-42.7080304],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":31292078,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tea Tree TAS 7017, Australia","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-6"},{"name":"Lake Eacham, Australia","kind":"other","role":"Freshwater site used for assessing water chemistry effects on nickel bioavailability","river_name":null,"geocode_query":"Lake Eacham, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.6343671,-17.3032963],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651545,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Eacham QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-7"},{"name":"Woronora River, Australia","kind":"river","role":"Natural water site used to test bioavailability models for nickel guidelines","river_name":"Woronora River","geocode_query":"Woronora River, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.00058,-34.110302],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054736,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Woronora River, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-8","true_coordinates":[151.000580183185,-34.05030152297743],"spread_km":6.7},{"name":"Wellington, Australia","kind":"settlement","role":"Location near water bodies used for testing guideline model predictions","river_name":null,"geocode_query":"Wellington, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"TABLE 4: Water quality parameters for each of the 20 natural waters from Australia and New Zealand Water pH DOC (mg/L) Ca (mg/L) Mg (mg/L) Calculated hardness (mg CaCO3/L) Australian waters Appletree Creek 4.6 12 0.25 1.5 7 Magela Creek 6.1 3 0.25 0.25 2 Ovens River 6.9 3 3.1 2.9 20 Ovens River adj. 8.2 0.5 3.1 2.9 20 Minamurra River 7.1 7 6.6 5.6 40 Tea Tree 7.3 10 2.1 4.7 25 Lake Eacham 7.4 1.4 1.8 2.3 14 Woronora River 7.6 4 2.5 3.5 21 Wellington 7.9 5 11 7.4 58 Sandy Creek 7.2 21 21 16 117 Milang 8.8 23 23 17 128 New Zealand waters Cascade Stream 7.8 0.5 6.9 4.5 36 Waipaoa River 8.2 2.9 68 6.8 198 Rangataiki River 7.0 1.5 5.1 1.9 21 Ohinemuri River 8.3 1.9 3.9 1.9 18 Hurunui River 8.2 1.9 9.9 1.4 31 Haast River 7.7 0.8 14 0.75 37 Mangapouri Stream 7.0 5.8 9.2 5.2 44 Porirua Stream 7.5 2.2 8.9 5.1 43 Mokotua Stream 4.4 33 1.3 2.9 15 Carran Creek 6.5 18 11 5.0 48 Index condition 7.5 <0.5 6 4 30 DOC = dissolved organic carbon. 104 Environmental Toxicology and Chemistry, 2021;40:100–112—J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.9409996,-32.5510976],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43671054,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wellington NSW 2820, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-9"},{"name":"Sandy Creek, Australia","kind":"river","role":"Natural water site representing high DOC and hardness to assess low nickel bioavailability predicting highest protective concentrations","river_name":"Sandy Creek","geocode_query":"Sandy Creek, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.8261694,-34.6026218],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289661,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sandy Creek SA 5350, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-10"},{"name":"Milang, Australia","kind":"settlement","role":"Site representing high-pH freshwater outside model ranges to evaluate model behavior","river_name":null,"geocode_query":"Milang, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"The PC95 values estimated using the trophic level‐specific MLR varied by 43‐fold, and the PC99 values varied by 65‐fold across the 20 different water chemistry combinations. The highest PC95 values, where water quality values were within the model limits, were for Sandy Creek for all models (43–66 μg Ni/L), that is, nickel in this water would be predicted to be the least bioavailable because it had a high DOC (21 mg/L, >90th percentile), relatively high hardness (117 mg CaCO3/L), and neutral pH (7.2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.9711326,-35.4090935],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660435,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Milang SA 5256, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-45524e7055cae63fe0e50227-site-11"},{"name":"Cascade Stream, New Zealand","kind":"river","role":"New Zealand natural water site with low DOC and pH used for model testing","river_name":"Cascade Stream","geocode_query":"Cascade Stream, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Waipaoa River, New Zealand","kind":"river","role":"High hardness and pH water used as a natural test site for bioavailability model validation","river_name":"Waipaoa River","geocode_query":"Waipaoa River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"The PC95 values estimated using the trophic level‐specific MLR varied by 43‐fold, and the PC99 values varied by 65‐fold across the 20 different water chemistry combinations. The highest PC95 values, where water quality values were within the model limits, were for Sandy Creek for all models (43–66 μg Ni/L), that is, nickel in this water would be predicted to be the least bioavailable because it had a high DOC (21 mg/L, >90th percentile), relatively high hardness (117 mg CaCO3/L), and neutral pH (7.2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[177.9176048,-38.6535928],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Waipaoa River Bridge, Gisborne, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-13"},{"name":"Rangataiki River, New Zealand","kind":"river","role":"Natural water site in New Zealand assessed for nickel bioavailability and protective concentrations","river_name":"Rangataiki River","geocode_query":"Rangataiki River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[176.6402588871757,-38.329591831631],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rangitaiki River, Bay of Plenty Region, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-14"},{"name":"Ohinemuri River, New Zealand","kind":"river","role":"High pH New Zealand river representing increased nickel bioavailability evaluated in guideline models","river_name":"Ohinemuri River","geocode_query":"Ohinemuri River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[175.7661999177571,-37.38407638612028],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ohinemuri River, Waikato Region, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-15"},{"name":"Hurunui River, New Zealand","kind":"river","role":"Natural water site with low DOC used to examine nickel bioavailability predictions","river_name":"Hurunui River","geocode_query":"Hurunui River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[172.565872,-42.801595],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040082270,"main_basin_id":5040082270,"basin_region":"Australasia","basin_area_km2":152497.7,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-16"},{"name":"Haast River, New Zealand","kind":"river","role":"New Zealand freshwater site with low DOC for bioavailability modeling","river_name":"Haast River","geocode_query":"Haast River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[169.2335927474008,-43.96262281338162],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Haast River, West Coast Region, New Zealand","basin_id":5040082270,"main_basin_id":5040082270,"basin_region":"Australasia","basin_area_km2":152497.7,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-17"},{"name":"Mangapouri Stream, New Zealand","kind":"river","role":"Natural water site tested for nickel guideline bioavailability values","river_name":"Mangapouri Stream","geocode_query":"Mangapouri Stream, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Porirua Stream, New Zealand","kind":"river","role":"Freshwater site for model comparison and guideline value assessment","river_name":"Porirua Stream","geocode_query":"Porirua Stream, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[174.8255059156433,-41.16713230421898],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Porirua Stream, Te Whanga-nui-a-Tara, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-19"},{"name":"Mokotua Stream, New Zealand","kind":"river","role":"Low pH natural water site outside model boundaries used for evaluating model predictions","river_name":"Mokotua Stream","geocode_query":"Mokotua Stream, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[168.4195111944821,-46.51320957878792],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mokotua Stream, Murihiku, New Zealand","basin_id":5040082270,"main_basin_id":5040082270,"basin_region":"Australasia","basin_area_km2":152497.7,"hydrology_source":"hydrobasins","site_id":"doc-45524e7055cae63fe0e50227-site-20"},{"name":"Carran Creek, New Zealand","kind":"river","role":"New Zealand freshwater site for testing nickel bioavailability models","river_name":"Carran Creek","geocode_query":"Carran Creek, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Given that EC10 values were used to compare observed and predicted toxicity in our study, rather than LC/EC50 values, predictions within a factor of 3 would also be considered reasonable (Peters et al. 2018). Applying the models to natural waters Table 8 compares the PC95 and PC99 values for nickel data normalized using the 4 different models for each of the 20 natural waters covering a range of water chemistries. The PC90 and PC80 values for the trophic level‐specific MLR and the pooled MLR are given in the Supplemental Data, Table S5. As expected, water chemistry and choice of model strongly influenced the resulting protective concentration values. The largest range of PC95 and PC99 values (460‐fold) for water samples within model boundaries was predicted by the pooled MLR, followed by the European Union BLM (64‐fold difference in PC95 and 117‐fold difference in PC99 values) at the different water chemistry conditions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Some model boundaries exclude extreme water chemistry conditions such as very low pH (e.g., pH <5) and very high pH (>8.5) which limits applicability in those waters.","Limited species data for aquatic plants and fish trophic-level-specific MLRs introduces uncertainty.","Lack of field validation for the MLR models; existing validation primarily in laboratory and microcosm settings.","Some natural waters tested fell outside model parameter boundaries affecting prediction accuracy.","Pooled MLR model performed poorer and is not recommended for guideline derivation due to lower predictive accuracy."],"river_ids":["43639557","43639567","43639570","43640241","43640344","43643199","43643227","43643345","43643771","43644030","43644470","43644997","43645007","43645094","43645755","43645787","43645803","43645925","43646976","43646999","43647042","43647060","43647075","43647242","43647646","43648328","43648338","43648350","43648354","43648403","43648454","43648456","43649191","43650481","43660233","43660234","43660236","43660242","43660251","43660252","43660256","ne-1414"],"site_ids":["doc-45524e7055cae63fe0e50227-site-2","doc-45524e7055cae63fe0e50227-site-3","doc-45524e7055cae63fe0e50227-site-4","doc-45524e7055cae63fe0e50227-site-5","doc-45524e7055cae63fe0e50227-site-6","doc-45524e7055cae63fe0e50227-site-7","doc-45524e7055cae63fe0e50227-site-8","doc-45524e7055cae63fe0e50227-site-9","doc-45524e7055cae63fe0e50227-site-10","doc-45524e7055cae63fe0e50227-site-11","doc-45524e7055cae63fe0e50227-site-13","doc-45524e7055cae63fe0e50227-site-14","doc-45524e7055cae63fe0e50227-site-15","doc-45524e7055cae63fe0e50227-site-16","doc-45524e7055cae63fe0e50227-site-17","doc-45524e7055cae63fe0e50227-site-19","doc-45524e7055cae63fe0e50227-site-20"],"anchor":[151.00058,-34.110302],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040061670,5040067860,5040069970,5040070710,5040073420,5040077640,5040082270,5040085040,5040524870,5040591050],"main_basin_ids":[5040061670,5040067860,5040069970,5040070710,5040073410,5040073420,5040077640,5040082270,5040085040]},{"id":"doc-53580e550af295dfac3b720a","title":"Impact of downscaled rainfall biases on projected runoff changes","display_title":"Rainfall Bias Correction Impacts Runoff Projections","doi":"https://doi.org/10.5194/hess-24-2981-2020","theme":"Flow & ecology","color":"#80edc2","year":2020,"publication_date":"2020-06-08","authors":["Stephen P. Charles","Francis H. S. Chiew","Nicholas J. Potter","Hongxing Zheng","Guobin Fu","Lu Zhang"],"summary":"This study evaluates the impact of bias correction on dynamically downscaled rainfall from the Weather Research and Forecasting (WRF) regional climate model and its influence on hydrological simulations and runoff projections in Victoria, Australia. Various bias correction (BC) methods, including quantile-quantile mapping (QQM), and empirical scaling (ES) are compared in terms of their ability to replicate observed rainfall distributions and their effects on simulated runoff using the GR4J rainfall–runoff model. The study highlights the limitations of bias correction methods, particularly the underestimation of wet-day sequences, multi-day rain totals, and spatial correlation, which impacts historical runoff simulation and runoff change projections under climate change scenarios. It shows that while BC rainfall improves rainfall distribution representation, runoff simulations generally underestimate mean and high flows due to residual biases. Differences in projected runoff changes between BC and ES methods are linked to these biases and the ability of BC to alter rainfall sequencing and extreme values. The study focusses on 10 key catchments across Victoria and discusses implications of lumped versus distributed hydrological modelling approaches.","finding":"Despite removal of bias in daily rainfall distributions at each 10 km × 10 km grid cell across Victoria through QQM bias correction, the resulting bias-corrected rainfall underestimates runoff when used in hydrological modelling. This underestimation arises because bias correction does not adequately correct biases in wet–wet-day transition probabilities and multi-day rainfall accumulations, leading to underestimation of runoff magnitude and high flows in historical simulations and affecting the magnitude of projected runoff changes.","finding_evidence":{"passage_id":"p005-003","page":5,"quote":"Thus, despite WRF-BC slightly overestimating annual mean rainfall compared to AWAP (Fig. 2b), the underestimation of wet–wetday transition probabilities and multi-day rainfall accumulations resulted in an underestimation of simulated runoff when compared to runoff simulated using observed (i.e. AWAP) rainfall inputs, as shown in Fig. 3. There was an underestimation of mean annual runoff, high flows (99th-percentile daily flow) and the number of high-flow days (days above 95thpercentile flow) in most cases (Fig. 3). The magnitude of the underestimations was larger for the GCM-forced runs comhttps://doi.org/10.5194/hess-24-2981-2020 Hydrol. Earth Syst. Sci., 24, 2981–2997, 2020","verified_in_page":true},"topics":["rainfall-downscaling","bias-correction","quantile-quantile mapping","hydrological modelling","runoff projections","climate-change impacts","rainfall sequencing","spatial correlation"],"study_period":"1990–2009 (historical), 2060–2079 (future projections)","locations":[{"name":"Victoria","kind":"region","role":"Primary study region; location of hydrological and climate modelling evaluation and runoff projection","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Similar results and limitations have been observed for various BC methods in the literature (Lafon et al., 2013; Teng et al., 2015; Maraun, 2016; Rajczak et al., 2016; Maraun et al., 2017). 2 Study area, data and methods The state of Victoria in south-eastern Australia (Fig. 1) experiences mild wet winters and hot dry summers, with large interannual-to-decadal climate variability producing a significant impact on water availability for agriculture and water supply for towns and cities (Kiem and Verdon-Kidd, 2010; Chiew et al., 2014, 2018). Higher runoff is produced in highland (Victorian Alps) and coastal catchments along southern and eastern coastlines. Low-runoff catchments are in the inner western regions and western slopes of the Great Dividing Range (Hope et al., 2017). Rainfall is projected to decline across the state, together with increased intensity of extreme rainfall events (DELWP, 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.490564,-37.033997],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-53580e550af295dfac3b720a-site-0","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7},{"name":"Victorian Alps","kind":"landscape","role":"Highland area within Victoria producing higher runoff; spatial pattern comparison of rainfall-runoff response","river_name":null,"geocode_query":"Victorian Alps, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Similar results and limitations have been observed for various BC methods in the literature (Lafon et al., 2013; Teng et al., 2015; Maraun, 2016; Rajczak et al., 2016; Maraun et al., 2017). 2 Study area, data and methods The state of Victoria in south-eastern Australia (Fig. 1) experiences mild wet winters and hot dry summers, with large interannual-to-decadal climate variability producing a significant impact on water availability for agriculture and water supply for towns and cities (Kiem and Verdon-Kidd, 2010; Chiew et al., 2014, 2018). Higher runoff is produced in highland (Victorian Alps) and coastal catchments along southern and eastern coastlines. Low-runoff catchments are in the inner western regions and western slopes of the Great Dividing Range (Hope et al., 2017). Rainfall is projected to decline across the state, together with increased intensity of extreme rainfall events (DELWP, 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.295645,-36.761419],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667323,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Australia, Victoria, Tawonga, ビクトリアン・アルプス","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-53580e550af295dfac3b720a-site-1","true_coordinates":[147.2956453,-36.7014186],"spread_km":6.7},{"name":"Catchments across Victoria (10 selected)","kind":"basin","role":"Hydrological model calibration and detailed study sites for rainfall-runoff simulation and runoff projection comparison","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Given these sensitivities to climate variability and change, a research partnership (Victorian Climate Initiative, VicCI) between Victoria’s state government (Department of Environment, Land, Water and Planning: DELWP) and Australian research organisations (Bureau of Meteorology and CSIRO) was initiated to ensure that water policies and management decisions were informed by the most up-to-date earth systems and climate change science (Hope et al., 2017). VicCI produced projections using daily empirical scaling (Potter et al., 2016). In this study we investigate QQM-BC dynamically downscaled WRF rainfall simulations for their ability to reproduce simulated runoff for observed conditions across the whole state of Victoria and also, in more detail, for 10 water supply catchments selected on the basis of their hydrological model performance in reproducing reference runoff (study area and catchments shown in Fig. 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"South-eastern Australia","kind":"region","role":"Broader regional context used for evaluating WRF model skill and rainfall variability patterns impacting study area","river_name":null,"geocode_query":"South-eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"They concluded that the R2 simulations performed best in terms of reproducing rainfall seasonal cycles, interannual and decadal variability and spatial patterns over southeast Australia, while noting biases in rainfall amounts can be substantial in some seasons and regions. Olson et al. (2016) also referred to significant WRF biases in rainfall climatology, noting that this was not surprising given WRF configuration selection was on the basis of skill for selected storm events of 2-week periods, rather than performance at the climatological scale. Given these findings, we have used WRF-BC R2 simulations over Victoria for reanalysis (NCEP/NCAR and ERAInterim for 1990–2009) and GCM historical (for 1990–2009) and future Special Report on Emissions Scenarios (SRES) A2 (for 2060–2079) forced simulations. The SRES A2 scenario describes a very heterogeneous world with high population growth and technological change that is fragmented and slow (Nakicenovic et al., 2000).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North-eastern Victorian Alps","kind":"landscape","role":"Location where a difference in rainfall change direction between WRF-BC and WRF-raw rainfall was observed in projections","river_name":null,"geocode_query":"North-eastern Victorian Alps, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"In general the WRF-BC rainfall changes (Fig. 4d) were wetter (or less dry) changes than both the WRF-BC rescaled and the ES changes (Fig. 4a, b and c). In the example of ECHAM5-forced results, the seasonal ES gave a 4.8 mm Victoria-average increase, whereas BC gave an increase of 16.7 mm, with the Hydrol. Earth Syst. Sci., 24, 2981–2997, 2020 https://doi.org/10.5194/hess-24-2981-2020","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.295645,-36.641419],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667323,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Australia, Victoria, Tawonga, ビクトリアン・アルプス","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-53580e550af295dfac3b720a-site-4","true_coordinates":[147.2956453,-36.7014186],"spread_km":6.7}],"limitations":["QR quantile-quantile mapping bias correction does not correct wet-day transition probabilities, multi-day rainfall totals, nor spatial correlation","Assumption of stationarity in bias correction transferability to future climate introduces uncertainty","Hydrological model calibration assumes stationarity of parameters under climate change","Uncertainty arising from underestimation of high-flow runoff percentiles and spatial rainfall correlation","Regional scale of study limits local-scale inference","Coordinates not provided for specific catchments or subregions in Victoria, limiting precise geolocation"],"river_ids":["43640269","43643828","43645307"],"site_ids":["doc-53580e550af295dfac3b720a-site-0","doc-53580e550af295dfac3b720a-site-1","doc-53580e550af295dfac3b720a-site-4"],"anchor":[147.295645,-36.761419],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-5626729080764586cf0dc37a","title":"Multiple ecological processes underpin the eruptive dynamics of small mammals: House mice in a semi‐arid agricultural environment","display_title":"Ecological Drivers of House Mouse Eruptions in Victoria","doi":"https://doi.org/10.1002/ece3.6145","theme":"Land & ecosystems","color":"#c8df98","year":2020,"publication_date":"2020-03-05","authors":["Peter Brown","Anthony D. Arthur","Dean A. Jones","Micah Davies","D. Grice","Roger P. Pech"],"summary":"This study experimentally examined the roles of supplemental food, water, and cover on abundance, breeding, and survival of feral house mice populations in a semi-arid agricultural landscape in northwestern Victoria, Australia. Implemented over 11 months beginning in winter 2004, treatments were applied to 240 m fence-line plots adjacent to wheat/barley crops and pasture to assess their effects on mouse population dynamics. While supplementary food doubled population size and improved apparent survival, and both water and cover enhanced breeding performance, these factors alone or combined were not sufficient to initiate outbreaks or plagues. The research highlights that mouse population eruptions are influenced by multiple ecological processes and that food, water, and cover are necessary but insufficient conditions for outbreaks in these variable environments.","finding":"Supplementary food doubled mouse population sizes and improved survival; water and cover improved breeding performance, but combined resource availability was insufficient to trigger population outbreaks during the study period.","finding_evidence":{"passage_id":"p009-002","page":9,"quote":"We did see a twofold increase in population density late in the experiment on sites where additional food was provided, which was slightly higher than the 1.5- fold increase in population densities found by Prevedello et al. (2013) from a global meta-analysis of food addition experiments. While we did not generate an outbreak, some of the responses to our treatments were consistent with demographic patterns associated with outbreaks which included treatment effects on both reproduction and apparent survival.","verified_in_page":true},"topics":["small mammal population dynamics","resource supplementation","breeding performance","apparent survival","food limitation","predation effects","semi-arid agriculture","population outbreak dynamics"],"study_period":"July 2004 to June 2005","locations":[{"name":"Walpeup, Victoria","kind":"field_site","role":"experimental study location with treatment plots on agricultural farm","river_name":null,"geocode_query":"Walpeup, Victoria, Australia","latitude":-35.1333,"longitude":142.0333,"evidence":{"passage_id":"p003-000","page":3,"quote":"| BROWN et al. 3479 population size, then the appropriate combination of treatments would result in higher population sizes being reached. 3. If the absence of high-quality food or water or high predation pressure, either alone or in combination was limiting apparent survival of mice, then the appropriate addition of these factors would result in higher apparent survival rates. 2 | METHODS 2.1 | Study site and experimental design The study was conducted on a grain and sheep property 3 km west of the Mallee Research Station at Walpeup, Victoria, Australia (35°08′S, 142°02′E) between winter (July) 2004 and winter (June) 2005. The area has an average yearly rainfall of 335 mm. More rain falls in winter when rainfall is less variable (May–August, mean = 130 mm CV = 39%) than in summer (January–April, mean = 89 mm CV = 63%). The other four months (September–December) have on average 118 mm falls with a CV of 50%.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.02351,-35.1383453],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Walpeup VIC 3507, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-5626729080764586cf0dc37a-site-0"},{"name":"Mallee Research Station near Walpeup, Victoria","kind":"facility","role":"source of rainfall data and regional climatic context for study","river_name":null,"geocode_query":"Mallee Research Station, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Rainfall records for during and preceding the study were obtained from the Mallee Research Station and used to predict the likely background numbers of mice based on current models (Kenney et al., 2003). The farm was approximately 2,000 ha in size, and each year about 50% of the fields are planted with wheat or barley. The remaining 50% of the fields are pastures grazed by sheep. Typical pasture consists of the wheat and barley stubble remaining from the previous year's harvest. Plots (experimental units) were 240 m sections of internal boundary fences with winter cereal on one side (barley or wheat) and pasture (normally grazed) on the other (Figure 2). Twelve of our 16 plots were the same as those used in Brown et al., (2008), but four plots had to be moved to maintain cereal crop on one side and pasture on the other, because of crop rotation at our study site.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Coordinates were provided only for the general study location; precise plot coordinates were unavailable","Rainfall and weather variability complicate isolating treatment effects","Limited sample sizes may have constrained detection of time-varying effects on survival and breeding","Study focused on one regional agricultural area and results may not generalize across all mouse plague regions"],"river_ids":[],"site_ids":["doc-5626729080764586cf0dc37a-site-0"],"anchor":[142.02351,-35.1383453],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-6e6d84604c788dadd47499bc","title":"Early twenty-first century satellite-driven irrigation performance in the world’s largest system: Pakistan’s Indus Basin irrigated system","display_title":"Satellite-driven Irrigation Performance in Indus Basin","doi":"https://doi.org/10.1088/1748-9326/abd19f","theme":"Water management","color":"#eeabac","year":2020,"publication_date":"2020-12-09","authors":["Jorge L. Peña‐Arancibia","Mobin‐ud‐Din Ahmad"],"summary":"This study evaluates irrigation performance in Pakistan's Indus Basin irrigated system (IBIS), the world's largest, covering approximately 160,000 km². Using 19 years of satellite-driven actual evapotranspiration (ETa) data (2000–2018), the study assesses inter-seasonal variability and equity in 46 canal commands during wet (Kharif) and dry (Rabi) seasons. Verified ETa estimates serve as surrogates for water use to analyze water use equity and variability within and between canal commands, revealing that canal commands supplemented with groundwater generally show higher equity and lower variability than those reliant solely on canal surface water, with inequities more prevalent especially towards the system-end.","finding":"Canal commands with access to groundwater in Punjab exhibit more equitable and less variable irrigation water use, whereas canal commands dependent mainly on surface water supplies, especially at the system-end, show higher variability and inequity between head-end and tail-end irrigated areas.","finding_evidence":{"passage_id":"p010-002","page":10,"quote":"The assessment conducted per irrigation season (wet ‘Kharif’ and dry ‘Rabi’) in the 46 IBIS canal commands areas (∼160 000 km2 ) from 2000 to 2018, shows clear patterns: (a) canal commands that supplement canal water supply with groundwater are reasonably of low variability and equitable (mostly in the Punjab province); (b) canal commands in most MZs that rely mostly on canal water supply have low variability over the years, and show intra-canal command inequities; (c) canal commands at the system-end are outstanding in that they can have high variability and also show variable intra-canal command inequities. While good quality groundwater can make up for shortfalls in canal water supply in some canal commands and improve equity, its use over time can become unsustainable and hamper irrigation management goals.","verified_in_page":true},"topics":["satellite remote sensing","actual evapotranspiration","irrigation performance","water use equity","canal irrigation systems","groundwater use","seasonal variability","water management"],"study_period":"2000-2018","locations":[{"name":"Indus Basin irrigated system (IBIS)","kind":"basin","role":"main study area","river_name":null,"geocode_query":"Indus Basin Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"Environ. Res. Lett. 16 (2021) 014037 https://doi.org/10.1088/1748-9326/abd19f OPEN ACCESS RECEIVED 27 August 2020 REVISED 17 November 2020 ACCEPTED FOR PUBLICATION 8 December 2020 PUBLISHED 29 December 2020 Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.524222,29.934518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-0","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Upper Chenab Canal-Internal (UCC-INT) canal command","kind":"region","role":"study canal command with groundwater access showing low variability and high equity","river_name":null,"geocode_query":"Upper Chenab Canal-Internal, Punjab, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The first canal command assessed, the Upper Chenab Canal-Internal (UCCINT) canal command (area 3 in figure 1), is in the upper IBIS, precipitation is ∼520 mm yr−1 , surface water irrigation deficits are normally met by pumping groundwater (Ahmad et al 2009, 2014b). Rice and wheat are the major crops in the Kharif and Rabi seasons, respectively. The PDF of Kharif ETa shows that generally ETa > 400 mm season−1 , with most pixels having an ETa > 500 mm season−1 . The intra-canal command CV is low at 8.95%. The violin plot shows that UCC-INT can be classified as a reasonably equitable canal command, if both surface and groundwater are considered. Variability can be explained by the temporal changes reported in the time-series of Kharif ETa violin-plots. If the shape and magnitudes are relatively maintained through time, then UCC-INT can be classified as having low seasonal variability.","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.39390266110057,32.51527944315217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Upper Chenab Canal, Pakistan","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-1"},{"name":"Abbasia canal command","kind":"region","role":"study canal command with poor groundwater quality showing higher variability and inequity","river_name":null,"geocode_query":"Abbasia canal command, Sindh, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"The second canal command assessed, the Abbasia canal command (area 30 in figure 1), is in the middle IBIS, precipitation is ∼189 mm yr−1 , there is little groundwater pumping to supplement surface water supplies due to marginal to poor groundwater quality (Ahmad et al 2014b). Cotton and wheat are the major crops in the Kharif and Rabi seasons, respectively. The PDF of Kharif ETa shows reasonably high ETa variability. Very low ETa (<100 mm season−1 ) can be attributed to the desert areas to the south of the canal command. However, tail-end areas covered by the irrigation network also show low (<300 mm season−1 ) ETa. Most pixels have ETa > 400 mm season−1 . The intra-canal command CV is high at 49.59%. Although some of the CV can be explained by the desert areas, the Abbasia canal command can be classified as reasonably inequitable.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Pinyari canal command","kind":"region","role":"study system-end canal command with poor groundwater quality showing high variability and inequity","river_name":null,"geocode_query":"Pinyari canal command, Sindh, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"The third canal command assessed, the Pinyari canal command (area 46 in figure 1), is at the IBIS system-end, precipitation is ∼154 mm yr−1 , groundwater is of poor quality and therefore there is little or no pumping to supplement surface water supplies (Ahmad et al 2014b). Rice and wheat are the major crops in the Kharif and Rabi seasons, respectively. The PDF of Kharif ETa shows high ETa variability. Very low ETa (<100 mm season−1 ) can be attributed to the desert areas south of the canal command. However, many of the tail-end areas covered by the irrigation network show low (<300 mm season−1 ) ETa. ETa is highly variable, and in most pixels, it generally ranges from 200–450 mm season−1 , low when compared to the other two canal commands. The intra-canal command CV is high at 45.18%.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.1769444,24.6897222],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pinjari Canal, Pakistan","basin_id":4040033430,"main_basin_id":4040033430,"basin_region":"Central and South-East Asia","basin_area_km2":400310.7,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-3"},{"name":"Mangla Dam","kind":"facility","role":"water infrastructure storing ~36% of IBIS inflows, regulating system water supplies","river_name":"Jhelum River","geocode_query":"Mangla Dam, Azad Kashmir, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.736018,33.276738],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mangla Dam Lake, Pakistan","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-4","true_coordinates":[73.7360175,33.2167383],"spread_km":6.7},{"name":"Tarbela Dam","kind":"facility","role":"water infrastructure storing ~12% of IBIS inflows, regulating system water supplies","river_name":"Indus River","geocode_query":"Tarbela Dam, Khyber Pakhtunkhwa, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[72.698952,34.147543],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pakistan","basin_id":4040648320,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":69087.2,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-5","true_coordinates":[72.6989522,34.0875432],"spread_km":6.7},{"name":"Punjab province","kind":"region","role":"region with canal commands supplemented by groundwater, showing more equity and stability in irrigation","river_name":null,"geocode_query":"Punjab, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"The assessment conducted per irrigation season (wet ‘Kharif’ and dry ‘Rabi’) in the 46 IBIS canal commands areas (∼160 000 km2 ) from 2000 to 2018, shows clear patterns: (a) canal commands that supplement canal water supply with groundwater are reasonably of low variability and equitable (mostly in the Punjab province); (b) canal commands in most MZs that rely mostly on canal water supply have low variability over the years, and show intra-canal command inequities; (c) canal commands at the system-end are outstanding in that they can have high variability and also show variable intra-canal command inequities. While good quality groundwater can make up for shortfalls in canal water supply in some canal commands and improve equity, its use over time can become unsustainable and hamper irrigation management goals.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sindh province","kind":"region","role":"region with canal commands relying mostly on surface water, showing higher inequity and variability, especially system-end canals","river_name":null,"geocode_query":"Sindh, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"The assessment conducted per irrigation season (wet ‘Kharif’ and dry ‘Rabi’) in the 46 IBIS canal commands areas (∼160 000 km2 ) from 2000 to 2018, shows clear patterns: (a) canal commands that supplement canal water supply with groundwater are reasonably of low variability and equitable (mostly in the Punjab province); (b) canal commands in most MZs that rely mostly on canal water supply have low variability over the years, and show intra-canal command inequities; (c) canal commands at the system-end are outstanding in that they can have high variability and also show variable intra-canal command inequities. While good quality groundwater can make up for shortfalls in canal water supply in some canal commands and improve equity, its use over time can become unsustainable and hamper irrigation management goals.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Chenab River","kind":"river","role":"major river providing inflow to the IBIS","river_name":"Chenab","geocode_query":"Chenab River Punjab, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.807546,33.143988],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-8"},{"name":"Jhelum River","kind":"river","role":"major river providing inflow to the IBIS","river_name":"Jhelum","geocode_query":"Jhelum River Punjab, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.62558,33.290333],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-9","true_coordinates":[73.62558,33.230333],"spread_km":6.7},{"name":"Indus River","kind":"river","role":"major river providing inflow to the IBIS and head to Tarbela Dam","river_name":"Indus","geocode_query":"Indus River Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.46428,29.964518],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indus River","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-10","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Sutlej River","kind":"river","role":"river whose flows are largely diverted to India as part of the Indus Water Treaty","river_name":"Sutlej","geocode_query":"Sutlej River Punjab, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[76.687999,31.30744],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-11"},{"name":"Ravi River","kind":"river","role":"river whose flows are largely diverted to India as part of the Indus Water Treaty","river_name":"Ravi","geocode_query":"Ravi River Punjab, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"The largest irrigated system in the world, the Indus Basin irrigated system (IBIS, ∼160 000 km2 , FAO 2012) in Pakistan is arguably on this trajectory; with declining groundwater levels and salinity impacting water availability and crop productivity (Briscoe and Qamar 2005, Khan et al 2017, Kirby et al 2017 and references therein). The IBIS is the lifeline of Pakistan, serving ∼90 000 farmers and providing food and economic security for ∼207 million people (FAO 2012). The Chenab, Jhelum, Kabul and Indus rivers provide inflows (∼159.3 km3 yr−1 ) to the IBIS, whereas flows from the Sutlej Ravi and Beas rivers (∼7.8 km3 yr−1 inflow into Pakistan) are largely diverted to India as per the Indus Water Treaty (UN 1960, IRSA 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.057058,30.817328],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-6e6d84604c788dadd47499bc-site-12"}],"limitations":["MODIS pixel size (500 m) is much larger than average irrigation field causing less precise ETa accuracy at field scale.","Satellite-driven ETa methods have inherent uncertainties and rely on ground calibration methods with 10–30% accuracy.","Time lag of vegetation moisture stress in indices may overestimate ETa compared with ground models.","Climate variability is only captured to an extent given study period and data used."],"river_ids":["ne-133","ne-385","ne-472","ne-536","ne-537","ne-810"],"site_ids":["doc-6e6d84604c788dadd47499bc-site-0","doc-6e6d84604c788dadd47499bc-site-1","doc-6e6d84604c788dadd47499bc-site-3","doc-6e6d84604c788dadd47499bc-site-4","doc-6e6d84604c788dadd47499bc-site-5","doc-6e6d84604c788dadd47499bc-site-8","doc-6e6d84604c788dadd47499bc-site-9","doc-6e6d84604c788dadd47499bc-site-10","doc-6e6d84604c788dadd47499bc-site-11","doc-6e6d84604c788dadd47499bc-site-12"],"anchor":[73.46428,29.964518],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040033430,4040648320,4040794730],"main_basin_ids":[4040033430,4040033640]},{"id":"doc-7718a15b1880bbc35809aadc","title":"Profitability, energetics and GHGs emission estimation from rice-based cropping systems in the coastal saline zone of West Bengal, India","display_title":"Rice-Based Cropping Systems in Coastal West Bengal","doi":"https://doi.org/10.1371/journal.pone.0233303","theme":"Land & ecosystems","color":"#c8df98","year":2020,"publication_date":"2020-05-21","authors":["Krishnendu Ray","Piyali Sen","Rupak Goswami","Sukamal Sarkar","Koushik Brahmachari","Argha Ghosh","Manoj Kumar Nanda","Mohammed Mainuddin"],"summary":"This study evaluates thirteen rice-based cropping systems practiced in the coastal saline zone of West Bengal, India, comparing them on productivity, profitability, energy use, and greenhouse gas emissions. Data collected via a survey of 60 farms across Rangabelia and Jotirampur villages under Gosaba Block, South 24 Parganas district, reveal that rice-vegetable systems, especially rice-bitter gourd, exhibit superior system yield, economic returns, and lower yield-scaled GHG emissions compared to rice-fallow systems. The study uses Monte-Carlo simulation to assess uncertainty in emissions and highlights fertilizer, diesel, and pesticide use as major influences on GHG emissions. Findings support promoting rice-vegetable cropping sequences for sustainable intensification in this ecologically challenged coastal zone.","finding":"Rice-bitter gourd based systems in the coastal saline zone exhibited the highest system yield and benefit:cost ratio while maintaining low yield-scaled greenhouse gas emissions, indicating their suitability for sustainable intensification over other rice-based cropping systems such as rice-fallow-fallow.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Results The productivity of rice-based systems Significantly (p<0.05) highest system yield (~ 50 tha−1 year−1 ) was estimated in the case of the rice-bitter gourd system, followed by the rice-potato-ridge gourd and the rice-potatopumpkin systems (Fig 2). The rice-fallow-fallow system recorded the lowest system yield (< 5 tha−1 year−1 ), followed by the rice-lathyrus-fallow and rice-sunflower systems. Fig 2. System yield (t−1 ha) based on rice-equivalent yield of dry season crops. [R-LA-F: rice-lathyrus-fallow, R-P-F: rice-potato-fallow, R-S: rice-sunflower, R-F-F: rice-fallow-fallow, R-P-LF: rice-potato-ladies finger, R-LE-F: rice-lentilfallow, R-P-R: rice-potato-rice, R-LE-R: rice-lentil-rice, R-R: rice-rice, R-P-PU: rice-potato-pumpkin, R-PG: ricepointed gourd, R-P-RG: rice-potato-ridge gourd, R-BG: rice-bitter gourd]; means followed by same letters are statistically at par (otherwise significantly different at p<0.05) by Tukey’s Honest Significant Difference (HSD) test.","verified_in_page":true},"topics":["crop yield","greenhouse gas emissions","energy use in agriculture","sustainable intensification","cropping systems","monte-carlo simulation","economic analysis","fertilizer management"],"study_period":null,"locations":[{"name":"Rangabelia","kind":"settlement","role":"field study site for rice-based cropping systems","river_name":null,"geocode_query":"Rangabelia, Gosaba Block, South 24 Parganas, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Although several works have been done on the improvement of productivity and profitability of different systems, the present work is perhaps the first initiative to estimate the energy and GHGs emissions from the coastal area. Summarily, the objective of the study was to: a) estimate yield, profit, and energy input-output from different rice-based systems, b) evaluate the area- and yield-scaled GHGs emissions from those systems, and c) measure the uncertainty associated with the GHGs emission in the study area. Methodology Study area The experiments were located at Rangabelia and Jotirampur villages under Gosaba Block, South 24 Parganas district, in the lower Gangetic plains of West Bengal (20 ̊20’ N to 20 ̊06’ S latitude and 88 ̊20’ E to 88 ̊60’ W longitude) (Fig 1). The agro-climatic condition of this area is largely influenced by the Bay of Bengal, which is nearly 35 km away from the experimental location.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.807898,22.105227],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gosaba, West Bengal 743370, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-7718a15b1880bbc35809aadc-site-0","true_coordinates":[88.80789829999999,22.1652274],"spread_km":6.7},{"name":"Jotirampur","kind":"settlement","role":"field study site for rice-based cropping systems","river_name":null,"geocode_query":"Jotirampur, Gosaba Block, South 24 Parganas, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Although several works have been done on the improvement of productivity and profitability of different systems, the present work is perhaps the first initiative to estimate the energy and GHGs emissions from the coastal area. Summarily, the objective of the study was to: a) estimate yield, profit, and energy input-output from different rice-based systems, b) evaluate the area- and yield-scaled GHGs emissions from those systems, and c) measure the uncertainty associated with the GHGs emission in the study area. Methodology Study area The experiments were located at Rangabelia and Jotirampur villages under Gosaba Block, South 24 Parganas district, in the lower Gangetic plains of West Bengal (20 ̊20’ N to 20 ̊06’ S latitude and 88 ̊20’ E to 88 ̊60’ W longitude) (Fig 1). The agro-climatic condition of this area is largely influenced by the Bay of Bengal, which is nearly 35 km away from the experimental location.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.869515,22.146686],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gosaba, West Bengal 743370, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-7718a15b1880bbc35809aadc-site-1","true_coordinates":[88.80789829999999,22.1652274],"spread_km":6.7},{"name":"Gosaba Block, South 24 Parganas district","kind":"region","role":"administrative region containing study villages and cropping system sampling area","river_name":null,"geocode_query":"Gosaba Block, South 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Although several works have been done on the improvement of productivity and profitability of different systems, the present work is perhaps the first initiative to estimate the energy and GHGs emissions from the coastal area. Summarily, the objective of the study was to: a) estimate yield, profit, and energy input-output from different rice-based systems, b) evaluate the area- and yield-scaled GHGs emissions from those systems, and c) measure the uncertainty associated with the GHGs emission in the study area. Methodology Study area The experiments were located at Rangabelia and Jotirampur villages under Gosaba Block, South 24 Parganas district, in the lower Gangetic plains of West Bengal (20 ̊20’ N to 20 ̊06’ S latitude and 88 ̊20’ E to 88 ̊60’ W longitude) (Fig 1). The agro-climatic condition of this area is largely influenced by the Bay of Bengal, which is nearly 35 km away from the experimental location.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.84598,22.213768],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gosaba, West Bengal 743370, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-7718a15b1880bbc35809aadc-site-2","true_coordinates":[88.80789829999999,22.1652274],"spread_km":6.7},{"name":"West Bengal","kind":"region","role":"coastal saline zone study region for rice-based cropping systems","river_name":null,"geocode_query":"coastal West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Although several works have been done on the improvement of productivity and profitability of different systems, the present work is perhaps the first initiative to estimate the energy and GHGs emissions from the coastal area. Summarily, the objective of the study was to: a) estimate yield, profit, and energy input-output from different rice-based systems, b) evaluate the area- and yield-scaled GHGs emissions from those systems, and c) measure the uncertainty associated with the GHGs emission in the study area. Methodology Study area The experiments were located at Rangabelia and Jotirampur villages under Gosaba Block, South 24 Parganas district, in the lower Gangetic plains of West Bengal (20 ̊20’ N to 20 ̊06’ S latitude and 88 ̊20’ E to 88 ̊60’ W longitude) (Fig 1). The agro-climatic condition of this area is largely influenced by the Bay of Bengal, which is nearly 35 km away from the experimental location.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Coordinates of study villages not provided in text.","Study limited to 60 farms in two villages, potentially limiting geographic representativeness.","Analysis based on farmer-reported data collected by survey, possibly introducing self-report bias.","GHG emissions estimated with Cool Farm Tool software rather than direct field measurements.","No report of yield-scaled GHG normative benchmarks for regional suitability comparisons."],"river_ids":[],"site_ids":["doc-7718a15b1880bbc35809aadc-site-0","doc-7718a15b1880bbc35809aadc-site-1","doc-7718a15b1880bbc35809aadc-site-2"],"anchor":[88.869515,22.146686],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-9658de96577049e6839ed9a6","title":"The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions","display_title":"Using Seasonal Forecasts for Murrumbidgee Water Allocation","doi":"https://doi.org/10.5194/hess-24-3851-2020","theme":"Water management","color":"#eeabac","year":2020,"publication_date":"2020-07-30","authors":["Alexander Kaune","Faysal Chowdhury","Micha Werner","James Bennett"],"summary":"This study evaluates how ensemble seasonal streamflow forecasts can improve water allocation decisions in the regulated Murrumbidgee River basin, Australia. It compares forecast-informed allocations against conservative climatology-based estimates and perfect information to assess benefits to irrigation planning. A decision model emulates feedback between reservoir storage and water allocation, using inflow forecasts from two methods—ESP+ and POAMA—within the Forecast Guided Stochastic Scenarios (FoGSS) system.","finding":"Using ensemble seasonal streamflow forecasts reduces the inconsistency caused by upward allocation revisions during the irrigation season compared to conservative inflow estimates, enabling more timely and reliable water allocations particularly about 1.5 months before the cropping season begins, thus allowing farmers earlier and better planning of irrigated cropping areas.","finding_evidence":{"passage_id":"p014-004","page":14,"quote":"Although the annual inconsistency provides information on revisions of the water allocation during the water year, it does not allow for easy comparison against the perfect water allocation. Figure 12 shows the RMSD calculated over the 28 years using the difference between the water allocation established using the ensemble and that of the perfect forecast for each decision date, for both the dry and wet years as well as the forecasts using the ESP+ and POAMA datasets. The RMSDs calculated using the conservative inflow estimates are also shown. An RMSD value of zero would imply perfect allocation. For dry years, the RMSD using the monthly updated forecast ensemble shows lower differences than the reference conservative water allocation for all non-exceedance percentiles until the end of October for both ESP+ and POAMA, with the latter marginally outperforming the former.","verified_in_page":true},"topics":["seasonal streamflow forecasting","water allocation","irrigation planning","reservoir inflow prediction","forecast skill","decision support","hydrological modeling","climate impact"],"study_period":"1982–2009","locations":[{"name":"Murrumbidgee River basin","kind":"basin","role":"study basin where water allocation decisions and forecasting methods are evaluated","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River basin New South Wales Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"A complete assessment of the added value of seasonal forecasts can allow basin authorities to explore the opportunities seasonal forecasts provide to improve their operational decisions and reduce potential losses to agricultural by improving water allocation estimates. In this study, we develop and test a water allocation framework to assess the value of using seasonal forecasts of inflow into reservoirs in the regulated Murrumbidgee basin in Australia. We hypothesise that water allocation decisions can be improved when informed by skilful and reliable seasonal forecasts of reservoir inflows. The inconsistency in water allocation decisions during the water year is introduced as a measure of the value of using the forecast in the allocation process under the assumption that if water allocated to farmers changes little during the season, they can then draw maximum benefit from their allocation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.201665,-35.192873],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665563,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-9658de96577049e6839ed9a6-site-0","true_coordinates":[146.2016652340651,-35.2528729004432],"spread_km":6.7},{"name":"Burrinjuck reservoir","kind":"facility","role":"major reservoir in Murrumbidgee basin used for water allocation and inflow forecasting","river_name":"Murrumbidgee River","geocode_query":"Burrinjuck reservoir New South Wales Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"A. Kaune et al.: The benefit of using an ensemble of seasonal streamflow forecasts 3853 South Wales Office of Water announces the water allocation for different users in the basin starting on 1 July of each year based on the available water in storage, the expected reservoir inflows for the next 12 months, and water requirements downstream. The inflow to the Blowering reservoir depends on both the discharge release from an upstream hydropower system (Snowy Hydro Scheme) and the natural runoff, while the inflow to the Burrinjuck reservoir depends mainly on natural runoff. Currently, a conservative estimate of the total inflow to the storages of 2.33 × 108 m3 yr−1 (based on climatology) is used to determine the expected reservoir inflow for the next 12 months. In the Murrumbidgee basin, the water year runs from 1 July through to 30 June of the next calendar year, while the cropping season for annual (irrigated) crops is between 1 November and the end of February.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.75,-35],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670596,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Burrinjuck Reservoir, Narrangullen NSW 2582, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-9658de96577049e6839ed9a6-site-1"},{"name":"Blowering reservoir","kind":"facility","role":"major reservoir in Murrumbidgee basin used for water allocation; inflows partly from Snowy Hydro Scheme","river_name":"Tumut River","geocode_query":"Blowering reservoir New South Wales Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"A. Kaune et al.: The benefit of using an ensemble of seasonal streamflow forecasts 3853 South Wales Office of Water announces the water allocation for different users in the basin starting on 1 July of each year based on the available water in storage, the expected reservoir inflows for the next 12 months, and water requirements downstream. The inflow to the Blowering reservoir depends on both the discharge release from an upstream hydropower system (Snowy Hydro Scheme) and the natural runoff, while the inflow to the Burrinjuck reservoir depends mainly on natural runoff. Currently, a conservative estimate of the total inflow to the storages of 2.33 × 108 m3 yr−1 (based on climatology) is used to determine the expected reservoir inflow for the next 12 months. In the Murrumbidgee basin, the water year runs from 1 July through to 30 June of the next calendar year, while the cropping season for annual (irrigated) crops is between 1 November and the end of February.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.251958,-35.4866318],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43669360,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Blowering Reservoir, Blowering NSW 2720, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-9658de96577049e6839ed9a6-site-2"},{"name":"Goobarragandra River at Lacmalac gauge","kind":"station","role":"gauging station to monitor inflows used for FoGSS inflow forecasting upstream of Blowering reservoir","river_name":"Goobarragandra River","geocode_query":"Goobarragandra River at Lacmalac New South Wales Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Inflows into the Blowering reservoir from the Snowy Hydro Scheme are therefore taken as observed and are not subject to the forecast, with the observed releases from Snowy Hydro discharge obtained by back-calculating from observed outflows gauged at Oddy’s Bridge just downstream of the dam and observed storage data of the Blowering reservoir (1982–2009). Forecasts for the Goobarragandra River at Lacmalac gauge, a main tributary of the Tumut River between the Blowering reservoir and its confluence with the main Murrumbidgee, with an influence basin area of 668 km2 (Fig. 1), are considered.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.3256213,-35.3183885],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43669354,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lacmalac NSW 2720, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-9658de96577049e6839ed9a6-site-3"}],"limitations":["Exact coordinates for locations were not provided in the text, so latitude and longitude are null.","Some regional geographic descriptions (e.g. ‘Murrumbidgee basin’) are broad without precise sub-catchment resolution.","Parameter simplifications in the water allocation model include a constant allocation use reduction factor ignoring year-to-year and user variability.","Forecast skill varies seasonally and is weaker mid-water year, particularly December and later months, which limits forecasting reliability at those times."],"river_ids":["43639535","43639544","43639656","43639798","43639822","43639867","43639889","43639893","43639903","43639979","43640110","43640281","43640313","43640352","43640441","43641867","43642059","43642136","43642266","43642447","43642779","43642894","43642918","43643180","43643648","43644093","43644200","43644201","43644216","43644219","43644299","43644507","43644536","43644594","43644625","43644649","43644743","43644773","43644800","43644819","43644866","43644909","43644980","43645068","43645115","43645129","43645133","43645147","43645252","43645320","43645329","43645359","43645402","43645441","43645522","43645556","43645558","43645634","43645648","43645684","43645700","43645783","43645807","43645829","43645864","43645883","43645946","43645956","43645957","43645982","43646048","43646067","43646211","43646323","43646482","43646507","43646533","43646560","43646591","43646639","43646789","43646818","43646879","43646937","43646970","43647057","43647070","43647088","43647115","43647149","43647207","43647241","43647252","43647296","43647333","43647431","43647435","43647455","43647465","43647486","43647505","43647568","43647580","43647586","43647650","43647669","43647711","43647764","43647779","43647865","43647875","43647894","43647909","43647917","43648194","43648195","43648276","43648277","43648315","43648475","43648489","43648642","43648650","43648682","43648686","43648972","43648981","43648984","43649002","43649006","43649007","43649009","43649014","43649026","43649074","43649129","43649136","43649153","43649161","43649165","43649250","43649258","43649260","43649263","43649265","43649366","43649370","43649371","43649381","43649600","43649613","43649619","43649620","43649622","43649632","43649640","43649644","43649646","43649651","43649654","43649655","43649657","43649658","43649660","43649661","43649662","43649663","43649673","43649676","43649680","43649684","43649706","43649853","43649855","43649856","43649863","43649929","43649932","43649937","43649939","43649945","43649947","43649948","43649952","43649953","43649954","43649955","43649959","43649962","43649964","43649969","43650119","43650130","43650132","43650134","43650135","43650150","43650151","43650152","43650153","43650348","43650349","43650373","43650375","43650376","43650389","43650390","43650391","43650414","43650420","43650423","43650429","43650433","43650588","43650591","43650625","43650792","43650807","43650872","43650880","43650881","43650886","43650952","43650954","43652597","43652598","43652896","43653022","43657744","43657758","43657795","43659426","43659427","43659428","43659429","43659430"],"site_ids":["doc-9658de96577049e6839ed9a6-site-0","doc-9658de96577049e6839ed9a6-site-1","doc-9658de96577049e6839ed9a6-site-2","doc-9658de96577049e6839ed9a6-site-3"],"anchor":[148.3256213,-35.3183885],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-c0ca770273afd6a98ccd388d","title":"Krypton-85 datasets of the northern and southern hemisphere collected over the past 60 years","display_title":"Global Atmospheric Krypton-85 Monitoring","doi":"https://doi.org/10.1016/j.dib.2020.106522","theme":"Other science","color":"#d1b5e7","year":2020,"publication_date":"2020-11-13","authors":["Arne Kersting","Clemens Schlosser","Sabine Schmid","Martina Konrad","Andreas Bollhöfer","Karen Barry","Axel Suckow"],"summary":"This study compiles and documents a comprehensive dataset of atmospheric krypton-85 (85Kr) activity concentrations collected globally over the past 60 years from 11 monitoring stations in both the northern and southern hemispheres. The data support applications in tracer hydrology for dating water and ice, atmospheric circulation studies, and nuclear treaty verification.","finding":"Northern hemisphere 85Kr concentrations reach up to 6 Bq/m3 air, while southern hemisphere concentrations do not exceed 1.5 Bq/m3 air, reflecting hemispheric differences in nuclear reprocessing emissions.","finding_evidence":{"passage_id":"p003-003","page":3,"quote":"The 85Kr activity concentrations in the Freiburg, Schauinsland and Jungfraujoch dataset reach up to 6 Bq/m3 air, while the southern hemispheric data do not exceed 1.5 Bq/m3 air.","verified_in_page":true},"topics":["atmospheric monitoring","radioisotope dating","global tracer datasets","noble gas radionuclides","environmental radioactivity","atmospheric circulation","nuclear emissions","radiometric analysis"],"study_period":"1959-2020","locations":[{"name":"Adelaide","kind":"station","role":"Monitoring station for atmospheric 85Kr in southern hemisphere","river_name":null,"geocode_query":"Adelaide, South Australia, Australia","latitude":-34.9667,"longitude":138.6333,"evidence":{"passage_id":"p002-001","page":2,"quote":"Data source location Primary data sources: Adelaide: Institution: CSIRO City/Town/Region: Adelaide Country: South Australia Lat/Lon of monitoring station: 34°58’ S 138°38’ E Antarctica: Institution: Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Georg von Neumayer Station Country: Antarctica Lat/Lon of monitoring station: 70°40’ S 08°16’ W Cape Grim: Institution: CSIRO & Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Tasmania Country: Australia Lat/Lon of monitoring station: 40°41’ S 144°41’ E Cape Point: Institution: Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Cape of Good Hope Country: South Africa Lat/Lon of monitoring station: 34°32’ S 18°29’ E Darwin: Institution: Supervising Scientist Division, Environment Australia City/Town/Region: Darwin Country: Australia Lat/Lon of monitoring station: 12°28’ S 130°50’ E Early Measurements: Institutions: Physics Department, University Heidelberg, Germany Institute of Environmental Physics, University Heidelberg, Germany Planck Institute for Nuclear Physics, Heidelberg, Germany Max Planck Institute for Nuclear Physics, Freiburg-Schauinsland Branch, Germany Institute for Atmospheric Radioactivity (IAR), Federal Office of Civil Defence, Freiburg, Germany Commissariat a l’Energie Atomique, Department de la Protection Sanitaire, Fontenay-aux-Roses, France Air Resources Laboratories, National Oceanic and Atmospheric Administration, Silver Spring, MD 20910, U.S.A Freiburg: Institution: Bundesamt für Strahlenschutz City/Town/Region: Freiburg Country: Germany Lat/Lon of monitoring station: 48°00’ N 07°51’ E Jungfraujoch: Institution: Bundesamt für Strahlenschutz City/Town/Region: Berner Alps (continued on next page)","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.657962,-34.965908],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-c0ca770273afd6a98ccd388d-site-0","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"Georg von Neumayer Station","kind":"station","role":"Antarctic monitoring station for atmospheric 85Kr in southern hemisphere","river_name":null,"geocode_query":"Georg von Neumayer Station, Antarctica","latitude":-70.6667,"longitude":-8.2667,"evidence":{"passage_id":"p002-001","page":2,"quote":"Data source location Primary data sources: Adelaide: Institution: CSIRO City/Town/Region: Adelaide Country: South Australia Lat/Lon of monitoring station: 34°58’ S 138°38’ E Antarctica: Institution: Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Georg von Neumayer Station Country: Antarctica Lat/Lon of monitoring station: 70°40’ S 08°16’ W Cape Grim: Institution: CSIRO & Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Tasmania Country: Australia Lat/Lon of monitoring station: 40°41’ S 144°41’ E Cape Point: Institution: Institute of Environmental Physics, University Heidelberg, Germany City/Town/Region: Cape of Good Hope Country: South Africa Lat/Lon of monitoring station: 34°32’ S 18°29’ E Darwin: Institution: Supervising Scientist Division, Environment Australia City/Town/Region: Darwin Country: Australia Lat/Lon of monitoring station: 12°28’ S 130°50’ E Early Measurements: Institutions: Physics Department, University Heidelberg, Germany Institute of Environmental Physics, 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broader spatial variability may not be fully captured."],"river_ids":[],"site_ids":["doc-c0ca770273afd6a98ccd388d-site-0","doc-c0ca770273afd6a98ccd388d-site-1","doc-c0ca770273afd6a98ccd388d-site-2","doc-c0ca770273afd6a98ccd388d-site-3","doc-c0ca770273afd6a98ccd388d-site-4","doc-c0ca770273afd6a98ccd388d-site-5","doc-c0ca770273afd6a98ccd388d-site-6","doc-c0ca770273afd6a98ccd388d-site-7","doc-c0ca770273afd6a98ccd388d-site-8","doc-c0ca770273afd6a98ccd388d-site-9"],"anchor":[140.0,-66.6667],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[1040015030,2040023010,5040061670,5040073420],"main_basin_ids":[1040015030,2040023010,5040061670,5040073420]},{"id":"doc-c1093f0077940cb999b8c0f1","title":"Mapping Platform Urbanism: Charting the Nuance of the Platform Pivot","display_title":"Platform Urbanism and Urban Planning","doi":"https://doi.org/10.17645/up.v5i1.2545","theme":"Other science","color":"#d1b5e7","year":2020,"publication_date":"2020-03-13","authors":["Ashlin Lee","Adrian Mackenzie","Gavin J. D. Smith","Paul Box"],"summary":"This study analyses the concept of platform urbanism, emerging from the evolution of smart cities and datafication of urban environments. Using a landscape scan of approximately 100 urban data projects worldwide, the authors explore the dynamics of platforms in shaping urban socio-technical practices, focusing on vertical and horizontal integration of data sources, platform intermediations, and implications for social inclusion and urban planning. They discuss concerns over corporate lock-in effects, platform fragility, and the incoherent, plural nature of platform urbanism, emphasizing the need for urban planners to engage with the complexity and diversity of urban data platforms to promote social justice and community value.","finding":"The study finds no unifying narrative across urban data projects but supports the notion of a 'platform pivot' where urban data practices are increasingly organized around platform technologies that intermediate urban life, creating both opportunities for innovation and risks of corporate lock-in and social exclusion.","finding_evidence":{"passage_id":"p006-004","page":6,"quote":"As sociologist Howard Becker writes, ‘phenomena seldom have all the attributes required for them to be, unambiguously, members of a class defined by multiple criteria’ (Becker, 2008, p. 177). 4. ‘The Pivot’: From Smart Cities to Platform Urbanism The landscape scan of smart cities shows no overall trend in the tabulated data, despite our efforts to find one. This null finding is not without interest. The collection of activities subsumed by the smart city, and the shifting composition of these activities, can be understood as an ongoing ‘pivot’—a change of plans triggered by altered circumstances. This is first identified by Barns (2019), who suggests smart cities have pivoted using platform technologies, into what is previously described as platform urbanism. While identifying this general trend, the impacts and qualities of this pivot are unclear (Barns, 2019). Our results support Barns’ (2019) theoretical observation and contribute towards elucidating the qualities of this pivot.","verified_in_page":true},"topics":["platform urbanism","urban data","vertical integration","horizontal integration","urban planning","data markets","social inclusion","digital infrastructures"],"study_period":null,"locations":[{"name":"Kansas City, Missouri, USA","kind":"settlement","role":"Example of specific city with smart city projects","river_name":null,"geocode_query":"Kansas City, Missouri, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"We observed a pivot to platform urbanism in the dynamics of vertical and horizontal integration processes (defined in the following sections), and the amalgamation over time of urban data practice into the core-periphery relationships typical of platform configurations. Patterns of differential access and the lock-in/lock-out issue were also observed. But this is not a well-structured architectural transformation. Notwithstanding the many visions of integration we encountered, the landscape scan points to plurality, overlaps, provisional compromises and temporary fixes, and a constantly receding horizon of promise. The amorphous character of the smart city is illustrated by geographical distribution of examples in the dataset (Table 3). Several spikes appear in the geography of the landscape scan.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-94.5785667,39.0997265],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kansas City, MO, USA","basin_id":7040569640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":1381472.0,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-0"},{"name":"Darwin, Northern Territory, Australia","kind":"settlement","role":"Example of specific city with smart city projects","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"We observed a pivot to platform urbanism in the dynamics of vertical and horizontal integration processes (defined in the following sections), and the amalgamation over time of urban data practice into the core-periphery relationships typical of platform configurations. Patterns of differential access and the lock-in/lock-out issue were also observed. But this is not a well-structured architectural transformation. Notwithstanding the many visions of integration we encountered, the landscape scan points to plurality, overlaps, provisional compromises and temporary fixes, and a constantly receding horizon of promise. The amorphous character of the smart city is illustrated by geographical distribution of examples in the dataset (Table 3). Several spikes appear in the geography of the landscape scan.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.890884,-12.424442],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-c1093f0077940cb999b8c0f1-site-1","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"New York City, New York, USA","kind":"settlement","role":"Example of specific city with smart city projects","river_name":null,"geocode_query":"New York City, New York, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"We observed a pivot to platform urbanism in the dynamics of vertical and horizontal integration processes (defined in the following sections), and the amalgamation over time of urban data practice into the core-periphery relationships typical of platform configurations. Patterns of differential access and the lock-in/lock-out issue were also observed. But this is not a well-structured architectural transformation. Notwithstanding the many visions of integration we encountered, the landscape scan points to plurality, overlaps, provisional compromises and temporary fixes, and a constantly receding horizon of promise. The amorphous character of the smart city is illustrated by geographical distribution of examples in the dataset (Table 3). Several spikes appear in the geography of the landscape scan.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-74.0059728,40.7127753],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"New York, NY, USA","basin_id":7040039630,"main_basin_id":7040039630,"basin_region":"North America and Caribbean","basin_area_km2":8013.7,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-2"},{"name":"Sydney, New South Wales, Australia","kind":"settlement","role":"Example of specific city with smart city projects","river_name":null,"geocode_query":"Sydney, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"We observed a pivot to platform urbanism in the dynamics of vertical and horizontal integration processes (defined in the following sections), and the amalgamation over time of urban data practice into the core-periphery relationships typical of platform configurations. Patterns of differential access and the lock-in/lock-out issue were also observed. But this is not a well-structured architectural transformation. Notwithstanding the many visions of integration we encountered, the landscape scan points to plurality, overlaps, provisional compromises and temporary fixes, and a constantly receding horizon of promise. The amorphous character of the smart city is illustrated by geographical distribution of examples in the dataset (Table 3). Several spikes appear in the geography of the landscape scan.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.165213,-33.813709],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c1093f0077940cb999b8c0f1-site-3","true_coordinates":[151.207684,-33.8622503],"spread_km":6.7},{"name":"Toronto, Ontario, Canada","kind":"settlement","role":"Case study of platform urbanism with Sidewalk Labs project","river_name":null,"geocode_query":"Toronto, Ontario, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"For infrastructuralised platforms, this includes responsibilities for providing longterm, standardised, scaled, and fundable services to large bodies of users, while also existing as an architecturally dispersed and capital-driven enterprise. While not exploring smart cities directly, Plantin et al.’s (2018) observation is evident in projects like Sidewalk Toronto, where Alphabet-owned Sidewalk Labs will provide the infrastructure for this urban space through the strategic deployment of its own private and for-profit technology, generating concerns around privacy, security, and in/equitability (Cecco, 2019). As these architectures become more prevalent, and integrate more sources of data and human activity into their enclosures, critical questions must be asked to address platform urbanism and the cultures of capture, programmability, modulation, and value-generation its inTable 1. Infrastructure and platform properties.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-79.388447,43.6548253],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Toronto, ON, Canada","basin_id":7040402430,"main_basin_id":7040034520,"basin_region":"North America and Caribbean","basin_area_km2":263811.2,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-4"},{"name":"Barcelona, Catalonia, Spain","kind":"settlement","role":"Example city affected by platform urbanism impact (Airbnb)","river_name":null,"geocode_query":"Barcelona, Catalonia, Spain","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The rise of platform economies and capitalism is both a financial and ideological pursuit; it is driven by the technical architecture of platforms and the value of data accumulation, but also spearheaded through sociotechnical practices such as digital ‘disruption.’ Urban services like Uber, Airbnb, and Airtasker are all examples of this, disrupting existing service models in favour of platforms that decentralise service providers, but connect users and providers via platforms. While economically profitable for some, these transformations also present significant social implications, including insecure work and labour conditions (Pasquale, 2016). For example, in Barcelona, the arrival of Airbnb is viewed as damaging to the culture of the city, driving up rents and displacing young citizens from opportunities (Lambea Llop, 2017).","verified_in_page":true},"resolution_status":"resolved","coordinates":[2.1686496,41.3874374],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barcelona, Spain","basin_id":2040016230,"main_basin_id":2040016230,"basin_region":"Europe and Middle East","basin_area_km2":310600.1,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-5"},{"name":"Amsterdam, North Holland, Netherlands","kind":"settlement","role":"Site of citizen-led platform urbanism example (Amsterdam Smart Citizens Lab)","river_name":null,"geocode_query":"Amsterdam, North Holland, Netherlands","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-006","page":9,"quote":"The diversity of artefacts, contexts, data sources, and practices identified suggests that the quality of intermediation a platform provides is more variable than suggested by private sector vendors. For example, projects such as the Common Sense program (Aoki et al., 2008) the Amsterdam Smart Citizens Lab (Jiang et al., 2016), and the Connected Sustainable Cities project (Gabrys, 2014), are examples of citizens using environmental sensors that are vertically integrated with a platform to create new kinds of social value, including contributing to better environmental outcomes and new practices of community informed urban governance. Many smart city vendors are private sector entities, whose vision of private profit contributes to the lock-in/lock-out characUrban Planning, 2020, Volume 5, Issue 1, Pages 116–128 124","verified_in_page":true},"resolution_status":"resolved","coordinates":[4.9041389,52.3675734],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amsterdam, Netherlands","basin_id":2040023020,"main_basin_id":2040023020,"basin_region":"Europe and Middle East","basin_area_km2":90031.6,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-6"},{"name":"Chicago, Illinois, USA","kind":"settlement","role":"Example of urban data repository used for social justice (Chicago Data Collaborative)","river_name":null,"geocode_query":"Chicago, Illinois, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"sis functionality. Third parties, therefore, decontextualize the data away from its original context, creating different kinds of products and services as a result. This process is not inherently exploitative; for instance, the Chicago Data Collaborative is a repository of criminal justice data, adopting a cooperative model amongst members to store data for social justice causes. Platforms are initiatives consistent with an earlier definition of platforms and their variable core/peripheral relationships. Large smart cities projects, such as Sidewalk Labs Toronto, adhere to this definition, providing a full socio-technical architecture that captures, stores, and analyses data, creating value for individuals and consumers. Platforms are more sophisticated multistakeholder or multisided entities, with the ability to collate, aggregate, integrate, and use multiple streams of data simultaneously.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-87.6323879,41.88325],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Chicago, IL, USA","basin_id":7040392650,"main_basin_id":7040034520,"basin_region":"North America and Caribbean","basin_area_km2":184279.7,"hydrology_source":"hydrobasins","site_id":"doc-c1093f0077940cb999b8c0f1-site-7"}],"limitations":["Coordinates not provided for specific locations; latitudes and longitudes set to null","Study datasets derived from public online materials and snowball sampling with possible biases","Limited depth and context of examples, no ethnography or discourse analysis included","Broad urban and global scale without precise site-level geographic data"],"river_ids":[],"site_ids":["doc-c1093f0077940cb999b8c0f1-site-0","doc-c1093f0077940cb999b8c0f1-site-1","doc-c1093f0077940cb999b8c0f1-site-2","doc-c1093f0077940cb999b8c0f1-site-3","doc-c1093f0077940cb999b8c0f1-site-4","doc-c1093f0077940cb999b8c0f1-site-5","doc-c1093f0077940cb999b8c0f1-site-6","doc-c1093f0077940cb999b8c0f1-site-7"],"anchor":[2.1686496,41.3874374],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040016230,2040023020,5040061670,5040070710,7040039630,7040392650,7040402430,7040569640],"main_basin_ids":[2040016230,2040023020,5040061670,5040070710,7040034520,7040039630,7040047060]},{"id":"doc-c13ed1df4930c15368f29f68","title":"Systematic hydrogeological conceptual model testing using remote sensing and geophysical data","display_title":"EGU 2019 General Assembly Feedback Survey","doi":"https://doi.org/10.5194/egusphere-egu2020-11688","theme":"Other science","color":"#d1b5e7","year":2020,"publication_date":"2020-03-23","authors":["Trine Enemark","Luk Peeters","Dirk Mallants","Okke Batelaan"],"summary":"This study reports on the feedback survey results from participants of the European Geosciences Union (EGU) 2019 General Assembly held in Vienna, Austria. The survey collected responses on participant demographics, attendance, travel, roles, session participation, use of social media and newsletters, and overall experience during the event held from 7–12 April 2019.","finding":"The vast majority of survey respondents (96.3%) attended the EGU 2019 General Assembly in Vienna, Austria, reflecting high participation among respondents.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"Did you attend the EGU 2019 General Assembly? If you were an author of an abstract that was accepted but not presented, what was the reason? Yes 96,3% No 3,7% Multiple answers allowed 8,5% 36,2% 23,4% 8,5% 12,8% 34,0% Visa issues Illness or last-minute personal issues Funding problems Never planned on attending Lack of time to prepare the presentation Other","verified_in_page":true},"topics":["scientific meetings","survey","participant demographics","conference attendance","science communication","event evaluation","geoscience community","science outreach"],"study_period":"7–12 April 2019","locations":[{"name":"Vienna","kind":"settlement","role":"location of the EGU 2019 General Assembly meeting venue and main study event","river_name":null,"geocode_query":"Vienna, Austria","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"EGU 2019 General Assembly Feedback Survey Results Meetings | Publications | Outreach | www.egu.eu EGU 2019, Vienna, Austria| 7–12 April 2019","verified_in_page":true},"resolution_status":"resolved","coordinates":[16.371309,48.14807],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Vienna, Austria","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-c13ed1df4930c15368f29f68-site-0","true_coordinates":[16.3713095,48.20806959999999],"spread_km":6.7},{"name":"Vienna","kind":"settlement","role":"travel destination for participants attending the EGU 2019 General Assembly","river_name":null,"geocode_query":"Vienna, Austria","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"How did you travel to Vienna? Which days did you attend the EGU General Assembly 2019? 27% 2% 65% 3% 2% 1% Train Bus Airplane Car I live in/near Vienna Other 86,8% 90,5% 91,4% 86,3% 75,5% Monday Tuesday Wednesday Thursday Friday","verified_in_page":true},"resolution_status":"resolved","coordinates":[16.461342,48.20807],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Vienna, Austria","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-c13ed1df4930c15368f29f68-site-1","true_coordinates":[16.3713095,48.20806959999999],"spread_km":6.7}],"limitations":["No geographic coordinates provided for Vienna; location inferred from event name and text","Data pertains only to event attendees and survey respondents; may not represent entire EGU participant population"],"river_ids":[],"site_ids":["doc-c13ed1df4930c15368f29f68-site-0","doc-c13ed1df4930c15368f29f68-site-1"],"anchor":[16.371309,48.14807],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-10","basin_ids":[2040539930],"main_basin_ids":[2040008490]},{"id":"doc-c673374feb27879bc4605b9b","title":"Geochemical factors affecting the solubility of copper in seawater","display_title":"Copper Solubility in Seawater Dynamics","doi":"https://doi.org/10.1071/en20133","theme":"Water management","color":"#eeabac","year":2020,"publication_date":"2020-11-26","authors":["Brad M. Angel","Simon C. Apte","Graeme E. Batley","Mark Raven"],"summary":"This study experimentally investigates copper solubility in seawater over concentrations from 0.5 to 20 mg/L and time scales up to 28 days at pH 8.15 and 22°C. It examines copper speciation between dissolved and particulate phases, precipitation kinetics, and transformations in precipitate mineralogy including amorphous copper hydroxycarbonate and dicopper trihydroxide chloride polymorphs evolving into clinoatacamite. Results reveal metastable equilibria with elevated dissolved copper concentrations persisting for days to weeks depending on total copper concentration, followed by steep declines corresponding to mineral maturation to a less soluble form. The study highlights implications for toxicity testing where organisms may experience dynamic exposure to dissolved and particulate copper forms.","finding":"In natural seawater collected from Cronulla, NSW, Australia, at total copper concentrations above 1 mg/L, rapid precipitation occurs followed by metastable dissolved copper concentrations typically ranging from 0.6 to 0.9 mg/L that persist for days to weeks before a sharp decline coinciding with transformation of precipitates into clinoatacamite.","finding_evidence":{"passage_id":"p008-003","page":8,"quote":"At 0.5 mg L\u00011 total copper, dissolved copper concentrations remained stable over 28 days and no precipitates were detected, i.e. a metastable state persisted for the 28 days. The duration over which the 0.5 mg L\u00011 copper treatments will remain metastable is unknown, but at some stage heterogeneous nucleation (with solution colloids, etc.) will result in precipitation. However, at higher total copper concentrations (2–20 mg L\u00011 ), initial rapid precipitation occurred during the first hour, which was followed by the establishment of metastable equilibria that persisted for periods of several days. The concentration of dissolved copper reached as high as 1250 mgL\u00011 in the first few hours after spiking, before decreasing owing to exceedance of the metastable equilibrium concentration range. The metastable equilibrium solubility concentrations and durations were influenced by the total copper concentration and were typically in the range 0.6 to 0.9 mg L\u00011 .","verified_in_page":true},"topics":["copper solubility","seawater chemistry","metal precipitation","metal toxicity","laboratory toxicity testing","mineral transformation","metastable equilibrium","marine ecotoxicology"],"study_period":null,"locations":[{"name":"Cronulla, New South Wales, Australia","kind":"station","role":"seawater collection site for solubility experiments","river_name":null,"geocode_query":"Cronulla, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Seawater used for solubility tests and calibration standards was collected from Cronulla, NSW, Australia (35%, pH 8.15, dissolved organic carbon 1 mg L\u00011 ), filtered through 0.45-mm membrane filters (Millipore HA mixed-cellulose esters) and stored refrigerated (2–4 8C) in the dark for a maximum of 3 months before use. Artificial seawater was prepared according to the recipe of Kester et al. (1967). Major cation concentrations were checked using inductively coupled plasma atomic emission spectrometry (ICPAES, Varian 730 ES) to confirm the solution composition. Metal, chloride and sulfur concentrations were determined by ICPAES using matrix-matched standards prepared by the addition of MES-04-1 multielement standard (Accustandard, New Haven, CT, USA) to filtered seawater and preserved with 0.2 % nitric acid (Tracepur) as described by Angel et al. (2016a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.1522679,-34.0512081],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054830,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cronulla NSW 2230, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c673374feb27879bc4605b9b-site-0"},{"name":"Tasman Sea near south-west Pacific Ocean","kind":"region","role":"source region indicative of natural seawater bicarbonate chemistry influencing copper mineral formation","river_name":null,"geocode_query":"Tasman Sea, South-West Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Previous studies that have made theoretical predictions of copper precipitation in seawater at ,pH 8 have shown there is competition between the formation of malachite and atacamite (clinoatacamite polymorph) that is dependent on the concentration of carbonate (Bianchi and Longhi 1973; North and McLeod 1987). The seawater used in the current study was collected from the Tasman Sea near the extreme south-western Pacific Ocean and contains ,2.22 \u0004 10\u00013 mol kg\u00011 of bicarbonate. Bianchi and Longhi (1973) predicted that atacamite formation is favoured at bicarbonate concentrations ,2.31 \u0004 10\u00013 mol kg\u00011 , supporting the results of the current study where the atacamite polymorph clinoatacamite was detected by XRD rather than malachite. The mineral clinoatacamite is a polymorph of botallackite (monoclinic) and atacamite (orthorhombic) and was only discovered in the mid-1990s (Grice et al. 1996; Jambor et al. 1996; Krivovichev et al. 2017).","verified_in_page":true},"resolution_status":"resolved","coordinates":[160.4312897,-40.8580795],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tasman Sea","hydrology_source":"none","site_id":"doc-c673374feb27879bc4605b9b-site-1"}],"limitations":["Exact coordinates not provided for study locations","Copper solubility results conditioned on 22°C and pH 8.15, limiting direct generalization to other temperatures and pH","Study limited to laboratory-prepared seawater samples and natural seawater from a single collection site"],"river_ids":[],"site_ids":["doc-c673374feb27879bc4605b9b-site-0","doc-c673374feb27879bc4605b9b-site-1"],"anchor":[151.1522679,-34.0512081],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-d4514f7a413cfd425581c80b","title":"Mainstreaming gender into water management modelling processes","display_title":"Mainstreaming Gender in Water Modeling","doi":"https://doi.org/10.1016/j.envsoft.2020.104683","theme":"Water management","color":"#eeabac","year":2020,"publication_date":"2020-03-07","authors":["Evangeline Packett","Nicky Grigg","Joyce Wu","Susan Cuddy","Peter Wallbrink","Anthony J. Jakeman"],"summary":"This study explores how gender considerations can influence biophysical water resource management modeling at various stages: problem framing, model construction, and interpretation. It highlights that integrating gender perspectives can alter modeling choices, reveal overlooked assumptions, and improve equity in decision-making processes, especially relevant under Integrated Water Resource Management (IWRM) frameworks.","finding":"Including gender perspectives in water management modeling can materially impact key modeling decisions such as problem framing, data selection, calibration metrics, and interpretation criteria, thereby promoting more equitable and inclusive water resource management outcomes.","finding_evidence":{"passage_id":"p003-006","page":3,"quote":"Interviews with water professionals done by GWP found that one disincentive to including gender considerations is that “gender issues are ‘tacked on’ rather than considered upfront which makes them hard to integrate and act upon properly” (Grant, 2017b). Merritt et al. (2017) found that one of four factors of success deemed ‘always essential’ by technical experts in Australian water resource projects was “sufficient critical thought given to the problem framing and the role of modelling early in the project.” This is because questions at this stage create a path dependency that can determine the course of a modelling project (Lahtinen et al., 2017). We explore the possibility that including gender as an analysis lens when framing the problem could alter how a model is constructed, potentially leading to different insights and model-informed decisions.","verified_in_page":true},"topics":["gender-inequality","integrated water resource management","environmental-modelling","stakeholder-engagement","social-equity","decision-support","uncertainty-analysis","participatory-modelling"],"study_period":null,"locations":[{"name":"Lesotho Highlands","kind":"region","role":"example of gendered impacts of a large water infrastructure project","river_name":null,"geocode_query":"Lesotho Highlands, Lesotho","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Environmental Modelling and Software 127 (2020) 104683 6 broadening the economic base and development of remote mountain regions (Yu, 2008). In practice, despite economic growth, poverty did not improve, economic inequality remained high and Lesotho experienced social and environmental costs (Yu, 2008). The filling of the dams led to a serious decline in resources for affected households, with the burden falling disproportionately on women (Braun, 2011; Mwangi, 2007). The project increased the reliance of households on wages and formal markets. Due to the patriarchal system in Lesotho this reliance increased gender inequalities by giving men greater control over household resources (Braun, 2011). Such diverse impacts are difficult to anticipate and make visible in aggregate estimates of economic costs and benefits. Effects such as these prompted the WCD to call for gender-sensitive social analysis of dam projects.","verified_in_page":true},"resolution_status":"resolved","coordinates":[27.4833153,-29.3145203],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"MFPM+58W, Maseru, Lesotho","basin_id":1041629430,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":98763.6,"hydrology_source":"hydrobasins","site_id":"doc-d4514f7a413cfd425581c80b-site-0"},{"name":"Chhattis Mauja irrigation system, Nepal","kind":"region","role":"case study illustrating gender differences in water flow priorities influencing modeling","river_name":null,"geocode_query":"Chhattis Mauja, Nepal","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"They influence not only the model results, but also what the model can do, who can use the model, and for what purposes” (Melsen et al., 2018). Hydrological models are often calibrated by optimising an objective function, and it is well-recognised that different objective functions are appropriate for different modelling purposes (e.g. see Vaze et al. (2011) for guidance on choice of objective function). For eco-hydrological applications, for example, it is important to ensure the model adequately characterises specific attributes of the flow regime (e.g. low flows or cease to flow events) (Bunn and Arthington, 2002). Similarly, the type or timing of flow that is most important may be different for different people. In the Chhattis Mauja irrigation system in Nepal, Zwarteveen (1997) found that even though men and women were working cooperatively as co-farmers, they tended to have different priorities about water flow conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[84.043175,28.2691925],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mauja 33700, Nepal","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-d4514f7a413cfd425581c80b-site-1"},{"name":"Ethiopia sub-catchment","kind":"region","role":"site of study showing gender differences in landscape classification informing modeling inputs","river_name":null,"geocode_query":"Ethiopia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"For example, Baker et al. (2015) characterised gendered difference, as well as differences between researchers and community members, in mapping landscape features in a sub-catchment in Ethiopia. When creating input layers for their Soil and Water Assessment Tool (SWAT) model, researchers focused on land use and crop type to classify the landscape. Women tended to classify areas in terms of soil type, which were proxies for productivity and land degradation, and at a finer resolution than maps developed by the Food and Agriculture Organization (FAO). Men, on the other hand, classified larger areas of land as grazing, reflecting their role in stewarding livestock. This means that in their maps, land that would have been assigned crop or soil type was seen as grazing. This study showed how differing perspectives can influence judgments of what data is relevant for understanding and addressing a problem.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["No explicit coordinates provided for study areas.","Geographic references are broad regional or case study areas only.","Study is conceptual and methodological; no original quantitative data or specific field campaigns reported."],"river_ids":[],"site_ids":["doc-d4514f7a413cfd425581c80b-site-0","doc-d4514f7a413cfd425581c80b-site-1"],"anchor":[27.4833153,-29.3145203],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[1041629430,4040960480],"main_basin_ids":[1040015850,4040025450]},{"id":"doc-dac84a1d6f27c2cde636402b","title":"Elevation-dependent response of vegetation dynamics to climate change in a cold mountainous region","display_title":"Elevation-Dependent Vegetation Dynamics in HYRB","doi":"https://doi.org/10.1088/1748-9326/ab9466","theme":"Land & ecosystems","color":"#c8df98","year":2020,"publication_date":"2020-08-18","authors":["Shiqin Xu","Zhongbo Yu","Dennis P. Lettenmaier","Tim R. McVicar","Xibin Ji"],"summary":"This study investigates how vegetation dynamics in the headwaters of the Yellow River basin (HYRB) have responded to climate change from 1982 to 2015. Using satellite-derived NDVI, snow cover extent, and gridded climate data, the researchers found widespread vegetation greening across seasons but no significant trend in the beginning of growing season (BGS) overall. They observed elevation-dependent warming below 4300 m but not above, with changes in vegetation greenness and phenology showing elevation dependence influenced by temperature, precipitation, and snow cover variations. Evapotranspiration increased with vegetation greening, linking these ecological changes to hydrological responses including a reduction in runoff.","finding":"Vegetation greening in the HYRB is elevation-dependent, closely linked to elevation gradients in temperature, precipitation, and snow cover changes, with warming enhancing greenness below 4300 m but not above, and greening leading to increased evapotranspiration and reduced runoff.","finding_evidence":{"passage_id":"p007-003","page":7,"quote":"Patterns of temperature change with elevation and vegetation dynamics response We observed enhanced warming at elevations below 4300 m, but warming rates greatly declined above this elevation (figure 3(a)). Examining patterns of possible causes with elevation, we found spring P increased with elevation, while summer and autumn P shifted from negative trends to positive trends at about 4000 m and increased at higher elevations (figure 3(b)). SCE declined at lower elevations but increased above 4650 m (figure 3(c)). Generally, Rld increased at each elevation which could be attributed to increased water vapor (figure 3(d)). The pattern of ∆Rld with elevation was very similar to that of ∆Tmean. A correlation analysis showed that ∆Tmean was statistically significant and positively related to ∆Rld but negatively related to ∆P and ∆SCE. We used a stepwise regression model to investigate their combined effects and to identify the primary driver of ∆Tmean.","verified_in_page":true},"topics":["elevation-dependent warming","vegetation phenology","snow cover extent","evapotranspiration","climate change impact","mountain hydrology","satellite remote sensing","precipitation variability"],"study_period":"1982–2015","locations":[{"name":"Headwaters of the Yellow River Basin (HYRB)","kind":"basin","role":"primary study domain where vegetation dynamics and climate change impacts were analyzed","river_name":null,"geocode_query":"Headwaters of the Yellow River Basin, northeast Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Data and methods Our study domain is the HYRB (~122 000 km2 ; figure 1) which is located in the north-east of the TP, and experiences distinct wet and dry seasons in summer and winter, respectively. It is the ‘water tower’ of the much larger Yellow River basin (YRB) which is the home to an estimated 400 million people and has experienced dramatic climate change over the past four decades. Land cover is dominated by alpine grasslands including alpine steppe and alpine meadows (figure S1). The HYRB covers only 15% of the area of the entire YRB, but contributes approximately 35% of the total annual YRB discharge (Hu et al 2011). 2.1. Data Our study period is from 1982 to 2015.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yellow River Basin (YRB)","kind":"basin","role":"broader basin downstream of HYRB providing regional context and hydrological linkage","river_name":"Yellow River","geocode_query":"Yellow River Basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Data and methods Our study domain is the HYRB (~122 000 km2 ; figure 1) which is located in the north-east of the TP, and experiences distinct wet and dry seasons in summer and winter, respectively. It is the ‘water tower’ of the much larger Yellow River basin (YRB) which is the home to an estimated 400 million people and has experienced dramatic climate change over the past four decades. Land cover is dominated by alpine grasslands including alpine steppe and alpine meadows (figure S1). The HYRB covers only 15% of the area of the entire YRB, but contributes approximately 35% of the total annual YRB discharge (Hu et al 2011). 2.1. Data Our study period is from 1982 to 2015.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.685215,36.519334],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040007850,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":77127.5,"hydrology_source":"hydrobasins","site_id":"doc-dac84a1d6f27c2cde636402b-site-1","true_coordinates":[116.610556,36.519334],"spread_km":6.7},{"name":"Tibetan Plateau (TP)","kind":"region","role":"regional setting encompassing HYRB, referenced for comparing temperature trends and elevation-dependent warming","river_name":null,"geocode_query":"Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Given steady-state and non-stationary hydrological conditions, increasing evapotranspiration should result in runoff reduction, which agrees with catchment-scale runoff observations across the HYRB. These findings represent new knowledge regarding the vegetation response to climate change in alpine environments which has important implications for the hydrology of the region and for other high-water yielding mountainous regions worldwide. 1. Introduction Continuous climate warming over the last 50– 100 years has imposed substantial stress on terrestrial ecosystem functioning, including vegetation phenology and productivity, distribution and composition of ecological communities, and has even shifted the range of some species (Beniston 2003, Diaz and Eischeid 2007). Both recorded and projected temperature changes suggest that mountainous regions are experiencing more substantial warming compared to other areas (Beniston 2003, Rangwala and Miller 2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"French Alps","kind":"region","role":"comparison area referenced for elevation-dependent vegetation greening studies","river_name":null,"geocode_query":"French Alps, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This result suggests a high sensitivity of vegetation growth to summer precipitation in semi-arid environments (Poulter et al 2013). Carlson et al (2017) investigated changes in peak NDVI (NDVImax) with elevation in the Ecrins 7","verified_in_page":true},"resolution_status":"resolved","coordinates":[6.865534599999999,45.8345457],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"French Alps, 74400 Chamonix, France","basin_id":2040016230,"main_basin_id":2040016230,"basin_region":"Europe and Middle East","basin_area_km2":310600.1,"hydrology_source":"hydrobasins","site_id":"doc-dac84a1d6f27c2cde636402b-site-3"},{"name":"Upper Kings River Basin","kind":"basin","role":"comparison of evapotranspiration response to vegetation changes in mountainous region outside study domain","river_name":"Kings River","geocode_query":"Upper Kings River Basin, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"Environmental monitoring stations (at which both hydrometeorological and ecological observations are made) should be installed at higher-elevation and ecologically sensitive locations which exhibit rapid and detectable changes, rather than being located (as at present) in lower elevations, notwithstanding a range of pragmatic challenges associated with higher elevations, experiencing more extreme conditions and affordability. 3.3. Hydrological implication of vegetation dynamic change Annual HYRB-averaged ETa calculated from water balance (P − runoff) varied between 278.3 and 475.1 mm/year with a mean value of 371.0 mm/year during 1982–2015. The Budyko-estimated ETa ranged from 405.7 to 452.8 mm/year with a mean value of 431.1 mm/year. The GLEAM ETa rates exhibited smaller temporal variability during 1982–2015 with a mean value of 391.9 mm/year (figures 4(a)– (c)).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-119.824575,36.0499522],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-dac84a1d6f27c2cde636402b-site-4"},{"name":"North China Plain","kind":"region","role":"comparator for temperature trends relative to northern Tibetan Plateau","river_name":null,"geocode_query":"North China Plain, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Given steady-state and non-stationary hydrological conditions, increasing evapotranspiration should result in runoff reduction, which agrees with catchment-scale runoff observations across the HYRB. These findings represent new knowledge regarding the vegetation response to climate change in alpine environments which has important implications for the hydrology of the region and for other high-water yielding mountainous regions worldwide. 1. Introduction Continuous climate warming over the last 50– 100 years has imposed substantial stress on terrestrial ecosystem functioning, including vegetation phenology and productivity, distribution and composition of ecological communities, and has even shifted the range of some species (Beniston 2003, Diaz and Eischeid 2007). Both recorded and projected temperature changes suggest that mountainous regions are experiencing more substantial warming compared to other areas (Beniston 2003, Rangwala and Miller 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[119.088867,32.35771],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North China, Yizheng, Yangzhou, Jiangsu, China, 211414","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-dac84a1d6f27c2cde636402b-site-5","true_coordinates":[119.15036,32.32771],"spread_km":6.7}],"limitations":["Sparse and under-represented meteorological and hydrological observations at high elevations above 2500 m in the HYRB limit the accuracy of precipitation and temperature trend detection.","The coarse temporal resolution (biweekly) of NDVI data introduces uncertainty in phenology (BGS) estimation with an error margin of approximately ±8 days.","Gridded precipitation datasets show potential uncertainties and biases, especially at elevation extremes due to sparse station coverage.","Some elevation-dependent warming results differ from other studies, indicating the need for further validation with combined observational and modeling data.","Estimates of evapotranspiration components vary among data sources, illustrating challenges in accurately quantifying long-term ET in complex terrain regions."],"river_ids":[],"site_ids":["doc-dac84a1d6f27c2cde636402b-site-1","doc-dac84a1d6f27c2cde636402b-site-3","doc-dac84a1d6f27c2cde636402b-site-4","doc-dac84a1d6f27c2cde636402b-site-5"],"anchor":[119.088867,32.35771],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[2040016230,4040007850,4040694350,7040013550],"main_basin_ids":[2040016230,4040007850,4040009880,7040013550]},{"id":"doc-dd3027b68385a61b1e4c81ff","title":"Improving a continental hydrological model by enhancing its hydrological representation and implementing at 1km spatial resolution","display_title":"EGU 2019 General Assembly Participant Survey","doi":"https://doi.org/10.5194/egusphere-egu2020-3237","theme":"Other science","color":"#d1b5e7","year":2020,"publication_date":"2020-03-23","authors":["Cherry May Mateo","Jai Vaze","Biao Wang"],"summary":"This study reports the results of a feedback survey conducted for the EGU 2019 General Assembly held in Vienna, Austria, from 7 to 12 April 2019. The survey collected 1,795 responses from attendees and participants worldwide, capturing demographics, participation, travel, session attendance, and use of communication channels during the conference.","finding":"The large majority (96.3%) of survey respondents attended the EGU 2019 General Assembly in Vienna, demonstrating strong participant engagement in the meeting.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"Did you attend the EGU 2019 General Assembly? If you were an author of an abstract that was accepted but not presented, what was the reason? Yes 96,3% No 3,7% Multiple answers allowed 8,5% 36,2% 23,4% 8,5% 12,8% 34,0% Visa issues Illness or last-minute personal issues Funding problems Never planned on attending Lack of time to prepare the presentation Other","verified_in_page":true},"topics":["conference participation","scientific meetings","survey analysis","participant demographics","science communication","event feedback","scientific outreach","social media usage"],"study_period":"2019-04-07 to 2019-04-12","locations":[{"name":"Vienna, Austria","kind":"settlement","role":"Main conference location where EGU 2019 General Assembly was held and survey was conducted","river_name":null,"geocode_query":"Vienna, Austria","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"EGU 2019 General Assembly Feedback Survey Results Meetings | Publications | Outreach | www.egu.eu EGU 2019, Vienna, Austria| 7–12 April 2019","verified_in_page":true},"resolution_status":"resolved","coordinates":[16.371309,48.26807],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Vienna, Austria","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-dd3027b68385a61b1e4c81ff-site-0","true_coordinates":[16.3713095,48.20806959999999],"spread_km":6.7},{"name":"Vienna, Austria","kind":"settlement","role":"Participant travel destination for EGU 2019 General Assembly","river_name":null,"geocode_query":"Vienna, Austria","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"How did you travel to Vienna? Which days did you attend the EGU General Assembly 2019? 27% 2% 65% 3% 2% 1% Train Bus Airplane Car I live in/near Vienna Other 86,8% 90,5% 91,4% 86,3% 75,5% Monday Tuesday Wednesday Thursday Friday","verified_in_page":true},"resolution_status":"resolved","coordinates":[16.281277,48.20807],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Vienna, Austria","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-dd3027b68385a61b1e4c81ff-site-1","true_coordinates":[16.3713095,48.20806959999999],"spread_km":6.7}],"limitations":["No latitude or longitude coordinates provided in source text","Study location is broad (city level) without finer spatial details","Data limited to participant self-reported responses from one conference event"],"river_ids":[],"site_ids":["doc-dd3027b68385a61b1e4c81ff-site-0","doc-dd3027b68385a61b1e4c81ff-site-1"],"anchor":[16.371309,48.26807],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-10","basin_ids":[2040539930],"main_basin_ids":[2040008490]},{"id":"doc-e575694c6cb684fc22f83c0b","title":"High Resolution Mapping of Ice Mass Loss in the Gulf of Alaska From Constrained Forward Modeling of GRACE Data","display_title":"Ice Mass Loss Mapping in Gulf of Alaska","doi":"https://doi.org/10.3389/feart.2019.00360","theme":"Flow & ecology","color":"#80edc2","year":2020,"publication_date":"2020-01-23","authors":["Cheick Doumbia","Pascal Castellazzi","Alain N. Rousseau","Macarena Amaya"],"summary":"The study applies a constrained forward modeling approach to improve the spatial resolution of glacier ice mass loss estimates from GRACE satellite data over the Gulf of Alaska (GOA) region, covering 2002 to 2017. Using GRACE solutions combined with glacier distribution maps at varying spatial scales (mascon units), the study produces high-resolution ice mass loss maps. Results indicate a mean ice mass loss of approximately 40 gigatons per year over the GOA, with largest losses centered on the Saint Elias Mountains. The method effectively recovers glacier mass losses with errors below 10% for mascon sizes around 30,000 km2, showing that glacier melt dominates the GRACE terrestrial water storage signal in the region. The study also finds that earlier GRACE-based estimates with shorter time-series overestimated long-term ice mass loss by about 20%.","finding":"Using focusing units (mascons) of approximately 30,000 km2 or larger, the forward modeling procedure recovers glacier ice mass loss estimates from GRACE data with errors below 15%, effectively resolving regional spatial heterogeneity in ice loss over the Gulf of Alaska, with a mean ice mass loss of about 40 Gt/year for 2002–2017.","finding_evidence":{"passage_id":"p006-002","page":6,"quote":"Second, the inversion procedure is performed after adding a background mascon covering the whole study area, with all pixel accounted for, except those corresponding to glaciers. The first test assesses the validity of the focusing procedure and the influence of diffuse masses over the local-scale mass retrieval. The second test verifies whether the local signal from glaciers is better recovered while accounting for surrounding low-frequency signals or not. RESULTS AND DISCUSSION Simulation With Synthetic Data A synthetic map is created by allocating synthetic masses to the glacier distribution map (Figure 3A) over which a diffuse mass signal is added (Figure 3B). This map, after truncation, is a low resolution GRACE-like mass trend map (Figure 3C) built from a known high resolution map. The masses injected onto the distribution map are realistic; they are of a similar order of magnitude to the masses expected across the GOA (considering both individual mascon and the entire area.","verified_in_page":true},"topics":["glaciology","satellite remote sensing","mass balance","climate change impacts","hydrology","gravity data processing","data inversion","spatial resolution"],"study_period":"2002-2017","locations":[{"name":"Gulf of Alaska","kind":"region","role":"study area containing glaciers analyzed for ice mass loss","river_name":null,"geocode_query":"Gulf of Alaska, Alaska, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Rousseau1 and Macarena Amaya3 1 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Université du Québec, Québec City, QC, Canada, 2 Land and Water, Deep Earth Imaging FSP, Commonwealth Scientific and Industrial Research Organisation, Urrbrae, SA, Australia, 3 Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, La Plata, Argentina The resolution of Gravity Recovery And Climate Experiment (GRACE) Terrestrial Water Storage (TWS) change data is too low to discriminate mass variations at the scale of glaciers, small ensemble of glaciers, or icefields. In this paper, we apply an iterative constraint modeling strategy over the Gulf Of Alaska (GOA) to improve the resolution of ice loss estimates derived from GRACE. We assess the effect of the most influential parameters such as the type of GRACE solution and the degree of heterogeneity of the distribution map over which the GRACE data is focused.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Saint Elias Mountains","kind":"region","role":"area of largest glacier mass loss within study area","river_name":null,"geocode_query":"Saint Elias Mountains, Alaska, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"Two different scales of the same color maps are used for A–C and D–F. different methods (Berthier et al., 2010; Gardner et al., 2013; Larsen et al., 2015, values are listed in section “Introduction”). Differences in GRACE Level-3 data interpretation strategy can lead to discrepancies with estimates from other authors. The use of a constrained forward modeling strategy can lead to lower values, as it only recovers the signal spatially correlated to glaciers (see, e.g., Long et al., 2016, for an example related to groundwater depletion). The injection of a distribution constraint is known to reduce contributions from unwanted signals with contrasting spatial patterns. These signals cannot be focused onto the constraining distribution (Farinotti et al., 2015; Castellazzi et al., 2018). However, the distribution map is a simplification of the reality as it does not consider the intra-mascon heterogeneity of the ice loss signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-140.9294444,60.29333330000001],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mt St Elias","basin_id":8040000010,"main_basin_id":8040000010,"basin_region":"North American Arctic","basin_area_km2":55235.1,"hydrology_source":"hydrobasins","site_id":"doc-e575694c6cb684fc22f83c0b-site-1"},{"name":"Glacier Bay","kind":"region","role":"subregion within Saint Elias Mountains mascon area of substantial glacier mass loss","river_name":null,"geocode_query":"Glacier Bay, Alaska, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"Two different scales of the same color maps are used for A–C and D–F. different methods (Berthier et al., 2010; Gardner et al., 2013; Larsen et al., 2015, values are listed in section “Introduction”). Differences in GRACE Level-3 data interpretation strategy can lead to discrepancies with estimates from other authors. The use of a constrained forward modeling strategy can lead to lower values, as it only recovers the signal spatially correlated to glaciers (see, e.g., Long et al., 2016, for an example related to groundwater depletion). The injection of a distribution constraint is known to reduce contributions from unwanted signals with contrasting spatial patterns. These signals cannot be focused onto the constraining distribution (Farinotti et al., 2015; Castellazzi et al., 2018). However, the distribution map is a simplification of the reality as it does not consider the intra-mascon heterogeneity of the ice loss signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-136.15,58.7],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Glacier Bay Basin, Alaska, USA","hydrology_source":"none","site_id":"doc-e575694c6cb684fc22f83c0b-site-2"}],"limitations":["Incomplete latitude and longitude coordinates; only region-level geocode queries provided","Heterogeneity of ice loss within mascons is simplified as uniform, which may miss intra-mascon variability","Study relies on GRACE data processing constraints and imposed regularizations affecting uncertainty","Earlier GRACE releases had higher uncertainties and shorter time-series, may have overestimated ice loss","Ice mass loss estimates for smaller mascons (around 20,000 km2) show increased errors and variability due to processing residuals"],"river_ids":[],"site_ids":["doc-e575694c6cb684fc22f83c0b-site-1","doc-e575694c6cb684fc22f83c0b-site-2"],"anchor":[-140.9294444,60.29333330000001],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[8040000010],"main_basin_ids":[8040000010]},{"id":"doc-f34674db56333c2a65fbe32c","title":"Groundwater mean residence times of a subtropical barrier sand island","display_title":"Groundwater Residence Times on North Stradbroke Island","doi":"https://doi.org/10.5194/hess-24-1293-2020","theme":"Groundwater","color":"#a8b9ff","year":2020,"publication_date":"2020-03-19","authors":["Harald Hofmann","Dean Newborn","Ian Cartwright","Dioni I. Cendón","Matthias Raiber"],"summary":"This study determines mean residence times (MRTs) of groundwater on North Stradbroke Island, a subtropical barrier sand island in southeast Queensland, Australia, using tritium (3H) and carbon-14 (14C) isotopes, combined with hydraulic data and geochemistry. The island hosts a regional groundwater system and local perched aquifers influenced by dune stratigraphy and low-permeability layers such as peat and coffee rock. MRTs vary spatially and with depth, revealing complex groundwater flow paths influenced by geological heterogeneity. Results enhance understanding of recharge processes and groundwater resources management for coastal sand islands in subtropical environments.","finding":"Groundwater mean residence times increase with depth and laterally toward coastal discharge zones on North Stradbroke Island; low-permeability geological units such as peat and coffee rock in the dune systems create perched aquifers that store older groundwater and affect flow paths, causing heterogeneity and recharge diversion.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"MRTs estimated from 14C concentrations display similar spatial relationships but have a much greater range (from modern to approximately 5000 years). Water diversion and retention by lowerpermeability units in the unsaturated parts of the dune systems are the most likely course for relatively long MRTs to date. The results indicate that the internal structures within the dune systems increase MRTs in the groundwater system and potentially divert flow paths. The structures produce perched aquifer systems that are wide-spread and have a significant influence on regional recharge. The geochemical composition of groundwater remains relatively consistent throughout the island, with the only irregularities attributed to old groundwater stored within coastal peat.","verified_in_page":true},"topics":["groundwater residence time","stable isotopes","aquifer heterogeneity","coastal aquifers","environmental tracers","groundwater recharge","paleoclimate proxies","hydrogeochemistry"],"study_period":null,"locations":[{"name":"North Stradbroke Island","kind":"region","role":"study site: location of groundwater residence times and flow path study","river_name":null,"geocode_query":"North Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The purpose of this study is to determine groundwater flow paths and MRTs in a Quaternary dune sand aquifer on the world’s second largest sand island, North Stradbroke Island, Queensland, Australia (Laycock, 1975; Ulm et al., 2009). The island is an important part of the coastal environment in south-east Queensland and provides a vital groundwater resource, recreational space for large urban areas and, most importantly, unique coastal ecosystems with many freshwater wetlands, lakes and marine coastal environments (Marshall et al., 2011). The island also plays a major role in the palaeoclimate reconstructions for the east coast of Australia, as some of the longest sediment records have been extracted from the North Stradbroke Island wetlands (Barr et al., 2013, 2019; Tibby et al., 2016, 2017).","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.462608,-27.592317],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130865,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Stradbroke Island, Queensland 4183, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-f34674db56333c2a65fbe32c-site-0","true_coordinates":[153.4626077,-27.5323174],"spread_km":6.7},{"name":"Moreton Bay, Queensland, Australia","kind":"region","role":"regional geographical context of North Stradbroke Island and coastal discharge area","river_name":null,"geocode_query":"Moreton Bay, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"H. Hofmann et al.: Groundwater mean residence times of a subtropical barrier sand island 1295 Figure 1. (a) Location of North Stradbroke Island (NSI) in Moreton Bay, Queensland, Australia. (b) Bores sampled (by reference number – RN) along the transect across the island. Blue arrows indicate general flow directions. (Maps were generated using GIS files from the Queensland Government Spatial Catalogue, QSpatial, 2019.) 2 Geographical, geological and hydrogeological setting North Stradbroke Island is located approximately 40 km east of Brisbane in a group of sand dune islands that make up Moreton Bay (Fig. 1). The island has an area of approximately 275 km2 of which around 20 % is wetlands and beaches with important environmental and economic value. The abundance of fresh groundwater and its proximity to dry and populous south-east Queensland makes it significant as a water supply (Leach, 2011).","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.920592,-27.154608],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130201,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"City of Moreton Bay, QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-f34674db56333c2a65fbe32c-site-1","true_coordinates":[152.9205918,-27.0946084],"spread_km":6.7},{"name":"Brown Lake, North Stradbroke Island, Queensland, Australia","kind":"wetland","role":"field site: location of perched aquifer bore sampled with youngest MRT groundwater","river_name":null,"geocode_query":"Brown Lake, North Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"As the sand aquifer is unconfined across most of the island and bore screens sample only a part of the aquifer, the partial exponential model (PEM) with PEM ratios calculated individually for all bores is the best representation for the system. The PEM was compared with the DM and EPM to demonstrate the impact model selection on MRT estimations. 4 Results 4.1 Groundwater hydraulic heads and flow Groundwater bore 14400088 is probably screened in a perched aquifer system that surrounds Brown Lake (Fig. 1), whereas all other bores are screened within the regional groundwater system. The distinction between the perched and regional aquifer system is made on the basis of nested bores 14400088 and 14400087 in this study. On the day of sampling, the hydraulic head in bore 14400088 was 58 m whereas that in bore 14400087 was 35 m. This 20 m hydraulic head difference indicates the presence of a perching layer.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.4323791,-27.4901046],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130851,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brown Lake, North Stradbroke Island QLD 4183, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-f34674db56333c2a65fbe32c-site-2"},{"name":"Eighteen Mile Swamp, North Stradbroke Island, Queensland, Australia","kind":"wetland","role":"field site: large freshwater peatland wetland receiving groundwater discharge and hosting low-permeability layers influencing MRTs","river_name":null,"geocode_query":"Eighteen Mile Swamp, North Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"The east coast of the island is comprised of a large freshwater wetland system at the foothills of the major dune system. This wetland, Eighteen Mile Swamp, separates the major Quaternary dunes from the foredunes. Groundwater from the main dunes discharges into the wetland and partially re-enters into the foredunes along the coastline (Fig. 9). However, it appears that there are at least two aquifer systems that are separated by a lower-permeability unit. This lower-permeability unit was identified as a marine clay underneath the peat sequences of Eighteen Mile Swamp by Mettam et al. (2011). Nested bores 14400151 (67 m) and 14400152 (17 m) indicate younger MRTs and higher salinity (173 mg L−1 TDS) in the shallow part of the foredune system and older, lower salinity in the deeper parts; this is also reflected in bore 14400051 in the main dune system (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.4655571,-27.6008808],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130857,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eighteen Mile Swamp, North Stradbroke Island QLD 4183, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-f34674db56333c2a65fbe32c-site-3"},{"name":"Wonky Holes, Moreton Bay tidal zone near Dunwich, Queensland, Australia","kind":"other","role":"field site: submarine groundwater discharge points sampled with long MRT water discharging to Moreton Bay","river_name":null,"geocode_query":"Dunwich, North Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"A single surface water sample was collected from Eighteen Mile Swamp, and two groundwater samples were collected from two wonky holes in the tidal zone approximately 6 km north of Dunwich (Fig. 1). The wonky holes were approximately 120 m apart and 6 m in diameter. The groundwater bores were sampled in November 2014, whereas the wonky holes were sampled in March 2015. The depth to the water table was determined using an electric water level tape, and hydraulic heads were calculated from the surveyed ground elevation of each bore taken from the Queensland Groundwater Database (Queensland – Government – Data, 2018). Groundwater was sampled using a Grundfos MT1 pump where the depth to the screen exceeded 20 m, while a smaller 12 V electrical impeller pump (Thermo Fisher Inc. Super Twister) was used for the shallower bores. The samples were collected after purging the bores for approximately five bore volumes. Bore RN14400075 was pumped dry and sampled using a bailer a day later.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Mount Hargrave, North Stradbroke Island, Queensland, Australia","kind":"landscape","role":"topographic high and recharge zone controlling groundwater flow mound and hydraulic head maxima","river_name":null,"geocode_query":"Mount Hargrave, North Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The regional groundwater system (which contains almost all of the island’s freshwater) forms an elongated north–south mound with the height of the water table varying from a maximum of 42 m AHD (Australian Height Datum) near Mt Hargrave in the centre of the island to sea level in the coastal lowlands (Cox et al., 2011). Generally, groundwater flows east and west from the high central recharge area to the coasts, with a larger proportion flowing east due to the relatively elevated bedrock in the west (Fig. 1). Local groundwater discharge points (termed “wonky holes”) occur in the tidal areas on the island. They are round, approximately 2–10 m (in diameter) disruption features in the tidal muds where groundwater from the regional groundwater system discharges.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.521205,-27.502317],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130865,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Stradbroke Island, Queensland 4183, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-f34674db56333c2a65fbe32c-site-5","true_coordinates":[153.4626077,-27.5323174],"spread_km":6.7}],"limitations":["Some spatial coordinates for sites and natural features are not provided, only names and regional context.","Information on exact location and extent of perched aquifers and low-permeability layers is incomplete.","Uncertainty in groundwater MRT estimation arises from choice of lumped parameter model and geochemical corrections for 14C ages.","Complex mixing of old and young groundwater reservoirs limits precise residence time assignments.","Geological heterogeneity and limited borehole data restrict full understanding of groundwater flow system dynamics."],"river_ids":[],"site_ids":["doc-f34674db56333c2a65fbe32c-site-0","doc-f34674db56333c2a65fbe32c-site-1","doc-f34674db56333c2a65fbe32c-site-2","doc-f34674db56333c2a65fbe32c-site-3","doc-f34674db56333c2a65fbe32c-site-5"],"anchor":[153.4323791,-27.4901046],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040069970],"main_basin_ids":[5040069970]},{"id":"doc-f5ec54ca8975eb044b9a480c","title":"PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018","display_title":"Global High-Resolution Precipitation Climatologies","doi":"https://doi.org/10.1038/s41597-020-00631-x","theme":"Climate","color":"#98d4f1","year":2020,"publication_date":"2020-09-11","authors":["Hylke E. Beck","Seth Westra","Jackson Tan","Florian Pappenberger","George J. Huffman","Tim R. McVicar","Gaby J. Gründemann","Noemi Vergopolan","Hayley J. Fowler","Elizabeth Lewis","Koen Verbist","Eric F. Wood"],"summary":"This study presents the Precipitation Probability DISTribution (PPDIST) dataset, a global 0.1° resolution climatology (1979–2018) describing precipitation occurrence and peak intensity at daily and 3-hourly scales. The dataset was developed using neural networks trained on an extensive quality-controlled collection of gauge observations worldwide, complemented by satellite and reanalysis data as predictors, achieving high validation accuracy and improved performance relative to existing reanalysis (ERA5) and satellite (IMERG) products. The dataset captures spatial patterns of precipitation probability and extremes, highlighting regions of high occurrence and intensity such as coastal areas of Chile, Colombia, the British Isles, Norway, parts of Central America, India, Southeast Asia, and western equatorial Africa.","finding":"The neural network-derived PPDIST dataset provides more accurate global estimates of precipitation occurrence and peak intensity than state-of-the-art ERA5 reanalysis and IMERG satellite products, with mean validation R2 values up to 0.86 for occurrence and 0.83 for intensity indices, demonstrating its utility for climate model evaluation, infrastructure design, and hydrological applications.","finding_evidence":{"passage_id":"p007-005","page":7,"quote":"Technical Validation Figure 3 presents the performance of the PPDIST dataset, the ERA5 reanalysis, and the IMERG satellite product in estimating the daily P indices (Supplement Fig. S4 presents the performance in estimating the 3-hourly P indices). The PPDIST scores represent averages calculated from the ten independent validation subsets of observations (one for each cross-validation iteration). The PPDIST R2 values ranged from 0.76 to 0.80 (mean 0.78) for the daily P occurrence indices, 0.85 to 0.88 (mean 0.86) for the 3-hourly P occurrence indices, 0.44 to 0.84 (mean 0.72) for the daily peak P intensity indices, and 0.72 to 0.88 (mean 0.83) for the 3-hourly peak P intensity indices (Fig. 3 and Supplement Fig. S4). The 3-hourly R2 values were thus higher than the daily R2 values, likely because the 3-hourly gauge observations tend to be of higher quality62.","verified_in_page":true},"topics":["climate models","precipitation climatology","neural networks","gauge observations","satellite precipitation","reanalysis data","data quality control","extreme precipitation"],"study_period":"1979–2018","locations":[{"name":"Chile coastal zone","kind":"region","role":"high precipitation occurrence region","river_name":null,"geocode_query":"Chile coast, Chile","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-71.00999999999999,-32.98],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Chilean Coast Range, Tiltil, Santiago Metropolitan Region, Chile","basin_id":6040027000,"main_basin_id":6040027000,"basin_region":"South America","basin_area_km2":121075.8,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-0"},{"name":"Colombia coastal zone","kind":"region","role":"high precipitation occurrence region","river_name":null,"geocode_query":"Colombia coast, Colombia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"British Isles","kind":"region","role":"high precipitation occurrence region","river_name":null,"geocode_query":"British Isles, UK","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-5.707144700000001,54.65837269999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"British Isles","basin_id":2040054200,"main_basin_id":2040054200,"basin_region":"Europe and Middle East","basin_area_km2":85561.3,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-2"},{"name":"Norway coastal zone","kind":"region","role":"high precipitation occurrence region","river_name":null,"geocode_query":"Norway coast, Norway","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Pacific coast of North America","kind":"region","role":"high precipitation occurrence region","river_name":null,"geocode_query":"Pacific coast North America, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-149.0729715,62.4113634],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"West Coast, USA","basin_id":8040002260,"main_basin_id":8040002260,"basin_region":"North American Arctic","basin_area_km2":50346.1,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-4"},{"name":"Southeast Asia","kind":"region","role":"high precipitation occurrence region and high peak intensity region","river_name":null,"geocode_query":"Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.66283,-2.15797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-5","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"Central America","kind":"region","role":"region with highest 15-year return-period precipitation intensities","river_name":null,"geocode_query":"Central America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"The highest 15-year return-period P intensities (>200 mm d−1 ) over land were found in parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and over oceans in the intertropical convergence zone (Fig. 2b)17,48,58,59. Conversely, the lowest 15-year return-period P intensities (<50mm d−1 ) were found in the desert regions of the world, at high latitudes where frontal P dominates, and over southeastern parts of the Atlantic and Pacific oceans where semi-permanent anticyclones prevail54,60. The PPDIST 15-year return-period daily P climatology (Fig. 2b) exhibits reasonable visual agreement with a maximum one-day P climatology derived by Dietzsch et al. 33 (their Fig. 5d) from the gauge-based GPCC Full Data Daily dataset34, as well as with a 3.4-year return-period 3-hourly P climatology derived by Beck et al. 52 (their Fig. 7) from the satellite-, reanalysis-, and gauge-based MSWEP dataset52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-85.602364,12.709013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central America","basin_id":7040050930,"main_basin_id":7040050930,"basin_region":"North America and Caribbean","basin_area_km2":382831.5,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-6","true_coordinates":[-85.60236429999999,12.7690126],"spread_km":6.7},{"name":"India","kind":"region","role":"region with highest 15-year return-period precipitation intensities","river_name":null,"geocode_query":"India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"The highest 15-year return-period P intensities (>200 mm d−1 ) over land were found in parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and over oceans in the intertropical convergence zone (Fig. 2b)17,48,58,59. Conversely, the lowest 15-year return-period P intensities (<50mm d−1 ) were found in the desert regions of the world, at high latitudes where frontal P dominates, and over southeastern parts of the Atlantic and Pacific oceans where semi-permanent anticyclones prevail54,60. The PPDIST 15-year return-period daily P climatology (Fig. 2b) exhibits reasonable visual agreement with a maximum one-day P climatology derived by Dietzsch et al. 33 (their Fig. 5d) from the gauge-based GPCC Full Data Daily dataset34, as well as with a 3.4-year return-period 3-hourly P climatology derived by Beck et al. 52 (their Fig. 7) from the satellite-, reanalysis-, and gauge-based MSWEP dataset52.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Western equatorial coast of Africa","kind":"region","role":"region with highest 15-year return-period precipitation intensities","river_name":null,"geocode_query":"Western equatorial coast Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"The highest 15-year return-period P intensities (>200 mm d−1 ) over land were found in parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and over oceans in the intertropical convergence zone (Fig. 2b)17,48,58,59. Conversely, the lowest 15-year return-period P intensities (<50mm d−1 ) were found in the desert regions of the world, at high latitudes where frontal P dominates, and over southeastern parts of the Atlantic and Pacific oceans where semi-permanent anticyclones prevail54,60. The PPDIST 15-year return-period daily P climatology (Fig. 2b) exhibits reasonable visual agreement with a maximum one-day P climatology derived by Dietzsch et al. 33 (their Fig. 5d) from the gauge-based GPCC Full Data Daily dataset34, as well as with a 3.4-year return-period 3-hourly P climatology derived by Beck et al. 52 (their Fig. 7) from the satellite-, reanalysis-, and gauge-based MSWEP dataset52.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Intertropical Convergence Zone (ITCZ) over oceans","kind":"region","role":"region with high peak precipitation intensities","river_name":null,"geocode_query":"Intertropical Convergence Zone","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"The highest 15-year return-period P intensities (>200 mm d−1 ) over land were found in parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and over oceans in the intertropical convergence zone (Fig. 2b)17,48,58,59. Conversely, the lowest 15-year return-period P intensities (<50mm d−1 ) were found in the desert regions of the world, at high latitudes where frontal P dominates, and over southeastern parts of the Atlantic and Pacific oceans where semi-permanent anticyclones prevail54,60. The PPDIST 15-year return-period daily P climatology (Fig. 2b) exhibits reasonable visual agreement with a maximum one-day P climatology derived by Dietzsch et al. 33 (their Fig. 5d) from the gauge-based GPCC Full Data Daily dataset34, as well as with a 3.4-year return-period 3-hourly P climatology derived by Beck et al. 52 (their Fig. 7) from the satellite-, reanalysis-, and gauge-based MSWEP dataset52.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Atacama Desert","kind":"region","role":"region of particularly low precipitation occurrence","river_name":null,"geocode_query":"Atacama Desert, Chile","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-69.1328491,-23.8634189],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Atacama Desert, Antofagasta, Chile","basin_id":6040028300,"main_basin_id":6040028300,"basin_region":"South America","basin_area_km2":77887.8,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-10"},{"name":"Sahara Desert","kind":"region","role":"region of particularly low precipitation occurrence","river_name":null,"geocode_query":"Sahara Desert, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The P occurrence was particularly high (>0.5mm d−1 on >50% of days) in windward coastal areas of Chile, Colombia, the British Isles, and Norway, along the Pacific coast of North America, and over parts of Southeast Asia (Fig. 1b)16,48–51. Conversely, the P occurrence was particularly low (>0.5 mm d−1 on <1% of days) in the Atacama and Sahara deserts. At a daily time-scale using a 0.5 mm d−1 threshold, P was found to occur 34.4%, 24.9%, and 38.9% of days, on average, over the global, land, and ocean domains, respectively (Fig. 1b). At a 3-hourly time-scale using a 0.1mm 3h−1 threshold, P was found to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively (Supplement Fig. S2b). These estimates are in line with previous estimates derived from the satellite-based CMORPH product16 and from the satellite-, reanalysis-, and gauge-based MSWEP product52.","verified_in_page":true},"resolution_status":"resolved","coordinates":[25.66283,23.476203],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sahara Desert","basin_id":1040040250,"main_basin_id":1040040250,"basin_region":"Africa","basin_area_km2":219482.9,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-11","true_coordinates":[25.66283,23.4162027],"spread_km":6.7},{"name":"United States, South Central region","kind":"region","role":"area with overestimated precipitation intensity in IMERG and somewhat in PPDIST due to known satellite product issues","river_name":null,"geocode_query":"South Central United States, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-118.2914872,33.9891116],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Los Angeles, Los Angeles, CA, USA","basin_id":7040008720,"main_basin_id":7040008720,"basin_region":"North America and Caribbean","basin_area_km2":177193.6,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-12"},{"name":"Eastern Africa","kind":"region","role":"area where ERA5 reanalysis strongly overestimates precipitation intensity (rain bombs)","river_name":null,"geocode_query":"Eastern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[37.297204,1.897709],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Africa","basin_id":1041145090,"main_basin_id":1040008100,"basin_region":"Africa","basin_area_km2":230636.1,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-13","true_coordinates":[37.2972044,1.957709],"spread_km":6.7},{"name":"Amazon River Basin","kind":"basin","role":"region where IMERG overestimates precipitation occurrence due to passive microwave retrieval misinterpretations","river_name":"Amazon River","geocode_query":"Amazon River Basin, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.617501,-1.601702],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-14","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Lena River Basin","kind":"basin","role":"region where IMERG overestimates precipitation occurrence due to passive microwave retrieval misinterpretations","river_name":"Lena River","geocode_query":"Lena River Basin, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[117.61249,63.596326],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040264710,"main_basin_id":3040009130,"basin_region":"Siberia","basin_area_km2":455235.5,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-15","true_coordinates":[117.6124900354569,63.65632566504951],"spread_km":6.7},{"name":"Ob River Basin","kind":"basin","role":"region where IMERG overestimates precipitation occurrence due to passive microwave retrieval misinterpretations","river_name":"Ob River","geocode_query":"Ob River Basin, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.728165,59.596797],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040386960,"main_basin_id":3040001840,"basin_region":"Siberia","basin_area_km2":1038714.1,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-16","true_coordinates":[74.72816505525375,59.65679690185249],"spread_km":6.7},{"name":"Japan","kind":"region","role":"region where IMERG tends to underestimate precipitation intensity due to complex topography","river_name":null,"geocode_query":"Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"West coast of India","kind":"region","role":"region where IMERG tends to underestimate precipitation intensity due to complex topography","river_name":null,"geocode_query":"West Coast India, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Additionally, ERA5 generally showed lower values for the highest return periods (Fig. 3), which is at least partly due to the spatial scale mismatch between point-scale observations and grid-box averages43,47,98,99. Conversely, ERA5 strongly overestimated the P intensity over several grid cells in eastern Africa in particular (Fig. 2c), likely due to the presence of so-called “rain bombs” caused by numerical artifacts100. The overly high P occurrence of IMERG in the tropics (Fig. 1d) may be at least partly attributable to the inclusion of relatively noisy P estimates from the SAPHIR sensor on board the tropical-orbiting Megha-Tropiques satellite46. Similar to the TMPA 3B42 and CMORPH satellite products, IMERG overestimated the P occurrence over several large rivers (notably the Amazon, the Lena, and the Ob), likely due to misinterpretation of the emissivity of small inland water bodies as P by the passive microwave P retrieval algorithms101.","verified_in_page":true},"resolution_status":"resolved","coordinates":[70.1232845,23.0838731],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ashapura Society, Plot No. 202, Bharat Nagar, Ward 11, Maheshwari Nagar, Gandhidham, Gujarat 370240, India","basin_id":4040032890,"main_basin_id":4040032890,"basin_region":"Central and South-East Asia","basin_area_km2":11393.5,"hydrology_source":"hydrobasins","site_id":"doc-f5ec54ca8975eb044b9a480c-site-18"}],"limitations":["Uncertainty estimates may be underestimated because only uncertainty in the mean annual precipitation predictor was accounted for, not in other predictors.","Low threshold precipitation occurrence estimates (≤0.25 mm) should be interpreted with caution due to gauge detection limits and underreporting of light precipitation.","Gauge observations suffer from wind-induced under-catch bias, underestimating rainfall and snowfall to some degree.","PPDIST climatologies represent point-scale precipitation; grid-box averages differ and require additional transformation for many applications.","ERA5 reanalysis artifacts, such as overestimated precipitation intensities in eastern Africa, are reflected in PPDIST estimates, albeit reduced.","PPDIST climatologies show spatial discontinuities at 60°N/S and at some coastlines due to data source differences and IMERG data coverage limitations."],"river_ids":[],"site_ids":["doc-f5ec54ca8975eb044b9a480c-site-0","doc-f5ec54ca8975eb044b9a480c-site-2","doc-f5ec54ca8975eb044b9a480c-site-4","doc-f5ec54ca8975eb044b9a480c-site-5","doc-f5ec54ca8975eb044b9a480c-site-6","doc-f5ec54ca8975eb044b9a480c-site-10","doc-f5ec54ca8975eb044b9a480c-site-11","doc-f5ec54ca8975eb044b9a480c-site-12","doc-f5ec54ca8975eb044b9a480c-site-13","doc-f5ec54ca8975eb044b9a480c-site-14","doc-f5ec54ca8975eb044b9a480c-site-15","doc-f5ec54ca8975eb044b9a480c-site-16","doc-f5ec54ca8975eb044b9a480c-site-18"],"anchor":[-5.707144700000001,54.65837269999999],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[1040040250,1041145090,2040054200,3040264710,3040386960,4040032890,5040015660,6040027000,6040028300,6040280400,7040008720,7040050930,8040002260],"main_basin_ids":[1040008100,1040040250,2040054200,3040001840,3040009130,4040032890,5040015660,6040007000,6040027000,6040028300,7040008720,7040050930,8040002260]},{"id":"doc-0a8299cd7702d12c9c43c71d","title":"Estimating surface solar irradiance from geostationary Himawari-8 over Australia: A physics-based method with calibration","display_title":"Solar Irradiance Estimation from Himawari-8","doi":"https://doi.org/10.1016/j.solener.2021.03.029","theme":"Other science","color":"#d1b5e7","year":2021,"publication_date":"2021-03-30","authors":["Yi Qin","Jing Huang","Tim R. McVicar","Sam West","Maryam Mahsal Khan","Andy Steven"],"summary":"This study developed and validated a physics-based method calibrated with ground measurements to accurately estimate surface solar irradiance components (global, direct, diffuse) across Australia from the Himawari-8 geostationary satellite data. Calibration accounted for factors such as water vapor and elevation effects. The method showed consistently low bias (~4 W/m2) and reliable separation of irradiance components over diverse climates and cloud conditions, suitable for operational and global application with minimal ancillary data.","finding":"The calibrated physics-based method using Himawari-8 data produces surface solar irradiance estimates across Australia with low biases (~4 W/m2) and successfully separates direct and diffuse components at multiple time scales and under all-sky and cloudy conditions.","finding_evidence":{"passage_id":"p010-001","page":10,"quote":"Conclusion A calibrated physics-based method to estimate the surface solar irradiance (SSI) from the Advanced Himawari Imager (AHI) sensor on the geostationary Himawari-8 satellite was developed. While major factors influencing SSI were considered by the physical modelling step, minor factors were accounted for through a calibration step against insitu measurements. This calibration step was conducted by deriving calibration coefficients using data from the preceding years and then applying the calibration to the following years. Comprehensive validation was conducted for all the SSI components (GHI, DNI, DirHI and DHI) under both all-sky and cloudy-sky conditions, and for four time scales (instantaneous, hourly, daily and monthly). Our method performed well showing consistently low biases (~4 W/m2 ) after calibration and was able to accurately separate the diffuse and direct components while maintaining relatively low RMSE.","verified_in_page":true},"topics":["satellite remote sensing","solar irradiance","solar energy","radiative transfer","cloud optical properties","surface radiation","calibration methods","validation studies"],"study_period":"2015-07 to 2019-04","locations":[{"name":"Australia","kind":"region","role":"study area for solar irradiance estimation and validation","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Steven d a CSIRO Oceans and Atmosphere, Canberra, Australia b CSIRO Land and Water, Canberra, Australia c CSIRO Energy, Newcastle, Australia d CSIRO Oceans and Atmosphere, Brisbane, Australia ARTICLE INFO Keywords: Solar energy Solar irradiance Solar radiation Solar insolation Solar flux Solar exposure Remote sensing Himawari AHI ABSTRACT Surface Solar Irradiance (SSI) is required for solar energy planning and adoption, and is a fundamental parameter in modelling weather, climate, ecosystem and agricultural activities. Herein a time series based radiative transfer model was developed to simultaneously retrieve properties of clouds, aerosols and surface albedo, which were in turn used to calculate the components of SSI: i.e., global, direct and diffuse irradiance. The calculated results were calibrated across the Australian continent against in-situ measurements to account for minor factors (e.g., water vapor) not considered by the physics-based method.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Darwin, Northern Territory, Australia","kind":"station","role":"ground validation and calibration site (latitude: -12.42, longitude: 130.89)","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-007","page":3,"quote":"Site ID Available (months) Latitude (◦N) Longitude (◦E) Elevation (m) Mean GHI (W/m2 ) Mean DNI (W/m2 ) Darwin DAR (1) 07/15 – 04/19 (44) − 12.42 130.89 25 597.4 562.7 Broome BRM (2) 07/15 – 04/19 (44) − 17.95 122.24 17 647.6 700.9 Townsville TVL (3) 01/17 – 04/19 (27) − 19.25 146.77 183 624.6 621.2 Rockhampton RHM (4) 07/15 – 04/19 (44) − 23.38 150.48 11 599.0 615.5 Alice Springs AS (5) 07/15 – 04/19 (46) − 23.80 133.89 545 650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.861757,-12.406164],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-0a8299cd7702d12c9c43c71d-site-1","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Broome, Western Australia, Australia","kind":"station","role":"ground validation and calibration site (latitude -17.95, longitude 122.24)","river_name":null,"geocode_query":"Broome, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-007","page":3,"quote":"Site ID Available (months) Latitude (◦N) Longitude (◦E) Elevation (m) Mean GHI (W/m2 ) Mean DNI (W/m2 ) Darwin DAR (1) 07/15 – 04/19 (44) − 12.42 130.89 25 597.4 562.7 Broome BRM (2) 07/15 – 04/19 (44) − 17.95 122.24 17 647.6 700.9 Townsville TVL (3) 01/17 – 04/19 (27) − 19.25 146.77 183 624.6 621.2 Rockhampton RHM (4) 07/15 – 04/19 (44) − 23.38 150.48 11 599.0 615.5 Alice Springs AS (5) 07/15 – 04/19 (46) − 23.80 133.89 545 650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[122.300072,-17.96182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102208974,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Broome WA 6725, 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Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.851018,-19.211337],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652811,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Townsville City QLD 4810, 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Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.571374,-23.352346],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12126666,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rockhampton City QLD 4700, 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133.89 545 650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.858336,-23.64166],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, 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650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.670577,-28.746135],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":80910278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Geraldton WA 6530, 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650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.526461,-30.719],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61119362,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kalgoorlie - Boulder WA, 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Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.672093,-34.915148],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, 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650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.36551,-35.042598],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wagga Wagga NSW 2650, 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Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.918416,-37.765087],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Melbourne VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-0a8299cd7702d12c9c43c71d-site-11","true_coordinates":[144.9630576,-37.8136276],"spread_km":6.7},{"name":"Cape Grim, Tasmania, Australia","kind":"station","role":"ground validation and calibration site (latitude -40.68, longitude 144.69)","river_name":null,"geocode_query":"Cape Grim, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-007","page":3,"quote":"Site ID Available (months) Latitude (◦N) Longitude (◦E) Elevation (m) Mean GHI (W/m2 ) Mean DNI (W/m2 ) Darwin DAR (1) 07/15 – 04/19 (44) − 12.42 130.89 25 597.4 562.7 Broome BRM (2) 07/15 – 04/19 (44) − 17.95 122.24 17 647.6 700.9 Townsville TVL (3) 01/17 – 04/19 (27) − 19.25 146.77 183 624.6 621.2 Rockhampton RHM (4) 07/15 – 04/19 (44) − 23.38 150.48 11 599.0 615.5 Alice Springs AS (5) 07/15 – 04/19 (46) − 23.80 133.89 545 650.9 771.2 Geraldton GRT (6) 01/17 – 04/19 (27) − 28.80 114.70 30 630.6 706.5 Kalgoorlie KGL (7) 01/17 – 04/19 (26) − 30.78 121.45 365 625.3 720.9 Adelaide ADL (8) 07/15 – 04/19 (45) − 34.95 138.52 7 568.1 605.7 Wagga Wagga WW (9) 07/15 – 04/19 (44) − 35.16 147.46 183 580.5 650.1 Canberra CBR 07/15 – 06/16 (12) − 35.27 149.11 583 557.1 637.6 Melbourne MEL (10) 07/15 – 04/19 (45) − 37.67 144.83 115 538.6 532.5 Cape Grim CG (11) 07/15 – 04/19 (44) − 40.68 144.69 70 529.2 463.6 Fig. 1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Calibration depends on availability of in-situ data, limiting applicability in data-sparse regions.","Latitude and elevation effects addressed via calibration rather than full physical modeling.","Coordinates of stations are not specified exactly, latitude and longitude given but no numerical values extracted due to lack of explicit coordinates in text.","Surface elevation treated as constant in physical model and corrected in calibration."],"river_ids":[],"site_ids":["doc-0a8299cd7702d12c9c43c71d-site-1","doc-0a8299cd7702d12c9c43c71d-site-2","doc-0a8299cd7702d12c9c43c71d-site-3","doc-0a8299cd7702d12c9c43c71d-site-4","doc-0a8299cd7702d12c9c43c71d-site-5","doc-0a8299cd7702d12c9c43c71d-site-6","doc-0a8299cd7702d12c9c43c71d-site-7","doc-0a8299cd7702d12c9c43c71d-site-8","doc-0a8299cd7702d12c9c43c71d-site-9","doc-0a8299cd7702d12c9c43c71d-site-10","doc-0a8299cd7702d12c9c43c71d-site-11"],"anchor":[133.858336,-23.64166],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073420,5040087690,5040491370,5040591050],"main_basin_ids":[5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073410,5040073420,5040087590,5040087690]},{"id":"doc-0c310dfd545f8b053b2535c6","title":"Streamflow stationarity in a changing world","display_title":"Global Streamflow Stationarity in a Changing World","doi":"https://doi.org/10.1088/1748-9326/ac08c1","theme":"Flow & ecology","color":"#80edc2","year":2021,"publication_date":"2021-06-01","authors":["Yuting Yang","Michael L. Roderick","Dawen Yang","Zhengrong Wang","Fangzheng Ruan","Tim R. McVicar","Shulei Zhang","Hylke E. Beck"],"summary":"This study comprehensively assesses the long-term stationarity of annual streamflow across 11,069 global catchments using extensive observational data. It finds that 79% of minimally human-impacted catchments maintain stationarity in annual streamflow despite historical climate change, while only 38% of catchments with substantial human impacts remain stationary. Human activities like irrigation, reservoir operations, and urbanization significantly increase the prevalence of non-stationary streamflow regimes.","finding":"Historical climate change alone has not led to non-stationarity in annual streamflow in most catchments (79%), whereas direct human interventions drastically reduce stationarity to 38%, demonstrating the dominant impact of human activities over climate change on freshwater system variability.","finding_evidence":{"passage_id":"p002-002","page":2,"quote":"E-mail: yuting_yang@tsinghua.edu.cn Keywords: streamflow stationarity, climate change, human intervention Supplementary material for this article is available online Abstract Whether river flows remain stationary is of great concern to hydrologists, water engineers, and society in general, yet is subject to substantial debate. Here we provide the first comprehensive assessment of the long-term stationarity of annual streamflow for 11 069 catchments globally. Our observation-based evidence shows that the long-term annual streamflow remains stationary in 79% of catchments with minimal human disturbance, indicating that historical climate change alone has not led to non-stationarity in annual streamflow series in most catchments. In direct contrast, we found streamflow has remained stationary in only 38% of those catchments where substantial human interventions have occurred.","verified_in_page":true},"topics":["streamflow stationarity","climate change","human intervention","trend analysis","hydrology","water management","catchment hydrology","anthropogenic impacts"],"study_period":null,"locations":[{"name":"central-north USA","kind":"region","role":"region showing widespread non-stationary increases in annual streamflow likely due to increased precipitation","river_name":null,"geocode_query":"central-north USA, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Beside central-north USA, nonstationary annual Q increases are generally concentrated in high latitudes of the North Hemisphere, which is likely caused by enhanced snow and/or glacier melting under warming in these regions (Huss et al 2010, Berghuijs et al 2014, IPCC 2019) (supplementary figure S8). In contrast, non-stationary annual Q decreases are more found in mid-low latitudes and in semiarid regions, which is found to be primarily caused by decreased local moisture recycling and atmospheric moisture inflow (so less precipitation) in these areas under climate change (Zhou et al 2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-81.657623,41.4928438],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central, Cleveland, OH, USA","basin_id":7040402430,"main_basin_id":7040034520,"basin_region":"North America and Caribbean","basin_area_km2":263811.2,"hydrology_source":"hydrobasins","site_id":"doc-0c310dfd545f8b053b2535c6-site-0"},{"name":"mid-Yangtze River basin (Hankou station)","kind":"station","role":"longest streamflow record (154 years) used to represent the mid-Yangtze River basin","river_name":"Yangtze River","geocode_query":"Hankou, mid-Yangtze River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"The gaps in the remaining Q series were filled with the climatological averages of observed Q in the remaining years and the resultant daily or monthly time series were aggregated to obtain annual Q series. As a quality control check, we compared the resulting mean annual Q with mean annual precipitation (Beck et al 2020) for each catchment and excluded a further 113 catchments where mean annual Q was higher than mean annual precipitation. As a result, 11 069 catchments with at least 30 years continuous annual Q record were identified and used for the analysis. The longest record is 154 years (1865–2018; the Hankou station representing the mid-Yangtze River basin, see supplementary figure S2) while the average (±1 standard deviation) record length is 56.7 (± 21.1) years (supplementary figure S1). Three types of human interventions on Q were considered: (a) irrigation; (b) water impoundments (dams and reservoirs); and (c) urban areas (supplementary figure S3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[112.719229,29.720415],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yangtze River, Shishou, Jingzhou, China, 434404","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-0c310dfd545f8b053b2535c6-site-1","true_coordinates":[112.650141,29.720415],"spread_km":6.7},{"name":"Oceania","kind":"region","role":"continent showing largest contrast in streamflow stationarity between natural and human-impacted catchments","river_name":null,"geocode_query":"Oceania","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North America","kind":"region","role":"continent included in global analysis showing human impacts increased non-stationarity","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.165581,54.495961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-0c310dfd545f8b053b2535c6-site-3","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Europe","kind":"region","role":"continent included in analysis showing increased non-stationarity with human interventions","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Asia","kind":"region","role":"continent included in analysis showing increased non-stationarity with human interventions","river_name":null,"geocode_query":"Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"South America","kind":"region","role":"continent included in analysis showing increased non-stationarity with human interventions","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Africa","kind":"region","role":"continent included in analysis showing increased non-stationarity with human interventions","river_name":null,"geocode_query":"Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Similar findings that human-impacted catchments show an evident higher proportion of nonstationary annual Q series than NAT catchments obtained at the global scale retains at the continental scale (figure 4). For the six major continents, the proportion of non-stationary annual Q series varies from 16.8% (Oceania) to 36.3% (Africa) in NAT catchments, and increases to 55.4% (Asia) ∼69.1% (South America) in HUMAN catchments. This result suggests that in all major continents: (a) historical natural Q remains largely stationary; and (b) direct human impacts greatly outweigh the climate change impacts on annual Q series. The largest contrast between NAT and HUMAN catchments are found in Oceania, where 59.8% of the HUMAN catchments registered a non-stationary annual Q series, which is about 3.6 times more than that of NAT catchments, followed by North America (3.1 times), Europe (2.7 times), Asia (2.6 times), South America (2.3 times) and Africa (1.8 times). 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"}],"limitations":["Use of static, one-time human intervention data to classify catchments may underestimate temporal changes in human impacts.","Limited temporal record lengths for many catchments restrict capacity to detect variance changes in streamflow, with an estimated requirement of ~296 years to robustly detect variance changes.","Uneven spatial distribution of streamflow stations, with data concentrated in North America and Europe, may influence global representativeness.","Human intervention time series data not available, so dynamic changes in interventions over time are not accounted for."],"river_ids":[],"site_ids":["doc-0c310dfd545f8b053b2535c6-site-0","doc-0c310dfd545f8b053b2535c6-site-1","doc-0c310dfd545f8b053b2535c6-site-3"],"anchor":[-81.657623,41.4928438],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[4040783120,7040021740,7040402430],"main_basin_ids":[4040009880,7040021740,7040034520]},{"id":"doc-2452e2d5d7b5ca42288b0d9b","title":"Groundwater sources for the Mataranka Springs (Northern Territory, Australia)","display_title":"Groundwater Sources for Mataranka Springs, NT","doi":"https://doi.org/10.1038/s41598-021-03701-1","theme":"Groundwater","color":"#a8b9ff","year":2021,"publication_date":"2021-12-20","authors":["Sébastien Lamontagne","Axel Suckow","Christoph Gerber","Alec Deslandes","Cornelia Wilske","S. J. Tickell"],"summary":"This study investigates the sources of groundwater sustaining the Mataranka Springs Complex in Northern Territory, Australia, which is the headwater for the Roper River. Using environmental tracers including isotopes and noble gases, the study identifies contributions from two regional groundwater flow paths within the Cambrian Limestone Aquifer (CLA): the Daly Basin to the north and the Georgina Basin to the south, as well as a local younger recharge component. Additionally, evidence for a deeper, older groundwater source entering the springs was found, suggesting vertical connectivity with underlying geological formations. This characterization is essential for assessing the potential impacts of groundwater extraction and unconventional gas development in the region.","finding":"The Mataranka Springs Complex is primarily fed by the Daly and Georgina regional groundwater flow paths within the Cambrian Limestone Aquifer, with a significant but smaller contribution from local young recharge, and also receives input from deeper, older groundwater sources beneath the CLA as indicated by elevated radiogenic helium concentrations.","finding_evidence":{"passage_id":"p006-002","page":6,"quote":"However, a component of local recharge feeding the springs was also anticipated owing the less permeable Cretaceous sediment cover overlying much of the CLA elsewhere being absent in the area surrounding Elsey National Park, which should facilitate local recharge processes. The findings of this study are largely consistent with Karp. Based on salinity, ionic composition, δ2 H and δ18O, Rainbow Spring is primarily fed by the Daly flow path of the CLA whilst Bitter Spring, Warloch Pond Spring and Fig Tree Spring are primarily fed by the Georgina flow path of the CLA. The regional scale of the flow systems is also reflected in the mixture of groundwater ages in the springs16. Tritium>0.1 TU in Fig Tree Spring and in several groundwater samples also demonstrated a contribution from recent (and probably local) recharge to the Georgina flow path of the CLA, especially at the eastern end of the complex.","verified_in_page":true},"topics":["groundwater flow","environmental tracers","isotopic analysis","hydrogeology","aquifer recharge","karst systems","groundwater age dating","vertical hydraulic connectivity"],"study_period":"2019","locations":[{"name":"Mataranka Springs Complex","kind":"region","role":"study area and main groundwater discharge zone","river_name":null,"geocode_query":"Mataranka Springs Complex, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2021) 11:24288 | https://doi.org/10.1038/s41598-021-03701-1 www.nature.com/scientificreports Groundwater sources for the Mataranka Springs (Northern Territory, Australia) Sébastien Lamontagne1*, Axel Suckow1 , ChristophGerber1 , Alec Deslandes1 , CorneliaWilske1,2 & StevenTickell3 The Mataranka Springs Complex is the headwater of the iconic Roper River of northern Australia. Using environmental tracers measured in springs and nearby boreholes, the origin of groundwater contributing to the springs was evaluated to help assess the impact of proposed groundwater extraction in the Cambrian Limestone Aquifer (CLA) for irrigation agriculture and for hydraulic fracturing in the Beetaloo Sub-basin (an anticipated world-class unconventional gas reserve).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.1349624,-14.9227356],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769444,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Elsey NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-2452e2d5d7b5ca42288b0d9b-site-0"},{"name":"Cambrian Limestone Aquifer (CLA)","kind":"aquifer","role":"regional groundwater system feeding the springs and subject to proposed groundwater extraction","river_name":null,"geocode_query":"Cambrian Limestone Aquifer, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"The Cambrian Limestone Aquifer (CLA) is an extensive (474,000 km2 ) regional groundwater system and provides water resources for social, economic and cultural activities for regional areas of the Northern Territory of Australia (NT)1–3 . Additional groundwater extraction from the CLA is being considered to help promote economic development, including for hydraulic fracturing in Beetaloo Sub-basin, anticipated to be a world-class unconventional gas reserve4,5 . The CLA has several distinct groundwater flow systems and, for hydraulic fracturing in Beetaloo Sub-basin, groundwater would be extracted from its Georgina flow path (Fig. 1). This flow path originates from the more arid interior of the NT and flows northward to discharge into the Mataranka Springs Complex. These springs are also thought to be sustained by a shorter flow system from the north (the Daly flow path) originating at a groundwater divide between Katherine and Mataranka (Fig. 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Roper River","kind":"river","role":"river originating at Mataranka Springs Complex, baseflow potentially influenced by groundwater sources studied","river_name":"Roper River","geocode_query":"Roper River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2021) 11:24288 | https://doi.org/10.1038/s41598-021-03701-1 www.nature.com/scientificreports Groundwater sources for the Mataranka Springs (Northern Territory, Australia) Sébastien Lamontagne1*, Axel Suckow1 , ChristophGerber1 , Alec Deslandes1 , CorneliaWilske1,2 & StevenTickell3 The Mataranka Springs Complex is the headwater of the iconic Roper River of northern Australia. Using environmental tracers measured in springs and nearby boreholes, the origin of groundwater contributing to the springs was evaluated to help assess the impact of proposed groundwater extraction in the Cambrian Limestone Aquifer (CLA) for irrigation agriculture and for hydraulic fracturing in the Beetaloo Sub-basin (an anticipated world-class unconventional gas reserve).","verified_in_page":true},"resolution_status":"resolved","coordinates":[134.137543,-14.678888],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":120769657,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-2452e2d5d7b5ca42288b0d9b-site-2"},{"name":"Daly Basin","kind":"region","role":"Northern regional flow path within CLA contributing to Mataranka Springs groundwater","river_name":null,"geocode_query":"Daly Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"The Cambrian Limestone Aquifer (CLA) is an extensive (474,000 km2 ) regional groundwater system and provides water resources for social, economic and cultural activities for regional areas of the Northern Territory of Australia (NT)1–3 . Additional groundwater extraction from the CLA is being considered to help promote economic development, including for hydraulic fracturing in Beetaloo Sub-basin, anticipated to be a world-class unconventional gas reserve4,5 . The CLA has several distinct groundwater flow systems and, for hydraulic fracturing in Beetaloo Sub-basin, groundwater would be extracted from its Georgina flow path (Fig. 1). This flow path originates from the more arid interior of the NT and flows northward to discharge into the Mataranka Springs Complex. These springs are also thought to be sustained by a shorter flow system from the north (the Daly flow path) originating at a groundwater divide between Katherine and Mataranka (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.76424,-13.734953],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102212844,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Daly River NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-2452e2d5d7b5ca42288b0d9b-site-3","true_coordinates":[130.7107425,-13.7649526],"spread_km":6.7},{"name":"Georgina Basin","kind":"region","role":"Southern regional flow path within CLA and location of Beetaloo Sub-basin contributing groundwater to Mataranka Springs","river_name":null,"geocode_query":"Georgina Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"The Cambrian Limestone Aquifer (CLA) is an extensive (474,000 km2 ) regional groundwater system and provides water resources for social, economic and cultural activities for regional areas of the Northern Territory of Australia (NT)1–3 . Additional groundwater extraction from the CLA is being considered to help promote economic development, including for hydraulic fracturing in Beetaloo Sub-basin, anticipated to be a world-class unconventional gas reserve4,5 . The CLA has several distinct groundwater flow systems and, for hydraulic fracturing in Beetaloo Sub-basin, groundwater would be extracted from its Georgina flow path (Fig. 1). This flow path originates from the more arid interior of the NT and flows northward to discharge into the Mataranka Springs Complex. These springs are also thought to be sustained by a shorter flow system from the north (the Daly flow path) originating at a groundwater divide between Katherine and Mataranka (Fig. 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Beetaloo Sub-basin","kind":"region","role":"Sub-basin within Georgina Basin, site of proposed hydraulic fracturing and potential source of deeper groundwater","river_name":null,"geocode_query":"Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2021) 11:24288 | https://doi.org/10.1038/s41598-021-03701-1 www.nature.com/scientificreports Groundwater sources for the Mataranka Springs (Northern Territory, Australia) Sébastien Lamontagne1*, Axel Suckow1 , ChristophGerber1 , Alec Deslandes1 , CorneliaWilske1,2 & StevenTickell3 The Mataranka Springs Complex is the headwater of the iconic Roper River of northern Australia. Using environmental tracers measured in springs and nearby boreholes, the origin of groundwater contributing to the springs was evaluated to help assess the impact of proposed groundwater extraction in the Cambrian Limestone Aquifer (CLA) for irrigation agriculture and for hydraulic fracturing in the Beetaloo Sub-basin (an anticipated world-class unconventional gas reserve).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.798706,-17.27706],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215708,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Beetaloo, Pamayu NT 0862, Australia","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-2452e2d5d7b5ca42288b0d9b-site-5","true_coordinates":[133.798706,-17.21706],"spread_km":6.7},{"name":"Rainbow Spring","kind":"other","role":"major spring sampled within Mataranka Springs Complex primarily fed by Daly Basin groundwater","river_name":null,"geocode_query":"Rainbow Spring, Mataranka, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"In general, the major ion composition for Rainbow Spring was most similar to the Daly flow path groundwater whilst the other springs were more similar to Georgina flow path groundwater. The highest salinity measured was in borehole RN034030, the only borehole located in the Antrim Plateau Volcanics within the complex. Borehole RN034230 is located in close proximity to Bitter Spring and had a nearly identical chemistry to the spring. Other boreholes located close to springs (RN035796 and RN034032) had chemistry similar to Georgina flow path groundwater. The boreholes on the northern (RN035860 and RN035519) and southern edge of the complex (RN035926, RN034031 and RN034038) were similar in ionic composition to Daly and Georgina Basin groundwater, respectively. Most spring and groundwater samples in the complex were depleted in Sr (0.005–1 μmol L–1) relative to regional CLA groundwater elsewhere (typically > 1 μmol L–1; Fig. 3e).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Bitter Spring","kind":"other","role":"major spring within Mataranka Springs Complex primarily fed by Georgina Basin groundwater","river_name":null,"geocode_query":"Bitter Spring, Mataranka, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"In general, the major ion composition for Rainbow Spring was most similar to the Daly flow path groundwater whilst the other springs were more similar to Georgina flow path groundwater. The highest salinity measured was in borehole RN034030, the only borehole located in the Antrim Plateau Volcanics within the complex. Borehole RN034230 is located in close proximity to Bitter Spring and had a nearly identical chemistry to the spring. Other boreholes located close to springs (RN035796 and RN034032) had chemistry similar to Georgina flow path groundwater. The boreholes on the northern (RN035860 and RN035519) and southern edge of the complex (RN035926, RN034031 and RN034038) were similar in ionic composition to Daly and Georgina Basin groundwater, respectively. Most spring and groundwater samples in the complex were depleted in Sr (0.005–1 μmol L–1) relative to regional CLA groundwater elsewhere (typically > 1 μmol L–1; Fig. 3e).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Fig Tree Spring","kind":"other","role":"minor spring with young groundwater component and high salinity suggesting local recharge input","river_name":null,"geocode_query":"Fig Tree Spring, Mataranka, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Major ions, Sr, 87Sr/86Sr, δ18O-H2O, δ2 H-H2O, 3 H, 14C-DIC were consistent with regional groundwater from the Daly and Georgina basins of the CLA as the sources of water sustaining the major springs (Rainbow and Bitter) and one of the minor springs (Warloch Pond). However, 3 H= 0.34 TU in another minor spring (Fig Tree) indicated an additional contribution from a young (probably local) source. High concentrations of radiogenic 4 He (> 10–7 cm3 STP g–1) at Rainbow Spring, Bitter Spring and in nearby groundwater also indicated an input of deeper, older groundwater. The presence of older groundwater within the CLA demonstrates the need for an appropriate baseline characterisation of the vertical exchange of groundwater in Beetaloo Sub-basin ahead of unconventional gas resource development.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Warloch Pond Spring","kind":"other","role":"minor spring sustained by Georgina Basin groundwater flow path","river_name":null,"geocode_query":"Warloch Pond Spring, Mataranka, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"There was a distinct 87Sr/86Sr signature between Daly (>0.7177) and Georgina flow path groundwater (<0.7177), with Rainbow Spring and Bitter Spring intermediate between the two (~0.7177). Fig Tree (0.7174) and Warloch Pond springs (0.7168) had 87Sr/86Sr within the range found for Georgina flow path groundwater. The Antrim Plateau Volcanics groundwater sample had the lowest 87Sr/86Sr (0.7159). The relatively low Sr concentrations in the complex indicate the geochemical conditions favour the precipitation of Sr minerals from groundwater. The Sr isotopic content indicate Rainbow Spring and Bitter Spring would be a mixture from the Daly and Georgina groundwater flow path whilst Fig Tree Spring and Warlock Pond Spring are sustained via the Georgina flow path. Stable isotopes of water. Two groups of samples can be distinguished based on δ2 H and δ18O.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Antrim Plateau Volcanics","kind":"other","role":"geological formation underlying CLA with groundwater showing highest radiogenic helium concentrations sampled via a borehole","river_name":null,"geocode_query":"Antrim Plateau Volcanics, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 Vol:.(1234567890) Scientific Reports | (2021) 11:24288 | https://doi.org/10.1038/s41598-021-03701-1 www.nature.com/scientificreports/ was consistent with an Exponential Model16, with inferred mean residence times ranging from 120 to 170 years for Fig Tree Spring to>1000 years for the other springs (Fig. 5a). Most spring and groundwater samples had radiogenic 4 He (that is, had 4 He concentrations in excess of atmospheric equilibrium, 4–5 × 10–8 cm3 Standard Temperature and Pressure (STP) g–1) with concentrations ranging from 4.6 × 10–8 to 2.6 × 10–6 cm3 STP g–1 (Figs. 5b,c). The highest 4 He concentration was found in the Antrim Plateau Volcanics groundwater sample. Whilst some accumulation of radiogenic 4 He during groundwater transport in the CLA is possible, the very high 4 He concentrations observed in many samples combined with the large spatial variability indicated a localised input of older groundwater from an underlying confined aquifer (or aquifers).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Elsey National Park","kind":"region","role":"protected area hosting Mataranka Springs with cultural and ecological significance; site access and cultural input acknowledged","river_name":null,"geocode_query":"Elsey National Park, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-005","page":10,"quote":"Hydrol. 555, 797–811. https://doi.org/10.1016/j.jhydr ol.2017.10.053 (2017). Acknowledgements We acknowledge the traditional owners (including the Mangarrayi and Yangman) as the custodian of the land and springs at Mataranka and are grateful for their input and support in accessing the springs, including at sacred sites. We thank Elsey National Park for granting access to the springs and the Northern Land Council for facilitating the collaboration with traditional owners. The study was funded by the Commonwealth Government’s Geological and Bioregional Assessment Program. Author contributions S.L. designed the study, interpreted the data and wrote the manuscript; A.S. designed the study; C.G. modelled the noble gas data; A.D. contributed to the design of the field program; S.T. helped design the study. C.W. interpreted the Sr and Sr isotopes data. All authors contributed to data interpretation and discussion of results and implications.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.167073,-14.9487307],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120771358,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Elsey National Park, Stuart Hwy, Mataranka NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-2452e2d5d7b5ca42288b0d9b-site-11"}],"limitations":["Lack of precise latitude and longitude coordinates for sites limits exact spatial referencing.","Absence of instrumentation and data from underlying geological formations limits quantification of deeper groundwater source contributions.","Data derived from a single sampling period at the end of the dry season might not capture seasonal variability.","Potential partial re-equilibration and degassing effects introduce uncertainty in noble gas and helium tracer interpretations.","The wider spatial extent and flowpath connectivity require further characterization to fully resolve aquifer interactions."],"river_ids":["ne-1081"],"site_ids":["doc-2452e2d5d7b5ca42288b0d9b-site-0","doc-2452e2d5d7b5ca42288b0d9b-site-2","doc-2452e2d5d7b5ca42288b0d9b-site-3","doc-2452e2d5d7b5ca42288b0d9b-site-5","doc-2452e2d5d7b5ca42288b0d9b-site-11"],"anchor":[133.1349624,-14.9227356],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040061670,5040064130,5040087780],"main_basin_ids":[5040061670,5040064130,5040087780]},{"id":"doc-272cbd7116a8b9076caa9ee3","title":"Standard assessments of climate forecast skill can be misleading","display_title":"Bias Correction Impacts ENSO Climate Forecast Skill","doi":"https://doi.org/10.1038/s41467-021-23771-z","theme":"Climate","color":"#98d4f1","year":2021,"publication_date":"2021-07-16","authors":["James S. Risbey","Dougal T. Squire","Amanda S. Black","Timothy DelSole","Chiara Lepore","Richard J. Matear","Didier P. Monselesan","Thomas Moore","Doug Richardson","Andrew Schepen","Michael K. Tippett","Carly R. Tozer"],"summary":"This study examines how different bias-correction methods affect assessments of forecast skill for the El Niño-Southern Oscillation (ENSO) using hindcast data from multiple climate models and simple regression models. By separating fair methods (using a climatology based only on past data) from unfair methods (using data from the forecast testing period itself), the authors demonstrate that unfair methods artificially inflate forecast skill by removing lead-time-dependent biases, thereby misrepresenting true forecast performance. They also show that model rankings and perceived skill for forecasting ENSO onset events vary with the chosen bias-correction approach and the periods tested.","finding":"Forecast skill assessments for ENSO are sensitive to bias-correction methods; specifically, 'unfair' methods that use the testing period data for bias correction artificially inflate skill and alter model rankings, while 'fair' methods reveal that most dynamical models do not outperform simple regression models.","finding_evidence":{"passage_id":"p005-001","page":5,"quote":"The relative model rankings at lead 6 (the longest lead for which we have all models) is shown for each of the bias correction methods. The changes in ranking between unfair and fair methods are striking. For example, florA and aer04 are among the most skillful models for unfair and among the least skillful for fair. The relative rankings change much less among the different variants of the fair method. Comparison of fair and unfair method Thus far we have seen that the dynamical models perform better relative to simple regression models when using unfair than fair. In this section we perform a direct comparison between unfair and fair methods for the same forecast model bias corrected in the two different ways. A direct comparison using fair and unfair methods for all dynamical models is shown in Fig. 3.","verified_in_page":true},"topics":["climate-forecasting","el-niño-southern-oscillation","model-bias","hindcast-skill","bias-correction","skill-assessment","seasonal-forecast","forecast-evaluation"],"study_period":"1982–2016","locations":[{"name":"Niño3.4 region (5°S–5°N; 170°W–120°W)","kind":"region","role":"study area defining ENSO SST index region","river_name":null,"geocode_query":"Niño3.4 region Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"whether dynamical models would perform better if forecast comparisons were restricted to just the more dynamically challenging onset aspects of El Niño and La Niña events. We drew no clear conclusions in this regard because the results for onset are critically sensitive to the direction of the change in forecast climatological bias in the models from calibration to testing periods. If this change in bias is toward warming, then warm event (El Niño) onset skill is high, and conversely if toward cooling, then cold event (La Niña) onset skill is high. However, since the change in climatological bias in the models is a function of the periods assessed (warm to cold, or cold to warm), then so too is the perception of relative skill for El Niño and La Niña onset. From the comparison of hindcast skill of fair and unfair methods here, we can deduce that the skill of actual forecasts of ENSO will be limited by the growth with lead time of climate-state-dependent biases in the dynamical models.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.447811,-8.783195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-272cbd7116a8b9076caa9ee3-site-0","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Pacific Ocean","kind":"basin","role":"region of principal ENSO climate variability and teleconnections","river_name":null,"geocode_query":"Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"This is mainly because it evolves on seasonal and longer timescales, and it provides discernable influences on weather systems across large swathes of the Pacific and Indian Ocean basins and into the Antarctic region6, so called teleconnections7. Here, we discuss the influence a number of methodological decisions can have on the assessment of predictive skill of ENSO forecasts. While we focus on evaluating ENSO forecast skill here, the issues arising in the interaction of forecast processing and skill apply more generally to all climate forecasts. History of ENSO forecast skill Forecasts of ENSO have been issued since the late 1980s8, with more routine forecasts emerging in the 1990’s to include those made through coupled general circulation models (CGCMs)9.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.455945,-8.753195],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"none","site_id":"doc-272cbd7116a8b9076caa9ee3-site-1","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Indian Ocean","kind":"basin","role":"influenced by ENSO teleconnections affecting climate variability","river_name":null,"geocode_query":"Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"This is mainly because it evolves on seasonal and longer timescales, and it provides discernable influences on weather systems across large swathes of the Pacific and Indian Ocean basins and into the Antarctic region6, so called teleconnections7. Here, we discuss the influence a number of methodological decisions can have on the assessment of predictive skill of ENSO forecasts. While we focus on evaluating ENSO forecast skill here, the issues arising in the interaction of forecast processing and skill apply more generally to all climate forecasts. History of ENSO forecast skill Forecasts of ENSO have been issued since the late 1980s8, with more routine forecasts emerging in the 1990’s to include those made through coupled general circulation models (CGCMs)9.","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.826172,-33.197551],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"none","site_id":"doc-272cbd7116a8b9076caa9ee3-site-2","true_coordinates":[81.826172,-33.137551],"spread_km":6.7},{"name":"Antarctic region","kind":"region","role":"teleconnection-affected downstream climate region related to ENSO","river_name":null,"geocode_query":"Antarctic region","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"This is mainly because it evolves on seasonal and longer timescales, and it provides discernable influences on weather systems across large swathes of the Pacific and Indian Ocean basins and into the Antarctic region6, so called teleconnections7. Here, we discuss the influence a number of methodological decisions can have on the assessment of predictive skill of ENSO forecasts. While we focus on evaluating ENSO forecast skill here, the issues arising in the interaction of forecast processing and skill apply more generally to all climate forecasts. History of ENSO forecast skill Forecasts of ENSO have been issued since the late 1980s8, with more routine forecasts emerging in the 1990’s to include those made through coupled general circulation models (CGCMs)9.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North American Multimodel Ensemble (NMME) hindcast region","kind":"region","role":"origin of dynamical model hindcast data set used in skill assessment","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The general results presented here are unchanged if Niño3 or Niño4 are used instead of Niño3.4. Dynamical models. The models used here are from the North American Multimodel Ensemble (NMME)16 and the Copernicus Climate Change Service (C3S)38. The advantage of the NMME archive is that it contains a rich set of runs from many CGCMs over a relatively long period from 1982 to the present. Another advantage is that the NMME archive has been the source of a number of prior ENSO skill assessments15,16,29,30,39–41. We selected models from these two archives that satisfied the criteria of: raw (uncorrected) hindcast data available, lead-time forecasts out to at least 6 months, at least 10 ensemble members per forecast, and hindcasts/forecasts span the period 1982–2015.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.15173,54.525961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-272cbd7116a8b9076caa9ee3-site-4","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Copernicus Climate Change Service (C3S) forecast region","kind":"region","role":"forecast data source for dynamical model skill comparisons","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"The general results presented here are unchanged if Niño3 or Niño4 are used instead of Niño3.4. Dynamical models. The models used here are from the North American Multimodel Ensemble (NMME)16 and the Copernicus Climate Change Service (C3S)38. The advantage of the NMME archive is that it contains a rich set of runs from many CGCMs over a relatively long period from 1982 to the present. Another advantage is that the NMME archive has been the source of a number of prior ENSO skill assessments15,16,29,30,39–41. We selected models from these two archives that satisfied the criteria of: raw (uncorrected) hindcast data available, lead-time forecasts out to at least 6 months, at least 10 ensemble members per forecast, and hindcasts/forecasts span the period 1982–2015.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Australia","kind":"region","role":"application area where incorrect bias-corrected hindcast skill has influenced agricultural climate forecast utility assessments","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-003","page":11,"quote":"For example, much of the assessed value of seasonal climate forecasts for Australian agriculture is based on hindcasts processed using unfair methods31, which therefore contain artificial skill. This can be misleading for farmers who must make tactical decisions using real forecasts, not hindcasts, and thus are likely to have lower forecast skill than expected. Path ahead When the assessment of model skill is fair, the skill of the NMME and C3S dynamical forecast models for ENSO is generally not demonstrably better than simple regression models. We tested Fig. 5 Skill scores for El Niño and La Niña onset using the fair method. Random walk skill score (RWSS) at 6 months lead relative to linear regression for El Niño and La Niña onset (a and d). The dashed lines in the panel represent forecast comparison periods that do not correspond to `onset' comparisons. The final random walk skill score, RWSSn, is shown as a function of lead time for El Niño and La Niña onset in panels b and e.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Study depends on limited hindcast observational record (1982–2016) with a small number of ENSO events limiting statistical power","Latitude/longitude coordinates for Niño3.4 and forecast model areas not precisely provided in text","Bias-correction methods and forecasts assessed only for specific model sets from NMME and C3S archives"],"river_ids":[],"site_ids":["doc-272cbd7116a8b9076caa9ee3-site-0","doc-272cbd7116a8b9076caa9ee3-site-1","doc-272cbd7116a8b9076caa9ee3-site-2","doc-272cbd7116a8b9076caa9ee3-site-4"],"anchor":[-105.15173,54.525961],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[7040021740],"main_basin_ids":[7040021740]},{"id":"doc-289dd493c461b4d3b5f69720","title":"A spatial causal network approach for multi-stressor risk analysis and mapping for environmental impact assessments","display_title":"Spatial causal network for gas impact EIA","doi":"https://doi.org/10.1016/j.scitotenv.2021.149845","theme":"Water management","color":"#eeabac","year":2021,"publication_date":"2021-08-23","authors":["Luk Peeters","Kate L. Holland","Cameron Huddlestone‐Holmes","Andrew J. Boulton"],"summary":"This study presents a spatial causal network approach for environmental impact assessment (EIA), applied to unconventional gas resource development in the Cooper Basin, central Australia. The method systematically identifies causal pathways from activities through stressors to valued assets and evaluates residual risk spatially, enabling transparent, consistent, and spatially explicit assessment. It reveals areas of potential concern for species and ecosystems and supports targeted monitoring and mitigation.","finding":"The spatial causal network approach applied in the Cooper Basin identified multiple pathways of potential concern from unconventional gas activities to valued assets, but no pathways of potentially high concern, with existing mitigation and regulation able to reduce most risks. Spatial mapping highlights where residual risks overlap with species and ecosystems, enabling focused monitoring and management.","finding_evidence":{"passage_id":"p010-002","page":10,"quote":"This reflects the regulatory frameworks currently in place to control unconventional gas resource development in the region and that many of the proponents already have published protocols demonstrating how their activities will meet regulatory requirements. On the right-hand side of the figure, there are many deep purple links. This indicates that for many natural processes, a change in their state will affect the AEs and, more importantly, that these effects are unavoidable and cannot be mitigated readily. The central upper part of the network diagram depicts the nodes related to subsurface features and to contamination and is dominated by links that are either not possible or are possible but not material. This reflects how the physical conditions of the Cooper region limit potential impacts of unconventional gas resource development in the region on subsurface features.","verified_in_page":true},"topics":["environmental-impact-assessment","spatial-analysis","causal-networks","risk-assessment","unconventional-gas-development","mitigation-strategies","monitoring","aquifers"],"study_period":null,"locations":[{"name":"Cooper Basin","kind":"basin","role":"study area","river_name":null,"geocode_query":"Cooper Basin, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"development in the Cooper Basin, central Australia. Unconventional gas resources are generally produced from complex geological systems that prevent or significantly limit the migration of gas and require innovative technological solutions for extraction. In the study area, the most prospective gas resources include deep coal, tight gas and shale gas (Holland et al., 2020). The maximum disturbance footprint for the development scenario is estimated to be 586-7350 km2 and assumes that up to 1180 wells would need to be drilled with 4 to 8 wells per well pad over a 50-year period. The study area covers approximately 130,000 km2 (Fig. 4) of mostly flat semi-arid floodplain and native grasslands with scattered woody vegetation. Surface water availability is unpredictable, reflecting highly variable rainfall, and is supplemented by runoff from the north.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Cooper Creek","kind":"river","role":"major river system in study area","river_name":"Cooper Creek","geocode_query":"Cooper Creek, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This runoff flows through the Barcoo and Thomson river systems and into Cooper Creek, which flows across expansive floodplains south of Windorah. Streamflow is reduced by about half due to high evapotranspiration rates between the junction of the Thomson and Barcoo rivers and the South Australian border (Holland et al., 2020). Natural systems in the Cooper region are driven by resource pulses and boom–bust ecological dynamics, shaping the high diversity of ecological communities and species (Arthington and Balcombe, 2011) The Cooper region supports the Ramsar-listed Coongie Lakes, many waterholes and terminal lakes, 8 nationally important wetlands and 26 taxa (plants, reptiles, birds and mammals) listed as threatened (critically endangered, endangered or vulnerable) (Holland et al., 2020). It also hosts several areas of Indigenous and cultural significance, especially along Cooper Creek and its associated waterholes which have a long history as part of a traditional trade route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.290244,-27.017703],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cooper Creek, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-289dd493c461b4d3b5f69720-site-1","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Barcoo River","kind":"river","role":"source of runoff into study region","river_name":"Barcoo River","geocode_query":"Barcoo River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This runoff flows through the Barcoo and Thomson river systems and into Cooper Creek, which flows across expansive floodplains south of Windorah. Streamflow is reduced by about half due to high evapotranspiration rates between the junction of the Thomson and Barcoo rivers and the South Australian border (Holland et al., 2020). Natural systems in the Cooper region are driven by resource pulses and boom–bust ecological dynamics, shaping the high diversity of ecological communities and species (Arthington and Balcombe, 2011) The Cooper region supports the Ramsar-listed Coongie Lakes, many waterholes and terminal lakes, 8 nationally important wetlands and 26 taxa (plants, reptiles, birds and mammals) listed as threatened (critically endangered, endangered or vulnerable) (Holland et al., 2020). It also hosts several areas of Indigenous and cultural significance, especially along Cooper Creek and its associated waterholes which have a long history as part of a traditional trade route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.361338,-25.091228],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006204,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barcoo, QLD, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-289dd493c461b4d3b5f69720-site-2","true_coordinates":[142.3613378,-25.0312277],"spread_km":6.7},{"name":"Thomson River","kind":"river","role":"source of runoff into study region","river_name":"Thomson River","geocode_query":"Thomson River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This runoff flows through the Barcoo and Thomson river systems and into Cooper Creek, which flows across expansive floodplains south of Windorah. Streamflow is reduced by about half due to high evapotranspiration rates between the junction of the Thomson and Barcoo rivers and the South Australian border (Holland et al., 2020). Natural systems in the Cooper region are driven by resource pulses and boom–bust ecological dynamics, shaping the high diversity of ecological communities and species (Arthington and Balcombe, 2011) The Cooper region supports the Ramsar-listed Coongie Lakes, many waterholes and terminal lakes, 8 nationally important wetlands and 26 taxa (plants, reptiles, birds and mammals) listed as threatened (critically endangered, endangered or vulnerable) (Holland et al., 2020). It also hosts several areas of Indigenous and cultural significance, especially along Cooper Creek and its associated waterholes which have a long history as part of a traditional trade route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.078154,-23.585345],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Thomson River, Longreach QLD 4730, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-289dd493c461b4d3b5f69720-site-3","true_coordinates":[144.0781535127636,-23.52534461181954],"spread_km":6.7},{"name":"Windorah","kind":"settlement","role":"regional town near study floodplains","river_name":null,"geocode_query":"Windorah, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This runoff flows through the Barcoo and Thomson river systems and into Cooper Creek, which flows across expansive floodplains south of Windorah. Streamflow is reduced by about half due to high evapotranspiration rates between the junction of the Thomson and Barcoo rivers and the South Australian border (Holland et al., 2020). Natural systems in the Cooper region are driven by resource pulses and boom–bust ecological dynamics, shaping the high diversity of ecological communities and species (Arthington and Balcombe, 2011) The Cooper region supports the Ramsar-listed Coongie Lakes, many waterholes and terminal lakes, 8 nationally important wetlands and 26 taxa (plants, reptiles, birds and mammals) listed as threatened (critically endangered, endangered or vulnerable) (Holland et al., 2020). It also hosts several areas of Indigenous and cultural significance, especially along Cooper Creek and its associated waterholes which have a long history as part of a traditional trade route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.655913,-25.48254],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Windorah QLD 4481, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-289dd493c461b4d3b5f69720-site-4","true_coordinates":[142.6559128,-25.4225397],"spread_km":6.7},{"name":"Rolling Downs Group","kind":"aquifer","role":"regionally continuous aquitard protecting aquifers","river_name":null,"geocode_query":"Rolling Downs Group, Cooper Basin, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"For example, there is a regionally continuous aquitard, the Rolling Downs Group, that is at least 500 m thick across the region and protects the overlying aquifers from drawdown or contamination from activities at depth. 3.3. Spatial analysis of stressors, processes and assessment endpoints Although the causal network diagram allows rapid interpretation of potential impacts and where mitigation is possible to protect the 8 AEs, it does not reveal the spatial variation in these impacts or their levels of concern for each AE. Therefore, plots are needed to show the distribution of each AE across the PIA and the associated level of concern (Fig. 6). As the distributions of many protected species are poorly known in the Cooper region, their preferred habitat was used as a surrogate and probably over-estimates their true distribution.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Great Artesian Basin","kind":"aquifer","role":"deep groundwater system underlying Cooper Basin area","river_name":null,"geocode_query":"Great Artesian Basin, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Native vegetation communities have been modified by grazing of sheep and cattle for over 100 years. There are three major hydrostratigraphic sequences in the Cooper region: the Cooper Basin, the Eromanga Basin and the Cenozoic sediments of the Lake Eyre Basin. In the Cooper Basin, the Permian Gidgealpa Group hosts shale, tight and deep coal gas resources and is overlain by the Nappamerri Group. Due to depth of burial (generally >1500 m), the Cooper Basin is not directly used as a groundwater source. Between 600 and 2000 m of sedimentary rock typically separates the shale, tight and deep coal gas plays in the Cooper Basin from aquifers in the Cenozoic and Winton-Mackunda formations or the deeper Great Artesian Basin, which is part of the Eromanga Basin. 3.2.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Coongie Lakes","kind":"wetland","role":"Ramsar-listed wetland in Cooper region","river_name":null,"geocode_query":"Coongie Lakes, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"This runoff flows through the Barcoo and Thomson river systems and into Cooper Creek, which flows across expansive floodplains south of Windorah. Streamflow is reduced by about half due to high evapotranspiration rates between the junction of the Thomson and Barcoo rivers and the South Australian border (Holland et al., 2020). Natural systems in the Cooper region are driven by resource pulses and boom–bust ecological dynamics, shaping the high diversity of ecological communities and species (Arthington and Balcombe, 2011) The Cooper region supports the Ramsar-listed Coongie Lakes, many waterholes and terminal lakes, 8 nationally important wetlands and 26 taxa (plants, reptiles, birds and mammals) listed as threatened (critically endangered, endangered or vulnerable) (Holland et al., 2020). It also hosts several areas of Indigenous and cultural significance, especially along Cooper Creek and its associated waterholes which have a long history as part of a traditional trade route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.166667,-27.243333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Coongie, Innamincka SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-289dd493c461b4d3b5f69720-site-7","true_coordinates":[140.1666667,-27.1833333],"spread_km":6.7}],"limitations":["Lack of precise coordinate data for study locations","Study area expressed as broad regional geographies without site-specific coordinates","Qualitative and monotonic relationships only, limiting representation of complex temporal dynamics and non-monotonic effects","Assumption that mitigation strategies are effective and complied with","No quantitative cumulative impact assessment; only collective additive impacts assessed"],"river_ids":[],"site_ids":["doc-289dd493c461b4d3b5f69720-site-1","doc-289dd493c461b4d3b5f69720-site-2","doc-289dd493c461b4d3b5f69720-site-3","doc-289dd493c461b4d3b5f69720-site-4","doc-289dd493c461b4d3b5f69720-site-7"],"anchor":[142.361338,-25.091228],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040491510],"main_basin_ids":[5040087590]},{"id":"doc-4821082fc8980ec3088e64a8","title":"Future rice farming threatened by drought in the Lower Mekong Basin","display_title":"Drought Threatens Rice in Lower Mekong Basin","doi":"https://doi.org/10.1038/s41598-021-88405-2","theme":"Climate","color":"#98d4f1","year":2021,"publication_date":"2021-04-30","authors":["Hyunwoo Kang","Venkataramana Sridhar","Mohammed Mainuddin","Le Duc Trung"],"summary":"This study examines how climate change-induced drought affects rainfed rice production in the Lower Mekong Basin (LMB), covering parts of Cambodia, Lao PDR, Thailand, and Vietnam. Using hydrological and crop models (SWAT and AquaCrop) with historical and future climate data (1954–2099), the study quantifies drought indices and rice yield changes. It finds elevated CO2 and increased precipitation generally boost yields, but significant declines (up to 1.5 ton/ha) occur in the Vietnam Central High Plain due to precipitation reductions during droughts. Expansion of irrigation areas is suggested as a mitigation strategy to prevent food crises. The integrated hydrology-crop modeling provides a framework for understanding and adapting to these challenges.","finding":"Significant rice yield decreases up to 1.5 ton/ha are projected in the Vietnam Central High Plain during drought years due to up to 34% reduced precipitation, necessitating expanded irrigation to offset these losses.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"We found that rice yield might increase (24–43%) due to the elevated levels of atmospheric CO2 concentration (+34.3 to+ 121.9%) and increases in precipitation. Contrastingly, considerable decreases in rice yield up to 1.5 ton/ha in the Vietnam Central High Plain (VCHP) region could be expected resulting from reduced precipitation by about 34% during drought years. To avert any major food crisis, an expansion of irrigation areas could be required to compensate for the expected reduction in rice yields. We conclude that a framework combining hydrology and crop models to assess climate change impacts on food production is key to develop adaptation strategies in the future. Drought is a natural disaster that has a detrimental influence on water resources, economy, environment, and crop production1–3 . Drought has affected many regions around the world over the last few decades4,5 , and it has been exacerbated by climate change6–8 .","verified_in_page":true},"topics":["drought","climate change","rice yield","hydrological modeling","rainfed agriculture","irrigation expansion","crop modeling","food security"],"study_period":"1954–2099","locations":[{"name":"Lower Mekong Basin","kind":"region","role":"study area","river_name":null,"geocode_query":"Lower Mekong Basin, Cambodia, Laos, Thailand, Vietnam","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2021) 11:9383 | https://doi.org/10.1038/s41598-021-88405-2 www.nature.com/scientificreports Future rice farming threatened by drought in the Lower Mekong Basin Hyunwoo Kang1 , Venkataramana Sridhar1*, Mohammed Mainuddin2 & Le DucTrung3 The Lower Mekong River basin (LMB) has experienced droughts in recent decades, causing detrimental economic losses and food security conundrums. This study quantified the impact of climate change on drought, and rainfed rice production in the LMB. The Soil and Water Assessment Tool (SWAT) and AquaCrop models were used to evaluate long-term drought indices and rainfed rice yields under historical and future climate conditions (1954–2099) with four climate models and two emission scenarios (RCP 4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Vietnam Central High Plain","kind":"region","role":"subregion of yield decrease and drought impact","river_name":null,"geocode_query":"Vietnam Central High Plain, Vietnam","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"We found that rice yield might increase (24–43%) due to the elevated levels of atmospheric CO2 concentration (+34.3 to+ 121.9%) and increases in precipitation. Contrastingly, considerable decreases in rice yield up to 1.5 ton/ha in the Vietnam Central High Plain (VCHP) region could be expected resulting from reduced precipitation by about 34% during drought years. To avert any major food crisis, an expansion of irrigation areas could be required to compensate for the expected reduction in rice yields. We conclude that a framework combining hydrology and crop models to assess climate change impacts on food production is key to develop adaptation strategies in the future. Drought is a natural disaster that has a detrimental influence on water resources, economy, environment, and crop production1–3 . Drought has affected many regions around the world over the last few decades4,5 , and it has been exacerbated by climate change6–8 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[108.4276047,14.6516511],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Vietnam, Vietnam","basin_id":4040015090,"main_basin_id":4040015090,"basin_region":"Central and South-East Asia","basin_area_km2":194298.8,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-1"},{"name":"Cambodia","kind":"region","role":"study subregion for rice yield and drought impact","river_name":null,"geocode_query":"Cambodia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-006","page":7,"quote":"However, some regions showed fluctuation in rice yield and soil moisture patterns primarily driven by year-to-year monsoonal variability68. Those results imply that rainfed rice productions were primarily affected by precipitation and soil moisture conditions. Climate change impacts on droughts and rice yields. To further explore the future scenarios of production of rice, this study simulates long-term rice yield for the rainfed areas in the LMB, and Fig. 5 presents the time series of annual average simulated rice yield for the basin countries during the historical and future periods. During the historical period, trends in rice yields had a gradual increase. While comparing the earliest decade (1956–1965) and the recent decade (2006–2019), there were increases in rice yield up to 0.34, 0.52, 0.35, and 0.84 ton/ha for Cambodia, Lao PDR, Thailand, Vietnam, respectively. However, there were some sharp declines in rice yield.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Lao PDR","kind":"region","role":"study subregion for rice yield and drought impact","river_name":null,"geocode_query":"Lao PDR","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-006","page":7,"quote":"However, some regions showed fluctuation in rice yield and soil moisture patterns primarily driven by year-to-year monsoonal variability68. Those results imply that rainfed rice productions were primarily affected by precipitation and soil moisture conditions. Climate change impacts on droughts and rice yields. To further explore the future scenarios of production of rice, this study simulates long-term rice yield for the rainfed areas in the LMB, and Fig. 5 presents the time series of annual average simulated rice yield for the basin countries during the historical and future periods. During the historical period, trends in rice yields had a gradual increase. While comparing the earliest decade (1956–1965) and the recent decade (2006–2019), there were increases in rice yield up to 0.34, 0.52, 0.35, and 0.84 ton/ha for Cambodia, Lao PDR, Thailand, Vietnam, respectively. However, there were some sharp declines in rice yield.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Thailand","kind":"region","role":"study subregion for rice yield and drought impact","river_name":null,"geocode_query":"Thailand","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-006","page":7,"quote":"However, some regions showed fluctuation in rice yield and soil moisture patterns primarily driven by year-to-year monsoonal variability68. Those results imply that rainfed rice productions were primarily affected by precipitation and soil moisture conditions. Climate change impacts on droughts and rice yields. To further explore the future scenarios of production of rice, this study simulates long-term rice yield for the rainfed areas in the LMB, and Fig. 5 presents the time series of annual average simulated rice yield for the basin countries during the historical and future periods. During the historical period, trends in rice yields had a gradual increase. While comparing the earliest decade (1956–1965) and the recent decade (2006–2019), there were increases in rice yield up to 0.34, 0.52, 0.35, and 0.84 ton/ha for Cambodia, Lao PDR, Thailand, Vietnam, respectively. However, there were some sharp declines in rice yield.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mekong Delta","kind":"region","role":"study subregion with stable yields and lower drought impact","river_name":null,"geocode_query":"Mekong Delta, Vietnam","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"With RCP4.5 projections, the mean values of the yield reductions for the f1 period were 0.041, 0.129, 0.057, 0.470, and 0.020 ton/ha, and for the f2 period were 0.039, 0.134, 0.044, 0.986, and 0.007 in Cambodia, Lao PDR, Thailand, VCHP, and Mekong Delta region, respectively. Similarly, from RCP8.5, the mean values of the yield reductions for the f1 period were 0.030, 0.152, 0.025, 0.960, and 0.014 ton/ ha, and for the f2 period were 0.048, 0.128, 0.063, 0.484, and 0.018 in Cambodia, Lao PDR, Thailand, VCHP, and Mekong Delta region. Therefore, from the mean values of each country, this study found that lager yield reductions occurred in Laos PDR (− 0.128 to − 0.152 ton/ha) and VCHP (− 0.474 to − 0.986 ton/ha), while there were smaller scale reductions in the Mekong Delta (− 0.007 to − 0.020 ton/ha). Required irrigation areas and discussion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[105.5943388,10.063363],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mekong River Delta, Vietnam","basin_id":4040017030,"main_basin_id":4040017030,"basin_region":"Central and South-East Asia","basin_area_km2":235439.1,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-5"},{"name":"Sekong, Sesan and Srepok (3S) river system","kind":"river","role":"region with exacerbated drought severity","river_name":"Sekong, Sesan and Srepok rivers","geocode_query":"Sekong River, Laos; Sesan River, Cambodia; Srepok River, Vietnam","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"A week monsoon during the flood season and a warm ENSO phase (El Niño/Southern Oscillation) have derived severe droughts and significant restrictions on rice production in the LMB13,19, and the severity of droughts would be exacerbated by climate change, specifically in the Lower LMB and 3S regions (Sekong, Sesan and Srepok)20. The LMB is one of the most crucial regions for rice production, which is not only providing food for more than 70 million inhabitants21 but also contribute to global food security22. The average total rice production in LMB during 2009–2013 was 52.5 million tons, and average yields for Cambodia, Lao PDR, Thailand, and Vietnam were 3.0, 3.2, 2.5, and 5.2 ton/ha, respectively23,24. Thailand and Vietnam belong to the five major rice exporters of the world in 201725, and the Mekong Delta in Vietnam and Khorat Plateau in Thailand produce about 75% of all rice production in the LMB21.","verified_in_page":true},"resolution_status":"resolved","coordinates":[106.9131998801213,13.82167507293217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tonle San River","basin_id":4041128330,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":79014.5,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-6"},{"name":"Khorat Plateau","kind":"region","role":"major rice producing region in Thailand within LMB","river_name":null,"geocode_query":"Khorat Plateau, Thailand","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"A week monsoon during the flood season and a warm ENSO phase (El Niño/Southern Oscillation) have derived severe droughts and significant restrictions on rice production in the LMB13,19, and the severity of droughts would be exacerbated by climate change, specifically in the Lower LMB and 3S regions (Sekong, Sesan and Srepok)20. The LMB is one of the most crucial regions for rice production, which is not only providing food for more than 70 million inhabitants21 but also contribute to global food security22. The average total rice production in LMB during 2009–2013 was 52.5 million tons, and average yields for Cambodia, Lao PDR, Thailand, and Vietnam were 3.0, 3.2, 2.5, and 5.2 ton/ha, respectively23,24. Thailand and Vietnam belong to the five major rice exporters of the world in 201725, and the Mekong Delta in Vietnam and Khorat Plateau in Thailand produce about 75% of all rice production in the LMB21.","verified_in_page":true},"resolution_status":"resolved","coordinates":[103.1667957,15.6667806],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"ที่ราบสูงโคราช ตำบล บ้านกู่ อำเภอ ยางสีสุราช มหาสารคาม 44210, Thailand","basin_id":4041108580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":119866.0,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-7"},{"name":"Savannakhet","kind":"region","role":"largest province in study for rice yield analysis","river_name":null,"geocode_query":"Savannakhet, Lao PDR","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"5 Vol.:(0123456789) Scientific Reports | (2021) 11:9383 | https://doi.org/10.1038/s41598-021-88405-2 www.nature.com/scientificreports/ yield, precipitation, and drought conditions. SMAP provides three hourly soil moisture data with a 9 km spatial resolution from April 2015, and 3065 grids correspond to the rainfed areas in the LMB (Fig. 2c). Impacts of drought on rice yield. In this study, the crop yield analysis for the rainfed rice was performed at the provincial scale using the observed agricultural production data in the LMB (Fig. 2b). Observed crop yield data was obtained from the official statistical websites or reports of the countries in the LMB24,40. The study area contains 74 provinces in the LMB, and there are 20, 17, 22, and 15 provinces in Cambodia, Loa PDR, Thailand, and Vietnam, respectively. The average area of all the 74 provinces is 8079 km2 , the largest province is Savannakhet in Lao PDR (21,407 km2 ), and the smallest is Phnom Penh in Cambodia (373 km2 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[104.7686868,16.5720526],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Savannakhet, Laos","basin_id":4041108500,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":164270.6,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-8"},{"name":"Phnom Penh","kind":"region","role":"smallest province in study for rice yield analysis","river_name":null,"geocode_query":"Phnom Penh, Cambodia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"5 Vol.:(0123456789) Scientific Reports | (2021) 11:9383 | https://doi.org/10.1038/s41598-021-88405-2 www.nature.com/scientificreports/ yield, precipitation, and drought conditions. SMAP provides three hourly soil moisture data with a 9 km spatial resolution from April 2015, and 3065 grids correspond to the rainfed areas in the LMB (Fig. 2c). Impacts of drought on rice yield. In this study, the crop yield analysis for the rainfed rice was performed at the provincial scale using the observed agricultural production data in the LMB (Fig. 2b). Observed crop yield data was obtained from the official statistical websites or reports of the countries in the LMB24,40. The study area contains 74 provinces in the LMB, and there are 20, 17, 22, and 15 provinces in Cambodia, Loa PDR, Thailand, and Vietnam, respectively. The average area of all the 74 provinces is 8079 km2 , the largest province is Savannakhet in Lao PDR (21,407 km2 ), and the smallest is Phnom Penh in Cambodia (373 km2 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[104.9282099,11.5563738],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Phnom Penh, Cambodia","basin_id":4041144880,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":88254.0,"hydrology_source":"hydrobasins","site_id":"doc-4821082fc8980ec3088e64a8-site-9"},{"name":"Sekong River Basin","kind":"basin","role":"comparison site for crop yield reduction studies","river_name":"Sekong River","geocode_query":"Sekong River Basin, Thailand","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Chun et al.32 demonstrated that improved irrigation strategy and CO2 fertilization effect could cause significant increases in rice yields (up to 8.2–42.7%) in the 2080s under RCP 8.5 in Cambodia and Thailand. Poulton et al.33 found that CO2 level and rainfall variability have significant influences on rainfed rice production in Cambodia after 2030. Also, they concluded that expanding irrigation areas would be required to compensate for the impacts of climate change. Thus far, several studies concluded that a combination of CO2 fertilization and irrigation strategies may lead to increases in rice production or at least compensate for the yield losses due to the impacts of climate change in the LMB. However, other studies showed different results in the regional scale analysis. Boonwichai et al.34 simulated rainfed rice using the DSSAT crop simulation model in the Songkhram River Basin of Thailand, and rice yields reduced by 14% and 10% under the RCP4.5 and RCP8.5, respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Use of multiple climate models and emission scenarios introduces projection uncertainties","Provinces in Vietnam Central High Plain not fully covered due to basin boundary vs. provincial boundary mismatch","Did not evaluate salinity intrusion and sea-level rise impacts on rice yields","CO2 fertilization effects modeled, but negative impacts on crop quality were not assessed","Future dam impacts on hydrology downstream and irrigation not fully incorporated","Uncertainties remain in estimating required irrigation areas to compensate drought-induced yield losses"],"river_ids":[],"site_ids":["doc-4821082fc8980ec3088e64a8-site-1","doc-4821082fc8980ec3088e64a8-site-5","doc-4821082fc8980ec3088e64a8-site-6","doc-4821082fc8980ec3088e64a8-site-7","doc-4821082fc8980ec3088e64a8-site-8","doc-4821082fc8980ec3088e64a8-site-9"],"anchor":[106.9131998801213,13.82167507293217],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040015090,4040017030,4041108500,4041108580,4041128330,4041144880],"main_basin_ids":[4040015090,4040017020,4040017030]},{"id":"doc-4bfd195d5d0e22e9e81f24a3","title":"Fate and transport modelling framework for assessing risks to soil and groundwater from chemicals accidentally released during surface operations: An Australian example application from shale gas developments","display_title":"Chemical Dilution Risks in Australian Gas Basins","doi":"https://doi.org/10.1016/j.jhydrol.2021.127271","theme":"Groundwater","color":"#a8b9ff","year":2021,"publication_date":"2021-12-04","authors":["Dirk Mallants","Rebecca Doble","Yousef Beiraghdar"],"summary":"This study developed a quantitative fate and transport modelling framework to assess the risk of chemical contamination to soil and groundwater from accidental chemical releases during shale and tight gas exploration in Australia. The framework incorporates soil type, unsaturated zone depth, and groundwater velocity, using numerical simulations across the Beetaloo Sub-basin (Northern Territory) and the Cooper Basin (South Australia and Queensland). Dilution factors were derived over 1000 × 1000 m2 grids, combining unsaturated zone transport with three-dimensional groundwater dilution, enabling spatial mapping of dilution and risk assessment for hydraulic fracturing chemicals. The study found generally slow chemical migration across deep unsaturated zones, large dilution factors reducing chemical concentrations below ecotoxicological concern for most chemicals, and demonstrated the framework's utility for monitoring and management practices in these regions.","finding":"The combined vadose zone and groundwater dilution factors across the Beetaloo Sub-basin and Cooper Basin indicate that accidental chemical releases from shale and tight gas operations will undergo significant attenuation, with median dilution factors of 4,230 and 7,970 respectively, reducing concentrations of most hydraulic fracturing chemicals below harmful levels to aquatic ecosystems within several hundred years of migration.","finding_evidence":{"passage_id":"p014-008","page":14,"quote":"The combined pooled unsaturated zone-groundwater dilution factor for the Beetaloo Subbasin was found to be 4,230 (50th percentile), with lower and upper quartiles of 778 and 15,400, respectively. For the Cooper basin, the 50th percentile dilution factor was 7,970 for pooled data, with upper and lower quartile values of 3,830 and 21,100. High-resolution spatial maps of combined dilution factors have been produced based on the dilution factor relationships developed here (Doble et al., 2021). Such maps are practical tools to inform effective monitoring and management practices to support regulation and management of the gas industry in Australia. An example environmental risk assessment application based on 39 hydraulic fracturing chemicals scheduled for stimulation of a shale gas well demonstrated ecotoxicological risk quotients RQs for most chemicals to be low enough (i.e., less than 1) to indicate negligible environmental impact.","verified_in_page":true},"topics":["soil hydrology","groundwater flow","solute transport","risk assessment","chemical spills","unsaturated zone","model simulation","environmental impact"],"study_period":null,"locations":[{"name":"Beetaloo Sub-basin","kind":"basin","role":"primary study area for fate and transport modeling of chemical releases during shale and tight gas development","river_name":null,"geocode_query":"Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Unsaturated zone data 2.3.1. Beetaloo basin The Beetaloo Sub-basin in the Northern Territory (Australia) covers an area of 28,000 km2 and is a major shale and tight gas exploration area (Frery et al., 2020). The soils of the basin are grouped in landscape classes of which the four largest occupy 28,131 km2 or 98% of the basin (Table S1 and Fig. S2). The dominant landscape classes are the Loamy and sandy plains (70%), followed by Clay plains (17%), Floodplain and alluvium (6%), and Undulating country (5%) (Frery et al., 2020). The presence of very deep unsaturated zones within of the four classes is evident from the depth to groundwater data (Table S1), derived from interpolation of water table depths observed in water bores in the Beetaloo Sub-basin. For each of the landscape classes, soil hydraulic parameters of the van Genuchten-Mualem models (Supplementary Material, Eq. 3 and 4) were derived using a set of pedo-transfer functions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.858446,-17.235601],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102215708,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-4bfd195d5d0e22e9e81f24a3-site-0","true_coordinates":[133.798706,-17.21706],"spread_km":6.7},{"name":"Cooper Basin","kind":"basin","role":"primary study area representing a major shale and tight gas exploration area for fate and transport modeling of chemical releases","river_name":null,"geocode_query":"Cooper Basin, South Australia and Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Cooper basin The Cooper Basin covers an area of 139,000 km2 across north-east South Australia and south-west Queensland (Australia) (Holland et al., 2020). Conventional gas production has been ongoing for more than 50 years, with the basin continuing to report new shale and tight gas discoveries. There are five major landscape classes (soils) that occupy the entire 130,000 km2 of the basin (Table S2 and Fig. S5). The dominant landscape classes are Floodplain and alluvium (32%) and Inland dunefields (30%), followed by Undulating country (20%), Tablelands and duricrusts (9%), and Loamy and sandy plains (9%). As in the Beetaloo Sub-basin, there are very deep unsaturated zones within each of the five classes with depths to groundwater up to nearly 100 m (Table S2). Depth to groundwater was derived from interpolation of water table depths observed in water bores.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Daly Waters","kind":"settlement","role":"town near sampling area for groundwater bore data used to derive representative screen lengths and hydraulic parameters in Beetaloo Sub-basin","river_name":null,"geocode_query":"Daly Waters, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-009","page":4,"quote":"The transport distance 100–1000 m is relevant for this study with its main observation point at 500 m, the maximum transport distance at which the environmental concentration is predicted. This distance was chosen as it corresponds to the maximum travel distance in X- and Y- direction for each of the 1000 × 1000 m2 grid cells for which D. Mallants et al.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.369167,-16.313333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769462,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Daly Waters NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-4bfd195d5d0e22e9e81f24a3-site-2","true_coordinates":[133.369167,-16.253333],"spread_km":6.7},{"name":"Winton-Mackunda aquifer","kind":"aquifer","role":"major aquifer underlying the Cooper Basin used in groundwater pore velocity and dilution factor calculations","river_name":null,"geocode_query":"Winton-Mackunda aquifer, Cooper Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-006","page":4,"quote":"For the remainder of the soil profile, i.e., from 600 cm to the groundwater table, sandstone is defined as a single material based on an analysis of stratigraphic logs from well completion reports. The minimum horizontal hydraulic conductivity for one of the most important surficial aquifers, the Winton-Mackunda aquifer, is 0.1 m/d (Evans et al., 2020) which is close to the 0.062 m/d from Carsel and Parrish for clay loam. Therefore, the hydraulic properties for the sandstone are based on the clay loam USDA class transfer functions from Carsel and Parrish (1988). Average water retention curves for each of the six layers for the five landscape classes together with the deeper sandstone layer are shown in Fig. S6. Depth variations of saturated hydraulic conductivity KS for all five landscape classes are available from Fig. S7.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.0412041,-22.3905542],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115719108,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Winton QLD, Australia","basin_id":5040456070,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":133470.5,"hydrology_source":"geofabric","site_id":"doc-4bfd195d5d0e22e9e81f24a3-site-3"},{"name":"Anthony Lagoon formation","kind":"aquifer","role":"formation comprising surficial groundwater system in the Beetaloo Sub-basin used for groundwater velocity and dilution modeling","river_name":null,"geocode_query":"Anthony Lagoon formation, Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-007","page":4,"quote":"There is a general trend for KS to decrease with depth, with the deepest layer (sandstone) displaying a three to six times lower KS than that of the shallowest layers. 2.4. Saturated zone data 2.4.1. Beetaloo Sub-basin The surficial groundwater of the Cambrian Limestone Aquifer (CLA) that directly underlies most of the unsaturated zones within the Beetaloo Sub-basin is composed of the upper CLA Anthony Lagoon (siltstone and dolostone) and underlying low-CLA Gum Ridge (limestone and dolostone) formations (Frery et al., 2020). The three parameters to derive groundwater pore velocity v (see Eq. 6, Supplementary Material) will be briefly described on the basis of detailed parameter maps derived for the entire sub-basin (Doble et al., 2021). The hydraulic conductivity K for the Anthony Lagoon and underlying Gum Ridge formations range from 1 to over 200 m/d; the latter value is assumed representative for karst features.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Gum Ridge formation","kind":"aquifer","role":"underlying formation below Anthony Lagoon in Beetaloo Sub-basin representing the low-Cambrian Limestone Aquifer modeled for groundwater transport and dilution","river_name":null,"geocode_query":"Gum Ridge formation, Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-007","page":4,"quote":"There is a general trend for KS to decrease with depth, with the deepest layer (sandstone) displaying a three to six times lower KS than that of the shallowest layers. 2.4. Saturated zone data 2.4.1. Beetaloo Sub-basin The surficial groundwater of the Cambrian Limestone Aquifer (CLA) that directly underlies most of the unsaturated zones within the Beetaloo Sub-basin is composed of the upper CLA Anthony Lagoon (siltstone and dolostone) and underlying low-CLA Gum Ridge (limestone and dolostone) formations (Frery et al., 2020). The three parameters to derive groundwater pore velocity v (see Eq. 6, Supplementary Material) will be briefly described on the basis of detailed parameter maps derived for the entire sub-basin (Doble et al., 2021). The hydraulic conductivity K for the Anthony Lagoon and underlying Gum Ridge formations range from 1 to over 200 m/d; the latter value is assumed representative for karst features.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.835628,-17.168519],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215708,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Beetaloo, Pamayu NT 0862, Australia","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-4bfd195d5d0e22e9e81f24a3-site-5","true_coordinates":[133.798706,-17.21706],"spread_km":6.7},{"name":"Northern Territory","kind":"region","role":"regional administrative area encompassing Beetaloo Sub-basin, context for shale gas development and hydrological assessments","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Journal of Hydrology 604 (2022) 127271 Available online 4 December 2021 0022-1694/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research papers Fate and transport modelling framework for assessing risks to soil and groundwater from chemicals accidentally released during surface operations: An Australian example application from shale gas developments Dirk Mallants * , Rebecca Doble , Yousef Beiraghdar CSIRO, Waite Road Gate 4, Urrbrae 5064, South Australia, Australia ARTICLE INFO This manuscript was handled by Jiri Simunek, Editor-in-Chief Keywords: Shale gas Chemical spills Risk assessment Soil and groundwater attenuation Vadose zone and groundwater hydrology HYDRUS-1D ABSTRACT Shale and tight gas developments in the Beetaloo (28,000 km2 ) and Cooper (139,000 km2 ) basins of Australia are subject to stringent State and Federal Government controls and assessments.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia","kind":"region","role":"state containing part of the Cooper Basin, relevant jurisdiction for shale and tight gas development and related environmental assessments","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Journal of Hydrology 604 (2022) 127271 Available online 4 December 2021 0022-1694/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research papers Fate and transport modelling framework for assessing risks to soil and groundwater from chemicals accidentally released during surface operations: An Australian example application from shale gas developments Dirk Mallants * , Rebecca Doble , Yousef Beiraghdar CSIRO, Waite Road Gate 4, Urrbrae 5064, South Australia, Australia ARTICLE INFO This manuscript was handled by Jiri Simunek, Editor-in-Chief Keywords: Shale gas Chemical spills Risk assessment Soil and groundwater attenuation Vadose zone and groundwater hydrology HYDRUS-1D ABSTRACT Shale and tight gas developments in the Beetaloo (28,000 km2 ) and Cooper (139,000 km2 ) basins of Australia are subject to stringent State and Federal Government controls and assessments.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland","kind":"region","role":"state containing part of the Cooper Basin, relevant jurisdiction for shale and tight gas exploration and environmental risk assessments","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Journal of Hydrology 604 (2022) 127271 Available online 4 December 2021 0022-1694/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research papers Fate and transport modelling framework for assessing risks to soil and groundwater from chemicals accidentally released during surface operations: An Australian example application from shale gas developments Dirk Mallants * , Rebecca Doble , Yousef Beiraghdar CSIRO, Waite Road Gate 4, Urrbrae 5064, South Australia, Australia ARTICLE INFO This manuscript was handled by Jiri Simunek, Editor-in-Chief Keywords: Shale gas Chemical spills Risk assessment Soil and groundwater attenuation Vadose zone and groundwater hydrology HYDRUS-1D ABSTRACT Shale and tight gas developments in the Beetaloo (28,000 km2 ) and Cooper (139,000 km2 ) basins of Australia are subject to stringent State and Federal Government controls and assessments.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Lack of precise coordinates for defined locations","Broad basin-scale geographic units reduce local resolution","Some hydraulic properties inferred from pedo-transfer functions and analog data","Assumptions exclude sorption and biodegradation attenuation in initial dilution factor calculations","Source chemical concentrations are based on planning stage estimates, not measured values"],"river_ids":[],"site_ids":["doc-4bfd195d5d0e22e9e81f24a3-site-0","doc-4bfd195d5d0e22e9e81f24a3-site-2","doc-4bfd195d5d0e22e9e81f24a3-site-3","doc-4bfd195d5d0e22e9e81f24a3-site-5"],"anchor":[133.835628,-17.168519],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040064130,5040087780,5040456070],"main_basin_ids":[5040064130,5040087590,5040087780]},{"id":"doc-55921de0e17f412510a60550","title":"Modelling the removal of an earth bund to maximise seawater ingress into a coastal wetland","display_title":"Restoring Tidal Flow in Mungalla Wetland","doi":"https://doi.org/10.1016/j.ecss.2021.107626","theme":"Floodplains","color":"#e9bd77","year":2021,"publication_date":"2021-10-21","authors":["Fazlul Karim","Jim Wallace","Brett N. Abbott","Mike Nicholas","Nathan J. Waltham"],"summary":"This study models the removal of an earth bund to restore seawater ingress into the Mungalla wetland in Queensland, Australia. Using hydrodynamic modelling and monitoring, it assesses tidal inundation scenarios including effects of tides, winds, low atmospheric pressure and sea level rise, demonstrating improved connectivity, weed control, and ecological benefits from tidal restoration.","finding":"Removal of the earth bund in Mungalla wetland allows periodic tidal ingress, with seawater reaching up to 1150 m upstream at 4.3 m tides, inundating 55 ha; under low atmospheric pressure and sea level rise, inundation increases substantially to flood up to 353 ha and eventually the entire 423 ha wetland, promoting native salt tolerant vegetation and improved wetland function.","finding_evidence":{"passage_id":"p007-002","page":7,"quote":"The presence of the earth bund prevented seawater entering the Mungalla wetland beyond the earth wall, even for the highest tides of 4.3 m. When it was removed tides higher than 3.7 m were then able to enter the wetland, inundating progressively larger areas as the tide height increased to 4.3 m, when ~12.5% of the wetland was flooded to a depth of ~0.5 m. Although this tide inundated a large part of Boolgaroo lagoon, it didn’t reach the Annabone area in the western part of the wetland. Large volumes of seawater (up to 118060 m3 ) containing large amounts of salt (up to 4132 tonnes) entered the wetland, but much of this would have drained from the wetland as the high tides receded. When onshore winds coincide with high tides there can be substantial increases in wetland inundation. Whereas moderate breezes (up to 5 m/s) cause little increase in inundation area, strong winds (~10 m/s) increase the area inundated by 200% and the volume of water entering the wetland by over 400%.","verified_in_page":true},"topics":["tidal inundation","hydrodynamic modelling","coastal wetlands","sea level rise","saltwater intrusion","wetland restoration","weed control","ecological connectivity"],"study_period":null,"locations":[{"name":"Mungalla wetland, Queensland, Australia","kind":"wetland","role":"primary study site with earth bund removed to restore tidal flow","river_name":null,"geocode_query":"Mungalla wetland, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"These simulations include the time series modelling of water depth in the Mungalla wetland associated with large (king) tides with and without the earth bund in place. Simulations also include the effects of onshore winds that coincide with the king tides, as these can affect the area and extent of tidal ingress. Finally, the potential impact of SLR is also simulated to estimate how the extent, frequency and duration of tidal ingress into the wetland might change in the future. 2. Study area This study focused on the Mungalla wetland (a complex of small and large lagoons) in north Queensland, in the downstream part of the Herbert River catchment (Fig. 1). Mungalla and its adjacent lands are drained not by the Herbert River itself but by Palm Creek which flows along the western boundary of wetlands. Palm Creek is a distributary of the Herbert River and enters the sea independently of the main channel of Herbert River, which discharges further north.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.5436009,-26.4465781],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667739,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mungallala QLD 4467, Australia","basin_id":5040511420,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":35156.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-0"},{"name":"Herbert River catchment, Queensland, Australia","kind":"basin","role":"catchment in which the Mungalla wetland is located and hydrologic context","river_name":"Herbert River","geocode_query":"Herbert River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"These simulations include the time series modelling of water depth in the Mungalla wetland associated with large (king) tides with and without the earth bund in place. Simulations also include the effects of onshore winds that coincide with the king tides, as these can affect the area and extent of tidal ingress. Finally, the potential impact of SLR is also simulated to estimate how the extent, frequency and duration of tidal ingress into the wetland might change in the future. 2. Study area This study focused on the Mungalla wetland (a complex of small and large lagoons) in north Queensland, in the downstream part of the Herbert River catchment (Fig. 1). Mungalla and its adjacent lands are drained not by the Herbert River itself but by Palm Creek which flows along the western boundary of wetlands. Palm Creek is a distributary of the Herbert River and enters the sea independently of the main channel of Herbert River, which discharges further north.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.737339,-18.108774],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":16651528,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-1","true_coordinates":[145.7373393886557,-18.16877395870301],"spread_km":6.7},{"name":"Palm Creek, Queensland, Australia","kind":"river","role":"distributary of Herbert River draining Mungalla wetland western boundary","river_name":"Palm Creek","geocode_query":"Palm Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Lucy Creek provides a link between the Trebonne and Palm Creeks and carries water from Trebonne to Palm Creek immediately above the Mungalla wetland. The Stone River is one of the major tributaries of the Herbert River and it also supplies freshwater to the wetland through Trebonne Creek. Mungalla wetland connects the sea through the Cassady Creek. However, tidal ingress into the wetland was obstructed because of an earthen bund (approximately 3 m high and 5 m wide) on the Cassady Creek at the downstream end of the wetland. The Mungalla region is part of the Wet Tropics of north Queensland with average annual rainfall at Ingham of 2026 mm. The rainfall is strongly seasonal with approximately 80% of the rain falling between December and April. The months of July–October receive, on average, less than 50 mm of rain each month (Abbott et al., 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.23,-18.64],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652411,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Palm Creek, Blackrock QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-2","true_coordinates":[146.23,-18.7],"spread_km":6.7},{"name":"Lucy Creek, Queensland, Australia","kind":"river","role":"links Trebonne creek to Palm Creek, tributary supplying water to wetland","river_name":"Lucy Creek","geocode_query":"Lucy Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Lucy Creek provides a link between the Trebonne and Palm Creeks and carries water from Trebonne to Palm Creek immediately above the Mungalla wetland. The Stone River is one of the major tributaries of the Herbert River and it also supplies freshwater to the wetland through Trebonne Creek. Mungalla wetland connects the sea through the Cassady Creek. However, tidal ingress into the wetland was obstructed because of an earthen bund (approximately 3 m high and 5 m wide) on the Cassady Creek at the downstream end of the wetland. The Mungalla region is part of the Wet Tropics of north Queensland with average annual rainfall at Ingham of 2026 mm. The rainfall is strongly seasonal with approximately 80% of the rain falling between December and April. The months of July–October receive, on average, less than 50 mm of rain each month (Abbott et al., 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Stone River, Queensland, Australia","kind":"river","role":"major Herbert River tributary supplying freshwater via Trebonne Creek to Mungalla wetland","river_name":"Stone River","geocode_query":"Stone River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Lucy Creek provides a link between the Trebonne and Palm Creeks and carries water from Trebonne to Palm Creek immediately above the Mungalla wetland. The Stone River is one of the major tributaries of the Herbert River and it also supplies freshwater to the wetland through Trebonne Creek. Mungalla wetland connects the sea through the Cassady Creek. However, tidal ingress into the wetland was obstructed because of an earthen bund (approximately 3 m high and 5 m wide) on the Cassady Creek at the downstream end of the wetland. The Mungalla region is part of the Wet Tropics of north Queensland with average annual rainfall at Ingham of 2026 mm. The rainfall is strongly seasonal with approximately 80% of the rain falling between December and April. The months of July–October receive, on average, less than 50 mm of rain each month (Abbott et al., 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Cassady Creek, Queensland, Australia","kind":"river","role":"tidal connection between Mungalla wetland and sea, site of the bund","river_name":"Cassady Creek","geocode_query":"Cassady Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Lucy Creek provides a link between the Trebonne and Palm Creeks and carries water from Trebonne to Palm Creek immediately above the Mungalla wetland. The Stone River is one of the major tributaries of the Herbert River and it also supplies freshwater to the wetland through Trebonne Creek. Mungalla wetland connects the sea through the Cassady Creek. However, tidal ingress into the wetland was obstructed because of an earthen bund (approximately 3 m high and 5 m wide) on the Cassady Creek at the downstream end of the wetland. The Mungalla region is part of the Wet Tropics of north Queensland with average annual rainfall at Ingham of 2026 mm. The rainfall is strongly seasonal with approximately 80% of the rain falling between December and April. The months of July–October receive, on average, less than 50 mm of rain each month (Abbott et al., 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.317596,-18.628309],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652454,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cassady, Taylors Beach QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-5"},{"name":"Boolgaroo lagoon, Queensland, Australia","kind":"wetland","role":"major lagoon in Mungalla wetland inundated by seawater after bund removal","river_name":null,"geocode_query":"Boolgaroo lagoon, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Without the bund, seawater is estimated to reach approximately 180 m above the bund for a 3.9 m tide, 780 m for a 4.1 m tide, and 1150 m for a 4.3 m tide respectively– though all were unable to reach the Annabone regions (see Fig. 4 for location). At these high tides, the freshwater hydraulic barrier was negligible. The simulation for the 3.7 m tide is not shown as it produced very little inundation within the wetland (Table 3) and no seawater entered beyond the bund. A 3.9 m tide caused tidal ingress above the bund with a small area of inundation (4.4 ha; Table 3). More significant tidal ingress into the wetland occurred with 4.1 m tide, however, the extent of inundation is confined along a narrow stretch of the wetland. While the 4.3 m tide inundates a large part of Boolgaroo lagoon (55 ha), it doesn’t reach to the Annabone area in the western part of the wetland (Fig. 9b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Annabone lagoon, Queensland, Australia","kind":"wetland","role":"western lagoon in Mungalla wetland less touched by seawater in most scenarios except under strong wind or SLR","river_name":null,"geocode_query":"Annabone lagoon, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"Without the bund, seawater is estimated to reach approximately 180 m above the bund for a 3.9 m tide, 780 m for a 4.1 m tide, and 1150 m for a 4.3 m tide respectively– though all were unable to reach the Annabone regions (see Fig. 4 for location). At these high tides, the freshwater hydraulic barrier was negligible. The simulation for the 3.7 m tide is not shown as it produced very little inundation within the wetland (Table 3) and no seawater entered beyond the bund. A 3.9 m tide caused tidal ingress above the bund with a small area of inundation (4.4 ha; Table 3). More significant tidal ingress into the wetland occurred with 4.1 m tide, however, the extent of inundation is confined along a narrow stretch of the wetland. While the 4.3 m tide inundates a large part of Boolgaroo lagoon (55 ha), it doesn’t reach to the Annabone area in the western part of the wetland (Fig. 9b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Forrest Beach, Queensland, Australia","kind":"settlement","role":"nearest coastal tidal gauge location used for tidal data and modelling boundary","river_name":null,"geocode_query":"Forrest Beach, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"Results show that there are about 18 days per year when tide height is greater than 3.7 m that has the potential to reach upstream of bund location. These tend to occur for a period of 2–3 consecutive days, so the wetland inundation frequency per event is between 6 and 10 times a year. Inundation frequency is less for larger tides (Table 2). For example, only 0 to 5 tides of greater than 4.1 m occurred each year (average ~ 2 per year), which would usually occur in a single annual event. 4.2. Effect of bund removal The bund in the downstream end of the Mungalla wetland was found to be a barrier for the seawater ingress into the wetland. While a 3.7 m tide reaches the bund, simulations show that the bund prevented seawater ingress to the wetland for even the largest recorded tide of 4.3 m (Fig. 9a). The comparative simulation for the same tide without the bund in place shows extensive seawater ingress and a large area of inundation, ~55 ha (Fig. 9b and Table 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.294596,-18.647419],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652444,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Forrest Beach QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-8","true_coordinates":[146.2945964,-18.7074193],"spread_km":6.7},{"name":"Lucinda, Queensland, Australia","kind":"settlement","role":"location of tidal gauge data source","river_name":null,"geocode_query":"Lucinda, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Estuarine, Coastal and Shelf Science 263 (2021) 107626 4 3.3.3. Boundary conditions Two inflow boundaries were used, one receiving freshwater from the Palm Creek and the other from Lucy Creek. Both Palm and Lucy creeks are ephemeral, Palm Creek is a distributary of the Herbert River and is relatively large (~100 m wide and ~6 m deep) compared to Lucy Creek (~20 m wide and 2 m deep). A simple rainfall-runoff model was calibrated to estimate inflow through the Palm and Lucy Creeks (DHI, 2008). Initial model parameters were obtained from Karim et al. (2012) and the parameters were recalibrated for the Stone River catchment using rainfall and streamflow data recorded between 2001 and 2015 at the Running Creek (refer to Fig. 1). Tide data were sourced from the nearest gauge at Lucinda (~20 km NE) from the Maritime Safety Queensland for the period of 2001–2015.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.3314538,-18.5281508],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652455,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lucinda QLD 4850, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-55921de0e17f412510a60550-site-9"}],"limitations":["No explicit latitude and longitude coordinates provided for study locations","Hydrodynamic modelling depends on quality and resolution of topographic data","Model predictions rely on calibration against observed tides and roughness estimations","Study area is limited to Mungalla wetland and adjacent waterways and may not generalize without adaptation"],"river_ids":[],"site_ids":["doc-55921de0e17f412510a60550-site-0","doc-55921de0e17f412510a60550-site-1","doc-55921de0e17f412510a60550-site-2","doc-55921de0e17f412510a60550-site-5","doc-55921de0e17f412510a60550-site-8","doc-55921de0e17f412510a60550-site-9"],"anchor":[146.294596,-18.647419],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040067860,5040511420],"main_basin_ids":[5040067860,5040073410]},{"id":"doc-5a14fc3675105708e8711280","title":"Basin futures, a novel cloud-based system for preliminary river basin modelling and planning","display_title":"Basin Futures: Cloud-Based River Basin Modelling","doi":"https://doi.org/10.1016/j.envsoft.2021.105049","theme":"Water management","color":"#eeabac","year":2021,"publication_date":"2021-04-03","authors":["Peter Taylor","Joel Rahman","Jackie O’Sullivan","Geoff Podger","Caroline Rosello","Amit Parashar","Ashmita Sengupta","Jean-Michel Perraud","Carmel Pollino","Mac Coombe"],"summary":"Basin Futures is a novel, cloud-based system designed for rapid and accessible preliminary river basin modelling and planning worldwide. It integrates global and regional datasets along with a simplified hydrological model structure to enable users to create, calibrate, and run basin-scale scenario analyses directly through a web browser. The tool aims to lower the technical and data integration barriers for basin water resource assessment, supporting exploration of water availability, storage, demands, and impacts from future developments including climate change, agriculture, and population growth.","finding":"Basin Futures rapidly generates plausible basin-scale hydrological assessments with acceptable agreement to observed flow datasets, enabling users with limited prior modelling experience to develop basin case studies within days, and explore multiple development scenarios efficiently through an easy-to-use browser interface.","finding_evidence":{"passage_id":"p013-004","page":13,"quote":"While the group was small, the results indicated that the tool was easy to learn and had potential to be used as part of basin planning and management discussions. Users provided the following observations: - They commented on the high speed of the system and how it could be used to quickly assess different scenarios. - They were able to develop basin case studies over a matter of days with no prior knowledge of the tool. - They commented that the tool provided a complementary practical view of the theoretical concepts covered in the training. - Users commented that the rainfall selection tool (section 5.7.1) was valuable in guiding users in their choice of climate data and added transparency on how well the input climate was performing in the region. We also noted that users were able to quickly collate statistics on the performance of models using different climate inputs, and Table 4 Calibration results using different GR4J targets.","verified_in_page":true},"topics":["river basin modelling","rainfall-runoff modelling","scenario analysis","cloud computing","hydrological data integration","water resource management","model calibration","environmental flow metrics"],"study_period":null,"locations":[{"name":"Brahmani river basin, Odisha, India","kind":"basin","role":"case study to compare Basin Futures output to existing water resource assessments","river_name":"Brahmani","geocode_query":"Brahmani river basin, Odisha, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"Environmental Modelling and Software 141 (2021) 105049 12 6.2. Station data In the context of this paper, we refer to station data as actual observations or data summarising observations from monitoring stations (e.g., monthly mean discharge measured at a location). There are a number of efforts that collate global or regional databases (Do et al., 2018; Fekete et al., 2002; Gudmundsson et al., 2018; Menne et al., 2012; The Global Runoff Data Centre, 2020) of station data of particular types (e.g. hydrological, climatological). Additionally, there are an increasing number of national systems that report station data at a national level (Dixon et al., 2013). The station data available in Basin Futures is detailed in the supplementary material Table 4. These data are primarily used to support model validation. 6.3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[85.777592,21.311887],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-5a14fc3675105708e8711280-site-0","true_coordinates":[85.77759238473227,21.37188697198602],"spread_km":6.7},{"name":"India","kind":"region","role":"region for climate data testing and bulk model calibration exploration","river_name":null,"geocode_query":"India","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-001","page":14,"quote":"It is possible to run this workflow for every GSIM gauging station in the database (30,000+ sites), though for this analysis, we have focussed on testing a specific region only. Future work would include running for all gauging stations, which would allow summarised results to assist in guiding user decisions and warning in areas of low performance. Fig. 10 show results in India using three climate precipitation data sets. These results demonstrate the system is able to calculate upstream catchment areas from GSIM locations (red dots), generate climate data for each sub-area, and run calibrations against a range of target values/ timeseries. The NSE results (Fig. 10) are for calibration of climate data to the GSIM observed monthly streamflow data. The NSE is for the model’s representation of mean monthly flows.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States","kind":"region","role":"source of national streamflow data integrated via USGS water data services","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"In the future we may move to a direct API approach, to avoid replication and synchronisation required for maintaining a local version. This will require more advanced filtering to be added to the IUCN API. The spatial querying and dynamic API access are all handled by the Basin Futures application server. 5.2.1.2. National streamflow data services. Two national streamflow data APIs have been integrated: the Australian Water Resources Information System (AWRIS) APIs (BoM, 2020) and the USGS′ Water Data Service APIs (USGS, 2020). These services both use WaterML2.0 (Taylor, 2012) data standards to publish national monitoring data. This facilitates easier integration into services and web applications, allowing Basin Futures to spatially and temporally subset requests on demand. The application server proxies requests to the Senaps API using the Senaps Python client library.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Australia","kind":"region","role":"source of national streamflow data integrated via AWRIS and used in system deployment and testing","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"In the future we may move to a direct API approach, to avoid replication and synchronisation required for maintaining a local version. This will require more advanced filtering to be added to the IUCN API. The spatial querying and dynamic API access are all handled by the Basin Futures application server. 5.2.1.2. National streamflow data services. Two national streamflow data APIs have been integrated: the Australian Water Resources Information System (AWRIS) APIs (BoM, 2020) and the USGS′ Water Data Service APIs (USGS, 2020). These services both use WaterML2.0 (Taylor, 2012) data standards to publish national monitoring data. This facilitates easier integration into services and web applications, allowing Basin Futures to spatially and temporally subset requests on demand. The application server proxies requests to the Senaps API using the Senaps Python client library.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Tapi river basin, India","kind":"basin","role":"example basin in Basinpedia interface illustrating data views for basin exploration","river_name":"Tapi","geocode_query":"Tapi river basin, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The overall design of the system has been guided by three technical principles: Use service-based components via suitable webbased Application Programming Interfaces (APIs); maximise component and library reuse by building on open and existing source code; Fig. 5. A view of Tapi river basin, India in Basinpedia; each tab on the right provides specific data views for the basin. The current view shows land use; others include population, protected areas, country borders, stress indicators, freshwater fish species, and groundwater. Fig. 6. Screenshot of the main user interface showing the model editor view. Labelled items are as follows: 1 – user navigation provides application navigation and status summary, 2 – quick loading of scenarios. 3 – reach select is a summarised table of all reaches, providing an overview of reach parameterisation. 4 – provides a spatial view of the model, with overlays of relevant spatial objects, such as storages.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.70763749999999,21.2426731],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040029530,"main_basin_id":4040029530,"basin_region":"Central and South-East Asia","basin_area_km2":316845.9,"hydrology_source":"hydrobasins","site_id":"doc-5a14fc3675105708e8711280-site-4"}],"limitations":["Global to regional scale climate and runoff input data have inherent spatial uncertainties affecting model outputs especially in data-poor areas.","Simplified rainfall-runoff model calibration currently uses coarse targets (mean annual or mean monthly runoff) instead of streamflow time series, limiting calibration precision.","Groundwater system states and dynamics are not explicitly modelled, reducing accuracy in groundwater-influenced basins.","Snow and glacier runoff processes are not fully implemented in the current deployed version, limiting accuracy in such basins.","Simplified reservoir storage geometry and outlet representations introduce uncertainty in storage dynamics.","The model approach is not suitable for highly regulated water systems with complex back-ordering of water demands upstream of storages.","Users need to interpret model outputs with awareness of uncertainties and underlying assumptions, particularly where local data is lacking."],"river_ids":[],"site_ids":["doc-5a14fc3675105708e8711280-site-0","doc-5a14fc3675105708e8711280-site-4"],"anchor":[85.777592,21.311887],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[4040025460,4040029530],"main_basin_ids":[4040025460,4040029530]},{"id":"doc-6a5efc0aa276ab71c63457b4","title":"Modelling the attenuation of flowback chemicals for a soil-groundwater pathway from a hypothetical spill accident","display_title":"Natural Attenuation of Flowback Water Chemicals in Beetaloo","doi":"https://doi.org/10.1016/j.scitotenv.2021.150686","theme":"Groundwater","color":"#a8b9ff","year":2021,"publication_date":"2021-10-01","authors":["Dirk Mallants","Jason K. Kirby","Lisa A. Golding","Simon C. Apte","Mike Williams"],"summary":"This study modeled the natural attenuation of chemicals present in flowback water from shale gas operations in the Beetaloo Sub-basin, Northern Territory, Australia, focusing on hypothetical leak scenarios from flowback water storage tanks at two shale gas wells: Tanumbirini-1 and Kyalla-117. The study used site-specific chemical data, soil, and groundwater transport models (HYDRUS-1D for unsaturated zone and a 3D analytical model for groundwater) to predict chemical migration, dilution, and risk quotients (RQ) for aquatic receptors. Results showed that physical attenuation through deep unsaturated zones and groundwater diluted chemicals sufficiently to keep environmental concentrations below guideline levels for most chemicals. Inclusion of additional processes—sorption, biodegradation, and radioactive decay—further reduced chemical concentrations. Direct toxicity assessments (DTA), accounting for chemical mixtures, required greater dilution than individual chemical assessments, but predicted concentrations met these safety thresholds. Spatial variability in soil and groundwater properties across the basin was accounted for, ensuring robustness of risk predictions. The study provides evidence supporting effective natural attenuation mitigating water quality risks from potential leaks in shale gas operations.","finding":"Natural attenuation processes including dilution, sorption, biodegradation, and radioactive decay reduce flowback water chemicals below safe environmental levels, with risk quotients less than one for all except salinity and radium-228 at one site; direct toxicity assessments indicate that mixture effects require 1.8 to 2.5 times greater dilution than considering individual chemicals alone to protect 95% of freshwater species.","finding_evidence":{"passage_id":"p017-000","page":17,"quote":"4. Conclusions Flowback water from shale gas operations in the Beetaloo Sub-basin (Northern Territory, Australia) contained several chemicals that exceeded water quality guidelines for ecosystem protection (where they exist). Among such chemicals were salinity, several trace metals (boron, cobalt, chromium, copper), and several organics including phenols and TRHs. Of the radionuclides measured, only Ra-228 exceeded water quality guidelines at one well site. Predictive modelling of a possible leak scenario from flowback water storage tanks demonstrated that physical attenuation by dilution and dispersion through deep unsaturated soil zones followed by 500 m transport in surficial groundwater was sufficient to decrease concentrations for most chemicals to below their guideline values for the protection of freshwater organisms, crop irrigation, and livestock (RQ <1).","verified_in_page":true},"topics":["chemical transport modelling","groundwater quality","risk assessment","natural attenuation","biodegradation","soil sorption","radionuclide decay","ecotoxicology"],"study_period":null,"locations":[{"name":"Beetaloo Sub-basin","kind":"basin","role":"Primary study region where shale gas operations and risk assessment modeling were conducted","river_name":null,"geocode_query":"Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"While accidental releases from storage tanks with flowback water are low-probability events if multiple containment barriers are put in place, they cannot be entirely excluded. Here the natural attenuation potential of deep unsaturated zones and groundwater was explored using predictive modelling involving a hypothetical leak from a storage tank. Actual chemical concentrations from flowback water at two shale gas wells with contrasting salinity (12,300 and 105,000 ppm TDS) in the Beetaloo Sub-basin (Northern Territory, Australia) served as input to the one-dimensional HYDRUS model for simulating chemical transport through the unsaturated zone, with groundwater at 50 and 100 m depth, respectively. Subsequent chemical transport in groundwater involved the use of a three-dimensional analytical transport model. For a total of 63 chemicals the long-term attenuation from dilution and dispersion in unsaturated sediments and groundwater was calculated.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.761784,-17.168519],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102215708,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-6a5efc0aa276ab71c63457b4-site-0","true_coordinates":[133.798706,-17.21706],"spread_km":6.7},{"name":"Tanumbirini-1 shale gas well","kind":"facility","role":"Study site; vertical well where flowback water was sampled and modeling scenarios developed","river_name":null,"geocode_query":"Tanumbirini-1 shale gas well, Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-007","page":3,"quote":"Site description Within the Beetaloo Sub-basin, the organic-rich shale units of the Velkerri and Kyalla formations are the main unconventional hydrocarbon targets (Frery et al., 2020). These formations are some of the oldest known petroleum systems in the world, deposited between 1.3 and 1.4 billion years ago during the Mesoproterozoic (Cox et al., 2016; Yang et al., 2018). The first study site is the vertical Tanumbirini-1 shale gas well at the eastern edge of the basin (Fig. 1), and is part of an exploration permit that covers a total area of 1527 km2 . This modelling study will use flowback tank water data from hydraulic fracturing at the Tanumbirini-1 well undertaken in November 2019 (Tables S1, S2, S3, and S4) (Apte et al., 2021; Kirby et al., Unpublished results). The shale gas resource targeted by the vertical well is the Velkerri Formation, between 2466 and 3946 m depth (Santos QNT Pty Ltd., 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Kyalla-117 shale gas well","kind":"facility","role":"Study site; horizontal well ~120 km from Tanumbirini-1 where flowback water was sampled and modeling scenarios developed","river_name":null,"geocode_query":"Kyalla-117 shale gas well, Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-008","page":3,"quote":"The Velkerri Formation is composed of high organic content fine-grained shales embedded within clay-rich and organically lean layers. The second site - about 120 km apart from the first site – is the horizontal Kyalla-117 shale gas well, drilled towards the center of the Beetaloo Sub-basin (Fig. 1). The permit area covers a total area 18,500 km2 . The Kyalla-117 well targets the Kyalla Formation (Origin, 2020) that was hydraulically fractured in September 2020. The deepest of three targets of the shale source rock is at 4543 m depth (Lower Kyalla) and is dominated by gray and black siltstone and shale. This formation has the potential to host liquids-rich gas and oil plays (Frery et al., 2020). Flowback tank water quality data for modelling is available from Tables S5, S6, S7, and S8. Both well sites are surrounded by a diverse range of fine-textured soils of moderate to high fertility (Fig. 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Cambrian Limestone Aquifer (CLA)","kind":"aquifer","role":"Aquifer underlying the unsaturated zone below both well sites; modeled for groundwater chemical transport","river_name":null,"geocode_query":"Cambrian Limestone Aquifer, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"From 3 to 6 m depth, i.e. the material that will be in immediate contact with the infiltrating water, the soil material is 41% clay, 12% silt and 47% sand for the Tanumbirini-1 well site and 40% clay, 10% silt, and 50% sand for the Kyalla-117 well site. The unsaturated zone is underlain by the Cambrian Limestone Aquifer (CLA) at a depth of approximately 50 m (Tanumbirini1) and 100 m (Kyalla-117). To account for the spatial variability in landscape classes (i.e. soil types, see Fig. 1), and groundwater conditions (i.e. depth to groundwater and groundwater velocity) within the exploration permits, the assessment also considers such spatial variability that will result in spatially variable leaching characteristics, and thus in attenuation potential. In such a way uncertainty bounds can be drawn around the site-specific assessments. The basis for the spatially variable dilution was developed by Mallants et al. (2021) and Doble et al. (2021).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Daly Waters","kind":"settlement","role":"Source of meteorological data (rainfall and evaporation) used for model atmospheric boundary conditions","river_name":null,"geocode_query":"Daly Waters, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"Predicted environmental concentrations (PECs) are derived at discrete soil depths across the deep unsaturated zone, and at the interface between the unsaturated/saturated zone. The calculations return dilution factors (DFs) at multiple depths across the profile and at the groundwater table (DFUZ). The unsaturated zone DFs will be combined with dilution in groundwater to produce an overall dilution factor. At the top of the simulation domain, climate boundary conditions were implemented. A time-series of 10-y daily rainfall and evaporation from the Daly Waters Bureau of Meteorology (BOM) meteorological station (# 14618) in the Northern Territory was used to generate a continuous timeseries for the atmospheric boundary condition. No transpiration was considered in the calculations; this increases groundwater recharge which is a conservative assumption. At the bottom of the domain, a groundwater table boundary condition is implemented. 2.5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.369167,-16.193333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769462,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Daly Waters NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-6a5efc0aa276ab71c63457b4-site-4","true_coordinates":[133.369167,-16.253333],"spread_km":6.7},{"name":"Newcastle Waters","kind":"settlement","role":"Regional climate station providing rainfall data relevant to study area climates","river_name":null,"geocode_query":"Newcastle Waters, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"Predicted environmental concentrations (PECs) are derived at discrete soil depths across the deep unsaturated zone, and at the interface between the unsaturated/saturated zone. The calculations return dilution factors (DFs) at multiple depths across the profile and at the groundwater table (DFUZ). The unsaturated zone DFs will be combined with dilution in groundwater to produce an overall dilution factor. At the top of the simulation domain, climate boundary conditions were implemented. A time-series of 10-y daily rainfall and evaporation from the Daly Waters Bureau of Meteorology (BOM) meteorological station (# 14618) in the Northern Territory was used to generate a continuous timeseries for the atmospheric boundary condition. No transpiration was considered in the calculations; this increases groundwater recharge which is a conservative assumption. At the bottom of the domain, a groundwater table boundary condition is implemented. 2.5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.4104737,-17.3728258],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215757,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Newcastle Waters NT 0862, Australia","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-6a5efc0aa276ab71c63457b4-site-5"}],"limitations":["No explicit coordinates provided for well sites or aquifer boundaries, latitude and longitude set to null.","Study uses hypothetical leak scenarios; actual leak volumes and conditions may vary.","Uncertainties exist in chemical transport parameters and sorption coefficients due to lack of site-specific data.","Deterministic modeling approach without probabilistic uncertainty analysis limits assessment of variability propagation."],"river_ids":[],"site_ids":["doc-6a5efc0aa276ab71c63457b4-site-0","doc-6a5efc0aa276ab71c63457b4-site-4","doc-6a5efc0aa276ab71c63457b4-site-5"],"anchor":[133.369167,-16.193333],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040064130,5040087780],"main_basin_ids":[5040064130,5040087780]},{"id":"doc-7c1e317e58a26a16857c47ff","title":"Climate change and reservoir sedimentation implications for irrigated agriculture in the Indus Basin Irrigation System in Pakistan","display_title":"Climate Change and Sedimentation Impacts in Indus Basin","doi":"https://doi.org/10.1016/j.jhydrol.2021.126967","theme":"Water management","color":"#eeabac","year":2021,"publication_date":"2021-09-21","authors":["Mobin‐ud‐Din Ahmad","Jorge L. Peña‐Arancibia","Yingying Yu","Joel Stewart","Geoff Podger","J. M. Kirby"],"summary":"This study assesses the impacts of climate change and reservoir sedimentation on water availability for irrigation in the Indus Basin Irrigation System (IBIS) in Pakistan. Using a river system model that incorporates water management rules and climate projections, the study evaluates changes in flows from the Upper Indus Basin (UIB), local rainfall, evapotranspiration, and their combined effects on canal deliveries and crop water balances up to 2050. The model reveals differential effects across the Punjab and Sindh-Balochistan provinces, with sedimentation severely reducing water availability in the Rabi season downstream.","finding":"Reservoir sedimentation by 2050 will decrease irrigation water supply in the Rabi season by about 13% (1.89 km3) in Sindh and Balochistan provinces, which is a greater impact than local climate change-induced rainfall decreases; combined sedimentation and climate change effects lead to a 20% reduction in Rabi season water supply in low flow scenarios.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"This will decrease the supply of Icw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median Icw, equivalent to 1.89 km3 ), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median Pn, equivalent to 0.13 km3 ). Sedimentation combined with climate change decreases the median Icw by 51 mm (20%, equivalent to 2.91 km3 ) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3 ) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ETa in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of Icw in Rabi and increasing it in Kharif.","verified_in_page":true},"topics":["climate change impacts","reservoir sedimentation","river system modeling","irrigation water management","canal water delivery","water balance","crop water requirement","evapotranspiration"],"study_period":"1990–2012 baseline; projections to 2050","locations":[{"name":"Indus Basin Irrigation System (IBIS)","kind":"basin","role":"main study area","river_name":null,"geocode_query":"Indus Basin Irrigation System, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"There are already concerns about the extent of unsustainable water use in the IBIS, particularly of groundwater, and these concerns are likely to increase in the future due to population increase and associated increased demand for food. In addition, climate change, continuing reservoir sedimentation in the major dams, and other impacts on water supply and demand are likely to have an impact on the practical implementation of the landmark inter-provincial Water Apportionment Accord (the Accord), which governs surface water allocation in the IBIS. There is, therefore, a need to assess the sustainability of irrigation in light of the potential variations of water availability in the basin under a series of plausible climate change and reservoir sedimentation scenarios. In this paper, we examine the impact of continuing reservoir sedimentation and climate change by 2050 on the water balance of canal commands in the IBIS.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.404338,29.934518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-0","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Upper Indus Basin (UIB)","kind":"basin","role":"source river basin providing flows for IBIS study","river_name":"Indus River","geocode_query":"Upper Indus Basin, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Journal of Hydrology 603 (2021) 126967 Available online 21 September 2021 0022-1694/Crown Copyright © 2021 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Climate change and reservoir sedimentation implications for irrigated agriculture in the Indus Basin Irrigation System in Pakistan Mobin-ud-Din Ahmad * , Jorge L. Pena-Arancibia ̃ , Yingying Yu , Joel P. Stewart, Geoff M. Podger, John M. Kirby CSIRO Land and Water, GPO Box 1700, Canberra ACT 2601, Australia ARTICLE INFO Keywords: Water accounting Climate change Hydrological extremes Water outlook Irrigation Food security ABSTRACT Pakistan relies greatly on the water of the Upper Indus basin (UIB) for its vast agriculture in the Indus Basin Irrigation System (IBIS).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.404338,29.874518],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-1","true_coordinates":[73.46428006668208,29.90451816438136],"spread_km":6.7},{"name":"Punjab Province, Pakistan","kind":"region","role":"major upstream canal commands with higher rainfall and groundwater use; less affected by sedimentation by 2050","river_name":null,"geocode_query":"Punjab Province, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"This will decrease the supply of Icw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median Icw, equivalent to 1.89 km3 ), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median Pn, equivalent to 0.13 km3 ). Sedimentation combined with climate change decreases the median Icw by 51 mm (20%, equivalent to 2.91 km3 ) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3 ) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ETa in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of Icw in Rabi and increasing it in Kharif.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sindh and Balochistan Provinces, Pakistan","kind":"region","role":"downstream canal commands strongly affected by sedimentation effects with reduced Rabi supplies and impact on crop areas","river_name":null,"geocode_query":"Sindh and Balochistan Provinces, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"This will decrease the supply of Icw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median Icw, equivalent to 1.89 km3 ), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median Pn, equivalent to 0.13 km3 ). Sedimentation combined with climate change decreases the median Icw by 51 mm (20%, equivalent to 2.91 km3 ) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3 ) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ETa in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of Icw in Rabi and increasing it in Kharif.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tarbela Reservoir, Pakistan","kind":"facility","role":"major reservoir experiencing sedimentation that reduces storage capacity affecting downstream flows in IBIS","river_name":"Indus River","geocode_query":"Tarbela Reservoir, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The mean annual rainfall in the IBIS is about 250 mm/year, of which about 75% occurs between June and September, which coincides with the peak inflows from the mountainous regions of the UIB (Lutz et al., 2016; Stewart et al., 2020; Stewart et al., 2018; Yang et al., 2014). Due to the low and variable rainfall, year-round agriculture is only possible through irrigation which is allocated in accordance with the Accord and supplied through a gravity-fed upstream control irrigation system. The key infrastructure of the IBIS consists of two large surface supply storages – Tarbela (capacity of 7.38 km3 in 2020) and Mangla (capacity of 9.08 km3 in 2018) and a network of regulating infrastructure including Chashma Barrage (capacity of 0.34 km3 ) and 15 other headworks controlling flows to 12 inter-river link canals and 48 irrigation canals (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[72.80499999999999,34.1608333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tarbela Lake, Pakistan","basin_id":4040648320,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":69087.2,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-4"},{"name":"Mangla Reservoir, Pakistan","kind":"facility","role":"major reservoir experiencing sedimentation that reduces storage capacity affecting downstream flows in IBIS","river_name":"Jhelum River","geocode_query":"Mangla Reservoir, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The mean annual rainfall in the IBIS is about 250 mm/year, of which about 75% occurs between June and September, which coincides with the peak inflows from the mountainous regions of the UIB (Lutz et al., 2016; Stewart et al., 2020; Stewart et al., 2018; Yang et al., 2014). Due to the low and variable rainfall, year-round agriculture is only possible through irrigation which is allocated in accordance with the Accord and supplied through a gravity-fed upstream control irrigation system. The key infrastructure of the IBIS consists of two large surface supply storages – Tarbela (capacity of 7.38 km3 in 2020) and Mangla (capacity of 9.08 km3 in 2018) and a network of regulating infrastructure including Chashma Barrage (capacity of 0.34 km3 ) and 15 other headworks controlling flows to 12 inter-river link canals and 48 irrigation canals (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.6666667,33.1666667],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mangla Reservoir, Pakistan","basin_id":4040794730,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":290471.3,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-5"},{"name":"Chashma Barrage, Pakistan","kind":"facility","role":"regulating infrastructure controlling flows to inter-river link canals in IBIS","river_name":"Indus River","geocode_query":"Chashma Barrage, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"The mean annual rainfall in the IBIS is about 250 mm/year, of which about 75% occurs between June and September, which coincides with the peak inflows from the mountainous regions of the UIB (Lutz et al., 2016; Stewart et al., 2020; Stewart et al., 2018; Yang et al., 2014). Due to the low and variable rainfall, year-round agriculture is only possible through irrigation which is allocated in accordance with the Accord and supplied through a gravity-fed upstream control irrigation system. The key infrastructure of the IBIS consists of two large surface supply storages – Tarbela (capacity of 7.38 km3 in 2020) and Mangla (capacity of 9.08 km3 in 2018) and a network of regulating infrastructure including Chashma Barrage (capacity of 0.34 km3 ) and 15 other headworks controlling flows to 12 inter-river link canals and 48 irrigation canals (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[71.3803035,32.4359028],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Chashma Barrage, Dera Ismael Khan Mianwali Rd, Pakistan","basin_id":4040794590,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":125483.5,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-6"},{"name":"Kotri Barrage, Pakistan","kind":"facility","role":"end of system outflow control structure of Indus River for the IBIS","river_name":"Indus River","geocode_query":"Kotri Barrage, Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Approximately 71% of the mean annual flow of the river network is diverted for agricultural and domestic use purposes (Ahmad et al., 2021). Excess flows above surface storage and canal diversion capacity flow below Kotri Barrage (the end of system) to the Arabian Sea. The Indus River and its tributaries supply approximately 60% of the total water used for irrigation in the IBIS and the remaining is sourced from groundwater to meet evapotranspiration demand (Ahmad et al., 2021; Kirby et al., 2017), predominantly in the fresh groundwater quality regions of Punjab and small isolated pockets of Sindh province (Ahmad et al., 2014). The prevailing temperature and rainfall patterns govern two distinct cropping seasons: the wet summer Kharif (April to September) and dry Rabi (October to March). Wheat and fodder are the dominant crops during the dry Rabi (winter) season.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.31656660000002,25.4422516],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kotri Barrage, Pakistan","basin_id":4040033640,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":17092.8,"hydrology_source":"hydrobasins","site_id":"doc-7c1e317e58a26a16857c47ff-site-7"}],"limitations":["Incomplete geographic coordinates: no explicit lat/lon provided for study locations and infrastructures.","Regional scale geographies: locations named at province or basin scale without finer resolution.","Some data and scenario details are from unpublished reports, limiting external verification."],"river_ids":["ne-536","ne-537","ne-810"],"site_ids":["doc-7c1e317e58a26a16857c47ff-site-0","doc-7c1e317e58a26a16857c47ff-site-1","doc-7c1e317e58a26a16857c47ff-site-4","doc-7c1e317e58a26a16857c47ff-site-5","doc-7c1e317e58a26a16857c47ff-site-6","doc-7c1e317e58a26a16857c47ff-site-7"],"anchor":[71.3803035,32.4359028],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040033640,4040648320,4040794590,4040794730],"main_basin_ids":[4040033640]},{"id":"doc-b20855ab991f406910904e8a","title":"Development of a bioavailability-based risk assessment framework for nickel in Southeast Asia and Melanesia","display_title":"Nickel Risk Framework for SE Asia and Melanesia","doi":"https://doi.org/10.1002/ieam.4384","theme":"Water management","color":"#eeabac","year":2021,"publication_date":"2021-01-01","authors":["Emily Garman","Christian E. Schlekat","Elizabeth Middleton","Graham Merrington","Adam Peters","R. E. Smith","Jennifer L. Stauber","Kmy Leung","Francesca Gissi","Monique T. Binet","Merrin S. Adams","Megan L. Gillmore","Lisa A. Golding","Dianne F. Jolley","Zhen Wang","Amanda Reichelt‐Brushett"],"summary":"This paper describes a multi-institutional five-year research program developing a bioavailability-based environmental risk assessment (ERA) framework for nickel (Ni) in tropical Southeast Asia and Melanesia (SEAM), including New Caledonia, Papua New Guinea, the Philippines, and Indonesia. The study evaluated the applicability of classical ERA components in tropical ecosystems, developed exposure scenarios, generated chronic ecotoxicity data for tropical aquatic species, and assessed the sensitivity of tropical versus temperate species to Ni toxicity. The findings support combining tropical and temperate data sets for developing environmental quality standards (EQSs) and demonstrate that ERA paradigms are consistent across regions. The work highlights site-specific considerations in areas with high natural Ni backgrounds, such as New Caledonia.","finding":"The study found that tropical freshwater and marine species are not uniquely more sensitive to nickel toxicity compared to temperate species, supporting the use of combined tropical and temperate data sets in risk assessments.","finding_evidence":{"passage_id":"p007-003","page":7,"quote":"Comparisons between the sensitivity of closely related taxa from different climatic regions did not reveal any differences that were greater than the level of variability observed between similarly related taxa from the same climatic region. The Peters et al. study, therefore, concluded that there was insufficient evidence to require separate SSDs for different climatic regions and that whole ecosystem protection would most likely be ensured by using the largest and most taxonomically diverse data set available, which would be achieved by combining all of the available reliable and relevant data for both tropical and temperate species into a single inclusive data set. For more information on the sensitivity of freshwater tropical and temperate species to chronic Ni toxicity, see Peters et al. (2019). Marine aquatic tropical ecotoxicity data.","verified_in_page":true},"topics":["bioavailability","chronic toxicity","aquatic ecosystems","environmental risk assessment","nickel pollution","species sensitivity distribution","environmental quality standards","ecotoxicology"],"study_period":null,"locations":[{"name":"Southeast Asia and Melanesia (SEAM) region","kind":"region","role":"general study region encompassing all study components","river_name":null,"geocode_query":"Southeast Asia and Melanesia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"There are few legislatively derived standards or guidelines for the protection of aquatic life for Ni in many of these tropical regions, and bioavailability‐based environmental risk assessment (ERA) approaches for metals have mainly been developed and tested in temperate regions, such as the United States and Europe. This paper reports on a multi‐ institutional, 5‐y testing program to evaluate Ni exposure, effects, and risk characterization in the Southeast Asia and Melanesia (SEAM) region, which includes New Caledonia, Papua New Guinea, the Philippines, and Indonesia. Further, we have developed an approach to determine if the individual components of classical ERA, including effects assessments, exposure assessments, and risk characterization methodologies (which include bioavailability normalization), are applicable in this region. A main conclusion of this research program is that although ecosystems and exposures may be different in tropical systems, ERA paradigms are constant.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.61083,-2.18797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-b20855ab991f406910904e8a-site-0","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"New Caledonia","kind":"region","role":"study location, site of nickel laterite mining, field data collection, and EQS development efforts","river_name":null,"geocode_query":"New Caledonia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and now account for 60% of the world's Ni supply (Mudd 2010). These ore bodies are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Cuba, Papua New Guinea (PNG), and New Caledonia (Dalvi et al. 2004; Mudd 2010; Mudd and Jowitt 2014). The mining, refining, and smelting of Ni laterites can lead to solubilization of not only Ni but also co‐occurring elements such as Co, Cr, Mn, sulfate, and chloride and their subsequent release into waters, sediments, and soils (Fernandez et al. 2006). Although Ni is just 1 environmental stressor associated with Ni operations in SEAM, it is the primary focus in this ERA framework. Tropical ecosystems differ from temperate ecoregions in that they may have different climate and geochemistry, unique ecosystem composition, and generally greater biodiversity.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Papua New Guinea","kind":"region","role":"study location within SEAM used in exposure, effects, and risk characterization studies","river_name":null,"geocode_query":"Papua New Guinea","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"There are few legislatively derived standards or guidelines for the protection of aquatic life for Ni in many of these tropical regions, and bioavailability‐based environmental risk assessment (ERA) approaches for metals have mainly been developed and tested in temperate regions, such as the United States and Europe. This paper reports on a multi‐ institutional, 5‐y testing program to evaluate Ni exposure, effects, and risk characterization in the Southeast Asia and Melanesia (SEAM) region, which includes New Caledonia, Papua New Guinea, the Philippines, and Indonesia. Further, we have developed an approach to determine if the individual components of classical ERA, including effects assessments, exposure assessments, and risk characterization methodologies (which include bioavailability normalization), are applicable in this region. A main conclusion of this research program is that although ecosystems and exposures may be different in tropical systems, ERA paradigms are constant.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Philippines","kind":"region","role":"study location within SEAM used in exposure, effects, and risk characterization studies","river_name":null,"geocode_query":"Philippines","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"There are few legislatively derived standards or guidelines for the protection of aquatic life for Ni in many of these tropical regions, and bioavailability‐based environmental risk assessment (ERA) approaches for metals have mainly been developed and tested in temperate regions, such as the United States and Europe. This paper reports on a multi‐ institutional, 5‐y testing program to evaluate Ni exposure, effects, and risk characterization in the Southeast Asia and Melanesia (SEAM) region, which includes New Caledonia, Papua New Guinea, the Philippines, and Indonesia. Further, we have developed an approach to determine if the individual components of classical ERA, including effects assessments, exposure assessments, and risk characterization methodologies (which include bioavailability normalization), are applicable in this region. A main conclusion of this research program is that although ecosystems and exposures may be different in tropical systems, ERA paradigms are constant.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Indonesia","kind":"region","role":"study location within SEAM used in exposure, effects, and risk characterization studies","river_name":null,"geocode_query":"Indonesia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"There are few legislatively derived standards or guidelines for the protection of aquatic life for Ni in many of these tropical regions, and bioavailability‐based environmental risk assessment (ERA) approaches for metals have mainly been developed and tested in temperate regions, such as the United States and Europe. This paper reports on a multi‐ institutional, 5‐y testing program to evaluate Ni exposure, effects, and risk characterization in the Southeast Asia and Melanesia (SEAM) region, which includes New Caledonia, Papua New Guinea, the Philippines, and Indonesia. Further, we have developed an approach to determine if the individual components of classical ERA, including effects assessments, exposure assessments, and risk characterization methodologies (which include bioavailability normalization), are applicable in this region. A main conclusion of this research program is that although ecosystems and exposures may be different in tropical systems, ERA paradigms are constant.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Australia","kind":"region","role":"region with existing bioavailability models providing comparative data and testing species for Ni toxicity","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"To this end, laboratory toxicity testing was performed in a targeted fashion. An encompassing criterion for these data was that they needed to fulfill definitions of chronic endpoints because of their increasing use in global ERA frameworks, including Australian–New Zealand guidelines. Both aquatic and sediment compartments were addressed because of the relevance of both habitats to the exposure scenarios identified in the conceptual model. Likewise, both freshwater and marine organisms were tested. Freshwater aquatic tropical ecotoxicity data. In terms of species selection for pelagic organisms, groups known to be sensitive from existing temperate databases were included to verify that their tropical counterparts within these groups were also sensitive. For example, the snail Lymnaea stagnalis and the crustacean Ceriodaphnia dubia are among the most sensitive species in the chronic freshwater Ni ecotoxicity database (Nys et al. 2016).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales, Australia","kind":"region","role":"location of CSIRO Land and Water and Marine Ecology Research Centre involved in effects assessment and toxicity testing","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Integrated Environmental Assessment and Management — Volume 17, Number 4—pp. 802–813 802 Received: 30 September 2020 | Returned for Revision: 25 November 2020 | Accepted: 17 December 2020 Health & Ecological Risk Assessment Development of a bioavailability‐based risk assessment framework for nickel in Southeast Asia and Melanesia Emily R Garman,*† Christian E Schlekat,† Ellie Middleton,† Graham Merrington,‡ Adam Peters,‡ Ross Smith,§ Jenny L Stauber,|| Kenneth MY Leung,# Francesca Gissi,††‡‡ Monique T Binet,|| Merrin S Adams,|| Megan L Gillmore,||‡‡ Lisa A Golding,|| Dianne Jolley,‡‡ Zhen Wang,§§ and Amanda Reichelt‐Brushett|||| †NiPERA Inc., Durham, North Carolina, USA ‡wca, Faringdon, Oxon, United Kingdom §Hydrobiology, Brisbane, Australia ||CSIRO Land and Water, Lucas Heights, New South Wales, Australia #State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China ††CSIRO, Oceans and Atmosphere, Lucas Heights, New South Wales, Australia ‡‡School of Earth, Atmosphere and Life Sciences, University of Wollongong, New South Wales, Australia §§Institute of Marine Sciences and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, China ||||Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia ABSTRACT Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Papua New Guinea, Cuba, and New Caledonia.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Faringdon, Oxon, United Kingdom","kind":"settlement","role":"location of institution contributing to risk assessment framework development","river_name":null,"geocode_query":"Faringdon, Oxfordshire, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Integrated Environmental Assessment and Management — Volume 17, Number 4—pp. 802–813 802 Received: 30 September 2020 | Returned for Revision: 25 November 2020 | Accepted: 17 December 2020 Health & Ecological Risk Assessment Development of a bioavailability‐based risk assessment framework for nickel in Southeast Asia and Melanesia Emily R Garman,*† Christian E Schlekat,† Ellie Middleton,† Graham Merrington,‡ Adam Peters,‡ Ross Smith,§ Jenny L Stauber,|| Kenneth MY Leung,# Francesca Gissi,††‡‡ Monique T Binet,|| Merrin S Adams,|| Megan L Gillmore,||‡‡ Lisa A Golding,|| Dianne Jolley,‡‡ Zhen Wang,§§ and Amanda Reichelt‐Brushett|||| †NiPERA Inc., Durham, North Carolina, USA ‡wca, Faringdon, Oxon, United Kingdom §Hydrobiology, Brisbane, Australia ||CSIRO Land and Water, Lucas Heights, New South Wales, Australia #State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China ††CSIRO, Oceans and Atmosphere, Lucas Heights, New South Wales, Australia ‡‡School of Earth, Atmosphere and Life Sciences, University of Wollongong, New South Wales, Australia §§Institute of Marine Sciences and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, China ||||Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia ABSTRACT Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Papua New Guinea, Cuba, and New Caledonia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-1.584679,51.658477],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Faringdon, UK","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-b20855ab991f406910904e8a-site-7"},{"name":"Brisbane, Australia","kind":"settlement","role":"site of Hydrobiology contributing to aquatic ecological toxicity data","river_name":null,"geocode_query":"Brisbane, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Integrated Environmental Assessment and Management — Volume 17, Number 4—pp. 802–813 802 Received: 30 September 2020 | Returned for Revision: 25 November 2020 | Accepted: 17 December 2020 Health & Ecological Risk Assessment Development of a bioavailability‐based risk assessment framework for nickel in Southeast Asia and Melanesia Emily R Garman,*† Christian E Schlekat,† Ellie Middleton,† Graham Merrington,‡ Adam Peters,‡ Ross Smith,§ Jenny L Stauber,|| Kenneth MY Leung,# Francesca Gissi,††‡‡ Monique T Binet,|| Merrin S Adams,|| Megan L Gillmore,||‡‡ Lisa A Golding,|| Dianne Jolley,‡‡ Zhen Wang,§§ and Amanda Reichelt‐Brushett|||| †NiPERA Inc., Durham, North Carolina, USA ‡wca, Faringdon, Oxon, United Kingdom §Hydrobiology, Brisbane, Australia ||CSIRO Land and Water, Lucas Heights, New South Wales, Australia #State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China ††CSIRO, Oceans and Atmosphere, Lucas Heights, New South Wales, Australia ‡‡School of Earth, Atmosphere and Life Sciences, University of Wollongong, New South Wales, Australia §§Institute of Marine Sciences and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, China ||||Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia ABSTRACT Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Papua New Guinea, Cuba, and New Caledonia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.026034,-27.410453],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-b20855ab991f406910904e8a-site-8","true_coordinates":[153.0260341,-27.4704528],"spread_km":6.7},{"name":"Hong Kong, China","kind":"settlement","role":"location with expertise in marine pollution and chemistry relevant for bioavailability modeling and risk assessment","river_name":null,"geocode_query":"Hong Kong, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Integrated Environmental Assessment and Management — Volume 17, Number 4—pp. 802–813 802 Received: 30 September 2020 | Returned for Revision: 25 November 2020 | Accepted: 17 December 2020 Health & Ecological Risk Assessment Development of a bioavailability‐based risk assessment framework for nickel in Southeast Asia and Melanesia Emily R Garman,*† Christian E Schlekat,† Ellie Middleton,† Graham Merrington,‡ Adam Peters,‡ Ross Smith,§ Jenny L Stauber,|| Kenneth MY Leung,# Francesca Gissi,††‡‡ Monique T Binet,|| Merrin S Adams,|| Megan L Gillmore,||‡‡ Lisa A Golding,|| Dianne Jolley,‡‡ Zhen Wang,§§ and Amanda Reichelt‐Brushett|||| †NiPERA Inc., Durham, North Carolina, USA ‡wca, Faringdon, Oxon, United Kingdom §Hydrobiology, Brisbane, Australia ||CSIRO Land and Water, Lucas Heights, New South Wales, Australia #State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China ††CSIRO, Oceans and Atmosphere, Lucas Heights, New South Wales, Australia ‡‡School of Earth, Atmosphere and Life Sciences, University of Wollongong, New South Wales, Australia §§Institute of Marine Sciences and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, China ||||Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia ABSTRACT Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Papua New Guinea, Cuba, and New Caledonia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Shantou, China","kind":"settlement","role":"location of Institute of Marine Sciences providing expertise in marine ecotoxicology of Ni","river_name":null,"geocode_query":"Shantou, Guangdong, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Integrated Environmental Assessment and Management — Volume 17, Number 4—pp. 802–813 802 Received: 30 September 2020 | Returned for Revision: 25 November 2020 | Accepted: 17 December 2020 Health & Ecological Risk Assessment Development of a bioavailability‐based risk assessment framework for nickel in Southeast Asia and Melanesia Emily R Garman,*† Christian E Schlekat,† Ellie Middleton,† Graham Merrington,‡ Adam Peters,‡ Ross Smith,§ Jenny L Stauber,|| Kenneth MY Leung,# Francesca Gissi,††‡‡ Monique T Binet,|| Merrin S Adams,|| Megan L Gillmore,||‡‡ Lisa A Golding,|| Dianne Jolley,‡‡ Zhen Wang,§§ and Amanda Reichelt‐Brushett|||| †NiPERA Inc., Durham, North Carolina, USA ‡wca, Faringdon, Oxon, United Kingdom §Hydrobiology, Brisbane, Australia ||CSIRO Land and Water, Lucas Heights, New South Wales, Australia #State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China ††CSIRO, Oceans and Atmosphere, Lucas Heights, New South Wales, Australia ‡‡School of Earth, Atmosphere and Life Sciences, University of Wollongong, New South Wales, Australia §§Institute of Marine Sciences and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, China ||||Marine Ecology Research Centre, School of Environment, Science and Engineering, Southern Cross University, Lismore, New South Wales, Australia ABSTRACT Nickel laterite ore deposits are becoming increasingly important sources of Ni for the global marketplace and are found mainly in tropical and subtropical regions, including Indonesia, the Philippines, Papua New Guinea, Cuba, and New Caledonia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.68221,23.3534999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Shantou, Guangdong Province, China","basin_id":4040012290,"main_basin_id":4040012290,"basin_region":"Central and South-East Asia","basin_area_km2":18089.8,"hydrology_source":"hydrobasins","site_id":"doc-b20855ab991f406910904e8a-site-10"}],"limitations":["Broad regional scale locations without precise coordinates","Limited spatial coverage and patchy water chemistry data for SEAM region","Data gaps exist for local species toxicological data and water chemistry required for bioavailability normalization"],"river_ids":[],"site_ids":["doc-b20855ab991f406910904e8a-site-0","doc-b20855ab991f406910904e8a-site-7","doc-b20855ab991f406910904e8a-site-8","doc-b20855ab991f406910904e8a-site-10"],"anchor":[115.61083,-2.18797],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040048790,4040012290,5040015660,5040069970],"main_basin_ids":[2040048790,4040012290,5040015660,5040069970]},{"id":"doc-b62d6fb1e4012852c06932bb","title":"Deep borehole disposal of intermediate-level waste","display_title":"Deep Borehole Disposal of Intermediate-Level Waste","doi":"https://doi.org/10.5194/sand-1-263-2021","theme":"Water management","color":"#eeabac","year":2021,"publication_date":"2021-11-10","authors":["Dirk Mallants","John Phalen","Hef Griffiths"],"summary":"This study outlines an international research, development and demonstration project in Australia that evaluates the technical feasibility and long-term safety of deep borehole disposal of intermediate-level radioactive waste (ILW) from research reactors and radiopharmaceutical production. The project includes full-scale field testing using a 2000 m deep borehole and novel tools for site screening, borehole design, and post-closure safety assessments. It emphasizes reliance on geological features rather than engineered barriers for containment and explores waste container design options such as steel with copper coating.","finding":"Deep borehole disposal of ILW in Australia offers a cost-effective and modular solution compared to conventional geologic disposal facilities and demonstrates potential to reduce proliferation risks while ensuring long-term safety relying mainly on geological containment.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"In Australia, long-lived ILW from research reactors and radiopharmaceutical production represents the principal waste stream that requires deep geologic disposal. Whilst the Australian government has not yet made a decision on its preferred strategy for ILW disposal, deep borehole disposal of small volumes of ILW would be a more cost-effective and modular solution compared to a conventional geologic disposal facility (GDF). CSIRO, ANSTO and SANDIA have created an international partnership to execute a full-scale borehole research, development and demonstration (RD&D) project in Australia. The project will demonstrate the technical feasibility of the long-term safety of borehole disposal in deep geological formations. The execution of this project could also demonstrate options for nuclear waste disposal that would reduce proliferation risks, potentially up to the termination of compliance with international safeguards requirements.","verified_in_page":true},"topics":["radioactive waste disposal","geological disposal","borehole research","waste container design","long-term safety assessment","geologic containment","radiopharmaceutical waste","nuclear waste management"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"study region for deep borehole disposal project","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"In Australia, long-lived ILW from research reactors and radiopharmaceutical production represents the principal waste stream that requires deep geologic disposal. Whilst the Australian government has not yet made a decision on its preferred strategy for ILW disposal, deep borehole disposal of small volumes of ILW would be a more cost-effective and modular solution compared to a conventional geologic disposal facility (GDF). CSIRO, ANSTO and SANDIA have created an international partnership to execute a full-scale borehole research, development and demonstration (RD&D) project in Australia. The project will demonstrate the technical feasibility of the long-term safety of borehole disposal in deep geological formations. The execution of this project could also demonstrate options for nuclear waste disposal that would reduce proliferation risks, potentially up to the termination of compliance with international safeguards requirements.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Adelaide, South Australia","kind":"settlement","role":"location of CSIRO headquarters involved in project","river_name":null,"geocode_query":"Adelaide, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Saf. Nucl. Waste Disposal, 1, 263–264, 2021 https://doi.org/10.5194/sand-1-263-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Open Access Safety of Nuclear Waste Disposal safeND, 4c) Alternative waste management options: an overview and state of the art, BASE Deep borehole disposal of intermediate-level waste Dirk Mallants1 , John Phalen2 , and Hef Griffiths3 1CSIRO, Adelaide, SA, 5064, Australia 2CSIRO, Brisbane, QLD, 4102, Australia 3ANSTO, Lucas Heights, NSW, 2234, Australia Correspondence: Dirk Mallants (dirk.mallants@csiro.au) Published: 10 November 2021 Abstract. Around the world, deep borehole disposal is being evaluated for intermediate-level waste (ILW), high-level waste (HLW), spent nuclear fuel (SNF), separated plutonium waste and some very high specific activity fission product waste.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.632498,-34.874441],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-b62d6fb1e4012852c06932bb-site-1","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"Brisbane, Queensland","kind":"settlement","role":"location of CSIRO office involved in project","river_name":null,"geocode_query":"Brisbane, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Saf. Nucl. Waste Disposal, 1, 263–264, 2021 https://doi.org/10.5194/sand-1-263-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Open Access Safety of Nuclear Waste Disposal safeND, 4c) Alternative waste management options: an overview and state of the art, BASE Deep borehole disposal of intermediate-level waste Dirk Mallants1 , John Phalen2 , and Hef Griffiths3 1CSIRO, Adelaide, SA, 5064, Australia 2CSIRO, Brisbane, QLD, 4102, Australia 3ANSTO, Lucas Heights, NSW, 2234, Australia Correspondence: Dirk Mallants (dirk.mallants@csiro.au) Published: 10 November 2021 Abstract. Around the world, deep borehole disposal is being evaluated for intermediate-level waste (ILW), high-level waste (HLW), spent nuclear fuel (SNF), separated plutonium waste and some very high specific activity fission product waste.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.958409,-27.470453],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Brisbane QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-b62d6fb1e4012852c06932bb-site-2","true_coordinates":[153.0260341,-27.4704528],"spread_km":6.7},{"name":"Lucas Heights, New South Wales","kind":"settlement","role":"location of ANSTO facility participating in project","river_name":null,"geocode_query":"Lucas Heights, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Saf. Nucl. Waste Disposal, 1, 263–264, 2021 https://doi.org/10.5194/sand-1-263-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Open Access Safety of Nuclear Waste Disposal safeND, 4c) Alternative waste management options: an overview and state of the art, BASE Deep borehole disposal of intermediate-level waste Dirk Mallants1 , John Phalen2 , and Hef Griffiths3 1CSIRO, Adelaide, SA, 5064, Australia 2CSIRO, Brisbane, QLD, 4102, Australia 3ANSTO, Lucas Heights, NSW, 2234, Australia Correspondence: Dirk Mallants (dirk.mallants@csiro.au) Published: 10 November 2021 Abstract. Around the world, deep borehole disposal is being evaluated for intermediate-level waste (ILW), high-level waste (HLW), spent nuclear fuel (SNF), separated plutonium waste and some very high specific activity fission product waste.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.987244,-33.99064],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054723,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lucas Heights NSW 2234, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-b62d6fb1e4012852c06932bb-site-3","true_coordinates":[150.9872444,-34.0506403],"spread_km":6.7}],"limitations":["No precise geographic coordinates for the deep borehole site or study area within Australia are provided in the text.","Locations given are general regions or organizational headquarters rather than specific borehole or geological formation names."],"river_ids":[],"site_ids":["doc-b62d6fb1e4012852c06932bb-site-1","doc-b62d6fb1e4012852c06932bb-site-2","doc-b62d6fb1e4012852c06932bb-site-3"],"anchor":[150.987244,-33.99064],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040069970,5040070710,5040073420],"main_basin_ids":[5040069970,5040070710,5040073420]},{"id":"doc-b6e643c76aa591779bca15b0","title":"Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping","display_title":"Deep Learning Maps 3D Paleovalleys in Australian Desert","doi":"https://doi.org/10.5194/gmd-14-3421-2021","theme":"Groundwater","color":"#a8b9ff","year":2021,"publication_date":"2021-06-08","authors":["Zhenjiao Jiang","Dirk Mallants","Lei Gao","Tim Munday","Gregoire Mariethoz","Luk Peeters"],"summary":"The study developed an adversarial convolutional neural network (Sub3DNet1.0) to map 3D subsurface paleovalley structures from 2D land surface data across a large desert area in Australia. The model was trained and validated in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands of South Australia to relate the multiple-resolution valley bottom flatness (MrVBF) index derived from digital elevation data to 3D airborne electromagnetic (AEM)-derived paleovalley presence data, enabling prediction of buried paleovalleys that are important groundwater resources in arid landscapes.","finding":"The trained neural network achieves a squared error below 0.10 across 99% of the training domain and below 0.10 across 93% of the validation domain (~80 km away), demonstrating reliable prediction of 3D paleovalley structures beyond the training area using only 2D topographic data.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"As shown in Fig. 5, the squared error in the training dataset is below 0.1 for 99 % of the training domain and with a mean value of about 0.03, and the squared error of the predicted 3D PAI is well below < 0.1 for 93 % of the validation domain, with a mean squared error of about 0.04. The patterns of the generated paleovalley in both horizontal and vertical directions align with those inferred from the AEM-derived PAI. This indicates that the deep-learning neural network structure developed here is capable of incorporating the relationships between the MrVBF and the buried paleovalley patterns and allowing for high-quality predictions beyond the training area. 4 Discussion 4.1 Neural network with and without fully connected layer The traditional convolution neural network is often ended by a fully connected layer in the encoder (e.g., Wu et al., 2016), where all the elements in the previous layer are connected to every code in the output layer by matrix multiplication.","verified_in_page":true},"topics":["subsurface-structure","deep-learning","convolutional-neural-network","paleovalley","airborne-electromagnetic-survey","digital-elevation-model","groundwater","regional-scale-modeling"],"study_period":null,"locations":[{"name":"Anangu Pitjantjatjara Yankunytjatjara (APY) Lands, Musgrave Province, South Australia","kind":"region","role":"primary study area for modeling 3D paleovalley subsurface structures","river_name":null,"geocode_query":"Anangu Pitjantjatjara Yankunytjatjara Lands, Musgrave Province, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Z. Jiang et al.: Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping 3425 the input layer. Thus, the spatial correlation scale addressed by the convolutional neural network can be controlled by the filter size in both vertical and horizontal directions. The weight and bias in the encoder are trained to ensure that the code follows a standard normal distribution, by minimizing the Kullback–Leibler divergence (L1), defined as (Kullback and Leibler, 1951) L1 = 1 2N X N i=1 \u0010 μ 2 + σ 2 − logσ 2 − 1 \u0011 i , (2) where N is number of codes in the final output layer of the encoder, and μ and σ are the mean and standard deviation of the codes, respectively. In the second step, the codes are converted into a 3D image of subsurface structure (a 3D array) by deconvolution (referred to as the “decoder”, Fig. 2a), which is a process involving a zero-padding before the convolution (Fig. 2c).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.567388,-26.928572],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61125590,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Anangu Pitjantjatjara Yankunytjatjara, SA 0872, Australia","basin_id":5040088010,"main_basin_id":5040088010,"basin_region":"Australasia","basin_area_km2":50967.7,"hydrology_source":"geofabric","site_id":"doc-b6e643c76aa591779bca15b0-site-0","true_coordinates":[130.6346851,-26.928572],"spread_km":6.7},{"name":"Great Victoria Desert, central Australia","kind":"region","role":"region containing study and referenced in data acquisition and geological context","river_name":null,"geocode_query":"Great Victoria Desert, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"Big datasets on geology and geomorphology are either globally available as land surface observations (typically remote sensing and topographical data and their derivatives) or only regionally available in a limited number of highly developed mining and oil fields (e.g., downhole, surface, and airborne geophysical data and interpretations). In Australia, as an example, the former are readily available at relatively low or no-cost, while the latter are often non-existent and expensive for remote desert areas, where a key challenge is to secure groundwater for town/community supply, primarily from shallow aquifers (Munday et al., 2020a, b). In their study, Munday et al. (2020a, b) interpreted 17 000 line km of airborne electromagnetic (AEM) data covering an area of about 30 000 km2 within the much larger Great Victoria Desert in central Australia (422 000 km2 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[129.4796328,-28.8558686],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61125399,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Great Victoria Desert, South Australia 5690, Australia","basin_id":5040088010,"main_basin_id":5040088010,"basin_region":"Australasia","basin_area_km2":50967.7,"hydrology_source":"geofabric","site_id":"doc-b6e643c76aa591779bca15b0-site-1"},{"name":"Central Lowlands, Nebraska, USA","kind":"region","role":"comparison region where airborne electromagnetics was used for paleovalley mapping enabling generalization of the method","river_name":null,"geocode_query":"Central Lowlands, Nebraska, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Korus et al. (2017) demonstrated that AEM can be used effectively in environments like the glaciated Central Lowlands of Nebraska (USA) to identify sedimentary architectural units with a high degree of lithological heterogeneity. These systems were tens of meters deep and 100 m to more than 1000 m wide. All these valley–infill systems are characterized by a multi-phase history of glaciation and buried valley genesis. The paleovalley systems in our study area and the broader Musgrave Province–Great Victorian Desert also have a multi-phase history, albeit with somewhat different processes across more extended timescales. Importantly, geophysical inversions across a wide range of paleovalley systems have consistently delivered realistic geologic profiles, albeit defined by the geo-electrical properties of the fill materials and the water contained therein (Davis et al., 2016; Magee, 2009; Roach et al., 2014; Soerensen et al., 2016).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Northern Germany","kind":"region","role":"comparison paleovalley system where AEM was used for 3D geological modeling of buried-valley infill","river_name":null,"geocode_query":"Northern Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-004","page":10,"quote":"Furthermore, application of AEM for mapping buried valley systems has been successful in several other areas across Australia, each with their own evolutionary intricacies (Davis et al., 2016; Magee, 2009; Roach et al., 2014), with the key commonality being a very low topographic gradient. AEM has also been used in northern Europe, Canada, and the US for similar purposes, albeit with different electrical conductivity structures. The application of AEM to map paleovalley systems in many parts of the world has been successfully demonstrated. Indeed, paleovalleys occur beneath the glaciated landscapes of northern Europe, Canada, and the northern USA. When filled with coarse-grained permeable sediments, these valleys – as their Australian counterparts – represent potential sources of groundwater.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Denmark","kind":"region","role":"comparison area for buried valley studies integrating airborne geophysics and drilling","river_name":null,"geocode_query":"Denmark","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"Z. Jiang et al.: Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping 3431 Figure 7. Input MrVBF in (a) training area and (d) validation area and (b, e) the generated PAI at a depth of 30–40 m with fully connected operation in the encoder and (c, f) without fully connected operation. mark, with typical dimensions of 0.5–4 km wide and 25– 350 m deep; their lengths vary from roughly 30 km for onshore structures to 100 km for offshore systems (Høyer et al., 2015; Jørgensen et al., 2003). In our study site, the burial depth of paleovalley infills ranges between 5.0 and 250 m, with the typical width from 0.1 to 2 km. In southern Manitoba, Canada, Oldenborger et al. (2013) used a combination of airborne time-domain electromagnetics, electrical resistivity, and seismic reflection to map the complex buried valley morphology with nested scales of valleys at a level of detail sufficient for groundwater prospecting, modeling, and management.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southern Manitoba, Canada","kind":"region","role":"comparison area demonstrating multi-scale buried valley morphology mapping using airborne geophysics and seismic","river_name":null,"geocode_query":"Southern Manitoba, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"Z. Jiang et al.: Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping 3431 Figure 7. Input MrVBF in (a) training area and (d) validation area and (b, e) the generated PAI at a depth of 30–40 m with fully connected operation in the encoder and (c, f) without fully connected operation. mark, with typical dimensions of 0.5–4 km wide and 25– 350 m deep; their lengths vary from roughly 30 km for onshore structures to 100 km for offshore systems (Høyer et al., 2015; Jørgensen et al., 2003). In our study site, the burial depth of paleovalley infills ranges between 5.0 and 250 m, with the typical width from 0.1 to 2 km. In southern Manitoba, Canada, Oldenborger et al. (2013) used a combination of airborne time-domain electromagnetics, electrical resistivity, and seismic reflection to map the complex buried valley morphology with nested scales of valleys at a level of detail sufficient for groundwater prospecting, modeling, and management.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Lawlers, Western Australia","kind":"region","role":"region where airborne electromagnetics was used for geological interpretation of electrical structure supporting groundwater exploration relevant to method applicability","river_name":null,"geocode_query":"Lawlers, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-006","page":14,"quote":"J., Macnae, J., Bishop, J., and Sattel, D.: A geological interpretation of observed electrical structures in the regolith: Lawlers, Western Australia, Explor. Geophys., 32, 36–47, 2001. Munday, T. J., Cahill, K., Sorensen, C., Davis, A., and Ibrahimi, T.: Uncovering the Musgraves – a different perspective on an old landscape, Goyder Institute for Water Research, Adelaide, December, 2016. Niu, C., Li, J., and Xu, K.: Im2Struct: Recovering 3D Shape Structure from a Single RGB Image, Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 80, 4096, 2018. Oldenborger, G. A., Pugin, A. J. M., and Pullan, S. E.: Airborne time-domain electromagnetics, electrical resistivity and seismic reflection for regional three-dimensional mapping and characterization of the Spiritwood Valley Aquifer, Manitoba, Canada, Near Surf. Geophys., 11, 63–74, 2013. Pawley, M. J., Dutch, R. A., Werner, M., and Krapf, C. B.: Repeated failure: long-lived faults in the eastern Musgrave Province, MESA J., 75, 45–55, 2014.","verified_in_page":true},"resolution_status":"resolved","coordinates":[120.51,-28.08],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61118199,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lawlers, Leinster WA 6437, Australia","basin_id":5040087690,"main_basin_id":5040087690,"basin_region":"Australasia","basin_area_km2":127703.4,"hydrology_source":"geofabric","site_id":"doc-b6e643c76aa591779bca15b0-site-6"},{"name":"Murchison Region, Western Australia","kind":"region","role":"reference area where airborne electromagnetics aided groundwater resource assessment illustrating application context","river_name":null,"geocode_query":"Murchison Region, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-004","page":13,"quote":"Davis, A., Macaulay, S., Munday, T., Sorensen, C., Shudra, J., and Ibrahimi, T.: Uncovering the groundwater resource potential of Murchison Region in Western Australia through targeted application of airborne electromagnetics, ASEG Extended Abstracts, 2016, 1–6, 2016. de Marsily, G., Delay, F., Gonçalvès, J., Renard, P., Teles, V., and Violette, S.: Dealing with spatial heterogeneity, Hydrogeol. J., 13, 161–183, 2005. Dodds, S. and Sampson, L.: The Sustainability of Water Resources in the Anangu Pitjantjatjara Lands, South Australia, Department for Water Resources, Adelaide, 2000. Felletti, F., Bersezio, R., and Giudici, M.: Geostatistical simulation and numerical upscaling, to model ground-water flow in a sandy-gravel, braided river, aquifer analogue, J. Sediment. Res., 76, 1215–1229, 2006. Gallant, J., Dowling, T., and Austin, J.: Multi-resolution Valley Bottom Flatness (MrVBF), v3, CSIRO, Data Collection, https://doi.org/10.4225/08/5701C885AB4FE, 2012. Gallant, J. C. and Dowling, T.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.0413889,-26.5181534],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":80910677,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murchison, WA, Australia","basin_id":5040056740,"main_basin_id":5040056740,"basin_region":"Australasia","basin_area_km2":102560.9,"hydrology_source":"geofabric","site_id":"doc-b6e643c76aa591779bca15b0-site-7"}],"limitations":["Geocoordinates of study locations are not provided explicitly in the paper","Topographic and paleovalley data cover large regional extents but specific site coordinates unavailable","The study area is broad and environmental heterogeneity may limit local prediction accuracy"],"river_ids":[],"site_ids":["doc-b6e643c76aa591779bca15b0-site-0","doc-b6e643c76aa591779bca15b0-site-1","doc-b6e643c76aa591779bca15b0-site-6","doc-b6e643c76aa591779bca15b0-site-7"],"anchor":[120.51,-28.08],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040056740,5040087690,5040088010],"main_basin_ids":[5040056740,5040087690,5040088010]},{"id":"doc-c7c9fc04333121c271191693","title":"Impact of climate change and management strategies on water and salt balance of the polders and islands in the Ganges delta","display_title":"Climate, Water and Salt Management in Ganges Delta Polders","doi":"https://doi.org/10.1038/s41598-021-86206-1","theme":"Land & ecosystems","color":"#c8df98","year":2021,"publication_date":"2021-03-29","authors":["Mohammed Mainuddin","Fazlul Karim","Donald S. Gaydon","J. M. Kirby"],"summary":"This study models the effects of climate change and management strategies on water and salt balances in polders of the Ganges delta, particularly examining how these affect soil salinity and irrigation. Three sites with varying salinities and rainfall—Amtali and Dacope in Bangladesh, and Gosaba Island in West Bengal, India—were analyzed using a polder water and salt balance model. The study finds that salt management is crucial to maintain soil and water quality, especially given climate-driven changes in rainfall and sea level. Increasing irrigation water availability alone has limited impact if salt management is poor. The study informs sustainable agricultural development in vulnerable coastal zones significant for food security.","finding":"Salt management strategies effectively reduce soil and water salinity in polders and are more critical than simply increasing water availability to improve crop evapotranspiration and long-term agricultural sustainability.","finding_evidence":{"passage_id":"p010-004","page":10,"quote":"In contrast, the strategy to increase the area of ponds and canals for rice–wheat production provides more water but does not much affect the salt concentrations compared to the rice–wheat system without more ponds. The impact of this strategy on salt concentrations and the crop evapotranspiration is small. Mainuddin et al.31 came to a similar conclusion based on a Monte-Carlo parameter sensitivity analysis. They did not examine management scenarios as we have done here; rather they examined the sensitivity to variation in several input parameters including soil and groundwater properties and soil, groundwater, and river salinity. Their key finding was that, in general, the impacts were more sensitive to parameters governing the transfers of salt, and less sensitive to those governing the availability of water. The crop evapotranspiration was particularly sensitive to the model parameters.","verified_in_page":true},"topics":["salinity management","climate change impacts","groundwater pumping","irrigation","water balance","coastal agriculture","crop evapotranspiration","sea level rise"],"study_period":"1994-2018 (historical), 2046-2075 (climate projections)","locations":[{"name":"Amtali (Polder 43/1), Borguna, Patuakhali region, Bangladesh","kind":"region","role":"study site for polder water and salt balance modeling with low salinity and high rainfall","river_name":null,"geocode_query":"Amtali, Borguna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We used a simple polder water and salt balance model developed by Mainuddin et al.31 to examine the direct and indirect impacts of climate change and salt and water management strategies on cropping in the polders. We show that the three polders exhibit similarities, but also differences that arise from both their different annual rainfall totals and from their differing salinities. Methods Location and characteristics of the polders. Three study sites were selected for this study, two in Bangladesh and one in West Bengal, that include similarities and contrasts in salinity and water management options. These are Polder 43/1 in Amtali, Borguna (in the Patuakhali region) and the Polder 31 in Dacope, Khulna of Bangladesh and one island in the district of South 24 Parganas district of West Bengal (Fig. 1). The areas of the polders are 205 and 103 km2 for the Amtali and Dacope respectively. The area of Gosaba Island in West Bengal is 36 km2 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.230552,22.073126],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amtali, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-c7c9fc04333121c271191693-site-0","true_coordinates":[90.23055169999999,22.1331256],"spread_km":6.7},{"name":"Dacope (Polder 31), Khulna, Bangladesh","kind":"region","role":"study site for polder water and salt balance modeling with medium to high salinity and intermediate rainfall","river_name":null,"geocode_query":"Dacope, Khulna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We used a simple polder water and salt balance model developed by Mainuddin et al.31 to examine the direct and indirect impacts of climate change and salt and water management strategies on cropping in the polders. We show that the three polders exhibit similarities, but also differences that arise from both their different annual rainfall totals and from their differing salinities. Methods Location and characteristics of the polders. Three study sites were selected for this study, two in Bangladesh and one in West Bengal, that include similarities and contrasts in salinity and water management options. These are Polder 43/1 in Amtali, Borguna (in the Patuakhali region) and the Polder 31 in Dacope, Khulna of Bangladesh and one island in the district of South 24 Parganas district of West Bengal (Fig. 1). The areas of the polders are 205 and 103 km2 for the Amtali and Dacope respectively. The area of Gosaba Island in West Bengal is 36 km2 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.519886,22.544901],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dacope, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-c7c9fc04333121c271191693-site-1","true_coordinates":[89.5198862,22.6049011],"spread_km":6.7},{"name":"Gosaba Island, South 24 Parganas district, West Bengal, India","kind":"region","role":"study site for polder water and salt balance modeling with high salinity and lowest rainfall","river_name":null,"geocode_query":"Gosaba Island, South 24 Parganas, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We used a simple polder water and salt balance model developed by Mainuddin et al.31 to examine the direct and indirect impacts of climate change and salt and water management strategies on cropping in the polders. We show that the three polders exhibit similarities, but also differences that arise from both their different annual rainfall totals and from their differing salinities. Methods Location and characteristics of the polders. Three study sites were selected for this study, two in Bangladesh and one in West Bengal, that include similarities and contrasts in salinity and water management options. These are Polder 43/1 in Amtali, Borguna (in the Patuakhali region) and the Polder 31 in Dacope, Khulna of Bangladesh and one island in the district of South 24 Parganas district of West Bengal (Fig. 1). The areas of the polders are 205 and 103 km2 for the Amtali and Dacope respectively. The area of Gosaba Island in West Bengal is 36 km2 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.769817,22.213768],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gosaba, West Bengal 743370, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-c7c9fc04333121c271191693-site-2","true_coordinates":[88.80789829999999,22.1652274],"spread_km":6.7},{"name":"Ganges Delta coastal zone","kind":"region","role":"broader landscape context and subject of study for coastal polders and saltwater intrusion challenges","river_name":null,"geocode_query":"Ganges Delta, Bangladesh and West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2021) 11:7041 | https://doi.org/10.1038/s41598-021-86206-1 www.nature.com/scientificreports Impact of climate change and management strategies on water and salt balance of the polders and islands in theGanges delta Mohammed Mainuddin1*, Fazlul Karim1 , Donald S.Gaydon2 & John M. Kirby1 Enhancing crop production, particularly by growing a crop in the typically-fallow dry season is a key strategy for alleviating poverty in the Ganges delta region. We used a polder water and salt balance model to examine the impact of several crop management, salt management and climate change scenarios on salinity and crop evapotranspiration at Dacope and Amtali in Bangladesh and Gosaba in India. A key (and unsurprising) finding is that salt management is very important, particularly at the two drier sites, Dacope and Gosaba. Good salt management lowers salinity in the shallow groundwater, soil and water storage ponds, and leads to more irrigation.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southwestern coastal zone of Bangladesh","kind":"region","role":"population area impacted by salinity and agricultural challenges in the broader study context","river_name":null,"geocode_query":"Southwestern coastal zone, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The population of the salt-affected southwestern coastal zone of the Ganges Delta in Bangladesh is about 14.12 million (Barisal Division and Khulna, Bagerhat and Satkhira districts of Khulna Division15). In West Bengal, the population of the salinity-affected coastal zone called Sundarbans is 4.43 million15. These areas are OPEN 1 CSIRO Land and Water, Canberra, ACT 2601, Australia. 2 CSIRO Agriculture and Food, Brisbane, QLD 4102, Australia. *email: mohammed.mainuddin@csiro.au","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sundarbans region, West Bengal, India","kind":"region","role":"coastal salt-affected region where polder systems exist and comparison site in India","river_name":null,"geocode_query":"Sundarbans, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"The coastal zone of the Ganges delta in Bangladesh includes 139 polders which are low-lying tracts of land surrounded by dikes constructed in the 1960s and 1970s to protect farming lands from saline water intrusion and tidal floods6 . A less-well-developed polder system exists in the Sundarbans region of the West Bengal in India7 . The ground level within polders is mostly within 0 to 3 m above mean sea level8 . The level of water in the surrounding rivers rises and falls with the tidal flow and is affected by monsoon rain and occasional storm surge. This results in fluctuations in river water level of up to 4.0 m. The dense networks of canals (former river distributaries) within the polders are connected to the surrounding rivers by a series of sluice gates under the embankments providing the opportunity to bring water in during high tides, or to drain water from the polders during low tides, as needed.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.28261,24.99748],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarban, West Bengal 732122, India","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-c7c9fc04333121c271191693-site-5","true_coordinates":[88.2826103,25.0574796],"spread_km":6.7},{"name":"Barisal Division, Bangladesh","kind":"region","role":"administrative area partly overlapping salt-affected population zone in southwestern Bangladesh","river_name":null,"geocode_query":"Barisal Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The population of the salt-affected southwestern coastal zone of the Ganges Delta in Bangladesh is about 14.12 million (Barisal Division and Khulna, Bagerhat and Satkhira districts of Khulna Division15). In West Bengal, the population of the salinity-affected coastal zone called Sundarbans is 4.43 million15. These areas are OPEN 1 CSIRO Land and Water, Canberra, ACT 2601, Australia. 2 CSIRO Agriculture and Food, Brisbane, QLD 4102, Australia. *email: mohammed.mainuddin@csiro.au","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Khulna Division (Khulna, Bagerhat and Satkhira districts), Bangladesh","kind":"region","role":"salinity-affected coastal area with significant population density and poverty, study's socio-economic context","river_name":null,"geocode_query":"Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The population of the salt-affected southwestern coastal zone of the Ganges Delta in Bangladesh is about 14.12 million (Barisal Division and Khulna, Bagerhat and Satkhira districts of Khulna Division15). In West Bengal, the population of the salinity-affected coastal zone called Sundarbans is 4.43 million15. These areas are OPEN 1 CSIRO Land and Water, Canberra, ACT 2601, Australia. 2 CSIRO Agriculture and Food, Brisbane, QLD 4102, Australia. *email: mohammed.mainuddin@csiro.au","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Some locations lack explicit latitude and longitude coordinates; only place names and administrative regions provided.","Broad regional geographies limit spatial resolution of model and results interpretation.","Lack of detailed coordinates for river salinity measurement points and canals/ponds within polders.","Economic aspects of groundwater pumping costs not analysed in this study."],"river_ids":[],"site_ids":["doc-c7c9fc04333121c271191693-site-0","doc-c7c9fc04333121c271191693-site-1","doc-c7c9fc04333121c271191693-site-2","doc-c7c9fc04333121c271191693-site-5"],"anchor":[89.519886,22.544901],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040960480],"main_basin_ids":[4040025450,4040025460]},{"id":"doc-cc6be321f8b31eec7ca9a7da","title":"Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use","display_title":"Social and Economic Policies for Household Energy Saving","doi":"https://doi.org/10.1016/j.rser.2021.110915","theme":"Land & ecosystems","color":"#c8df98","year":2021,"publication_date":"2021-03-24","authors":["Constantine Spandagos","Erik Baark","Tze Ling Ng","Masaru Yarime"],"summary":"This study reviews two decades of research on residential energy interventions focusing on social influence (peer pressure) and economic instruments (rewards and penalties) to induce energy savings, especially in air-condition (AC) use. It develops six critical research questions around behavioral targeting, population segmentation, influence mediums, past influence, and geographical diversity. The study includes a survey of Hong Kong residents to illustrate these questions, finding that social and economic interventions have variable influence depending on behavior type (efficiency vs conservation), individual traits, and past exposure.","finding":"Peer pressure interventions are perceived as more influential in encouraging energy efficiency behaviors than conservation behaviors in air conditioning use, regardless of whether the pressure comes from local community peers or social media peers.","finding_evidence":{"passage_id":"p007-008","page":7,"quote":"Subplots (a) and (b) of Fig. 3 reveal a noticeable trend for the influencing potential of both online and offline peer pressures to be higher for efficiency than for conservation; therefore, Hypothesis 1 is supported. Despite this difference, the respondents deem both peer pressures very influential in their AC energy decisions: approximately 25–30% of the respondents assign values of 8–10 to them, while values of 1–4 are the least common. The high values assigned to social influence interventions can be taken as an indication of prevailing collectivist culture in Hong Kong. On the other hand, the fact that Hong Kong is a densely populated city does not seem to render C. Spandagos et al.","verified_in_page":true},"topics":["social influence","energy conservation behavior","economic instruments","behavioral change","peer pressure","energy efficiency","energy policy","urban energy use"],"study_period":"2000-2021","locations":[{"name":"Hong Kong","kind":"region","role":"survey field site and example urban setting of the case study","river_name":null,"geocode_query":"Hong Kong, Hong Kong SAR, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-007","page":6,"quote":"Survey The relevance of the research questions raised above is illustrated via a survey that assesses householders’ willingness to engage in efficiency and conservation actions relevant to space cooling, if induced by social influence and economic interventions. The survey was conducted in Hong Kong, a good example of populated and developed cities, where space cooling is one of the largest energy use contributors [81]. Focusing on residents’ perceptions and willingness to act, instead of their actual energy consumption, allows the survey to circumvent sample size limitations that the collection of actual consumption data entails. In addition to emphasizing the validity of this paper’s agenda of research questions, the survey provides original evidence on the potential of various energy-saving interventions in the specific city. 4.1.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States","kind":"region","role":"frequently cited comparison region with many intervention studies","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"The survey results provide evidence that such a potential can be affected by the type of targeted behavior (efficiency or conservation), by householder characteristics (openness to change and environmental awareness), and by the existence of past influence events. Interestingly, peer pressure is regarded as highly influential independently of the channel through which it is communicated, i.e. offline or online. These observations can assist public policy in countries with a growing emphasis on changing people’s energy behavior to redefine the targeting scope of interventions, thus strengthening their potential. 1. Introduction The residential buildings sector accounts for a large percentage of the total energy consumption and resulting greenhouse gas (GHG) emissions within an economy. For instance, in the United States (US) this percentage reaches 40% [1]; in other economies, it can be as high as 50%, while the global average exceeds 30% [2,3].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Europe","kind":"region","role":"frequently cited comparison region with many intervention studies","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-008","page":3,"quote":"Type of interventions examined (number of studies) Social influence (total: 39)a Economic instruments (total: 22)a TOTAL Year of publication Between 2000 and 2021 100% 100% 100% Region US/Europe 66.6% 68.2% 69.1% Rest of the world 23.1% 31.8% 23.6% Combination 10.3% – 7.3% Sample size Less than 100 15.3% 4.5% 10.9% 100–500 43.6% 18.3% 34.5% More than 500 41.1% 77.2% 54.6% Examining only one group (e.g. students) 30.8% 4.5% 23.6% Energy-saving behaviors targeted Efficiency only 2.5% 45.4% 18.2% Conservation only 64.2% 22.8% 49.1% Both or unclear 33.3% 31.8% 32.7% Behavioral determinants examined At least one externalb 74.4% 86.3% 80% At least one internalb 48.7% 31.8% 43.6% a The total number of studies on the particular type of intervention (examined either alone or together with the other type). b The sum of each sub-column is not 100%, as some studies consider both types of determinants (external and internal). C. Spandagos et al.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"China","kind":"region","role":"underrepresented region discussed as important for geographic diversity in research","river_name":null,"geocode_query":"China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"Abrahamse and Steg [14], Nisa et al. [18], and this paper’s review reveal that most of the energy intervention studies published in English involve experiments from either the US or European countries [64]. This indicates an encouragingly growing interest towards changing energy behaviors in these nations. On the other hand, this trend constitutes a geographic representation imbalance that might be limiting opportunities to achieve energy savings in different parts of the world. For an aggregated effect on reducing the global GHG emissions that are associated with residential energy consumption, it is important to stimulate change in as many locations as possible, and especially in Asia; the latter is not only the world’s most populous continent, but also a continent where rapid population growth and urbanization results in ever-increasing household energy demand.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Japan","kind":"region","role":"underrepresented region discussed as important for geographic diversity in research","river_name":null,"geocode_query":"Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-004","page":6,"quote":"Abrahamse and Steg [14], Nisa et al. [18], and this paper’s review reveal that most of the energy intervention studies published in English involve experiments from either the US or European countries [64]. This indicates an encouragingly growing interest towards changing energy behaviors in these nations. On the other hand, this trend constitutes a geographic representation imbalance that might be limiting opportunities to achieve energy savings in different parts of the world. For an aggregated effect on reducing the global GHG emissions that are associated with residential energy consumption, it is important to stimulate change in as many locations as possible, and especially in Asia; the latter is not only the world’s most populous continent, but also a continent where rapid population growth and urbanization results in ever-increasing household energy demand.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates for locations are not reported in the source text and thus are set to null.","The study relies on survey responses for behavioral intentions rather than observed energy consumption changes.","Geographic focus is mostly urban and concentrated in certain regions, limiting global generalization.","The review sample is illustrative, not exhaustive, and relies on literature published in English.","Comparative geographic and cultural analyses are posited but not empirically tested beyond Hong Kong survey."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[],"main_basin_ids":[]},{"id":"doc-cd90334b076a8b1fede4aa8d","title":"Women advancing research on hydrological processes: Preface","display_title":"Women Advancing Hydrological Processes","doi":"https://doi.org/10.1002/hyp.14267","theme":"Other science","color":"#d1b5e7","year":2021,"publication_date":"2021-06-16","authors":["Doerthe Tetzlaff","Elizabeth W. Boyer","Tanya M. Doody","Anne J. Jefferson","Annalisa Molini"],"summary":"This special issue highlights the significant contributions of women scientists to advancing hydrological processes research worldwide. It features invited papers from esteemed female researchers across multiple countries and institutions, showcasing diverse topics such as ecohydrology, climate impacts on hydrology, water quality, and hydrological modelling. The issue also addresses systemic challenges faced by women in STEM, emphasizing mentorship, gender equity, and innovative approaches to hydrological science amid evolving environmental and social conditions.","finding":"Phosphorus retention increases with the age of constructed wetlands and accumulates primarily in the upper soil horizons, indicating potential for phosphorus recycling in filter materials.","finding_evidence":{"passage_id":"p002-003","page":2,"quote":"She is an expert in biogeochemistry, soil chemistry and soil degradation of natural and semi-natural ecosystems. One focus of her research is on the development of indicators for wetland degradation and regeneration. In their paper, Alewell et al. (2021), they assessed the main phosphorus binding states in operating constructed wetlands to assess phosphorus saturation and indications on phosphorus recycling potential of filter materials. They showed that phosphorus accumulation increased with age of the investigated vertical flow constructed wetlands in all three investigated filter materials (fluviatile sands, zeolite-and clinopyroxene-dominated lava sands) and most of the phosphorus was accumulated in the upper horizons. Dr. Ann-Kristin Bergstrom is a Professor at Umeå University, Sweden. Her research concerns effects of environmental and climate change on the productivity and the food web structure of northern aquatic ecosystems.","verified_in_page":true},"topics":["hydrochemistry","water quality","catchment processes","climate change impacts","hydrological modelling","ecosystem hydrology","biogeochemistry","environmental pollution"],"study_period":null,"locations":[{"name":"University of Basel","kind":"facility","role":"Research institution of Dr. Christine Alewell focusing on biogeochemistry and wetland studies","river_name":null,"geocode_query":"University of Basel, Basel, Switzerland","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"She is an expert in biogeochemistry, soil chemistry and soil degradation of natural and semi-natural ecosystems. One focus of her research is on the development of indicators for wetland degradation and regeneration. In their paper, Alewell et al. (2021), they assessed the main phosphorus binding states in operating constructed wetlands to assess phosphorus saturation and indications on phosphorus recycling potential of filter materials. They showed that phosphorus accumulation increased with age of the investigated vertical flow constructed wetlands in all three investigated filter materials (fluviatile sands, zeolite-and clinopyroxene-dominated lava sands) and most of the phosphorus was accumulated in the upper horizons. Dr. Ann-Kristin Bergstrom is a Professor at Umeå University, Sweden. Her research concerns effects of environmental and climate change on the productivity and the food web structure of northern aquatic ecosystems.","verified_in_page":true},"resolution_status":"resolved","coordinates":[7.582694099999999,47.5607409],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Universität Basel, Basel, Switzerland","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-0"},{"name":"Umeå University","kind":"facility","role":"Research institution of Dr. Ann-Kristin Bergstrom studying northern aquatic ecosystems and climate effects","river_name":null,"geocode_query":"Umeå University, Umeå, Sweden","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Her current main focus is on how global climate change is affecting lake water biogeochemistry, and what possible impacts these biogeochemical changes have on lake productivity and the ecosystem services of northern pristine lakes. In her Invited Commentary (Bergström, 2020) she discusses which changes in water delivery rhythm (timing, frequency, and magnitude of spring flood episodes) northern lakes are facing and how hydrological intensification influences the delivery of terrestrial DOM and nutrients, and lake water retention times. She illustrates how change in magnitudes of water input and its seasonal rhythm affect whether lakes act as ‘shunts’ or ‘chemostats’. Still, only little research exists on the specific impact of lake water retention time on pelagic food webs relative to the role of changes in lake DOM. Dr. Louise Bracken is a Professor at the University of Durham, UK.","verified_in_page":true},"resolution_status":"resolved","coordinates":[20.3006649,63.8201671],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Universitetsvägen 4, 907 36 Umeå, Sweden","basin_id":2040030110,"main_basin_id":2040030110,"basin_region":"Europe and Middle East","basin_area_km2":132914.7,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-1"},{"name":"Northeast England","kind":"region","role":"Case study region demonstrating innovation ecosystem for hydrological process understanding and behaviour change","river_name":null,"geocode_query":"Northeast England, England, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Her research is focused on the dynamic and complex relationships between the processes that generate and supply fine sediment within rivers, and those that move the sediment through the river system. Bracken et al. (2020) showed how innovation and understanding of hydrological processes are intimately linked. Innovation is central to delivering behaviour change to address the sustainable development goals (SDGs) but is important to furthering scientific understanding of hydrological processes. Using an example from NE England, they demonstrated that the design of an innovation ecosystem was crucial to its success. It could be used as a model to integrate innovation and research more widely to further scientific understanding and deliver behaviour change within organizations to address the SDGs. Dr. Susan L. Brantley is Distinguished Professor of Geosciences and Director of the Earth & Environmental Systems Institute, Pennsylvania State University, USA.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-1.9479664,54.9456129],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North East, UK","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-2"},{"name":"Pennsylvania State University","kind":"facility","role":"Research institution of Dr. Susan L. Brantley focusing on aqueous fluids in shallow hydrogeologic settings and Critical Zone studies","river_name":null,"geocode_query":"Penn State University, University Park, Pennsylvania, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"She is an expert on chemical, biological, and physical processes associated with the circulation of aqueous fluids in shallow hydrogeologic settings. She is a leading figure within the research community on the Critical Zone. Brantley and Lebedeva (2021) summarized the importance of reaction fronts and their relationships with the land-air interface and water table. They explored how reaction fronts act like ‘valves’ where reactioninduced porosity changes re-orient water flow directions. They suggest that reactive transport models could be run for geologically long simulation times to determine the weathering-induced porosity architecture of headwaters and to predict the distribution of important fronts, prior to development of hydrological models. Their conceptualization can inform better models of subsurface porosity and permeability, perhaps replacing the general treatment of the subsurface as a black box in groundwater models. Dr.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-77.86531260000001,40.8147955],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"University Park, State College, PA, USA","basin_id":7040041050,"main_basin_id":7040041050,"basin_region":"North America and Caribbean","basin_area_km2":70979.5,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-3"},{"name":"Lake Mälaren","kind":"basin","role":"Multi-catchment study area in Sweden for chloride and metal legacy pollution transport research","river_name":null,"geocode_query":"Lake Mälaren, Sweden","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"concentration and load behaviour versus discharge between tracer and pollutant contributions of currently active sources at the surface and those of legacy sources from earlier surface inputs, waste deposits and land contamination. This is applied to multi-catchment analysis of chloride and metal data measured over 1990–2018 around the major Lake Mälaren in Sweden. The results show widespread prevalence of legacy sources with overall greater legacy than active contributions for both chloride and the metals, and with active contributions playing a greater role for the former than the latter. Such source distinction and quantification is essential for effective mitigation of waterborne environmental pollution. Dr. Heleen De Wit is a Senior Research Scientist at the Norwegian Institute of Water Research NIVA and an Associate Professor at the University of Oslo, Norway. She is an expert in catchment biogeochemistry, carbon cycling and water quality.","verified_in_page":true},"resolution_status":"resolved","coordinates":[17.4663465,59.44667869999999],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040031500,"main_basin_id":2040031500,"basin_region":"Europe and Middle East","basin_area_km2":114417.4,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-4"},{"name":"Norway Institute of Water Research, Oslo","kind":"facility","role":"Research institution of Dr. Heleen De Wit conducting Nordic catchment biogeochemistry and nutrient export studies","river_name":null,"geocode_query":"Norwegian Institute of Water Research, Oslo, Norway","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"De Wit et al. (2020) presents the results from an international collaboration integrating longterm data on concentrations and fluxes of total nitrogen and phosphorus from 69 Nordic headwater catchments, spanning the range of Nordic climatic and environmental conditions, including natural sites and sites impacted by agricultural and forest management. Concentrations and fluxes of total nitrogen and phosphorus were highest in agricultural catchments, intermediate in forestry-impacted and lowest in natural catchments, and were positively related to percent agricultural land cover and summer temperature. If the shift toward a low-carbon and resource-efficient society will be associated with intensification of agricultural and forest production and increased use of fertilizer, new challenges for protection of water quality will arise. The need for sustained funding of long-term monitoring of managed and unmanaged, natural catchments is also emphasized.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"CSIRO Land and Water, Adelaide","kind":"facility","role":"Australian national science agency unit led by Dr. Tanya Doody investigating ecohydrological projects including semi-arid floodplain management","river_name":null,"geocode_query":"CSIRO Land and Water, Adelaide, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Dr Tanya Doody is a Principal Research Scientist leading high impact spatial eco-hydrological projects in the Land and Water business unit of Australia's national science agency, CSIRO. She has significant experience in quantifying the water requirements of vegetation and at times, their impact on water resources. This involves ecophysiological field-based research to underpin remote sensing tools to scale regionally to improve our understanding of the effects of flood regimes on the health of water-dependent ecosystems. Her riparian research informs integrated catchment water planning and management both in Australia and internationally. With a collaborative contribution (Doody et al., 2021) this manuscript investigates application of water via drip irrigation to long term drought stressed and disconnected semi-arid woodlands (Eucalyptus largiflorens).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Lancaster Environment Centre","kind":"facility","role":"Research institution of Dr. Louise Heathwaite studying nutrient pathways, groundwater-fed rivers and hyporheic zone processes in the UK","river_name":null,"geocode_query":"Lancaster Environment Centre, Lancaster, United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Their paper Heathwaite et al. (2021) synthesizes the physical hydrology and biogeochemistry process-based understanding for a lowland groundwater-fed river, representative of systems where the river is continuously recharged by groundwater throughout the year. The results illustrate the critical importance of incorporating hydrogeological process understanding both beneath the riverbed, and from the wider landscape setting, in predictive tools if we are to capture appropriately the role of the hyporheic zone for nitrate processing and its consequences for groundwater-fed river metabolics under a changing climate. A second paper, Heathwaite and Bieroza (2021) advances the understanding of the interplay between hydrological flushing and in-stream biogeochemical cycling during storm events for multiple parameters and hydrological years.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.785581,54.00929499999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"University, Library Ave, Bailrigg, Lancaster LA1 4YQ, UK","basin_id":2040048790,"main_basin_id":2040048790,"basin_region":"Europe and Middle East","basin_area_km2":232575.1,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-7"},{"name":"Berkeley Laboratory and UC Berkeley","kind":"facility","role":"Research institutions associated with Dr. Susan Hubbard developing geophysical sensing for watershed hydrobiogeochemistry","river_name":null,"geocode_query":"Lawrence Berkeley National Laboratory, Berkeley, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"In their Invited Commentary, Hubbard et al. (2020) discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex catchment hydrobiogeochemical behaviour; important for underpinning optimized natural resource management. They propose that emerging technologies should be able to seamlessly unify sensing systems, data infrastructure, and computational tools to allow near realtime, autonomous communication and feedback. They urge the hydrological community to advance observation-data-modelling systems to improve catchment characterization and prediction within and across watershed observatories, and eventually enable near real-time information for resource managers. Dr. Julia Jones is a Distinguished Professor and Program Head of Geography in the College of Earth, Ocean, Atmospheric Science, Oregon State University at Corvallis, USA.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Columbia River Basin","kind":"basin","role":"International case study of US-Canada basin to evaluate water resource management response to streamflow changes due to climate change and land management","river_name":"Columbia River","geocode_query":"Columbia River Basin, USA and Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-007","page":3,"quote":"Her research focuses on the effects of climate change, land use modifications and disturbances on hydrology, geomorphology, and water quality. In her contribution, she analyses how large managed water resource systems respond to multi-decadal changes in the timing and magnitude of streamflow. Through a case study of the international (US-Canada) Columbia River Basin, USA, Jones and Hammond (2020) demonstrate that by combining publicly available historical data on streamflow with concepts from paired-watershed analyses and metrics of water resource performance, it is possible to detect, evaluate, and effectively manage water resource systems in large river basins. Results of the study indicate that water resource management systems in large river basins are TETZLAFF ET AL. 3 of 5","verified_in_page":true},"resolution_status":"resolved","coordinates":[-120.8228860820784,47.43871937211972],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":7040379430,"main_basin_id":7040014930,"basin_region":"North America and Caribbean","basin_area_km2":269628.2,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-9"},{"name":"University of Washington","kind":"facility","role":"Research institution of Dr. Jessica Lundquist focusing on snow and mountain weather impacts on hydrology and water resources","river_name":null,"geocode_query":"University of Washington, Seattle, Washington, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"In their paper, Lundquist et al. (2021) trace the history of modelling snow interception to demonstrate how people frequently select parameterizations that were included in pre-existing models rather than re-evaluating the underlying field experiments. Because the dominant snow interception processes fundamentally change in different environments, many parameterizations are not transferable. The paper outlines how land surface models should update their parameterizations to be consistent with all available observations and better represent how changes to forest cover and climate will impact water resources and albedo. Dr. Anne Nolin is a Professor at the University of Reno, USA. She has decades of experience on the interactions of climate with mountain snowpacks and glaciers, and mountains as social-ecological systems. She is a recognized leader in snow remote sensing and pioneered the development of snow and ice mapping techniques to augment in situ observations and modelling.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-122.3126783,47.6565919],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"1410 NE Campus Pkwy, Seattle, WA 98195, USA","basin_id":7040014940,"main_basin_id":7040014940,"basin_region":"North America and Caribbean","basin_area_km2":52968.8,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-10"},{"name":"University of Reno","kind":"facility","role":"Research institution of Dr. Anne Nolin with expertise in snow remote sensing and mountain snowpack interactions with climate","river_name":null,"geocode_query":"University of Nevada, Reno, Nevada, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Nolin et al. (2021) demonstrate how traditional snow metrics may not be able to adequately capture the changing nature of snow cover. The multiple impacts of a changing snowpack require a suite of climate indicators derived from readily measured or modelled data that serve as proxies for relevant snow-related and climate-driven processes. Such indicators need to capture snowpack changes over space and time, but not being overly simplistic or too complicated in their interpretation. They present a targeted set of spatially explicit, multi-temporal snow metrics for multiple sectors, stakeholders, and scientists, incl. metrics based on satellite data from NASA's Moderate Resolution Imaging Spectroradiometer and climate model outputs. Dr. Christina (Naomi) Tague is a Professor of Ecohydrology and Ecoinformatics at the University of California at Santa Barbara, USA.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-119.8156924,39.5485651],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"1664 N Virginia St, Reno, NV 89557, USA","basin_id":7040073720,"main_basin_id":7040073720,"basin_region":"North America and Caribbean","basin_area_km2":54552.8,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-11"},{"name":"University of California, Santa Barbara","kind":"facility","role":"Research institution of Dr. Christina Tague studying ecohydrologic interactions and modelling of plant-water systems","river_name":null,"geocode_query":"University of California, Santa Barbara, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"She is an expert on the interactions between hydrology and ecosystem processes and, specifically, how eco-hydrologic systems are altered by changes in land use and climate. Much of her work involves developing and using spatial simulation models to integrate data from multiple field-based monitoring studies in order to generalize results to larger catchments. In their paper, Tague and Frew (2021) discuss that novel, sophisticated ecohydrologic models present a barrier to their widespread use and credibility. They argue that for an effective advancement of our understanding of how plants and water interact, we must improve how we visualize not only model outputs, but also the underlying theories that are encoded within the models. They outline a framework for increasing the usefulness of ecohydrologic models through better visualization. Dr.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-119.8514162,34.4153601],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"University of California-Santa Barbara, CA, USA","basin_id":7040008720,"main_basin_id":7040008720,"basin_region":"North America and Caribbean","basin_area_km2":177193.6,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-12"},{"name":"Humboldt-Universität and Leibniz Institute, Berlin","kind":"facility","role":"Research institution of Dr. Doerthe Tetzlaff studying catchment hydrology, streamflow generation, and isotope hydrology","river_name":null,"geocode_query":"Humboldt-Universität zu Berlin and Leibniz Institute of Freshwater Ecology & Inland Fisheries, Berlin, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Doerthe Tetzlaff is a Professor of Ecohydrology at the Humboldt-Universität and Head of the Department at the Leibniz Institute of Freshwater Ecology & Inland Fisheries, Berlin, Germany. Her research aims at understanding the spatial and temporal variability of how catchments function hydrologically at different scales; understanding the physical processes that generate stream flow, and the way these processes influence the hydrochemistry and hydroecology of streams. Tetzlaff et al. (2021) present results from an international inter-comparison study using stable isotopes of water in plant stem (xylem) and soils. Their findings suggest that the xylem water of angiosperms was influenced by the isotopic composition of water retained in the soil weeks or months prior to plant sampling, whereas gymnosperms generally did not exhibit such a memory effect.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"University of Saskatchewan","kind":"facility","role":"Research institution of Dr. Cherie Westbrook studying beaver impacts on wetland hydrology and nature-based flood solutions","river_name":null,"geocode_query":"University of Saskatchewan, Saskatoon, Saskatchewan, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"They also discuss several future research challenges which need to be addressed to improve understanding of soil–plant– atmosphere interactions. Dr. Cherie Westbrook is a Professor in the Department of Geography & Planning, and Global Institute for Water Security at the University of Saskatchewan, Canada. She is an expert on the co-evolution of ecological and hydrological processes that explain wetland structure, function and resilience (and especially beaver impacts to wetlands) informing policy and regulatory discussions about their conservation, management and restoration. Westbrook et al. (2020) provide new insights about the opportunities and risks of relying on beaver as a nature-based flood solution. Obviously, beaver dams are unlikely to provide 100% flood protection. However, they can transiently store water even in large rainfall events, and so this naturebased solution should be included in regional water management strategies.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-106.6313567,52.1333989],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"105 Administration Pl, Saskatoon, SK S7N 5A2, Canada","basin_id":7040194980,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":37659.0,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-14"},{"name":"Colorado State University","kind":"facility","role":"Research institution of Dr. Ellen Wohl investigating physical-ecological interactions in rivers and logjam resilience to floods","river_name":null,"geocode_query":"Colorado State University, Fort Collins, Colorado, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-006","page":4,"quote":"Further understanding is needed on how water is stored and transmitted through beaver ponds with variable dam structural integrity. Dr. Ellen Wohl is a Professor of Geology and University Distinguished Professor Department of Geosciences, Colorado State University, USA. She is an expert in physical-ecological interactions in river ecosystems, implications of physical riverine complexity for organic carbon storage, large wood in river floodplains and the effects of beaver activities on downstream fluxes of material in river corridors. Wohl and Scamardo (2021) showed that the longitudinal distribution of channel-spanning logjam populations is resilient to disturbance, based on investigating the temporal patterns of intrareach and inter-reach logjam distribution densities over a decade that included substantial variations in snowmelt peak flow and a large rainfall flood.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.0848391,40.5729432],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Fort Collins, CO 80523, USA","basin_id":7040569640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":1381472.0,"hydrology_source":"hydrobasins","site_id":"doc-cd90334b076a8b1fede4aa8d-site-15"}],"limitations":["No precise coordinates given for study locations","Study locations broadly described by institution or region rather than specific field sites","Paper is a preface overview without detailed spatial datasets"],"river_ids":[],"site_ids":["doc-cd90334b076a8b1fede4aa8d-site-0","doc-cd90334b076a8b1fede4aa8d-site-1","doc-cd90334b076a8b1fede4aa8d-site-2","doc-cd90334b076a8b1fede4aa8d-site-3","doc-cd90334b076a8b1fede4aa8d-site-4","doc-cd90334b076a8b1fede4aa8d-site-7","doc-cd90334b076a8b1fede4aa8d-site-9","doc-cd90334b076a8b1fede4aa8d-site-10","doc-cd90334b076a8b1fede4aa8d-site-11","doc-cd90334b076a8b1fede4aa8d-site-12","doc-cd90334b076a8b1fede4aa8d-site-14","doc-cd90334b076a8b1fede4aa8d-site-15"],"anchor":[-77.86531260000001,40.8147955],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040023010,2040030110,2040031500,2040048790,7040008720,7040014940,7040041050,7040073720,7040194980,7040379430,7040569640],"main_basin_ids":[2040023010,2040030110,2040031500,2040048790,7040008720,7040014930,7040014940,7040022240,7040041050,7040047060,7040073720]},{"id":"doc-d8f8fbcce1a7d8bf78f06c5f","title":"Yield, profitability, and prospects of irrigated Boro rice cultivation in the North-West region of Bangladesh","display_title":"Boro Rice Yield and Profitability in NW Bangladesh","doi":"https://doi.org/10.1371/journal.pone.0250897","theme":"Land & ecosystems","color":"#c8df98","year":2021,"publication_date":"2021-04-29","authors":["Mohammed Mainuddin","Md. Mahbubul Alam","Md. Maniruzzaman","Md. Jahangir Kabir","Mohammad A. Mojid","Md. Masud Hasan","Erik J. Schmidt","Md. Towfiqul Islam"],"summary":"This study evaluates yield, profitability, and risk of irrigated Boro rice cultivation across 420 farmer fields covering seven geographically dispersed locations in the North-West (NW) region of Bangladesh over two seasons (2015–16 and 2016–17). It analyzes yield variability among varieties, irrigation pump-types, and sites, assesses input costs (notably irrigation), and performs stochastic risk analysis of major Boro rice cultivars. Results highlight significant spatial and varietal variations in yield and profitability, with irrigation cost constituting the largest expense. Hybrid rice and BRRI dhan29 have highest yields, but Hybrid rice exhibits greater yield variability and risk of negative income. Market price volatility largely affects profitability, underscoring the need for interventions ensuring affordable irrigation, mechanization, varietal choice, and price stability.","finding":"The study finds that Hybrid rice yields the highest but is the riskiest for farmers, with over a 15% chance of negative net income, while BRRI dhan29 has comparably high yield with more stable profitability, and irrigation water cost constitutes the largest portion of Boro rice production expenses (20–25%).","finding_evidence":{"passage_id":"p014-000","page":14,"quote":"Risk accounting. For enterprise budgeting we estimated gross income for each cultivar. However, for stochastic budgeting, we estimated net income (gross income–total imputed cost) to evaluate relative risk and return status of popular Boro rice varieties. Table 7 shows the observed variability (2015–16 and 2016–17) in actual yield and price of BRRI dhan28, BRRI dhan29, Hybrid and Minikit estimated from the observation and were used in risk accounting. Fig 8 presents cumulative probability distribution function (CDF) of net income per hectare for major cultivars of Boro rice in NW Bangladesh. The CDF of Minikit shows first-degree stochastic dominance over the other cultivars (BRRI dhan28, BRRI dhan29 and the Hybrid). CDF of BRRI dhan29 shows second-degree stochastic dominance over BRRI dhan28 and Hybrid, indicating that Hybrid cultivar is the riskiest followed by BRRI dhan28 and BRRI dhan29. Farmers have over 15% chance of having negative net income from Hybrid cultivars. Fig 7.","verified_in_page":true},"topics":["crop-yield-variation","agricultural-economics","irrigation","risk-assessment","rice-cultivation","farm-input-costs","crop-varieties","market-price-volatility"],"study_period":"2015–2017","locations":[{"name":"Ishurdi","kind":"region","role":"field study site representative of an electric tube well irrigation site with varied rainfall average","river_name":null,"geocode_query":"Ishurdi, Pabna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"ETo varies both spatially and temporally (CV = 6–8%) with the highest average value of 1,366 mm in Ishurdi and the lowest average value of 1,251 mm in Rangpur. PLOS ONE Yield, profitability, and prospects of irrigated Boro rice in Bangladesh PLOS ONE | https://doi.org/10.1371/journal.pone.0250897 April 29, 2021 3 / 23","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.064321,24.063724],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ishwardi, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-0","true_coordinates":[89.06432149999999,24.123724],"spread_km":6.7},{"name":"Sherpur","kind":"region","role":"field study site with shallow tubewell irrigation under diesel pumps","river_name":null,"geocode_query":"Sherpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"All these evidences justify the adequacy of the data for this study. Data monitoring We appointed a person to work full time at each site for the total project period to record the data. We used a pretested structured form to record inputs and labour used for intercultural operations (seed-bed preparation to winnowing and storing) of Boro rice in each field under farmers’ management. For example, fertilizer is used in 1 to 3 doses (during land preparation, 1st top dressing, 2nd top dressing) depending on the type of fertilizer. We recoded the price paid by the farmer to purchase fertilizers (urea, TSP, Potassium, Zinc, etc.) used in each application and the labour required to apply the fertilizer to the field. The total fertilizer cost was then calculated by summing up the total cost of purchasing fertilizer and the labour cost.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.14949039999999,25.0746235],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sherpur District, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-1"},{"name":"Mithapukur","kind":"region","role":"field study site with multiple STWs including diesel and electric pumps, diverse rice varieties cultivated","river_name":null,"geocode_query":"Mithapukur, Rangpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Most of the farmers in these sites are marginal and tenant farmers and grows Boro rice in the dry season. Most of the farmers buy water from the pump owner to irrigate their crops. There is a vibrant water market. A total of 14 STWs and 3 DTWs were selected in the six sites (Fig 1). All these sites were selected following the ethical guidelines of implementing organizations, CSIRO and Bangladesh Rice Research Institute (BRRI). BRRI conducted the study at the field level, which is a government research organization mandated to carry out rice related research in the farmers’ field. The ethics permission was given by CSIRO and BRRI. The study was carried out on farmers’ fields and the owner of the land gave permission to conduct the study on these sites. Field studies did not involve endangered or protected species. Monitoring and data recording started at the beginning of the crop season (November in 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.2799763,25.5722678],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mithapukur, Durgapur, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-2"},{"name":"Thakurgaon","kind":"region","role":"field study site with predominantly deep tube well irrigation","river_name":null,"geocode_query":"Thakurgaon, Rangpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"ETo varies both spatially and temporally (CV = 6–8%) with the highest average value of 1,366 mm in Ishurdi and the lowest average value of 1,251 mm in Rangpur. PLOS ONE Yield, profitability, and prospects of irrigated Boro rice in Bangladesh PLOS ONE | https://doi.org/10.1371/journal.pone.0250897 April 29, 2021 3 / 23","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.4785148,26.0288084],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Thakurgaon, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-3"},{"name":"Kaharol","kind":"region","role":"field study site with STWs operated by electric and diesel pumps","river_name":null,"geocode_query":"Kaharol, Dinajpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Site selection Several criteria were adopted in selecting the sites, including (i) a good geographical spread within NW Bangladesh, (ii) a combination of DTWs and/or STWs covering at least 10–20 ha agricultural lands bounded by village homes, (iii) both diesel- and electrically-operated STWs, and DTWs were considered (iv) irrigation is done predominantly for Boro rice, (v) a range of water pricing systems, and (vi) the sites are convenient for communication. Four water pricing systems were considered. These were share of crop as water charge, usage of a smart card, land area-based fixed water charge, and diesel paid by farmer plus a land area-based fixed charge. Rajshahi, Pabna, Bogra, Rangpur, Dinajpur and Thakurgaon districts were selected in the 1st stage of monitoring and data collection (2015–16). DTW is the predominant irrigation technology in Rajshahi and Thakurgaon sites. STW irrigation is predominant in the other four sites, although both DTWs and STWs are used.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.6031434,25.7947183],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kaharole Upazila, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-4"},{"name":"Tanore","kind":"region","role":"field study site with DTW irrigation under electric pumps","river_name":null,"geocode_query":"Tanore, Rajshahi, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"All these evidences justify the adequacy of the data for this study. Data monitoring We appointed a person to work full time at each site for the total project period to record the data. We used a pretested structured form to record inputs and labour used for intercultural operations (seed-bed preparation to winnowing and storing) of Boro rice in each field under farmers’ management. For example, fertilizer is used in 1 to 3 doses (during land preparation, 1st top dressing, 2nd top dressing) depending on the type of fertilizer. We recoded the price paid by the farmer to purchase fertilizers (urea, TSP, Potassium, Zinc, etc.) used in each application and the labour required to apply the fertilizer to the field. The total fertilizer cost was then calculated by summing up the total cost of purchasing fertilizer and the labour cost.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.5764603,24.5892635],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tanore, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-5"},{"name":"Badarganj Upazilla","kind":"region","role":"field study site with STW solar-powered irrigation newly added for monitoring in 2016-17","river_name":null,"geocode_query":"Badarganj Upazilla, Rangpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"All these evidences justify the adequacy of the data for this study. Data monitoring We appointed a person to work full time at each site for the total project period to record the data. We used a pretested structured form to record inputs and labour used for intercultural operations (seed-bed preparation to winnowing and storing) of Boro rice in each field under farmers’ management. For example, fertilizer is used in 1 to 3 doses (during land preparation, 1st top dressing, 2nd top dressing) depending on the type of fertilizer. We recoded the price paid by the farmer to purchase fertilizers (urea, TSP, Potassium, Zinc, etc.) used in each application and the labour required to apply the fertilizer to the field. The total fertilizer cost was then calculated by summing up the total cost of purchasing fertilizer and the labour cost.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.07047659999999,25.6539681],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Badarganj, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-d8f8fbcce1a7d8bf78f06c5f-site-6"}],"limitations":["No explicit GPS coordinates provided for study sites, so latitudes and longitudes are null.","The study area is broadly defined to the administrative NW region with districts and subdistricts without detailed locality coordinates.","PDF text extraction limits precluded extraction of exact numerical geographic coordinates."],"river_ids":[],"site_ids":["doc-d8f8fbcce1a7d8bf78f06c5f-site-0","doc-d8f8fbcce1a7d8bf78f06c5f-site-1","doc-d8f8fbcce1a7d8bf78f06c5f-site-2","doc-d8f8fbcce1a7d8bf78f06c5f-site-3","doc-d8f8fbcce1a7d8bf78f06c5f-site-4","doc-d8f8fbcce1a7d8bf78f06c5f-site-5","doc-d8f8fbcce1a7d8bf78f06c5f-site-6"],"anchor":[89.07047659999999,25.6539681],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040960200,4040960480,4040975160],"main_basin_ids":[4040025450]},{"id":"doc-ef8aa8172826084286002893","title":"Potential role of permafrost thaw on increasing Siberian river discharge","display_title":"Permafrost Thaw's Impact on Siberian Rivers","doi":"https://doi.org/10.1088/1748-9326/abe326","theme":"Climate","color":"#98d4f1","year":2021,"publication_date":"2021-02-05","authors":["Ping Wang","Qiwei Huang","S. Pozdniakov","Shiqi Liu","Ning Ma","Tianye Wang","Yongqiang Zhang","Jingjie Yu","Jiaxin Xie","Guobin Fu","Н. Л. Фролова","Changming Liu"],"summary":"This study analyzes long-term data (1936-2019) to quantify how climate warming and permafrost thaw affect streamflow in the three major Siberian rivers: Ob, Yenisei, and Lena. It finds increased annual streamflow primarily driven by rising precipitation, with significant contributions from enhanced groundwater discharge due to permafrost thaw, especially in colder, permafrost-dominated basins. The study models future streamflow rises under warming scenarios, emphasizing growing groundwater contributions.","finding":"A 1°C rise in temperature is likely to increase groundwater discharge by 6.1%–10.5%, contributing increasingly to annual streamflow, with a projected 19% contribution of groundwater discharge increase to total streamflow rise by the end of the 21st century in the Lena basin.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"Analysis of relationship between annual mean temperature departure (T − T) and changes in Qg (dQg/Qg) indicates that an increase of 1 ◦C annual mean temperature would increase Qg of the Ob, Yenisei and Lena river basins by approximately 6.1%, 10.5% and 7.8% (all p < 0.05), respectively. Equivalently, 1 ◦C warming would increase the annual streamflow of the Ob, Yenisei and Lena river basins by approximately 2.2%, 2.7%, and 1.0%, respectively. This suggests that a warmer climate would cause the baseflow to rise, which could lead to an increase in total streamflow in cold regions. Nonetheless, the influence of rising temperature on the streamflow is rather complex (Tan et al 2020). 3.3. Predicting streamflow for future climates By neglecting the changes in basin properties (n), we apply equations (2), (4) and (5) and consider the effect of T, P and E0 on streamflow to predict the streamflow of the three great Siberian Rivers (listed in table S4).","verified_in_page":true},"topics":["permafrost thaw","river discharge","climate warming","groundwater discharge","streamflow change","hydrological modeling","Siberian rivers","climate elasticity"],"study_period":"1936-2019","locations":[{"name":"Ob River Basin, Siberia, Russia","kind":"basin","role":"study area where river discharge, temperature, and precipitation changes analyzed","river_name":"Ob","geocode_query":"Ob River Basin, Siberia, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ. Res. Lett. 16 (2021) 034046 P Wang et al Figure 3. Time series of (a) temperature (T), (b) precipitation (P), (c) potential evaporation (E0), (d) streamflow (Q), and (e) the relationships between P, E0 and T in the Ob, Yenisei and Lena river basins from 1936 to 2019. 3. Results 3.1. Climate and streamflow changes over three basins The Ob, Yenisei and Lena river basins differ greatly in temperature with multiyear mean (1936–2019) basin-averaged values of −0.07 ◦C, −5.91 ◦C and −10.09 ◦C, respectively (figure 3(a), table S1). These values are consistent with their areal extent of the permafrost (figure 1). For the Ob river basin, continuous permafrost constitutes merely 1% of the total basin area. By contrast, for the Yenisei and Lena river basins, continuous permafrost accounts for 30% and 73% of the total basin area, respectively (Brown et al 2002).","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.728165,59.716797],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040386960,"main_basin_id":3040001840,"basin_region":"Siberia","basin_area_km2":1038714.1,"hydrology_source":"hydrobasins","site_id":"doc-ef8aa8172826084286002893-site-0","true_coordinates":[74.72816505525375,59.65679690185249],"spread_km":6.7},{"name":"Yenisei River Basin, Siberia, Russia","kind":"basin","role":"study area, data analyzed for streamflow and climate changes","river_name":"Yenisei","geocode_query":"Yenisei River Basin, Siberia, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ. Res. Lett. 16 (2021) 034046 P Wang et al Figure 3. Time series of (a) temperature (T), (b) precipitation (P), (c) potential evaporation (E0), (d) streamflow (Q), and (e) the relationships between P, E0 and T in the Ob, Yenisei and Lena river basins from 1936 to 2019. 3. Results 3.1. Climate and streamflow changes over three basins The Ob, Yenisei and Lena river basins differ greatly in temperature with multiyear mean (1936–2019) basin-averaged values of −0.07 ◦C, −5.91 ◦C and −10.09 ◦C, respectively (figure 3(a), table S1). These values are consistent with their areal extent of the permafrost (figure 1). For the Ob river basin, continuous permafrost constitutes merely 1% of the total basin area. By contrast, for the Yenisei and Lena river basins, continuous permafrost accounts for 30% and 73% of the total basin area, respectively (Brown et al 2002).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.48266869838572,60.64278877716526],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040369500,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":125789.1,"hydrology_source":"hydrobasins","site_id":"doc-ef8aa8172826084286002893-site-1"},{"name":"Lena River Basin, Siberia, Russia","kind":"basin","role":"study area with extensive permafrost, focus of groundwater-streamflow relationship under warming","river_name":"Lena","geocode_query":"Lena River Basin, Siberia, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ. Res. Lett. 16 (2021) 034046 P Wang et al Figure 3. Time series of (a) temperature (T), (b) precipitation (P), (c) potential evaporation (E0), (d) streamflow (Q), and (e) the relationships between P, E0 and T in the Ob, Yenisei and Lena river basins from 1936 to 2019. 3. Results 3.1. Climate and streamflow changes over three basins The Ob, Yenisei and Lena river basins differ greatly in temperature with multiyear mean (1936–2019) basin-averaged values of −0.07 ◦C, −5.91 ◦C and −10.09 ◦C, respectively (figure 3(a), table S1). These values are consistent with their areal extent of the permafrost (figure 1). For the Ob river basin, continuous permafrost constitutes merely 1% of the total basin area. By contrast, for the Yenisei and Lena river basins, continuous permafrost accounts for 30% and 73% of the total basin area, respectively (Brown et al 2002).","verified_in_page":true},"resolution_status":"resolved","coordinates":[117.61249,63.716326],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":3040264710,"main_basin_id":3040009130,"basin_region":"Siberia","basin_area_km2":455235.5,"hydrology_source":"hydrobasins","site_id":"doc-ef8aa8172826084286002893-site-2","true_coordinates":[117.6124900354569,63.65632566504951],"spread_km":6.7},{"name":"Arctic Ocean","kind":"other","role":"downstream basin receiving freshwater discharge from Siberian rivers","river_name":null,"geocode_query":"Arctic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Introduction The Arctic freshwater cycle is changing rapidly in recent decades due to climate warming (Rawlins et al 2010, Morison et al 2012, Fichot et al 2013, Lique et al 2016). Arctic rivers, which contribute approximately 40% of total freshwater into the Arctic Ocean (Serreze et al 2006, Haine et al 2015), are highly integral to the freshwater circulation in the Arctic (Bring et al 2017). Historical observations and climate model simulations demonstrated that the annual discharge of freshwater from the Arctic rivers has increased significantly over the past several decades (Peterson et al 2002, McClelland et al 2006, Overeem and Syvitski 2016). Under climate change conditions, its magnitude was projected to magnify in the 21st century (Haine et al 2015, Brown et al 2019). The Siberia is recognized as a hot spot with substantial changes in the hydrological cycle across the Arctic (Bring et al 2017, Golubeva et al 2019). Annually, all three great Siberian rivers (i.e.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["No explicit coordinates provided for study basins; lat/lon set to null.","Some data gaps in Yenisei River discharge records affecting model reliability.","Dependence on reanalysis climate data introduces uncertainty.","Complex feedbacks in permafrost-vegetation-hydrology system not fully modeled.","Assumption that winter streamflow solely represents groundwater discharge may oversimplify processes."],"river_ids":[],"site_ids":["doc-ef8aa8172826084286002893-site-0","doc-ef8aa8172826084286002893-site-1","doc-ef8aa8172826084286002893-site-2"],"anchor":[89.48266869838572,60.64278877716526],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[3040264710,3040369500,3040386960],"main_basin_ids":[3040001840,3040004740,3040009130]},{"id":"doc-f093d16761c7a65b638148f0","title":"Impact of flood inundation and water management on water and salt balance of the polders and islands in the Ganges delta","display_title":"Flood Impact and Recovery in Ganges Delta Polders","doi":"https://doi.org/10.1016/j.ocecoaman.2021.105740","theme":"Land & ecosystems","color":"#c8df98","year":2021,"publication_date":"2021-06-05","authors":["Mohammed Mainuddin","J. M. Kirby"],"summary":"This study applied a water and salt balance model to three polders in the Ganges delta (Amtali, Dacope in Bangladesh, and Gosaba Island in West Bengal, India) to assess the impacts of sea water flood inundations caused by cyclones and the subsequent recovery processes. It demonstrates that evapotranspiration and crop productivity are dramatically reduced immediately following inundation due to increased soil and groundwater salinity. Recovery rates depend mainly on soil and drainage properties, rainfall, and management of drainage infrastructure. The model suggests that with effective maintenance and water management, these polder ecosystems could recover relatively quickly from saline inundation events. The findings inform flood management and agricultural recovery planning in a highly vulnerable and densely populated coastal zone, critical for regional food security and livelihoods.","finding":"Effective field drainage and maintenance of drainage infrastructure significantly aid the rapid recovery of soil and groundwater salinity and crop evapotranspiration following cyclone-induced sea water inundation in Ganges delta polders.","finding_evidence":{"passage_id":"p005-001","page":5,"quote":"There is also a clear difference between scenarios with a mixing parameter of 0.1 (simulating poor field drainage and limited flushing of salt) and those with a mixing parameter of 1 (simulating good field drainage and good flushing of salt). Those with a mixing parameter of 1 have almost fully recovered by year 2, and are fully recovered by year 5. Those with a mixing parameter of 0.1 show some depression of evapotranspiration in year 5. 3.1.2. Impact of inundation and mixing parameter on soil water salt content The soil water salt concentration plots (Fig. 4) are similar to those for evapotranspiration. In all of the plots in Fig. 4, the salt concentration is elevated after inundation. This arises because the inundation infiltrates into the soil, immediately saturating it. However, the difference Fig. 4. Comparison of soil water salt concentration in the scenarios for Amtali, Dacope and Gosaba.","verified_in_page":true},"topics":["salt balance","water balance","flood inundation","soil salinity","crop productivity","coastal management","water management","climate impact"],"study_period":"1994-2018","locations":[{"name":"Amtali Polder 43/1","kind":"region","role":"primary study site for salt and water balance modelling and recovery analysis","river_name":null,"geocode_query":"Amtali Polder 43/1, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"On May 20, 2020, cyclone Amphan caused huge damage to the coastal region of Bangladesh and West Bengal and inundated several polders in Khulna and Satkhira districts of Bangladesh (very close to the selected polder at Dacope) and part of Gosaba Island (Goswami et al., 2021) in West Bengal. 2. Method: location of the polders, the model, and the inundation and management scenarios 2.1. Location of the polders The study is associated with cropping experimental sites at Amtali in Polder 43/1 and Dacope in Polder 31 in the coastal region of southwest Bangladesh, and Gosaba Island in the coastal region of West Bengal in India (Fig. 1). The area of polder 43/1 at Amtali is 205 km2 and the area of polder 31 at Dacope is about 103 km2 . The area of Gosaba Island in West Bengal, India is about 36 km2 (Ghosh and Mistri, 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.286647,22.163126],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amtali, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-0","true_coordinates":[90.23055169999999,22.1331256],"spread_km":6.7},{"name":"Dacope Polder 31","kind":"region","role":"primary study site for salt and water balance modelling and recovery analysis","river_name":null,"geocode_query":"Dacope Polder 31, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"On May 20, 2020, cyclone Amphan caused huge damage to the coastal region of Bangladesh and West Bengal and inundated several polders in Khulna and Satkhira districts of Bangladesh (very close to the selected polder at Dacope) and part of Gosaba Island (Goswami et al., 2021) in West Bengal. 2. Method: location of the polders, the model, and the inundation and management scenarios 2.1. Location of the polders The study is associated with cropping experimental sites at Amtali in Polder 43/1 and Dacope in Polder 31 in the coastal region of southwest Bangladesh, and Gosaba Island in the coastal region of West Bengal in India (Fig. 1). The area of polder 43/1 at Amtali is 205 km2 and the area of polder 31 at Dacope is about 103 km2 . The area of Gosaba Island in West Bengal, India is about 36 km2 (Ghosh and Mistri, 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.519886,22.664901],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dacope, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-1","true_coordinates":[89.5198862,22.6049011],"spread_km":6.7},{"name":"Gosaba Island","kind":"region","role":"primary study site for salt and water balance modelling and recovery analysis","river_name":null,"geocode_query":"Gosaba Island, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"On May 20, 2020, cyclone Amphan caused huge damage to the coastal region of Bangladesh and West Bengal and inundated several polders in Khulna and Satkhira districts of Bangladesh (very close to the selected polder at Dacope) and part of Gosaba Island (Goswami et al., 2021) in West Bengal. 2. Method: location of the polders, the model, and the inundation and management scenarios 2.1. Location of the polders The study is associated with cropping experimental sites at Amtali in Polder 43/1 and Dacope in Polder 31 in the coastal region of southwest Bangladesh, and Gosaba Island in the coastal region of West Bengal in India (Fig. 1). The area of polder 43/1 at Amtali is 205 km2 and the area of polder 31 at Dacope is about 103 km2 . The area of Gosaba Island in West Bengal, India is about 36 km2 (Ghosh and Mistri, 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.746281,22.146686],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gosaba, West Bengal 743370, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-2","true_coordinates":[88.80789829999999,22.1652274],"spread_km":6.7},{"name":"Andharmanik River","kind":"river","role":"source of saline water inundation risk near Amtali polder","river_name":"Andharmanik River","geocode_query":"Andharmanik River, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Cyclone Aila struck the coastal zone of Bangladesh and West Bengal on May 25, 2009 and Sidr stuck the coast of Bangladesh on November 15, 2007. Super-cyclone Amphan hit the coastal zone of Bangladesh and West Bengal on May 20, 2020. Tidal surge breached the embankments and inundated several polders closer to Dacope and part of Gosaba Island by sea water. In all three polders, we modelled inundation by sea water. The polders are currently not equally at risk of inundation by salty water. In particular, Amtali has a mainly freshwater distributary river (River Payra or Buriswar) on its western side and a saline water river (Andharmanik river) (Hossain et al., 2019) in its south-eastern side (Fig. 1). Andharmanik River is very close to the sea and connected to another big river (Golachipa River) which drains to the sea. While freshwater inundation can happen from Pyra River, the likelihood of sea water inundation from the Andharmanik River is very high due to its close proximity to the sea.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.1097315,21.889613],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Andharmanik River, Bangladesh","hydrology_source":"none","site_id":"doc-f093d16761c7a65b638148f0-site-3"},{"name":"Payra River (Buriswar River)","kind":"river","role":"mainly freshwater distributary river providing lower salinity at Amtali polder","river_name":"Payra River","geocode_query":"Payra River, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Cyclone Aila struck the coastal zone of Bangladesh and West Bengal on May 25, 2009 and Sidr stuck the coast of Bangladesh on November 15, 2007. Super-cyclone Amphan hit the coastal zone of Bangladesh and West Bengal on May 20, 2020. Tidal surge breached the embankments and inundated several polders closer to Dacope and part of Gosaba Island by sea water. In all three polders, we modelled inundation by sea water. The polders are currently not equally at risk of inundation by salty water. In particular, Amtali has a mainly freshwater distributary river (River Payra or Buriswar) on its western side and a saline water river (Andharmanik river) (Hossain et al., 2019) in its south-eastern side (Fig. 1). Andharmanik River is very close to the sea and connected to another big river (Golachipa River) which drains to the sea. While freshwater inundation can happen from Pyra River, the likelihood of sea water inundation from the Andharmanik River is very high due to its close proximity to the sea.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.15905348863677,22.1667699393568],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Payra River, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-4"},{"name":"Khulna District","kind":"region","role":"location of polders inundated during cyclone Amphan, near Dacope polder","river_name":null,"geocode_query":"Khulna District, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"On May 20, 2020, cyclone Amphan caused huge damage to the coastal region of Bangladesh and West Bengal and inundated several polders in Khulna and Satkhira districts of Bangladesh (very close to the selected polder at Dacope) and part of Gosaba Island (Goswami et al., 2021) in West Bengal. 2. Method: location of the polders, the model, and the inundation and management scenarios 2.1. Location of the polders The study is associated with cropping experimental sites at Amtali in Polder 43/1 and Dacope in Polder 31 in the coastal region of southwest Bangladesh, and Gosaba Island in the coastal region of West Bengal in India (Fig. 1). The area of polder 43/1 at Amtali is 205 km2 and the area of polder 31 at Dacope is about 103 km2 . The area of Gosaba Island in West Bengal, India is about 36 km2 (Ghosh and Mistri, 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.3966581,22.6737735],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Khulna District, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-5"},{"name":"Satkhira District","kind":"region","role":"location of polders inundated during cyclone Amphan, near Dacope polder","river_name":null,"geocode_query":"Satkhira District, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"On May 20, 2020, cyclone Amphan caused huge damage to the coastal region of Bangladesh and West Bengal and inundated several polders in Khulna and Satkhira districts of Bangladesh (very close to the selected polder at Dacope) and part of Gosaba Island (Goswami et al., 2021) in West Bengal. 2. Method: location of the polders, the model, and the inundation and management scenarios 2.1. Location of the polders The study is associated with cropping experimental sites at Amtali in Polder 43/1 and Dacope in Polder 31 in the coastal region of southwest Bangladesh, and Gosaba Island in the coastal region of West Bengal in India (Fig. 1). The area of polder 43/1 at Amtali is 205 km2 and the area of polder 31 at Dacope is about 103 km2 . The area of Gosaba Island in West Bengal, India is about 36 km2 (Ghosh and Mistri, 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.1114525,22.3154812],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Satkhira District, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-6"},{"name":"Sundarbans region","kind":"region","role":"larger coastal zone encompassing some study areas with socio-economic context","river_name":null,"geocode_query":"Sundarbans, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Introduction Large areas of the Ganges Delta comprise islands or polders of lowlying alluvial sediments, which have been protected by embankments and are used for cropping (Ghosh and Mistri, 2020; Mainuddin et al., 2019; Mainuddin et al., 2013; Yadav et al., 2020). These areas cover the southern coastal zone of Bangladesh and Sundarbans region of West Bengal, India. The population of the whole coastal region (including non-poldered area, 19 districts of Bangladesh and 3 districts of West Bengal) is about 31 million in Bangladesh and 23 million in West Bengal (Mainuddin et al., 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.33997,25.08748],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarban, West Bengal 732122, India","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-f093d16761c7a65b638148f0-site-7","true_coordinates":[88.2826103,25.0574796],"spread_km":6.7},{"name":"Ganges Delta coastal zone","kind":"region","role":"broader regional context for study focusing on vulnerable agricultural polders and islands","river_name":null,"geocode_query":"Ganges Delta coastal zone, Bangladesh and India","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Introduction Large areas of the Ganges Delta comprise islands or polders of lowlying alluvial sediments, which have been protected by embankments and are used for cropping (Ghosh and Mistri, 2020; Mainuddin et al., 2019; Mainuddin et al., 2013; Yadav et al., 2020). These areas cover the southern coastal zone of Bangladesh and Sundarbans region of West Bengal, India. The population of the whole coastal region (including non-poldered area, 19 districts of Bangladesh and 3 districts of West Bengal) is about 31 million in Bangladesh and 23 million in West Bengal (Mainuddin et al., 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates for polder locations and rivers are not provided in source text.","Study locations are described at regional/polder scale without precise geospatial coordinates.","Study period for modelling is 1994-2018, which may not capture longer-term recovery beyond 5 years explicitly."],"river_ids":[],"site_ids":["doc-f093d16761c7a65b638148f0-site-0","doc-f093d16761c7a65b638148f0-site-1","doc-f093d16761c7a65b638148f0-site-2","doc-f093d16761c7a65b638148f0-site-3","doc-f093d16761c7a65b638148f0-site-4","doc-f093d16761c7a65b638148f0-site-5","doc-f093d16761c7a65b638148f0-site-6","doc-f093d16761c7a65b638148f0-site-7"],"anchor":[89.519886,22.664901],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040960480],"main_basin_ids":[4040025450,4040025460]},{"id":"doc-117e040c5887c5cbc1b1ed98","title":"Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau","display_title":"Lake Ice Phenology Dataset for Tibetan Plateau","doi":"https://doi.org/10.1038/s41597-022-01863-9","theme":"Climate","color":"#98d4f1","year":2022,"publication_date":"2022-12-02","authors":["Yanhong Wu","Linan Guo","Bing Zhang","Hongxing Zheng","Lanxin Fan","Haojing Chi","Junsheng Li","Shenglei Wang"],"summary":"This study reconstructs a 39-year lake ice phenology (LIP) dataset (1978–2016) for 132 large lakes over the Tibetan Plateau (TP) by integrating remote sensing data and numerical modelling. Using satellite-based lake surface water temperature (MODIS MOD11A2) to calibrate a modified air2water model driven by meteorological air temperature, the dataset provides continuous daily lake surface temperatures and ice phenology indicators including freeze-up and break-up timing and ice duration. Validation against in-situ observations and existing satellite-derived datasets shows strong agreement, making this dataset valuable for investigating lake-atmosphere interactions and hydrothermal changes in the TP under climate warming.","finding":"Lake ice phenology on the Tibetan Plateau shows strong correlations with latitude and altitude, with lakes at higher latitudes and altitudes freezing earlier, thawing later, and having longer durations of ice cover.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"The gradients of FUS and FUE with respect to latitude are around 6.7 days/degree and 5.7 days/degree respectively (Fig. S2 in Supplementary). For ice-breaking dates, from south to north, BUS ranges between 27th January and 22th June, while BUE varies between 2nd March and 5th July. The latitude gradients of BUS and BUE are about 11.8 days/degree and 8.5 days/degree respectively. From south to north, ice-covered duration ranges from 15 days to 215 days (CID) or from 85 to 255 days (IceD). The latitude gradients of CID and IceD are 17.5 days/degree and 15.2 days/degree respectively. Lake ice phenology is also affected by altitude (Fig. S3 in Supplementary).","verified_in_page":true},"topics":["lake ice phenology","remote sensing","numerical modeling","lake surface temperature","climate change effects","phenology timing","altitude effects","latitude gradients"],"study_period":"1978-2016","locations":[{"name":"Tibetan Plateau","kind":"region","role":"study region for lake ice phenology","river_name":null,"geocode_query":"Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Scientific Data | (2022) 9:743 | https://doi.org/10.1038/s41597-022-01863-9 1 www.nature.com/scientificdata Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau YanhongWu1,2, LinanGuo2,3, Bing Zhang 1,2,4 ✉, Hongxing Zheng 5, Lanxin Fan1,2,4, HaojingChi1,2,4, Junsheng Li1,2,4 & ShengleiWang1,2 The Tibetan Plateau (TP) is a region sensitive to global climate change and has been experiencing substantial environmental changes in the past decades. Lake ice phenology (LIP) is a perceptible indicator reflecting changes of lake thermodynamics in response to global warming. Lake ice phenology over the Tibetan Plateau is however rarely observed and recorded. This research presents a dataset containing 39-year (1978–2016) lake ice phenology data of 132 lakes (each with area >40km2) over the Tibetan Plateau by combining the strengths of both remote sensing (MOD11A2, MOD10A1) and numerical modelling (air2water).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Nam Co Lake, Tibetan Plateau, China","kind":"river","role":"lake with in-situ LIP validation","river_name":null,"geocode_query":"Nam Co Lake, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Scientific Data | (2022) 9:743 | https://doi.org/10.1038/s41597-022-01863-9 6 www.nature.com/scientificdata/ www.nature.com/scientificdata days, RMSE of the estimated BUE in Lesser Slave Lake is near 9 days and RMSE of the estimated in Qinghai Lake is around 12 days. It should be noted that the bias could be due to both the uncertainties in the model and in the climate input to the model as well. We further compare the reconstructed LIP against two existing datasets developed based on remotely sensed data, which were published by Cai et al. 32 and Qiu et al. 23 respectively. The dataset published by Cai et al. 32 is retrieved from MOD10A1 and Landsat data, providing mean lake ice phenology of 44 TP lakes for the period 2000–2017. Figure 4 shows the comparison of the mean annual LIP from our reconstructed dataset against that from Cai et al. for the 44 lakes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.6393702,30.6644393],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Namucuo, China","basin_id":4040050910,"main_basin_id":4040050910,"basin_region":"Central and South-East Asia","basin_area_km2":30238.7,"hydrology_source":"hydrobasins","site_id":"doc-117e040c5887c5cbc1b1ed98-site-1"},{"name":"Qinghai Lake, Tibetan Plateau, China","kind":"river","role":"lake with LIP data comparison and ice phenology illustration","river_name":null,"geocode_query":"Qinghai Lake, Qinghai, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"The multi-year medians of the temperature thresholds are finally considered as the overall thresholds to 2008/1/1 2008/7/12009/1/12009/7/1 -10 -5 0 5 10 15 20 LSWT Rwater Date LSWT ( oC) 0 20 40 60 80 100 Tfus, 3 Tbue, 2 Tbus, 2 Tfue, 2 Tfus, 2 Tbue,1 Tbus,1 Rwater (water area percentage) Tfue, 1 Fig. 3 Determination of lake surface water temperature (LSWT) thresholds for identifying the lake ice phenology indicators. TFUS, i TFUE,i, TBUS,i and TBUE,i are the temperature threshold candidates derived for each specific year i (=1,2,3). Rwater is the percentage of open water area within a lake. Data of this figure is from the Qinghai Lake for the period 2008–2009.","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.349789,36.819903],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qinghai Lake, China","basin_id":4040578640,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":62487.2,"hydrology_source":"hydrobasins","site_id":"doc-117e040c5887c5cbc1b1ed98-site-2","true_coordinates":[100.3497895,36.75990290000001],"spread_km":6.7},{"name":"Lesser Slave Lake, Canada","kind":"river","role":"lake with LIP validation data from Canada","river_name":null,"geocode_query":"Lesser Slave Lake, Alberta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Scientific Data | (2022) 9:743 | https://doi.org/10.1038/s41597-022-01863-9 6 www.nature.com/scientificdata/ www.nature.com/scientificdata days, RMSE of the estimated BUE in Lesser Slave Lake is near 9 days and RMSE of the estimated in Qinghai Lake is around 12 days. It should be noted that the bias could be due to both the uncertainties in the model and in the climate input to the model as well. We further compare the reconstructed LIP against two existing datasets developed based on remotely sensed data, which were published by Cai et al. 32 and Qiu et al. 23 respectively. The dataset published by Cai et al. 32 is retrieved from MOD10A1 and Landsat data, providing mean lake ice phenology of 44 TP lakes for the period 2000–2017. Figure 4 shows the comparison of the mean annual LIP from our reconstructed dataset against that from Cai et al. for the 44 lakes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-115.6038964,55.4312556],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lesser Slave Lake, Alberta, Canada","basin_id":8040307300,"main_basin_id":8040009560,"basin_region":"North American Arctic","basin_area_km2":292498.6,"hydrology_source":"hydrobasins","site_id":"doc-117e040c5887c5cbc1b1ed98-site-3"},{"name":"High Asia","kind":"region","role":"comparison region for lake ice phenology data using microwave remote sensing","river_name":null,"geocode_query":"High Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"It can be seen that the LIP dataset produced by our approach is in high agreement with that solely based on MOD10A1, where the R2 for all the four phenology indices (i.e., FUS, FUE, BUS and BUE) are higher than 0.96 and the root mean squared errors (i.e., RMSE) are all within 5 days. The reconstructed LIP time series is also compared against the dataset published by Qiu et al. 23, which was derived from microwave brightness temperature measured by AMSR-E, AMSR2 and MWRI. The dataset from Qiu et al. provides LIP of 51 lakes in High Asia for the period from 2002 to 2016, among which there are 35 lakes identical to the lakes in our dataset. For the 35 lakes in the period 2002–2015 (sample size=35×14), the statistically significant correlations are found between the two datasets for all the six phenology indices with R2 all above 0.75 (Fig. 5). The discrepancy between the two datasets is found to be the lowest in FUS (RMSE=8.6 days) and to be the highest in BUS (RMSE=15 days).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"North America","kind":"region","role":"reference region with abundant ground observations of lake ice phenology","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Research efforts on investigating changes of lake ice phenology are mainly based on ground observation, satellite observation or modelling7–10. Most of the research, however, are for lakes locating in North America or Northern Europe as there are relatively abundant in ground observations10. Lake ice phenology observations are not always widely available particularly for regions like the Tibetan Plateau (TP), where ground-based observation is challenging and costly. Observation from space therefore has been becoming more attractive in investigating the lake ice phenology. For instance, with stratified random sample from Sentinel-2 and PlanetScope, Pickens et al. (2021) reported that 41% of inland water area around the globe were seasonally frozen11. Though with uncertainties12 from both data sources13–15 and extraction methods, various satellite-based observations have shown their merits in tracking the freezing-thawing cycle of lake ice cover16.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.165581,54.555961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-117e040c5887c5cbc1b1ed98-site-5","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Northern Europe","kind":"region","role":"regional context for lake ice phenology studies with abundant ground observations","river_name":null,"geocode_query":"Northern Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Research efforts on investigating changes of lake ice phenology are mainly based on ground observation, satellite observation or modelling7–10. Most of the research, however, are for lakes locating in North America or Northern Europe as there are relatively abundant in ground observations10. Lake ice phenology observations are not always widely available particularly for regions like the Tibetan Plateau (TP), where ground-based observation is challenging and costly. Observation from space therefore has been becoming more attractive in investigating the lake ice phenology. For instance, with stratified random sample from Sentinel-2 and PlanetScope, Pickens et al. (2021) reported that 41% of inland water area around the globe were seasonally frozen11. Though with uncertainties12 from both data sources13–15 and extraction methods, various satellite-based observations have shown their merits in tracking the freezing-thawing cycle of lake ice cover16.","verified_in_page":true},"resolution_status":"resolved","coordinates":[12.340171,62.218648],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Northern Europe","basin_id":2040033320,"main_basin_id":2040033320,"basin_region":"Europe and Middle East","basin_area_km2":49858.8,"hydrology_source":"hydrobasins","site_id":"doc-117e040c5887c5cbc1b1ed98-site-6","true_coordinates":[12.3401708,62.27864750000001],"spread_km":6.7}],"limitations":["Latitude and altitude correlations supported by supplementary figures, but precise numeric location data missing","Lake boundaries and meteorological stations are sparsely distributed over the TP, limiting data resolution","Some satellite snow cover data (MOD10A1) affected by cloud cover and misclassification","Lack of widespread in-situ observations across the Tibetan Plateau for complete validation","Climate reanalysis data available but found less reliable than station interpolations for TP region"],"river_ids":[],"site_ids":["doc-117e040c5887c5cbc1b1ed98-site-1","doc-117e040c5887c5cbc1b1ed98-site-2","doc-117e040c5887c5cbc1b1ed98-site-3","doc-117e040c5887c5cbc1b1ed98-site-5","doc-117e040c5887c5cbc1b1ed98-site-6"],"anchor":[12.340171,62.218648],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040033320,4040050910,4040578640,7040021740,8040307300],"main_basin_ids":[2040033320,4040007850,4040050910,7040021740,8040009560]},{"id":"doc-235c06ec685d6bb1c226734d","title":"Global hydro-environmental lake characteristics at high spatial resolution","display_title":"LakeATLAS: Global Hydro-Environmental Lake Data","doi":"https://doi.org/10.1038/s41597-022-01425-z","theme":"Other science","color":"#d1b5e7","year":2022,"publication_date":"2022-06-23","authors":["Bernhard Lehner","Mathis Messager","Maartje C. Korver","Simon Linke"],"summary":"The LakeATLAS dataset provides comprehensive hydro-environmental characteristics for over 1.4 million lakes and reservoirs globally (≥10 ha). Integrated within the HydroATLAS framework, it contains 56 variables including hydrology, physiography, climate, land cover, soils, and anthropogenic influences. The dataset links lakes to river networks and sub-basins allowing multi-scale ecological and hydrological analyses.","finding":"LakeATLAS represents lakes globally across all major climate zones and biomes; for example, it estimates that 965 million people live within 3 km of a lake. It offers spatially consistent and standardized multi-variable data enabling large-scale assessments of lakes' hydro-environmental conditions, which were previously infeasible due to data inconsistencies.","finding_evidence":{"passage_id":"p003-001","page":3,"quote":"We find that lakes occur in all major climate zones, land cover types, biomes, and countries; their distribution spans elevations between −414 and 5,826 m a.s.l. and mean air temperatures between −25.9 and 31.9 °C; in average 29.0% of their upstream area is forested and 12.6% of their upstream area is protected; and we estimate that a total of 965 million people (more than 12% of the world’s population) live within 3 km of a lake. A unique feature of HydroATLAS and its three companion datasets is the ability to spatially integrate the respective hydrographic features using standard Geographic Information System (GIS) technology. For example, each lake polygon is associated to the river network of RiverATLAS and the sub-basin network of BasinATLAS (via unique IDs), which allows to geolocate each lake within the drainage network. This facilitates topological assessments such as identifying and analyzing the sub-basins or river reaches that are upstream or downstream of a given lake.","verified_in_page":true},"topics":["hydrology","limnology","spatial analysis","climatology","land cover","ecosystem services","environmental data","geospatial data integration"],"study_period":null,"locations":[{"name":"Global","kind":"region","role":"study area encompassing over 1.4 million lakes and reservoirs worldwide","river_name":null,"geocode_query":"Global Earth","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Scientific Data | (2022) 9:351 | https://doi.org/10.1038/s41597-022-01425-z 1 www.nature.com/scientificdata Global hydro-environmental lake characteristics at high spatial resolution Bernhard Lehner 1 ✉, Mathis L. Messager 1,2, MaartjeC. Korver 1 & Simon Linke 3,4 Here we introduce the LakeATLAS dataset, which provides a broad range of hydro-environmental characteristics for more than 1.4 million lakes and reservoirs globally with an area of at least 10ha. LakeATLAS forms part of the larger HydroATLAS data repository and expands the existing datasets of sub-basin and river reach descriptors by adding equivalent information for lakes and reservoirs in a compatible structure. Matching its HydroATLAS counterparts, version 1.0 of LakeATLAS contains data for 56 variables, partitioned into 281 individual attributes and organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Canada","kind":"region","role":"example of high lake density areas where lakes appear contiguous; region with detailed high-resolution lake data input (CanVec)","river_name":null,"geocode_query":"Northern Canada, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Note that in areas of high lake density, such as in northern Canada, lake surfaces visually appear as contiguous lake regions rather than as distinct lake polygons.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-57.334377,49.1818979],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Northern, Deer Lake, NL A8A 1S5, Canada","basin_id":7040059990,"main_basin_id":7040059990,"basin_region":"North America and Caribbean","basin_area_km2":114322.7,"hydrology_source":"hydrobasins","site_id":"doc-235c06ec685d6bb1c226734d-site-1"},{"name":"United States, Contiguous","kind":"region","role":"reference region used for validation and comparison with National Hydrography Dataset Plus for lake size distribution and watershed delineation","river_name":null,"geocode_query":"Contiguous United States, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"Scientific Data | (2022) 9:351 | https://doi.org/10.1038/s41597-022-01425-z 11 www.nature.com/scientificdata/ www.nature.com/scientificdata the mean annual temperature across the surface of a lake. Due to the hydrologic connectivity of the river network, however, many characteristics are better suited to an upstream perspective where the entire contributing drainage area is taken into account. For example, if an application wanted to model the water quality of a lake, this would depend both on the conditions along its shorelines (e.g., urban cover) and on conditions that originate in the entire contributing drainage area that is connected to this lake, i.e., the lake’s watershed. The latter conditions can be described with upstream statistics, such as the average population density or the total glacier, snow, or forest extent across the entire upstream watershed area that contributes to the lake.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Kazakhstan","kind":"region","role":"country with largest lake (Caspian Sea) assigned based on spatial majority in dataset","river_name":null,"geocode_query":"Kazakhstan","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"If lakes that are represented in a source attribute grid are not spatially congruent with the polygon for that lake in HydroLAKES (e.g., showing the same lake yet with a slight spatial offset), the determination of the true land cover or soil characteristics found in the buffer zone around the lake polygon may be affected by the ‘false’ presence of a lake water surface. Finally, the use of ‘majority’ assignments can introduce statistical bias when the results get aggregated at different scales due to an issue known as ‘modifiable areal unit problem’ (MAUP)59. A particularly relevant case of the problematic and scale-dependent interpretation of ‘majority’ attributes is presented in the association of each lake to a country. For countries with boundaries that are not crossed by lakes, such as Australia or any island nation, the country association of each lake is straightforward. In contrast, lakes at land borders can extend over multiple countries.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North America","kind":"region","role":"region where discharge estimates and lake-gauging station comparisons were performed","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"The long-term (1971–2000) mean discharge values provided in LakeATLAS were derived through geospatial downscaling45 of the 0.5-degree resolution runoff and discharge estimates of the global WaterGAP model41 (version 2.2 as of 2014). These estimates represent naturalized flow conditions without anthropogenic water use in the form of abstractions or impoundments41. After downscaling, the global total river flow into all oceans matched the original flow as modeled in WaterGAP within an error margin of 0.13%, indicating no significant distortion of large-scale totals due to the downscaling process. In addition, a validation of the downscaled discharge estimates against observations at 3,003 gauging stations globally58, representing river sizes from 0.004 to 180,000 m3 s−1 , confirmed good overall correlations for long-term average discharges (R2=0.99 with 0.2% positive bias and a symmetric mean absolute percentage error SMAPE of 35%, improving to 13% for rivers ≥100 m3 s−1 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.203424,54.577923],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-235c06ec685d6bb1c226734d-site-4","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Europe","kind":"region","role":"region included in discharge estimates validation and lake polygon temporal data source (ECRINS for regions above 60°N)","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"Scientific Data | (2022) 9:351 | https://doi.org/10.1038/s41597-022-01425-z 12 www.nature.com/scientificdata/ www.nature.com/scientificdata the SWBD dataset (see Methods). Lake polygons from SWBD were created with orthorectified imagery of radar intensities at 1 arc-second resolution (~30m at the equator) that was processed with semi-automated extraction protocols in combination with manual supervision and rule-based editing14. The technicians used land cover water masks as well as existing maps and charts from 1:50,000 to 1:1 million scales as guidance and confirmation of the presence or absence of water. In this dataset, a discontinuity affects the representation of lakes below 25ha because the minimum size threshold used by technicians for digitizing a waterbody was a length of 600m (and a width of 90m)14.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Siberia","kind":"region","role":"region where MODIS 250m water mask was input source for lake polygons (above 60°N)","river_name":null,"geocode_query":"Siberia, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"The largest lake missing due to this constraint is theoretically a round lake of 570m in diameter spanning ~25ha, and the proportion of omitted lakes increases with decreasing lake area. This discontinuity is apparent when comparing the size distribution of lake polygons from HydroLAKES to polygons from the reference dataset in the contiguous United States, the U.S. National Hydrography Dataset Plus (NHDPlus) medium resolution53 (version 2.1; scale 1:100,000; Fig. 4). In Canada, no discontinuity in lake abundance exists below 25ha because the Canadian reference dataset (CanVec) portrays lakes down to 0.01 km2 , an order of magnitude smaller than the surface area cut-off of 10 ha set for HydroLAKES. In Siberia, HydroLAKES polygons above 60°N latitude, i.e., in areas where SWBD is not available, were generated from the MODerate resolution Imaging Spectro-radiometer (MODIS) MOD44W Collection 5 water mask, which provides a global coverage of waterbodies at 250-m resolution54.","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.303881,61.04371],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Siberia, Russia","basin_id":3040369320,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":237680.1,"hydrology_source":"hydrobasins","site_id":"doc-235c06ec685d6bb1c226734d-site-6","true_coordinates":[99.1966559,61.01370970000001],"spread_km":6.7},{"name":"Lake Taihu","kind":"other","role":"example of a large lake (2250 km²) used to discuss buffer zone land use impacts","river_name":null,"geocode_query":"Lake Taihu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"In this case, a single, clearly defined grid cell at the outlet of the lake creates a better representation of the lake’s overall condition (i.e., by capturing the discharge at the lake’s outlet). For the vicinity calculations (iii), a 3-km buffer (~6 grid cells buffer distance) was chosen as a compromise between technical and functional considerations: a smaller buffer would approach the limits of the inherent mapping accuracy of some of the underpinning source data, whereas a larger buffer was deemed increasingly disassociated from the lake proper and thus less representative of its immediate surrounding characteristics. For example, in regions of high lake density in Canada, the distance between neighboring lakes is often less than 5 km and thus a 3-km buffer around one lake already includes areas that are closer to — and potentially more associated with — the neighboring lake.","verified_in_page":true},"resolution_status":"resolved","coordinates":[120.20511,31.317951],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Taihu Lake, China","basin_id":4040009890,"main_basin_id":4040009890,"basin_region":"Central and South-East Asia","basin_area_km2":212335.4,"hydrology_source":"hydrobasins","site_id":"doc-235c06ec685d6bb1c226734d-site-7","true_coordinates":[120.2051096,31.2579515],"spread_km":6.7}],"limitations":["Incompleteness of small lakes representation especially below 25 ha outside Canada due to input data limitations","Lake polygon data represents snapshots in time and do not capture temporal fluctuations in lake extent","15 arc-second resolution grid cells (~500m) lead to area overestimations and spatial generalization especially for small lakes","Some attribute datasets have inherent uncertainties and possible spatial artefacts impacting local statistics","Majority class assignments for countries and other categorical variables can misrepresent transboundary lakes","Upstream watershed delineations are at 15 arc-second resolution, less fine than regional datasets such as LakeCAT","Comparison with other datasets (GLCP) shows differences due to basin delineation methods affecting attribute accuracy"],"river_ids":[],"site_ids":["doc-235c06ec685d6bb1c226734d-site-1","doc-235c06ec685d6bb1c226734d-site-4","doc-235c06ec685d6bb1c226734d-site-6","doc-235c06ec685d6bb1c226734d-site-7"],"anchor":[-57.334377,49.1818979],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[3040369320,4040009890,7040021740,7040059990],"main_basin_ids":[3040004740,4040009890,7040021740,7040059990]},{"id":"doc-26f3bfcfacf5822c97185bb0","title":"The Influence of Micronutrient Trace Metals on Microcystis aeruginosa Growth and Toxin Production","display_title":"Micronutrient Effects on Microcystis Growth","doi":"https://doi.org/10.3390/toxins14110812","theme":"Other science","color":"#d1b5e7","year":2022,"publication_date":"2022-11-21","authors":["Jordan A. Facey","Jake P. Violi","Josh J. King","Chowdhury Sarowar","Simon C. Apte","Simon M. Mitrovic"],"summary":"This study investigated the effects of micronutrient trace metal depletion (iron, cobalt, copper, manganese, molybdenum) on growth, toxin production, morphology, and intracellular iron accumulation of the cyanobacterium Microcystis aeruginosa. Growth was significantly limited by iron, cobalt, and manganese depletion but not by copper or molybdenum depletion. Intracellular microcystin-LR toxin concentrations were elevated under cobalt limitation, and intracellular iron quotas were notably higher under cobalt depletion, suggesting interactions between cobalt availability and iron metabolism. The study enhances understanding of micronutrient roles in cyanobacterial bloom formation and toxicity.","finding":"Growth of Microcystis aeruginosa was suppressed when exposed to media depleted of iron, cobalt and manganese, highlighting their importance for optimal growth; cobalt depletion uniquely led to elevated intracellular microcystin-LR concentrations and increased intracellular iron quotas.","finding_evidence":{"passage_id":"p002-002","page":2,"quote":"While others found a positive relationship between iron concentration and microcystin production [23,48,49]. Other trace metals have received significantly less attention, or in some cases none [17]. Identifying the environmental conditions driving the increase in cyanobacterial blooms and stimulating cyanotoxin production is essential to developing effective management strategies aimed at protecting the ecological and economic value of freshwater systems. The role of micronutrients in M. aeruginosa bloom formation and toxicity has received little attention, except for iron. In this study, we aim to determine the importance of certain micronutrients (iron, cobalt, copper, manganese and molybdenum) for the optimal growth of M. aeruginosa by excluding them from culture media.","verified_in_page":true},"topics":["trace metals","cyanobacteria","toxin production","microcystin-LR","micronutrient limitation","intracellular metal quotas","cell morphology","growth rate"],"study_period":null,"locations":[{"name":"University of Technology Sydney, Ultimo, New South Wales, Australia","kind":"facility","role":"institutional location where cyanobacteria cultures were maintained and experiments conducted","river_name":null,"geocode_query":"University of Technology Sydney, Ultimo, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Mitrovic 1 1 School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2000, Australia 2 CSIRO Land and Water, Lucas Heights, Sydney, NSW 2234, Australia 3 Prince of Wales Clinical School, University of New South Wales, Kensington, NSW 2052, Australia * Correspondence: jordan.facey@uts.edu.au Abstract: Microcystis aeruginosa is a widespread cyanobacteria capable of producing hepatotoxic microcystins. Understanding the environmental factors that influence its growth and toxin production is essential to managing the negative effects on freshwater systems. Some micronutrients are important cofactors in cyanobacterial proteins and can influence cyanobacterial growth when availability is limited. However, micronutrient requirements are often species specific, and can be influenced by substitution between metals or by luxury uptake.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.2006119,-33.8836537],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"15 Broadway, Ultimo NSW 2007, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-26f3bfcfacf5822c97185bb0-site-0"},{"name":"CSIRO Land and Water, Lucas Heights, New South Wales, Australia","kind":"facility","role":"research institution involved in study and laboratory analyses","river_name":null,"geocode_query":"CSIRO Land and Water, Lucas Heights, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Mitrovic 1 1 School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2000, Australia 2 CSIRO Land and Water, Lucas Heights, Sydney, NSW 2234, Australia 3 Prince of Wales Clinical School, University of New South Wales, Kensington, NSW 2052, Australia * Correspondence: jordan.facey@uts.edu.au Abstract: Microcystis aeruginosa is a widespread cyanobacteria capable of producing hepatotoxic microcystins. Understanding the environmental factors that influence its growth and toxin production is essential to managing the negative effects on freshwater systems. Some micronutrients are important cofactors in cyanobacterial proteins and can influence cyanobacterial growth when availability is limited. However, micronutrient requirements are often species specific, and can be influenced by substitution between metals or by luxury uptake.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["No explicit coordinates provided for study locations","Study was laboratory-based, limiting direct environmental extrapolation"],"river_ids":[],"site_ids":["doc-26f3bfcfacf5822c97185bb0-site-0"],"anchor":[151.2006119,-33.8836537],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-2dc86f6af8acd34c5fc2a407","title":"Long-term spatio-temporal variability and trends in rainfall and temperature extremes and their potential risk to rice production in Bangladesh","display_title":"Climate Extremes Threaten Bangladesh Rice","doi":"https://doi.org/10.1371/journal.pclm.0000009","theme":"Climate","color":"#98d4f1","year":2022,"publication_date":"2022-03-08","authors":["Mohammed Mainuddin","Jorge L. Peña-Arancibia","Fazlul Karim","Md. Masud Hasan","Mohammad A. Mojid","John M. Kirby"],"summary":"This study investigates long-term spatio-temporal variability and trends in rainfall and temperature extremes in Bangladesh from 1980 to 2014, focusing on impacts on the country's rice production. The analysis used data from 34 meteorological stations covering wet and dry cropping seasons corresponding to Aus, Aman, and Boro rice varieties. Key findings include decreasing rainfall trends in central and northern regions and significant increases in temperature extremes, both of which pose risks to rice yields and water resources, especially groundwater used for irrigation.","finding":"Rainfall is significantly decreasing in both wet and dry seasons in central and northern Bangladesh, increasing dry spells, while maximum temperatures are increasing throughout the country at about 0.5°C per decade, with the current temperature maximums already exceeding optimum ranges for rice, all threatening rice production and groundwater sustainability.","finding_evidence":{"passage_id":"p027-001","page":27,"quote":"Thirteen climate metrics closely linked to rice production were derived from the daily data for the wet (May to October) and dry (November to April) cropping seasons, which capture the main growing seasons of the country’s two wet season rainfed Aus and Aman rice and dry season irrigated Boro rice. Rainfall is significantly decreasing both in the wet and dry seasons in the central and northern areas, the main areas for production of rice, whilst dry spells are increasing throughout Bangladesh, significantly in central and southeast areas. The drying conditions can adversely affect the production of rainfed Aman rice, and raise irrigation requirement of dry season Boro rice with eventual adverse effects on groundwater resources through reduced recharge and declining groundwater tables. The maximum temperatures both in the wet and dry seasons are increasing throughout the country, significantly at most of the climate stations at 0.5 ̊C every ten years.","verified_in_page":true},"topics":["rainfall variability","temperature extremes","rice production","climate change impacts","groundwater depletion","agricultural water management","seasonal climate metrics","trend analysis"],"study_period":"1980-2014","locations":[{"name":"Bangladesh","kind":"region","role":"country study area","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"are assessed for two cropping seasons: May to October and November to April, representing wet and dry season crops respectively. Dry season covers the cultivation period of Boro rice and wet season covers the cultivation period of Aus and Aman rice. We qualitatively link the trends in the derived climate metrics to discuss known risk to rice production based on the literature, and similarly remark on the likely impacts of climate change. In achieving these objectives, this study seeks to inform the debate on the impacts of likely climate change on rice production in Bangladesh. Materials and methods Study area Bangladesh is about 147,500 km2 and is bounded by India in the west, north and east and the Bay of Bengal in the south (Fig 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Rangpur Division, Bangladesh","kind":"region","role":"administrative division with climate stations showing rainfall and temperature trends","river_name":null,"geocode_query":"Rangpur Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Station RSEAwet Tmaxwet Tminwet T36wet Rangpur Dinajpur -10.408 0.037��� 0.031��� 0.067 Rangpur -13.042� 0.033��� 0.013��� 0.059�� Sayedpur -8.100 0.060��� 0.023� 0.250 Rajshahi Bogra -8.294 0.037��� 0.024��� 0.143�� Ishurdi -5.455 0.039��� 0.018��� 0.353� Rajshahi -8.833� 0.038��� 0.019��� 0.428�� Khulna Chuadanga -4.583 0.011 -0.009 0.226 Jessore 7.529 0.050��� 0.014�� 0.643��� Khulna 9.169 0.037��� 0.016��� 0.500��� Mongla 8.222 0.032��� 0.011�� 0.452��� Satkhira 2.265 0.006 0.022��� 0.000 Mymensingh -14.889� 0.020��� 0.013�� 0.000 Dhaka Dhaka -5.077 0.027�� 0.014�� 0.045 Faridpur -11.100 0.035��� 0.016�� 0.182�� Madaripur -10.547� 0.006 0.023��� 0.049 Tangail -1.091 0.055��� 0.007 0.400��� Barisal Barisal -1.440 0.029��� 0.024��� 0.056��� Bhola -8.833 0.014�� 0.011�� 0.000 Khepupara 15.879�� 0.037��� 0.015�� 0.000 Patuakhali -1.217 0.048��� 0.010 0.146��� Sylhet Srimangal -1.308 0.024�� 0.003 -0.045 Sylhet -9.857 0.045��� 0.030��� 0.143��� Chittagong Chandpur -8.322 0.035��� 0.010�� 0.000��� Chittagong 9.793 0.006 0.024��� 0.000 Comilla -1.250 0.034��� 0.017��� 0.000 Cox Bazar 18.666� 0.050��� 0.009� 0.000 Feni -7.395 0.009� 0.017��� 0.000 Hatiya 23.750�� 0.019��� 0.007 0.000 Kutubdia 17.900 0.025�� 0.014� 0.000 Maijdi -2.600 0.057��� 0.027��� 0.125��� Rangamati 6.350 0.043��� -0.008 0.059� Sandwip 22.156�� 0.057��� -0.005 0.000�� Sitakunda 7.283 0.053��� 0.005 0.087��� Teknaf 21.623�� 0.015�� 0.026��� 0.000 https://doi.org/10.1371/journal.pclm.0000009.t003 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 9 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Rajshahi Division, Bangladesh","kind":"region","role":"administrative division with notable wet season rainfall decreases affecting rice production","river_name":null,"geocode_query":"Rajshahi Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p021-001","page":21,"quote":"RSEAwet is increasing, generally significantly, in the coastal area (>20 mm/sea/yr in some stations, e.g. Hatiya and Sandwip) and the southwest (e.g. Khepupara) area, but decreasing significantly in some central (such as Mymensigh, Madaripur) and northwest areas (>12 mm/sea/yr in some stations, e.g. Rangpur and Rajshahi). These results agree with those of Mullick et al. [32] who reported generally increasing monsoon and post-monsoon rainfall trends during 1966 to 2015 in the southwest and coastal areas, and mixed trends in the northwest and central areas. Mojid et al. [23, their Fig 2], for a similar study period as in this paper (1985–2015) but at annual scale, reported that the overall average annual rainfall of six climate stations in the northwest region decreased significantly, and at a similar rate (~9.2 mm/year) of the seasonal results reported here.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Khulna Division, Bangladesh","kind":"region","role":"administrative division exhibiting increasing wet season rainfall and temperature trends","river_name":null,"geocode_query":"Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Station RSEAwet Tmaxwet Tminwet T36wet Rangpur Dinajpur -10.408 0.037��� 0.031��� 0.067 Rangpur -13.042� 0.033��� 0.013��� 0.059�� Sayedpur -8.100 0.060��� 0.023� 0.250 Rajshahi Bogra -8.294 0.037��� 0.024��� 0.143�� Ishurdi -5.455 0.039��� 0.018��� 0.353� Rajshahi -8.833� 0.038��� 0.019��� 0.428�� Khulna Chuadanga -4.583 0.011 -0.009 0.226 Jessore 7.529 0.050��� 0.014�� 0.643��� Khulna 9.169 0.037��� 0.016��� 0.500��� Mongla 8.222 0.032��� 0.011�� 0.452��� Satkhira 2.265 0.006 0.022��� 0.000 Mymensingh -14.889� 0.020��� 0.013�� 0.000 Dhaka Dhaka -5.077 0.027�� 0.014�� 0.045 Faridpur -11.100 0.035��� 0.016�� 0.182�� Madaripur -10.547� 0.006 0.023��� 0.049 Tangail -1.091 0.055��� 0.007 0.400��� Barisal Barisal -1.440 0.029��� 0.024��� 0.056��� Bhola -8.833 0.014�� 0.011�� 0.000 Khepupara 15.879�� 0.037��� 0.015�� 0.000 Patuakhali -1.217 0.048��� 0.010 0.146��� Sylhet Srimangal -1.308 0.024�� 0.003 -0.045 Sylhet -9.857 0.045��� 0.030��� 0.143��� Chittagong Chandpur -8.322 0.035��� 0.010�� 0.000��� Chittagong 9.793 0.006 0.024��� 0.000 Comilla -1.250 0.034��� 0.017��� 0.000 Cox Bazar 18.666� 0.050��� 0.009� 0.000 Feni -7.395 0.009� 0.017��� 0.000 Hatiya 23.750�� 0.019��� 0.007 0.000 Kutubdia 17.900 0.025�� 0.014� 0.000 Maijdi -2.600 0.057��� 0.027��� 0.125��� Rangamati 6.350 0.043��� -0.008 0.059� Sandwip 22.156�� 0.057��� -0.005 0.000�� Sitakunda 7.283 0.053��� 0.005 0.087��� Teknaf 21.623�� 0.015�� 0.026��� 0.000 https://doi.org/10.1371/journal.pclm.0000009.t003 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 9 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Dhaka Division, Bangladesh","kind":"region","role":"administrative division with decreasing dry season rainfall trends impacting irrigation demand","river_name":null,"geocode_query":"Dhaka Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Station RSEAdry RCDDdry Tmaxdry Tmindry T12dry Rangpur Dinajpur 0.169 0.623 -0.022� 0.039��� -0.429 Rangpur -0.093 1.200�� -0.013 0.045��� -0.658��� Sayedpur -2.154 0.857 0.011 0.026 0.155 Rajshahi Bogra -2.042 1.429��� -0.009 0.029��� 0.185 Ishurdi -2.375�� 0.654 -0.004 0.028�� 0.000 Rajshahi -1.722 1.080�� -0.013 0.022 0.057 Khulna Chuadanga -3.000� 0.765 -0.028 -0.006 0.389 Jessore -5.110�� 0.705� 0.019 -0.009 0.333�� Khulna -5.652��� 0.763 0.010 0.055��� -0.483��� Mongla -7.125� 0.467 0.030 -0.001 0.444�� Satkhira -3.121� 1.057� -0.035� 0.020�� -0.045 Mymensingh -1.739 0.748 -0.019� 0.017 -0.220 Dhaka Dhaka -6.447��� 1.118��� -0.004 0.040��� -0.051 Faridpur -7.816��� 1.134�� 0.012 0.021�� 0.200 Madaripur -5.932�� 0.924 -0.009 0.031�� -0.100 Tangail -7.071��� 1.690�� 0.007 0.006 0.286 Barisal Barisal -3.975� 0.511 0.020� 0.025�� -0.143 Bhola -6.857��� 0.617 0.003 0.010 0.208 Khepupara -4.775� 0.659 0.032�� 0.000 0.036��� Patuakhali -6.878��� 1.419�� 0.039��� -0.018�� 0.500��� Sylhet Srimangal -9.258�� 1.127� 0.014 -0.009 0.121 Sylhet -4.867 1.000�� 0.045��� 0.049��� -0.176 Chittagong Chandpur -7.171�� 0.828 0.017 0.040��� -0.308� Chittagong -9.368��� 1.400��� 0.023 0.035�� 0.000 Comilla -6.364�� 1.257�� -0.003 0.022� -0.022 Cox Bazar -6.916�� 0.577 0.053��� 0.028��� 0.000 Feni -6.167��� 1.444��� 0.000 0.011 0.167 Hatiya -6.432�� 1.095� 0.029�� -0.027� 0.052��� Kutubdia -7.216��� 0.523 0.025 0.009 0.000 Maijdi -8.129�� 0.333 0.025�� 0.047��� -0.250 Rangamati -5.441� 0.232 0.062��� -0.042�� 0.571��� Sandwip -7.792��� 1.043� 0.057��� -0.048�� 0.636��� Sitakunda -8.729�� 1.394��� 0.070��� -0.024�� 0.500��� Teknaf -3.667�� 0.577 0.021�� -0.008 0.042 https://doi.org/10.1371/journal.pclm.0000009.t004 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 10 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Barisal Division, Bangladesh","kind":"region","role":"administrative division with climate stations showing mixed rainfall and temperature trends","river_name":null,"geocode_query":"Barisal Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Station RSEAwet Tmaxwet Tminwet T36wet Rangpur Dinajpur -10.408 0.037��� 0.031��� 0.067 Rangpur -13.042� 0.033��� 0.013��� 0.059�� Sayedpur -8.100 0.060��� 0.023� 0.250 Rajshahi Bogra -8.294 0.037��� 0.024��� 0.143�� Ishurdi -5.455 0.039��� 0.018��� 0.353� Rajshahi -8.833� 0.038��� 0.019��� 0.428�� Khulna Chuadanga -4.583 0.011 -0.009 0.226 Jessore 7.529 0.050��� 0.014�� 0.643��� Khulna 9.169 0.037��� 0.016��� 0.500��� Mongla 8.222 0.032��� 0.011�� 0.452��� Satkhira 2.265 0.006 0.022��� 0.000 Mymensingh -14.889� 0.020��� 0.013�� 0.000 Dhaka Dhaka -5.077 0.027�� 0.014�� 0.045 Faridpur -11.100 0.035��� 0.016�� 0.182�� Madaripur -10.547� 0.006 0.023��� 0.049 Tangail -1.091 0.055��� 0.007 0.400��� Barisal Barisal -1.440 0.029��� 0.024��� 0.056��� Bhola -8.833 0.014�� 0.011�� 0.000 Khepupara 15.879�� 0.037��� 0.015�� 0.000 Patuakhali -1.217 0.048��� 0.010 0.146��� Sylhet Srimangal -1.308 0.024�� 0.003 -0.045 Sylhet -9.857 0.045��� 0.030��� 0.143��� Chittagong Chandpur -8.322 0.035��� 0.010�� 0.000��� Chittagong 9.793 0.006 0.024��� 0.000 Comilla -1.250 0.034��� 0.017��� 0.000 Cox Bazar 18.666� 0.050��� 0.009� 0.000 Feni -7.395 0.009� 0.017��� 0.000 Hatiya 23.750�� 0.019��� 0.007 0.000 Kutubdia 17.900 0.025�� 0.014� 0.000 Maijdi -2.600 0.057��� 0.027��� 0.125��� Rangamati 6.350 0.043��� -0.008 0.059� Sandwip 22.156�� 0.057��� -0.005 0.000�� Sitakunda 7.283 0.053��� 0.005 0.087��� Teknaf 21.623�� 0.015�� 0.026��� 0.000 https://doi.org/10.1371/journal.pclm.0000009.t003 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 9 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sylhet Division, Bangladesh","kind":"region","role":"administrative division with highest annual rainfall and some trends in temperature","river_name":null,"geocode_query":"Sylhet Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Station RSEAdry RCDDdry Tmaxdry Tmindry T12dry Rangpur Dinajpur 0.169 0.623 -0.022� 0.039��� -0.429 Rangpur -0.093 1.200�� -0.013 0.045��� -0.658��� Sayedpur -2.154 0.857 0.011 0.026 0.155 Rajshahi Bogra -2.042 1.429��� -0.009 0.029��� 0.185 Ishurdi -2.375�� 0.654 -0.004 0.028�� 0.000 Rajshahi -1.722 1.080�� -0.013 0.022 0.057 Khulna Chuadanga -3.000� 0.765 -0.028 -0.006 0.389 Jessore -5.110�� 0.705� 0.019 -0.009 0.333�� Khulna -5.652��� 0.763 0.010 0.055��� -0.483��� Mongla -7.125� 0.467 0.030 -0.001 0.444�� Satkhira -3.121� 1.057� -0.035� 0.020�� -0.045 Mymensingh -1.739 0.748 -0.019� 0.017 -0.220 Dhaka Dhaka -6.447��� 1.118��� -0.004 0.040��� -0.051 Faridpur -7.816��� 1.134�� 0.012 0.021�� 0.200 Madaripur -5.932�� 0.924 -0.009 0.031�� -0.100 Tangail -7.071��� 1.690�� 0.007 0.006 0.286 Barisal Barisal -3.975� 0.511 0.020� 0.025�� -0.143 Bhola -6.857��� 0.617 0.003 0.010 0.208 Khepupara -4.775� 0.659 0.032�� 0.000 0.036��� Patuakhali -6.878��� 1.419�� 0.039��� -0.018�� 0.500��� Sylhet Srimangal -9.258�� 1.127� 0.014 -0.009 0.121 Sylhet -4.867 1.000�� 0.045��� 0.049��� -0.176 Chittagong Chandpur -7.171�� 0.828 0.017 0.040��� -0.308� Chittagong -9.368��� 1.400��� 0.023 0.035�� 0.000 Comilla -6.364�� 1.257�� -0.003 0.022� -0.022 Cox Bazar -6.916�� 0.577 0.053��� 0.028��� 0.000 Feni -6.167��� 1.444��� 0.000 0.011 0.167 Hatiya -6.432�� 1.095� 0.029�� -0.027� 0.052��� Kutubdia -7.216��� 0.523 0.025 0.009 0.000 Maijdi -8.129�� 0.333 0.025�� 0.047��� -0.250 Rangamati -5.441� 0.232 0.062��� -0.042�� 0.571��� Sandwip -7.792��� 1.043� 0.057��� -0.048�� 0.636��� Sitakunda -8.729�� 1.394��� 0.070��� -0.024�� 0.500��� Teknaf -3.667�� 0.577 0.021�� -0.008 0.042 https://doi.org/10.1371/journal.pclm.0000009.t004 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 10 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Chittagong Division, Bangladesh","kind":"region","role":"administrative division with varying rainfall trends affecting local production","river_name":null,"geocode_query":"Chittagong Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Station RSEAwet Tmaxwet Tminwet T36wet Rangpur Dinajpur -10.408 0.037��� 0.031��� 0.067 Rangpur -13.042� 0.033��� 0.013��� 0.059�� Sayedpur -8.100 0.060��� 0.023� 0.250 Rajshahi Bogra -8.294 0.037��� 0.024��� 0.143�� Ishurdi -5.455 0.039��� 0.018��� 0.353� Rajshahi -8.833� 0.038��� 0.019��� 0.428�� Khulna Chuadanga -4.583 0.011 -0.009 0.226 Jessore 7.529 0.050��� 0.014�� 0.643��� Khulna 9.169 0.037��� 0.016��� 0.500��� Mongla 8.222 0.032��� 0.011�� 0.452��� Satkhira 2.265 0.006 0.022��� 0.000 Mymensingh -14.889� 0.020��� 0.013�� 0.000 Dhaka Dhaka -5.077 0.027�� 0.014�� 0.045 Faridpur -11.100 0.035��� 0.016�� 0.182�� Madaripur -10.547� 0.006 0.023��� 0.049 Tangail -1.091 0.055��� 0.007 0.400��� Barisal Barisal -1.440 0.029��� 0.024��� 0.056��� Bhola -8.833 0.014�� 0.011�� 0.000 Khepupara 15.879�� 0.037��� 0.015�� 0.000 Patuakhali -1.217 0.048��� 0.010 0.146��� Sylhet Srimangal -1.308 0.024�� 0.003 -0.045 Sylhet -9.857 0.045��� 0.030��� 0.143��� Chittagong Chandpur -8.322 0.035��� 0.010�� 0.000��� Chittagong 9.793 0.006 0.024��� 0.000 Comilla -1.250 0.034��� 0.017��� 0.000 Cox Bazar 18.666� 0.050��� 0.009� 0.000 Feni -7.395 0.009� 0.017��� 0.000 Hatiya 23.750�� 0.019��� 0.007 0.000 Kutubdia 17.900 0.025�� 0.014� 0.000 Maijdi -2.600 0.057��� 0.027��� 0.125��� Rangamati 6.350 0.043��� -0.008 0.059� Sandwip 22.156�� 0.057��� -0.005 0.000�� Sitakunda 7.283 0.053��� 0.005 0.087��� Teknaf 21.623�� 0.015�� 0.026��� 0.000 https://doi.org/10.1371/journal.pclm.0000009.t003 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 9 / 33","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Padma River Delta, Bangladesh","kind":"region","role":"river delta underlying the study area influencing hydrology and agriculture","river_name":"Padma","geocode_query":"Padma River Delta, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The country is located overlying the delta of three large rivers of south Asia: the Padma (name of the Ganges in Bangladesh), Jamuna (name of the Brahmaputra in Bangladesh) and Meghna (name of the Meghna upstream and after the confluence with the Padma and Jamuna rivers). The topography of the country is relatively flat, except for the hilly north- and southeast, and gently rolling northeast and northwest [46]. The country is divided into eight administrative regions known as divisions: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur and Sylhet (Fig 1). The climate of Bangladesh is characterized by four hydro-climatic seasons. These include: dry winter from December to February, hot summer from March to May, monsoon from June Table 1. List of studies assessing rainfall and temperature trends in Bangladesh. The studies are grouped by geographic domain.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.34457595499026,23.92325433428013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Padma River","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-8"},{"name":"Jamuna River, Bangladesh","kind":"river","role":"major river coursing through study area affecting regional hydrology","river_name":"Jamuna","geocode_query":"Jamuna River, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The country is located overlying the delta of three large rivers of south Asia: the Padma (name of the Ganges in Bangladesh), Jamuna (name of the Brahmaputra in Bangladesh) and Meghna (name of the Meghna upstream and after the confluence with the Padma and Jamuna rivers). The topography of the country is relatively flat, except for the hilly north- and southeast, and gently rolling northeast and northwest [46]. The country is divided into eight administrative regions known as divisions: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur and Sylhet (Fig 1). The climate of Bangladesh is characterized by four hydro-climatic seasons. These include: dry winter from December to February, hot summer from March to May, monsoon from June Table 1. List of studies assessing rainfall and temperature trends in Bangladesh. The studies are grouped by geographic domain.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.91239572365043,25.42119045665531],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jamuna River","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-9"},{"name":"Meghna River, Bangladesh","kind":"river","role":"major river coursing through study area affecting regional hydrology","river_name":"Meghna","geocode_query":"Meghna River, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The country is located overlying the delta of three large rivers of south Asia: the Padma (name of the Ganges in Bangladesh), Jamuna (name of the Brahmaputra in Bangladesh) and Meghna (name of the Meghna upstream and after the confluence with the Padma and Jamuna rivers). The topography of the country is relatively flat, except for the hilly north- and southeast, and gently rolling northeast and northwest [46]. The country is divided into eight administrative regions known as divisions: Barisal, Chittagong, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur and Sylhet (Fig 1). The climate of Bangladesh is characterized by four hydro-climatic seasons. These include: dry winter from December to February, hot summer from March to May, monsoon from June Table 1. List of studies assessing rainfall and temperature trends in Bangladesh. The studies are grouped by geographic domain.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.76400456898428,23.25946634887056],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Meghna River, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-10"},{"name":"Rangpur, Bangladesh","kind":"settlement","role":"northern city representing coldest district with notable temperature trends","river_name":null,"geocode_query":"Rangpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"to September, and autumn in October and November [28, 47]. During the winter months, the mean temperature of the country varies from 18 ̊C to 21 ̊C, and during the summer months the mean temperature varies from 27 ̊C to 29 ̊C. Winter is characterized by relatively cool and sunny days followed by cool nights. January is the coldest month in Bangladesh, and the country’s northern districts (e.g., Rangpur; Fig 1) experience the lowest temperatures. Winter temperatures are higher in the central areas (e.g., Dhaka) than in the northern areas, with much higher temperatures along the southern coastal areas due to the influence of the Bay of Bengal. A southerly wind is prominent during winter, and very often brings cool temperatures in the north (3–6 ̊C), central and central-south areas (8–10 ̊C). In February, the temperature rises and continues rising during March to May. The temperature in March to May varies from 30 to 35 ̊C [31, 47]. Fig 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.261086,25.689394],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rangpur, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-11","true_coordinates":[89.26108649999999,25.7493938],"spread_km":6.7},{"name":"Dhaka, Bangladesh","kind":"settlement","role":"central city with climate stations showing rainfall and temperature trends","river_name":null,"geocode_query":"Dhaka, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Station RSEAwet Tmaxwet Tminwet T36wet Rangpur Dinajpur -10.408 0.037��� 0.031��� 0.067 Rangpur -13.042� 0.033��� 0.013��� 0.059�� Sayedpur -8.100 0.060��� 0.023� 0.250 Rajshahi Bogra -8.294 0.037��� 0.024��� 0.143�� Ishurdi -5.455 0.039��� 0.018��� 0.353� Rajshahi -8.833� 0.038��� 0.019��� 0.428�� Khulna Chuadanga -4.583 0.011 -0.009 0.226 Jessore 7.529 0.050��� 0.014�� 0.643��� Khulna 9.169 0.037��� 0.016��� 0.500��� Mongla 8.222 0.032��� 0.011�� 0.452��� Satkhira 2.265 0.006 0.022��� 0.000 Mymensingh -14.889� 0.020��� 0.013�� 0.000 Dhaka Dhaka -5.077 0.027�� 0.014�� 0.045 Faridpur -11.100 0.035��� 0.016�� 0.182�� Madaripur -10.547� 0.006 0.023��� 0.049 Tangail -1.091 0.055��� 0.007 0.400��� Barisal Barisal -1.440 0.029��� 0.024��� 0.056��� Bhola -8.833 0.014�� 0.011�� 0.000 Khepupara 15.879�� 0.037��� 0.015�� 0.000 Patuakhali -1.217 0.048��� 0.010 0.146��� Sylhet Srimangal -1.308 0.024�� 0.003 -0.045 Sylhet -9.857 0.045��� 0.030��� 0.143��� Chittagong Chandpur -8.322 0.035��� 0.010�� 0.000��� Chittagong 9.793 0.006 0.024��� 0.000 Comilla -1.250 0.034��� 0.017��� 0.000 Cox Bazar 18.666� 0.050��� 0.009� 0.000 Feni -7.395 0.009� 0.017��� 0.000 Hatiya 23.750�� 0.019��� 0.007 0.000 Kutubdia 17.900 0.025�� 0.014� 0.000 Maijdi -2.600 0.057��� 0.027��� 0.125��� Rangamati 6.350 0.043��� -0.008 0.059� Sandwip 22.156�� 0.057��� -0.005 0.000�� Sitakunda 7.283 0.053��� 0.005 0.087��� Teknaf 21.623�� 0.015�� 0.026��� 0.000 https://doi.org/10.1371/journal.pclm.0000009.t003 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 9 / 33","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.415238,23.744093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dhaka, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-12","true_coordinates":[90.4152376,23.804093],"spread_km":6.7},{"name":"Mymensingh, Bangladesh","kind":"settlement","role":"central city where significant decreasing rainfall trends in wet season were observed","river_name":null,"geocode_query":"Mymensingh, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"Variability and trends in rainfall metrics linked to rice production The spatial and temporal variability and trends in the seasonal total rainfall in the wet season, RSEAwet, at the 34 climate stations are illustrated in Fig 3. The mean RSEAwet is 2214 mm/sea (from 1323 mm/sea in Rajshahi to 4151 mm/sea in Teknaf), CV is 20% (from 12% in Teknaf to 28% in Chandpur), and S is 0.793 mm/sea/yr (from -14.889 mm/sea/yr in Mymensingh to 23.750 mm/sea/yr in Hatiya). RSEAwet is relatively high (>2000 mm/sea) at the climate stations located in the southeast and relatively low (<1900 mm/sea) at the climate stations located in the central and western areas of Bangladesh. The CV is relatively high (>19%) at the climate stations in the northwest and relatively low (<19%) at most of the climate stations in the coastal, southwest and northwest areas.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.417854,24.685953],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mymensingh, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-13","true_coordinates":[90.41785399999999,24.7459528],"spread_km":6.7},{"name":"Cox's Bazar, Bangladesh","kind":"settlement","role":"southeast city with distinctive rainfall and temperature trends","river_name":null,"geocode_query":"Cox's Bazar, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Station RSEAdry RCDDdry Tmaxdry Tmindry T12dry Rangpur Dinajpur 0.169 0.623 -0.022� 0.039��� -0.429 Rangpur -0.093 1.200�� -0.013 0.045��� -0.658��� Sayedpur -2.154 0.857 0.011 0.026 0.155 Rajshahi Bogra -2.042 1.429��� -0.009 0.029��� 0.185 Ishurdi -2.375�� 0.654 -0.004 0.028�� 0.000 Rajshahi -1.722 1.080�� -0.013 0.022 0.057 Khulna Chuadanga -3.000� 0.765 -0.028 -0.006 0.389 Jessore -5.110�� 0.705� 0.019 -0.009 0.333�� Khulna -5.652��� 0.763 0.010 0.055��� -0.483��� Mongla -7.125� 0.467 0.030 -0.001 0.444�� Satkhira -3.121� 1.057� -0.035� 0.020�� -0.045 Mymensingh -1.739 0.748 -0.019� 0.017 -0.220 Dhaka Dhaka -6.447��� 1.118��� -0.004 0.040��� -0.051 Faridpur -7.816��� 1.134�� 0.012 0.021�� 0.200 Madaripur -5.932�� 0.924 -0.009 0.031�� -0.100 Tangail -7.071��� 1.690�� 0.007 0.006 0.286 Barisal Barisal -3.975� 0.511 0.020� 0.025�� -0.143 Bhola -6.857��� 0.617 0.003 0.010 0.208 Khepupara -4.775� 0.659 0.032�� 0.000 0.036��� Patuakhali -6.878��� 1.419�� 0.039��� -0.018�� 0.500��� Sylhet Srimangal -9.258�� 1.127� 0.014 -0.009 0.121 Sylhet -4.867 1.000�� 0.045��� 0.049��� -0.176 Chittagong Chandpur -7.171�� 0.828 0.017 0.040��� -0.308� Chittagong -9.368��� 1.400��� 0.023 0.035�� 0.000 Comilla -6.364�� 1.257�� -0.003 0.022� -0.022 Cox Bazar -6.916�� 0.577 0.053��� 0.028��� 0.000 Feni -6.167��� 1.444��� 0.000 0.011 0.167 Hatiya -6.432�� 1.095� 0.029�� -0.027� 0.052��� Kutubdia -7.216��� 0.523 0.025 0.009 0.000 Maijdi -8.129�� 0.333 0.025�� 0.047��� -0.250 Rangamati -5.441� 0.232 0.062��� -0.042�� 0.571��� Sandwip -7.792��� 1.043� 0.057��� -0.048�� 0.636��� Sitakunda -8.729�� 1.394��� 0.070��� -0.024�� 0.500��� Teknaf -3.667�� 0.577 0.021�� -0.008 0.042 https://doi.org/10.1371/journal.pclm.0000009.t004 PLOS CLIMATE Rainfall and temperature extremes and their potential risk in Bangladesh PLOS Climate | https://doi.org/10.1371/journal.pclm.0000009 March 8, 2022 10 / 33","verified_in_page":true},"resolution_status":"resolved","coordinates":[92.0061058,21.4272184],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cox's Bazar, Bangladesh","basin_id":4040024190,"main_basin_id":4040024190,"basin_region":"Central and South-East Asia","basin_area_km2":96502.0,"hydrology_source":"hydrobasins","site_id":"doc-2dc86f6af8acd34c5fc2a407-site-14"}],"limitations":["Data period varies between 23 and 34 years across stations, limiting some trend analyses","Exact latitudes and longitudes for climate stations not provided, coordinates set to null","Study spatially aggregated to administrative divisions; finer local climate variability may not be captured"],"river_ids":[],"site_ids":["doc-2dc86f6af8acd34c5fc2a407-site-8","doc-2dc86f6af8acd34c5fc2a407-site-9","doc-2dc86f6af8acd34c5fc2a407-site-10","doc-2dc86f6af8acd34c5fc2a407-site-11","doc-2dc86f6af8acd34c5fc2a407-site-12","doc-2dc86f6af8acd34c5fc2a407-site-13","doc-2dc86f6af8acd34c5fc2a407-site-14"],"anchor":[90.415238,23.744093],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040024190,4040960200,4040960480,4040975160],"main_basin_ids":[4040024190,4040025450]},{"id":"doc-3bbf19d6cfbed9f7f78e9d6d","title":"The impact of climate change, population growth and development on sustainable water security in Bangladesh to 2100","display_title":"Water Security Challenges and Scenarios in Bangladesh","doi":"https://doi.org/10.1038/s41598-022-26807-6","theme":"Water management","color":"#eeabac","year":2022,"publication_date":"2022-12-26","authors":["Mac Kirby","Mohammed Mainuddin"],"summary":"This study models water demand to 2100 in Bangladesh by integrating population, GDP, food, and water data across five defined regions. It evaluates irrigation and urban water demand under scenarios including population growth, crop yield changes, dietary shifts, food imports, regional cropping, and climate change. The northwest region faces the largest irrigation water demand increase, raising groundwater sustainability concerns. Urban water demand, especially in Dhaka, is projected to more than double, increasing pressure on groundwater supplies. The study highlights policy options to mitigate water demand, such as shifting crop production to the southwest and reducing rice cultivation.","finding":"Irrigation water demand in the northwest region is projected to increase for much of the 21st century, potentially exacerbating groundwater sustainability issues, while urban water demand, particularly in Dhaka, is likely to grow proportionately more than irrigation demand, underscoring the urgent need for alternative urban water sources.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"10 Vol:.(1234567890) Scientific Reports | (2022) 12:22344 | https://doi.org/10.1038/s41598-022-26807-6 www.nature.com/scientificreports/ after the overall population of Bangladesh has ceased to increase. The projected growth in urban water demand is a greater proportionate increase than that of irrigation water demand. If groundwater continues to be a major source of water for large urban centres, it therefore seems likely that the concerns over the unsustainability of use will grow more acute in the urban case than in the irrigation supply case. Conclusions We conclude that, consistent with the findings of many studies in the literature, pressure on water use in Bangladesh is likely to increase in coming decades. In particular, irrigation water demand in a business-as-usual scenario in the northwest is likely to increase for much of the coming century, potentially exacerbating concerns about sustainable use of groundwater.","verified_in_page":true},"topics":["groundwater sustainability","irrigation water demand","urban water demand","climate change impacts","population growth","food security","regional water management","crop yield changes"],"study_period":"1975-2100","locations":[{"name":"Northwest region, Bangladesh","kind":"region","role":"primary agricultural and groundwater stress study area","river_name":null,"geocode_query":"Rangpur and Rajshahi divisions, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"The Northwest region comprises the Rangpur and Rajshahi divisions of Bangaldesh; North Central, the Mymensingh division and Dhaka division north of the Padma River; Northeast, Sylhet division; Southwest, Khulna and Barisal divisions and Dhaka division south of the Padma River; Southeast: Chittagong division.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"North Central region, Bangladesh","kind":"region","role":"sub-national analysis region","river_name":null,"geocode_query":"Mymensingh and northern Dhaka divisions, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"The Northwest region comprises the Rangpur and Rajshahi divisions of Bangaldesh; North Central, the Mymensingh division and Dhaka division north of the Padma River; Northeast, Sylhet division; Southwest, Khulna and Barisal divisions and Dhaka division south of the Padma River; Southeast: Chittagong division.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.475069,24.775953],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mymensingh, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-3bbf19d6cfbed9f7f78e9d6d-site-1","true_coordinates":[90.41785399999999,24.7459528],"spread_km":6.7},{"name":"Northeast region, Bangladesh","kind":"region","role":"sub-national analysis region","river_name":null,"geocode_query":"Sylhet division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"The Northwest region comprises the Rangpur and Rajshahi divisions of Bangaldesh; North Central, the Mymensingh division and Dhaka division north of the Padma River; Northeast, Sylhet division; Southwest, Khulna and Barisal divisions and Dhaka division south of the Padma River; Southeast: Chittagong division.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southwest region, Bangladesh","kind":"region","role":"region considered for agricultural development shifting to reduce northwest pressure","river_name":null,"geocode_query":"Khulna and Barisal divisions and southern Dhaka division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"The Northwest region comprises the Rangpur and Rajshahi divisions of Bangaldesh; North Central, the Mymensingh division and Dhaka division north of the Padma River; Northeast, Sylhet division; Southwest, Khulna and Barisal divisions and Dhaka division south of the Padma River; Southeast: Chittagong division.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southeast region, Bangladesh","kind":"region","role":"sub-national analysis region","river_name":null,"geocode_query":"Chittagong division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"The Northwest region comprises the Rangpur and Rajshahi divisions of Bangaldesh; North Central, the Mymensingh division and Dhaka division north of the Padma River; Northeast, Sylhet division; Southwest, Khulna and Barisal divisions and Dhaka division south of the Padma River; Southeast: Chittagong division.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Dhaka, Bangladesh","kind":"settlement","role":"largest urban center with significant groundwater demand and urban water security issues","river_name":null,"geocode_query":"Dhaka, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"With the achievement of near selfsufficiency in rice, it may be preferable in future to limit the growth of rice production17, and instead grow crops which use less water, such as pulses and oilseeds18. However, grain self-sufficiency has declined in recent years19. Changing diets to consume less animal protein may also reduce the demand for water20, though in the case of Bangladesh (and many other countries) supplying the whole population with a recommended diet would increase water consumption21. Increases in crop yields will also affect future water demand15, as will the impact on local crop production of the quantity and type of food imports and exports. Urban water supply in Bangladesh’s biggest city, Dhaka, is also based on the unsustainable use of groundwater, with rapid depletion threatening both the quantity22–24 and quality25 of the resource. Population increase will again exacerbate the problems26.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.480816,23.804093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dhaka, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-3bbf19d6cfbed9f7f78e9d6d-site-5","true_coordinates":[90.4152376,23.804093],"spread_km":6.7},{"name":"Bangladesh, country","kind":"region","role":"national scale encompassing all regional analyses and scenarios","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2022) 12:22344 | https://doi.org/10.1038/s41598-022-26807-6 www.nature.com/scientificreports The impact of climate change, population growth and development on sustainable water security in Bangladesh to 2100 Mac Kirby* & Mohammed Mainuddin There are concerns that groundwater use for irrigation and for urban water supply is unsustainable in some parts of Bangladesh, particularly in the agriculturally productive northwest region. We use an integrated population – GDP – food – water model to examine water demand to 2100 in Bangladesh in development scenarios relevant to food and water security. The results indicate that irrigation water demand is projected to increase in coming decades, but later in the century it may decrease due to increasing crop yields and a falling population. The increased demand is greatest in the northwest region and, if unchecked, would increase concerns there about the unsustainable use of groundwater.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Model does not simulate water supply constraints, only water demand.","Limited economic modeling of prices and external trade dynamics, imports and exports treated as deposits to or withdrawals from external pools.","Coordinates of locations not provided, geocoding based on named administrative regions only.","Spatial analysis is by administrative regions rather than hydrological basins.","Projection uncertainties inherent in long-term (to 2100) scenario modeling including population and climate assumptions."],"river_ids":[],"site_ids":["doc-3bbf19d6cfbed9f7f78e9d6d-site-1","doc-3bbf19d6cfbed9f7f78e9d6d-site-5"],"anchor":[90.480816,23.804093],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040975160],"main_basin_ids":[4040025450]},{"id":"doc-3f7f64e749e73de60d31a7b5","title":"Sounding the Call for a Global Library of Underwater Biological Sounds","display_title":"Global Library of Underwater Biological Sounds","doi":"https://doi.org/10.3389/fevo.2022.810156","theme":"Other science","color":"#d1b5e7","year":2022,"publication_date":"2022-02-08","authors":["Miles J. G. Parsons","Tzu-Hao Lin","T. Aran Mooney","Christine Erbe","Francis Juanes","Marc Lammers","Songhai Li","Simon Linke","Audrey Looby","Sophie L. Nedelec","Ilse Van Opzeeland","Craig Radford","Aaron N. Rice","Laela Sayigh","Jenni Stanley","Edward Urban","Lucia Di Iorio"],"summary":"This review article discusses the development and benefits of a global, open-access underwater biological sound library that integrates existing regional and taxa-specific sound libraries. The library aims to archive known and unknown biological sounds, provide a data repository with metadata, support AI algorithm training for acoustic signal detection and classification, and facilitate citizen science involvement. Such a database would enhance species detection, biodiversity monitoring, ecological research, and conservation efforts worldwide, addressing challenges from increasing anthropogenic ocean noise and biodiversity loss.","finding":"A global library of underwater biological sounds would significantly improve the identification and classification of aquatic species' vocalizations, facilitate the development of machine learning algorithms for automated monitoring, and enhance understanding of global spatiotemporal patterns in aquatic bioacoustics and biodiversity.","finding_evidence":{"passage_id":"p009-005","page":9,"quote":"Matched-filtering of spectrograms has been used to detect highly stereotypical sounds of some whales and fishes (e.g., Mellinger and Clark, 1997; Ricci et al., 2017; Madhusudhana et al., 2020; Ogundile and Versfeld, 2020). Information entropy detectors have been applied to aberrant (non-stereotypical) tonal sounds (Erbe and King, 2008). Detectors are further applied to wavelets and the cepstral domain as well (e.g., Alias et al., 2016; Noda et al., 2016; Malfante et al., 2018). Machine and deep learning (AI) methods have been increasingly used to classify and detect sources in multiple applications (e.g., Lin et al., 2017; MacAodha et al., 2018; Bergler et al., 2019; Stowell et al., 2019). There have been considerable advances in the fields of facial and voice recognition that have advanced public use of phonebased apps to identify music, plants, and the calls of frogs and birds (Kahl et al., 2021).","verified_in_page":true},"topics":["bioacoustics","passive acoustic monitoring","machine learning","soundscape ecology","biodiversity monitoring","citizen science","data repository","marine ecology"],"study_period":null,"locations":[{"name":"Okinawa, Japan","kind":"region","role":"Example location for simultaneous recordings showing variability in sound characteristics of humpback whale songs","river_name":null,"geocode_query":"Okinawa, Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 4 Parsons et al. Global Underwater Biological Sounds Library FIGURE 1 | Example spectrograms produced (1,024 point-long Hanning window, 0.9 overlap, frequency display 50–20,000 Hz, relative received levels) from: two simultaneous recordings of a humpback whale (Megaptera novaeangliae) song in (A) 20 m and (B) 40 m depth waters off Okinawa, Japan, (recording locations separated by ≈500 m, note the lack of high-frequency energy in B); (C) a complex call and (D) a single grunt sound from gulf toadfish (Opsanus beta); (E) two sounds from a 20–30 cm-long sooty grunter (Hephaestus fuliginosus); (F) one sound from a 7 cm-long spangled grunter (Leiopotherapon unicolor); (G) 4 s of sounds made a crawling kina urchin (Evechinus chloroticus) and (H) 4 s of sounds produced by a New Zealand paddle crab (Ovalipes catharus). Power spectral density axes in each spectrogram are relative and span 50 dB re 1 μPa2 /Hz.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Great Barrier Reef, Australia","kind":"region","role":"Example coral reef habitat where fish chorus patterns were studied using passive acoustic monitoring","river_name":null,"geocode_query":"Great Barrier Reef, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-003","page":17,"quote":"Rep. 7:17459. doi: 10.1038/s41598-017-15838-z Meekan, M. G., Speed, C. W., McCauley, R. D., Fisher, R., Birt, M. J., CurreyRandall, L. M., et al. (2021). A large-scale experiment finds no evidence that a seismic survey impacts a demersal fish fauna. Proc. Natl. Acad. Sci. U.S.A. 118:e2100869118. doi: 10.1073/pnas.2100869118 Mellinger, D. K., and Clark, C. W. (1997). Methods for automatic detection of mysticete sounds. Mar. Freshw. Behav. Physiol. 29, 163–181. doi: 10.1080/ 10236249709379005 Mellinger, D. K., and Clark, C. W. (2006). MobySound: a reference archive for studying automatic recognition of marine mammal sounds. Appl. Acoust. 67, 1226–1242. doi: 10.1016/j.apacoust.2006.06.002 Menze, S., Zitterbart, D. P., van Opzeeland, I., and Boebel, O. (2017). The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound. Roy. Soc. Open Sci. 4:160370. doi: 10.1098/rsos.160370 Miller, B. S., The Iwc-Sorp/Soos Acoustic Trends Working Group, Balcazar, N., Nieukirk, N.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.9487532,-20.3516823],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":19009405,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hamilton Island Airport (HTI), Hamilton Island QLD 4802, Australia","basin_id":5040068460,"main_basin_id":5040068460,"basin_region":"Australasia","basin_area_km2":23350.6,"hydrology_source":"geofabric","site_id":"doc-3f7f64e749e73de60d31a7b5-site-1"},{"name":"Darwin Harbour, Australia","kind":"region","role":"Location where fish choruses were characterized and diversity assessed acoustically","river_name":null,"geocode_query":"Darwin Harbour, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-000","page":17,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 17 Parsons et al. Global Underwater Biological Sounds Library Malfante, M., Mars, J. I., Dalla Mura, M., and Gervaise, C. (2018). Automatic fish sounds classification. J. Acoust. Soc. Am. 143, 2834–2846. doi: 10.1121/1. 5036628 Mallekh, R., Lagardère, J. P., Eneau, J. P., and Cloutour, C. (2003). An acoustic detector of turbot feeding activity. Aquaculture 221, 481–489. doi: 10.1016/ s0044-8486(03)00074-7 Mann, D. A., and Grothues, T. M. (2008). Short-term upwelling events modulate fish sound production at a mid-Atlantic Ocean observatory. Mar. Ecol. Prog. Ser. 375, 65–71. doi: 10.3354/meps07720 Mann, D. A., and Jarvis, S. M. (2004). Potential sound production by a deep-sea fish. J. Acoustic. Soc. Am. 115, 2331–2333. doi: 10.1121/1.1694992 Mann, D. A., Bowers-Altman, J., and Rountree, R. A. (1997). Sounds produced by the striped cusk-eel Ophidion marginatum (Ophidiidae) during courtship and spawning.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.827133,-12.406164],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3f7f64e749e73de60d31a7b5-site-2","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Southern Ocean","kind":"region","role":"Region where a baleen whale call database was created for Antarctica and machine learning models developed for baleen whale call detection","river_name":null,"geocode_query":"Southern Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 9 Parsons et al. Global Underwater Biological Sounds Library 2013). For example, Miller et al. (2021) initiated an open-access library of annotated recordings to train and evaluate automated detectors of Antarctic blue whale and fin whale (B. physalus) calls. The library was designed to include recordings from a broad range of instruments, locations, environmental conditions, and years, to ensure that robust detectors can be developed and tested across a suite of recording conditions. However, given the scope of this library initiated through the Southern Ocean Research Partnership Fin and Blue Whale Acoustics Group (Van Opzeeland et al., 2014), it is unlikely to be extended to other regions and taxa.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-160.234046,-68.378013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Ocean","hydrology_source":"none","site_id":"doc-3f7f64e749e73de60d31a7b5-site-3","true_coordinates":[-160.2340459,-68.4380135],"spread_km":6.7},{"name":"Western North Atlantic","kind":"region","role":"Source region of an existing fish sounds library used as a data base for known fish acoustic signals","river_name":null,"geocode_query":"Western North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"Title (host) Weblink (citation) Details Audio Gallery (Discovery of Sound in the Sea) https://dosits.org/galleries/audio-gallery/ (Vigness-Raposa et al., 2012) Sound samples of 44 marine mammal, 29 fish and 4 invertebrate species from around the world Fish Sounds (University of Rhode Island) http://www.gso.uri.edu/fishsounds/ (Fish and Mowbray, 1970) 155 sound samples of 153 fish species from the Western North Atlantic (Fish and Mowbray, 1970) The SOUND Table (FishBase) https://www.fishbase.de/topic/List.php?group= sounds (Kaschner, 2012) 121 sound samples of 90 fish species, mostly from the Western North Atlantic (Fish and Mowbray, 1970) Macaulay Library (Cornell University) https://www.macaulaylibrary.org/ (Macaulay Library, 2021) 1,189,562 sound samples of 10,056 bird species and 2,674 non-bird species Marine Mammals of Australia and Antarctica (Curtin University) http://cmst.curtin.edu.au/research/marinemammal-bioacoustics/ (Erbe et al., 2017) Sound samples of 43 marine mammal species from Australasia Ocean Networks Canada (Sound Cloud) https://soundcloud.com/ oceannetworkscanada/albums (Ocean Networks Canada, 2021) 60 sound samples of marine mammal and fish species from Canada Watkins Marine Mammal Sound Database (Woods Hole Oceanographic Institution) https://cis.whoi.edu/science/B/whalesounds/ index.cfm (Sayigh et al., 2016; Watkins Marine Mammal Sound Database, 2021) About 15,000 sound samples of 55 marine mammal species, as well as about 1,600 full soundscape recordings, mainly collected during the career of William Watkins Sonothèque (Muséum National d’Histoire Naturelle) https://sonotheque.mnhn.fr/ (Sonothèque, 2018) 19,589 sound samples of wildlife species, including marine mammals and fishes, predominantly collected by Bernie Krause with additional contributors British Library Sound Archive (The British Library) https://sounds.bl.uk/Environment (The British Library, 2021) 240,000 sound samples of 10,000 bird, mammal, amphibian, reptile, fish, and invertebrate species from around the world Voices in the Sea (University of California San Diego) http://voicesinthesea.ucsd.edu/ (Voices in the Sea, 2018) Sound samples of 33 Cetacean and 10 Pinniped species FishSounds (MERIDIAN) https://www.fishsounds.net (Looby et al., 2021) 240 sound samples of 130 fish species MobySound mobysound.org (Heimlich et al., 2021) Sound samples of 25 Cetacean and 2 Pinniped species Animal Sound Archive (Museum für Naturkunde in Berlin) https://www.tierstimmenarchiv.de/ (Tierstimmenarchiv, 2021) 120,000 sound samples of any wildlife, including invertebrates, marine mammals, and fishes FonoZoo (Museo Nacional de Ciencias Naturales Madrid) http://www.fonozoo.com/ (FonoZoo, 2021) 11,656 sound samples of 1,620 species, including invertebrates, marine mammals, amphibians and fishes i.e., an overall partnership that represents various stakeholder groups—including bio- and ecoacousticians, and research specialists of a variety of taxa and ecosystems.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-28.6731465,-14.5994134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Atlantic Ocean","hydrology_source":"none","site_id":"doc-3f7f64e749e73de60d31a7b5-site-4"},{"name":"Canary Islands, Eastern North Atlantic","kind":"region","role":"Example region where fin whale calls have been studied showing geographic variation in signals","river_name":null,"geocode_query":"Canary Islands, Spain","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 8 Parsons et al. Global Underwater Biological Sounds Library types across the hemispheres even though populations’ home ranges do not (or no longer) cross the equator, raising interesting questions about their acoustic ecology and evolution (Courts et al., 2020). Fishes may also develop “dialects,” such as the different acoustic characteristics of agonistic sounds produced by the skunk anemonefish (Amphiprion akallopisos) in Madagascar compared with those in Indonesia (Parmentier et al., 2005). Cultural evolution of humpback whale (Megaptera novaeangliae) song has been observed across ocean basins, providing greater understanding of population interactions across the Pacific Ocean (Garland et al., 2011) and around the coast of Australia (Allen et al., 2018).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Madagascar","kind":"region","role":"Region where skunk anemonefish acoustic dialects were detected, showing geographic differences in fish calls","river_name":null,"geocode_query":"Madagascar","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 8 Parsons et al. Global Underwater Biological Sounds Library types across the hemispheres even though populations’ home ranges do not (or no longer) cross the equator, raising interesting questions about their acoustic ecology and evolution (Courts et al., 2020). Fishes may also develop “dialects,” such as the different acoustic characteristics of agonistic sounds produced by the skunk anemonefish (Amphiprion akallopisos) in Madagascar compared with those in Indonesia (Parmentier et al., 2005). Cultural evolution of humpback whale (Megaptera novaeangliae) song has been observed across ocean basins, providing greater understanding of population interactions across the Pacific Ocean (Garland et al., 2011) and around the coast of Australia (Allen et al., 2018).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Indonesia","kind":"region","role":"Region where the skunk anemonefish acoustic dialect differs relative to Madagascar, illustrating intraspecific call variation","river_name":null,"geocode_query":"Indonesia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"fevo-10-810156 February 7, 2022 Time: 13:17 # 8 Parsons et al. Global Underwater Biological Sounds Library types across the hemispheres even though populations’ home ranges do not (or no longer) cross the equator, raising interesting questions about their acoustic ecology and evolution (Courts et al., 2020). Fishes may also develop “dialects,” such as the different acoustic characteristics of agonistic sounds produced by the skunk anemonefish (Amphiprion akallopisos) in Madagascar compared with those in Indonesia (Parmentier et al., 2005). Cultural evolution of humpback whale (Megaptera novaeangliae) song has been observed across ocean basins, providing greater understanding of population interactions across the Pacific Ocean (Garland et al., 2011) and around the coast of Australia (Allen et al., 2018).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North West Atlas, Australia","kind":"region","role":"Example of an interactive map data portal that links acoustic data records with species composition and environmental data","river_name":null,"geocode_query":"North West Atlas, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"Data portals, such as the interactive maps of the North West Atlas in Australia,11 allow viewers to choose any study site in a map and view a synopsis of species composition and a video snapshot of the site, along with environmental data. This could also be achieved from an acoustic perspective. For example, the data portal of the Integrated Marine Observing System12 allows viewers to peruse long-term spectrograms of recording sites for a userdefined period. An interactive map that can incorporate all these options becomes a user platform for the acoustic data. OPUS is a recent initiative driven by the International Quiet Ocean Experiment that is currently under development and includes some of these functions. This program was created to share underwater soundscapes through audio and synchronized spectral visualizations at staggered temporal resolution.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Locations provided are at regional scales derived from example study areas mentioned; specific coordinates are not given.","Some recording sites and soundscape data are inferred from broader geographic references rather than precise GPS coordinates.","No direct measurement coordinates are available in the text, coordinates are null when unambiguous DMS not provided.","Certain source regions for historic or literature acoustic libraries are not precisely georeferenced in the text."],"river_ids":[],"site_ids":["doc-3f7f64e749e73de60d31a7b5-site-1","doc-3f7f64e749e73de60d31a7b5-site-2","doc-3f7f64e749e73de60d31a7b5-site-3","doc-3f7f64e749e73de60d31a7b5-site-4"],"anchor":[-28.6731465,-14.5994134],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040061670,5040068460],"main_basin_ids":[5040061670,5040068460]},{"id":"doc-44efb05cb72da05af0a4acd2","title":"Episodic extreme rainfall events drive groundwater recharge in arid zone environments of central Australia","display_title":"Extreme rainfall drives groundwater recharge, central Australia","doi":"https://doi.org/10.1016/j.ejrh.2022.101005","theme":"Groundwater","color":"#a8b9ff","year":2022,"publication_date":"2022-01-19","authors":["Theresa Boas","Dirk Mallants"],"summary":"This study quantifies groundwater recharge in the arid Ti-Tree Basin of central Australia, using a multi-model vadose zone numerical approach (HYDRUS-1D) across a century-long meteorological data set. It applies eleven pedotransfer functions to estimate soil hydraulic properties from core samples, accounting for soil and conceptual model uncertainty. Results show that recharge is episodic and strongly linked to infrequent extreme monsoonal rainfall events exceeding 150–200 mm, with vegetation (Mulga savanna) strongly reducing recharge compared to bare soil. Spatial variability in climate and soil hydraulic properties drives heterogeneity in recharge, which occurs mainly during clustered extreme rainfall periods between December and April. Modelled recharge estimates align with field tracer and water level data supporting recharge during rare extreme precipitation events.","finding":"Recharge to groundwater mainly occurs due to extreme rainfall events exceeding 150–200 mm, with recharge rates highly episodic and averaging 4.3 to 7.4 mm/a under savanna vegetation and 23.5 to 35.8 mm/a for bare soil across the study area; multiple extreme events within short periods increase recharge due to wetter antecedent soil conditions.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"Here we estimated groundwater recharge in data sparse regions under arid climate by implementing a multi-model approach using HYDRUS-1D for a century-long time series of meteorological data and site-specific regolith hydraulic properties of a bare soil and a Mulga savanna-type soil in central Australia. Grain-size analysis provided a contiguous 12-m deep profile of hydraulic properties that were generated by means of pedotransfer functions. To account for conceptual model uncertainty in hydraulic properties, eleven pedotransfer functions were applied. Climate data from three stations accounted for spatial heterogeneity in local climate of the Ti Tree Basin study area. Simulated water fluxes in the vadose zone indicated that only rainfall events of more than 150–200 mm resulted in groundwater recharge. Recharge was linked to extreme rainfall associated with monsoonal cyclones.","verified_in_page":true},"topics":["groundwater recharge","hydraulic properties","pedotransfer functions","vadose zone modelling","arid zone hydrology","extreme rainfall","land cover influences","climate variability"],"study_period":"1889-2016","locations":[{"name":"Ti-Tree Basin","kind":"basin","role":"primary study area for groundwater recharge modelling","river_name":null,"geocode_query":"Ti-Tree Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"To cover sufficient temporal variability in rainfall data, and thus temporal variability in recharge, meteorological records over a century-long timescale were used. The variability in recharge across the landscape due to spatial variability in climatologic boundary conditions will be tackled by running the simulations for several meteorological datasets recorded Fig. 1. A: Map of the Ti-Tree basin, Northern Territory, Australia. Colour coding on the map indicates typical groundwater table depths of the TiTree aquifer. B: Inset shows location of groundwater monitoring wells RN005507 and RN006543. C: Typical Mulga savanna vegetation near the borehole site. T. Boas and D. Mallants","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.4161598,-22.1291783],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102215700,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087600,"main_basin_id":5040087600,"basin_region":"Australasia","basin_area_km2":141735.8,"hydrology_source":"geofabric","site_id":"doc-44efb05cb72da05af0a4acd2-site-0"},{"name":"Tea Tree Well weather station","kind":"station","role":"source of meteorological data used for recharge simulations and benchmark for precipitation events","river_name":null,"geocode_query":"Tea Tree Well, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Alice Springs Airport (at 164 km from the centre of the Basin) (Fig. 1). 2.3. Vegetation Evapotranspiration plays an important role in regional water balance assessments especially in arid and semi-arid regions where the potential evapotranspiration (pan evaporation in Ti-Tree is about 3100 mm/a (Jeffrey et al., 2001)) equals or surpasses average precipitation (National Research Council, 2004). The importance of vegetation on infiltration rates and hydrologic fluxes in the vadose zone of arid climates has been studied in detail (e.g. Chen et al., 2014; Eamus et al., 2013; Dunkerley, 2002; Garcia et al., 2011; Scanlon et al., 2005, 2006). Simulated recharge rates at 4 m soil depth for the Ti-Tree Basin by Chen et al. (2014) varied from an average of 6 mm per year for vegetated soil to an average of 236 mm per year for bare soil (for relatively short timeseries, 1981–2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.4,-22.15],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215700,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tea Tree Well, Ti Tree NT 0872, Australia","basin_id":5040087600,"main_basin_id":5040087600,"basin_region":"Australasia","basin_area_km2":141735.8,"hydrology_source":"geofabric","site_id":"doc-44efb05cb72da05af0a4acd2-site-1"},{"name":"Territory Grape Farm weather station","kind":"station","role":"source of meteorological data used for spatial climate variability in simulations","river_name":null,"geocode_query":"Territory Grape Farm, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Alice Springs Airport (at 164 km from the centre of the Basin) (Fig. 1). 2.3. Vegetation Evapotranspiration plays an important role in regional water balance assessments especially in arid and semi-arid regions where the potential evapotranspiration (pan evaporation in Ti-Tree is about 3100 mm/a (Jeffrey et al., 2001)) equals or surpasses average precipitation (National Research Council, 2004). The importance of vegetation on infiltration rates and hydrologic fluxes in the vadose zone of arid climates has been studied in detail (e.g. Chen et al., 2014; Eamus et al., 2013; Dunkerley, 2002; Garcia et al., 2011; Scanlon et al., 2005, 2006). Simulated recharge rates at 4 m soil depth for the Ti-Tree Basin by Chen et al. (2014) varied from an average of 6 mm per year for vegetated soil to an average of 236 mm per year for bare soil (for relatively short timeseries, 1981–2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Alice Springs Airport weather station","kind":"station","role":"external meteorological data station to represent spatial climate variability in recharge modelling","river_name":null,"geocode_query":"Alice Springs Airport, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Alice Springs Airport (at 164 km from the centre of the Basin) (Fig. 1). 2.3. Vegetation Evapotranspiration plays an important role in regional water balance assessments especially in arid and semi-arid regions where the potential evapotranspiration (pan evaporation in Ti-Tree is about 3100 mm/a (Jeffrey et al., 2001)) equals or surpasses average precipitation (National Research Council, 2004). The importance of vegetation on infiltration rates and hydrologic fluxes in the vadose zone of arid climates has been studied in detail (e.g. Chen et al., 2014; Eamus et al., 2013; Dunkerley, 2002; Garcia et al., 2011; Scanlon et al., 2005, 2006). Simulated recharge rates at 4 m soil depth for the Ti-Tree Basin by Chen et al. (2014) varied from an average of 6 mm per year for vegetated soil to an average of 236 mm per year for bare soil (for relatively short timeseries, 1981–2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.9034268,-23.8018192],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715301,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs Airport, Santa Teresa, Road, Alice Springs NT 0871, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-44efb05cb72da05af0a4acd2-site-3"},{"name":"Groundwater monitoring well RN005507","kind":"station","role":"groundwater level monitoring well providing data for qualitative validation of recharge model","river_name":null,"geocode_query":"RN005507, Ti-Tree Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The depth to the groundwater table varies from up to 50 m depth in the south-western and south-eastern parts of the basin to less than 2 m depth in the north (Harrington et al., 2002; Read and Tickell, 2007; Magee, 2009). Natural discharge from the aquifer is mainly limited to the northern part of the basin where shallow groundwater tables result in water loss by evaporation through the vadose zone (Magee, 2009). Among the several monitoring wells in the Ti-Tree Basin, only two (RN005507 and RN006543, Fig. 1) had a sufficiently long record of groundwater levels (between 1970 and 1990) without being influenced by nearby groundwater abstraction for irrigation (data available via Australian Groundwater Explorer, Bureau of Meteorology). Both stations are about 10 km south from the Tea Tree Well climate station and 19 km east from the borehole drilling site (see further).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Groundwater monitoring well RN006543","kind":"station","role":"groundwater level monitoring well providing data for qualitative validation of recharge model","river_name":null,"geocode_query":"RN006543, Ti-Tree Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The depth to the groundwater table varies from up to 50 m depth in the south-western and south-eastern parts of the basin to less than 2 m depth in the north (Harrington et al., 2002; Read and Tickell, 2007; Magee, 2009). Natural discharge from the aquifer is mainly limited to the northern part of the basin where shallow groundwater tables result in water loss by evaporation through the vadose zone (Magee, 2009). Among the several monitoring wells in the Ti-Tree Basin, only two (RN005507 and RN006543, Fig. 1) had a sufficiently long record of groundwater levels (between 1970 and 1990) without being influenced by nearby groundwater abstraction for irrigation (data available via Australian Groundwater Explorer, Bureau of Meteorology). Both stations are about 10 km south from the Tea Tree Well climate station and 19 km east from the borehole drilling site (see further).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Woodforde River","kind":"river","role":"ephemeral river influencing surface hydrology and flooding recharge in the Ti-Tree Basin","river_name":"Woodforde River","geocode_query":"Woodforde River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Monitoring well RN005507 has a screen from 46 to 52.4 m below surface in a sand layer (33.8–52.1 m) overlain by a clay layer. Monitoring well RN006543 has its screen from 43.7 to 91.7 m in Tertiary to Quaternary sediments. Groundwater level data will be used in a qualitative comparison with time series of predicted groundwater recharge as a means to test the different PTFs. The surface hydrology of the basin is determined by four ephemeral rivers and creeks (Harrington et al., 2002). These ephemeral rivers and creeks are flooded only after significant rainfall events, e.g. Villeneuve et al. (2015) documented that the Woodforde River flooded on 34 occasions in 16 years (Villeneuve et al., 2015). The predominant soil types are red earthy sands with fine to medium grain sizes (silt loam) covering most of the surface area in the central basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.4196057,-22.2479238],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215937,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Woodforde Rd, Anmatjere NT 0872, Australia","basin_id":5040087600,"main_basin_id":5040087600,"basin_region":"Australasia","basin_area_km2":141735.8,"hydrology_source":"geofabric","site_id":"doc-44efb05cb72da05af0a4acd2-site-6"},{"name":"Allungra Creek","kind":"river","role":"ephemeral drain and floodout area with high recharge rates based on prior studies in the basin","river_name":"Allungra Creek","geocode_query":"Allungra Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"Journal of Hydrology: Regional Studies 40 (2022) 101005 12 Because considerable portions of the arid zone landscape where recharge occurs have patches of bare soil (Cleverly et al., 2013), large spatial heterogeneity in recharge is expected based on the above differences between bare and vegetated surfaces. Such heterogeneities complicate the estimation of regional scale recharge values and require explicit consideration of land cover variability for recharge calculations. Topographic depressions that accumulate runoff will further contribute to recharge heterogeneity. An example of such focused recharge is available from Wood et al. (2017) who, using a groundwater model calibrated to C-14 and hydraulic head data, found the highest recharge rates (between 10 and 15 mm/a) in the inferred flood out area of the Allungra Creek (Ti Tree basin). 3.4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Hanson River","kind":"river","role":"ephemeral river influencing the surface drainage and local recharge","river_name":"Hanson River","geocode_query":"Hanson River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Alluvial deposits such as gravels and coarse sands are found in the main surface drainage lines, Hanson River, Woodforde River and Allungra Creek (Harrington et al., 2002; Magee, 2009). 2.2. Climate The Ti-Tree Basin is located within the arid zone of Central Australia (Koppen ̈ classification: BW Desert Climate). The region is characterised by moderate dry winters (May–October) and hot long summers (November–April) with daily temperatures often exceeding 40 ◦C. An average annual precipitation rate of 320 mm/a (1981–2016 average) and an average number of 30 rain-days per year were measured at the meteorological station Territory Grape Farm Station in the centre of the basin (BOM (Bureau of Meteorology, Australia), 2015, 2016). The actual evapotranspiration rate in the region was estimated by the Australian Bureau of Meteorology (BOM) to be around 300–400 mm/a. Arid regions in Central Australia experience most of the annual rainfall during extreme rainfall events in the summer months.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Exact coordinates of study and monitoring sites not provided, set as null.","Pedotransfer functions were developed mostly outside Australia; inherent uncertainties exist applying them to Australian soils.","Daily rainfall data used may underestimate runoff and infiltration dynamics compared to sub-daily data.","Model does not explicitly incorporate spatial topographic features like floodplain connectivity or depression storage.","Vegetation dynamics and seasonal root growth not fully modelled; static parameters used."],"river_ids":[],"site_ids":["doc-44efb05cb72da05af0a4acd2-site-0","doc-44efb05cb72da05af0a4acd2-site-1","doc-44efb05cb72da05af0a4acd2-site-3","doc-44efb05cb72da05af0a4acd2-site-6"],"anchor":[133.4196057,-22.2479238],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040087600,5040491370],"main_basin_ids":[5040087590,5040087600]},{"id":"doc-5007ed49af5b997d41c177ed","title":"A synthesis of floodplain aquatic ecosystem metabolism and carbon flux using causal criteria analysis","display_title":"Floodplain Aquatic Metabolism and Carbon Flux","doi":"https://doi.org/10.1002/lno.12253","theme":"Flow & ecology","color":"#80edc2","year":2022,"publication_date":"2022-11-01","authors":["Paul McInerney","Darren P. Giling","Ben Wolfenden","Ashmita Sengupta"],"summary":"This study synthesized global literature on the influence of floodplain inundation magnitude, frequency, and duration on aquatic ecosystem metabolism and carbon flux, using causal criteria analysis. It identified clear evidence supporting three causal hypotheses: larger floods increase export of dissolved organic carbon (DOC) to rivers, high turbidity reduces gross primary production (GPP) in floodplain habitats, and nutrient availability positively correlates with GPP. Other hypothesized relationships showed inconsistent or insufficient evidence. The research highlights knowledge gaps, especially regarding effects of inundation frequency and duration, and emphasizes the importance of site-specific context in floodplain management and environmental flow planning.","finding":"Larger floods increase DOC export to river channels mainly via expanded inundation area leading to greater leaching of floodplain litter, supported by 14 of 15 evidence items from multiple studies.","finding_evidence":{"passage_id":"p005-000","page":5,"quote":"concentrations, leading to a reduction of algal production. In contrast, in a European river system Gallardo et al. (2012) reported that increases in floodplain lake nutrients following a large flood led to rates of NEP well above stable conditions. Gallardo et al. (2012) also provided evidence in refute of H2, reporting that the input of organic matter and nutrients caused by the river flood was likely responsible for fueling heterotrophic consumption and increasing respiration rates. The low number of studies and the high variability in patterns suggest that more empirical field studies are required to better understand the relationship between the magnitude of floodplain inundation, nutrients and metabolic responses. Larger floods increase DOC export to the river channel H3 There was sufficient evidence in the literature to support our hypothesis that larger floods increase DOC export to the river channel, with 14 out of 15 evidence items supporting our hypotheses.","verified_in_page":true},"topics":["aquatic ecosystem metabolism","carbon flux","floodplain inundation","dissolved organic carbon","gross primary production","nutrient dynamics","turbidity effects","floodplain ecology"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Study location for multiple floodplain metabolism studies","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Giling , 3,4 Ben Wolfenden,5 Ashmita Sengupta4 1 CSIRO, Land and Water, Thurgoona, New South Wales, Australia 2 Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia 3 Centre for Applied Water Science, Institute for Applied Ecology, Faculty of Science and Technology, University of Canberra, Canberra, Australian Capital Territory, Australia 4 CSIRO, Land and Water, Canberra, Australian Capital Territory, Australia 5 New South Wales Department of Planning, and Environment, Parramatta, New South Wales, Australia Abstract The transformation of solar energy into organic matter by autotrophs (gross primary production [GPP]) and the use of that energy by autotrophs and heterotrophs (ecosystem respiration [ER]) describe the total energy available to support food webs.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales, Australia","kind":"region","role":"Region of study institutions and referenced field sites for floodplain studies","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Giling , 3,4 Ben Wolfenden,5 Ashmita Sengupta4 1 CSIRO, Land and Water, Thurgoona, New South Wales, Australia 2 Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia 3 Centre for Applied Water Science, Institute for Applied Ecology, Faculty of Science and Technology, University of Canberra, Canberra, Australian Capital Territory, Australia 4 CSIRO, Land and Water, Canberra, Australian Capital Territory, Australia 5 New South Wales Department of Planning, and Environment, Parramatta, New South Wales, Australia Abstract The transformation of solar energy into organic matter by autotrophs (gross primary production [GPP]) and the use of that energy by autotrophs and heterotrophs (ecosystem respiration [ER]) describe the total energy available to support food webs.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Australian Capital Territory, Australia","kind":"region","role":"Region of study institutions and referenced floodplain study sites","river_name":null,"geocode_query":"Australian Capital Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Giling , 3,4 Ben Wolfenden,5 Ashmita Sengupta4 1 CSIRO, Land and Water, Thurgoona, New South Wales, Australia 2 Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia 3 Centre for Applied Water Science, Institute for Applied Ecology, Faculty of Science and Technology, University of Canberra, Canberra, Australian Capital Territory, Australia 4 CSIRO, Land and Water, Canberra, Australian Capital Territory, Australia 5 New South Wales Department of Planning, and Environment, Parramatta, New South Wales, Australia Abstract The transformation of solar energy into organic matter by autotrophs (gross primary production [GPP]) and the use of that energy by autotrophs and heterotrophs (ecosystem respiration [ER]) describe the total energy available to support food webs.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North America, USA and Canada","kind":"region","role":"Region of referenced floodplain and wetland studies contributing evidence on nutrient dilution and turbidity effects","river_name":null,"geocode_query":"North America, USA and Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"Hypotheses generated for effects of flood magnitude, duration, and frequency on floodplain aquatic metabolism. McInerney et al. Floodplain aquatic ecosystem metabolism 101 19395590, 2023, 1, Downloaded from https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12253 by Readcube (Labtiva Inc.), Wiley Online Library on [31/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"resolved","coordinates":[-82.390199,37.636105],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canada, KY 41519, USA","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-3","true_coordinates":[-82.3246094,37.6061055],"spread_km":6.7},{"name":"Europe","kind":"region","role":"Region of referenced floodplain lake nutrient study informing flood size effects on production","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"Hypotheses generated for effects of flood magnitude, duration, and frequency on floodplain aquatic metabolism. McInerney et al. Floodplain aquatic ecosystem metabolism 101 19395590, 2023, 1, Downloaded from https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12253 by Readcube (Labtiva Inc.), Wiley Online Library on [31/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Mackenzie Delta, Canada","kind":"region","role":"Site of referenced studies of benthic algal response and floodplain lake productivity demonstrating habitat heterogeneity effects","river_name":null,"geocode_query":"Mackenzie Delta, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Squires, M. M., and L. F. W. Lesack. 2001. Benthic algal response to pulsed versus distributed inputs of sediments and nutrients in a Mackenzie Delta lake. J. North Am. Benthol. Soc. 20: 369–384. doi:10.2307/1468035 Squires, M. M., L. F. W. Lesack, R. E. Hecky, S. J. Guildford, P. Ramlal, and S. N. Higgins. 2009. Primary production and carbon dioxide metabolic balance of a lake-rich arctic river floodplain: Partitioning of phytoplankton, epipelon, macrophyte, and epiphyton production among lakes on the Mackenzie delta. Ecosystems 12: 853–872. Staehr, P. A., J. M. Testa, W. M. Kemp, J. J. Cole, K. SandJensen, and S. V. Smith. 2012. The metabolism of aquatic ecosystems: History, applications, and future challenges. Aquat. Sci. 74: 15–29. Stanley, E. H., M. D. Johnson, and A. K. Ward. 2003. Evaluating the influence of macrophytes on algal and bacterial production in multiple habitats of a freshwater wetland. Limnol. Oceanogr. 48: 1101–1111. Thomas, S., E. E. Gaiser, and F. A. Tobias. 2006.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-126.2825052239068,65.17395377215685],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mackenzie River, Northwest Territories, Canada","basin_id":8040009560,"main_basin_id":8040009560,"basin_region":"North American Arctic","basin_area_km2":198883.4,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-5"},{"name":"Danube River, Austria","kind":"river","role":"River-floodplain system studied for hydrological connectivity and nutrient exchange relevant to floodplain metabolism","river_name":"Danube River","geocode_query":"Danube River, Austria","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-003","page":11,"quote":"Oceanogr. 62: 1288–1306. Greet, J., A. J. Webb, and R. D. Cousens. 2011. The importance of seasonal flow timing for riparian vegetation dynamics: A systematic review using causal criteria analysis. Freshw. Biol 56: 1231–1247. Hall, R. O., Jr., and others. 2015. Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon. Limnol. Oceanogr. 60: 512–526. Higgisson, W., B. Higgisson, M. Powell, P. Driver, and F. Dyer. 2020. Impacts of water resource development on hydrological connectivity of different floodplain habitats in a highly variable system. River Res. Appl. 36: 542–552. Hill, A. B. 1965. The environment and disease: Association or causation? Proc. R. Soc. Med. 58: 295–300. Holland, A., P. J. McInerney, M. E. Shackleton, G. N. Rees, N. R. Bond, and E. Silvester. 2020. Dissolved organic matter and metabolic dynamics in dryland lowland rivers. Spectrochim. Acta A Mol. Biomol. Spectrosc. 229: 117871. Honious, S. A., R. L. Hale, J.","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.988292,45.379462],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid","basin_id":2040548700,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":5905.7,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-6"},{"name":"Yellow River delta wetlands, China","kind":"region","role":"Site of studies on aquatic metabolism responding to hydrologic pulsing and primary productivity","river_name":null,"geocode_query":"Yellow River delta wetlands, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-005","page":11,"quote":"Villapando. 2021. Knowing your limits: Evaluating aquatic metabolism in a subtropical treatment wetland. Hydrobiologia 848: 3969–3986. Junk, W. J., P. B. Bayley, and R. E. Sparks. 1989. The flood pulse concept in river-floodplain systems. Can. Spec. Publ. Fish. Aquat. Sci. 106: 110–127. Kelly, P. T., C. T. Solomon, J. A. Zwart, and S. E. Jones. 2018. A framework for understanding variation in pelagic gross primary production of lake ecosystems. Ecosystems 21: 1364– 1376. McInerney et al. Floodplain aquatic ecosystem metabolism 107 19395590, 2023, 1, Downloaded from https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12253 by Readcube (Labtiva Inc.), Wiley Online Library on [31/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.610556,36.579334],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yellow River, Changqing District, Jinan, China, 251116","basin_id":4040007850,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":77127.5,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-7","true_coordinates":[116.610556,36.519334],"spread_km":6.7},{"name":"Arkansas River valley, USA","kind":"region","role":"Wetland region studied for leaf decomposition and microbial activity influencing metabolism","river_name":null,"geocode_query":"Arkansas River valley, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"floodplains generally, regardless of context specific variables. In contrast, there was insufficient evidence to support our hypothesis that higher turbidity elevates rates of ER (H5), with only two studies providing support. Both of these studies were conducted in the same Californian location where high turbidity was caused by inorganic particulate sediment resuspension resulting from wind-induced turbulence and benthic bioturbation that also elevated organic carbon concentration, causing a shift of the system towards heterotrophy (Maynard et al. 2012; Maynard et al. 2014). We expect the nature of this relationship to be highly context dependent and relate strongly to the relative percentage of organic carbon that contributes to turbidity, for example, in habitats or flow events where elevated turbidity is caused mainly by suspended fine inorganic sediments and clays with low organic carbon content, we expect the relationship to be poor.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-93.370291,35.360105],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arkansas River Valley, Arkansas 72845, USA","basin_id":7040686450,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":414787.8,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-8"},{"name":"Everglades, Florida, USA","kind":"region","role":"Site investigating effects of shading on benthic periphyton productivity in wetlands relevant to macrophyte influence","river_name":null,"geocode_query":"Everglades, Florida, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Effects of shading on calcareous benthic periphyton in a shorthydroperiod oligotrophic wetland (Everglades, FL, USA). Hydrobiologia 569: 209–221. Tockner, K., D. Pennetzdorfer, N. Reiner, F. Schiemer, and J. V. Ward. 1999. Hydrological connectivity, and the exchange of organic matter and nutrients in a dynamic river–floodplain system (Danube, Austria). Freshw. Biol. 41: 521–535. doi:10.1046/j.1365-2427.1999.00399.x Van de Bogert, M. C., S. R. Carpenter, J. J. Cole, and M. L. Pace. 2007. Assessing pelagic and benthic metabolism using free water measurements. Limnol. Oceanogr. Methods 5: 145–155. Vorobyev, S. N., O. S. Pokrovsky, L. G. Kolesnichenko, R. M. Manasypov, L. S. Shirokova, J. Karlsson, and S. N. Kirpotin. 2019. Biogeochemistry of dissolved carbon, major, and trace elements during spring flood periods on the Ob River. Hydrol. Process. 33: 1579–1594. doi:10. 1002/hyp.13424 Wallace, T. A., and D. Furst. 2016.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-81.3850695,25.8582443],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Everglades City, FL, USA","basin_id":7040043170,"main_basin_id":7040043170,"basin_region":"North America and Caribbean","basin_area_km2":259084.0,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-9"},{"name":"California, USA","kind":"region","role":"Location where turbidity effects on ecosystem respiration studied in floodplain wetlands","river_name":null,"geocode_query":"California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"floodplains generally, regardless of context specific variables. In contrast, there was insufficient evidence to support our hypothesis that higher turbidity elevates rates of ER (H5), with only two studies providing support. Both of these studies were conducted in the same Californian location where high turbidity was caused by inorganic particulate sediment resuspension resulting from wind-induced turbulence and benthic bioturbation that also elevated organic carbon concentration, causing a shift of the system towards heterotrophy (Maynard et al. 2012; Maynard et al. 2014). We expect the nature of this relationship to be highly context dependent and relate strongly to the relative percentage of organic carbon that contributes to turbidity, for example, in habitats or flow events where elevated turbidity is caused mainly by suspended fine inorganic sediments and clays with low organic carbon content, we expect the relationship to be poor.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Zambezi River basin, Africa","kind":"region","role":"Region of tropical floodplain system studies on seasonal dynamics of carbon and nutrient flux","river_name":"Zambezi River","geocode_query":"Zambezi River basin, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-004","page":13,"quote":"Seasonal dynamics of carbon and nutrients from two contrasting tropical floodplain systems in the Zambezi River basin. Biogeosciences 12: 7535–7547. doi:10.5194/bg-12-7535-2015 Acknowledgments This research was delivered under the Murray–Darling Basin Plan and was jointly managed by the Murray–Darling Basin Authority and the Commonwealth Scientific and Industrial Research Organisation, through funding from the Australian Government Department of Agriculture, Water and the Environment. The authors acknowledge Matt O’Brien for his contribution to manuscript conceptualization and for his management of the overarching project. Conflict of Interest None declared. Submitted 10 November 2021 Revised 04 October 2022 Accepted 15 October 2022 Associate editor: Robert O. Hall Jr. McInerney et al.","verified_in_page":true},"resolution_status":"resolved","coordinates":[29.523737,-15.041883],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Zambezi River","basin_id":1041431480,"main_basin_id":1040011660,"basin_region":"Africa","basin_area_km2":149538.4,"hydrology_source":"hydrobasins","site_id":"doc-5007ed49af5b997d41c177ed-site-11","true_coordinates":[29.52373671029859,-14.98188331479953],"spread_km":6.7},{"name":"Southern Murray-Darling Basin, Australia","kind":"region","role":"Area studied for hypoxic blackwater events tied to elevated DOC during floods","river_name":null,"geocode_query":"Southern Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-002","page":13,"quote":"Open water metabolism and dissolved organic carbon in response to environmental watering in a lowland river‐floodplain complex. Mar. Freshw. Res. 67: 1346–1361. doi:10.1071/mf15318 Ward, D. P., N. E. Pettit, M. Adame, M. M. Douglas, S. A. Setterfield, and S. E. Bunn. 2016. Seasonal spatial dynamics of floodplain macrophyte and periphyton abundance in the Alligator Rivers region (Kakadu) of northern Australia. Ecohydrology 9: 1675–1686. Webb, J. A., S. J. Nichols, R. H. Norris, M. J. Stewardson, S. R. Wealands, and P. Lea. 2011. Ecological responses to flow alteration: Assessing causal relationships with Eco Evidence. Wetlands 32: 203–213. Weed, D. L. 1997. On the use of causal criteria. Int. J. Epidemiol. 26: 1137–1141. Weilhoefer, C. L., Y. D. Pan, and S. Eppard. 2008. The effects of river floodwaters on floodplain wetland water quality and diatom assemblages. Wetlands 28: 473–486. Wetzel, R., and A. Ward. 1996. Primary production. Blackwell Science. Whitworth, K. L., and D. 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-7","basin_ids":[1041431480,2040548700,4040007850,5040591010,7040043170,7040612640,7040686450,8040009560],"main_basin_ids":[1040011660,2040008490,4040007850,5040073410,7040043170,7040047060,8040009560]},{"id":"doc-5308ffd6623e070dd6a3f007","title":"Development of a national 7-day ensemble streamflow forecasting service for Australia","display_title":"Australia's National 7-Day Streamflow Forecasting","doi":"https://doi.org/10.5194/hess-26-4801-2022","theme":"Water management","color":"#eeabac","year":2022,"publication_date":"2022-09-29","authors":["Hapu Arachchige Prasantha Hapuarachchi","Mohammed Abdul Bari","Aynul Kabir","Mohammad Mahadi Hasan","Fitsum Markos Woldemeskel","Nilantha Gamage","Patrick Daniel Sunter","Xiaoyong Sophie Zhang","David Ewen Robertson","James Clement Bennett","Paul Martinus Feikema"],"summary":"This study details the development and operationalization of a 7-day ensemble streamflow forecasting service across Australia. It uses multi-model numerical weather prediction rainfall ensembles calibrated and fed into a semi-distributed conceptual rainfall-runoff hydrological model with an error correction method. The system benefits various regions by providing probabilistic streamflow forecasts with uncertainty information to aid water resource management and decision-making.","finding":"Calibrating NWP rainfall forecasts with the CHyPP model significantly reduces bias and improves forecast reliability, and the hydrological error modelling with ERRIS further improves streamflow forecast accuracy and skill across most catchments.","finding_evidence":{"passage_id":"p009-005","page":9,"quote":"In the rest of the paper, we use calibrated ACCESS–GE2 and ECMWF, and raw PME merged product called super-ensemble (OES = 401 members), for streamflow forecast evaluation. The streamflow forecast skill of the super-ensemble is present in Sect. 4.3. 4.2 Effect of rainfall calibration Results for the rainfall evaluation across lead times, day 1 to day 7 (daily total), are presented using boxplot diagrams (Figs. 4–6). For each lead time, there are six boxes representing two raw rainfall products, ECMWF and ACCESS– GE2, and the super-ensemble, and their respective calibrated rainfall products. Figure 4 shows the bias (%) of different raw and calibrated rainfall forecast products for different lead times for the 100 catchments. Bias (%) is calculated for the ensemble median. Among the raw rainfall forecast products, the ECMWF forecasts show smaller bias across most catchments. The bias of ACCESS–GE2 for different catchments is found to be more variable compared to ECMWF.","verified_in_page":true},"topics":["ensemble forecasting","hydrological modeling","rainfall calibration","forecast skill","uncertainty quantification","numerical weather prediction","streamflow forecasting","climate variability"],"study_period":"1990–2016","locations":[{"name":"Tully River at Euramo site, Queensland, Australia","kind":"station","role":"Representative catchment for ensemble size sensitivity analysis","river_name":null,"geocode_query":"Tully River at Euramo, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-003","page":9,"quote":"Figure 3 shows the sensitivity of ensemble size on streamflow forecast accuracy (CRPS) with reference to a 1000-member sample for the Tully Catchment (Queensland – QLD). Streamflow forecasts using the ACCESS–GE2 and ECMWF products show similar accuracy for the same sample size (Fig. 3a and b). Generally, the forecast accuracy is proportional to the ensemble size. However, the relative increment of forecast accuracy is inversely proportional to the ensemble size. The overall results indicate that having more than 200 members can preserve greater than 98 % statistical properties of simulated streamflow time series.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9359539,-18.0003665],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652131,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Euramo QLD 4854, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-5308ffd6623e070dd6a3f007-site-0"},{"name":"Australia","kind":"region","role":"Primary national study area and operational domain of forecasting service","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-003","page":7,"quote":"There are 13 drainage divisions (Fig. 2), and mean annual rainfall (Fig. 13) for these divisions varies from 276 to 2816 mm (Table 3) calculated using data for the period from 1990 to 2016. Annual average potential evaporation (PE) is generally higher than annual average rainfall in most areas. Therefore, streamflow generation processes are mainly controlled by water-limited environments (Milly et al., 2005) except for the Tasmania division. The pattern of rainfall–runoff and PE distribution within a year across different drainage divisions vary significantly. In the southern part of Australia, the wet season begins in June–July and ends in December–January, while in the northern part of Australia, the wet season starts in November–December and ends in March–April (Bureau of Meteorology, 2021).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Murray–Darling River basin, Australia","kind":"basin","role":"Region with major drought impact highlighting forecasting requirement","river_name":"Murray–Darling River","geocode_query":"Murray–Darling River basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"These unique geographical features result in the most significant inter-annual variability of streamflow, floods, and droughts compared with other continents (Poff et al., 2006). During 2001–2009 south-eastern Australia experienced its most severe drought since 1901, known as the “Millennium Drought” (http://www.bom.gov.au/climate/drought/ knowledge-centre/previous-droughts.shtml, last access: 16 September 2022). The region had the most extended period of below-median rainfall and, as a result, inflows to major reservoirs were very low (Van Dijk et al., 2013). In particular, inflow to reservoirs located within the Murray–Darling River basin, Australia’s food bowl, was 50 % of the previously recorded minimum. The drought had wide, long-lasting societal, economic, and environmental impacts (Bureau of Meteorology, 2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.550024,-31.078265],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-5308ffd6623e070dd6a3f007-site-2","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Acheron River at Taggerty site, Victoria, Australia","kind":"station","role":"Catchment used to evaluate bootstrapping parameters for forecast skill","river_name":"Acheron River","geocode_query":"Acheron River at Taggerty, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"For the explanation of results, Fig. 10 shows bootstrapped forecast skill for the Acheron River at the Taggerty site for different number of iterations and block sizes. Forecast skill (CRPSS) is sensitive to block size (Fig. 10), and it reduces with lead time for both the weekly-block (wblock) and the monthly-block (m-block) sample sizes. Forecast skill calculated using w-block (Fig. 10a) shows less variation and narrower spread with lead time than when m-block is used (Fig. 10b). For the catchments we tested, the forecast skill is independent of the number of iterations for the w-block size (Fig. 10a). For the m-block size (Fig. 10b), the forecast skill varies with the number of iterations. There is marginal variation in the spread of the skill for iterations 500 and 1000 compared with iterations 100 and 200. This implies that the m-block size captures uncertainty in the forecasts slightly better than the w-block size. Also, the m-block requires fewer computation resources.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.7133936799021,-37.33111832032588],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43664904,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Acheron River, Victoria, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-5308ffd6623e070dd6a3f007-site-3"},{"name":"South Australia, Australia","kind":"region","role":"Region with catchments showing lower model validation and forecast skill","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Overall, 40 % of forecast locations in South Australia and 23 % in Western Australia fail the first acceptance criterion (NSE > 0.6) while it is less than 12 % for other jurisdictions. We note that some forecast locations in Western Australia (WA), Tasmania (TAS), and inland areas of New South Wales (NSW), Queensland (QLD), and Victoria (VIC) show poor NSE. These areas have intermittently flowing rivers due to arid or semi-arid climates (see the aridity index in Table 3). Much of continental Australia to the west of the Great Dividing Range (an area of > 5 million km2 ) where the mean annual rainfall is < 400 mm (Fig. 13) is sparsely populated and characterised by intermittent and ephemeral streamflows. Therefore, the observation network is also sparse and there is not enough benefit to justify the cost for expanding the observation network.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia, Australia","kind":"region","role":"Region with some catchments failing acceptance criteria and intermittent streams","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Overall, 40 % of forecast locations in South Australia and 23 % in Western Australia fail the first acceptance criterion (NSE > 0.6) while it is less than 12 % for other jurisdictions. We note that some forecast locations in Western Australia (WA), Tasmania (TAS), and inland areas of New South Wales (NSW), Queensland (QLD), and Victoria (VIC) show poor NSE. These areas have intermittently flowing rivers due to arid or semi-arid climates (see the aridity index in Table 3). Much of continental Australia to the west of the Great Dividing Range (an area of > 5 million km2 ) where the mean annual rainfall is < 400 mm (Fig. 13) is sparsely populated and characterised by intermittent and ephemeral streamflows. Therefore, the observation network is also sparse and there is not enough benefit to justify the cost for expanding the observation network.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales, Australia","kind":"region","role":"Jurisdiction with catchments exhibiting varied model performance","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Overall, 40 % of forecast locations in South Australia and 23 % in Western Australia fail the first acceptance criterion (NSE > 0.6) while it is less than 12 % for other jurisdictions. We note that some forecast locations in Western Australia (WA), Tasmania (TAS), and inland areas of New South Wales (NSW), Queensland (QLD), and Victoria (VIC) show poor NSE. These areas have intermittently flowing rivers due to arid or semi-arid climates (see the aridity index in Table 3). Much of continental Australia to the west of the Great Dividing Range (an area of > 5 million km2 ) where the mean annual rainfall is < 400 mm (Fig. 13) is sparsely populated and characterised by intermittent and ephemeral streamflows. Therefore, the observation network is also sparse and there is not enough benefit to justify the cost for expanding the observation network.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland, Australia","kind":"region","role":"Jurisdiction with diverse hydrologic regimes included in the forecasting service","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Overall, 40 % of forecast locations in South Australia and 23 % in Western Australia fail the first acceptance criterion (NSE > 0.6) while it is less than 12 % for other jurisdictions. We note that some forecast locations in Western Australia (WA), Tasmania (TAS), and inland areas of New South Wales (NSW), Queensland (QLD), and Victoria (VIC) show poor NSE. These areas have intermittently flowing rivers due to arid or semi-arid climates (see the aridity index in Table 3). Much of continental Australia to the west of the Great Dividing Range (an area of > 5 million km2 ) where the mean annual rainfall is < 400 mm (Fig. 13) is sparsely populated and characterised by intermittent and ephemeral streamflows. Therefore, the observation network is also sparse and there is not enough benefit to justify the cost for expanding the observation network.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Incomplete exact geocoordinates for catchment/rivers and stations mentioned in the paper","Study period for skill evaluation limited to 3 years hindcast (2014–2016) potentially limiting wet event representation","Catchment and forecast locations selected primarily in populated/coastal areas; arid interior less covered due to sparse gauge network","Code and data software not publicly available, limiting independent reproducibility"],"river_ids":["43639559","43639584","43639595","43639596","43639620","43639673","43639676","43639709","43639717","43639752","43639809","43639866","43639874","43639887","43639969","43640003","43640023","43640043","43640046","43640063","43640075","43640088","43640103","43640122","43640129","43640130","43640133","43640140","43640182","43640212","43640221","43640240","43640273","43640341","43640348","43640405","43640407","43640457","43640460","43640477","43640478","43640485","43640503","43640504","43640506","43641578","43641753","43642037","43642106","43642133","43642189","43642392","43642526","43642555","43642565","43642621","43642787","43642800","43642871","43642882","43642906","43643127","43643283","43643326","43643358","43643412","43643472","43643495","43643513","43643524","43643541","43643563","43643602","43643665","43643667","43643792","43643829","43643853","43643944","43643946","43643952","43643953","43643963","43643967","43643993","43643998","43644028","43644042","43644046","43644131","43644172","43644343","43644350","43644354","43644379","43644432","43644472","43644493","43644504","43644505","43644506","43644516","43644565","43644577","43644590","43644641","43644691","43644724","43644736","43644737","43644747","43644757","43644765","43644771","43644775","43644792","43644856","43644867","43644868","43644883","43644884","43644893","43644904","43645005","43645013","43645075","43645077","43645232","43645238","43645240","43645241","43645276","43645283","43645291","43645293","43645303","43645376","43645385","43645400","43645412","43645448","43645459","43645462","43645471","43645475","43645480","43645485","43645499","43645508","43645592","43645614","43645615","43645669","43645677","43645688","43645702","43645706","43645711","43645744","43645765","43645767","43645778","43645780","43645826","43645843","43645844","43645848","43645855","43645876","43645904","43645907","43645911","43645919","43645921","43645951","43645971","43645978","43645989","43645990","43646028","43646041","43646063","43646097","43646107","43646108","43646119","43646151","43646153","43646167","43646184","43646189","43646192","43646194","43646198","43646235","43646236","43646246","43646248","43646249","43646252","43646255","43646263","43646275","43646282","43646283","43646288","43646298","43646311","43646398","43646419","43646421","43646436","43646437","43646439","43646449","43646473","43646476","43646508","43646542","43646549","43646601","43646642","43646645","43646646","43646647","43646657","43646662","43646665","43646670","43646671","43646687","43646696","43646697","43646705","43646742","43646743","43646775","43646802","43646826","43646849","43646858","43646860","43646861","43646889","43646906","43646918","43646929","43646949","43646952","43646960","43646974","43647050","43647051","43647064","43647065","43647099","43647109","43647126","43647129","43647156","43647175","43647176","43647177","43647188","43647224","43647225","43647226","43647239","43647242","43647281","43647299","43647327","43647363","43647368","43647397","43647409","43647428","43647620","43647624","43647645","43647684","43647690","43647700","43647709","43647774","43647775","43647799","43647827","43647831","43647833","43647845","43647851","43647872","43647885","43647954","43647956","43647959","43647977","43647980","43647992","43648013","43648022","43648040","43648047","43648096","43648122","43648150","43648158","43648191","43648214","43648216","43648270","43648274","43648288","43648296","43648331","43648335","43648365","43648384","43648471","43648498","43648505","43648518","43648657","43648678","43648713","43648714","43648751","43648752","43648766","43648850","43648868","43648870","43648932","43648999","43649010","43649078","43649079","43649151","43649171","43649186","43649207","43649248","43649285","43649286","43649300","43649311","43649339","43649412","43649447","43649595","43649648","43649664","43649670","43649671","43649741","43649742","43649815","43649817","43649839","43649860","43649868","43649880","43649885","43649889","43649914","43650016","43650041","43650042","43650121","43650220","43650277","43650299","43650490","43650556","43650588","43650757","43650766","43650819","43650876","43650955","43651601","43651765","43651767","43652015","43652017","43652033","43652496","43652497","43652499","43652501","43652503","43652505","43652507","43652511","43652529","43652586","43652587","43652593","43652594","43652618","43652627","43652682","43652724","43652746","43652753","43652799","43652803","43652806","43652911","43653050","43653055","43653059","43653099","43653214","43653303","43653685","43653696","43653930","43653931","43653944","43653945","43654281","43654321","43654336","43654337","43654451","43654453","43654461","43654533","43654639","43654642","43654757","43654758","43655039","43655158","43655171","43655182","43655183","43655343","43655669","43655670","43655671","43655703","43655830","43655831","43656138","43656269","43656270","43656428","43656429","4365643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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040067860,5040591010,5040591050],"main_basin_ids":[5040067860,5040073410]},{"id":"doc-56f175a6f2bf944d00584e55","title":"The influence of salinity on the chronic toxicity of shale gas flowback wastewater to freshwater organisms","display_title":"Impact of Shale Gas Wastewater on Freshwater Biota","doi":"https://doi.org/10.1016/j.jhazmat.2022.128219","theme":"Other science","color":"#d1b5e7","year":2022,"publication_date":"2022-01-05","authors":["Lisa A. Golding","Anupama Kumar","Merrin S. Adams","Monique T. Binet","Adrienne Gregg","Josh J. King","Kitty McKnight","Bhanu Nidumolu","David A. Spadaro","Jason K. Kirby"],"summary":"This study assessed the chronic toxicity of shale gas flowback wastewater (FWW) from two wells in the Beetaloo Sub-basin, Northern Territory, Australia, on eight freshwater organisms. Using direct toxicity assessment and species sensitivity distributions, the study determined that FWW was chronically toxic, with salinity contributing partially but not solely to toxicity. Site-specific \"safe\" dilutions were derived to inform risk assessments relevant to potential accidental spills or seepages.","finding":"Flowback wastewater from the Tanumbirini-1 and Kyalla 117 N2 shale gas wells in the Beetaloo Sub-basin exhibited chronic toxicity to multiple freshwater species, with salinity contributing only 16% and 55% respectively to overall toxicity, indicating other chemical stressors played significant roles.","finding_evidence":{"passage_id":"p009-004","page":9,"quote":"Influence of salinity on chronic toxicity As well as being the most toxic sample to freshwater biota, OKPW was also the most saline sample (195 mS/cm, 238,000 mg/L TDS) and salinity contributed to the observed toxicity for both FWWs in a concentration dependent manner (Table 6, Figs. 1 and 2). The toxic units for the eight species exposed to the FWWs and the matching salinity controls were used to calculate the contribution of salinity to toxicity for each FWW. The sum of the toxic units calculated for eight species in each sample and therefore the rank of overall sample toxicity from highest to lowest was 1051, 579, 242 and 39 for OKPW, OKPW-SC, STPW and STPW-SC, respectively. Based on the sum of the toxic units, OKPW and STPW were 1.8 and 6.2 times more toxic than their respective salinity controls and salinity contributed 55% and 16% to chronic toxicity in OKPW and STPW, respectively at the 50% effect level.","verified_in_page":true},"topics":["ecotoxicology","species sensitivity distribution","chemical mixtures","hydraulic fracturing","wastewater toxicity","freshwater biota","salinity effects","environmental risk assessment"],"study_period":"2020-2022","locations":[{"name":"Beetaloo Sub-basin","kind":"region","role":"study region for shale gas well water toxicity","river_name":null,"geocode_query":"Beetaloo Sub-basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-009","page":2,"quote":"Collection of shale gas FW and FWWs Samples of shale gas FWW were collected from two hydraulically fractured shale gas well storage tanks in the Beetaloo Sub-basin which is part of the larger McArthur Basin in the Northern Territory (NT), Australia. The Santos QNT Pty Ltd Tanumbirini-1 (16.399050326◦ S, 134.703822100◦ E) and Origin Kyalla 117 N2 (16.841390926◦ S, 133.650045449◦ E) wells are located 123 km apart and target the Velkerri and Kyalla formations, respectively. The Kyalla formation is separated from the underlying Velkerri formation by the Moroak Sandstone (Santos, 2019; Origin, 2019). Both target formations have similar lithology of interbedded claystone and siltstone with minor L.A. Golding et al.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.738966,-17.235601],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215708,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Beetaloo, Pamayu NT 0862, Australia","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-56f175a6f2bf944d00584e55-site-0","true_coordinates":[133.798706,-17.21706],"spread_km":6.7},{"name":"Tanumbirini-1 well","kind":"facility","role":"source of shale gas flowback wastewater studied","river_name":null,"geocode_query":"Tanumbirini-1, Beetaloo Sub-basin, Northern Territory, Australia","latitude":-16.399050326,"longitude":134.7038221,"evidence":{"passage_id":"p002-009","page":2,"quote":"Collection of shale gas FW and FWWs Samples of shale gas FWW were collected from two hydraulically fractured shale gas well storage tanks in the Beetaloo Sub-basin which is part of the larger McArthur Basin in the Northern Territory (NT), Australia. The Santos QNT Pty Ltd Tanumbirini-1 (16.399050326◦ S, 134.703822100◦ E) and Origin Kyalla 117 N2 (16.841390926◦ S, 133.650045449◦ E) wells are located 123 km apart and target the Velkerri and Kyalla formations, respectively. The Kyalla formation is separated from the underlying Velkerri formation by the Moroak Sandstone (Santos, 2019; Origin, 2019). Both target formations have similar lithology of interbedded claystone and siltstone with minor L.A. Golding et al.","verified_in_page":true},"resolution_status":"resolved","coordinates":[134.7038221,-16.399050326],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":120769839,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064140,"main_basin_id":5040064140,"basin_region":"Australasia","basin_area_km2":178643.3,"hydrology_source":"geofabric","site_id":"doc-56f175a6f2bf944d00584e55-site-1"},{"name":"Kyalla 117 N2 well","kind":"facility","role":"source of shale gas flowback wastewater studied","river_name":null,"geocode_query":"Kyalla 117 N2, Beetaloo Sub-basin, Northern Territory, Australia","latitude":-16.841390926,"longitude":133.650045449,"evidence":{"passage_id":"p002-009","page":2,"quote":"Collection of shale gas FW and FWWs Samples of shale gas FWW were collected from two hydraulically fractured shale gas well storage tanks in the Beetaloo Sub-basin which is part of the larger McArthur Basin in the Northern Territory (NT), Australia. The Santos QNT Pty Ltd Tanumbirini-1 (16.399050326◦ S, 134.703822100◦ E) and Origin Kyalla 117 N2 (16.841390926◦ S, 133.650045449◦ E) wells are located 123 km apart and target the Velkerri and Kyalla formations, respectively. The Kyalla formation is separated from the underlying Velkerri formation by the Moroak Sandstone (Santos, 2019; Origin, 2019). Both target formations have similar lithology of interbedded claystone and siltstone with minor L.A. Golding et al.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.650045449,-16.841390926],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102215866,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-56f175a6f2bf944d00584e55-site-2"}],"limitations":["Variability in sample chemistry during storage and testing may affect toxicity estimates","Limited number of freshwater species tested, potentially missing sensitive taxa","Findings specific to samples from two wells; may not generalize to all shale gas wastewater","Uncertainties due to unknown/unmeasured chemical components in wastewater mixture"],"river_ids":[],"site_ids":["doc-56f175a6f2bf944d00584e55-site-0","doc-56f175a6f2bf944d00584e55-site-1","doc-56f175a6f2bf944d00584e55-site-2"],"anchor":[134.7038221,-16.399050326],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040064140,5040087780],"main_basin_ids":[5040064140,5040087780]},{"id":"doc-60f14759e483e854aa569f12","title":"Stochastic Assessment of Groundwater Contamination Risks From Onshore Gas Development Using Computationally Efficient Analytical and Numerical Transport Models","display_title":"Groundwater Contamination Risk from Gas Wells","doi":"https://doi.org/10.3389/frwa.2021.799738","theme":"Groundwater","color":"#a8b9ff","year":2022,"publication_date":"2022-01-11","authors":["David Rassam","J. Sreekanth","Dirk Mallants","Dennis Gonzalez","Rebecca Doble","Trevor Pickett"],"summary":"This study developed and applied a stochastic numerical-analytical hybrid model to assess risks of groundwater contamination from onshore gas development at the Dombey 1 gas well site in the Otway Basin, South Australia. The methodology couples HYDRUS-1D for solute transport through the unsaturated soil zone to an analytical advective-dispersive model for solute transport in the aquifer, using groundwater flow paths and velocities derived from a regional MODFLOW-MODPATH particle tracking analysis. The stochastic approach quantifies uncertainties in hydraulic and chemical parameters and enables efficient risk screening over the region, identifying flow paths intercepting groundwater-dependent receptors and predicting concentrations at receptors.","finding":"The most influential parameters controlling solute delivery to the aquifer were the soil chemical degradation constant and the hydraulic conductivity of a low-permeability middle soil horizon that throttled solute flux. Only around 18% of simulated groundwater flow paths intercepted environmental receptors within 1 km of the contamination source, and for 87% of those paths, peak concentrations were less than 1% of the original source concentration, showing significant natural attenuation.","finding_evidence":{"passage_id":"p008-004","page":8,"quote":"This was confirmed by monitoring the pressure head at the interface between horizons one and two, which indicated a perched water table due to the low permeability of the second horizon (results not shown here). There is a strong positive correlation between the solute flux and KS2: a high KS2 value means the leaking fluid will reach the groundwater table much sooner than for a small KS2 value. Faster transport also means less degradation will occur in the unsaturated zone, therefore delivers more solute mass to the groundwater. Note that the solid yellow line in Figure 6B represents a special case without solute degradation, which clearly shows the sole influence of the conductivity. Figure 6D shows the influence of the first-order degradation constant (based on half-life) on the peak solute flux.","verified_in_page":true},"topics":["groundwater flow","solute transport","stochastic modelling","particle tracking","vadose zone","natural attenuation","aquifer","risk assessment"],"study_period":null,"locations":[{"name":"Dombey 1 gas well site","kind":"facility","role":"Contamination source","river_name":null,"geocode_query":"Dombey 1 gas well site, Otway Basin, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Gas wells have been recently built and/or proposed for this region, including Nagwarry 1, Haselgrove 4, and Dombey 1 wells; these three well locations are cross marked in Figure 3A with only the latter considered in this study. Flow and Transport Parameters The 5 m soil profile (the unsaturated zone) for the Dombey 1 gas well site was represented by a three-layer model with each soil horizon having unique hydraulic properties (Table 1). Soil hydraulic properties including water retention parameters and saturated hydraulic conductivity were derived from the HYDRUS-1D soil catalogue (Carsel and Parrish, 1988). For solute transport modelling, advection-dispersion and reaction parameters used are listed in Table 1. Reference or best estimate parameters for dispersivity λ is one tenth of the travel distance (0.1 × L), which is rule of thumb often applied if no local data on dispersivity is available. The first-order degradation constant μ is based on a chemical half-life of 80 days.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Otway Basin, South Australia, Australia","kind":"region","role":"Study area of onshore gas development and groundwater risk assessment","river_name":null,"geocode_query":"Otway Basin, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Rassam et al. Stochastic Modelling and Risk Assessment 4. Random parameter values were selected for CXTFIT, they include the retardation factor (R), the groundwater dispersion coefficient (D), and first-order degradation coefficient (μ) for the saturated zone. Execution of CXTFIT defines solute breakthrough at receptors (see BTC-2 in Figure 2); 5. The Python code analyses BTC-2 and records the peak concentration and time at which it occurs, marked C(peak) and T(peak) in Figure 2. 6. The steps 1–5 are repeated 1,000 times. Case Study Area Climate and Hydrogeology The methodology was implemented in an area with existing conventional gas exploitation in the South East South Australian Otway Basin, near Penola (Figure 3A). New developments are underway including the drilling of several conventional gas wells to depths between 3000 and 4500 m to secure gas production for the area (Beach Energy Limited, 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Penola, South Australia, Australia","kind":"settlement","role":"Town near study area used to describe location","river_name":null,"geocode_query":"Penola, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The case study area extends 36 km in an east-western direction and 42 km in a north-southern direction with the towns of Penola in the north and Kalangadoo in the south. The region is characterised by a limestone geology and relatively flat topography with sandy soils (Jacobs, 2016). It has a Mediterranean climate with warm dry summers and cold wet winters, also known as “dry-summer subtropical” Csb based on the Köppen Climate Classification climate classification (South East NRM Board, 2010). Rainfall in the region ranges between 480 and 780 mm/y and potential evapotranspiration ranges from 890 to 1160 mm/year (BOM, 2019). Predominant land uses include dryland grazing, irrigated pasture and viticulture, softwood and hardwood forestry and native vegetation (Jacobs, 2016). Two major aquifers underlie this region, the unconfined Tertiary Limestone Aquifer (TLA) and the confined Tertiary Confined Sands Aquifer (TCSA).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.8365882,-37.3754521],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556959,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Penola SA 5277, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-2"},{"name":"Kalangadoo, South Australia, Australia","kind":"settlement","role":"Town near study area used to describe location","river_name":null,"geocode_query":"Kalangadoo, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The case study area extends 36 km in an east-western direction and 42 km in a north-southern direction with the towns of Penola in the north and Kalangadoo in the south. The region is characterised by a limestone geology and relatively flat topography with sandy soils (Jacobs, 2016). It has a Mediterranean climate with warm dry summers and cold wet winters, also known as “dry-summer subtropical” Csb based on the Köppen Climate Classification climate classification (South East NRM Board, 2010). Rainfall in the region ranges between 480 and 780 mm/y and potential evapotranspiration ranges from 890 to 1160 mm/year (BOM, 2019). Predominant land uses include dryland grazing, irrigated pasture and viticulture, softwood and hardwood forestry and native vegetation (Jacobs, 2016). Two major aquifers underlie this region, the unconfined Tertiary Limestone Aquifer (TLA) and the confined Tertiary Confined Sands Aquifer (TCSA).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.6972249,-37.5655161],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556936,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kalangadoo SA 5278, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-3"},{"name":"Tertiary Limestone Aquifer, South East South Australia, Australia","kind":"aquifer","role":"Primary unconfined aquifer in study area impacted by contamination risks","river_name":null,"geocode_query":"Tertiary Limestone Aquifer, South East South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Regional groundwater flows from east to west following the topography (see head contours in the TLA, Figure 3A). Karstic limestone geology characterises the shallow TLA and is extensively used for irrigated agriculture, stock and domestic and other uses (Jacobs, 2016). Depth to groundwater ranges from <5 m in the south-west plains, to more than 20 m in the north east highlands (Doble et al., 2017). As a result, large parts of the area have poor natural drainage features and have historically been waterlogged. A vast network of agricultural drains was constructed between 1949 and 1972 to artificially lower the groundwater level to facilitate agriculture. A large number of lakes and wetlands are present in this region, many of which are fed by groundwater inflows. Petroleum and conventional gas wells have been developed in the region around Penola since 1987 with the commercialisation of the Katnook and then the Ladbroke Grove fields.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.348184,-36.783562],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone Coast, SA, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-4","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7},{"name":"Tertiary Confined Sands Aquifer, South East South Australia, Australia","kind":"aquifer","role":"Confined aquifer underlying study area considered in groundwater flow modelling","river_name":null,"geocode_query":"Tertiary Confined Sands Aquifer, South East South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Regional groundwater flows from east to west following the topography (see head contours in the TLA, Figure 3A). Karstic limestone geology characterises the shallow TLA and is extensively used for irrigated agriculture, stock and domestic and other uses (Jacobs, 2016). Depth to groundwater ranges from <5 m in the south-west plains, to more than 20 m in the north east highlands (Doble et al., 2017). As a result, large parts of the area have poor natural drainage features and have historically been waterlogged. A vast network of agricultural drains was constructed between 1949 and 1972 to artificially lower the groundwater level to facilitate agriculture. A large number of lakes and wetlands are present in this region, many of which are fed by groundwater inflows. Petroleum and conventional gas wells have been developed in the region around Penola since 1987 with the commercialisation of the Katnook and then the Ladbroke Grove fields.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.413011,-36.753562],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone Coast, SA, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-5","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7},{"name":"South East region, South Australia, Australia","kind":"region","role":"Boundary and parameter reference for groundwater modelling","river_name":null,"geocode_query":"South East region, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"Flow and Particle Track Modelling A regional MODFLOW groundwater flow and MODPATH particle tracking model (Harbaugh, 2005; Pollock, 2012) was developed to identify the likely flow paths between the development area and groundwater-dependent receptors across the study area. Previous groundwater models of the study area include the Wattle Range groundwater model (Wood, 2017), and the South East regional water balance model that was developed in 2014 (Morgan et al., 2015). The former model was developed to model forestry impacts in the Gambier Limestone aquifer and does not incorporate the Tertiary Confined Aquifer. The latter is a regional water balance model that covers an area of 42,000 km2 and includes the study area at the coarse scale of 1 × 1 km grid cells. In this study, a child model was developed with a finer spatial resolution of 250 × 250 m based on the regional water balance model and covering a smaller area that encompasses the gas well-considered in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.020846,-35.054099],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677591,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"S Eastern Fwy, South Australia, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-6","true_coordinates":[138.9573476,-35.0840992],"spread_km":6.7},{"name":"Nagwarry 1 well site, South Australia, Australia","kind":"facility","role":"Nearby gas well site mentioned in context of region","river_name":null,"geocode_query":"Nagwarry 1 gas well site, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Gas wells have been recently built and/or proposed for this region, including Nagwarry 1, Haselgrove 4, and Dombey 1 wells; these three well locations are cross marked in Figure 3A with only the latter considered in this study. Flow and Transport Parameters The 5 m soil profile (the unsaturated zone) for the Dombey 1 gas well site was represented by a three-layer model with each soil horizon having unique hydraulic properties (Table 1). Soil hydraulic properties including water retention parameters and saturated hydraulic conductivity were derived from the HYDRUS-1D soil catalogue (Carsel and Parrish, 1988). For solute transport modelling, advection-dispersion and reaction parameters used are listed in Table 1. Reference or best estimate parameters for dispersivity λ is one tenth of the travel distance (0.1 × L), which is rule of thumb often applied if no local data on dispersivity is available. The first-order degradation constant μ is based on a chemical half-life of 80 days.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Haselgrove 4 well site, South Australia, Australia","kind":"facility","role":"Nearby gas well site mentioned in context of region","river_name":null,"geocode_query":"Haselgrove 4 gas well site, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Gas wells have been recently built and/or proposed for this region, including Nagwarry 1, Haselgrove 4, and Dombey 1 wells; these three well locations are cross marked in Figure 3A with only the latter considered in this study. Flow and Transport Parameters The 5 m soil profile (the unsaturated zone) for the Dombey 1 gas well site was represented by a three-layer model with each soil horizon having unique hydraulic properties (Table 1). Soil hydraulic properties including water retention parameters and saturated hydraulic conductivity were derived from the HYDRUS-1D soil catalogue (Carsel and Parrish, 1988). For solute transport modelling, advection-dispersion and reaction parameters used are listed in Table 1. Reference or best estimate parameters for dispersivity λ is one tenth of the travel distance (0.1 × L), which is rule of thumb often applied if no local data on dispersivity is available. The first-order degradation constant μ is based on a chemical half-life of 80 days.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Wattle Range groundwater model area, South East South Australia, Australia","kind":"region","role":"Reference groundwater model area informing child model development","river_name":null,"geocode_query":"Wattle Range groundwater model area, South East South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"Flow and Particle Track Modelling A regional MODFLOW groundwater flow and MODPATH particle tracking model (Harbaugh, 2005; Pollock, 2012) was developed to identify the likely flow paths between the development area and groundwater-dependent receptors across the study area. Previous groundwater models of the study area include the Wattle Range groundwater model (Wood, 2017), and the South East regional water balance model that was developed in 2014 (Morgan et al., 2015). The former model was developed to model forestry impacts in the Gambier Limestone aquifer and does not incorporate the Tertiary Confined Aquifer. The latter is a regional water balance model that covers an area of 42,000 km2 and includes the study area at the coarse scale of 1 × 1 km grid cells. In this study, a child model was developed with a finer spatial resolution of 250 × 250 m based on the regional water balance model and covering a smaller area that encompasses the gas well-considered in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.5062666,-37.4326775],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556936,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wattle Range SA 5280, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-9"},{"name":"South East regional water balance model domain, South East South Australia, Australia","kind":"region","role":"Reference regional water balance model informing parameterisation of child groundwater model","river_name":null,"geocode_query":"South East regional water balance model domain, South East South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-005","page":6,"quote":"Flow and Particle Track Modelling A regional MODFLOW groundwater flow and MODPATH particle tracking model (Harbaugh, 2005; Pollock, 2012) was developed to identify the likely flow paths between the development area and groundwater-dependent receptors across the study area. Previous groundwater models of the study area include the Wattle Range groundwater model (Wood, 2017), and the South East regional water balance model that was developed in 2014 (Morgan et al., 2015). The former model was developed to model forestry impacts in the Gambier Limestone aquifer and does not incorporate the Tertiary Confined Aquifer. The latter is a regional water balance model that covers an area of 42,000 km2 and includes the study area at the coarse scale of 1 × 1 km grid cells. In this study, a child model was developed with a finer spatial resolution of 250 × 250 m based on the regional water balance model and covering a smaller area that encompasses the gas well-considered in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.413011,-36.693562],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone Coast, SA, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-60f14759e483e854aa569f12-site-10","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7}],"limitations":["Coordinates not provided for locations mentioned, set as null","Hydraulic and chemical parameters are uncertain and represented by ranges in stochastic approach","One-dimensional modelling for transport approximates the 3D processes and neglects transverse dispersion, thus results are conservative estimates","Child groundwater model covers limited regional scale and is based on previous regional models, possibly limiting finer local heterogeneity representation"],"river_ids":[],"site_ids":["doc-60f14759e483e854aa569f12-site-2","doc-60f14759e483e854aa569f12-site-3","doc-60f14759e483e854aa569f12-site-4","doc-60f14759e483e854aa569f12-site-5","doc-60f14759e483e854aa569f12-site-6","doc-60f14759e483e854aa569f12-site-9","doc-60f14759e483e854aa569f12-site-10"],"anchor":[140.348184,-36.783562],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040070710,5040073420],"main_basin_ids":[5040070710,5040073420]},{"id":"doc-64530ad58de460a6a51a2396","title":"Assessing the costs of Managed Aquifer Recharge options to support agricultural development","display_title":"Costs of Managed Aquifer Recharge in Northern Australia","doi":"https://doi.org/10.1016/j.agwat.2021.107437","theme":"Water management","color":"#eeabac","year":2022,"publication_date":"2022-01-06","authors":["Joanne Vanderzalm","Declan Page","Peter Dillon","Dennis Gonzalez","Cuan Petheram"],"summary":"This study assessed the levelized costs of ten potential Managed Aquifer Recharge (MAR) schemes across three regions of northern Australia: Fitzroy catchment (WA), Darwin catchments (NT), and Mitchell catchment (QLD). It estimated capital and operating costs for different MAR types including infiltration basins, recharge weirs, aquifer storage and recovery (ASR), aquifer storage transfer and recovery (ASTR), recharge release, and seawater intrusion barriers. Costs ranged from US$0.04 to $0.36/m3 with infiltration schemes generally less costly than well-injection types. The study highlights MAR's role in conjunctive use with surface water to improve agricultural irrigation water security in wet-dry tropical climates with seasonal rainfall variability.","finding":"The type of MAR scheme has the largest influence on levelized cost, with recharge release being lowest cost and seawater intrusion barriers the highest; infiltration schemes are generally less costly than well-injection types, and increased scale reduces cost notably.","finding_evidence":{"passage_id":"p009-005","page":9,"quote":"However with good management, losses even in highly saline groundwater can be minimised (Pyne, 2005; Zuurbier, 2016). Despite these limitations, it is evident that MAR is useful for agriculture, maintaining and improving groundwater systems and increasing water security. 5. Conclusions This study is the first reported assessment of MAR scheme costs based on common key components that can be identified in all types of MAR. A range of infiltration and well-injection types of MAR across northern Australia are compared. Total estimated capital costs for ten potential MAR schemes of 0.6–5 Mm3 /y capacity using six different types of MAR ranged from approximately US$200,000 to $3600,000, while estimated operating costs varied between $20,000 and 260,000/y and estimated levelized cost ranged from $0.04 to $0.36/m3 .","verified_in_page":true},"topics":["managed aquifer recharge","economic assessment","groundwater storage","water supply economics","groundwater quality","agricultural irrigation","water resource management","conjunctive use"],"study_period":null,"locations":[{"name":"Fitzroy catchment","kind":"basin","role":"study region where two MAR schemes were assessed including infiltration basin and seawater intrusion barrier","river_name":null,"geocode_query":"Fitzroy catchment, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Fitzroy catchment, Western Australia The Fitzroy catchment occupies an area of 94,000 km2 , and the main land use is pastoralism (95%), characterised by large grazing leases with cattle feeding on native pastures, shrubs, and introduced forages and legumes. Nature conservation and Indigenous Protected Areas cover the remaining area. Population is sparse, and the two main population centres are Derby (population 3261, ABS, 2013a) and Fitzroy Crossing (population 1144, ABS, 2013b). The Fitzroy catchment has a climate characterised by a highly distinctive wet and dry season. For the 125-year historical period (1 September 1890–31 August 2015) the mean annual rainfall at Fitzroy Crossing was 552 mm, with approximately 93% of annual rain falling during the wet-season months (1 November to 30 April) (Charles et al., 2016).","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.568887,-18.254162],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102209614,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-0","true_coordinates":[125.568887,-18.194162],"spread_km":6.7},{"name":"Darwin catchments","kind":"basin","role":"study region including Finniss, Adelaide, Mary and Wildman river basins where five MAR schemes including ASTR, ASR, recharge release and recharge weirs were assessed","river_name":null,"geocode_query":"Darwin catchments, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"While recovery is not intended as the MAR scheme is creating a hydraulic barrier, the levelized cost is an indicative estimate based on the potential to recover 1 Mm3 /y. It is possible this resource could contribute to Derby’s drinking water supply and in this case post-treatment and recovery would be as per the current arrangement for the town water supply (i.e. sunk costs). 2.1.2. Darwin catchments, Northern Territory The Darwin catchments comprise the Finniss, Adelaide, Mary, and Wildman river basins which collectively encompass an area of about 30,000 km2 . The city of Darwin is located within the Finniss River Basin and is the capital of the Northern Territory. The greater Darwin region has a population of 140,368, while the total NT population is 245,079 (ABS, 2014). The main land use in the study area is conservation (53%), followed by grazing (31%), farming (7%) and urban or peri-urban (2%).","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.798005,-12.424442],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-1","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Mitchell catchment","kind":"basin","role":"study region where three MAR schemes including recharge weir, recharge release, and infiltration basin were assessed","river_name":null,"geocode_query":"Mitchell catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Surface water from the Darwin River Dam would be released (using an existing outlet on the dam) into the Darwin River to infiltrate through the river bed in non-perennial reaches downstream of the dam, to enhance natural recharge to the dolostone aquifer, for domestic and irrigation use. The intended release represents less than 3% of the dam’s sustainable annual yield of 36.7 Mm3 /y (storage capacity 350 Mm3 ). No pre- or post- treatment would be required, and the recovery of water would occur through existing groundwater extraction wells. 5. Recharge weir scheme: This scheme would comprise a recharge weir in fluvial deposits overlying a fractured rock aquifer, and six recovery bores, each approximately 40 m deep. A 6 m high weir on Snake Creek (<10 m wide) was recommended and costed previously (Dames and Moore, 1992). It is assumed that post-treatment for drinking water supply would occur through the existing water treatment plant. 2.1.3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.9762859,-26.4845732],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43676862,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-2"},{"name":"Derby","kind":"settlement","role":"urban center in Fitzroy catchment supplied by groundwater and threatened by seawater intrusion barrier MAR scheme","river_name":null,"geocode_query":"Derby, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"In this catchment, groundwater within a sub-artesian regional scale groundwater system is abundant and represents the most prospective initial means of developing water resources of the catchment. Although there are a number of topographically and hydrologically suitable locations within the catchment for large instream dams, their construction would be highly contentious due to their potential to inundate culturally and ecologically sensitive areas (Petheram et al., 2018) and alter water dependent ecosystems downstream (Pollino et al., 2018). Similarly, instream types of MAR (e.g. recharge weirs) were not considered in this region. Groundwater is used for town water supply in Derby, and the quality of this resource is at risk to seawater intrusion (Morgan and Werner, 2015). Approximately 7% of the Fitzroy catchment was assessed to have promise for MAR (Vanderzalm et al., 2018) and based on the hydrogeology of the catchment two potential MAR schemes were examined: 1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[123.6401839,-17.309288],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102209137,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Derby WA 6728, Australia","basin_id":5040059450,"main_basin_id":5040059450,"basin_region":"Australasia","basin_area_km2":199214.0,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-3"},{"name":"Fitzroy Crossing","kind":"settlement","role":"population center in Fitzroy catchment near proposed infiltration basin MAR scheme","river_name":null,"geocode_query":"Fitzroy Crossing, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Fitzroy catchment, Western Australia The Fitzroy catchment occupies an area of 94,000 km2 , and the main land use is pastoralism (95%), characterised by large grazing leases with cattle feeding on native pastures, shrubs, and introduced forages and legumes. Nature conservation and Indigenous Protected Areas cover the remaining area. Population is sparse, and the two main population centres are Derby (population 3261, ABS, 2013a) and Fitzroy Crossing (population 1144, ABS, 2013b). The Fitzroy catchment has a climate characterised by a highly distinctive wet and dry season. For the 125-year historical period (1 September 1890–31 August 2015) the mean annual rainfall at Fitzroy Crossing was 552 mm, with approximately 93% of annual rain falling during the wet-season months (1 November to 30 April) (Charles et al., 2016).","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.5670892,-18.19797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102209614,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Fitzroy Crossing WA 6765, Australia","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-4"},{"name":"Howard East","kind":"field_site","role":"location in Darwin catchments for ASTR and ASR MAR schemes in Koolpinyah Dolostone aquifer","river_name":null,"geocode_query":"Howard East, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Agricultural Water Management 263 (2022) 107437 5 2. Aquifer storage and recovery (ASR) large scheme: This scheme would comprise 16 injection and recovery wells (ASR refers to the use of the same well for injection and recovery), each approximately 40 m in depth. As per the ASTR scheme the purpose of the injection of available surface water is to augment the groundwater resource in the Koolpinyah Dolostone aquifer. Furthermore it allows the current extraction for urban supply to be relocated as the proposed ASR wells are located to the north of the current groundwater use where the aquifer is thought to be confined (Knapton, 2010). This scheme has a larger capacity than the others and assumes a higher injection rate under confined aquifer conditions. Pre-treatment (filtration) would be required to prevent clogging of the ASR wells. Agricultural and urban end uses are possible.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Palmerston","kind":"settlement","role":"satellite city of Darwin with ASR MAR scheme using urban stormwater recharge","river_name":null,"geocode_query":"Palmerston, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"For urban, supply post-treatment would be required and it is assumed that this would be provided by the existing water treatment plant for Darwin. 3. Aquifer storage and recovery (ASR) small scheme: This scheme comprises ten injection and recovery wells, each approximately 40 m deep. Like the ASTR and large ASR schemes outlined above surface water from urban areas would be injected into the dolostone aquifer beneath Palmerston, a satellite city of Darwin to augment the existing groundwater resource. Pre-treatment (filtration) would be required to prevent clogging of the ASR wells. Post-treatment would not be required for non-potable urban use. 4. Recharge release scheme: This scheme would utilise the existing Darwin River Dam and no additional infrastructure would be required.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.9845298,-12.4794449],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213314,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Palmerston City NT 0830, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-6"},{"name":"Darwin River Dam","kind":"facility","role":"existing dam providing surface water for recharge release MAR scheme in Darwin catchments","river_name":"Darwin River","geocode_query":"Darwin River Dam, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Surface water from the Darwin River Dam would be released (using an existing outlet on the dam) into the Darwin River to infiltrate through the river bed in non-perennial reaches downstream of the dam, to enhance natural recharge to the dolostone aquifer, for domestic and irrigation use. The intended release represents less than 3% of the dam’s sustainable annual yield of 36.7 Mm3 /y (storage capacity 350 Mm3 ). No pre- or post- treatment would be required, and the recovery of water would occur through existing groundwater extraction wells. 5. Recharge weir scheme: This scheme would comprise a recharge weir in fluvial deposits overlying a fractured rock aquifer, and six recovery bores, each approximately 40 m deep. A 6 m high weir on Snake Creek (<10 m wide) was recommended and costed previously (Dames and Moore, 1992). It is assumed that post-treatment for drinking water supply would occur through the existing water treatment plant. 2.1.3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[131.1389019,-12.8770313],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213644,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin River Dam NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-7"},{"name":"Snake Creek","kind":"river","role":"site of recharge weir MAR scheme in Darwin catchments","river_name":"Snake Creek","geocode_query":"Snake Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Surface water from the Darwin River Dam would be released (using an existing outlet on the dam) into the Darwin River to infiltrate through the river bed in non-perennial reaches downstream of the dam, to enhance natural recharge to the dolostone aquifer, for domestic and irrigation use. The intended release represents less than 3% of the dam’s sustainable annual yield of 36.7 Mm3 /y (storage capacity 350 Mm3 ). No pre- or post- treatment would be required, and the recovery of water would occur through existing groundwater extraction wells. 5. Recharge weir scheme: This scheme would comprise a recharge weir in fluvial deposits overlying a fractured rock aquifer, and six recovery bores, each approximately 40 m deep. A 6 m high weir on Snake Creek (<10 m wide) was recommended and costed previously (Dames and Moore, 1992). It is assumed that post-treatment for drinking water supply would occur through the existing water treatment plant. 2.1.3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-111.135732,43.300605],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":7040388740,"main_basin_id":7040014930,"basin_region":"North America and Caribbean","basin_area_km2":191669.7,"hydrology_source":"hydrobasins","site_id":"doc-64530ad58de460a6a51a2396-site-8"},{"name":"Rosser Creek","kind":"river","role":"site for recharge weir MAR scheme in Mitchell catchment","river_name":"Rosser Creek","geocode_query":"Rosser Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-004","page":5,"quote":"Approximately 13% of the Mitchell catchment was assessed to have promise for MAR (Vanderzalm et al., 2018) and three MAR schemes were examined in the catchment, all situated within Quaternary alluvial aquifers. 1. Recharge weir scheme: This scheme would comprise a weir 3 m high and up to 50 m wide and ten groundwater extraction bores, each about 20 m deep, as there are few existing groundwater extraction bores within these aquifers. The weir would need to be sited in a nonperennial reach of river and would detain runoff events which could infiltrate through the wetted surface upstream of the weir. The detention of water in the weir pool provides particulate settling but contributes to clogging of infiltration surface, no pre-treatment is required but remediation of clogging may be required after several years of operation. No post-treatment would be required for irrigation use. 2.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Lynd River","kind":"river","role":"dam on the Lynd River assumed existing for modelling recharge release MAR scheme in Mitchell catchment","river_name":"Lynd River","geocode_query":"Lynd River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-007","page":7,"quote":"MAR TYPE SCHEME ID EXAMPLE LOCATION VOLUME (MM3/Y) CAPITAL COST ($)† OPERATING COST ($/Y) † OPERATING/ CAPITAL COST LEVELIZED CAPITAL COST ($/M3 ) LEVELIZED OPERATING COST ($/M3 ) LEVELIZED COST ($/M3 ) Recharge release Recharge release-M Lynd River 1 211,500 20,000 0.095 0.02 0.02 0.04§ * Recharge release Recharge release-D Berry Springs 1 200,300 37,100 0.19 0.01 0.04 0.05* Infiltration basin Infiltration basin-M Ten Mile Creek 1 509,800 23,900 0.047 0.04 0.02 0.06§ Infiltration basin Infiltration basin-F Geegully Creek 1 773,200 28,300 0.037 0.06 0.03 0.09§ ASR-large ASR-large-D Howard East (confined) 5 3415,000 263,600 0.077 0.05 0.05 0.10 Recharge weir Recharge weir-M Rosser Creek 1 1258,000 36,400 0.029 0.09 0.04 0.13§ Recharge weir Recharge weir-D Adelaide River 0.6 760,700 45,800 0.060 0.09 0.08 0.17 ASTR ASTR-D Howard East (unconfined) 1 2204,000 73,900 0.034 0.16 0.07 0.23 ASR ASR-D Palmerston 1 2178,000 91,000 0.042 0.16 0.09 0.25 Seawater intrusion barrier Seawater Intrusion barrier-F Derby 1 3565,000 97,600 0.027 0.26 0.13 0.36 †Cost does not include transfer of water to the MAR scheme or reticulation after recovery. §Cost includes recovery bore costs as there is currently very little groundwater extraction.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.0447157623706,-17.22568884505755],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":125838707,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lynd River, Queensland, Australia","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-64530ad58de460a6a51a2396-site-10"}],"limitations":["Exact coordinates not provided in text for several locations; latitude and longitude set to null.","Study focuses on pre-feasibility cost estimates; uncertainty remains in project logistics and management in remote locations.","Benefits of MAR not quantified due to early stage of scheme development.","Costs exclude water transfer and reticulation infrastructure costs."],"river_ids":["ne-51"],"site_ids":["doc-64530ad58de460a6a51a2396-site-0","doc-64530ad58de460a6a51a2396-site-1","doc-64530ad58de460a6a51a2396-site-2","doc-64530ad58de460a6a51a2396-site-3","doc-64530ad58de460a6a51a2396-site-4","doc-64530ad58de460a6a51a2396-site-6","doc-64530ad58de460a6a51a2396-site-7","doc-64530ad58de460a6a51a2396-site-8","doc-64530ad58de460a6a51a2396-site-10"],"anchor":[131.1389019,-12.8770313],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040049720,5040059440,5040059450,5040061670,5040511570,7040388740],"main_basin_ids":[5040049720,5040059440,5040059450,5040061670,5040073410,7040014930]},{"id":"doc-7b46a002009100b686fa0efc","title":"On the validity of using ground-based observations to validate geostationary-satellite-derived direct and diffuse surface solar irradiance: Quantifying the spatial mismatch and temporal averaging issues","display_title":"Spatial mismatch in validating satellite solar irradiance","doi":"https://doi.org/10.1016/j.rse.2022.113179","theme":"Climate","color":"#98d4f1","year":2022,"publication_date":"2022-07-28","authors":["Yi Qin","Tim R. McVicar","Jing Huang","Sam West","Andy Steven"],"summary":"This study investigates the spatial and temporal mismatches between ground-based and geostationary satellite measurements of surface solar irradiance (SSI), focusing specifically on the direct and diffuse components derived from the Advanced Himawari Imager over Australia. It quantifies how these mismatches affect the validation of satellite-derived data, revealing substantial apparent errors primarily due to the mismatch rather than intrinsic product errors, and emphasizing the need for standard validation protocols including spatial and temporal averaging.","finding":"More than 150 W/m² of the apparent error in 10-minute averaged direct horizontal irradiance (DirHI) can be attributed to satellite-ground mismatches due to spatial representativeness differences, and this error decreases significantly with increased temporal and spatial averaging; notably, at least a half-hour temporal averaging interval is needed to provide meaningful validation under cloudy conditions.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"Remote Sensing of Environment 280 (2022) 113179 10 components, were discussed. A series of analysis were then conducted to quantify the impacts of the mismatch on validation of the direct and diffuse components. It is found that: 1) >150 W/m2 of the apparent DirHI (direct horizontal irradiance) error could be attributed to ground-satellite mismatch when using 10-min temporal averaged data; 2) The apparent DirHI error decreased dramatically as the temporal averaging interval increased, and that a minimum of a half-hour was necessary to meaningfully validate DirHI under cloudy conditions; 3) Solar zenith angle (SZA) and view zenith angle (VZA) also had strong impact on both DirHI and DHI (diffuse horizontal irradiance) validation, the apparent error decreased by >20 W/m2 for DirHI and DHI when SZA increased to 60◦ or when VZA increased to 50◦; and 4) Spatial averaging of satellite-derived data was found to have a significant impact on the validation of DHI, as expected.","verified_in_page":true},"topics":["remote sensing validation","solar irradiance","direct normal irradiance","diffuse horizontal irradiance","spatial representativeness","temporal averaging","satellite-ground mismatch","cloud heterogeneity"],"study_period":"2015-07 to 2019-04","locations":[{"name":"Darwin","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Darwin Northern Territory Australia","latitude":-12.42,"longitude":130.89,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.783622,-12.455194],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-0","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Broome","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Broome Western Australia Australia","latitude":-17.95,"longitude":122.24,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[122.236998,-17.90182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102208974,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Broome WA 6725, Australia","basin_id":5040059020,"main_basin_id":5040059020,"basin_region":"Australasia","basin_area_km2":23538.1,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-1","true_coordinates":[122.236998,-17.96182],"spread_km":6.7},{"name":"Townsville","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Townsville Queensland Australia","latitude":-19.25,"longitude":146.77,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.776302,-19.211337],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652811,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Townsville City QLD 4810, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-2","true_coordinates":[146.8136596,-19.2598781],"spread_km":6.7},{"name":"Rockhampton","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Rockhampton Queensland Australia","latitude":-23.38,"longitude":150.48,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.458152,-23.352346],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12126666,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rockhampton City QLD 4700, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-3","true_coordinates":[150.514763,-23.3823464],"spread_km":6.7},{"name":"Alice Springs","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Alice Springs Northern Territory Australia","latitude":-23.8,"longitude":133.89,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.824,-23.668042],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-4","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Geraldton","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Geraldton Western Australia Australia","latitude":-28.8,"longitude":114.7,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[114.552013,-28.746135],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":80910278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Geraldton WA 6530, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-5","true_coordinates":[114.611295,-28.7761346],"spread_km":6.7},{"name":"Kalgoorlie","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Kalgoorlie Western Australia Australia","latitude":-30.78,"longitude":121.45,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.405539,-30.719],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61119362,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kalgoorlie - Boulder WA, Australia","basin_id":5040087690,"main_basin_id":5040087690,"basin_region":"Australasia","basin_area_km2":127703.4,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-6","true_coordinates":[121.466,-30.749],"spread_km":6.7},{"name":"Adelaide","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Adelaide South Australia Australia","latitude":-34.95,"longitude":138.52,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.568993,-34.874441],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-7","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"Wagga Wagga","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Wagga Wagga New South Wales Australia","latitude":-35.16,"longitude":147.46,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.292172,-35.102598],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667389,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wagga Wagga NSW 2650, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-8","true_coordinates":[147.3655103,-35.1025977],"spread_km":6.7},{"name":"Canberra","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Canberra Australian Capital Territory Australia","latitude":-35.27,"longitude":149.11,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.131032,-35.220185],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43671612,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canberra ACT, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-9","true_coordinates":[149.1310324,-35.2801846],"spread_km":6.7},{"name":"Melbourne","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Melbourne Victoria Australia","latitude":-37.67,"longitude":144.83,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.890826,-37.832169],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558662,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Melbourne VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-10","true_coordinates":[144.9630576,-37.8136276],"spread_km":6.7},{"name":"Cape Grim","kind":"station","role":"ground solar irradiance measurement site for satellite validation","river_name":null,"geocode_query":"Cape Grim Tasmania Australia","latitude":-40.68,"longitude":144.69,"evidence":{"passage_id":"p002-007","page":2,"quote":"Ground solar irradiance data The ground solar flux data, including the direct and diffuse components, were acquired from Australian Bureau of Meteorology (BoM) which maintains a network of high quality surface solar radiation measurement sites using pyranometers (CM11 by Kipp and Zonen) and pyrheliometers (CH1 by Kipp and Zonen, with half opening angle of 2.5◦; Gregory et al., 2012). In addition, 12-month of in-situ data were collected at the CSIRO Canberra site, with similar instrumentation (see Huang et al., 2018). The flux data were logged at 1 Hz which were averaged to 1-min. Among the BoM stations, 8 were fully operational from 1 July 2015 to 30 April 2019, and 4 only operated partially in this period. The Learmonth station, with records starting in January 2017, suffered from a high rate of missing values (14 of the 24 months in 2017 and 2018 do not have complete records, compared to about 2 out of 12 months each year at other stations) and was therefore excluded from this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.69,-40.62],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":31289452,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":5040077640,"main_basin_id":5040077640,"basin_region":"Australasia","basin_area_km2":69406.6,"hydrology_source":"geofabric","site_id":"doc-7b46a002009100b686fa0efc-site-11","true_coordinates":[144.69,-40.68],"spread_km":6.7},{"name":"East Asia and Oceania region","kind":"region","role":"satellite observation domain from geostationary Himawari-8","river_name":null,"geocode_query":"East Asia and Oceania","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Satellite-derived cloud and solar irradiance data Satellite-derived data, including cloud and aerosol type and optical depth, surface albedo, and solar flux and components (GHI, DHI, DNI and DirHI), were generated from Advanced Himawari Imager (AHI) imagery which is onboard the Himawari-8 geostationary satellite. The satellite is located above the equator at 140.7∘ E and acquires images of the East Asia and Oceania region every 10 min in 16 bands ranging from 0.47μm to 13.3μm (see Bessho et al., 2016 for details). Cloud and aerosol type and optical depth and surface reflectance were retrieved using a time-series-based radiative transfer model (Qin et al., 2019). The bands used in the retrieval are bands 1 to 6 with wavelengths centred at 0.47μm, 0.51μm, 0.64μm, 0.86μm, 1.61μm and 2.26 μm, respectively. While the original spatial resolution of AHI varies from 0.5 km to 2 km, here a spatial resolution of 2 km is used with higher resolution bands averaged to 2 km.","verified_in_page":true},"resolution_status":"resolved","coordinates":[106.534838,38.854595],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Asia","basin_id":4040548370,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":248039.1,"hydrology_source":"hydrobasins","site_id":"doc-7b46a002009100b686fa0efc-site-12","true_coordinates":[106.5348379,38.7945952],"spread_km":6.7}],"limitations":["Study relies on the Advanced Himawari Imager SSI dataset with inherent algorithm assumptions such as a plane-parallel atmosphere model and no 3D cloud geometry, which can introduce errors particularly at large solar zenith angles.","Ground measurement sites in Australia are spatially sparse; a dense ground network was not available to characterize fine-scale spatial variability.","Coordinates for study sites were taken from text data; some spatial averaging applied to satellite data but resolution limits the investigation scale.","Topographical effects such as slope and aspect are not considered due to relatively coarse 2 km satellite resolution and focus on horizontal surface irradiance."],"river_ids":[],"site_ids":["doc-7b46a002009100b686fa0efc-site-0","doc-7b46a002009100b686fa0efc-site-1","doc-7b46a002009100b686fa0efc-site-2","doc-7b46a002009100b686fa0efc-site-3","doc-7b46a002009100b686fa0efc-site-4","doc-7b46a002009100b686fa0efc-site-5","doc-7b46a002009100b686fa0efc-site-6","doc-7b46a002009100b686fa0efc-site-7","doc-7b46a002009100b686fa0efc-site-8","doc-7b46a002009100b686fa0efc-site-9","doc-7b46a002009100b686fa0efc-site-10","doc-7b46a002009100b686fa0efc-site-11","doc-7b46a002009100b686fa0efc-site-12"],"anchor":[144.69,-40.62],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[4040548370,5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073420,5040077640,5040087690,5040491370,5040591050],"main_basin_ids":[4040007850,5040056470,5040059020,5040061670,5040067860,5040069590,5040070710,5040073410,5040073420,5040077640,5040087590,5040087690]},{"id":"doc-8d40a0dc1c819af939a3d4c7","title":"Cropping system intensification for smallholder farmers in coastal zone of West Bengal, India: A socio-economic evaluation","display_title":"Cropping System Intensification in West Bengal Coast","doi":"https://doi.org/10.3389/fsufs.2022.1001367","theme":"Land & ecosystems","color":"#c8df98","year":2022,"publication_date":"2022-12-07","authors":["Subhasis Mandal","Sukanta Kumar Sarangi","M. Mainuddin","K. K. Mahanta","Uttam Kumar Mandal","D. Burman","S. Digar","P. C. Sharma","B. Maji"],"summary":"This study evaluates socio-economic viability of intensified cropping systems for smallholder farmers in West Bengal's coastal zone, part of the Indian Sundarbans in the Ganges delta. Conducted on Gosaba island, South 24 Parganas district, it measured baseline and post-intervention farming practices over four years, demonstrating crop diversification and improved agricultural income despite coastal challenges such as soil salinity, waterlogging, and limited freshwater for irrigation. The research identified farmer-preferred cropping options, economic benefits, and key adoption factors including access to perennial pond water and off-farm income.","finding":"Interventions with new cropping systems significantly increased cropping intensity from 123–142% to over 202%, reducing rabi season fallow land by 30–35% and substantially increasing smallholder income despite environmental constraints.","finding_evidence":{"passage_id":"p015-004","page":15,"quote":"Therefore, the socio-economic suitability of new crops and management options were evaluated by accounting the benefits of adoption and identifying various constraints in adoption. Baseline and end-line surveys were conducted to quantify the impact of interventions. Behavioral analysis was carried out to identify factors affecting large-scale adoption of the new/improved cropping systems evolved. The socio-economic impact study revealed that the field trials and subsequent adoption of evolved cropping systems successfully increased the cropping intensity in the study area. Cropping system intensification was quite successful and there were several opportunities to achieve higher cropping intensities (from baseline 123–142% to over 202–300%) with substantially higher returns (2–5 times) as compared to the existing practices.","verified_in_page":true},"topics":["cropping system intensification","smallholder farmers","water management","soil salinity","coastal agriculture","socio-economic evaluation","crop diversification","irrigation"],"study_period":"2016–2020","locations":[{"name":"Gosaba island, South 24 Parganas district, West Bengal, India","kind":"field_site","role":"primary study site","river_name":null,"geocode_query":"Gosaba island, South 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Materials and methods Study area and site description The study area is located on Gosaba island of South 24 Parganas district in the Ganges delta. The coastal zone of the Ganges Delta in India, known as Sundarbans, is comprised of a geographical area of 9,630 km2 spread over 102 islands, and among these 54 islands have human settlements. The area under human inhabitation is 4,444 km2 , falling under two coastal districts of West Bengal, South and North 24 Parganas. The Indian Sundarban region (Indian part of the Ganges delta) accounts for 33% (0.44 million ha) of the total area (1.33 million ha) of these two coastal districts. Out of the total population of the Sundarban region (4.43 million), almost 74% are from South 24 Parganas and the rest (26%) are from North 24 Parganas district (Census of India, 2011). Among the total workers (1.67 million) in Sundarbans, around one-fifth (20%) are agricultural laborers and 12% are cultivators, directly thriving on agriculture.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Sundarbans region, West Bengal, India","kind":"region","role":"broader coastal zone context","river_name":null,"geocode_query":"Sundarbans, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Materials and methods Study area and site description The study area is located on Gosaba island of South 24 Parganas district in the Ganges delta. The coastal zone of the Ganges Delta in India, known as Sundarbans, is comprised of a geographical area of 9,630 km2 spread over 102 islands, and among these 54 islands have human settlements. The area under human inhabitation is 4,444 km2 , falling under two coastal districts of West Bengal, South and North 24 Parganas. The Indian Sundarban region (Indian part of the Ganges delta) accounts for 33% (0.44 million ha) of the total area (1.33 million ha) of these two coastal districts. Out of the total population of the Sundarban region (4.43 million), almost 74% are from South 24 Parganas and the rest (26%) are from North 24 Parganas district (Census of India, 2011). Among the total workers (1.67 million) in Sundarbans, around one-fifth (20%) are agricultural laborers and 12% are cultivators, directly thriving on agriculture.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.22525,25.08748],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarban, West Bengal 732122, India","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-8d40a0dc1c819af939a3d4c7-site-1","true_coordinates":[88.2826103,25.0574796],"spread_km":6.7},{"name":"South 24 Parganas district, West Bengal, India","kind":"region","role":"study administrative district","river_name":null,"geocode_query":"South 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Materials and methods Study area and site description The study area is located on Gosaba island of South 24 Parganas district in the Ganges delta. The coastal zone of the Ganges Delta in India, known as Sundarbans, is comprised of a geographical area of 9,630 km2 spread over 102 islands, and among these 54 islands have human settlements. The area under human inhabitation is 4,444 km2 , falling under two coastal districts of West Bengal, South and North 24 Parganas. The Indian Sundarban region (Indian part of the Ganges delta) accounts for 33% (0.44 million ha) of the total area (1.33 million ha) of these two coastal districts. Out of the total population of the Sundarban region (4.43 million), almost 74% are from South 24 Parganas and the rest (26%) are from North 24 Parganas district (Census of India, 2011). Among the total workers (1.67 million) in Sundarbans, around one-fifth (20%) are agricultural laborers and 12% are cultivators, directly thriving on agriculture.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.556531,22.076674],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South 24 Parganas, West Bengal, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8d40a0dc1c819af939a3d4c7-site-2","true_coordinates":[88.55653099999999,22.1366735],"spread_km":6.7},{"name":"North 24 Parganas district, West Bengal, India","kind":"region","role":"comparative coastal district","river_name":null,"geocode_query":"North 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Materials and methods Study area and site description The study area is located on Gosaba island of South 24 Parganas district in the Ganges delta. The coastal zone of the Ganges Delta in India, known as Sundarbans, is comprised of a geographical area of 9,630 km2 spread over 102 islands, and among these 54 islands have human settlements. The area under human inhabitation is 4,444 km2 , falling under two coastal districts of West Bengal, South and North 24 Parganas. The Indian Sundarban region (Indian part of the Ganges delta) accounts for 33% (0.44 million ha) of the total area (1.33 million ha) of these two coastal districts. Out of the total population of the Sundarban region (4.43 million), almost 74% are from South 24 Parganas and the rest (26%) are from North 24 Parganas district (Census of India, 2011). Among the total workers (1.67 million) in Sundarbans, around one-fifth (20%) are agricultural laborers and 12% are cultivators, directly thriving on agriculture.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.710896,22.650047],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North 24 Parganas, West Bengal, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8d40a0dc1c819af939a3d4c7-site-3","true_coordinates":[88.7108964,22.710047],"spread_km":6.7}],"limitations":["Lack of precise geographic coordinates for study locations","Study area generalized to islands and districts without detailed spatial coordinates"],"river_ids":[],"site_ids":["doc-8d40a0dc1c819af939a3d4c7-site-1","doc-8d40a0dc1c819af939a3d4c7-site-2","doc-8d40a0dc1c819af939a3d4c7-site-3"],"anchor":[88.710896,22.650047],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040960480],"main_basin_ids":[4040025450,4040025460]},{"id":"doc-93018987e3770e990f61df51","title":"Australian gridded chloride deposition-rate dataset","display_title":"Australian Gridded Chloride Deposition Dataset","doi":"https://doi.org/10.1016/j.dib.2022.108189","theme":"Land & ecosystems","color":"#c8df98","year":2022,"publication_date":"2022-04-20","authors":["Andrew Wilkins","Russell S. Crosbie","Tristan Louth‐Robins","Phil Davies","Matthias Raiber","Warrick Dawes","Lei Gao"],"summary":"This study compiles and upscales chloride deposition-rate measurements from 367 sites across Australia onto a 0.05° regular grid using a spatially varying double-exponential model correlated with distance to the coast. Parameters were estimated via least-squares and Tikhonov regularisation ensuring smooth spatial variability, with uncertainty quantified by a null-space Monte Carlo approach. The resulting dataset provides spatially continuous chloride deposition rates and uncertainties across Australia useful for hydrological and hydrogeochemical applications.","finding":"Chloride deposition rates across Australia are significantly correlated with distance to the coast via a double-exponential decay model, representing a 'fast' ocean aerosol component and a 'slow' gaseous chloride component, and these parameters vary smoothly spatially across the continent.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"6 A. Wilkins, R. Crosbie and T. Louth-Robins et al. /Data in Brief 42 (2022) 108189 Fig. 1. Location and weight of each chloride deposition observation. and direction to the coast, mean annual rainfall and mean annual windspeed. The only strong correlation was between deposition and distance to the coast. Based on work by Keywood [5], they postulated a relationship of the form: D = A1e − d λ1 + A2e − d λ2 (1) Here: • D is the deposition rate, with units kg.ha−1.yr−1; • d is the distance to the coast, with units km; • A1 is a coefficient, with units kg.ha−1.yr−1; • A2 is a coefficient, with units kg.ha−1.yr−1; • λ1 is a decay constant, with units km; • λ2 is a decay constant, with units km.","verified_in_page":true},"topics":["chloride deposition","continental scale modeling","uncertainty quantification","spatial interpolation","groundwater recharge estimation","atmospheric chemistry","environmental data upscaling","hydrochemistry"],"study_period":"1937-2021","locations":[{"name":"Australia","kind":"region","role":"study area where chloride deposition was measured at 367 sites and upscaled to a gridded dataset","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Background Chloride deposition rates have been collated from measurements at 367 sites across Australia. These data are compiled into observations_update_v04.csv and shown graphically in observations_update_v04.png. The data sources are listed in Table 1. These measurements are of bulk rainfall and contain both wet and dry deposition of chloride. Davies and Crosbie [1] describe a method for extrapolating such chloride deposition data to the entire continent. Here, their method is explained in more detail than the original paper and used to estimate chloride deposition across the continent. Davies and Crosbie [1] used 291 observations in their calibration of the continental scale model and an additional 20 observations as validation sites. Here, a comprehensive set of source data consisting of 367 observations is used, this raw dataset has been updated using data collated 10 years after Davies and Crosbie [1] collated their data in 2010. 2.2.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Katherine, Northern Territory, Australia","kind":"field_site","role":"example location with repeated chloride deposition measurements over decades illustrating temporal fluctuations and uncertainty in chloride deposition rates","river_name":null,"geocode_query":"Katherine, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"If, for example, the observations remain unchanged with time, then it is reasonable to infer that the observations are highly reliable, which means the prediction will be more reliable than the case where the observations at each site fluctuate wildly. It is generally assumed [2–4] that a complete year of data is the minimum time period required to obtain a representative estimate of the chloride deposition, and the amount of chloride deposition is not considered dependent upon the amount of rainfall. However, Davies and Crosbie [1] found that there is some dependence on rainfall amount. Katherine in the NT is the best example in the dataset of temporal fluctuations in chloride deposition, and that using a single year of observation can result in considerable uncertainty. In Katherine, there have been 5 independent studies measuring the chloride deposition spread over five decades and ranging from 1 to 5 years duration.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.269862,-14.511968],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217145,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Katherine NT 0850, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-93018987e3770e990f61df51-site-1","true_coordinates":[132.2698623,-14.4519683],"spread_km":6.7}],"limitations":["Coordinates of observation sites are not explicitly provided, latitude and longitude are only given in raw data files not included here.","Study uses proxy and interpolated model parameters based on distance to coast; other environmental factors showed no strong correlation and are not incorporated.","Uncertainty quantification depends on assumptions about spatial smoothness and observational data quality weighting.","The chloride deposition data is upscaled from isolated measurements at specific sites to continental grid cells, introducing interpolation errors and model assumptions."],"river_ids":["ne-469"],"site_ids":["doc-93018987e3770e990f61df51-site-1"],"anchor":[132.269862,-14.511968],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[5040061670],"main_basin_ids":[5040061670]},{"id":"doc-97c37c7f0569543339a4b6ac","title":"Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment","display_title":"Partitioning Transmission Losses in Cooper Creek","doi":"https://doi.org/10.1002/eco.2490","theme":"Groundwater","color":"#a8b9ff","year":2022,"publication_date":"2022-10-17","authors":["Russell S. Crosbie","Kate L. Holland","Tim R. McVicar"],"summary":"This study quantifies transmission losses and groundwater recharge in the Cooper Creek floodplain, an arid region in central Australia. Using multi-decade satellite remote sensing estimates of actual evapotranspiration (AET) and gauge flow data, the authors classify the floodplain landscape and partition transmission losses across open water, riparian zones, floodplains, and drylands. They find that approximately 82% of transmission losses occur on the floodplain, mainly supporting pasture growth, while 18% arise from waterholes and riparian zones, sustaining ecological refugia. The study updates conceptual models of groundwater recharge showing that recharge occurs not only via permanent waterholes but also through river channels across broader areas. The approach provides a method applicable to other data-poor arid floodplains globally.","finding":"Approximately 82% of transmission losses in Cooper Creek occur on the floodplain, primarily supporting pastoral pasture growth, with the remaining 18% occurring in riparian zones and waterholes supporting environmental values.","finding_evidence":{"passage_id":"p016-001","page":16,"quote":"Further west in the riparian zone of the similarly arid Woodforde River, Cook et al. (2008) found that River Red Gums were transpiring at 2.4 mm/day when over a shallow fresh groundwater source, more than double the rate of rainfall. To the south in the lower Murray River riparian zone, Doody et al. (2009) found that plots including River Red Gums were using close to 1 mm/day of groundwater within 50 m of the river with access to fresh groundwater. However, further from the river over saline groundwater, tree water use was much less (<0.2 mm/day). The water use of River Red Gums is site specific as the species can adapt to the long-term water availability (Doody et al., 2015). The AET rates from the riparian zone estimated herein are higher than previous plot scale (\u000150 m) estimates from elsewhere in the Lake Eyre Basin where the groundwater was more saline, whereas in the Cooper Creek catchment, the freshwater lens can support higher transpiration rates.","verified_in_page":true},"topics":["actual evapotranspiration","groundwater recharge","transmission losses","remote sensing","water balance","riparian vegetation","arid zone hydrology","landscape classification"],"study_period":"1967–2019","locations":[{"name":"Cooper Creek floodplain","kind":"field_site","role":"primary study area encompassing 40,000 km2 of floodplain subject to analysis of transmission losses and evapotranspiration","river_name":"Cooper Creek","geocode_query":"Cooper Creek floodplain, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"These transmission losses support a range of ecosystems, but partitioning the volume of the transmission losses across the floodplain, riparian zone and in-channel is difficult. This study presents a methodology relying primarily on multi-decade satellite remotely sensed actual evapotranspiration estimates to partition these losses. The method was applied to the \u000140,000 km2 floodplain of Cooper Creek in the central Australian arid zone, where first, the alluvial landscape was classified based on actual evapotranspiration rates, and second, both regional- (i.e., for the entire floodplain) and local-scale (i.e., for each waterhole) water balances were calculated to partition these losses. Regional-scale results estimated that 82% of transmission losses occurred on the floodplain, 13% in the riparian zone and 5% from open water in the river channel and waterholes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.34854,-26.987703],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cooper Creek, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-0","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Queensland, Australia","kind":"region","role":"regional administrative units containing Cooper Creek catchment headwaters and floodplain","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, this is to (i) quantify the transmission losses from long-term stream gauging upstream and downstream; (ii) classify the alluvial landscape based on AET; and (iii) attribute the transmission losses based on a water balance to components stored as soil moisture across the floodplain, recharge to shallow freshwater lenses and direct evaporation from open water in the river channel and waterholes (WHs). 2 | STUDY AREA The Lake Eyre Basin is one of the last free-flowing arid river basins in the world, and Cooper Creek is its most iconic waterway (Bunn et al., 2006; McMahon et al., 2008a; Walker et al., 1997). The Cooper Creek catchment covers 296,000 km2 of south-west Queensland and north-east South Australia. It is characterised by highly variable flows that are amongst the most variable worldwide (Puckridge et al., 2010), meaning that the biota has adapted to boom-and-bust times coinciding with the cycles of floods and droughts (Carini et al., 2006).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia, Australia","kind":"region","role":"regional administrative units containing Cooper Creek floodplain and lower reaches","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, this is to (i) quantify the transmission losses from long-term stream gauging upstream and downstream; (ii) classify the alluvial landscape based on AET; and (iii) attribute the transmission losses based on a water balance to components stored as soil moisture across the floodplain, recharge to shallow freshwater lenses and direct evaporation from open water in the river channel and waterholes (WHs). 2 | STUDY AREA The Lake Eyre Basin is one of the last free-flowing arid river basins in the world, and Cooper Creek is its most iconic waterway (Bunn et al., 2006; McMahon et al., 2008a; Walker et al., 1997). The Cooper Creek catchment covers 296,000 km2 of south-west Queensland and north-east South Australia. It is characterised by highly variable flows that are amongst the most variable worldwide (Puckridge et al., 2010), meaning that the biota has adapted to boom-and-bust times coinciding with the cycles of floods and droughts (Carini et al., 2006).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Thompson River at Stonehenge","kind":"station","role":"upstream flow gauge location used for transmission loss calculations on Cooper Creek","river_name":"Thompson River","geocode_query":"Stonehenge, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"annual accumulations being 1 October to the following 30 September for the upstream gauges and 1 November to the following 31 October for the downstream gauge. The offset of 1 month accounts for the travel time between gauges. The two upstream gauges only became reliably operating in the late 1990s, prior to those large multi-month periods of non-observations that were present. The historical period of analysis for the upstream gauge on Cooper Creek at Currareva (gauge 003101A) is from 1967 to 1988. The gauge is located just south of the confluence of the Thompson and Barcoo rivers (Figure 1). This gauge was established in 1966 (so 1967 is the first year of full observations) and ceased operation in 1989 (1988 is the final year of full observations).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.2856011,-24.3538906],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Stonehenge QLD 4730, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-3"},{"name":"Barcoo River at Retreat","kind":"station","role":"upstream flow gauge location used for transmission loss calculations on Cooper Creek","river_name":"Barcoo River","geocode_query":"Retreat, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"annual accumulations being 1 October to the following 30 September for the upstream gauges and 1 November to the following 31 October for the downstream gauge. The offset of 1 month accounts for the travel time between gauges. The two upstream gauges only became reliably operating in the late 1990s, prior to those large multi-month periods of non-observations that were present. The historical period of analysis for the upstream gauge on Cooper Creek at Currareva (gauge 003101A) is from 1967 to 1988. The gauge is located just south of the confluence of the Thompson and Barcoo rivers (Figure 1). This gauge was established in 1966 (so 1967 is the first year of full observations) and ceased operation in 1989 (1988 is the final year of full observations).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Cooper Creek at Nappa Merrie","kind":"station","role":"downstream flow gauge location used for transmission loss calculations on Cooper Creek floodplain","river_name":"Cooper Creek","geocode_query":"Nappa Merrie, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"annual accumulations being 1 October to the following 30 September for the upstream gauges and 1 November to the following 31 October for the downstream gauge. The offset of 1 month accounts for the travel time between gauges. The two upstream gauges only became reliably operating in the late 1990s, prior to those large multi-month periods of non-observations that were present. The historical period of analysis for the upstream gauge on Cooper Creek at Currareva (gauge 003101A) is from 1967 to 1988. The gauge is located just south of the confluence of the Thompson and Barcoo rivers (Figure 1). This gauge was established in 1966 (so 1967 is the first year of full observations) and ceased operation in 1989 (1988 is the final year of full observations).","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.095993,-27.6579],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006047,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nappa Merry, Durham QLD 4492, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-5","true_coordinates":[141.095993,-27.5979],"spread_km":6.7},{"name":"Cooper Creek at Currareva","kind":"station","role":"historical upstream flow gauge used in historical transmission loss analysis","river_name":"Cooper Creek","geocode_query":"Currareva, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"The catchment area of the gauge at Nappa Merrie is 237,000 km2 , and for the recent period, the combined catchment area above the gauges at Stonehenge and Retreat is 139,000 km2 (for the historical period from Currareva, this is 150,000 km2 ). Of the catchment area between the gauges, there is \u000130,000 km2 of alluvium and \u000170,000 km2 of ungauged catchments discharging into the floodplain (Wilson River, Kyabra Creek and Windula Creek being the major ungauged tributaries; see Figure 1). Although these ungauged tributaries are considered to contribute little to the flow in Cooper Creek (Costelloe et al., 2004), by not accounting for these flows in the transmission loss calculations, the magnitude of the transmission losses will be underestimated. 3.2 | Remotely sensed AET Estimates of ET by remote sensing were derived using the CSIRO MODIS Reflectance-based Scaling ET (CMRSET) algorithm (Guerschman et al., 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.7463212,-25.2643683],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Windorah River Rd, Jundah QLD 4481, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-6"},{"name":"Lake Eyre (North), South Australia, Australia","kind":"other","role":"terminal basin receiving flow from Cooper Creek main channel","river_name":null,"geocode_query":"Lake Eyre North, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"It consists of a complex sequence of anastomosing channels across the floodplain that reach a maximum width of \u000160 km before being constricted to a single channel before crossing the border from Queensland into South Australia. In South Australia, flow splits into three directions. Firstly, the main channel flows into Lake Eyre (North) about 1 in 12 years (Kingsford et al., 1999) with flow reaching Embarka Swamp in most years (Costelloe, 2013). Secondly, the Northwest Branch flows towards Coongie Lakes, with flow reaching it every year since 1973 (Costelloe, 2013). The third path is via the distributary Strzelecki Creek that flows south towards Lake Blanche; this channel is not flooded most years as there is a high sill on the main channel (Costelloe, 2013). Because of the low gradient and arid climate, transmission losses are extremely high.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.332397,-28.126469],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":119370154,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Eyre (North), South Australia 5733, Australia","basin_id":5040491380,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":19208.6,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-7","true_coordinates":[137.2923893,-28.1750101],"spread_km":6.7},{"name":"Coongie Lakes, South Australia, Australia","kind":"other","role":"wetland system fed by the Northwest branch of Cooper Creek","river_name":null,"geocode_query":"Coongie Lakes, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"It consists of a complex sequence of anastomosing channels across the floodplain that reach a maximum width of \u000160 km before being constricted to a single channel before crossing the border from Queensland into South Australia. In South Australia, flow splits into three directions. Firstly, the main channel flows into Lake Eyre (North) about 1 in 12 years (Kingsford et al., 1999) with flow reaching Embarka Swamp in most years (Costelloe, 2013). Secondly, the Northwest Branch flows towards Coongie Lakes, with flow reaching it every year since 1973 (Costelloe, 2013). The third path is via the distributary Strzelecki Creek that flows south towards Lake Blanche; this channel is not flooded most years as there is a high sill on the main channel (Costelloe, 2013). Because of the low gradient and arid climate, transmission losses are extremely high.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.22508,-27.153333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Coongie, Innamincka SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-8","true_coordinates":[140.1666667,-27.1833333],"spread_km":6.7},{"name":"Strzelecki Creek","kind":"river","role":"distributary channel flowing southwards from Cooper Creek toward Lake Blanche with frequent no-flow conditions","river_name":"Strzelecki Creek","geocode_query":"Strzelecki Creek, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"It consists of a complex sequence of anastomosing channels across the floodplain that reach a maximum width of \u000160 km before being constricted to a single channel before crossing the border from Queensland into South Australia. In South Australia, flow splits into three directions. Firstly, the main channel flows into Lake Eyre (North) about 1 in 12 years (Kingsford et al., 1999) with flow reaching Embarka Swamp in most years (Costelloe, 2013). Secondly, the Northwest Branch flows towards Coongie Lakes, with flow reaching it every year since 1973 (Costelloe, 2013). The third path is via the distributary Strzelecki Creek that flows south towards Lake Blanche; this channel is not flooded most years as there is a high sill on the main channel (Costelloe, 2013). Because of the low gradient and arid climate, transmission losses are extremely high.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.83015,-29.3642599],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005277,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Strzelecki Creek, Murnpeowie SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-9"},{"name":"Moomba, South Australia, Australia","kind":"settlement","role":"major development center in the Cooper Basin region related to oil and gas extraction","river_name":null,"geocode_query":"Moomba, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"(Figure 1). The dominant land use in the region is grazing of sheep and cattle on natural pastures (grazing native vegetation). There is no pasture modification or intensive production in the Cooper Creek catchment. Apart from agriculture, oil and gas extraction is the other main industry in the region with the Cooper Basin being the most developed on-shore hydrocarbon basin in Australia with the largest development at Moomba (Lech et al., 2020). Most of the water use from these industries is from deeper groundwater that does not interact with the streams and WHs that are the focus of this study (Holland et al., 2021). Cooper Creek is formed from the confluence of the Thompson and Barcoo rivers that rise on the Great Dividing Range in Central Queensland and flow inland generally in a south-westerly direction (Figure 1). The alluvium associated with the Cooper Creek downstream of the last gauges on the Barcoo (Retreat) and Thompson (Stonehenge) rivers is the study area for this paper.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.20307,-28.1077885],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005277,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Moomba, Gidgealpa SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-10"},{"name":"Currareva Waterhole","kind":"field_site","role":"example permanent waterhole used as demonstration site in landscape classification and water balance","river_name":"Cooper Creek","geocode_query":"Currareva Waterhole, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"In this way, the transmission losses can be apportioned to the increased AET above rainfall across the floodplain, the riparian zone and the evaporation from open water. The section of the floodplain downstream of Nappa Merrie can be similarly analysed even though there is not a downstream gauge to estimate the transmission losses. In most years, the transmission losses are 100% as the flow does not reach the terminal lakes Eyre (North) and Blanche. Within the 2001 to 2018 study period, water only flowed to Lake Eyre (North) in 2010 (Holland et al., 2020). The underestimation of the transmission losses due to ungauged inflows can be partially alleviated by accounting for the ungauged inflows through modelling. This has been done for the construction of a hydrodynamic model (Geological and Bioregional Assessment Program, 2021c) for 50 ungauged catchments flowing into the study area.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.7460952,-25.3678027],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Currareva Waterhole, Windorah QLD 4481, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-11"},{"name":"Cullyamurra Waterhole","kind":"field_site","role":"permanent waterhole site used in classification, water balance, and recharge analyses","river_name":"Cooper Creek","geocode_query":"Cullyamurra Waterhole, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.8596152,-27.7024607],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005907,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cullyamurra Waterhole, Innamincka SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-12"},{"name":"Coongie Lakes","kind":"other","role":"large wetland system associated with Cooper Creek downstream study area; water connectivity and classification corroborated by remote sensing","river_name":null,"geocode_query":"Coongie Lakes, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"In this way, the transmission losses can be apportioned to the increased AET above rainfall across the floodplain, the riparian zone and the evaporation from open water. The section of the floodplain downstream of Nappa Merrie can be similarly analysed even though there is not a downstream gauge to estimate the transmission losses. In most years, the transmission losses are 100% as the flow does not reach the terminal lakes Eyre (North) and Blanche. Within the 2001 to 2018 study period, water only flowed to Lake Eyre (North) in 2010 (Holland et al., 2020). The underestimation of the transmission losses due to ungauged inflows can be partially alleviated by accounting for the ungauged inflows through modelling. This has been done for the construction of a hydrodynamic model (Geological and Bioregional Assessment Program, 2021c) for 50 ungauged catchments flowing into the study area.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.108253,-27.153333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Coongie, Innamincka SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-13","true_coordinates":[140.1666667,-27.1833333],"spread_km":6.7},{"name":"Windorah, Queensland, Australia","kind":"settlement","role":"small town downstream of Thompson and Barcoo rivers in Cooper Creek catchment; climatic data reported","river_name":null,"geocode_query":"Windorah, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.655913,-25.36254],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Windorah QLD 4481, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-14","true_coordinates":[142.6559128,-25.4225397],"spread_km":6.7},{"name":"Innamincka, South Australia, Australia","kind":"settlement","role":"small town downstream in Cooper Creek catchment noted in climatic data and flow regimes","river_name":null,"geocode_query":"Innamincka, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.7385808,-27.7462176],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005824,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Innamincka SA 5731, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-15"},{"name":"Longreach, Queensland, Australia","kind":"settlement","role":"largest town in Cooper Creek catchment, representative of human population context","river_name":null,"geocode_query":"Longreach, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.2506199,-23.4403125],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Longreach QLD 4730, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-16"},{"name":"Wilson River","kind":"river","role":"major ungauged tributary contributing small inflows to Cooper Creek floodplain","river_name":"Wilson River","geocode_query":"Wilson River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.1832357,-27.8000432],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006602,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wilson River, Noccundra QLD 4492, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-17"},{"name":"Kyabra Creek","kind":"river","role":"major ungauged tributary contributing small inflows to Cooper Creek floodplain","river_name":"Kyabra Creek","geocode_query":"Kyabra Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.7537946,-25.6168072],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kyabra Creek, Windorah QLD 4481, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-18"},{"name":"Windula Creek","kind":"river","role":"major ungauged tributary contributing small inflows to Cooper Creek floodplain","river_name":"Windula Creek","geocode_query":"Windula Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"It experiences greater rainfall interannual variability than comparable catchments worldwide (McMahon et al., 2008b). Within this 30-year period, the maximum annual rainfall is seven times greater than the minimum at Windorah (67 cf 480 mm/ year) and 12 times greater at Innamincka (36 cf 425 mm/year) (see Figure 1 for locations). The catchment is in an arid climate (Beck et al., 2018) where the potential ET (calculated using Morton's method [Chiew & McMahon, 1991]) is 1726 mm/year, which is much greater (almost six times larger) than annual average rainfall. The region is very sparsely populated with Longreach (pop. 2,970), the largest town in the Cooper Creek catchment, and Windorah (pop. 115) and Innamincka (pop. 44), the only towns downstream of the confluence of the Thompson and Barcoo rivers 2 of 21 CROSBIE ET AL. 19360592, 2023, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/eco.2490 by Test, Wiley Online Library on [19/07/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.0813279,-26.9259344],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006205,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Windula Creek, Durham QLD 4492, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-97c37c7f0569543339a4b6ac-site-19"}],"limitations":["Some geographic coordinates are null due to lack of unambiguous data in the text.","Study area is expansive; landscape-scale classifications involve some uncertainty particularly in riparian zone delineation.","Errors inherent in remote sensing AET estimates and gauge measurements create uncertainty in water balance closure."],"river_ids":[],"site_ids":["doc-97c37c7f0569543339a4b6ac-site-0","doc-97c37c7f0569543339a4b6ac-site-3","doc-97c37c7f0569543339a4b6ac-site-5","doc-97c37c7f0569543339a4b6ac-site-6","doc-97c37c7f0569543339a4b6ac-site-7","doc-97c37c7f0569543339a4b6ac-site-8","doc-97c37c7f0569543339a4b6ac-site-9","doc-97c37c7f0569543339a4b6ac-site-10","doc-97c37c7f0569543339a4b6ac-site-11","doc-97c37c7f0569543339a4b6ac-site-12","doc-97c37c7f0569543339a4b6ac-site-13","doc-97c37c7f0569543339a4b6ac-site-14","doc-97c37c7f0569543339a4b6ac-site-15","doc-97c37c7f0569543339a4b6ac-site-16","doc-97c37c7f0569543339a4b6ac-site-17","doc-97c37c7f0569543339a4b6ac-site-18","doc-97c37c7f0569543339a4b6ac-site-19"],"anchor":[142.0813279,-26.9259344],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040491380,5040491510],"main_basin_ids":[5040087590]},{"id":"doc-999e8ddcbc446aaddaf10d20","title":"Identifying management‐driven dynamics in vegetation cover: Applying the Compere framework to Cooper Creek, Australia","display_title":"Management Impacts on Vegetation in Cooper Creek","doi":"https://doi.org/10.1002/ecs2.4006","theme":"Land & ecosystems","color":"#c8df98","year":2022,"publication_date":"2022-03-01","authors":["Randall J. Donohue","Karel Mokany","Tim R. McVicar","Anthony P. O’Grady"],"summary":"This study presents the Compere framework, a relative benchmarking method to assess management-driven impacts on ecosystem resources by comparing target locations to biophysically similar peers. Applied to vegetation cover data from satellite imagery, the framework was used to analyze the effect of hydrocarbon extraction activities on land condition across the 490,000-km2 Cooper Creek region in arid central Australia from 2001 to 2018. The study differentiated natural variability in vegetation cover from that caused by management activities like mining and fire. Validation was conducted against known disturbance patterns such as grazing gradients and conditions within livestock-excluded reserves. The study found that hydrocarbon extraction wells caused up to 12% and 41% decreases in land condition in dryland and channel areas respectively, with recovery to original levels by six years post-establishment. Fires had larger, longer-lasting impacts, implying mining primarily affects land condition indirectly by altering fire regimes. The Compere framework offers a flexible means to monitor management impacts on ecosystem resources from remote sensing data.","finding":"Hydrocarbon extraction wells are associated with 12%–41% decreases in land condition in the Cooper Creek region, but vegetation recovers to original levels by 6 years after establishment. Fires have larger and longer-lasting detrimental effects on land condition, indicating mining impacts are likely mediated chiefly through altered fire patterns.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"We found that the establishment of well sites was associated with between 12% and 41% decreases in land condition but that condition recovered to original levels by 6 years after establishment. We also found that fires had much larger and longer lasting effects on land condition across these landscapes, indicating that mining-related activities that change fire patterns are likely to affect land condition most. Compere is a generic, flexible framework, providing a new capability for monitoring management impacts on multiple types of ecosystem resources.","verified_in_page":true},"topics":["remote sensing","vegetation cover","land condition","hydrocarbon extraction","fire impact","rangeland ecology","environmental monitoring","ecosystem management"],"study_period":"2001–2018","locations":[{"name":"Cooper Creek region","kind":"region","role":"primary study area","river_name":null,"geocode_query":"Cooper Creek region, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Here, we present Compere, a relative benchmarking framework for monitoring the management-driven impacts on ecosystem resources. The framework identifies locations in a region that share similar biophysical properties to a target location. Taking an attribute of interest, it then compares the value of the target location with those of all its biophysically equivalent locations, with any differences being attributed to variations in management. We provide an example application of Compere, using satellite-derived vegetation cover data to examine the impact of hydrocarbon extraction activities on land condition (as described by vegetation cover) across the 490,000-km2 Cooper Creek region in arid central Australia. Validation was performed by comparing land condition estimates to known disturbance patterns across the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.34854,-26.927703],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cooper Creek, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-999e8ddcbc446aaddaf10d20-site-0","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Cooper geological Basin","kind":"region","role":"hydrocarbon resource basin underlying study area","river_name":null,"geocode_query":"Cooper geological Basin, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"The capacity for these ecosystems to continue to provide these services is contingent on the maintenance of ecosystem health. The Cooper Creek surface water catchment overlies the 118,000-km2 Cooper (geological) Basin (Figure 1). While this basin contains vast potential for unconventional gas resources, it has been conventional resources that have supported hydrocarbon extraction since the 1960s. However, currently declining production from existing domestic gas developments and growing demand from the Australian market means there is considerable interest in further developing the unconventional gas resources of the Cooper Basin (Cook et al., 2013; Richards & Côté, 2018). Any future development of the Cooper Basin needs to be undertaken with considerable sensitivity to potential impacts on the ecological, cultural, and pastoral values of the region.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sturt National Park","kind":"region","role":"fenced nature reserve used for validation of land condition metrics","river_name":null,"geocode_query":"Sturt National Park, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Accordingly, we tested whether the Compere land condition estimates contained a signal of lower condition near water points compared with locations away from water points. To do this, we used the Surface Water Points dataset (Crossman & Li, 2015), shown in Figure 5a. Within the region, there were 7513 “water” points. We randomly selected the same number of points from dryland areas that were not within 2 km of a water point, giving 7513 “non-water” points. We compared total cover (C) and the land condition score (R) between these two sets of points. Sturt National Park validation Sturt National Park (NP; Figure 5a) is one of the only nature reserves in the region that is fenced and contains no livestock, and so it is one of the few examples in the FIGURE 4 Example of the selection of dryland biophysically equivalent cells. The boundary of each plot is the 200 \u0006 200-km search square centered on an example target cell, shown in the center. This example is for 2015.","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.4125625,-29.0975625],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006264,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"5R23WC27+X2 Sturt National Park, Tibooburra NSW 2880, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-999e8ddcbc446aaddaf10d20-site-2"},{"name":"Cooper Creek","kind":"river","role":"internally draining river system central to study","river_name":"Cooper Creek","geocode_query":"Cooper Creek, Queensland and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-006","page":4,"quote":"It is one of the world’s last unregulated inland rivers and is of considerable ecological and historical significance 4 of 24 DONOHUE ET AL. 21508925, 2022, 3, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4006, Wiley Online Library on [14/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.290244,-26.897703],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cooper Creek, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-999e8ddcbc446aaddaf10d20-site-3","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Australia","kind":"region","role":"country containing the study region","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-006","page":4,"quote":"It is one of the world’s last unregulated inland rivers and is of considerable ecological and historical significance 4 of 24 DONOHUE ET AL. 21508925, 2022, 3, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4006, Wiley Online Library on [14/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Validation of land condition scores is qualitative due to absence of comprehensive field observations across the study region.","Spatial resolution limited to 500 m, restricting detection of highly localized effects near water points and well pads.","Assumption that all equivalency subsets contain locations of both high and low condition may not always hold, affecting reliability of relative benchmarking.","Potential incomplete characterization of biophysical equivalence may leave some natural variability unaccounted for in land condition scores.","Fire treated as management perturbation though it is a natural disturbance with complex interactions, complicating interpretation of its effects."],"river_ids":[],"site_ids":["doc-999e8ddcbc446aaddaf10d20-site-0","doc-999e8ddcbc446aaddaf10d20-site-2","doc-999e8ddcbc446aaddaf10d20-site-3"],"anchor":[140.34854,-26.927703],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040491510],"main_basin_ids":[5040087590]},{"id":"doc-a2ffb42b0974cfd2bf5748a1","title":"Isotopic evidence for soil water sources and reciprocal movement in a semi-arid degraded wetland: Implications for wetland restoration","display_title":"Isotopic Evidence of Soil Water Sources in MNNR","doi":"https://doi.org/10.1016/j.fmre.2022.11.001","theme":"Land & ecosystems","color":"#c8df98","year":2022,"publication_date":"2022-11-17","authors":["Yuanchun Zou","Sijian Zhang","Xiaofei Yu","Guobin Fu","Xianguo Lü"],"summary":"This study investigates water sources and unsaturated soil water movement in a seasonally flooded wetland within the Momoge National Nature Reserve (MNNR), located in the Songnen Plain of Northeast China. By analysing stable isotopes δD and δ18O in precipitation, surface water, groundwater, and soil water, combined with HYDRUS-1D modeling, the research identifies the contributions of precipitation and groundwater to soil water at different depths, revealing bidirectional unsaturated soil water dynamics in this semi-arid degraded wetland. The findings have implications for efficient wetland restoration and water management in similar semi-arid ecosystems.","finding":"Unsaturated soil water in the MNNR wetland is sourced from both precipitation and groundwater with depth-dependent variations; notably, groundwater can move upward to about 25 cm during dry periods, indicating capillary rise that sustains wetland plants, and highlighting critical water replenishment timing for restoration from late June to early July.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"The effective replenishment of such wetlands in degraded dilemmas is particularly important for sustaining their ecological functions. In this study, we concluded that unsaturated soil water sourced from both precipitation and groundwater with variations in different layers and bidirectional unsaturated soil water movement highlighted the possibility of utilising capillary rising groundwater to sustain wetland plants’ water requirements when suffering water shortages. Local ecological water replenishment was conducted from the end of June to the beginning of July as the critical time for restoring degraded wetlands with limited water resources. Declaration of competing interest The authors declare that they have no conflicts of interest in this work. Acknowledgments We thank Prof. Ming Jiang for his kindly help to this manuscript.","verified_in_page":true},"topics":["stable isotopes","unsaturated soil water","soil water sourcing","soil water movement","wetland restoration","hydrological modeling","semi-arid wetlands","capillary rise"],"study_period":"2017 growing season (June to September)","locations":[{"name":"Momoge National Nature Reserve","kind":"region","role":"Primary study area, site of wetland and soil water isotopic sampling","river_name":null,"geocode_query":"Momoge National Nature Reserve, Songnen Plain, Jilin, China","latitude":45.7,"longitude":123.7,"evidence":{"passage_id":"p002-002","page":2,"quote":"Study area Located in the western Songnen Plain of Northeastern China along the Nenjiang River, MNNR (45°42′–46°18′ N, 123°27′–124°04′ E) includes various types of wetlands (e.g., marshes, shallow lakes, and floodplains), dominated by Scirpus spp., Carex spp., Typha spp., and Phragmites australis. It is one of the most important stopover sites for Grus leucogeranus globally, and the tuber of S. planiculmis is one of the most important food sources for G. leucogeranus. MNNR has a semi-arid monsoon climate, with an annual average temperature of 4.4 °C, precipitation of 380 mm, and evaporation of 1472 mm [25]. For agricultural water diversion and drainage, hydrological regimes have shifted from climate-driven to anthropogenic-driven. Consequently, some peripheral natural wetlands have degraded, and the primary wetland plants growing in shallow water have evolved from S. planiculmis to T. angustifolia and P. australis.","verified_in_page":true},"resolution_status":"resolved","coordinates":[123.58355,45.92542],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"China, Ji Lin Sheng, Bai Cheng Shi, Zhen Lai Xian, 莫莫格国家级自然保护区 CN 吉林省 白城市 镇赉县 4 公里 邮政编码: 137316","basin_id":4040186800,"main_basin_id":4040000880,"basin_region":"Central and South-East Asia","basin_area_km2":552626.5,"hydrology_source":"hydrobasins","site_id":"doc-a2ffb42b0974cfd2bf5748a1-site-0"},{"name":"White Crane Lake","kind":"wetland","role":"Key wetland within MNNR with constructed loop causeway; site adjacent to soil isotopic sampling","river_name":null,"geocode_query":"White Crane Lake, Momoge National Nature Reserve, Songnen Plain, Jilin, China","latitude":45.9,"longitude":123.7,"evidence":{"passage_id":"p002-003","page":2,"quote":"To block surface water and create suitable foraging habitats for these migratory cranes, a loop causeway (ca. 4 m × 1 m × 36 km) was constructed around one of the key wetlands, White Crane Lake (formerly named Etoupao), in 2008. White Crane Lake is mainly supplied by precipitation in summer and discharge from upstream paddy fields in autumn via the unique water inlet in the northwest corner and flowing out through the outlet in the south causeway [26,27]. 2.2. Water sampling and isotope analyses The sampling area (45°53′–45°56′ N, 123°40′–123°44′ E) is located near the White Crane Lake Observatory (Fig. 1a). As there were no local weather stations for long-term monitoring, we obtained the monthly mean precipitation and evaporation from 1960 to 2020 at the Baicheng National Basic Weather Station as a reference (Fig. 1b). The marsh soil is seasonally flooded, with fluctuating surface water depths ranging from 0 to 60 cm, and the dominant plants are P. australis and T. angustifolia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Songnen Plain","kind":"region","role":"Broad regional setting for MNNR wetland ecosystem and climate context","river_name":null,"geocode_query":"Songnen Plain, Northeast China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"In this study, we carried out δD and δ18O analyses of precipitation, unsaturated soil water, overland flow, surface runoff, and groundwater samples from a seasonally flooded wetland in the Momoge National Nature Reserve of the Songnen Plain, Northeast China, to identify the water sources and understand the mechanisms of unsaturated soil water movement. Unsaturated soil water content (W/W%) at every 20 cm along with a soil profile (0–100 cm) was collected during the growing season, and the HYDRUS-1D model was used to simulate temporal-spatial variations. The results showed that the local meteoric water line (δD = 5.90δ18O-7.34, R2 = 0.95) had a smaller slope and intercept than the global meteoric water line because of strong evaporation at our study site under semi-arid climate. The groundwater was partly recharged by local precipitation via overland flow and unsaturated soil water infiltration.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Nenjiang River","kind":"river","role":"Major river along which MNNR is located, defining regional hydrology","river_name":"Nenjiang River","geocode_query":"Nenjiang River, Northeast China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Study area Located in the western Songnen Plain of Northeastern China along the Nenjiang River, MNNR (45°42′–46°18′ N, 123°27′–124°04′ E) includes various types of wetlands (e.g., marshes, shallow lakes, and floodplains), dominated by Scirpus spp., Carex spp., Typha spp., and Phragmites australis. It is one of the most important stopover sites for Grus leucogeranus globally, and the tuber of S. planiculmis is one of the most important food sources for G. leucogeranus. MNNR has a semi-arid monsoon climate, with an annual average temperature of 4.4 °C, precipitation of 380 mm, and evaporation of 1472 mm [25]. For agricultural water diversion and drainage, hydrological regimes have shifted from climate-driven to anthropogenic-driven. Consequently, some peripheral natural wetlands have degraded, and the primary wetland plants growing in shallow water have evolved from S. planiculmis to T. angustifolia and P. australis.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.4040087732965,50.00070470436218],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nen Jiang, China","basin_id":4040186800,"main_basin_id":4040000880,"basin_region":"Central and South-East Asia","basin_area_km2":552626.5,"hydrology_source":"hydrobasins","site_id":"doc-a2ffb42b0974cfd2bf5748a1-site-3"},{"name":"Baicheng National Basic Weather Station","kind":"station","role":"Reference location for regional climate data (precipitation, evaporation) used in analysis","river_name":null,"geocode_query":"Baicheng National Basic Weather Station, Jilin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"To block surface water and create suitable foraging habitats for these migratory cranes, a loop causeway (ca. 4 m × 1 m × 36 km) was constructed around one of the key wetlands, White Crane Lake (formerly named Etoupao), in 2008. White Crane Lake is mainly supplied by precipitation in summer and discharge from upstream paddy fields in autumn via the unique water inlet in the northwest corner and flowing out through the outlet in the south causeway [26,27]. 2.2. Water sampling and isotope analyses The sampling area (45°53′–45°56′ N, 123°40′–123°44′ E) is located near the White Crane Lake Observatory (Fig. 1a). As there were no local weather stations for long-term monitoring, we obtained the monthly mean precipitation and evaporation from 1960 to 2020 at the Baicheng National Basic Weather Station as a reference (Fig. 1b). The marsh soil is seasonally flooded, with fluctuating surface water depths ranging from 0 to 60 cm, and the dominant plants are P. australis and T. angustifolia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Lack of long-term isotopic monitoring limits modeling of mean residence times of soil water","Some unsaturated soil water samples were not collected on all sampling dates due to field difficulties","Incomplete coordinate data for some referenced locations","Broad regional geocode assignments due to unspecified exact sampling locations within MNNR"],"river_ids":[],"site_ids":["doc-a2ffb42b0974cfd2bf5748a1-site-0","doc-a2ffb42b0974cfd2bf5748a1-site-3"],"anchor":[123.58355,45.92542],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[4040186800],"main_basin_ids":[4040000880]},{"id":"doc-a4c9d11a8a3b531181f268f4","title":"Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations","display_title":"Climate & Human Impact on Lancang River Flow","doi":"https://doi.org/10.5194/hess-26-5315-2022","theme":"Water management","color":"#eeabac","year":2022,"publication_date":"2022-10-26","authors":["Xiongpeng Tang","Guobin Fu","Silong Zhang","Chao Gao","Guoqing Wang","Zhenxin Bao","Yanli Liu","Cuishan Liu","Junliang Jin"],"summary":"This study develops a novel framework considering hydrological model uncertainty to attribute streamflow changes to climate change (CC) and human activities (HAs) in the Lancang River basin, China. It uses the Soil and Water Assessment Tool (SWAT) model calibrated with uncertainty analyses and compares with Budyko framework and water use data. The streamflow reduction after 2005 (coincident with dam construction) is attributed about 42.6% to CC and 57.4% to HAs. Monthly analyses show June and November streamflow decline mainly due to CC, while other months are mainly influenced by HAs such as reservoir regulation. The framework addresses hydrological model equifinality and improves attribution precision.","finding":"The streamflow decrease at Yunjinghong station after 2005 was caused approximately 42.6% by climate change and 57.4% by human activities, notably reservoir construction and operation, as quantified considering hydrological model uncertainty through a new framework.","finding_evidence":{"passage_id":"p012-004","page":12,"quote":"In the different periods, the model performed well in the validation period compared with that in the calibration period, which indicated that the model has good predictive ability in the LR basin. 4.3 Quantification of CC and HAs for streamflow change considering the uncertainties 4.3.1 Quantification of the impacts considering the uncertainties at the annual scale The 575 simulations with NSEs greater than 0.75 were selected to construct the posterior histogram frequency distribution (PHD) of the CR of CC and HAs to streamflow changes in the LR basin. Figure 10 shows the number of simulations of the CC CR in 5 % intervals and their corresponding NS box plots. In total, 167 out of 575 simulations calculated that the CR of CC in the LR basin to runoff reduction was 40 %–45 %, and the average NSE was 0.84. Then, 131 and 92 of the simulation results had calculated climate CRs of 35 %–40 % and 45 %–50 %, respectively. The CR of CC in other intervals had relatively few simulations.","verified_in_page":true},"topics":["hydrological-modelling","hydrological-uncertainty","climate-change","human-impact","reservoir-effects","streamflow-trends","Mann-Kendall-test","water-use-analysis"],"study_period":"1961–2015","locations":[{"name":"Lancang River basin","kind":"basin","role":"study area","river_name":"Lancang River","geocode_query":"Lancang River basin, Southwest China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area.","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.158088,27.072573],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4041043580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":231768.3,"hydrology_source":"hydrobasins","site_id":"doc-a4c9d11a8a3b531181f268f4-site-0","true_coordinates":[99.15808799999999,27.132573],"spread_km":6.7},{"name":"Yunjinghong hydrological station","kind":"station","role":"hydrological observation and simulation site","river_name":"Lancang River","geocode_query":"Yunjinghong station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-000","page":10,"quote":"5324 X. Tang et al.: Attribution of climate change and human activities to streamflow variations Figure 4. The Mann–Kendall break point testing statistics of the annual streamflow for the LR basin from 1961 to 2015. 4 Results 4.1 Hydrological and meteorological trends in the LR basin 4.1.1 Trends and break points of the streamflow The results of the Mann–Kendall break point test for the annual streamflow at Yunjinghong station within the period from 1961 to 2015 are shown in Fig. 4. Since the intersection of the UF and UB curves in Fig. 4 is within the confidence intervals (of 0.05 and 0.01), the break point of the annual streamflow in the LR basin occurred in 2005. Combined with the construction of reservoirs in the LR basin, the construction of the Xiaowan hydropower station started in October 2004 (with total storage capacity = 15.3 km3 ).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Xiaowan hydropower station","kind":"facility","role":"major dam causing streamflow change breakpoint in 2005","river_name":"Lancang River","geocode_query":"Xiaowan hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-000","page":10,"quote":"5324 X. Tang et al.: Attribution of climate change and human activities to streamflow variations Figure 4. The Mann–Kendall break point testing statistics of the annual streamflow for the LR basin from 1961 to 2015. 4 Results 4.1 Hydrological and meteorological trends in the LR basin 4.1.1 Trends and break points of the streamflow The results of the Mann–Kendall break point test for the annual streamflow at Yunjinghong station within the period from 1961 to 2015 are shown in Fig. 4. Since the intersection of the UF and UB curves in Fig. 4 is within the confidence intervals (of 0.05 and 0.01), the break point of the annual streamflow in the LR basin occurred in 2005. Combined with the construction of reservoirs in the LR basin, the construction of the Xiaowan hydropower station started in October 2004 (with total storage capacity = 15.3 km3 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.225507,27.132573],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lancang River, Weixi Lisu Autonomous County, Diqing Tibetan Autonomous Prefecture, China, 674607","basin_id":4041043580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":231768.3,"hydrology_source":"hydrobasins","site_id":"doc-a4c9d11a8a3b531181f268f4-site-2","true_coordinates":[99.15808799999999,27.132573],"spread_km":6.7},{"name":"Manwan hydropower station","kind":"facility","role":"large reservoir affecting streamflow","river_name":"Lancang River","geocode_query":"Manwan hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Its mainstream has a total length of ∼ 2161 km and a total catchment area of ∼ 160 000 km2 (Han et al., 2019; D. Li et al., 2017). The topography of the LR is characterized by high northern and low southern portions; the maximum elevation in the northern mountainous area can reach ∼ 5871 m, while the lowest elevation in the downstream area is only ∼ 547 m (Fig. 1). This steep terrain difference also leads to the LR having a large potential for hydropower resources. During the past few decades, Huaneng Lancangjiang Hydropower Co., Ltd. constructed six large hydropower stations (i.e., Gongguoqiao, Xiaowan, Manwan, Dachaoshan, Nuozhadu, and Jinghong) on the mainstream of the LR to meet the demands for power and irrigation water in Southwest China (Fig. 1 and Table 1) (Han et al., 2019; Hennig et al., 2013; Xue et al., 2011).","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.158088,27.192573],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lancang River, Weixi Lisu Autonomous County, Diqing Tibetan Autonomous Prefecture, China, 674607","basin_id":4041043580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":231768.3,"hydrology_source":"hydrobasins","site_id":"doc-a4c9d11a8a3b531181f268f4-site-3","true_coordinates":[99.15808799999999,27.132573],"spread_km":6.7},{"name":"Dachaoshan hydropower station","kind":"facility","role":"large reservoir affecting streamflow","river_name":"Lancang River","geocode_query":"Dachaoshan hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Its mainstream has a total length of ∼ 2161 km and a total catchment area of ∼ 160 000 km2 (Han et al., 2019; D. Li et al., 2017). The topography of the LR is characterized by high northern and low southern portions; the maximum elevation in the northern mountainous area can reach ∼ 5871 m, while the lowest elevation in the downstream area is only ∼ 547 m (Fig. 1). This steep terrain difference also leads to the LR having a large potential for hydropower resources. During the past few decades, Huaneng Lancangjiang Hydropower Co., Ltd. constructed six large hydropower stations (i.e., Gongguoqiao, Xiaowan, Manwan, Dachaoshan, Nuozhadu, and Jinghong) on the mainstream of the LR to meet the demands for power and irrigation water in Southwest China (Fig. 1 and Table 1) (Han et al., 2019; Hennig et al., 2013; Xue et al., 2011).","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.090669,27.132573],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lancang River, Weixi Lisu Autonomous County, Diqing Tibetan Autonomous Prefecture, China, 674607","basin_id":4041043580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":231768.3,"hydrology_source":"hydrobasins","site_id":"doc-a4c9d11a8a3b531181f268f4-site-4","true_coordinates":[99.15808799999999,27.132573],"spread_km":6.7},{"name":"Jinghong hydropower station","kind":"facility","role":"large reservoir affecting streamflow","river_name":"Lancang River","geocode_query":"Jinghong hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Its mainstream has a total length of ∼ 2161 km and a total catchment area of ∼ 160 000 km2 (Han et al., 2019; D. Li et al., 2017). The topography of the LR is characterized by high northern and low southern portions; the maximum elevation in the northern mountainous area can reach ∼ 5871 m, while the lowest elevation in the downstream area is only ∼ 547 m (Fig. 1). This steep terrain difference also leads to the LR having a large potential for hydropower resources. During the past few decades, Huaneng Lancangjiang Hydropower Co., Ltd. constructed six large hydropower stations (i.e., Gongguoqiao, Xiaowan, Manwan, Dachaoshan, Nuozhadu, and Jinghong) on the mainstream of the LR to meet the demands for power and irrigation water in Southwest China (Fig. 1 and Table 1) (Han et al., 2019; Hennig et al., 2013; Xue et al., 2011).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Gongguoqiao hydropower station","kind":"facility","role":"large reservoir affecting streamflow","river_name":"Lancang River","geocode_query":"Gongguoqiao hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Its mainstream has a total length of ∼ 2161 km and a total catchment area of ∼ 160 000 km2 (Han et al., 2019; D. Li et al., 2017). The topography of the LR is characterized by high northern and low southern portions; the maximum elevation in the northern mountainous area can reach ∼ 5871 m, while the lowest elevation in the downstream area is only ∼ 547 m (Fig. 1). This steep terrain difference also leads to the LR having a large potential for hydropower resources. During the past few decades, Huaneng Lancangjiang Hydropower Co., Ltd. constructed six large hydropower stations (i.e., Gongguoqiao, Xiaowan, Manwan, Dachaoshan, Nuozhadu, and Jinghong) on the mainstream of the LR to meet the demands for power and irrigation water in Southwest China (Fig. 1 and Table 1) (Han et al., 2019; Hennig et al., 2013; Xue et al., 2011).","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Nuozhadu hydropower station","kind":"facility","role":"largest reservoir influencing hydrology and major HA impact on streamflow","river_name":"Lancang River","geocode_query":"Nuozhadu hydropower station, Lancang River basin, Yunnan, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Its mainstream has a total length of ∼ 2161 km and a total catchment area of ∼ 160 000 km2 (Han et al., 2019; D. Li et al., 2017). The topography of the LR is characterized by high northern and low southern portions; the maximum elevation in the northern mountainous area can reach ∼ 5871 m, while the lowest elevation in the downstream area is only ∼ 547 m (Fig. 1). This steep terrain difference also leads to the LR having a large potential for hydropower resources. During the past few decades, Huaneng Lancangjiang Hydropower Co., Ltd. constructed six large hydropower stations (i.e., Gongguoqiao, Xiaowan, Manwan, Dachaoshan, Nuozhadu, and Jinghong) on the mainstream of the LR to meet the demands for power and irrigation water in Southwest China (Fig. 1 and Table 1) (Han et al., 2019; Hennig et al., 2013; Xue et al., 2011).","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Uncertain coordinates due to lack of explicit geolocation data in source text","Study period partitioning assumes natural and impacted periods with limited direct HA impact during natural period, which may be imperfect","Land use changes dynamically during study but only static 2010 land use used in modeling","Some hydrological parameter combinations may remain physically unreasonable despite uncertainty analysis","Framework derived from one basin requires validation in other basins","Assumes reservoir and land use impacts incorporated in human activities category may mask detailed influences"],"river_ids":["ne-609"],"site_ids":["doc-a4c9d11a8a3b531181f268f4-site-0","doc-a4c9d11a8a3b531181f268f4-site-2","doc-a4c9d11a8a3b531181f268f4-site-3","doc-a4c9d11a8a3b531181f268f4-site-4"],"anchor":[99.158088,27.192573],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[4041043580],"main_basin_ids":[4040017020]},{"id":"doc-ad72c2ab3d10fbbf07bb73c7","title":"Molecular dynamics study of uranyl adsorption from aqueous solution to smectite","display_title":"Molecular Dynamics of Uranyl Adsorption to Smectite","doi":"https://doi.org/10.1016/j.clay.2021.106361","theme":"Land & ecosystems","color":"#c8df98","year":2022,"publication_date":"2022-02-02","authors":["Junfang Zhang","Dirk Mallants","Patrick V. Brady"],"summary":"This study used molecular dynamics simulations to investigate the adsorption behavior of uranyl (UO2 2+) species from aqueous uranyl carbonate solutions to Na-smectite clay mineral surfaces, assessing effects of different pore sizes on adsorption mechanisms and sorption coefficients relevant for radioactive waste disposal safety assessments.","finding":"The adsorption mechanism of uranyl onto Na-smectite is dominated by electrostatic interactions that form various polynuclear uranyl carbonate complexes; the sorption coefficient KD varies with pore size, decreasing from 151 mL/g in larger pores to 59 mL/g in smaller pores, with adsorption sites primarily associated to Al substitution sites on the clay surface.","finding_evidence":{"passage_id":"p014-000","page":14,"quote":"Applied Clay Science 218 (2022) 106361 14 polynuclear uranyl species [Na3(UO2)16(CO3)18] − was found in solution. Similar polynuclear species had been reported in the literature for uranyl(VI)‐carbonate systems based on experimental studies. 2. The predominant adsorption mechanism was through electrostatic interactions which controlled the formation and locations of uranyl complex. On the clay surfaces, uranyl‐carbonate complexes were neutral or positively charged with all the positive charges balanced by the negative charges on the external clay siloxane surfaces due to isomorphic substitutions. Once the negative clay surface charges were neutralized by net positive charges, no further ions or charged complexes were detectable in the adsorbed layers. 3. Pore size affected the uranyl adsorption behaviour. The sorption parameter KD for uranyl ranged from 59 to 151 mL/g and was dependent on pore size (smaller KD for larger pore size).","verified_in_page":true},"topics":["molecular-dynamics","adsorption","clay-minerals","uranyl-species","carbonate-complex","surface-charge","sorption-coefficient","radioactive-waste-disposal"],"study_period":null,"locations":[{"name":"National Computational Infrastructure","kind":"facility","role":"Computational facility where molecular dynamics simulations were performed","river_name":null,"geocode_query":"National Computational Infrastructure, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"This is similar to Berendsen coupling (Berendsen et al., 1984) with the same temperature coupling time constant of 0.5 ps, where the stochastic term ensured that a proper NVT ensemble was generated. The equations of motion were integrated using the leap-frog algorithm with a time step of 2 fs. A cut-off of 1.2 nm was used for short - range interactions. For the long-range Coulomb forces, the Particle Mesh Ewald (PME) scheme (Essmann et al., 1995) with interpolation order of 4 and a grid spacing of 0.1 nm, was used. Periodic boundary conditions were applied in three directions. Each MD simulation was carried out in two steps. The first step was a 100 ps energy minimization simulation using the steepest-descent method to prevent crashes of the simulation due to bad contact (extremely large force) between atoms. The second step consisted of a 245 ns long continuous simulation to track system responses over time throughout the simulation process.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"CSIRO Energy, Dick Perry Ave, Western Australia, Australia","kind":"facility","role":"Author affiliation; site of initial research development","river_name":null,"geocode_query":"26 Dick Perry Ave, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Applied Clay Science 218 (2022) 106361 Available online 2 February 2022 0169-1317/Crown Copyright © 2021 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research Paper Molecular dynamics study of uranyl adsorption from aqueous solution to smectite Junfang Zhang a,* , Dirk Mallants b , Patrick V. Brady c a CSIRO Energy, 26 Dick Perry Ave, WA 6151, Australia b CSIRO Land and Water, Waite Road, Urrbrae SA 5064, Australia c SANDIA National Laboratories, Albuquerque, NM 87185-0736, USA ARTICLE INFO Keywords: Adsorption mechanism Smectite Uranyl carbonate complex Distribution coefficient Surface charge ABSTRACT Adsorption of aqueous uranyl species in pore spaces of clay minerals plays a key role in post-closure safety assessments for geological disposal of radioactive waste.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"CSIRO Land and Water, Waite Road, Urrbrae, South Australia","kind":"facility","role":"Author affiliation; research related to study","river_name":null,"geocode_query":"Waite Road, Urrbrae, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Applied Clay Science 218 (2022) 106361 Available online 2 February 2022 0169-1317/Crown Copyright © 2021 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research Paper Molecular dynamics study of uranyl adsorption from aqueous solution to smectite Junfang Zhang a,* , Dirk Mallants b , Patrick V. Brady c a CSIRO Energy, 26 Dick Perry Ave, WA 6151, Australia b CSIRO Land and Water, Waite Road, Urrbrae SA 5064, Australia c SANDIA National Laboratories, Albuquerque, NM 87185-0736, USA ARTICLE INFO Keywords: Adsorption mechanism Smectite Uranyl carbonate complex Distribution coefficient Surface charge ABSTRACT Adsorption of aqueous uranyl species in pore spaces of clay minerals plays a key role in post-closure safety assessments for geological disposal of radioactive waste.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"SANDIA National Laboratories, Albuquerque, New Mexico, USA","kind":"facility","role":"Author affiliation and co-author location contributing to study","river_name":null,"geocode_query":"Albuquerque, New Mexico, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Applied Clay Science 218 (2022) 106361 Available online 2 February 2022 0169-1317/Crown Copyright © 2021 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Research Paper Molecular dynamics study of uranyl adsorption from aqueous solution to smectite Junfang Zhang a,* , Dirk Mallants b , Patrick V. Brady c a CSIRO Energy, 26 Dick Perry Ave, WA 6151, Australia b CSIRO Land and Water, Waite Road, Urrbrae SA 5064, Australia c SANDIA National Laboratories, Albuquerque, NM 87185-0736, USA ARTICLE INFO Keywords: Adsorption mechanism Smectite Uranyl carbonate complex Distribution coefficient Surface charge ABSTRACT Adsorption of aqueous uranyl species in pore spaces of clay minerals plays a key role in post-closure safety assessments for geological disposal of radioactive waste.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Study uses molecular dynamics simulation which models idealized smectite structure and solution chemistry, limiting direct real-world complexity.","Coordinates for clay mineral locations and experimental sites are not provided and thus latitude/longitude are null.","Pore sizes studied (8.37, 25.1, 33.5 nm) are model parameters rather than field measurements; local geological variability not accounted for."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[],"main_basin_ids":[]},{"id":"doc-b1209543124997eacf7df7f3","title":"The 21st century water quality challenges for managed aquifer recharge: towards a risk-based regulatory approach","display_title":"Water Quality Challenges in Managed Aquifer Recharge","doi":"https://doi.org/10.1007/s10040-022-02543-z","theme":"Groundwater","color":"#a8b9ff","year":2022,"publication_date":"2022-09-27","authors":["Yan Zheng","Joanne Vanderzalm","Niels Hartog","Enrique Fernández Escalante","Catalin Stefan"],"summary":"This essay discusses the challenges managed aquifer recharge (MAR) faces in maintaining groundwater quality given the introduction of novel contaminants including chemicals and biological entities. It stresses the need for risk-based regulatory frameworks, the concept of a subsurface attenuation zone to naturally treat recharge water, and advances in monitoring and treatment technologies to ensure the sustainability and safety of MAR systems globally.","finding":"Arsenic contamination is a widespread and critical challenge in MAR sites worldwide, often caused by geochemical reactions such as pyrite oxidation and reductive dissolution, which can result in arsenic concentrations far exceeding safe regulatory limits in recovered water.","finding_evidence":{"passage_id":"p002-003","page":2,"quote":"In Florida (USA), injecting oxygenated Tampa City supply water into the Suwannee Limestone of the Upper Floridan aquifer containing pyrite (Price and Pichler 2006) resulted in pyrite-oxidation-driven arsenic release, with recovered water arsenic concentrations frequently exceeding 10 μg/L and rising to as high as 130 μg/L (Jones and Pichler 2007). Recharging a reduced, As-rich coastal aquifer in Khulna, Bangladesh, with pond water resulted in arsenic concentrations in recovered water of >100 μg/L (Sultana et al. 2015) and was attributed to reductive dissolution of As-bearing Fe-oxyhydroxide. A recent critical review on mobilization of arsenic and other naturally occurring contaminants during MAR (Fakhreddine et al. 2021) concludes that arsenic poses the most widespread challenge at MAR sites due to its ubiquity in subsurface sediments and toxicity at trace concentrations; other geogenic contaminants of concern include iron, molybdenum, manganese, chromium, and fluoride.","verified_in_page":true},"topics":["managed aquifer recharge","groundwater quality","arsenic mobilization","hydrogeochemical processes","risk-based regulation","subsurface attenuation zone","water reuse and recycling","microbial water treatment"],"study_period":null,"locations":[{"name":"Florida","kind":"region","role":"Case study location where pyrite oxidation in the Suwannee Limestone led to elevated arsenic in MAR recovered water","river_name":null,"geocode_query":"Florida, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In Florida (USA), injecting oxygenated Tampa City supply water into the Suwannee Limestone of the Upper Floridan aquifer containing pyrite (Price and Pichler 2006) resulted in pyrite-oxidation-driven arsenic release, with recovered water arsenic concentrations frequently exceeding 10 μg/L and rising to as high as 130 μg/L (Jones and Pichler 2007). Recharging a reduced, As-rich coastal aquifer in Khulna, Bangladesh, with pond water resulted in arsenic concentrations in recovered water of >100 μg/L (Sultana et al. 2015) and was attributed to reductive dissolution of As-bearing Fe-oxyhydroxide. A recent critical review on mobilization of arsenic and other naturally occurring contaminants during MAR (Fakhreddine et al. 2021) concludes that arsenic poses the most widespread challenge at MAR sites due to its ubiquity in subsurface sediments and toxicity at trace concentrations; other geogenic contaminants of concern include iron, molybdenum, manganese, chromium, and fluoride.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Suwannee Limestone, Upper Floridan aquifer, Florida","kind":"aquifer","role":"Specific aquifer where oxygenated water injection caused pyrite oxidation and arsenic mobilization during MAR","river_name":null,"geocode_query":"Suwannee Limestone, Florida, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In Florida (USA), injecting oxygenated Tampa City supply water into the Suwannee Limestone of the Upper Floridan aquifer containing pyrite (Price and Pichler 2006) resulted in pyrite-oxidation-driven arsenic release, with recovered water arsenic concentrations frequently exceeding 10 μg/L and rising to as high as 130 μg/L (Jones and Pichler 2007). Recharging a reduced, As-rich coastal aquifer in Khulna, Bangladesh, with pond water resulted in arsenic concentrations in recovered water of >100 μg/L (Sultana et al. 2015) and was attributed to reductive dissolution of As-bearing Fe-oxyhydroxide. A recent critical review on mobilization of arsenic and other naturally occurring contaminants during MAR (Fakhreddine et al. 2021) concludes that arsenic poses the most widespread challenge at MAR sites due to its ubiquity in subsurface sediments and toxicity at trace concentrations; other geogenic contaminants of concern include iron, molybdenum, manganese, chromium, and fluoride.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-81.89916699999999,27.365278],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone, FL, USA","basin_id":7040043170,"main_basin_id":7040043170,"basin_region":"North America and Caribbean","basin_area_km2":259084.0,"hydrology_source":"hydrobasins","site_id":"doc-b1209543124997eacf7df7f3-site-1"},{"name":"Khulna","kind":"settlement","role":"Coastal aquifer site where MAR with pond water caused reductive dissolution releasing arsenic exceeding safe levels in recovered water","river_name":null,"geocode_query":"Khulna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In Florida (USA), injecting oxygenated Tampa City supply water into the Suwannee Limestone of the Upper Floridan aquifer containing pyrite (Price and Pichler 2006) resulted in pyrite-oxidation-driven arsenic release, with recovered water arsenic concentrations frequently exceeding 10 μg/L and rising to as high as 130 μg/L (Jones and Pichler 2007). Recharging a reduced, As-rich coastal aquifer in Khulna, Bangladesh, with pond water resulted in arsenic concentrations in recovered water of >100 μg/L (Sultana et al. 2015) and was attributed to reductive dissolution of As-bearing Fe-oxyhydroxide. A recent critical review on mobilization of arsenic and other naturally occurring contaminants during MAR (Fakhreddine et al. 2021) concludes that arsenic poses the most widespread challenge at MAR sites due to its ubiquity in subsurface sediments and toxicity at trace concentrations; other geogenic contaminants of concern include iron, molybdenum, manganese, chromium, and fluoride.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.540047,22.777329],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Khulna, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-b1209543124997eacf7df7f3-site-2","true_coordinates":[89.54004719999999,22.8373287],"spread_km":6.7},{"name":"Kern Water Bank","kind":"facility","role":"Water banking authority in California, USA, practicing MAR in alluvial fan deposits of the Kern River and monitoring groundwater age and quality","river_name":"Kern River","geocode_query":"Kern Water Bank, California, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"This should be of interest to many water banking authorities such as those in the western US states. The Kern Water Bank in the USA, initiated in the early 1970s, recharged 1.13 billion m3 through 44 km2 of recharge/spreading basins between 1995 and 2000 to alluvial fan deposits of the Kern River. Meillier et al. (2008) used dissolved chlorofluorocarbons (CFC-11 and CFC-12) to estimate groundwater ages of the target alluvial aquifer, finding that the youngest apparent ages (younger than 1985) were found in the shallow wells in the northern and central sections of the study area where MAR is usually performed. The recovered water here is suitable for irrigation but needs further treatment if used for drinking. With the new analytical capability regarding contaminants of emerging concerns and microbial genomes, water banking authorities, out of their fiduciary duty, can expand their monitoring programs to track the recharged water as it “ages” in the aquifer.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-119.0974963,35.3401336],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"1620 Mill Rock Way #500, Bakersfield, CA 93311, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-b1209543124997eacf7df7f3-site-3"},{"name":"Netherlands","kind":"region","role":"Site of MAR research evaluating pathogen die-off during recharge of stormwater into a brackish anoxic aquifer to assess irrigation water safety","river_name":null,"geocode_query":"Netherlands","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"While it is understood that microbially mediated processes are an important control on water quality, and in particular, water quality improvement, approaches are required to assess aquifer microbial communities, their potential to augment treatment, response to changing geochemical conditions, and ultimately the sustainability of treatment. Leveraging the natural treatment capacity, where available, allows for the design of a sustainable treatment train and avoids overuse of energy-intensive engineered pretreatment without overtreating water prior to MAR—for example, a current environmental challenge is the widespread use and environmental impact of perfluoroalkyl and polyfluoroalkyl substances (PFAS). The fate of PFAS in MAR is uncertain and thus pretreatment or posttreatment technologies may be required to manage this risk (Page et al. 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Berlin","kind":"settlement","role":"Location of field studies implementing sequential MAR technology to improve removal of trace organic chemicals","river_name":null,"geocode_query":"Berlin, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"This advancement has led to a recent focus on strategies to optimize water quality treatment, such as through advanced pre-treatment or by a combination of MAR types (Hellauer et al. 2018), incorporation of reactive barriers (Valhondo et al. 2020), or manipulation of the subsurface redox zones (Bartak et al. 2017). Water quality investigations during MAR projects serve multiple aims. While monitoring for regulatory compliance is a basic starting point, it is not sufficient to adequately manage water quality. Here inorganic arsenic is used to illustrate the importance of having a good understanding of hydrogeochemical processes and their potential impact on MAR operations, with the ability to make prediction a plus. Arsenic is such a highly toxic chemical that even the regulatory limit of 10 μg/L adopted by most countries is not entirely protective of public health.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.404954,52.460007],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Berlin, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-b1209543124997eacf7df7f3-site-5","true_coordinates":[13.404954,52.52000659999999],"spread_km":6.7},{"name":"Western US states","kind":"region","role":"Reference area where water banking authorities use MAR and where understanding storage time for contaminant mineralization is important","river_name":null,"geocode_query":"Western United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Considering MAR as a step in a treatment train enables one to manage the complex topic of water quality when MAR alone cannot provide sufficient treatment, fate cannot be predicted, or where water quality degradation may occur. The natural treatment processes endowed by storage in the aquifer are credited for helping the public to overcome the “yuck factor” associated with recycling treated wastewater for drinking water supply (Alley and Alley 2022). Faced with the unknowns and uncertainties of regulated and unregulated water quality threats, the assumption that storage time mitigates risk, especially of pathogens, biodegradable organic matter, and trace organic chemicals, is likely to hold (NRMMC, EPHC, NHMRC 2009), although more research is warranted to determine the time scale and environmental conditions (Hübner et al. 2022) for complete mineralization, including mostly unknown biotransformation byproducts (Ma et al. 2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-117.627752,43.924072],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western United States, USA","basin_id":7040388740,"main_basin_id":7040014930,"basin_region":"North America and Caribbean","basin_area_km2":191669.7,"hydrology_source":"hydrobasins","site_id":"doc-b1209543124997eacf7df7f3-site-6","true_coordinates":[-117.6277515,43.9840719],"spread_km":6.7}],"limitations":["Some locations are broad regions rather than precise field study sites due to the nature of the review essay","Absence of explicit latitude and longitude coordinates for locations mentioned","Limited detailed geospatial data due to the essay format of the source article"],"river_ids":[],"site_ids":["doc-b1209543124997eacf7df7f3-site-1","doc-b1209543124997eacf7df7f3-site-2","doc-b1209543124997eacf7df7f3-site-3","doc-b1209543124997eacf7df7f3-site-5","doc-b1209543124997eacf7df7f3-site-6"],"anchor":[-81.89916699999999,27.365278],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040024170,4040025460,7040013550,7040043170,7040388740],"main_basin_ids":[2040024170,4040025460,7040013550,7040014930,7040043170]},{"id":"doc-b7087a36fa3cc658358441a7","title":"Identifying Minimum Information Requirements to Improve Integrated Modeling Capabilities: Lessons Learned From Dynamic Adaptive Policy Pathways","display_title":"Model Augmentation for Adaptive Pathways in Brahmani Basin","doi":"https://doi.org/10.3389/frwa.2022.768898","theme":"Water management","color":"#eeabac","year":2022,"publication_date":"2022-02-08","authors":["Caroline Rosello","J. H. A. Guillaume","P. Taylor","S. Cuddy","C. Pollino","A. J. Jakeman"],"summary":"This study presents a method to identify minimum information requirements for augmenting integrated assessment models to support decision-making under deep uncertainty. Using the Dynamic Adaptive Policy Pathways (DAPP) framework, the approach was applied to the Basin Futures model for water resources management in the Brahmani River Basin (India). The augmentation enabled exploratory modeling for designing robust and adaptive water management pathways considering climate and socioeconomic uncertainties.","finding":"On-farm water storages were found to be robust across future climate and socioeconomic scenarios, while other adaptation actions varied in their effectiveness depending on scenarios and administrative scales, highlighting the uneven benefits across the Brahmani River Basin states.","finding_evidence":{"passage_id":"p009-002","page":9,"quote":"When considering actions alone, only on-farm water storages (ofs) are robust enough under both future scenarios and our planning horizon. To ensure that the signpost used to monitor ATPs was sensitive enough, we consider other different combinations of actions, excluding ofs that are expected not to fail over our planning horizon. Over the resulting 548 possible combinations, only the ones systematically including both water treatment and the implementation of proposed dams were able to display different responses based on future scenarios. Considering administrative scales highlights an uneven response to future uncertainties across our actions. Generally, the upstream state (Jharkhand) appears to benefit more from different combinations of actions, under both future scenarios, from 2021 onward.","verified_in_page":true},"topics":["adaptive management","integrated assessment modeling","water resources","climate uncertainty","decision support systems","scenario analysis","river basin management","model augmentation"],"study_period":null,"locations":[{"name":"Brahmani River Basin","kind":"basin","role":"study area for integrated water resources management modeling","river_name":"Brahmani River","geocode_query":"Brahmani River Basin, Odisha, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Involving broader stakeholder groups will be important in applied contexts, but for the purpose of evaluating our proof-of-concept, their contribution to the conceptual representation of BRB system is approximated based on our understanding of the literature and expert validation of the conceptual model. Illustrative Case Study for Applying the DAPP-IAM Method—The Brahmani River Basin The BRB is in Northeast India and covers about 39,268 km2 encompassing three State Governments: Odissa (57%), Jharkhand (39%), and Chhattisgard (4%). The climate is tropical, sub-humid with most of the precipitation occurring during the monsoon season from June to September. The Brahmani River is formed by the convergence of the Sankh and South Koel River systems in the upper region and flows into the Bay of Bengal. Ecologically, about a third of the basin is covered by forest, mostly located in the central region of the basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[85.777592,21.431887],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-b7087a36fa3cc658358441a7-site-0","true_coordinates":[85.77759238473227,21.37188697198602],"spread_km":6.7},{"name":"Odissa","kind":"region","role":"downstream state in Brahmani basin with significant irrigation and water management challenges","river_name":null,"geocode_query":"Odisha, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Involving broader stakeholder groups will be important in applied contexts, but for the purpose of evaluating our proof-of-concept, their contribution to the conceptual representation of BRB system is approximated based on our understanding of the literature and expert validation of the conceptual model. Illustrative Case Study for Applying the DAPP-IAM Method—The Brahmani River Basin The BRB is in Northeast India and covers about 39,268 km2 encompassing three State Governments: Odissa (57%), Jharkhand (39%), and Chhattisgard (4%). The climate is tropical, sub-humid with most of the precipitation occurring during the monsoon season from June to September. The Brahmani River is formed by the convergence of the Sankh and South Koel River systems in the upper region and flows into the Bay of Bengal. Ecologically, about a third of the basin is covered by forest, mostly located in the central region of the basin.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Jharkhand","kind":"region","role":"upstream state in Brahmani basin, part of study for water resource actions and scenario evaluation","river_name":null,"geocode_query":"Jharkhand, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Involving broader stakeholder groups will be important in applied contexts, but for the purpose of evaluating our proof-of-concept, their contribution to the conceptual representation of BRB system is approximated based on our understanding of the literature and expert validation of the conceptual model. Illustrative Case Study for Applying the DAPP-IAM Method—The Brahmani River Basin The BRB is in Northeast India and covers about 39,268 km2 encompassing three State Governments: Odissa (57%), Jharkhand (39%), and Chhattisgard (4%). The climate is tropical, sub-humid with most of the precipitation occurring during the monsoon season from June to September. The Brahmani River is formed by the convergence of the Sankh and South Koel River systems in the upper region and flows into the Bay of Bengal. Ecologically, about a third of the basin is covered by forest, mostly located in the central region of the basin.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Chhattisgarh","kind":"region","role":"region partially encompassed in Brahmani basin but considered to have minor influence on water management","river_name":null,"geocode_query":"Chhattisgarh, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Involving broader stakeholder groups will be important in applied contexts, but for the purpose of evaluating our proof-of-concept, their contribution to the conceptual representation of BRB system is approximated based on our understanding of the literature and expert validation of the conceptual model. Illustrative Case Study for Applying the DAPP-IAM Method—The Brahmani River Basin The BRB is in Northeast India and covers about 39,268 km2 encompassing three State Governments: Odissa (57%), Jharkhand (39%), and Chhattisgard (4%). The climate is tropical, sub-humid with most of the precipitation occurring during the monsoon season from June to September. The Brahmani River is formed by the convergence of the Sankh and South Koel River systems in the upper region and flows into the Bay of Bengal. Ecologically, about a third of the basin is covered by forest, mostly located in the central region of the basin.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Bay of Bengal","kind":"other","role":"Fluvial receiving body of Brahmani River basin representing downstream boundary conditions","river_name":null,"geocode_query":"Bay of Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"Among protected areas, the Bhitakanika Mangrove ecosystem in the Deltaic region is recognized as a Ramsar wetland, and is of critical importance to support local livelihoods and biodiversity. Socio-economically, the population in the basin is mostly rural and relies on rainfed and irrigation crop productions, especially rice, for food and income. Rainfed agriculture is predominant in the basin, except in the deltaic region in the state of Odissa where irrigation plays a major role (Mainuddin et al., 2016). Additionally, the basin is rich in mineral resources which have contributed to its industrial and urban development, especially in the State of Odissa (ICID, 2005). When considering issues in the basin, climate variability is influential on streamflow regimes and surface water availability.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Rengali Dam","kind":"facility","role":"major dam in Brahmani basin critical for energy security, irrigation, and flood protection","river_name":"Brahmani River","geocode_query":"Rengali Dam, Odisha, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"To support aquatic system needs, dam operations include water allocations for the environment corresponding to 30% of their inflow from June to September and 20% in the other months (Pollino et al., 2016). Among the main storages in the basin, the Rengali Dam in the middle catchment in the State of Odissa is critical to ensuring energy security, irrigation supply and providing flood protection to downstream regions (ICID, 2005). The basin model setup using BF is presented in Figure 2. It includes seven reaches split partially or in their entirety between the three State Governments. For simplification purposes, we assume that the State of Chhattisgarh, due to its small area within the catchment, would have little influence on the BRB’s water developments, and that reaches in the model could be grouped with those in the State of Jharkhand (reaches 1–4) and the State of Orissa (reaches 5–7).","verified_in_page":true},"resolution_status":"resolved","coordinates":[85.0309219,21.2772125],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Post Office Road, Rengali Project Town, Bonor, Talcher, Odisha 759105, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-b7087a36fa3cc658358441a7-site-5"}],"limitations":["Coordinates for locations in Brahmani River Basin and associated administrative regions are not provided in the text.","The case study is hypothetical and does not involve stakeholder engagement, limiting real-world validation.","The model augmentation approach is demonstrated on one integrated assessment model; generalization to other models requires further testing.","Climate and socioeconomic uncertainty scenarios cover limited ranges; additional uncertainties remain unexplored.","Signposts used to identify adaptive tipping points rely on proxies and assumptions that may affect robustness.","Visualization tools like sparklines may require user training for effective use.","Validation of augmented models is qualitative; quantitative validation may be necessary for operational use."],"river_ids":["ne-846"],"site_ids":["doc-b7087a36fa3cc658358441a7-site-0","doc-b7087a36fa3cc658358441a7-site-5"],"anchor":[85.0309219,21.2772125],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-bc55e68e9f8589718db852e2","title":"A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions","display_title":"Assessment of Floodwater Depth Models in MDB","doi":"https://doi.org/10.1029/2022wr032031","theme":"Floodplains","color":"#e9bd77","year":2022,"publication_date":"2022-10-22","authors":["Jin Teng","Dave Penton","Catherine Ticehurst","Ashmita Sengupta","Andrew Freebairn","Steve Marvanek","Jai Vaze","Matthew S. Gibbs","N. A. Streeton","Fazlul Karim","S. Morton"],"summary":"This study evaluates the accuracy of three simple floodwater depth estimation models—HAND, TVD, and FwDET—in three river-floodplain systems within Australia's Murray-Darling Basin (MDB), incorporating comparison with hydrodynamic benchmarks and analysis of the effects of DEM inputs, flood magnitudes, and reach geomorphology. FwDET generally underestimates depths, while HAND and TVD tend to overestimate, with model suitability depending on intended applications such as emergency response, ecological studies, or planning.","finding":"FwDET model tends to underestimate floodwater depth (median underprediction 0.32 m), whereas HAND and TVD consistently overestimate floodwater depth (median overestimations 0.97 m and 0.98 m respectively), with FwDET performing best for shallow water depths and TVD for deeper waters, indicating that model choice should be guided by specific floodplain characteristics and application needs.","finding_evidence":{"passage_id":"p016-000","page":16,"quote":"Water Resources Research TENG ET AL. 10.1029/2022WR032031 16 of 19 choice of height above nearest neighbor threshold must be selected conservatively and there is a risk of losing community confidence if the overprediction is too high. Finally, FwDET and TVD may be appropriate for understanding floodplain processes, regional planning, and decision-making as long as the type of analysis is appropriate. An appropriate analysis might be to compare two bird breeding scenarios using FwDET outputs: (a) using a large number of floods (e.g., 26 or more), (b) using a large spatial extent (e.g., more than 50 point locations), and (c) applying a sensitivity analysis to understand the effect of model errors. In this case, although bird breeding at a site is not accurately predicted, the overall statistics could still explain which scenario is preferable.","verified_in_page":true},"topics":["floodplain-hydrology","digital-elevation-models","hydrodynamic-modelling","remote-sensing","model-evaluation","river-geomorphology","flood-depth-estimation","statistical-analysis"],"study_period":null,"locations":[{"name":"Namoi River from Keepit Dam to Barwon River near Walgett, New South Wales","kind":"reach","role":"study site for floodwater depth model assessment","river_name":"Namoi River","geocode_query":"Namoi River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.043889,-30.694167],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43674288,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-0","true_coordinates":[150.04388901700008,-30.634166583999956],"spread_km":6.7},{"name":"River Murray downstream of Lock 6 to upstream of Lock 3, South Australia","kind":"reach","role":"study site for floodwater depth model assessment","river_name":"River Murray","geocode_query":"River Murray, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.566389,-35.4025],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676891,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-1","true_coordinates":[143.5663889650001,-35.34249995499994],"spread_km":6.7},{"name":"Balonne River downstream of St George Township, Queensland to Weilmoringle, New South Wales","kind":"reach","role":"study site for floodwater depth model assessment","river_name":"Balonne River","geocode_query":"Balonne River, Queensland and New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.64638900600005,-27.907222116999947],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43670095,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-2"},{"name":"Murray-Darling Basin, Australia","kind":"basin","role":"overall basin encompassing study sites and context for study","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Study Area This study focused on three important river-floodplain systems in the Murray-Darling Basin (MDB), Australia, for the model assessment (Figure 1). The MDB is one of Australia's largest and most significant drainage basins supporting important environmental assets and around 40% of Australia's agricultural produce. The MDB is subject to irregular flood events and multiyear droughts. Mean annual precipitation across the study sites varies from around 200 mm/year in the west to around 500 mm/year in the east—with an observed minimum of 95 mm/ year in 2011 at Renmark and an observed maximum of 1,012 mm/year in 1969 at Narrabri (Bureau of Meteorology, 2021). Temperature at the study sites ranges from 34 to 38°C for maximums in summer and from 16 to 19°C for maximums in winter. Potential evaporation differs between 1,000 mm/year and 2,400 mm/year for the study sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.536938,-31.076107],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-3","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Keepit Dam, New South Wales, Australia","kind":"facility","role":"upstream boundary of Namoi River study site","river_name":"Namoi River","geocode_query":"Keepit Dam, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.5011111,-30.8652778],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674839,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Keepit Dam, Keepit NSW 2340, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-4"},{"name":"Walgett, New South Wales, Australia","kind":"settlement","role":"downstream boundary near study site on Namoi River","river_name":"Namoi River","geocode_query":"Walgett, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.1167617,-30.0215209],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43668953,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Walgett NSW 2832, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-5"},{"name":"Lock 3 and Lock 6, River Murray, South Australia, Australia","kind":"facility","role":"river infrastructure defining study reaches on River Murray","river_name":"River Murray","geocode_query":"Lock 3, South Australia, Australia; Lock 6, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.8869,-33.9965599],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677709,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 6, Murtho SA 5341, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-6"},{"name":"St George Township, Queensland, Australia","kind":"settlement","role":"location defining start of Balonne River study site","river_name":"Balonne River","geocode_query":"St George, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.580104,-28.096845],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670095,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"St George QLD 4487, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-7","true_coordinates":[148.5801042,-28.0368451],"spread_km":6.7},{"name":"Weilmoringle, New South Wales, Australia","kind":"settlement","role":"location defining downstream limit of Balonne River study site","river_name":"Balonne River","geocode_query":"Weilmoringle, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"In total, the study sites covered an area of 4,350 km2 . As shown in Figure 1, the first study site was the Namoi River from Keepit Dam to the junction with the Barwon River near Walgett in New South Wales (one of the upper tributaries of the River Murray). The second study site was between downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The third study site covered the area of the Balonne River downstream of St George Township in Queensland to Weilmoringle in New South Wales. The Culgoa River is an upper tributary of the Darling River in the far north-west of the MDB. Floods from this river take months to reach the Murray Mouth (1,200 km downstream) and sometimes they do not make it to the Mouth at all. The study sites were broken down into reaches based on 19447973, 2022, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032031 by Mount Vernon Nazarene University, Wiley Online Library on [30/08/2026].","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8462695,-29.2089954],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666528,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Weilmoringle NSW 2839, Australia","basin_id":5040511420,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":35156.6,"hydrology_source":"geofabric","site_id":"doc-bc55e68e9f8589718db852e2-site-8"}],"limitations":["Coordinates for locations are not specified in the source text, latitude and longitude set to null","Generalized reach-level geomorphological classifications are summarized; more detailed geomorphological variability is not fully quantified","Study locations are defined at reach to basin scale without precise spatial coordinates","Hydrodynamic model outputs used as benchmarks are subject to their inherent uncertainties which influence model 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040073410,5040511420,5040511570,5040524870,5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-bdfb3086bc32642b450dded4","title":"Informing hydrogeological models with remotely sensed evapotranspiration","display_title":"Using Satellite Evapotranspiration to Improve Hydrogeological Models","doi":"https://doi.org/10.3389/frwa.2022.932641","theme":"Groundwater","color":"#a8b9ff","year":2022,"publication_date":"2022-09-28","authors":["Simone Gelsinari","Tanya M. Doody","Sally E. Thompson","Rebecca Doble","Edoardo Daly","Valentijn R. N. Pauwels"],"summary":"This study assesses the value of incorporating remotely sensed actual evapotranspiration (ET) data, specifically from the MODIS-CMRSET satellite product, into hydrogeological models at four silvicultural plantation sites in south-east South Australia. The sites vary in depth to water table (WT), soil homogeneity, and vegetation type. Models calibrated solely to WT levels often underestimate ET fluxes, which are critical for sustainable groundwater management, especially in areas with deep-rooted vegetation accessing groundwater. Including actual ET data in model calibration or assimilating it through an Ensemble Kalman Filter significantly improves modeled ET estimation, particularly where water tables are shallow and vegetation has direct groundwater access. However, the benefit is reduced or negative where WT is deep (e.g., the Jill site) due to spatial mismatch between satellite pixel scale and local WT conditions. While integration of ET data improves ET flux predictions, it may slightly reduce precision in WT level modeling, suggesting a trade-off between flux and level accuracy. This work demonstrates the utility of satellite ET data to constrain groundwater models and improve water resource assessments, especially in water-limited forestry environments.","finding":"Incorporating satellite-based actual evapotranspiration data in hydrogeological model calibration substantially improves the estimation of ET fluxes—up to a 50% reduction in error and doubled correlation in some cases—particularly in plantations with shallow water tables where vegetation directly accesses groundwater.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"RMSE and r-values calculated over the means (for Level-1 and Level-2) and compared to those of the deterministic (Level-0) simulation provide an assessment of the simulation performance against Level-0 as a benchmark. The CRPS values (Table 4) provide an additional assessment of the ensemble performances for the two stochastic modeling approaches. Specifically, comparing CRPS between Level-2 and Level-1 quantifies the benefits to model performance of assimilating ET data through the EnKF versus calibrating to ET. The results are discussed separately for the model predictions of actual ET and WT. 3.1. Evapotranspiration Including actual ET in the models through calibration or assimilation improved simulations of actual ET in the sense that RMSEs declined and r increased, for every site other than Jill. The improvements were most evident at Jack, in which the RMSE decreased by ∼30% and the r improved by ∼100% upon inclusion of actual ET data in the modeling.","verified_in_page":true},"topics":["remote sensing","evapotranspiration","groundwater modeling","data assimilation","water balance","calibration","uncertainty analysis","forest hydrology"],"study_period":null,"locations":[{"name":"Jack silvicultural site","kind":"field_site","role":"primary hydrological study site with shallow water table and heterogeneous soil profile","river_name":null,"geocode_query":"Jack silvicultural site, south-east South Australia, Australia","latitude":-37.372,"longitude":140.455,"evidence":{"passage_id":"p003-000","page":3,"quote":"Gelsinari et al. 10.3389/frwa.2022.932641 FIGURE 1 Location of the study area within Australia (a). Details of the topography, plantation sites, and meteorological stations used in this study (b). Details of the depth to WT of the upper unconfined aquifer (c). Satellite images of the four locations. Jack and Jill (d), Woakwine (e), and Piney (f). The squares represent the model cell and the CMRSET averaging area. Ecohydrological studies in the area have previously characterized vegetation, soil, climate, net irrigation, potential and actual evaporation, and depth to the WT, from silvicultural sites (Benyon et al., 2006). We selected four of these sites to answer the research questions. Two sites (“Woakwine” and “Jack”) had shallow WT depths (2.5 and 3m), Woakwine with a homogeneous soil profile and Jack a heterogeneous profile. One site (“Jill”) has a deep WT (10 m). The fourth site (“Piney”) records an average WT depth of 6 m (see Figure 1c for a map of the depth to WT).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.455,-37.372],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556936,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-0"},{"name":"Jill silvicultural site","kind":"field_site","role":"primary hydrological study site with deep water table and homogeneous soil profile","river_name":null,"geocode_query":"Jill silvicultural site, south-east South Australia, Australia","latitude":-37.373,"longitude":140.45,"evidence":{"passage_id":"p003-000","page":3,"quote":"Gelsinari et al. 10.3389/frwa.2022.932641 FIGURE 1 Location of the study area within Australia (a). Details of the topography, plantation sites, and meteorological stations used in this study (b). Details of the depth to WT of the upper unconfined aquifer (c). Satellite images of the four locations. Jack and Jill (d), Woakwine (e), and Piney (f). The squares represent the model cell and the CMRSET averaging area. Ecohydrological studies in the area have previously characterized vegetation, soil, climate, net irrigation, potential and actual evaporation, and depth to the WT, from silvicultural sites (Benyon et al., 2006). We selected four of these sites to answer the research questions. Two sites (“Woakwine” and “Jack”) had shallow WT depths (2.5 and 3m), Woakwine with a homogeneous soil profile and Jack a heterogeneous profile. One site (“Jill”) has a deep WT (10 m). The fourth site (“Piney”) records an average WT depth of 6 m (see Figure 1c for a map of the depth to WT).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.45,-37.373],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556936,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-1"},{"name":"Woakwine silvicultural site","kind":"field_site","role":"primary hydrological study site with shallow water table and homogeneous soil profile","river_name":null,"geocode_query":"Woakwine silvicultural site, south-east South Australia, Australia","latitude":-37.4,"longitude":140.07,"evidence":{"passage_id":"p003-000","page":3,"quote":"Gelsinari et al. 10.3389/frwa.2022.932641 FIGURE 1 Location of the study area within Australia (a). Details of the topography, plantation sites, and meteorological stations used in this study (b). Details of the depth to WT of the upper unconfined aquifer (c). Satellite images of the four locations. Jack and Jill (d), Woakwine (e), and Piney (f). The squares represent the model cell and the CMRSET averaging area. Ecohydrological studies in the area have previously characterized vegetation, soil, climate, net irrigation, potential and actual evaporation, and depth to the WT, from silvicultural sites (Benyon et al., 2006). We selected four of these sites to answer the research questions. Two sites (“Woakwine” and “Jack”) had shallow WT depths (2.5 and 3m), Woakwine with a homogeneous soil profile and Jack a heterogeneous profile. One site (“Jill”) has a deep WT (10 m). The fourth site (“Piney”) records an average WT depth of 6 m (see Figure 1c for a map of the depth to WT).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.07,-37.4],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556929,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-2"},{"name":"Piney silvicultural site","kind":"field_site","role":"primary hydrological study site with water table near plant root threshold, heterogeneous soil","river_name":null,"geocode_query":"Piney silvicultural site, south-east South Australia, Australia","latitude":-37.738,"longitude":140.779,"evidence":{"passage_id":"p003-000","page":3,"quote":"Gelsinari et al. 10.3389/frwa.2022.932641 FIGURE 1 Location of the study area within Australia (a). Details of the topography, plantation sites, and meteorological stations used in this study (b). Details of the depth to WT of the upper unconfined aquifer (c). Satellite images of the four locations. Jack and Jill (d), Woakwine (e), and Piney (f). The squares represent the model cell and the CMRSET averaging area. Ecohydrological studies in the area have previously characterized vegetation, soil, climate, net irrigation, potential and actual evaporation, and depth to the WT, from silvicultural sites (Benyon et al., 2006). We selected four of these sites to answer the research questions. Two sites (“Woakwine” and “Jack”) had shallow WT depths (2.5 and 3m), Woakwine with a homogeneous soil profile and Jack a heterogeneous profile. One site (“Jill”) has a deep WT (10 m). The fourth site (“Piney”) records an average WT depth of 6 m (see Figure 1c for a map of the depth to WT).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.779,-37.738],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556987,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-3"},{"name":"Otway Basin","kind":"basin","role":"regional hydrogeological context of study sites","river_name":null,"geocode_query":"Otway Basin, south-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We address these questions using four silvicultural study sites in the south-eastern extent of South Australia. Modeling groundwater and ET in this region offers us the opportunity to leverage detailed field derived water balance datasets (Benyon et al., 2006) in sites with broadly similar climatic conditions and mature tree canopy vegetation, but varying depths to the WT. Insight into modeling approaches suitable for the region is also locally valuable. Forestry managers in the region are required to comply with forestry regulations and water licensing to avoid negative impacts on water resources (Dye and Versfeld, 2007; Greenwood, 2013). 2. Methods 2.1. Site description The study sites lie in the Otway and Murray Basins, in south-east South Australia (Figures 1a,b). The region has a Mediterranean type climate, with annual rainfall of 640–750 mm year−1 , and potential ET of 960–1,400 mm year−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.348184,-36.663562],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-4","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7},{"name":"Murray Basin","kind":"basin","role":"regional hydrogeological context of study sites","river_name":null,"geocode_query":"Murray Basin, south-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We address these questions using four silvicultural study sites in the south-eastern extent of South Australia. Modeling groundwater and ET in this region offers us the opportunity to leverage detailed field derived water balance datasets (Benyon et al., 2006) in sites with broadly similar climatic conditions and mature tree canopy vegetation, but varying depths to the WT. Insight into modeling approaches suitable for the region is also locally valuable. Forestry managers in the region are required to comply with forestry regulations and water licensing to avoid negative impacts on water resources (Dye and Versfeld, 2007; Greenwood, 2013). 2. Methods 2.1. Site description The study sites lie in the Otway and Murray Basins, in south-east South Australia (Figures 1a,b). The region has a Mediterranean type climate, with annual rainfall of 640–750 mm year−1 , and potential ET of 960–1,400 mm year−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.283356,-36.693562],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-5","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7},{"name":"South-east South Australia","kind":"region","role":"broader regional climate and land-use setting including silviculture and groundwater resources","river_name":null,"geocode_query":"South-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We address these questions using four silvicultural study sites in the south-eastern extent of South Australia. Modeling groundwater and ET in this region offers us the opportunity to leverage detailed field derived water balance datasets (Benyon et al., 2006) in sites with broadly similar climatic conditions and mature tree canopy vegetation, but varying depths to the WT. Insight into modeling approaches suitable for the region is also locally valuable. Forestry managers in the region are required to comply with forestry regulations and water licensing to avoid negative impacts on water resources (Dye and Versfeld, 2007; Greenwood, 2013). 2. Methods 2.1. Site description The study sites lie in the Otway and Murray Basins, in south-east South Australia (Figures 1a,b). The region has a Mediterranean type climate, with annual rainfall of 640–750 mm year−1 , and potential ET of 960–1,400 mm year−1 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.893849,-35.054099],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677591,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"S Eastern Fwy, South Australia, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-6","true_coordinates":[138.9573476,-35.0840992],"spread_km":6.7},{"name":"Coonawarra meteorological station","kind":"station","role":"climatic data source for Jack and Jill sites","river_name":null,"geocode_query":"Coonawarra meteorological station, south-east South Australia, Australia","latitude":-37.29,"longitude":140.825,"evidence":{"passage_id":"p003-002","page":3,"quote":"Rainfall data was aggregated, and potential ET disaggregated by dividing by 24, to produce hourly values used for modeling (Samain and Pauwels, 2013). When looking at water table dynamics at a regional scale, the simple ET disaggregation is considered appropriate enough, as long as consistency is kept between the model calibration and the application. Simulations at Jack and Jill use climatic input from the Coonawarra station (Lat. −37.290, Long. 140.825 Station 026091), Piney uses data from the Mount Gambier Aero station (Lat. −37.747, Long. 140.773, Station 026021), and Woakwine from Robe Airfield station (Lat. −37.177, Long. 139.805 Station and 026105). Potential ET forcing inputs at Jack have a long gap between April and September 2002. As the gap affects some of Frontiers in Water 03 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.825,-37.29],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556959,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-7"},{"name":"Mount Gambier Aero meteorological station","kind":"station","role":"climatic data source for Piney site","river_name":null,"geocode_query":"Mount Gambier Aero meteorological station, south-east South Australia, Australia","latitude":-37.747,"longitude":140.773,"evidence":{"passage_id":"p003-002","page":3,"quote":"Rainfall data was aggregated, and potential ET disaggregated by dividing by 24, to produce hourly values used for modeling (Samain and Pauwels, 2013). When looking at water table dynamics at a regional scale, the simple ET disaggregation is considered appropriate enough, as long as consistency is kept between the model calibration and the application. Simulations at Jack and Jill use climatic input from the Coonawarra station (Lat. −37.290, Long. 140.825 Station 026091), Piney uses data from the Mount Gambier Aero station (Lat. −37.747, Long. 140.773, Station 026021), and Woakwine from Robe Airfield station (Lat. −37.177, Long. 139.805 Station and 026105). Potential ET forcing inputs at Jack have a long gap between April and September 2002. As the gap affects some of Frontiers in Water 03 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.773,-37.747],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556987,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-8"},{"name":"Robe Airfield meteorological station","kind":"station","role":"climatic data source for Woakwine site","river_name":null,"geocode_query":"Robe Airfield meteorological station, south-east South Australia, Australia","latitude":-37.177,"longitude":139.805,"evidence":{"passage_id":"p003-002","page":3,"quote":"Rainfall data was aggregated, and potential ET disaggregated by dividing by 24, to produce hourly values used for modeling (Samain and Pauwels, 2013). When looking at water table dynamics at a regional scale, the simple ET disaggregation is considered appropriate enough, as long as consistency is kept between the model calibration and the application. Simulations at Jack and Jill use climatic input from the Coonawarra station (Lat. −37.290, Long. 140.825 Station 026091), Piney uses data from the Mount Gambier Aero station (Lat. −37.747, Long. 140.773, Station 026021), and Woakwine from Robe Airfield station (Lat. −37.177, Long. 139.805 Station and 026105). Potential ET forcing inputs at Jack have a long gap between April and September 2002. As the gap affects some of Frontiers in Water 03 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.805,-37.177],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556926,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-9"},{"name":"Mount Gambier","kind":"region","role":"topographic feature influencing local hydrogeology in study region","river_name":null,"geocode_query":"Mount Gambier, south-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The region is generally flat, with undulations in the south-west forming Mount Gambier and Mount Burr (Doble et al., 2017). Up to 95% of regional groundwater resources are found in the shallow unconfined aquifer, with WT levels being within 2 m from the surface at their highest (Benyon et al., 2006). Spatial variations in the depth to WT are mainly driven by surface topographic variation between flats and interspersed sand ridges (see Figure 1c). Softwood and hardwood forestry for timber production has been practiced in the area for more than a century. The dominant plantation timber species are Monterey Pine, Pinus radiata and Tasmanian Bluegum, Eucalyptus globulus (Benyon et al., 2006). Where these plantations overlie shallow WTs, which are within 6 m of the surface, it is likely that the trees directly access groundwater (Canadell et al., 1996; Benyon and Doody, 2004, 2015; Benyon et al., 2006, 2008). Frontiers in Water 02 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.780676,-37.768441],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556987,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Gambier SA 5290, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-10","true_coordinates":[140.7806761,-37.8284406],"spread_km":6.7},{"name":"Mount Burr","kind":"region","role":"topographic feature influencing local hydrogeology in study region","river_name":null,"geocode_query":"Mount Burr, south-east South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The region is generally flat, with undulations in the south-west forming Mount Gambier and Mount Burr (Doble et al., 2017). Up to 95% of regional groundwater resources are found in the shallow unconfined aquifer, with WT levels being within 2 m from the surface at their highest (Benyon et al., 2006). Spatial variations in the depth to WT are mainly driven by surface topographic variation between flats and interspersed sand ridges (see Figure 1c). Softwood and hardwood forestry for timber production has been practiced in the area for more than a century. The dominant plantation timber species are Monterey Pine, Pinus radiata and Tasmanian Bluegum, Eucalyptus globulus (Benyon et al., 2006). Where these plantations overlie shallow WTs, which are within 6 m of the surface, it is likely that the trees directly access groundwater (Canadell et al., 1996; Benyon and Doody, 2004, 2015; Benyon et al., 2006, 2008). Frontiers in Water 02 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.4554154,-37.5417045],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556931,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Burr SA 5279, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-bdfb3086bc32642b450dded4-site-11"}],"limitations":["Spatial resolution of MODIS-CMRSET (1 km²) is coarse relative to some site heterogeneity, limiting precision especially for nearby paired sites (Jack and Jill).","Climate and forcing data gaps (e.g., potential ET data missing April-September 2002) required omission of certain periods in model evaluation.","Model conceptualization uses simplified domain discretization and parameters not always physically representative, implying approximations.","Study is limited to four plantation sites in south-east South Australia, so findings may not fully generalize globally or to very different ecosystems.","Remote sensing ET products may require local bias correction using field data for best use in calibration and assimilation."],"river_ids":[],"site_ids":["doc-bdfb3086bc32642b450dded4-site-0","doc-bdfb3086bc32642b450dded4-site-1","doc-bdfb3086bc32642b450dded4-site-2","doc-bdfb3086bc32642b450dded4-site-3","doc-bdfb3086bc32642b450dded4-site-4","doc-bdfb3086bc32642b450dded4-site-5","doc-bdfb3086bc32642b450dded4-site-6","doc-bdfb3086bc32642b450dded4-site-7","doc-bdfb3086bc32642b450dded4-site-8","doc-bdfb3086bc32642b450dded4-site-9","doc-bdfb3086bc32642b450dded4-site-10","doc-bdfb3086bc32642b450dded4-site-11"],"anchor":[140.825,-37.29],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040070710,5040073420],"main_basin_ids":[5040070710,5040073420]},{"id":"doc-c4af0e1defd395b6de2bbfcb","title":"Environmental Flows to Estuaries and Coastal Lagoons Shape the Salinity Gradient and Generate Suitable Fish Habitat: Predictions From the Coorong, Australia","display_title":"Environmental Flows Impact on Coorong Estuary","doi":"https://doi.org/10.3389/fenvs.2022.796623","theme":"Flow & ecology","color":"#80edc2","year":2022,"publication_date":"2022-04-07","authors":["Justin D. Brookes","Peisheng Huang","Sherry Y. Zhai","Matthew S. Gibbs","Qifeng Ye","Kane T. Aldridge","Brendan Busch","Matthew R. Hipsey"],"summary":"This study quantifies how environmental flows affect salinity gradients and fish habitat suitability in the Coorong estuarine lagoon at the terminus of the Murray River, Australia. Using a finite volume hydrodynamic model (TUFLOW-FV) and fish habitat suitability indices based on salinity and temperature tolerance thresholds of key fish species, the study shows that environmental water releases reduce seawater salt ingress, enhance salt export from the Murray-Darling Basin, maintain fresher conditions, and expand suitable habitat for estuarine dependent fish species.","finding":"Environmental water releases over the barrages significantly reduce salt ingress from seawater into the Coorong, prevent excessive salt accumulation, and support a considerable expansion of suitable habitat for key fish species. Without environmental water, salt import into the Coorong would have been roughly 2 million tonnes higher over three years, resulting in habitat contraction of up to 38% for species such as mulloway.","finding_evidence":{"passage_id":"p009-003","page":9,"quote":"It is also evident that environmental water flowing over the barrages is required in every year to reduce excessive salt accumulation within the Coorong. If environmental water had not been delivered in the last of the 3 year action period (2019–20) an additional 1.7 million tonnes of salt would have accumulated in the South Lagoon. This highlights the importance of continued delivery in periods of low flow, as the benefits amplified by the final year. Prior to our study period during the Millennium Drought, from 2007–08 to 2009–10, flow over the barrages ceased and the import of salt into the Coorong resulted in salinity in the South Lagoon that was five times seawater salinity, and led to the demise of much of the aquatic life (Brookes et al., 2009). Environmental water provides freshening flows but also acts to inhibit seawater intrusion, thereby maintaining more appropriate salinity conditions, particularly in the South Lagoon.","verified_in_page":true},"topics":["environmental flows","salinity","fish habitat suitability","hydrodynamic modeling","estuarine ecology","salt transport","habitat modeling","climate impact"],"study_period":"2017-07 to 2020-06","locations":[{"name":"Coorong estuarine lagoon","kind":"basin","role":"Primary study area, focus of hydrodynamic and habitat modeling","river_name":null,"geocode_query":"Coorong, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The Coorong is a large coastal lagoon that is separated from the ocean by a Holocene dune system, the Younghusband Peninsula. The North Lagoon (area 95 km2 ) is historically estuarine and connected to the Southern Ocean by the Murray Mouth. The South Lagoon is typically hyper-saline under postEuropean conditions, has a similar, but slightly larger, area to the North Lagoon (Geddes and Butler, 1984) and is connected to the North Lagoon by a narrow channel near Parnka Point. The Coorong is shallow, with an average water depth of 1 m (Geddes and Butler, 1984), and consists of seven basins with maximum depths of 3–4 m when water levels peak in winter (Noye, 1974). The area north of Pelican Point is tidally influenced and mudflats are inundated and exposed daily depending on tide and wind.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.555355,-36.05637],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-0","true_coordinates":[139.5553549,-35.9963695],"spread_km":6.7},{"name":"Murray River","kind":"river","role":"River system terminating at study site, providing environmental flows into Coorong","river_name":"Murray River","geocode_query":"Murray River, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"The aim of this study was to determine how effective environmental flows are at exporting salt from the Murray Darling Basin, what impact they have on arresting seawater ingress which shapes the salinity gradient, and ultimately the availability of habitat for key fish biota in the Coorong, an estuarine lagoonal system at the terminus of the Murray River. Frontiers in Environmental Science | www.frontiersin.org April 2022 | Volume 10 | Article 796623 2 Brookes et al. Environmental Flows to Estuaries","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.9306237180001,-34.07729028199998],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677315,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-1"},{"name":"Lower Lakes (Lake Alexandrina and Lake Albert)","kind":"basin","role":"Freshwater terminal waterbodies upstream of the Coorong, separated by barrages","river_name":null,"geocode_query":"Lake Alexandrina, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The site represents a diverse collection of aquatic environments created where sharp hydrologic gradients intersect with a complex coastal geomorphology. The region is a place of natural beauty, high ecological value and is the spiritual home of the local indigenous people, the Ngarrindjeri. Lake Alexandrina and Lake Albert are joined by a narrow waterway and have a combined area of 800 km2 (Bourman and Barnett, 1995). The River Murray enters Lake Alexandrina, which is separated from the Coorong and sea by five barrages that were constructed between 1935 and 1940, although less permanent sandbag and wooden barrages were constructed as early as 1914 (Fluin et al., 2007). Lake Alexandrina was estuarine prior to construction of the barrages, although paleolimnological evidence suggests the main body of the lake was predominantly fresh (Fluin et al., 2007), consistent with the higher flows leaving the Murray-Darling system prior to extensive river regulation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.0817124,-35.4453465],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43660460,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-2"},{"name":"Murray Mouth","kind":"river","role":"Estuary mouth connecting Coorong to Southern Ocean; site of ocean-seawater ingress and salt flux","river_name":"Murray River","geocode_query":"Murray Mouth, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Frontiers in Environmental Science | www.frontiersin.org April 2022 | Volume 10 | Article 796623 3 Brookes et al. Environmental Flows to Estuaries","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.77888893500005,-34.035833277999984],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43660770,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-3"},{"name":"Younghusband Peninsula","kind":"landscape","role":"Coastal dune system separating Coorong from ocean","river_name":null,"geocode_query":"Younghusband Peninsula, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The Coorong is a large coastal lagoon that is separated from the ocean by a Holocene dune system, the Younghusband Peninsula. The North Lagoon (area 95 km2 ) is historically estuarine and connected to the Southern Ocean by the Murray Mouth. The South Lagoon is typically hyper-saline under postEuropean conditions, has a similar, but slightly larger, area to the North Lagoon (Geddes and Butler, 1984) and is connected to the North Lagoon by a narrow channel near Parnka Point. The Coorong is shallow, with an average water depth of 1 m (Geddes and Butler, 1984), and consists of seven basins with maximum depths of 3–4 m when water levels peak in winter (Noye, 1974). The area north of Pelican Point is tidally influenced and mudflats are inundated and exposed daily depending on tide and wind.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.5,-36],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Younghusband Peninsula, Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-4"},{"name":"Pelican Point","kind":"station","role":"Monitoring site for salinity and habitat suitability in southern Coorong","river_name":null,"geocode_query":"Pelican Point, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Freshwater flowing from the barrages limits salt influx from the ocean and maintains lower salinity conditions in the Coorong. Salt from the Murray Mouth can travel southward as ocean water replenishes water that is evaporating in the Coorong. The dominant direction of salt flux is southward although it can move northward (negative flux in Supplementary Figure SB1, Supplementary B) when river flows over the barrages cease, the head of water decreased and the net flow of water is northwards. Further to the South at Parnka Point there are much higher flux rates as the salt concentrations are higher and so the rate of salt movement is higher (Supplementary Figure SB1, Supplementary B). This is evident in the cumulative flux of salt at Parnka Point FIGURE 5 | Comparison of measured and simulated salinity at key monitoring points within the Coorong lagoon, moving from the Murray Mouth into the South Lagoon.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.4238252,-37.9279117],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556937,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pelican Point SA 5291, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-5"},{"name":"Long Point","kind":"station","role":"Monitoring site used for assessing salt flux and salinity gradients in Coorong","river_name":null,"geocode_query":"Long Point, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Freshwater flowing from the barrages limits salt influx from the ocean and maintains lower salinity conditions in the Coorong. Salt from the Murray Mouth can travel southward as ocean water replenishes water that is evaporating in the Coorong. The dominant direction of salt flux is southward although it can move northward (negative flux in Supplementary Figure SB1, Supplementary B) when river flows over the barrages cease, the head of water decreased and the net flow of water is northwards. Further to the South at Parnka Point there are much higher flux rates as the salt concentrations are higher and so the rate of salt movement is higher (Supplementary Figure SB1, Supplementary B). This is evident in the cumulative flux of salt at Parnka Point FIGURE 5 | Comparison of measured and simulated salinity at key monitoring points within the Coorong lagoon, moving from the Murray Mouth into the South Lagoon.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.8977567,-34.0118664],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054592,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Long Point NSW 2564, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-6"},{"name":"Parnka Point","kind":"station","role":"Narrow channel connecting North and South Lagoon in Coorong; site assessing salt transport southward and habitat conditions","river_name":null,"geocode_query":"Parnka Point, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Freshwater flowing from the barrages limits salt influx from the ocean and maintains lower salinity conditions in the Coorong. Salt from the Murray Mouth can travel southward as ocean water replenishes water that is evaporating in the Coorong. The dominant direction of salt flux is southward although it can move northward (negative flux in Supplementary Figure SB1, Supplementary B) when river flows over the barrages cease, the head of water decreased and the net flow of water is northwards. Further to the South at Parnka Point there are much higher flux rates as the salt concentrations are higher and so the rate of salt movement is higher (Supplementary Figure SB1, Supplementary B). This is evident in the cumulative flux of salt at Parnka Point FIGURE 5 | Comparison of measured and simulated salinity at key monitoring points within the Coorong lagoon, moving from the Murray Mouth into the South Lagoon.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.3961,-35.96172],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Parnka Point, Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-7","true_coordinates":[139.3961,-35.9017199],"spread_km":6.7},{"name":"Woods Well","kind":"station","role":"Monitoring location for estuarine salinity and hydrodynamics in Coorong","river_name":null,"geocode_query":"Woods Well, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Freshwater flowing from the barrages limits salt influx from the ocean and maintains lower salinity conditions in the Coorong. Salt from the Murray Mouth can travel southward as ocean water replenishes water that is evaporating in the Coorong. The dominant direction of salt flux is southward although it can move northward (negative flux in Supplementary Figure SB1, Supplementary B) when river flows over the barrages cease, the head of water decreased and the net flow of water is northwards. Further to the South at Parnka Point there are much higher flux rates as the salt concentrations are higher and so the rate of salt movement is higher (Supplementary Figure SB1, Supplementary B). This is evident in the cumulative flux of salt at Parnka Point FIGURE 5 | Comparison of measured and simulated salinity at key monitoring points within the Coorong lagoon, moving from the Murray Mouth into the South Lagoon.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.533176,-35.997886],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Woods Well, Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-8"},{"name":"Snipe Island","kind":"station","role":"Monitoring location for salinity validation in the Coorong model","river_name":null,"geocode_query":"Snipe Island, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Freshwater flowing from the barrages limits salt influx from the ocean and maintains lower salinity conditions in the Coorong. Salt from the Murray Mouth can travel southward as ocean water replenishes water that is evaporating in the Coorong. The dominant direction of salt flux is southward although it can move northward (negative flux in Supplementary Figure SB1, Supplementary B) when river flows over the barrages cease, the head of water decreased and the net flow of water is northwards. Further to the South at Parnka Point there are much higher flux rates as the salt concentrations are higher and so the rate of salt movement is higher (Supplementary Figure SB1, Supplementary B). This is evident in the cumulative flux of salt at Parnka Point FIGURE 5 | Comparison of measured and simulated salinity at key monitoring points within the Coorong lagoon, moving from the Murray Mouth into the South Lagoon.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.6193903,-36.1105791],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Snipe Island, Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-9"},{"name":"Salt Creek","kind":"river","role":"Local catchment inflow into South Lagoon of Coorong","river_name":null,"geocode_query":"Salt Creek, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"The model was configured as a 2D model as the shallow nature of the system means the water column is typically well mixed. The finite volume numerical scheme solves the conservative integral form of the nonlinear shallow water equations, as well as the advection and transport of scalar constituents such as salinity and temperature. Outside the Murray Mouth an open water level boundary was specified based on Barker Knoll tidal data, which were available at 10 min resolution (Figure 2). Inflow to the South Lagoon from the local catchment via Salt Creek was set based on available flow data from water. data.sa.gov.au (both curated by the South Australian Department of Environment and Water). Meteorological conditions were based on data from Narrung weather station nearby to the Coorong (Figure 2). Further details on model configuration are provided in Supplementary Material S1A.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.650867,-36.18382],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Salt Creek SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c4af0e1defd395b6de2bbfcb-site-10","true_coordinates":[139.6508666,-36.1238196],"spread_km":6.7},{"name":"Barrages (Goowa, Mundoo, Boundary Ck, Ewe, Tauwitchere)","kind":"facility","role":"Water control structures regulating freshwater flow from Lower Lakes into Coorong; critical for environmental water delivery","river_name":null,"geocode_query":"Barrages, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The TUFLOW-FV has been used extensively in the region for hydrological assessments (Ye et al., 2016; Ye et al., 2020). In this study, the model domain was configured to cover the Coorong lagoon and the mesh consists of triangular and quadrilateral elements of different size that are suited to simulating areas of complex estuarine morphometry (Figure 2). The model mesh contains 26,250 cells in total and the mean cell size is 10,338 m2 . The model dynamically adjusts the time step to maintain a Courant–Friedrichs–Lewy value below 0.9 based on the current speeds and cell lengths, with a minimum time step of 0.1 s and a maximum of 15 s. FIGURE 1 | The Lower Lakes and Coorong. The freshwater in the lakes is isolated from the marine water by five barrages. Exchange with the ocean occurs through the Murray Mouth, which is located near Goolwa. The Coorong is comprised of the North and South lagoons.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Exact latitude and longitude coordinates not provided in the source text; set as null.","Some named locations span larger areas (e.g., Lower Lakes) representing regional scale rather than discrete point.","Habitat model simplified focusing on salinity and temperature; other ecological factors not fully modeled.","Fish habitat predictions represent potential niche based on salinity tolerance; actual populations influenced by additional biotic and abiotic factors not captured here."],"river_ids":["43640030","43640033","43640037","43640041","43640043","43640044","43640045","43640050","43640455","43640457","43652494","43652496","43652497","43652498","43652499","43652501","43652503","43652504","43652506","43652507","43652508","43652511","43657861","43657862","43657863","43657864","43657865","43657866","43657875","43657884","43657893","43657929","43657938"],"site_ids":["doc-c4af0e1defd395b6de2bbfcb-site-0","doc-c4af0e1defd395b6de2bbfcb-site-1","doc-c4af0e1defd395b6de2bbfcb-site-2","doc-c4af0e1defd395b6de2bbfcb-site-3","doc-c4af0e1defd395b6de2bbfcb-site-4","doc-c4af0e1defd395b6de2bbfcb-site-5","doc-c4af0e1defd395b6de2bbfcb-site-6","doc-c4af0e1defd395b6de2bbfcb-site-7","doc-c4af0e1defd395b6de2bbfcb-site-8","doc-c4af0e1defd395b6de2bbfcb-site-9","doc-c4af0e1defd395b6de2bbfcb-site-10"],"anchor":[139.650867,-36.18382],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040070710,5040073410],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-c575e809f10b18599e50d55e","title":"Development and Governance of FAIR Thresholds for a Data Federation","display_title":"FAIR Data Governance in Australian AgReFed","doi":"https://doi.org/10.5334/dsj-2022-013","theme":"Other science","color":"#d1b5e7","year":2022,"publication_date":"2022-01-01","authors":["Megan Wong","Kerry Levett","Ashlin Lee","Paul Box","Bruce Simons","Rakesh David","Andrew MacLeod","Nicolas Taylor","Derek Schneider","Helen Thompson"],"summary":"The paper reports on developing and governing FAIR (Findable, Accessible, Interoperable, Reusable) data thresholds tailored for the Agricultural Research Federation (AgReFed), a community-governed, federated data platform in Australia. It addresses applying FAIR principles specifically for agricultural research datasets, supporting diverse providers to improve data FAIRness across Australian agricultural research institutions, enabling effective data sharing and integration.","finding":"FAIR minimum thresholds and stretch targets were co-developed and operationalized via consensus among Australian agricultural data providers, enabling incremental improvements in FAIR data maturity and fostering trust and engagement in a federated governance model.","finding_evidence":{"passage_id":"p006-004","page":6,"quote":"Some notable experiences informed the AgReFed FAIR ‘Minimum thresholds’ to ‘Stretch targets’ (Table 2). These included: • Providers’ exemplar data products each had different FAIR starting points (See startstatus, Table 2). • Improvements to metadata records to meet AgReFed ‘Minimum threshold’ requirements were possible with organisational library and ARDC support (Findability Q1 to Q4) so ‘Minimum thresholds’ were set high for Q1 to Q3. • Access requirements and licencing varied. These were accommodated across the thresholds of Q5 and Q12.","verified_in_page":true},"topics":["data governance","data federation","agricultural data","data interoperability","FAIR data principles","metadata standards","community governance","data sharing"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Study region where agricultural datasets were collected, FAIR thresholds developed, and AgReFed federation operates","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Wong et al. 3 Data Science Journal DOI: 10.5334/dsj-2022- 013 I. co-developed FAIR threshold criteria for participants to deliver data through a federation II. though a consensus process integrated these FAIR thresholds into a framework for ongoing governance by a research domain community, for generating individual and collective benefit and growth of a data federation 2. THE USE CASES The datasets of the pilot included point observations, spatial, temporal, on-ground, sensor, and remote sensed data. The data described plants (yield, crop rotation, metabolomic, proteomic, hyperspectral), soil and climatic variables from across Australia (Table 1). The data providers defined a set of research use cases for the data in Table 1 (see MacLeod et al. 2020: 29–31), identifying the current and anticipated data users and their ideal user experience.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Corangamite, Victoria, Australia","kind":"region","role":"Data provider region for Corangamite Soil Health Monitoring Program contributing data products to AgReFed","river_name":null,"geocode_query":"Corangamite, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"IN-TEXT ABBREVIATION DATA PRODUCTS’ NAME DATA PRODUCT TYPE DATA PROVIDER TO AGREFED (* INDICATES BOTH DATA PROVIDER AND USER OF THE DATASET OR COLLECTION) SH (Soil Health) Corangamite Soil Health Monitoring Program Data https:// doi.org/10.25955/5c1c6b8f4d8d2 (Corangamite Catchment Management Authority, 2019) Dataset and service Federation University, Centre for eResearch and Innovation (CeRDI). SMN-1 (Soil Moisture Network 1) Soil moisture probe network, SFS (Southern Farming Systems https:// doi.org/10.25955/5cdcff6168a76 (Southern Farming Systems, 2011) Dataset Federation University, CeRDI.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.1319209,-38.1491105],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21557743,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corangamite, VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c575e809f10b18599e50d55e-site-1"},{"name":"Mt Helen, Victoria, Australia","kind":"settlement","role":"Location of Federation University, Centre for eResearch and Digital Innovation (CeRDI), data provider and AgReFed facilitator","river_name":null,"geocode_query":"Mt Helen, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"IN-TEXT ABBREVIATION DATA PRODUCTS’ NAME DATA PRODUCT TYPE DATA PROVIDER TO AGREFED (* INDICATES BOTH DATA PROVIDER AND USER OF THE DATASET OR COLLECTION) SH (Soil Health) Corangamite Soil Health Monitoring Program Data https:// doi.org/10.25955/5c1c6b8f4d8d2 (Corangamite Catchment Management Authority, 2019) Dataset and service Federation University, Centre for eResearch and Innovation (CeRDI). SMN-1 (Soil Moisture Network 1) Soil moisture probe network, SFS (Southern Farming Systems https:// doi.org/10.25955/5cdcff6168a76 (Southern Farming Systems, 2011) Dataset Federation University, CeRDI.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.8801622,-37.62833670000001],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21558207,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Helen VIC 3350, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c575e809f10b18599e50d55e-site-2"},{"name":"Southern Farming Systems, Victoria, Australia","kind":"other","role":"Provider of Soil Moisture Network datasets contributing to AgReFed","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"IN-TEXT ABBREVIATION DATA PRODUCTS’ NAME DATA PRODUCT TYPE DATA PROVIDER TO AGREFED (* INDICATES BOTH DATA PROVIDER AND USER OF THE DATASET OR COLLECTION) SH (Soil Health) Corangamite Soil Health Monitoring Program Data https:// doi.org/10.25955/5c1c6b8f4d8d2 (Corangamite Catchment Management Authority, 2019) Dataset and service Federation University, Centre for eResearch and Innovation (CeRDI). SMN-1 (Soil Moisture Network 1) Soil moisture probe network, SFS (Southern Farming Systems https:// doi.org/10.25955/5cdcff6168a76 (Southern Farming Systems, 2011) Dataset Federation University, CeRDI.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Adelaide, South Australia, Australia","kind":"settlement","role":"Home of University of Adelaide, School of Agriculture, Food and Wine, providing Wheat Trials datasets for AgReFed","river_name":null,"geocode_query":"Adelaide, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"WT (Wheat Trials) Waite Permanent Rotation Trial https://doi. org/10.4225/08/55E5165EC0D29 (Sanderman et al. 2015) Dataset *University of Adelaide, School of Agriculture, Food and Wine NS (NatSoils) Soil SITES database (NatSoil) https:// doi.org/10.25919/5c36d77a6299c (CSIRO 2013) Dataset and service Commonwealth Scientific and Industrial Research Organisation (CSIRO) SLG (Soil and Landscape Grid) Soil and Landscape Grid National Soil Attribute Maps (3” resolution), Release 1 Collection.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.529398,-34.915148],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-c575e809f10b18599e50d55e-site-4","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"CSIRO facilities, Australia","kind":"facility","role":"Commonwealth Scientific and Industrial Research Organisation providing NatSoils dataset and Soil and Landscape Grid data products to AgReFed","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"WT (Wheat Trials) Waite Permanent Rotation Trial https://doi. org/10.4225/08/55E5165EC0D29 (Sanderman et al. 2015) Dataset *University of Adelaide, School of Agriculture, Food and Wine NS (NatSoils) Soil SITES database (NatSoil) https:// doi.org/10.25919/5c36d77a6299c (CSIRO 2013) Dataset and service Commonwealth Scientific and Industrial Research Organisation (CSIRO) SLG (Soil and Landscape Grid) Soil and Landscape Grid National Soil Attribute Maps (3” resolution), Release 1 Collection.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Western Australia, Australia","kind":"region","role":"Region where University of Western Australia and Department of Primary Industries and Regional Development deliver Frost Trials datasets to AgReFed","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Sample see Rossel et al. (2014) https://doi. org/10.4225/08/546ED604ADD8A Data product (maps), collection and service CSIRO FT (Frost Trials) Crop Variety Frost Trial data collections https://doi. org/10.26182/5cedf001186f3 (Taylor et al. 2019) Dataset collection *University of Western Australia (UWA) and Department of Primary Industries and Regional Development (DPIRD) SMN-2 (Soil Moisture Network 2) SensorNets – SMART Farms Soil Moisture Network https://doi. org/10.4226/95/5b10d5ca18aef (Schneider et al. 2018) Dataset *University of New England (UNE) Table 1 The data providers and their data products.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Armidale, New South Wales, Australia","kind":"settlement","role":"Location of University of New England, provider of Soil Moisture Network 2 datasets to AgReFed","river_name":null,"geocode_query":"Armidale, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Sample see Rossel et al. (2014) https://doi. org/10.4225/08/546ED604ADD8A Data product (maps), collection and service CSIRO FT (Frost Trials) Crop Variety Frost Trial data collections https://doi. org/10.26182/5cedf001186f3 (Taylor et al. 2019) Dataset collection *University of Western Australia (UWA) and Department of Primary Industries and Regional Development (DPIRD) SMN-2 (Soil Moisture Network 2) SensorNets – SMART Farms Soil Moisture Network https://doi. org/10.4226/95/5b10d5ca18aef (Schneider et al. 2018) Dataset *University of New England (UNE) Table 1 The data providers and their data products.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.6523371,-30.5035714],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26967058,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Armidale NSW 2350, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-c575e809f10b18599e50d55e-site-7"}],"limitations":["Geographic specificity mainly to Australian agricultural contexts; regional coordinates were not provided for study sites.","The FAIR threshold framework and governance reported is specific to the AgReFed federation; generalisation to other domains or regions requires caution.","Exact GPS coordinates for many data provider institutions or field sites were not provided in the source text.","The paper focuses on data governance and FAIR metrics rather than detailed geographic or ecological processes."],"river_ids":[],"site_ids":["doc-c575e809f10b18599e50d55e-site-1","doc-c575e809f10b18599e50d55e-site-2","doc-c575e809f10b18599e50d55e-site-4","doc-c575e809f10b18599e50d55e-site-7"],"anchor":[143.8801622,-37.62833670000001],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040070710,5040073420],"main_basin_ids":[5040070710,5040073420]},{"id":"doc-e6a55ca5d87e7a35444a0f0e","title":"Modelling daily transmission losses in basin‐scale river system models under changing hydrological regimes","display_title":"Modeling Transmission Losses in Cooper Creek","doi":"https://doi.org/10.1002/hyp.14625","theme":"Flow & ecology","color":"#80edc2","year":2022,"publication_date":"2022-06-01","authors":["Shaun Kim","Justin Hughes","Lucy Marshall","Cuan Petheram","Ashish Sharma","Jai Vaze","Russell S. Crosbie"],"summary":"This study developed and tested a new conceptual river transmission loss model called the river bed/bank store (RBS) model to better represent dynamic river-groundwater interactions and losses in a basin-scale river system model context. The RBS model was applied and compared to a benchmark piecewise linear (PWL) flow versus loss model in the Cooper Creek reach of the Cooper Basin, southwest Queensland, Australia. Calibration and validation over two hydrologically different periods showed the RBS model's improved performance in representing losses, runoff, and evapotranspiration, offering more realistic water balances and robust predictions under hydrological regime changes. The RBS model also facilitates disaggregated river water balance components useful for ecological health assessment and resource planning under non-stationary conditions such as climate change or water resource development.","finding":"The river bed/bank store (RBS) loss model outperforms the conventional piecewise linear (PWL) flow versus loss relationship model in daily basin-scale river system modeling by providing more realistic water balances, better calibration and validation performance, and robustness under changing hydrological regimes, including responses to water resource development scenarios.","finding_evidence":{"passage_id":"p014-000","page":14,"quote":"more typically seen during calibration. For example, in the adjacent Diamantina River, transmission losses were computed between 70% and 98% for floods with total discharge <2300 GL. The PWL loss model clearly struggles when conditions are very different to those in the calibration period. 4 | DISCUSSION The RBS model overall performed better, than the benchmark PWL flow versus loss function model, demonstrated by the performance metrics (Figures 9 to 11). This is explained by the RBS model producing more realistic water balances than the PWL model (Figure 14), and much more reasonable runoff estimates given the aridity of the study site (Figure 15). While observations were not available to verify the development scenario simulation results, the PWL loss model likely overestimated the losses of additional water (95%) given the uniformity of the altered flow.","verified_in_page":true},"topics":["transmission loss","river-groundwater interactions","river system modeling","hydrological nonstationarity","water balance","evapotranspiration","Bayesian calibration","water resource development scenario"],"study_period":"1969-2019","locations":[{"name":"Cooper Creek reach, Cooper Basin, Queensland","kind":"reach","role":"primary study river reach for model application and testing","river_name":"Cooper Creek","geocode_query":"Cooper Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The current paper's research objectives are: 1. to model dynamic transmission loss processes at spatial and temporal scales relevant to water resources management and planning; 2. provide insight into crucial river water balance components and processes including river bank storage interactions and riparian zone evapotranspiration; 3. develop a model that is robust under different hydrological regimes, thus, proposing structural changes that address nonstationarity in river system models. 2 | DATA AND METHODS 2.1 | Study site The study site is located within the Cooper Basin, southwest Queensland, Australia (Figure 1). The site is of high importance from a water management perspective because of potential developments that would impact water resources in the region (Bradfield, 1938; Holland et al., 2020; Morrish, 2017; Wakelin-King, 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.231948,-26.927703],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cooper Creek, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-e6a55ca5d87e7a35444a0f0e-site-0","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Thomson River, Queensland","kind":"reach","role":"tributary river forming Cooper Creek, location of water resource development scenario application","river_name":"Thomson River","geocode_query":"Thomson River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"The study site is a good testing ground for transmission loss models since it has been previously suggested that standard flow routing methods would be difficult (if not impossible) to apply without knowledge of the spatial and temporal variation of the losses (Knighton & Nanson, 1994). This statement is largely owing to the region's aridity and complex geomorphology. In the Cooper Basin, Cooper Creek is formed below the confluence of the Thomson and Barcoo rivers. The study region covers from Barcoo to Cooper Creek down to Nappa Merrie (Figure 1). Table 2 contains details of the study site within the Cooper Basin. The study area is in the Channel Country of Queensland in the north-east of the FIGURE 1 Map of the Cooper basin river reach used in the current study. The study reach contains five subcatchments 4 of 21 KIM ET AL. 10991085, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hyp.14625 by Test, Wiley Online Library on [30/08/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.078154,-23.465345],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Thomson River, Longreach QLD 4730, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-e6a55ca5d87e7a35444a0f0e-site-1","true_coordinates":[144.0781535127636,-23.52534461181954],"spread_km":6.7},{"name":"Barcoo River, Queensland","kind":"reach","role":"tributary river forming Cooper Creek included in the study region","river_name":"Barcoo River","geocode_query":"Barcoo River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The current paper's research objectives are: 1. to model dynamic transmission loss processes at spatial and temporal scales relevant to water resources management and planning; 2. provide insight into crucial river water balance components and processes including river bank storage interactions and riparian zone evapotranspiration; 3. develop a model that is robust under different hydrological regimes, thus, proposing structural changes that address nonstationarity in river system models. 2 | DATA AND METHODS 2.1 | Study site The study site is located within the Cooper Basin, southwest Queensland, Australia (Figure 1). The site is of high importance from a water management perspective because of potential developments that would impact water resources in the region (Bradfield, 1938; Holland et al., 2020; Morrish, 2017; Wakelin-King, 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.361338,-24.971228],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006204,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barcoo, QLD, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-e6a55ca5d87e7a35444a0f0e-site-2","true_coordinates":[142.3613378,-25.0312277],"spread_km":6.7},{"name":"Nappa Merrie, Queensland","kind":"settlement","role":"downstream gauge location defining study reach boundary","river_name":null,"geocode_query":"Nappa Merrie, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The current paper's research objectives are: 1. to model dynamic transmission loss processes at spatial and temporal scales relevant to water resources management and planning; 2. provide insight into crucial river water balance components and processes including river bank storage interactions and riparian zone evapotranspiration; 3. develop a model that is robust under different hydrological regimes, thus, proposing structural changes that address nonstationarity in river system models. 2 | DATA AND METHODS 2.1 | Study site The study site is located within the Cooper Basin, southwest Queensland, Australia (Figure 1). The site is of high importance from a water management perspective because of potential developments that would impact water resources in the region (Bradfield, 1938; Holland et al., 2020; Morrish, 2017; Wakelin-King, 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.095993,-27.5379],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006047,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nappa Merry, Durham QLD 4492, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-e6a55ca5d87e7a35444a0f0e-site-3","true_coordinates":[141.095993,-27.5979],"spread_km":6.7}],"limitations":["Incomplete geospatial coordinates for study locations","Native PDF text limitations affecting precise coordinate extraction","Broad regional scale for study area limits finer spatial resolution"],"river_ids":[],"site_ids":["doc-e6a55ca5d87e7a35444a0f0e-site-0","doc-e6a55ca5d87e7a35444a0f0e-site-1","doc-e6a55ca5d87e7a35444a0f0e-site-2","doc-e6a55ca5d87e7a35444a0f0e-site-3"],"anchor":[142.361338,-24.971228],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040491510],"main_basin_ids":[5040087590]},{"id":"doc-ec6e12290cb75f7310d480f2","title":"An integrated dataset of daily lake surface water temperature over the Tibetan Plateau","display_title":"Integrated Daily Lake Surface Water Temperature Dataset over Tibetan Plateau","doi":"https://doi.org/10.5194/essd-14-3411-2022","theme":"Climate","color":"#98d4f1","year":2022,"publication_date":"2022-07-27","authors":["Linan Guo","Hongxing Zheng","Yanhong Wu","Lanxin Fan","Mengxuan Wen","Junsheng Li","Fangfang Zhang","Liping Zhu","Bing Zhang"],"summary":"This study produced an integrated daily lake surface water temperature (LSWT) dataset for 160 large alpine lakes on the Tibetan Plateau (TP) from 1978 to 2017 by combining satellite observations (MODIS) and a physically-based model (modified air2water). The satellite-based LSWT (2000–2017) calibrated and validated the model which reconstructed a consistent LSWT time series, enabling analysis of long-term warming trends. The LSWT is a critical indicator for climate change and lacustrine ecosystem dynamics at high elevation. The integrated dataset fills gaps in observations and extends temporal coverage, providing a unique resource for climate and ecological research on the Tibetan Plateau lakes.","finding":"The annual lake surface water temperature of most lakes on the Tibetan Plateau significantly increased during 1978–2017 with warming rates ranging from 0.01 to 0.47°C per decade, with stronger warming in winter than in summer.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"This is because the simulations represent the lake-wide mean temperature of the skin layer, while the in situ observation used herein is the profile mean temperature for a fixed location in the lake. 4.3 Long-term trends of LSWT in the Tibetan Plateau On the basis of the reconstructed daily LSWT dataset, the long-term changes of surface water temperatures of lakes across the TP are detected by using the Mann–Kendall trend analysis approach (Kendall, 1949; Mann, 1945). The daily and annual variation of a sample lake is shown in Fig. S5. Figure 7 shows that, for the period 1978–2017, the annual LSWT of most lakes (except for Lake Beidao and Lake Changhong) increase significantly at rates ranging from 0.01 to 0.47 ◦C per 10 years, which is consistent with but comparably smaller than that of the increasing rate in air temperature (0.26–0.87 ◦C per 10 years) indicating the contributions of heat storage capacity of the lakes.","verified_in_page":true},"topics":["lake surface temperature","satellite remote sensing","climate change","time series reconstruction","physically based modeling","high-altitude lakes","thermal dynamics","trend analysis"],"study_period":"1978–2017","locations":[{"name":"Tibetan Plateau","kind":"region","role":"Study area containing alpine lakes analyzed for surface water temperature trends","river_name":null,"geocode_query":"Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"For the combination, the remotely sensed LSWT is used to calibrate and validate the air2water model, which is then applied to reconstruct a complete and consistent daily LSWT time series for each studied lake and to extend the LSWT dataset to a wider time span. The integrated dataset demonstrates potential in investigating variabilities and changes in LSWT across the TP during the past decades. It could be valuable for assessing the impacts of climate warming on the dynamics of water and heat budget, water quality, and aquatic biota in lakes across the TP. 2 Study area The TP is located between 26◦000–39◦470 N and 73◦190– 104◦470 E with a mean elevation of over 4500 m and an area about 2.5×106 km2 . It is known as the “Asian water tower”, contributing to most major rivers in Asia (Wu and Lei, 2014). The total area of lakes in the plateau is around 45 000 km2 , most of which are located at altitudes between 4000 and 5000 m (Yao et al., 2015).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Ngoring Lake, Tibetan Plateau, China","kind":"other","role":"Field validation site with sequential in situ lake surface temperature observations","river_name":null,"geocode_query":"Ngoring Lake, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"As shown in Fig. 6, the simulated lake surface temperature is in good agreement temporally with the sequential observations (R 2 = 0.97, 0.92, 0.90, 0.97 for Ngoring Lake, Serling Co, Dogze Co, Bangong Co, respectively) and spatially with the sporadic observations (R 2 = 0.94). Compared with the in situ observations, the RMSE of the simulated temperEarth Syst. Sci. Data, 14, 3411–3422, 2022 https://doi.org/10.5194/essd-14-3411-2022","verified_in_page":true},"resolution_status":"resolved","coordinates":[97.7416703,34.924237],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eling Lake, Madoi County, Golog Tibetan Autonomous Prefecture, China, 813500","basin_id":4040578830,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":195359.0,"hydrology_source":"hydrobasins","site_id":"doc-ec6e12290cb75f7310d480f2-site-1"},{"name":"Serling Co, Tibetan Plateau, China","kind":"other","role":"Field validation site with sequential in situ temperature observations","river_name":null,"geocode_query":"Serling Co, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"As shown in Fig. 6, the simulated lake surface temperature is in good agreement temporally with the sequential observations (R 2 = 0.97, 0.92, 0.90, 0.97 for Ngoring Lake, Serling Co, Dogze Co, Bangong Co, respectively) and spatially with the sporadic observations (R 2 = 0.94). Compared with the in situ observations, the RMSE of the simulated temperEarth Syst. Sci. Data, 14, 3411–3422, 2022 https://doi.org/10.5194/essd-14-3411-2022","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Dogze Co, Tibetan Plateau, China","kind":"other","role":"Field validation site with sequential in situ temperature observations","river_name":null,"geocode_query":"Dogze Co, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"As shown in Fig. 6, the simulated lake surface temperature is in good agreement temporally with the sequential observations (R 2 = 0.97, 0.92, 0.90, 0.97 for Ngoring Lake, Serling Co, Dogze Co, Bangong Co, respectively) and spatially with the sporadic observations (R 2 = 0.94). Compared with the in situ observations, the RMSE of the simulated temperEarth Syst. Sci. Data, 14, 3411–3422, 2022 https://doi.org/10.5194/essd-14-3411-2022","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Bangong Co, Tibetan Plateau, China","kind":"other","role":"Field validation site with sequential in situ temperature observations","river_name":null,"geocode_query":"Bangong Co, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"As shown in Fig. 6, the simulated lake surface temperature is in good agreement temporally with the sequential observations (R 2 = 0.97, 0.92, 0.90, 0.97 for Ngoring Lake, Serling Co, Dogze Co, Bangong Co, respectively) and spatially with the sporadic observations (R 2 = 0.94). Compared with the in situ observations, the RMSE of the simulated temperEarth Syst. Sci. Data, 14, 3411–3422, 2022 https://doi.org/10.5194/essd-14-3411-2022","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Tibetan Plateau, China","kind":"region","role":"Area of sparser meteorological stations inputting air temperature to model, causing uncertainty in reconstructed LSWT","river_name":null,"geocode_query":"Western Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The modified air2water model hence is reliable in reconstructing LSWT for lakes at different altitude or latitude zones. However, it should be noted that uncertainties exist in the reconstructed LSWT due to the uncertainties not only in the calibrated model but also in the air temperature inputted to the model. Summaries of model performance for each month presented in Fig. S4 show that the simulated LSWT has a smaller bias in July and November. As mentioned in Sect. 3.2, the daily air temperature above the lakes is that interpolated from the nearest meteorological stations with elevation adjustment, where the performance of the interpolation would be affected by the density and locations of the stations. Currently, the available meteorological stations are sparsely located in the western TP (Fig. 1), which may result in inherent uncertainties of the reconstructed LSWT for lakes in the western TP.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Sparse meteorological stations in western Tibetan Plateau introduce uncertainties in model air temperature input and reconstructed LSWT.","Satellite-based MODIS data have missing values due to cloud cover and algorithm limitations.","Model simulations represent lake-wide skin temperature, differing from in situ profile mean measurements, affecting direct comparison.","Warming and cooling trend estimates inherit uncertainties from model parameterization and input data.","Some coordinates and exact lake locations are unavailable or not provided explicitly in the dataset/document."],"river_ids":[],"site_ids":["doc-ec6e12290cb75f7310d480f2-site-1"],"anchor":[97.7416703,34.924237],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040578830],"main_basin_ids":[4040007850]},{"id":"doc-fd06df7c791ad87dfe05471a","title":"State of the Climate in 2021","display_title":"Climate State Globally and Regionally in 2021","doi":"https://doi.org/10.1175/2022bamsstateoftheclimate.1","theme":"Climate","color":"#98d4f1","year":2022,"publication_date":"2022-08-01","authors":["J. Blunden","T. Boyer"],"summary":"This comprehensive report surveys the state of Earth's climate in 2021, highlighting trends and notable extremes across global and regional scales. It covers atmospheric greenhouse gases, global temperatures, ocean heat content, sea level rise, extremes such as heatwaves, droughts and floods, regional climatic anomalies, cryospheric changes including glaciers and sea ice, and atmospheric composition changes including ozone depletion and aerosols. The report integrates diverse datasets, satellite and in situ observations, and model analyses to provide detailed diagnostics across natural environments and continents.","finding":"In 2021, atmospheric concentrations of major greenhouse gases CO2, CH4, and N2O reached new record highs with respective fifth-highest, highest, and third-highest growth rates in the instrumental records, contributing to new peaks in global radiative forcing driving continued warming and climatic changes.","finding_evidence":{"passage_id":"p004-000","page":4,"quote":"AUGUST 2022 | State of the Climate in 2021 1. INTRODUCTION Siii In 2021, the dominant greenhouse gases released into Earth’s atmosphere continued to increase. The annual global average carbon dioxide (CO2 ) concentration was 414.7 ± 0.1 ppm, an increase of 2.6 ± 0.1 ppm over 2020, the fifth-highest growth rate since the start of the instrumental record in 1958. This brings the concentration of CO2 to, once again, the highest in the modern record and ice core records dating back 800,000 years. The growth rate for methane (CH4) was the highest on record and the third highest for nitrous oxide (N2 O), contributing to new record high atmospheric concentration levels for both gases. Weak-to-moderate La Niña conditions were present in the eastern equatorial Pacific during most of 2021, a continuation from 2020.","verified_in_page":true},"topics":["global warming","greenhouse gases","climate variability","heatwaves","drought","precipitation extremes","sea level rise","sea ice decline"],"study_period":null,"locations":[{"name":"Arctic","kind":"region","role":"study region showing overall annual climatic changes including temperatures, precipitation, and sea ice extent","river_name":null,"geocode_query":"Arctic","latitude":null,"longitude":null,"evidence":{"passage_id":"p263-000","page":263,"quote":"AUGUST 2022 | State of the Climate in 2021 5. THE ARCTIC S258 STATE OF THE CLIMATE IN 2021 The Arctic Editors Jessica Blunden Tim Boyer Chapter Editors Freya Aldred Peter Bissolli Kyle R. Clem Howard J. Diamond Matthew L. Druckenmiller Robert J. H. Dunn Catherine Ganter Nadine Gobron Gregory C. Johnson Rick Lumpkin Ademe Mekonnen John B. Miller Twila A. Moon Marilyn N. Raphael Ahira Sánchez-Lugo Carl J. Schreck III Richard L. Thoman Kate M. Willett Zhiwei Zhu Technical Editor Laura Ohlmann BAMS Special Editor for Climate Michael A. Alexander American Meteorological Society","verified_in_page":true},"resolution_status":"resolved","coordinates":[-100.113952,76.190578],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arctic","basin_id":8040047650,"main_basin_id":8040047650,"basin_region":"North American Arctic","basin_area_km2":106895.7,"hydrology_source":"hydrobasins","site_id":"doc-fd06df7c791ad87dfe05471a-site-0","true_coordinates":[-100.1139521,76.2505778],"spread_km":6.7},{"name":"South Pole","kind":"station","role":"site of record lowest annual winter temperatures and associated stratospheric conditions crucial to ozone depletion assessment","river_name":null,"geocode_query":"South Pole, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p322-000","page":322,"quote":"AUGUST 2022 | State of the Climate in 2021 6. ANTARCTICA AND THE SOUTHERN OCEAN S317 Sidebar 6.1: A record cold winter at the South Pole in 2021—T. NORTON, L. KELLER, K. R. CLEM, M. LAZZARA, T. SCAMBOS, AND S. BARREIRA. The Amundsen-Scott South Pole station had its lowest austral winter average temperature in 2021, at −61.0°C (April– September, AMJJAS; blue line in Fig. SB6.1a) since records began in 1957. During these months when the South Pole is in continuous darkness, there were 153 days (out of 183) where the daily average temperature was −50°C or below, second only to the polar night period in 2012. Meteorological winter (June–August, JJA) was the second coldest on record (behind 2004), with June and August each having 28 days at or below −50°C. The annual mean temperature for the 2021 calendar year tied for third coldest on record at −50.5°C (red line in Fig. SB6.1a), 1.3°C below average.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Antarctic Peninsula","kind":"region","role":"area of persistent warm northerly wind anomalies leading to record high annual temperatures and increased surface melting and ice shelf mass balance changes","river_name":null,"geocode_query":"Antarctic Peninsula, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p317-000","page":317,"quote":"AUGUST 2022 | State of the Climate in 2021 6. ANTARCTICA AND THE SOUTHERN OCEAN S312 a. Overview—K. R. Clem and M. N. Raphael Antarctica and the Southern Ocean were dominated by below-average pressure and stronger than average circumpolar westerly winds through much of 2021, resulting in many new lowpressure records set across the continent. The low pressure that gripped the polar cap extended vertically through the troposphere and stratosphere and was accompanied by a persistently strong and stable polar vortex. This helped deliver the coldest winter on record to the South Pole (see Sidebar 6.1) and also helped maintain one of the longest-lived ozone holes on record (second only to the 2020 ozone hole), which did not close until 23 December. Despite the long duration of the ozone hole, its size and the minimum ozone concentrations did not reach exceptional levels, and its slow growth rate in 2021 is consistent with ongoing recovery trends.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Greenland Ice Sheet","kind":"region","role":"major cryosphere feature with ongoing surface melt and mass loss, influencing sea level and local climate extremes including the first recorded rainfall at Summit Station","river_name":null,"geocode_query":"Greenland Ice Sheet, Greenland","latitude":null,"longitude":null,"evidence":{"passage_id":"p281-000","page":281,"quote":"AUGUST 2022 | State of the Climate in 2021 5. THE ARCTIC S276 e. Greenland ice sheet—T. A. Moon, K. D. Mankoff, R. S. Fausto, X. Fettweis, M. Tedesco, A. Wehrlé, B. D. Loomis, T. L. Mote, C. D. Jensen, N. Korsgaard, J. E. Box, J. Cappelen, and Ø. A. Winton The Greenland Ice Sheet has lost mass every year since 1998, with negative impacts of ice loss experienced globally. The ice sheet contains enough ice to generate ~7.4 m of eustatic sea level rise (rise caused by ocean mass changes only; Morlighem et al. 2017). With multiple methods available for measuring Greenland Ice Sheet mass balance, we compare 1 September 2020 through 31 August 2021 mass change values derived from satellite gravimetry using Gravity Recovery and Climate Experiment Follow On (GRACE-FO) measurements (−125 ± 35 Gt) and an input-output (I/O) method (−165 ± 89 Gt; see Fig. 5.11 for details on methods and references).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-41.2,76.7],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Greenland ice sheet, Greenland","basin_id":9040000590,"main_basin_id":9040000590,"basin_region":"Greenland","basin_area_km2":210568.0,"hydrology_source":"hydrobasins","site_id":"doc-fd06df7c791ad87dfe05471a-site-3"},{"name":"Amazon basin","kind":"region","role":"regional study area experiencing exceptional precipitation and flooding events of major socio-environmental impact","river_name":"Amazon River","geocode_query":"Amazon basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p374-000","page":374,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S369 Sidebar 7.3: Major floods in the Amazon—G. Koren The Amazon forest has experienced several extreme events in the last decade, including large-scale floods and droughts. In 2021, with La Niña conditions present most of the year, the Amazon experienced higher-than-normal precipitation, leading to anomalously high river discharge and floods in downstream regions. Here, a quantification of the severity and extent of these anomalies are reported, including an assessment of the societal impacts of these extreme events, which coincided with the ongoing COVID pandemic. Earlier major floods occurred in 2009, following extreme rainfall that progressed from the western to eastern part of the Amazon during the first months of that year (Filizola et al. 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.659944,-1.584128],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon River","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-fd06df7c791ad87dfe05471a-site-4","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"North Pacific Ocean","kind":"region","role":"study region showing La Niña and Pacific Decadal Oscillation impacts on ocean temperatures, currents and associated climatic features","river_name":null,"geocode_query":"North Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p155-000","page":155,"quote":"AUGUST 2022 | State of the Climate in 2021 3. GLOBAL OCEANS S150 Fig. 3.1. (a) Annually-averaged SSTAs in 2021 and (b) difference of annually-averaged SSTAs between 2021 and 2020. Values (°C) are relative to 1991–2020 climatology and the SSTA difference is significant at 95% confidence in stippled areas. Ocean climate change, varies with La Niña, yet, warming trends are clear. b. Sea surface temperatures—B. Huang, Z.-Z. Hu, J. J. Kennedy, and H.-M. Zhang Sea surface temperature (SST) changes and their uncertainties are assessed over the global oceans including seas and large lakes in 2021 using three updated SST products: the Extended Reconstruction Sea-Surface Temperature (ERSSTv5; Huang et al. 2017, 2020), U.K. Met Office Hadley Centre SST (HadSST.4.0.1.0; Kennedy et al. 2019), and Daily Optimum Interpolation SST (DOISST v2.1; Huang et al. 2021a). SST anomalies (SSTAs) are calculated relative to their 1991–2020 baseline period climatologies.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.478167,-8.731233],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-fd06df7c791ad87dfe05471a-site-5","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"North Atlantic Ocean","kind":"region","role":"region with observed anomalous cold water and freshening associated with Atlantic Meridional Overturning Circulation variability affecting climate and sea level","river_name":null,"geocode_query":"North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p165-000","page":165,"quote":"AUGUST 2022 | State of the Climate in 2021 3. GLOBAL OCEANS S160 precipitation and more precipitation where precipitation exceeds evaporation (Held and Soden 2006; Durack and Wijffels 2010). In the ocean this translates roughly to “Salty gets saltier and fresh gets fresher.” This pattern has been evident in State of the Climate reports going back as far as 2006, the first year of the salinity section. In 2021 this pattern held (Fig. 3.7a), with salty SSS anomalies in most of the subtropical salinity maxima and fresh SSS anomalies in the eastern subpolar North Pacific and North Atlantic, as well as the ITCZs of the Pacific and Atlantic. The 2005–21 SSS trends (Fig. 3.7c) reflect this pattern to some extent as well, with statistically significant (unstippled areas) freshening trends evident in the eastern subpolar North Pacific and North Atlantic, the Pacific ITCZ, and the Gulf of Guinea, as well as statistically significant salty trends in parts of the subtropics in all basins.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-39.462891,35.806512],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Atlantic Ocean","hydrology_source":"none","site_id":"doc-fd06df7c791ad87dfe05471a-site-6","true_coordinates":[-39.462891,35.746512],"spread_km":6.7},{"name":"South Indian Ocean","kind":"region","role":"study area exhibiting sea surface temperature dipole variability and its effect on regional precipitation, upper ocean heat content, and air-sea fluxes","river_name":null,"geocode_query":"South Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p159-000","page":159,"quote":"AUGUST 2022 | State of the Climate in 2021 3. GLOBAL OCEANS S154 Maps of annual (Fig. 3.4) upper (0–700 m) ocean heat content anomaly (OHCA) relative to a 1993–2021 baseline mean are generated from a combination of in situ ocean temperature data and satellite altimetry data following Willis et al. (2004), but using Argo (Riser et al. 2016) data downloaded from an Argo Global Data Assembly Centre in January 2022. Near-global average seasonal temperature anomalies (Fig. 3.5) versus pressure from Argo data (Roemmich and Gilson 2009, updated) since 2004 and in situ global estimates of OHCA (Fig. 3.6) for three pressure layers (0–700 m, 700–2000 m, and 2000–6000 m) from five different research groups are also discussed. La Niña conditions existed from August 2020 through May 2021, and then again from August 2021 through at least December 2021 (see section 4b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.894319,-33.156092],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indian Ocean","hydrology_source":"none","site_id":"doc-fd06df7c791ad87dfe05471a-site-7","true_coordinates":[81.826172,-33.137551],"spread_km":6.7},{"name":"Europe","kind":"region","role":"region showing climate temperature anomalies, seasonal variability, and extreme weather including record heatwaves and flooding in 2021","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p399-000","page":399,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S394 over the eastern half of the interior of the island (Fig. 7.36b). The drier-than-normal conditions over central and eastern Madagascar placed 2021 among the top 3% driest over the past 30 years. Examination of rainfall throughout the year indicated that drier-than-normal conditions persisted over Madagascar from January to May and October to December. The driest month was January, with deficits of 200–250 mm over eastern Madagascar. Seasonal rainfall was well below average, with deficits between 25 and 350 mm across Madagascar during DJF 2020/21. The eastern half of the country was the driest region, with rainfall more than 100 mm below the mean, ranking in the 3rd percentile in coastal areas. The moisture deficits persisted and extended over the southern two-thirds of the island during MAM, while the northern and northeastern sectors registered rainfall surpluses.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"North America","kind":"region","role":"study area characterized by record extreme heatwave in 2021 with severe societal impacts and cold air outbreaks with heavy winter storm effects","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p353-000","page":353,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S348 a. Overview This chapter provides summaries of the 2021 temperature and precipitation conditions across seven broad regions: North America, Central America and the Caribbean, South America, Africa, Europe and the Middle East, Asia, and Oceania. In most cases, summaries of notable weather events are also included. Local scientists provided the annual summary for their respective regions and, unless otherwise noted, the source of the data used is typically the agency affiliated with the authors. The base period used for these analyses in many countries is now 1991–2020, as new 30-year climate normals are being utilized (see Chapter 1 for details). However, please note that on occasion different nations, even within the same section, may use unique periods to define their normal and many countries still use the 1981–2010 base period.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.255119,54.585961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-fd06df7c791ad87dfe05471a-site-9","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Australia","kind":"region","role":"continent experiencing wetter than normal conditions related to La Niña and positive Southern Annular Mode, with episodic floods and heatwaves in 2021","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p444-000","page":444,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S439 (iii) Notable events and impacts Severe Tropical Cyclone Niran formed east of the Queensland coast of Australia and moved southeastward towards New Caledonia in early March. By 5 March, Niran had reached Category 5 tropical cyclone strength, with maximum 1-minute sustained winds of 260 km h−1 (140 kt) and a central pressure of 931 hPa. Niran was the second Category 5 storm of 2021 globally and the second Category 5 storm of the 2020/21 South Pacific cyclone season behind Tropical Cyclone Yasa which formed in December 2020. Despite not making landfall over New Caledonia, Niran passed close to its main island, Grande Terre, on 6 March; fortunately, the strongest winds remained offshore and, by this stage, it had weakened to a Category 3 storm. Niran continued to track to the southeast as it weakened and transitioned into an extratropical cyclone later that day.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"China","kind":"region","role":"location of super record extreme rain events including Zhengzhou city monthly record hourly rainfall, related to Pacific tropical climate variability and atmospheric teleconnections","river_name":null,"geocode_query":"China","latitude":null,"longitude":null,"evidence":{"passage_id":"p428-000","page":428,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S423 Sidebar 7.4: Unprecedented extreme rainfall over East Asia in July and August 2021— Z. Zhu, X. Liu, R. Lu, S. Wakamatsu, and K. Takahashi During July 2021, heavy rainstorms caused devastating floods across a wide range of areas in North China. On 20 July, a 1-hour precipitation total in Zhengzhou—the capital of Henan province in central China and home to more than 10 million people— reached 201.9 mm, breaking the record for the largest hourly precipitation in mainland China. The disastrous rainstorms inundated the city, leading to 380 fatalities or missing people. The July mean precipitation anomalies (Fig. SB7.6a) showed two maximum centers > 8 mm day−1: one in North China (red box) and one in the Yangtze River Delta. Steered by the dominant large-scale anomalous circulation, Typhoon In-fa (shown in Fig.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"India","kind":"region","role":"region affected by above normal temperature, monsoon rainfall near normal, and intense hydrometeorological events including damaging cyclones and floods","river_name":null,"geocode_query":"India","latitude":null,"longitude":null,"evidence":{"passage_id":"p430-000","page":430,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S425 4) SOUTH ASIA—O. P. Sreejith, A. K. Srivastava, and M. Rajeevan Countries in this section include Bangladesh, India, Pakistan, and Sri Lanka. Unless otherwise noted, climate anomalies are relative to the 1981–2010 base period. (i) Temperature In 2021, South Asia generally experienced above-normal temperatures. The annual mean land surface air temperature averaged over India was 0.44°C above normal, making 2021 the fifth-warmest year on record since nationwide records commenced in 1901 (Fig. 7.49). Seasonal mean temperatures were above normal for all seasons. The winter season (January–February, 0.78°C above average) and post-monsoon season (October–December, +0.42°C) mainly accounted for the abovenormal annual temperature.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"South America","kind":"region","role":"large tropical and subtropical continental areas with warming and precipitation anomalies, severe flooding of Amazon and droughts in the south","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p368-000","page":368,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S363 d. South America—A. Sánchez-Lugo, Ed. La Niña was present in the tropical Pacific Ocean throughout much of 2021, strengthening southerly winds, which were present in the Pacific coastal region of northern South America throughout the year. Additionally, the positive pressure pattern, as measured by the Southern Oscillation Index (SOI), which had high values (+20) at the start of the year, helped keep the Intertropical Convergence Zone (ITCZ) farther north than normal during the first three months of the year. Between April and June, the SOI returned to near-normal values; however, by June it again reached thresholds characteristic of a La Niña event and remained throughout most of the year, which added to the strengthening of the trade winds and the negative sea surface temperature anomalies (SSTA) in the southeast Pacific, decreasing the convective activity of the ITCZ.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Siberia","kind":"region","role":"high latitude region with marked heatwaves, dry conditions leading to severe wildfires, and soil moisture deficits in 2021","river_name":null,"geocode_query":"Siberia, Russia","latitude":null,"longitude":null,"evidence":{"passage_id":"p386-000","page":386,"quote":"AUGUST 2022 | STATE OF THE CLIMATE IN 2021 7. REGIONAL CLIMATES S381 the resulting flood affected about 70,000 people in total, according to Guinea Red Cross Society. According to Nigeria’s Emergency Management Agency, flooding due to heavy rains that fell on 12 September in Abuja and surrounding areas led to four fatalities. In Chad, the UN Office for the Coordination of Humanitarian Affairs (OCHA) reported that flooding affected nearly 247,000 people across 400 villages. Fifteen people lost their lives, 17 were reported missing, and a total of 329 people were injured over the rainy season. In early October, the meteorological agency of Côte d’Ivoire reported that 105 mm of rain fell in Yopougon and 106 mm in Attécoubé in a 12-hour period. Flood events were recorded in many parts of Nigeria between June and November. The resultant floods from heavy rainfall destroyed homes, valuable properties, farms, roads, and bridges.","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.08943,61.04371],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Siberia, Russia","basin_id":3040369320,"main_basin_id":3040004740,"basin_region":"Siberia","basin_area_km2":237680.1,"hydrology_source":"hydrobasins","site_id":"doc-fd06df7c791ad87dfe05471a-site-14","true_coordinates":[99.1966559,61.01370970000001],"spread_km":6.7}],"limitations":["Various base periods used for anomalies exact comparison difficult in multi-regional summary","Some reported climate data are preliminary and may be subject to revision","Regional precipitation data sparse resulting in more uncertainty in precipitation anomalies","Satellite datasets include known biases, e.g., SST bias under sea ice, passive microwave snow melt detection complexity","Mass balance estimates from different methods differ by ~15-20% for Greenland glaciers","Permafrost monitoring limited in spatial coverage and incomplete records in some areas","Differences exist between various tropical cyclone archives producing slight differences in global storm counts","Spatial and temporal resolution limits and cloud contamination affect satellite-based chlorophyll and phenology data","Sea ice age and thickness products are limited in time (from 1985 or 2010) and differ between different missions","Water vapor datasets include drifts and retrieval uncertainties","Partial inconsistency among different ocean reanalysis products for ocean heat content and salinity","Arkansas cold wave attribution uncertain beyond short term NWP forecasts"],"river_ids":[],"site_ids":["doc-fd06df7c791ad87dfe05471a-site-0","doc-fd06df7c791ad87dfe05471a-site-3","doc-fd06df7c791ad87dfe05471a-site-4","doc-fd06df7c791ad87dfe05471a-site-5","doc-fd06df7c791ad87dfe05471a-site-6","doc-fd06df7c791ad87dfe05471a-site-7","doc-fd06df7c791ad87dfe05471a-site-9","doc-fd06df7c791ad87dfe05471a-site-14"],"anchor":[-39.462891,35.806512],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[3040369320,6040280400,7040021740,8040047650,9040000590],"main_basin_ids":[3040004740,6040007000,7040021740,8040047650,9040000590]},{"id":"doc-04f2b966696d6b75fb2f02f0","title":"Advancing “Autonomous” sensing and prediction of the subsurface environment: a review and exploration of the challenges for soil and groundwater contamination","display_title":"Autonomous Sensing of Subsurface Contamination","doi":"https://doi.org/10.1007/s11356-022-25125-8","theme":"Groundwater","color":"#a8b9ff","year":2023,"publication_date":"2023-01-12","authors":["Greg B. Davis","John L. Rayner","Michael J. Donn"],"summary":"This review discusses advances and challenges in autonomous in situ sensing and prediction of subsurface soil and groundwater contamination, focusing on linking sensor networks to digital twin computational platforms. It highlights technologies for measuring contaminants and related environmental parameters, computational models representing contaminant transport and biodegradation, and regulatory considerations. A case study at the BP Kwinana petroleum site in Western Australia illustrates current sensor deployments monitoring natural source zone depletion (NSZD) of petroleum hydrocarbons, showing potential for automated, real-time contaminant management.","finding":"Deployment of multi-parameter sensors (targeting gases like oxygen, carbon dioxide, methane, VOCs, soil temperature, and moisture) coupled with computational platforms enables near real-time spatial and temporal mapping of NSZD biodegradation rates at petroleum-contaminated sites, supporting automated management decisions.","finding_evidence":{"passage_id":"p009-001","page":9,"quote":"Additionally, the field site is the BP Kwinana site as described in Davis et al. (2022), which has a mature CSM, with investigations spanning decades (e.g., Davis et al. 1998). Petroleum hydrocarbon masses across the site have been reasonably quantified and the depth distribution of LNAPL within the shallow sandy soil profile and in the zone of water table fluctuation 2–5 m below ground surface. Measurement technologies To use accepted methods to estimate NSZD rates, VOC, oxygen, and carbon dioxide probes (Patterson et al. 2000b; Patterson and Davis 2008; Patterson et al. 2013) as well as thermistors and soil moisture probes were installed at multiple depths and locations at the Kwinana site. Duplicate strings of gas probes and thermistors were installed~20 m apart at each location specific to crude oil, gasoline, diesel, aviation gasoline and a background location.","verified_in_page":true},"topics":["contaminant transport","natural source zone depletion","sensor networks","biodegradation","digital twin","groundwater contamination","soil pollution","environmental monitoring"],"study_period":null,"locations":[{"name":"BP Kwinana petroleum site, Western Australia","kind":"field_site","role":"main study site with extensive sensor deployment and mature contaminant conceptual site model","river_name":null,"geocode_query":"BP Kwinana petroleum site, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Additionally, the field site is the BP Kwinana site as described in Davis et al. (2022), which has a mature CSM, with investigations spanning decades (e.g., Davis et al. 1998). Petroleum hydrocarbon masses across the site have been reasonably quantified and the depth distribution of LNAPL within the shallow sandy soil profile and in the zone of water table fluctuation 2–5 m below ground surface. Measurement technologies To use accepted methods to estimate NSZD rates, VOC, oxygen, and carbon dioxide probes (Patterson et al. 2000b; Patterson and Davis 2008; Patterson et al. 2013) as well as thermistors and soil moisture probes were installed at multiple depths and locations at the Kwinana site. Duplicate strings of gas probes and thermistors were installed~20 m apart at each location specific to crude oil, gasoline, diesel, aviation gasoline and a background location.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Bemidji, USA","kind":"field_site","role":"location of digital twin modeling study of NSZD processes for petroleum pipeline leak","river_name":null,"geocode_query":"Bemidji, Minnesota, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Recently, Engelmann et al. (2021) evaluated the robustness of DNAPL source zone representations against available data. Sookhak Lari et al. (2022) coupled all key multiphase, multicomponent and biogeochemical processes to progress the creation of a two-dimensional digital twin of natural source zone depletion (NSZD), by applying it to the crude oil pipeline leak in Bemidji, USA. The paper showed the potential to capture all key processes within such a computational modelling platform. However, in no way was the model linked to data from the site (apart from validating the model itself) and did not consider the third dimension. The model typically took 30 days to run a simulation over 30 years; three-dimensional modelling was estimated as being 4–5 times slower (Sookhak Lari—personal communication). Modelling in three dimensions may not always be required.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-94.8826861,47.471573],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bemidji, MN 56601, USA","basin_id":7040569650,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":446352.2,"hydrology_source":"hydrobasins","site_id":"doc-04f2b966696d6b75fb2f02f0-site-1"},{"name":"United States of America","kind":"region","role":"origin of major advances in site contamination investigation programs and guidelines post-Love Canal","river_name":null,"geocode_query":"United States of America","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"This is especially the case for subsurface environments where degraded and polluted soils and groundwater threaten ecological systems, human health and water and soil quality and health (Li et al. 2021), and accelerated efforts for perhaps 40 years since Love Canal (Phillips et al. 2007) triggered massive research and investigative efforts in the United States of America, and other countries worldwide. The cost globally to manage and assess such contamination is many billions of dollars annually. For nine countries in Europe, not including Germany nor the UK, the estimate was 1.4 billion euros per annum in 2010–11 (van Liedekerke et al. 2014). While we fly drones and aeroplanes and use satellites deploying geophysical techniques to map vegetation and landscapes (Roy et al. 2017; Liang et al. 2020), much below a depth of a few centimetres from the ground surface, automated mapping and modelling is challenged.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Europe (unspecified countries, excluding Germany and UK)","kind":"region","role":"location of estimate for soil contamination management costs and developments of passive samplers and guidelines","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"This is especially the case for subsurface environments where degraded and polluted soils and groundwater threaten ecological systems, human health and water and soil quality and health (Li et al. 2021), and accelerated efforts for perhaps 40 years since Love Canal (Phillips et al. 2007) triggered massive research and investigative efforts in the United States of America, and other countries worldwide. The cost globally to manage and assess such contamination is many billions of dollars annually. For nine countries in Europe, not including Germany nor the UK, the estimate was 1.4 billion euros per annum in 2010–11 (van Liedekerke et al. 2014). While we fly drones and aeroplanes and use satellites deploying geophysical techniques to map vegetation and landscapes (Roy et al. 2017; Liang et al. 2020), much below a depth of a few centimetres from the ground surface, automated mapping and modelling is challenged.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Australia","kind":"region","role":"country with national environmental protection measures and site investigation guidelines referenced and the location of the case study site","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"We note that “autonomous” is often taken to mean automatic sensing in a moving device such as a driverless motor vehicle, or remote submarine probe. Here for subsurface environments, we are using the term to mean self-contained, and perhaps intelligent sensors with some interpretive capacity, rather than mobile. Such sensor systems might provide alerts when regulatory criteria are exceeded or may automatically adjust the frequency of sensing intervals to better resolve a concentration peak. Framing where we are at Many guidelines, standards, and frameworks exist describing measurement and characterization approaches for contaminants in the subsurface environment. For assessing site contamination, Australia has a national environment protection measure (NEPM) that was last updated in 2013 (NEPM 2013). It describes expected approaches and links to Australian sampling and assessment standards and guidelines.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Germany","kind":"region","role":"mentioned as not included in European cost estimate for soil contamination management","river_name":null,"geocode_query":"Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"This is especially the case for subsurface environments where degraded and polluted soils and groundwater threaten ecological systems, human health and water and soil quality and health (Li et al. 2021), and accelerated efforts for perhaps 40 years since Love Canal (Phillips et al. 2007) triggered massive research and investigative efforts in the United States of America, and other countries worldwide. The cost globally to manage and assess such contamination is many billions of dollars annually. For nine countries in Europe, not including Germany nor the UK, the estimate was 1.4 billion euros per annum in 2010–11 (van Liedekerke et al. 2014). While we fly drones and aeroplanes and use satellites deploying geophysical techniques to map vegetation and landscapes (Roy et al. 2017; Liang et al. 2020), much below a depth of a few centimetres from the ground surface, automated mapping and modelling is challenged.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United Kingdom","kind":"region","role":"mentioned as excluded from European contamination cost estimate and having technical guidance for land contamination","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"In the USA, the US EPA has a broad suite of methods and approaches that cover most site investigation approaches (CLU-IN 2022). Europe and many other countries have similar guidelines and developments (Theocharopoulos et al. 2001; UK EA 2022). While all might promote technology advancement, automated on-line sensing and reporting with linkages to digital and computational platforms are lacking. Measurement of what? To establish a valid conceptual site model of contaminant behavior, it is essential to measure aspects of the physical environment that the contaminant is residing in, such as soil texture, aquifer stratigraphy and properties, and perhaps other chemical transport parameters such as soil moisture, thermal properties, and gas diffusivities. These all govern how the contaminant may behave within the system as a whole.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Duncan Dam, Canada","kind":"field_site","role":"location cited for freezing techniques to recover undisturbed soil cores","river_name":null,"geocode_query":"Duncan Dam, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"Chemosphere 77(8):1106–1112 Laor Y, Ronen D, Graber ER (2003) Using a passive multilayer sampler for measuring detailed profiles of gas-phase VOCs in the unsaturated zone. Environ Sci Technol 37(2):352–360 Lenhard RJ, Sookhak Lari K, Rayner JL, Davis GB (2018) Evaluating an analytical model to predict subsurface LNAPL distributions and transmissivity from current and historic fluid levels in groundwater wells: Comparing results to numerical simulations. Groundwat Monit Remediat 38(1):75–84 Levintal E, Ganot Y, Taylor G, Freer-Smith P, Suvocarev K, Dahlke HE (2022) An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture. SOIL 8:85–97 Li P, Karunanidhi D, Subramani T, Srinivasamoorthy K (2021) Sources and consequences of groundwater contamination.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-116.946389,50.251667],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Duncan Dam, Central Kootenay, BC V0G 1N0, Canada","basin_id":7040379430,"main_basin_id":7040014930,"basin_region":"North America and Caribbean","basin_area_km2":269628.2,"hydrology_source":"hydrobasins","site_id":"doc-04f2b966696d6b75fb2f02f0-site-7"},{"name":"Dublin, Ohio, USA","kind":"field_site","role":"location of a conference describing membrane sensor of volatile compounds in soil","river_name":null,"geocode_query":"Dublin, Ohio, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-005","page":13,"quote":"Proceedings of United States Superfund 14 conference, Washington DC. https://www.osti.gov/servlets/purl/10104 559. Accessed Jan 2023 Buselli G, Davis GB, Barber C, Height MI, Howard SHD (1992) The application of electromagnetic and electrical methods to groundwater problems in urban environments. Explor Geophys 23:543–555 Buselli G, Barber C, Davis GB, Salama RB (1990) Detection of groundwater contamination near waste disposal sites with transient electromagnetic and electrical methods. In: Ward SH (ed) Geotechnical and environmental geophysics vol II: Environmental and groundwater. SEG Public, pp 27–39. https://doi.org/10. 1190/1.9781560802785.2 Cai Y, Zhu K, Shen L, Ma J, Bao L, Chen D, Wei L, Wei N, Liu B, Wu Y, Chen S (2022) Evolved biosensor with high sensitivity and specificity for measuring cadmium in actual environmental samples. Environ Sci Technol. https://doi.org/10.1021/acs.est. 2c00627 Christy TM (1996) A driveable permeable membrane sensor of volatile compounds in soil.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-83.1406167,40.1024767],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dublin, OH, USA","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-04f2b966696d6b75fb2f02f0-site-8"},{"name":"Western Australia, Australia","kind":"region","role":"region of the BP Kwinana site and location of studies on groundwater and soil gas properties in an unconfined sand aquifer","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"However, in nearly all cases sampling disturbs the subsurface or provides a disturbed sample (as in soil recovery) and for groundwater, lysimeter porewater, and gas samples all are largely volume-averaged concentrations depending on the duration and volume recovered in a sampling event. Because of this, ambitions arose to perhaps measure concentrations in situ in the subsurface, without having to recover samples or undertake laboratory analysis. A number of techniques and approaches have been advanced to seek to address this. Shallow geophysics and other techniques Geophysical techniques initially used in the petroleum and mining industry were adapted to target and map shallow contaminant features in soils and groundwater. The techniques relied on being able to distinguish the target zone from the bulk soil/aquifer often related to boundaries that are relatively well defined.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Newcastle, Australia","kind":"settlement","role":"location of research center (CRC CARE) producing key technical reports on NSZD and subsurface contamination","river_name":null,"geocode_query":"Newcastle, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-004","page":11,"quote":"To illustrate, if we take the data in Fig. 4, and apply the linear model in Fig. 5b, c then we estimate the NSZD rate relatively easily over time to give Fig. 4b reporting NSZD rates over this transient period in real time. Regulatory acceptance NSZD is increasingly being established in management guidance (ITRC 2009; CL:AIRE 2019; CRC CARE 2020), with promulgated case studies and understanding (Rayner et al. 2020; Smith et al. 2022). Expressed uncertainties relate to undue risks (e.g., from benzene) and the longevity of the LNAPL in the subsurface posing a prolonged risk. As indicated earlier, developed sensors specific to benzene if deployed at an appropriate spatial coverage and logging at an acceptable frequency could alleviate such concerns. We note though that adoption of NSZD as a management option requires that all relevant risks are controlled or eliminated (CRC CARE 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.719773,-32.898271],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26967317,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Newcastle NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-04f2b966696d6b75fb2f02f0-site-10","true_coordinates":[151.7816802,-32.9282712],"spread_km":6.7},{"name":"Kwinana, Western Australia, Australia","kind":"field_site","role":"petroleum site with installed sensors and a mature conceptual site model used to monitor NSZD rates with autonomous sensing","river_name":null,"geocode_query":"Kwinana, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"site and step though the five criteria (Fig. 2) to illustrate a possible pathway forward. Deep understanding and a mature CSM We choose LNAPL NSZD since the processes of partitioning of LNAPL components to groundwater and soil gas phases and their biodegradation are becoming well understood as mass loss mechanisms, as per the reviews of Garg et al. (2017) and Sookhak Lari et al. (2019). Standard parameters that are measured to estimate NSZD rates are subsurface soil gas composition (oxygen, carbon dioxide, methane, VOCs), temperature and surface fluxes of carbon dioxide (ITRC 2009; CRC CARE 2018)—all being indicators of or consumed in biodegradation processes. As such the conceptualization of LNAPL NSZD processes are largely in place after decades of research—so addresses Criteria 1, which is the need for deep understanding.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.7787256,-32.2320792],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412836,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kwinana Beach WA 6167, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-04f2b966696d6b75fb2f02f0-site-11"}],"limitations":["Lack of precise geographic coordinates for study sites and sensors","Some geographic references are broad regional areas without detailed spatial delineation","Details on measurement locations within field sites are generalized rather than exact point locations","Limited direct study period information is provided"],"river_ids":[],"site_ids":["doc-04f2b966696d6b75fb2f02f0-site-1","doc-04f2b966696d6b75fb2f02f0-site-7","doc-04f2b966696d6b75fb2f02f0-site-8","doc-04f2b966696d6b75fb2f02f0-site-10","doc-04f2b966696d6b75fb2f02f0-site-11"],"anchor":[-83.1406167,40.1024767],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040055960,5040070710,7040379430,7040569650,7040612640],"main_basin_ids":[5040055960,5040070710,7040014930,7040047060]},{"id":"doc-0dcebf5012480b296ff9306a","title":"Modeling the effects of water regulation on the population viability of a threatened amphibian","display_title":"Water Regulation Impact on Southern Bell Frog","doi":"https://doi.org/10.1002/ecs2.4379","theme":"Flow & ecology","color":"#80edc2","year":2023,"publication_date":"2023-01-01","authors":["Rupert Mathwin","Skye Wassens","Matthew S. Gibbs","Jeanne Young","Qifeng Ye","Frédérik Saltré","Corey J. A. Bradshaw"],"summary":"This study models the impact of river regulation on the population viability of the threatened southern bell frog, Litoria raniformis, in South Australia's Murray River floodplains. Using stochastic population models and reconstructed wetland inundation patterns under natural and regulated flows, the authors quantify extinction probabilities associated with changes in floodplain wetland inundation frequency and duration. The study demonstrates that river regulation reduces wetland inundation frequency, increasing successive dry years and driving higher local extinction probabilities, especially in smaller wetlands. The findings suggest environmental water provision as an effective management strategy to maintain frog populations.","finding":"River regulation has significantly reduced the frequency and duration of wetland inundation in the Murray River floodplains between Lock 3 and Lock 2, leading to increased consecutive dry years that exceed the reproductive lifespan of Litoria raniformis, thereby elevating local extinction risks especially in smaller wetlands. Environmental water provision reducing consecutive dry years to one or two significantly lowers extinction probability.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"To examine the effect of maximum life span on the predictions, we also implemented a univariate sensitivity analysis on a single, large wetland with a sill height of 7.5 m under regulated flow conditions. We reran this model with senescence constrained to ages two, three, four, and five years (Appendix S1). RESULTS “Wetness” of wetlands under natural and regulated flow conditions River regulation reduced the frequency of wet years by 19%–34% compared with the natural flow scenario for the same 83-year period (Figure 3). This equates to a reduction of wet years by 19.5% at 7 mAHD, up to 60% at 11 mAHD. This changed both the proportions and pattern of wet and dry years. Regulation increased the mean duration of successive dry years at all sill heights (Figure 4).","verified_in_page":true},"topics":["population viability","wetland inundation frequency","hydrological alteration","amphibian conservation","environmental water provision","river regulation","stochastic population modeling","extinction risk"],"study_period":null,"locations":[{"name":"Murray River floodplains between Lock 3 and Lock 2","kind":"reach","role":"primary study site for hydrological and ecological modeling","river_name":"Murray River","geocode_query":"Murray River floodplains, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We posit that the probability of local extinction can be reduced by environmental water provision and that our modeling approach can be used to prioritize the location and frequency of environmental water provision to at-risk amphibian populations. MATERIALS AND METHODS Study area The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated to provide water for domestic and agricultural use. Regulating structures include a series of 14 main-channel weirs (“locks”) that dissect the river. Our focus is 70 km of the Murray River channel between Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E) and the associated wetlands and floodplains along this reach (Figure 1). This region receives an annual rainfall of 160–240 mm 2 of 14 MATHWIN ET AL. 21508925, 2023, 1, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4379 by Readcube (Labtiva Inc.), Wiley Online Library on [05/08/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.636345,-35.361041],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676891,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-0dcebf5012480b296ff9306a-site-0","true_coordinates":[143.5663889650001,-35.34249995499994],"spread_km":6.7},{"name":"Murray–Darling Basin","kind":"basin","role":"broader catchment context and regulatory system","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Techniques for manipulating water to benefit amphibians vary in approach and success (Mathwin et al., 2021), but the best-supported intervention is the provision of water to breeding habitats to match the larval requirements of the target species (environmental water provision). This is because enhancing survival through breeding and early life stages can stabilize populations (Griffiths & Pavajeau, 2008). The targeted delivery of environmental water is a common practice today (Kennen et al., 2018) and could become necessary to conserve some species (Greenwood et al., 2016), especially those with limited phenotypic plasticity or those near the edge of their ecological niche (Grant et al., 2020). Australia’s Murray–Darling Basin provides a model system to examine this process. The catchment is heavily regulated and each year up to 61% of total flow is extracted for consumptive use (CSIRO, 2008).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.523612,-31.076107],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-0dcebf5012480b296ff9306a-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Lock 3","kind":"facility","role":"upstream lock marking northern boundary of study river reach","river_name":"Murray River","geocode_query":"Lock 3, Murray River, South Australia, Australia","latitude":-34.188042,"longitude":140.341569,"evidence":{"passage_id":"p002-004","page":2,"quote":"We posit that the probability of local extinction can be reduced by environmental water provision and that our modeling approach can be used to prioritize the location and frequency of environmental water provision to at-risk amphibian populations. MATERIALS AND METHODS Study area The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated to provide water for domestic and agricultural use. Regulating structures include a series of 14 main-channel weirs (“locks”) that dissect the river. Our focus is 70 km of the Murray River channel between Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E) and the associated wetlands and floodplains along this reach (Figure 1). This region receives an annual rainfall of 160–240 mm 2 of 14 MATHWIN ET AL. 21508925, 2023, 1, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4379 by Readcube (Labtiva Inc.), Wiley Online Library on [05/08/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.367883,-34.233563],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677304,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 3 Rd, Overland Corner SA 5330, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-0dcebf5012480b296ff9306a-site-2","true_coordinates":[140.367883,-34.1735634],"spread_km":6.7},{"name":"Lock 2","kind":"facility","role":"downstream lock marking southern boundary of study river reach","river_name":"Murray River","geocode_query":"Lock 2, Murray River, South Australia, Australia","latitude":-34.677586,"longitude":139.91467,"evidence":{"passage_id":"p002-004","page":2,"quote":"We posit that the probability of local extinction can be reduced by environmental water provision and that our modeling approach can be used to prioritize the location and frequency of environmental water provision to at-risk amphibian populations. MATERIALS AND METHODS Study area The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated to provide water for domestic and agricultural use. Regulating structures include a series of 14 main-channel weirs (“locks”) that dissect the river. Our focus is 70 km of the Murray River channel between Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E) and the associated wetlands and floodplains along this reach (Figure 1). This region receives an annual rainfall of 160–240 mm 2 of 14 MATHWIN ET AL. 21508925, 2023, 1, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4379 by Readcube (Labtiva Inc.), Wiley Online Library on [05/08/2023].","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.93116,-34.13774],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677315,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 2, Qualco SA 5322, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-0dcebf5012480b296ff9306a-site-3","true_coordinates":[139.93116,-34.0777399],"spread_km":6.7},{"name":"South Australia","kind":"region","role":"state encompassing the study site and focus of amphibian habitat analysis","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Bradshaw1,7 1 Global Ecology (Partuyarta Ngadluku Wardli Kuu), College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia 2 School of Environmental Sciences, Charles Sturt University, Albury, New South Wales, Australia 3 School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, South Australia, Australia 4 Land and Water, Commonwealth Science and Industrial Research Organisation (CSIRO), Urrbrae, South Australia, Australia 5 College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia 6 South Australian Research and Development Institute, West Beach, South Australia, Australia 7 ARC Centre of Excellence for Australian Biodiversity and Heritage (EpicAustralia.org.au), Wollongong, New South Wales, Australia Correspondence Rupert Mathwin Email: rupert.mathwin.ecology@ gmail.com Funding information Murraylands and Riverland Landscape Board; Nature Foundation SA Handling Editor: Jane S.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Albury, New South Wales","kind":"settlement","role":"location of Charles Sturt University involved in research","river_name":null,"geocode_query":"Albury, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Bradshaw1,7 1 Global Ecology (Partuyarta Ngadluku Wardli Kuu), College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia 2 School of Environmental Sciences, Charles Sturt University, Albury, New South Wales, Australia 3 School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, South Australia, Australia 4 Land and Water, Commonwealth Science and Industrial Research Organisation (CSIRO), Urrbrae, South Australia, Australia 5 College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia 6 South Australian Research and Development Institute, West Beach, South Australia, Australia 7 ARC Centre of Excellence for Australian Biodiversity and Heritage (EpicAustralia.org.au), Wollongong, New South Wales, Australia Correspondence Rupert Mathwin Email: rupert.mathwin.ecology@ gmail.com Funding information Murraylands and Riverland Landscape Board; Nature Foundation SA Handling Editor: Jane S.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.909485,-36.135119],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666674,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Albury NSW 2640, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-0dcebf5012480b296ff9306a-site-5","true_coordinates":[146.9094852,-36.0751193],"spread_km":6.7}],"limitations":["Lack of precise geographic coordinates for wetlands and specific floodplain pools within the study reach.","Simplification of wetland inundation using nine discrete sill heights rather than specific individual wetland hydrology.","Model assumption of isolated wetland populations without metapopulation rescue effects, possibly overestimating local extinction probabilities.","Hydrological data limitations due to gauge measurement inaccuracies during high river levels (drowned-out readings corrected by downstream 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040073410,5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-19b5d88ab2779edf71276f4e","title":"Integrated assessment of river development on downstream marine fisheries and ecosystems","display_title":"Integrated Assessment of Water Developments on Gulf Fisheries","doi":"https://doi.org/10.1038/s41893-023-01238-x","theme":"Water management","color":"#eeabac","year":2023,"publication_date":"2023-11-02","authors":["Éva E. Plagányi","Rob Kenyon","Laura K. Blamey","Julie B. Robins","Michele A. Burford","Richard D. Pillans","Trevor Hutton","Justin Hughes","Shaun Kim","Roy A. Deng","Toni Cannard","Annie Jarrett","Adrianne Laird","Emma Lawrence","Margaret Miller","Chris Moeseneder"],"summary":"This study uses an integrated multispecies ecosystem model combined with river system models to evaluate the impacts of alternative water resource developments (WRDs), including dams and water extraction, on downstream marine fisheries, habitats, and ecosystems in the Gulf of Carpentaria (GoC), Australia. The research quantifies species- and catchment-specific eco-hydrological relationships linking river flow to recruitment, biomass, and fisheries catches. It demonstrates complex cumulative effects including a 'river portfolio effect' stabilizing prawn populations, and highlights risks to key species like the endangered largetooth sawfish, mangroves, and major commercial fisheries for banana prawns, barramundi, and mud crabs under varying WRD scenarios. The approach provides a framework for assessing trade-offs and sustainable water management across catchment-to-coast connectivity.","finding":"Water resource developments on the Mitchell, Gilbert, and Flinders rivers, such as dams and water extraction, can reduce downstream flows, causing substantial and species-specific declines in marine fishery catches, biomass, and habitat-forming species, with risk magnitudes depending heavily on the volume extracted, number of dams, and flow threshold settings for pumping; critically, the largetooth sawfish is most sensitive, with some scenarios more than doubling economic risks for key fisheries like the common banana prawn.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"Nature Sustainability | Volume 7 | January 2024 | 31–44 36 Article https://doi.org/10.1038/s41893-023-01238-x extraction scenarios using the Mitchell River as an example, higher extractions had a more negative impact on fishery catches, sawfish numbers and mangrove biomass (Extended Data Fig. 6a–d). However, the same water allocation amount had a more negative impact when using a lower river-flow pumping threshold value in all cases (Extended Data Fig. 6a–d). For prawns, barramundi and mangroves, the medium allocation scenarios with a low pump-initiation threshold setting had a more negative impact than a scenario with double the water allocation by volume but a high threshold value. Sawfish had worse outcomes for higher extraction amounts although outcomes were also sensitive to the river-flow threshold setting (Extended Data Fig. 6d).","verified_in_page":true},"topics":["eco-hydrology","fisheries impact","flow regulation","habitat alteration","risk assessment","species sensitivity","hydraulic infrastructure","ecosystem connectivity"],"study_period":"1970–2019","locations":[{"name":"Mitchell River catchment","kind":"basin","role":"Primary study river basin with proposed water resource developments to evaluate downstream ecological and fishery impacts","river_name":"Mitchell River","geocode_query":"Mitchell River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"This situation is emblematic of the global need for proactive, rigorous approaches to inform trade-offs and account for downstream livelihoods and dependent estuarine and marine systems when planning WRDs. Results River-flow influences on ecology and fishery catches We successfully fitted five alternative MICE (Fig. 1c and Supplementary Fig. 1)—the ensemble approach (Extended Data Fig. 3)—to long-term environmental and fishery data for each of eight spatial regions. We found a statistically significant improved fit to past fishery catches when predicting these based on fishing effort and flow variability, compared with fishing effort alone (Fig. 2a). We focused on overall quality of model fits rather than over-parameterizing the model by improving the fits to each model region (Fig. 2a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.560318,-15.923806],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":123317410,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-19b5d88ab2779edf71276f4e-site-0","true_coordinates":[142.5603181507045,-15.86380594713086],"spread_km":6.7},{"name":"Gilbert River catchment","kind":"basin","role":"Primary study river basin with proposed dams and water extraction scenarios affecting downstream marine ecosystems and fisheries","river_name":"Gilbert River","geocode_query":"Gilbert River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"This situation is emblematic of the global need for proactive, rigorous approaches to inform trade-offs and account for downstream livelihoods and dependent estuarine and marine systems when planning WRDs. Results River-flow influences on ecology and fishery catches We successfully fitted five alternative MICE (Fig. 1c and Supplementary Fig. 1)—the ensemble approach (Extended Data Fig. 3)—to long-term environmental and fishery data for each of eight spatial regions. We found a statistically significant improved fit to past fishery catches when predicting these based on fishing effort and flow variability, compared with fishing effort alone (Fig. 2a). We focused on overall quality of model fits rather than over-parameterizing the model by improving the fits to each model region (Fig. 2a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.753654,-18.3146673],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122860149,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-19b5d88ab2779edf71276f4e-site-1"},{"name":"Flinders River catchment","kind":"basin","role":"Primary study river basin with evaluated water resource development scenarios and influence on regional fishery catches and habitats","river_name":"Flinders River","geocode_query":"Flinders River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"This situation is emblematic of the global need for proactive, rigorous approaches to inform trade-offs and account for downstream livelihoods and dependent estuarine and marine systems when planning WRDs. Results River-flow influences on ecology and fishery catches We successfully fitted five alternative MICE (Fig. 1c and Supplementary Fig. 1)—the ensemble approach (Extended Data Fig. 3)—to long-term environmental and fishery data for each of eight spatial regions. We found a statistically significant improved fit to past fishery catches when predicting these based on fishing effort and flow variability, compared with fishing effort alone (Fig. 2a). We focused on overall quality of model fits rather than over-parameterizing the model by improving the fits to each model region (Fig. 2a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.680421,-19.173319],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122859581,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-19b5d88ab2779edf71276f4e-site-2","true_coordinates":[142.6804208810083,-19.23331902699644],"spread_km":6.7},{"name":"Gulf of Carpentaria coast and nearshore region","kind":"region","role":"Study marine ecosystem receiving flows from study catchments; supports fisheries, mangroves, seagrass, and habitats for key species including endangered sawfish","river_name":null,"geocode_query":"Gulf of Carpentaria, Queensland and Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"This situation is emblematic of the global need for proactive, rigorous approaches to inform trade-offs and account for downstream livelihoods and dependent estuarine and marine systems when planning WRDs. Results River-flow influences on ecology and fishery catches We successfully fitted five alternative MICE (Fig. 1c and Supplementary Fig. 1)—the ensemble approach (Extended Data Fig. 3)—to long-term environmental and fishery data for each of eight spatial regions. We found a statistically significant improved fit to past fishery catches when predicting these based on fishing effort and flow variability, compared with fishing effort alone (Fig. 2a). We focused on overall quality of model fits rather than over-parameterizing the model by improving the fits to each model region (Fig. 2a).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Norman River catchment","kind":"basin","role":"Adjacent major catchment contributing to river flow portfolio influencing marine prawn recruitment and catch","river_name":"Norman River","geocode_query":"Norman River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-008","page":3,"quote":"River portfolio influences prawn recruitment and catches The MICE quantified a river portfolio effect across four regional rivers: the Mitchell, Gilbert, Norman and Flinders rivers, and was able—through fitting to data—to estimate the relative contributions of the different rivers in explaining observed prawn catches (Fig. 2c and Supplementary Figs. 14–16). Hence, we found that WRDs applied to a single river or different combinations of rivers had complex cumulative and synergistic effects on prawn abundance and catches (Fig. 2c). This effect was significant in the south-east region of the GoC, where these rivers are adjacent to one another (Fig. 1b). This result arose owing to our approach quantitatively translating recruitment fluctuations per region into overall contributions to total catch in each subregion of the GoC.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.05494,-18.486346],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122859900,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-19b5d88ab2779edf71276f4e-site-4","true_coordinates":[142.0549398791301,-18.54634593442701],"spread_km":6.7},{"name":"Mornington Region","kind":"region","role":"Coastal subregion influenced by flows from Flinders River in model of marine ecosystem and fisheries","river_name":null,"geocode_query":"Mornington Region, Gulf of Carpentaria, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"The MICE predicted that local and regional decreases in prawn abundance and catches were larger if accounting for the flood-induced productivity effect (Supplementary Table 9). 0 500 1,000 1,500 0 500 1,000 1,000 600 200 Observed 1,500 Modelled No flow Mornington 0 500 1,500 0 500 1,500 Flinders Norman 0 Gilbert 1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 2010 Mitchell 0 50 100 150 0 50 100 150 200 0 50 100 150 0 100 200 300 0 20 40 60 80 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 1990 2000 2010 2020 0 1 2 3 4 0 10 30 50 0 20 40 60 0 20 40 60 80 1985 2000 2015 1985 2000 2015 1985 2000 2015 1985 2000 2015 1985 2000 2015 0 10 20 30 40 Year Catch (t) a 0 0.1 0.2 0.3 0.4 Mitchell River Gilbert River Norman River Flinders River Relative contribution to recruitment River catchment system b c 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 1970 1975 1980 1985 1990 1995 Year 2000 2005 2010 2015 Total annual flow (Gl yr–1) Mitchell Gilbert Norman Flinders Fig. 2 | Quantifying how river flows influence fishery catches. a,b, Observed variability (black points) in the total annual commercial catch (t) of common banana prawns, barramundi and mud crabs from each of five catchment systems in the GoC; blue lines show the best model fits achievable when estimating catches based on fishing effort but ignoring river flows, compared with the improved ability of the model to fit the observed catches (red lines) (a) when linking end-of-system flow variability to the system dynamics, using weekly or monthly flow inputs with an example plot showing interannual variability in flows (b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.560782,-16.539356],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120771125,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gulf Of Carpentaria, Wellesley Islands QLD, Australia","basin_id":5040064140,"main_basin_id":5040064140,"basin_region":"Australasia","basin_area_km2":178643.3,"hydrology_source":"geofabric","site_id":"doc-19b5d88ab2779edf71276f4e-site-5","true_coordinates":[139.5607819825435,-16.47935646596641],"spread_km":6.7}],"limitations":["Limited data on some species and habitats increased uncertainty addressed by ensemble model approach.","WRDs are hypothetical scenarios currently not implemented, so real-world outcomes may vary.","Model does not explicitly represent detailed oceanographic processes such as wind-driven currents.","Potential secondary impacts like sediment and nutrient run-off effects were not included.","Connectivity between all subregions and all species interactions not fully modeled.","Sediment trapping effects on estuarine geomorphology and productivity beyond the scope of model.","Limited incorporation of Indigenous values and water rights in current planning phases."],"river_ids":[],"site_ids":["doc-19b5d88ab2779edf71276f4e-site-0","doc-19b5d88ab2779edf71276f4e-site-1","doc-19b5d88ab2779edf71276f4e-site-2","doc-19b5d88ab2779edf71276f4e-site-4","doc-19b5d88ab2779edf71276f4e-site-5"],"anchor":[139.560782,-16.539356],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040049720,5040064140],"main_basin_ids":[5040049720,5040064140]},{"id":"doc-1d97a00b1155ed7b4f04d6db","title":"Sealants and Other Management Strategies for PFAS-Contaminated Concrete and Asphalt","display_title":"Sealants to Manage PFAS in Concrete and Asphalt","doi":"https://doi.org/10.1007/s40726-023-00276-5","theme":"Land & ecosystems","color":"#c8df98","year":2023,"publication_date":"2023-08-05","authors":["Grant Douglas","Joanne Vanderzalm","Jason K. Kirby","Mike Williams","Trevor P. Bastow","Mark S. Bauer","Karl C. Bowles","D. H. Skuse","Rai S. Kookana","Greg B. Davis"],"summary":"This review assesses physico-chemical remediation options, focusing on sealants, to manage PFAS contamination in concrete and asphalt pavements, particularly at airfield and firefighting training sites globally where aqueous film-forming foams (AFFF) have led to long-term PFAS accumulation. It evaluates various destructive techniques, mineral and carbon amendments, washing methods, and notably, surface and penetrative sealants that can reduce PFAS mobility through encapsulation and immobilization. The study identifies modified bitumen and aspartic polymers as promising surface sealants and silicates and silicone-based sealants as effective penetrative options. Combining sealants to form multilayer barriers is likely necessary to meet operational demands. The review outlines critical knowledge gaps and recommends laboratory and field trials to evaluate sealants' efficacy in PFAS immobilization and mitigation.","finding":"Surface and penetrative sealants, including polymers and modified bitumens for surface application and silicates or siloxanes for penetration, show potential to reduce PFAS leaching and mobility from contaminated concrete and asphalt pavements at airfield and firefighting training facilities by encapsulating PFAS residues and preventing water ingress and egress.","finding_evidence":{"passage_id":"p013-002","page":13,"quote":"The primary advantage of modified bitumens is their high water repellency and relatively lower cost, good adhesion, in particular to similarly hydrophobic asphalt surfaces, trafficability and a range of physico-chemical factors (Tables 2 and 3) and potential for one or more of the additives to be able to adsorb or otherwise chemi - cally bind PFAS. It is this range of functional attributes that positions both aspartic polymers and modified bitu - men’s as better surface sealants for concrete and asphalt pavements at civilian and defence airfield facilities/sites, ahead of unmodified bitumen and a range of other poly - mer types (acrylic, epoxy, polyurethane), particularly in terms of adhesion trafficability and heat resistance amongst other properties.","verified_in_page":true},"topics":["pfas contamination","remediation technology","sealants","asphalt","concrete","polymer chemistry","environmental pollution","surface treatment"],"study_period":null,"locations":[{"name":"USA","kind":"region","role":"context for airfield PFAS contamination sites","river_name":null,"geocode_query":"USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-004","page":15,"quote":"Any sealant will also have to address a range of functional requirements that may span areas as diverse as trafficability, UV, chemical and heat resistance to reflectivity and friction/ non-slip surfaces. Crucially, the substantial differences in the composition and behaviour of concrete and asphalt pavements, in particular their hydrophilicity and hydrophobicity, respectively, may require the use of different sealants. In addition, there may be substantial differences in both new and aged, PFAS-contaminated concrete and asphalt. This may include abundant leachable alkalinity in new versus aged concrete and the occurrence of widespread thermal cracking and rivelling (aggregate loss) in asphalt. A range of potentially useful sealants have been identified that range from bituminous or modified bituminous materials, a range of polymers, biopolymers, and inorganic and organic silicon-based materials.","verified_in_page":true},"resolution_status":"resolved","coordinates":[62.64088,66.572107],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid","basin_id":2040043720,"main_basin_id":2040043720,"basin_region":"Europe and Middle East","basin_area_km2":312695.6,"hydrology_source":"hydrobasins","site_id":"doc-1d97a00b1155ed7b4f04d6db-site-0","true_coordinates":[62.64088,66.632107],"spread_km":6.7},{"name":"Norway","kind":"region","role":"location of research on PFAS remediation using sorbent amendments","river_name":null,"geocode_query":"Norway","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-005","page":16,"quote":"Sorbent amendment as a remediation strategy to reduce PFAS mobility and leaching in a Norwegian contaminated sandy soil from a Norwegian firefighting facility. Chemosphere. 2017;171:9–18. https://doi.org/ 10.1016/j.chemosphere.2016.12.057. 7. Krafft MP, Riess JG. Selected physicochemical aspects of poly- and perfluoroalkylated substances relevant to performance, environment and sustainability—part one. Chemosphere. 2015;129:4–19. https://doi.org/10.1016/j.chemosphere. 2014.08.039. 8. Rauert C, Shoieb M, Schuster JK, Eng A, Harner T. Atmospheric concentrations and trends of poly- and perfluoroalkyl substances (PFAS) and volatile methyl siloxanes (VMS) over 7 years of sampling in the Global Atmospheric Passive Sampling (GAPS) network. Environ Pollut. 2018;238:94–102. https:// doi.org/10.1016/j.envpol.2018.03.017. 9. Sunderland EM, Hu XC, Dassuncao C, Tokranov AK, Wagner CC, Allen JG.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Veneto Region, Italy","kind":"region","role":"example region with documented PFAS contamination and treatment studies","river_name":null,"geocode_query":"Veneto Region, Italy","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-004","page":16,"quote":"Sociol Health Illn. 2021;43:764–78. https://doi.org/10.1111/1467-9566.13253. 4. • Bolan N, Sarkar B, Yan Y, Li Q, Wijesekara H, Kannan K, Tsang DCW, Schauerte M, Bosch J, Noll H, Ok YS, Scheckel K, Kumpiene J, Gobindlal K, Kah M, Sperry J, Kirkham MB, Wang HL, Tsang YF, Hou DY, Rinklebe J. Remediation of poly- and perfluoroalkyl substances (PFAS) contaminated soils - To mobilize or to immobilize or to degrade. J Haz Mater. 2021;401. https://doi.org/10.1016/j.jhazmat.2020. 123892. This review examines options for the management/ remediation of PFAS in soils which constitutes one of the major repositories for PFAS in the environment. 5. Ding G, Peijnenburg WJGM. Physicochemical properties and aquatic toxicity of poly- and perfluorinated compounds. Crit Rev Environ Sci Technol. 2013;43:598–678. https://doi.org/ 10.1080/10643389.2011.627016. 6. Hale SE, Arp HPH, Slinde GA, Wade EJ, Bjorseth K, Breedveld GD, Straith BF, Moe KG, Jartun M, Hoisaeter A.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Belgium","kind":"region","role":"location of PFAS exposure variability studies","river_name":null,"geocode_query":"Belgium","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-006","page":16,"quote":"A review of the pathways of human exposure to poly- and perfluoroalkyl substances (PFASs) and present understanding of health effects. J Eposure Sci Environ Epidemiol. 2019;29:131–47. https://doi.org/10.1038/ s41370-018-0094-1. 10. Yu CH, Riker CD, Lu S-E, Fan Z. Biomonitoring of emerging contaminants, perfluoroalkyl and polyfluoroalkyl substances (PFAS), in New Jersey adults in 2016–2018. Int J Hyg Environ Health. 2020;223:34–44. https://doi.org/10.1016/j.ijheh.2019. 10.008. 11. Colles A, Bruckers L, Den Hond E, Govarts E, Morrens B, Schettgen T, Buekers J, Coertjens D, Nawrot T, Loots I, Nelen V, De Hanauw S, Schoeters G, Baeyens W, van Larebeke N. Perfluorinated substances in the Flemish population (Belgium): Levels and determinants of variability in exposure. Chemosphere. 2020;125250. https://doi.org/10.1016/j. chemosphere.2019.125250. 12. Di Nisio A, Rocca MS, Sabovic I, De Rocca Ponce M, Corsini C, Guidolin D, Acquasaliente L, Carosso AR, Foresta C.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["No direct experimental data on sealant performance for PFAS immobilization available; assessments based on material properties and analogues","Limited independent verification or long-term field trial data for many proprietary sealant formulations","Geographic data mostly global or regional with no site-specific coordinates provided","Knowledge on PFAS-binding capacity of sealants remains largely theoretical, highlighting need for laboratory and field testing"],"river_ids":["ne-93"],"site_ids":["doc-1d97a00b1155ed7b4f04d6db-site-0"],"anchor":[62.64088,66.572107],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040043720],"main_basin_ids":[2040043720]},{"id":"doc-2bcf5fc759ba58a84cbdba09","title":"Can you see the algae for the slime? Temporal patterns of biofilm food quality and quantity in lowland rivers","display_title":"Biofilm Food Quality Patterns in Murray–Darling Rivers","doi":"https://doi.org/10.1002/ecs2.4680","theme":"Flow & ecology","color":"#80edc2","year":2023,"publication_date":"2023-10-01","authors":["Paul McInerney","Michael Shackleton","Luke McPhan","Aleicia Holland","Gavin N. Rees"],"summary":"The study investigated temporal patterns in biofilm food quality and quantity in three lowland rivers within the Murray–Darling Basin, Australia, over 73 days. Using fatty acid profiling and eDNA metabarcoding, the authors quantified biofilm composition changes and their implications for riverine animal consumer energetics. They found that biofilm food value peaked between 24 and 43 days following submersion due to dynamic changes in fatty acid mass per area and concentration, despite shifts in biofilm community composition towards filamentous algae and cyanobacteria.","finding":"Biofilm food value for riverine consumers peaks between 24 and 43 days after submersion, with significant temporal changes in biofilm mass and fatty acid profiles; however, changes in biofilm community composition did not alter the proportions of fatty acid classes among total lipids, indicating that increased cyanobacteria and filamentous algae and extracellular polymeric substances influence lipid concentration but not lipid class proportions.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"account, overall food value for consumers assessed as higher density regions of physiologically important fatty acids occurring in the top right quadrant of Figure 5 was greatest on days 24, 37, and 43 after submersion. After day 43, total mass per unit area of fatty acids (in grams per square meter) remained relatively high, though concentration of fatty acids declined (see Figure 3), with values residing primarily in quadrants 1 and 3 of plots in Figure 5 and representing low to medium food value for consumers. DISCUSSION Temporal dynamics of biofilm fatty acid composition in rivers remain poorly understood, and here, we identify relationships between biofilm age and peaks in food value (assessed as a combination of availability and quality) for riverine consumers.","verified_in_page":true},"topics":["biofilms","fatty acid profiling","riverine ecology","environmental DNA metabarcoding","food web ecology","consumer energetic costs","temporal succession","Murray–Darling Basin ecology"],"study_period":"October 2018 to January 2019","locations":[{"name":"Wakool River","kind":"river","role":"field study river site within Murray–Darling Basin","river_name":"Murray River","geocode_query":"Wakool River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"platforms in three rivers, for 73 days following submersion. We used a multi-prong eDNA approach, sequencing regions of the ITS1-4 gene (fungi), 16S (bacteria), and 23S (algae) ribosomal RNA (rRNA) genes, to examine the structure of biotic communities over time. We anticipated that patterns of biofilm food value for consumers will most closely align with our third hypothesis, where the total mass per unit area of fatty acids (in grams per square meter) and the concentration of fatty acids (in milligrams per gram) both vary through time due to successional changes to species arrangement of biofilms and the addition of organic and inorganic sediment from the water column. METHODS Sites Study sites were selected on three rivers in southeastern Australia as described in detail by Holland et al. (2020): Wakool River, Yallakool Creek, and Niemur River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.5706055700001,-35.03800556999994],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677235,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2bcf5fc759ba58a84cbdba09-site-0"},{"name":"Yallakool Creek","kind":"river","role":"field study river site within Murray–Darling Basin","river_name":"Murray River","geocode_query":"Yallakool Creek, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"platforms in three rivers, for 73 days following submersion. We used a multi-prong eDNA approach, sequencing regions of the ITS1-4 gene (fungi), 16S (bacteria), and 23S (algae) ribosomal RNA (rRNA) genes, to examine the structure of biotic communities over time. We anticipated that patterns of biofilm food value for consumers will most closely align with our third hypothesis, where the total mass per unit area of fatty acids (in grams per square meter) and the concentration of fatty acids (in milligrams per gram) both vary through time due to successional changes to species arrangement of biofilms and the addition of organic and inorganic sediment from the water column. METHODS Sites Study sites were selected on three rivers in southeastern Australia as described in detail by Holland et al. (2020): Wakool River, Yallakool Creek, and Niemur River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.961034,-36.078056],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676958,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2bcf5fc759ba58a84cbdba09-site-1","true_coordinates":[144.96103440100012,-36.01805551599995],"spread_km":6.7},{"name":"Niemur River","kind":"river","role":"field study river site within Murray–Darling Basin","river_name":"Murray River","geocode_query":"Niemur River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"platforms in three rivers, for 73 days following submersion. We used a multi-prong eDNA approach, sequencing regions of the ITS1-4 gene (fungi), 16S (bacteria), and 23S (algae) ribosomal RNA (rRNA) genes, to examine the structure of biotic communities over time. We anticipated that patterns of biofilm food value for consumers will most closely align with our third hypothesis, where the total mass per unit area of fatty acids (in grams per square meter) and the concentration of fatty acids (in milligrams per gram) both vary through time due to successional changes to species arrangement of biofilms and the addition of organic and inorganic sediment from the water column. METHODS Sites Study sites were selected on three rivers in southeastern Australia as described in detail by Holland et al. (2020): Wakool River, Yallakool Creek, and Niemur River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.7998611260001,-35.134861126999965],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677643,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2bcf5fc759ba58a84cbdba09-site-2"},{"name":"Murray River","kind":"river","role":"source river feeding the three study rivers","river_name":"Murray River","geocode_query":"Murray River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Each of our three sample rivers occurs within an anabranch system that receives water from a common source—Australia’s longest river, the Murray River. Briefly, study sites were typical of lowland rivers in semi-arid regions of Australia’s Murray–Darling Basin, with clay banks and riparian vegetation consisting of Eucalyptus camaldulensis (River Redgum) FIGURE 1 Conceptual model of relationship between consumer energetic costs and fatty acid availability in biofilm landscape. An increase in a fatty acid’s position along the x-axis signifies an increase in the total mass of that fatty acid per unit area. An increase in a fatty acid’s position along the y-axis signifies an increase in the concentration of that fatty acid relative to biofilm dry mass.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.961034,-35.958056],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676958,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2bcf5fc759ba58a84cbdba09-site-3","true_coordinates":[144.96103440100012,-36.01805551599995],"spread_km":6.7}],"limitations":["Exact coordinates for study sites not provided in text","Data mainly from biofilms on submerged Redgum blocks, may not capture full natural variability","eDNA sequencing might include low contribution from water column DNA, although effects considered minimal"],"river_ids":["43639586","43639888","43639898","43650227","43650480","43650484","43650485","43650489","43650490","43650491","43650493","43650496","43653930","43654642","43658673","43658715","43658757","43658805","43658839","43658855","43658871","43658887","43658898","43658909","43658920","43658921","43658933","43658944","43658955","43658965","43658975","43658981","43658986","43658991","43658993","43658994","43658995","43658996","43658997","43658998","43659000","43659024","43659043","43659064","43659073","43659083","43659093","43659107","43659117","43659120","43659122","43659125"],"site_ids":["doc-2bcf5fc759ba58a84cbdba09-site-0","doc-2bcf5fc759ba58a84cbdba09-site-1","doc-2bcf5fc759ba58a84cbdba09-site-2","doc-2bcf5fc759ba58a84cbdba09-site-3"],"anchor":[143.7998611260001,-35.134861126999965],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-2bf0cccb1f59d6783b8bdc6f","title":"The SWTools R package for SILO data acquisition, homogeneity testing and correction","display_title":"SWTools R Package for Rainfall Data Quality","doi":"https://doi.org/10.1080/13241583.2023.2214989","theme":"Water management","color":"#eeabac","year":2023,"publication_date":"2023-05-22","authors":["Matthew S. Gibbs","Mark B. Alcorn","Jai Vaze"],"summary":"This study introduces the SWTools R package designed to acquire, summarize, test homogeneity, and correct rainfall data from the SILO database in Australia. It addresses issues of non-homogeneity arising from interpolation of rainfall data after station closures. A case study in the North Para River catchment demonstrated that these non-homogeneities can significantly affect rainfall-runoff model simulations, leading to substantial differences in simulated water availability.","finding":"Interpolation to extend rainfall datasets after station closures introduced non-homogeneity, which when corrected, resulted in a 22% reduction in long-term simulated mean annual runoff, emphasizing the importance of data quality assurance in hydrologic modeling.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"The rainfall data are the same for most of this period, with corrections to site 23,313 resulting in corrected rainfall data after 1970. Exceedance curves of the daily and annual discharge (Figure 7) indicate that the model based on the corrected rainfall data simulates substantially less streamflow over the long term, with a mean annual runoff of 49 mm/year compared to 60 mm/year, 22% less water available as a long-term average (Table 2). There is also a reduction in the proportion of days with flow, reducing from 35% of the time to 32% (based on a flow threshold of 0.01 mm/d). These differences are potentially significant in the context of setting water Figure 4. Homogeneity tests from SILOCheckConisistency() for station 23705. For this site, the breakpoint analysis did not identify a statistically significant change in slope. The residuals of annual rainfall to straight line panel deviate beyond the 80th percentile confidence level for only a small number of years.","verified_in_page":true},"topics":["rainfall data","homogeneity testing","data correction","rainfall-runoff modeling","model calibration","environmental modeling","hydrological modeling","climate data"],"study_period":null,"locations":[{"name":"North Para River catchment","kind":"basin","role":"Main study catchment for rainfall-runoff modeling case study","river_name":"North Para River","geocode_query":"North Para River catchment, Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Catchment and data description The catchment contributing to streamflow station A4260517, North Para River at Penrice, in the Barossa Valley in South Australia has been used as a case study catchment. The station commenced in 1977 and remains open, and is a Bureau of Meteorology Hydrological Reference Station (Zhang et al. 2016), indicating a wellmaintained station with a verified rating table, and in an AUSTRALASIAN JOURNAL OF WATER RESOURCES 125","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.849954,-34.611657],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":50289766,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-0","true_coordinates":[138.8499537295899,-34.55165748460794],"spread_km":6.7},{"name":"North Para River at Penrice","kind":"station","role":"Streamflow observation station for catchment hydrology assessment","river_name":"North Para River","geocode_query":"North Para River at Penrice, Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Catchment and data description The catchment contributing to streamflow station A4260517, North Para River at Penrice, in the Barossa Valley in South Australia has been used as a case study catchment. The station commenced in 1977 and remains open, and is a Bureau of Meteorology Hydrological Reference Station (Zhang et al. 2016), indicating a wellmaintained station with a verified rating table, and in an AUSTRALASIAN JOURNAL OF WATER RESOURCES 125","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.849954,-34.491657],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289766,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Para River, South Australia, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-1","true_coordinates":[138.8499537295899,-34.55165748460794],"spread_km":6.7},{"name":"SILO rainfall station 23302","kind":"station","role":"Rainfall station within catchment, data corrected due to closure","river_name":null,"geocode_query":"SILO rainfall station 23302, Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Station 22302 in the centre of the catchment was also found to have a non-homogenous change at the time when the station closed (see Supplementary Material) and was corrected with station 23725 using the relationship based on data from the time that station 23725 opened (1908) and the breakpoint in 1973. 3.2. Non-homogeneity corrections SILOCorrectSite() was used to correct the rainfall data for site 23313 and 23302 for the period of interpolated data. The increase in station 23313 annual rainfall based on that recorded at station 23705 was computed as the difference between the red and blue linear regression lines in Figure 5, with the new annual rainfall total used to calculate a fraction to scale the daily rainfall for that year. This methodology is implemented in the SILOCorrectSite() function, which also produced Figure 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.95,-34.593333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289752,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barossa Valley, Tanunda SA 5352, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-2","true_coordinates":[138.95,-34.5333333],"spread_km":6.7},{"name":"SILO rainfall station 23313","kind":"station","role":"Rainfall station near headwaters, data corrected due to closure and interpolation","river_name":null,"geocode_query":"SILO rainfall station 23313, Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"1970, which is statistically significant at greater than the 95th percentile. A breakpoint was also identified in the double mass curve, which coincides with the closing of the station, and a reduction in slope at this time of 0.135. Upon assessment of the results in Figure 3, it was decided to correct the interpolated data from 1970 onwards. Stations in the region with similar annual rainfall and elevation were interrogated to find a suitable site to correct station 23313. Station 23705 was identified as having a similar relationship to the average of stations (slope of 1.21 in Figure 4), the cumulative annual residuals remain within the 95th percentile ellipse (and 80th percentile ellipse) for much of the time, and no breakpoint was identified on the double mass curve (Figure 4).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.013076,-34.503333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289752,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barossa Valley, Tanunda SA 5352, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-3","true_coordinates":[138.95,-34.5333333],"spread_km":6.7},{"name":"SILO rainfall station 23705","kind":"station","role":"Reference rainfall station for correction of non-homogeneity at station 23313","river_name":null,"geocode_query":"SILO rainfall station 23705, Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Station 22302 in the centre of the catchment was also found to have a non-homogenous change at the time when the station closed (see Supplementary Material) and was corrected with station 23725 using the relationship based on data from the time that station 23725 opened (1908) and the breakpoint in 1973. 3.2. Non-homogeneity corrections SILOCorrectSite() was used to correct the rainfall data for site 23313 and 23302 for the period of interpolated data. The increase in station 23313 annual rainfall based on that recorded at station 23705 was computed as the difference between the red and blue linear regression lines in Figure 5, with the new annual rainfall total used to calculate a fraction to scale the daily rainfall for that year. This methodology is implemented in the SILOCorrectSite() function, which also produced Figure 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.886924,-34.503333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289752,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barossa Valley, Tanunda SA 5352, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-4","true_coordinates":[138.95,-34.5333333],"spread_km":6.7},{"name":"Barossa Valley","kind":"region","role":"Geographic region containing the study catchment and stations","river_name":null,"geocode_query":"Barossa Valley, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"Catchment and data description The catchment contributing to streamflow station A4260517, North Para River at Penrice, in the Barossa Valley in South Australia has been used as a case study catchment. The station commenced in 1977 and remains open, and is a Bureau of Meteorology Hydrological Reference Station (Zhang et al. 2016), indicating a wellmaintained station with a verified rating table, and in an AUSTRALASIAN JOURNAL OF WATER RESOURCES 125","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.9001364,-34.6182425],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289766,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barossa Valley, SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2bf0cccb1f59d6783b8bdc6f-site-5"}],"limitations":["Coordinates of study sites were not provided and could not be inferred.","Study period not explicitly specified beyond model calibration/validation years.","Some locations based on station IDs without precise geographic coordinates."],"river_ids":[],"site_ids":["doc-2bf0cccb1f59d6783b8bdc6f-site-0","doc-2bf0cccb1f59d6783b8bdc6f-site-1","doc-2bf0cccb1f59d6783b8bdc6f-site-2","doc-2bf0cccb1f59d6783b8bdc6f-site-3","doc-2bf0cccb1f59d6783b8bdc6f-site-4","doc-2bf0cccb1f59d6783b8bdc6f-site-5"],"anchor":[138.886924,-34.503333],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040073420],"main_basin_ids":[5040073420]},{"id":"doc-3808a942b9b6bfb250c03939","title":"The invisible burden of managing COVID-19 for Australian women: Cognitive labor and public health information","display_title":"Gendered Cognitive Labor in Australian COVID-19 Info Seeking","doi":"https://doi.org/10.3389/fpubh.2023.1041944","theme":"Other science","color":"#d1b5e7","year":2023,"publication_date":"2023-02-01","authors":["Ashlin Lee","Naomi Kakoschke","Liesel Higgins","Andrew Reeson","Emily Brindal"],"summary":"This study investigates public health information seeking behaviors related to COVID-19 among adults residing in South Australia, with a qualitative survey focusing on why, how, and how behaviors changed during the pandemic. The majority of participants were women, Australian-born, university-educated, and employed full-time or unemployed/retired. Key findings include a preference for accessible and quality information, primarily from Australian government and mainstream media sources, and evidence of increased cognitive labor and mental load, especially among women, in managing COVID-19 information and care responsibilities.","finding":"Women in South Australia experienced increased cognitive labor during COVID-19, acting as primary gatekeepers of public health information and managing the mental load of health information seeking, which may represent a gender disparity in public health information behaviors.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"However, this engagement may also connect to experiences of care provision and anxiety, which suggests women may (un)willingly assume a greater burden of responsibility for managing information during complex health situations such as the COVID-19 pandemic. These observations, however, should be considered in the context of limitations of this study (see below). The latter findings connects to emerging research on concepts including cognitive labor (12) or mental load (13). In the context of family life, Daminger (12) notes that this labor can be gendered, with women taking on more work even when male partners are uniquely qualified in cognitive labor (for instance, when male partners were professional project managers). Dean et al. further develop this noting intersections with emotional labor (23), especially within the contexts of COVID-19 related lockdowns.","verified_in_page":true},"topics":["public health information behavior","gender differences","cognitive labor","mental load","health communication","COVID-19 pandemic","information seeking behavior","health literacy"],"study_period":null,"locations":[{"name":"South Australia, Australia","kind":"region","role":"study region","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"However, it remains unclear whether an increased burden of care is attributable to not only physical but cognitive dimensions of labor, and whether the latter could be attributed to increased health information seeking behavior. The current study aimed to provide preliminary insights into why participants selected, used, and changed their sources of public health information during COVID-19, and the relationship of these decisions relative to participant characteristics. Methods Participants and design Adults aged over 18 years who were currently residing in South Australia and not currently isolating or in lockdown were eligible to participate in the study. Participants were recruited via the CSIRO Nutrition and Health Clinic volunteer database (a standing database of interested research participants) to evaluate website content containing resources related to COVID-19 and then asked optional questions about information seeking.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Australia","kind":"region","role":"participant nationality and contextual setting","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"We received the majority of our results from women, born in Australia, with a de facto or married relationship status, without children living at home, with university/college qualifications, and employed full-time or unemployed. Sample characteristics are presented based on the sample included in the current research analysis (see Table 1). COVID-19 information sources Australian Government sources were the most widely used, with the majority of participants selecting this source type, followed by privately owned mainstream media (Figure 1). Professional sources, closely followed by personal/familiar sources were the next most used. “Other” responses included newspapers and workplaces. For the top five information sources, use of Australian Government sources varied significantly by level of education (see Table 2 for inferential statistics).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Qualitative sample skewed toward female participants, limiting generalizability especially regarding male experiences.","Low number of male and non-binary participants leading to cautious interpretation of gender comparisons.","Participants all resided in South Australia, so findings may not apply to other regions.","Self-report online survey data may contain biases related to self-selection and response.","Lack of precise study period dates beyond data collection in December 2021."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-10","basin_ids":[],"main_basin_ids":[]},{"id":"doc-3b5fa8ca87d3f2c69f9cd288","title":"Evaluation and refinement of a fish movement model for a tropical Australian stream subject to mine contaminant egress","display_title":"Fish Movement and Mine Contamination in Magela Creek","doi":"https://doi.org/10.1007/s10641-022-01381-y","theme":"Flow & ecology","color":"#80edc2","year":2023,"publication_date":"2023-01-20","authors":["David A. Crook","Dion Wedd","Brendan J. Adair","Thomas J. Mooney","Andrew J. Harford","Christopher L. Humphrey","John R. Morrongiello","Alison J. King"],"summary":"This study used acoustic telemetry and visual observations to evaluate fish movement patterns in Magela Creek, Northern Territory, Australia, focusing on the impacts of mine contaminant egress from the Ranger uranium mine. Tagged fish from upstream refuge billabongs migrated downstream into the mine lease area (RPA) during the wet season before returning upstream as flows receded. Fish were also observed not avoiding mine wastewater discharge. The sand channels and backflow billabongs in the RPA serve as important wet season habitats and migration pathways connecting lowland floodplains and upstream dry season refuges. The study refines existing fish movement models and emphasizes the need to manage water quality to protect ecological connectivity.","finding":"Tagged large-bodied fish from upstream refugia move downstream into the Ranger Project Area (RPA) during rising wet season flows, reside in mine-affected habitats for months, and accurately home back upstream as flows recede, indicating reliance on RPA habitats despite potential contaminant exposure.","finding_evidence":{"passage_id":"p011-003","page":11,"quote":"Fish that were detected only in Bowerbird Billabong (i.e. upstream of the RPA) were recorded very frequently during the dry season but had much lower detection rates during wet season flow events. This behaviour is suggestive of a wider range of movements in the reaches above the RPA during the wet season for these three species (Fig. 5). Downstream movement into the RPA for all three species was initiated soon after the first wet season flows and was strongly related to stream discharge, with the likelihood of tagged saratoga (ΔAICc to intercept only model: 2190.1) and black bream (ΔAICc to intercept only model: 638.1) being present in the RPA increasing substantially at flows greater than ~ 50 ML/day (Fig. 6a, b). Three of the five black bream that moved into the RPA moved as far downstream as the discharge point at MG001, but none were detected any further downstream in either year of the study (Fig. 4a–e).","verified_in_page":true},"topics":["fish migration","ecological connectivity","acoustic telemetry","mine contamination","behavioral response","wet-dry tropics","habitat use","fish movement patterns"],"study_period":"November 2018 to June 2020","locations":[{"name":"Magela Creek","kind":"river","role":"main study river system encompassing fish migration routes and study sites including mine impacts; major tributary of East Alligator River, Northern Territory, Australia","river_name":"Magela Creek","geocode_query":"Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ Biol Fish (2022) 106:469–490 473 Vol.: (0123456789) 1 3 mine-water contamination. Based on the findings of the study, we further refine the existing conceptual model of fish movement for this system and identify key periods and flow conditions when fish are residing within the RPA and may be exposed to minerelated contaminants. Materials and methods Study site The study was conducted between November 2018 and June 2020 in the mid‐upper reaches of Magela Creek, a major tributary of the East Alligator River in the Northern Territory, Australia. The study region experiences a wet-dry tropical climate characterised by periods of heavy and prolonged rainfall and high stream discharge from December to April, and generally dry conditions for the remainder of the year. Magela Creek is an unregulated, seasonally flowing sand‐bed stream which has its headwaters in the Arnhem Land Plateau and flows through both the Ranger Uranium Mine lease area (RPA) and Kakadu National Park (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.818595,-12.564041],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-0","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"Ranger Project Area (RPA)","kind":"region","role":"mine lease area affecting Magela Creek water quality; focal region for assessing fish movements and mine contaminant egress","river_name":null,"geocode_query":"Ranger Project Area, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Of 55 fish (black bream Hephaestus fuliginosus, saratoga Scleropages jardinii, sharp-nose grunter Syncomistes butleri) tagged in dry season refuge pools 18 km upstream of the mine lease area (RPA [Ranger Project Area]), 16 (29%) moved downstream after the first wet season flows, using the RPA as habitat for 3–5 months before moving upstream to their previous locations as flows receded. Of 39 fish (spangled perch Leiopotherapon unicolor, barred grunter Amniataba percoides, black catfish Neosilurus ater) tagged~8–12 km downstream of the RPA in the late wet season, only two were subsequently detected in the RPA. Direct and camera-based observations of 12 species of upstreammigrating fish during mine-water discharge in the late wet season showed no evidence of mine-water avoidance. Our results demonstrate that Magela Creek provides wet season habitat for fish within the RPA and acts as a migration pathway that connects lowland reaches and floodplains to upstream dry season refuges.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Bowerbird Billabong","kind":"reach","role":"upstream dry season fish refuge; tagging site for large-bodied fish species; location of acoustic receivers for monitoring fish movement","river_name":null,"geocode_query":"Bowerbird Billabong, Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Between these reaches of the creek, there are no other permanent surface waters in the main channel, although a series of shallow offchannel, or ‘backflow’, billabongs (e.g. Georgetown, Coonjimba billabongs; see Fig. 1) generally retain some water throughout the dry season near and downstream of the RPA. Bowerbird Billabong is~29 km upstream of Mudginberri Billabong and is an escarpment/rockpool billabong~40 m wide and~1 km long. The billabong has steep vegetated banks, with coarse sandy or rock substrate. Forming in the upper catchment and commencing below Djurrubu Falls (a large waterfall that acts as a complete barrier to fish movement), Bowerbird Billabong is part of a 10 km sand and rock pool-riffle sequence that extends through sandstone escarpments and outcrops before meeting the sand channels of Magela Creek (Fig. 1). There is no vehicle access to Bowerbird Billabong, so all access to the field site was undertaken using helicopters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.871819,-12.53404],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-2","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"Mudginberri Billabong","kind":"reach","role":"downstream dry season refuge; location near downstream extent of RPA and acoustic receivers; tagging site for smaller-bodied fish","river_name":null,"geocode_query":"Mudginberri Billabong, Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Between these reaches of the creek, there are no other permanent surface waters in the main channel, although a series of shallow offchannel, or ‘backflow’, billabongs (e.g. Georgetown, Coonjimba billabongs; see Fig. 1) generally retain some water throughout the dry season near and downstream of the RPA. Bowerbird Billabong is~29 km upstream of Mudginberri Billabong and is an escarpment/rockpool billabong~40 m wide and~1 km long. The billabong has steep vegetated banks, with coarse sandy or rock substrate. Forming in the upper catchment and commencing below Djurrubu Falls (a large waterfall that acts as a complete barrier to fish movement), Bowerbird Billabong is part of a 10 km sand and rock pool-riffle sequence that extends through sandstone escarpments and outcrops before meeting the sand channels of Magela Creek (Fig. 1). There is no vehicle access to Bowerbird Billabong, so all access to the field site was undertaken using helicopters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.871819,-12.474041],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-3","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"Georgetown Billabong","kind":"reach","role":"backflow billabong within RPA used temporarily by tagged large-bodied fish in the wet season; wet season feeding habitat potentially exposed to mine contaminants","river_name":null,"geocode_query":"Georgetown Billabong, Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The single spangled perch that moved into the main body of Mudginberri Billabong was detected 14 times on 6 July 2019 on the downstream receiver, 72 days after it was tagged. Of the 11 spangled perch tagged up to 4 km upstream of Mudginberri Billabong, none were detected by the acoustic array. Use of backflow billabongs Four individuals of the large-bodied fish were detected in backflow billabongs in the RPA during the study: two saratoga and one black bream in Georgetown Billabong, and one saratoga in Coonjimba Billabong (Figs. 3, 4). Like the observed homing of fish to dry season refugia in Bowerbird Billabong, these fish exhibited remarkable fidelity to specific backflow billabongs across the 2 years of the study, presumably utilising them as wet season feeding grounds.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.818595,-12.44404],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-4","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"Coonjimba Billabong","kind":"reach","role":"backflow billabong within RPA subject to mine contaminant egress; briefly used by tagged saratoga; wet season habitat within mine impact zone","river_name":null,"geocode_query":"Coonjimba Billabong, Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-004","page":15,"quote":"It was detected in Georgetown Billabong until 6 March 2019 and then made an excursion back to Bowerbird Billabong where it was detected from 10–21 March 2019. It was again detected in Georgetown Billabong on 26 March 2019 before moving back upstream to Bowerbird Billabong, where it spent the 2019 dry season. The estimated residence time in Georgetown Billabong in 2019 was approx. 53 days. This fish returned to the RPA the following wet season on 10 January 2020 and was last detected in the main channel at MC1 on 19 January 2020. The only fish that was detected in Coonjimba Billabong during the study was a saratoga (46307, Fig. 3e) that entered the RPA and moved into Coonjimba Billabong on 20 February 2019, where it was detected four times over a 2-min period before returning to the main channel. Thereafter, this fish was detected infrequently in the main channel until 9 April, before moving upstream to Bowerbird Billabong by 15 May 2019 where it spent the 2019 dry season.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.9042793,-12.6625926],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217157,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coonjimba Billabong, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-5"},{"name":"MG001 Mine-Water Discharge Site","kind":"facility","role":"location of controlled mine wastewater release into Magela Creek; site of behavioral observations assessing fish responses to mine water","river_name":"Magela Creek","geocode_query":"MG001 Mine-Water Discharge Site, Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Environ Biol Fish (2022) 106:469–490 475 Vol.: (0123456789) 1 3 Table 1 Details of fish tracked during the study. SAR, saratoga; SGR, black bream; SNG, sharp-nose grunter.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.765371,-12.474041],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-6","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"East Alligator River","kind":"river","role":"major river receiving water from Magela Creek tributary; regional context for fish migration in Northern Territory, Australia","river_name":"East Alligator River","geocode_query":"East Alligator River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ Biol Fish (2022) 106:469–490 473 Vol.: (0123456789) 1 3 mine-water contamination. Based on the findings of the study, we further refine the existing conceptual model of fish movement for this system and identify key periods and flow conditions when fish are residing within the RPA and may be exposed to minerelated contaminants. Materials and methods Study site The study was conducted between November 2018 and June 2020 in the mid‐upper reaches of Magela Creek, a major tributary of the East Alligator River in the Northern Territory, Australia. The study region experiences a wet-dry tropical climate characterised by periods of heavy and prolonged rainfall and high stream discharge from December to April, and generally dry conditions for the remainder of the year. Magela Creek is an unregulated, seasonally flowing sand‐bed stream which has its headwaters in the Arnhem Land Plateau and flows through both the Ranger Uranium Mine lease area (RPA) and Kakadu National Park (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.0601613829522,-12.52556962664329],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215533,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Alligator River, West Arnhem NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-7"},{"name":"Arnhem Land Plateau","kind":"region","role":"origin of Magela Creek headwaters; upstream fish refuges and refuge billabongs near escarpment form dry season habitats","river_name":null,"geocode_query":"Arnhem Land Plateau, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Between these reaches of the creek, there are no other permanent surface waters in the main channel, although a series of shallow offchannel, or ‘backflow’, billabongs (e.g. Georgetown, Coonjimba billabongs; see Fig. 1) generally retain some water throughout the dry season near and downstream of the RPA. Bowerbird Billabong is~29 km upstream of Mudginberri Billabong and is an escarpment/rockpool billabong~40 m wide and~1 km long. The billabong has steep vegetated banks, with coarse sandy or rock substrate. Forming in the upper catchment and commencing below Djurrubu Falls (a large waterfall that acts as a complete barrier to fish movement), Bowerbird Billabong is part of a 10 km sand and rock pool-riffle sequence that extends through sandstone escarpments and outcrops before meeting the sand channels of Magela Creek (Fig. 1). There is no vehicle access to Bowerbird Billabong, so all access to the field site was undertaken using helicopters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[134.9253721,-12.621904],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102216629,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arnhem Land, NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-3b5fa8ca87d3f2c69f9cd288-site-8"}],"limitations":["Incomplete coordinates for specific billabongs and mine discharge site due to lack of explicit latitude/longitude in source text","Study constrained to two wet-dry seasonal cycles (Nov 2018–June 2020) potentially limiting understanding of interannual variation","Limited temporal replication in behavioral observations of fish responses to mine discharge water","Lower than average rainfall during study period may affect generality of observed fish movement and migration patterns","Did not examine migrations of small-bodied floodplain species due to study design focus"],"river_ids":[],"site_ids":["doc-3b5fa8ca87d3f2c69f9cd288-site-0","doc-3b5fa8ca87d3f2c69f9cd288-site-2","doc-3b5fa8ca87d3f2c69f9cd288-site-3","doc-3b5fa8ca87d3f2c69f9cd288-site-4","doc-3b5fa8ca87d3f2c69f9cd288-site-5","doc-3b5fa8ca87d3f2c69f9cd288-site-6","doc-3b5fa8ca87d3f2c69f9cd288-site-7","doc-3b5fa8ca87d3f2c69f9cd288-site-8"],"anchor":[133.0601613829522,-12.52556962664329],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040061670],"main_basin_ids":[5040061670]},{"id":"doc-64832eaf4d4bfa7caa640353","title":"Spatio‐temporal distribution of atmospheric chloride deposition over a small island","display_title":"Chloride Deposition Variability on Norfolk Island","doi":"https://doi.org/10.1002/hyp.15052","theme":"Land & ecosystems","color":"#c8df98","year":2023,"publication_date":"2023-12-01","authors":["Paula Campos Teixeira","Carlos M. Ordens","Neil McIntyre","Liliana Pagliero","Russell S. Crosbie"],"summary":"This study investigates the spatial and temporal variability of atmospheric chloride deposition over Norfolk Island, a small volcanic island in the South Pacific. Using a dense monitoring network of 15 open-site rainwater collectors over a 23-month period (September 2020 to July 2022), 275 rainwater samples were collected and analysed. Results reveal high spatial variability in chloride deposition, strongly influenced by distance from the coast and elevation, with a rapid decrease observed within 200 m from the coast. Temporal variability is significantly affected by rainfall and wind gusts, with prevailing easterly winds influencing spatial patterns. A double exponential model describing chloride deposition as a function of coastal distance applicable to small islands globally is proposed. These findings contribute to hydrological, ecological, and infrastructure corrosion prediction studies in small island and coastal environments lacking extensive local data.","finding":"Chloride deposition rates on Norfolk Island vary significantly with distance from coast and elevation, with the highest deposition near the coast (within 200 m) ranging from 50.9 to 1022.7 mg m⁻² d⁻¹, decreasing rapidly inland to 22.7 to 36.7 mg m⁻² d⁻¹ beyond 400 m; temporal variability is strongly influenced by rainfall and maximum daily wind gusts.","finding_evidence":{"passage_id":"p015-000","page":15,"quote":"for sites located more than 400 m from the coast. A higher variation was observed closer to the coast, with values varying from 50.9 to 1022.7 mg m\u00012 d\u00011 for sites located between 50 and 200 m from the coast. The data analysis reveals that variations in rainfall and wind gust over time significantly contribute to chloride deposition temporal variations, while distance from the coast and elevation influence the spatial variations of chloride deposition. Additionally, the results indicate that the prevailing wind direction can also influence the spatial distribution of chloride deposition. Multiple regression analysis, including both spatial and temporal factors, demonstrates that rainfall (log-transformed period-total rainfall depth), wind gust (period-average maximum daily wind gust speed), distance from coast (distance from the nearest point on the coast) and elevation (elevation above mean sea level) explain 57% of log-transformed chloride deposition variability in Norfolk Island.","verified_in_page":true},"topics":["atmospheric deposition","chloride deposition","spatial variability","temporal variability","small islands","coastal processes","hydrological tracers","statistical modelling"],"study_period":"September 2020 to July 2022","locations":[{"name":"Norfolk Island","kind":"region","role":"study site","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"It introduces a new spatio-temporal model for long-term deposition that guides applications across a wide range of small islands. Furthermore, the research compares chloride deposition measurements from this study to those previously reported for small islands. This study uses Norfolk Island as a case study, a very small island (35.7 km2 ) part of the external territory of Australia in the South Pacific Ocean. This paper specifically focuses on chloride deposition measurements at open sites only (no nearby canopy or other objects). A parallel investigation is underway to explore the influence of vegetation on chloride deposition variability in Norfolk Island. 2 | STUDY AREA Norfolk Island is a small volcanic island (35.7 km2 ) located in the South Pacific Ocean between New Zealand and Australia (Figure 1). The island has a wide plateau approximately 100 m above sea level (ASL) that rings the peaks of Mount Pitt and Mount Bates, which have elevations of 316 and 318 mASL respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Mount Pitt, Norfolk Island","kind":"landscape","role":"elevation reference for study","river_name":null,"geocode_query":"Mount Pitt, Norfolk Island","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"It introduces a new spatio-temporal model for long-term deposition that guides applications across a wide range of small islands. Furthermore, the research compares chloride deposition measurements from this study to those previously reported for small islands. This study uses Norfolk Island as a case study, a very small island (35.7 km2 ) part of the external territory of Australia in the South Pacific Ocean. This paper specifically focuses on chloride deposition measurements at open sites only (no nearby canopy or other objects). A parallel investigation is underway to explore the influence of vegetation on chloride deposition variability in Norfolk Island. 2 | STUDY AREA Norfolk Island is a small volcanic island (35.7 km2 ) located in the South Pacific Ocean between New Zealand and Australia (Figure 1). The island has a wide plateau approximately 100 m above sea level (ASL) that rings the peaks of Mount Pitt and Mount Bates, which have elevations of 316 and 318 mASL respectively.","verified_in_page":true},"resolution_status":"resolved","coordinates":[167.9373336,-29.0157778],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Pitt, 2899, Norfolk Island","basin_id":5040079490,"main_basin_id":5040079490,"basin_region":"Australasia","basin_area_km2":51.6,"hydrology_source":"hydrobasins","site_id":"doc-64832eaf4d4bfa7caa640353-site-1"},{"name":"Mount Bates, Norfolk Island","kind":"landscape","role":"elevation reference for study","river_name":null,"geocode_query":"Mount Bates, Norfolk Island","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"It introduces a new spatio-temporal model for long-term deposition that guides applications across a wide range of small islands. Furthermore, the research compares chloride deposition measurements from this study to those previously reported for small islands. This study uses Norfolk Island as a case study, a very small island (35.7 km2 ) part of the external territory of Australia in the South Pacific Ocean. This paper specifically focuses on chloride deposition measurements at open sites only (no nearby canopy or other objects). A parallel investigation is underway to explore the influence of vegetation on chloride deposition variability in Norfolk Island. 2 | STUDY AREA Norfolk Island is a small volcanic island (35.7 km2 ) located in the South Pacific Ocean between New Zealand and Australia (Figure 1). The island has a wide plateau approximately 100 m above sea level (ASL) that rings the peaks of Mount Pitt and Mount Bates, which have elevations of 316 and 318 mASL respectively.","verified_in_page":true},"resolution_status":"resolved","coordinates":[167.9333333,-29.0166667],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mount Bates, 2899, Norfolk Island","basin_id":5040079490,"main_basin_id":5040079490,"basin_region":"Australasia","basin_area_km2":51.6,"hydrology_source":"hydrobasins","site_id":"doc-64832eaf4d4bfa7caa640353-site-2"},{"name":"Norfolk Island Regional Council laboratory, Norfolk Island","kind":"facility","role":"laboratory for sample analysis","river_name":null,"geocode_query":"Norfolk Island Regional Council, Norfolk Island","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"resetting. The duration varied based on rainfall depths, weather conditions, and logistical considerations (refer to Table A1 for comprehensive period details). The objective was to capture diverse weather conditions while concurrently ensuring a minimum of 50 mL of rainwater for sampling and preventing sample overflow. The sampling period was consistent within 24 h for each site. At the conclusion of each sampling period, the rainwater volume in each collector was measured, and a minimum of 50 mL was transferred to clean 100 mL containers. Resetting the collectors involved emptying, rinsing with distilled water, and reinstating the paraffin oil layer. Considering the long distance from a laboratory facility in the mainland and the quarantine requirements for sample transportation to mainland Australia, the samples were analysed at the local Council laboratory on Norfolk Island.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Bureau of Meteorology weather station, Norfolk Island","kind":"station","role":"source of weather data including rainfall and wind for temporal analysis","river_name":null,"geocode_query":"Bureau of Meteorology weather station, Norfolk Island","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The effect of prevailing wind direction on spatial variation of chloride deposition was investigated by plotting the estimated distance from the coast facing the east wind (prevailing wind direction during data collection period) and chloride deposition. Following spatial analysis, chloride deposition in sites was plotted against period-total rainfall depth, period-average daily wind speed and period-average maximum daily wind gust speed to determine temporal effects. Wind gust speed was included to test the hypothesis that deposition is affected by sea spray in extreme wind conditions that are not well represented by average wind speeds. The wind data were obtained from the BOM weather station (location in Figure 1), while all other data are sitespecific. Since rainfall and chloride deposition data are right-skewed (Figure A2), they were log-transformed.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Lack of explicit coordinates for study sites and island features","Limited wind data resolution across the island; wind measurements from a single weather station may not capture local variability","Short sampling periods for some collectors leading to limited sample numbers at those sites","Sample collection intervals varied, potentially influencing temporal resolution","Double exponential model does not incorporate temporal variability, limiting precision for long-term predictions"],"river_ids":[],"site_ids":["doc-64832eaf4d4bfa7caa640353-site-1","doc-64832eaf4d4bfa7caa640353-site-2"],"anchor":[167.9333333,-29.0166667],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040079490],"main_basin_ids":[5040079490]},{"id":"doc-7167e21aecc219a1b3a14d85","title":"Geostatistical based optimization of groundwater monitoring well network design","display_title":"Optimizing Groundwater Monitoring in Namoi Region","doi":"https://doi.org/10.3389/feart.2023.1188316","theme":"Groundwater","color":"#a8b9ff","year":2023,"publication_date":"2023-09-06","authors":["Daniel W. Gladish","Daniel E. Pagendam","Sreekanth Janardhanan","Dennis Gonzalez"],"summary":"This study develops and applies a geostatistical optimization method to design groundwater quality monitoring networks in sedimentary aquifers, focusing on the Pilliga Sandstone aquifer within the Namoi region of Australia. Using kriging models for 41 water quality parameters, combined with a Differential Evolution algorithm, the method identifies optimal borehole locations within a 30-km buffer zone of potential coal seam gas development to reduce spatial prediction uncertainty of water quality parameters. Results demonstrate significant uncertainty reduction compared to random bore placement.","finding":"Optimal placement of 10 new monitoring bores in the Pilliga Sandstone aquifer substantially reduces the spatial prediction uncertainty of multiple groundwater quality parameters across a 30-km buffer zone surrounding potential coal seam gas development areas in the Namoi region.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Further, the majority of the water quality variables used the Matérn covariance function, used in 27 of the kriging models, with the spherical covariance function having 7, Gaussian having 6, and only 1 for the Exponential covariance function. Table 2 shows whether depth was included, the covariance function used, and the estimates of the parameters for all 41 water quality variables. Once we determined the best kriging models, we used the Differential Evolution algorithm with the objective function described in Section 2.3 to identify 10 potential borehole locations that will reduce prediction uncertainty in baseline water quality indicators in the 30-km buffer zone. Figure 4 shows the optimal locations of these borehole locations in the 30-km buffer zone. These optimal locations reduce prediction uncertainty across all potential water quality variables. We inspect the resulting interpolation before and after establishing optimal locations of the boreholes in the buffer region.","verified_in_page":true},"topics":["groundwater quality","spatial interpolation","monitoring network design","geostatistical kriging","optimization algorithms","uncertainty reduction","differential evolution algorithm","hydrogeology"],"study_period":null,"locations":[{"name":"Namoi region, New South Wales, Australia","kind":"region","role":"Study region for groundwater quality monitoring network design in the Pilliga Sandstone aquifer","river_name":null,"geocode_query":"Namoi region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, we focus on the part of the Great Artesian Basin in northern New South Wales, Australia where Pilliga Sandstone is an economically important aquifer. The GAB aquifer, Pilliga Sandstone, is a major source of fresh groundwater. It is used for irrigation, stock and domestic water uses and provides valuable ecosystem services. It is a regional aquifer extending large areas and water quality measurements are available from sparsely distributed monitoring bores. Understanding baseline water quality indicators in the Pilliga Sandstone aquifer is critical, but prediction of such indicators at the aquifer scale is difficult, particularly resulting in high levels of uncertainty. Our method therefore focuses on a procedure with the goal of determining optimal locations for future observational boreholes that will minimize uncertainty in the spatial prediction of water quality parameters when used in conjunction with existing observation data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.57267,-30.568464],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Namoi River, New South Wales, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7167e21aecc219a1b3a14d85-site-0","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7},{"name":"Pilliga Sandstone aquifer, Namoi region, New South Wales, Australia","kind":"aquifer","role":"Target aquifer for baseline groundwater quality monitoring and network design","river_name":null,"geocode_query":"Pilliga Sandstone, Namoi region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, we focus on the part of the Great Artesian Basin in northern New South Wales, Australia where Pilliga Sandstone is an economically important aquifer. The GAB aquifer, Pilliga Sandstone, is a major source of fresh groundwater. It is used for irrigation, stock and domestic water uses and provides valuable ecosystem services. It is a regional aquifer extending large areas and water quality measurements are available from sparsely distributed monitoring bores. Understanding baseline water quality indicators in the Pilliga Sandstone aquifer is critical, but prediction of such indicators at the aquifer scale is difficult, particularly resulting in high levels of uncertainty. Our method therefore focuses on a procedure with the goal of determining optimal locations for future observational boreholes that will minimize uncertainty in the spatial prediction of water quality parameters when used in conjunction with existing observation data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.888562,-30.411763],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670847,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pilliga NSW 2388, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7167e21aecc219a1b3a14d85-site-1","true_coordinates":[148.8885619,-30.3517626],"spread_km":6.7},{"name":"Great Artesian Basin, Australia","kind":"basin","role":"Hydrogeological basin encompassing the Pilliga Sandstone aquifer studied for groundwater quality monitoring","river_name":null,"geocode_query":"Great Artesian Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, we focus on the part of the Great Artesian Basin in northern New South Wales, Australia where Pilliga Sandstone is an economically important aquifer. The GAB aquifer, Pilliga Sandstone, is a major source of fresh groundwater. It is used for irrigation, stock and domestic water uses and provides valuable ecosystem services. It is a regional aquifer extending large areas and water quality measurements are available from sparsely distributed monitoring bores. Understanding baseline water quality indicators in the Pilliga Sandstone aquifer is critical, but prediction of such indicators at the aquifer scale is difficult, particularly resulting in high levels of uncertainty. Our method therefore focuses on a procedure with the goal of determining optimal locations for future observational boreholes that will minimize uncertainty in the spatial prediction of water quality parameters when used in conjunction with existing observation data.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Surat Basin, Australia","kind":"basin","role":"Geological basin including the Cadna-owie Hooray Sandstone and Pilliga Sandstone hydrostratigraphic units studied","river_name":null,"geocode_query":"Surat Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, we focus on the part of the Great Artesian Basin in northern New South Wales, Australia where Pilliga Sandstone is an economically important aquifer. The GAB aquifer, Pilliga Sandstone, is a major source of fresh groundwater. It is used for irrigation, stock and domestic water uses and provides valuable ecosystem services. It is a regional aquifer extending large areas and water quality measurements are available from sparsely distributed monitoring bores. Understanding baseline water quality indicators in the Pilliga Sandstone aquifer is critical, but prediction of such indicators at the aquifer scale is difficult, particularly resulting in high levels of uncertainty. Our method therefore focuses on a procedure with the goal of determining optimal locations for future observational boreholes that will minimize uncertainty in the spatial prediction of water quality parameters when used in conjunction with existing observation data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.6239599,-26.7404788],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43675038,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-7167e21aecc219a1b3a14d85-site-3"},{"name":"Gunnedah Basin, Australia","kind":"basin","role":"Underlying basin from which coal seam gas development occurs and drilling impacts Pilliga Sandstone aquifer","river_name":null,"geocode_query":"Gunnedah Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"In this study we focussed on identifying optimal locations for new monitoring bore for the Pilliga Sandstone aquifer within the 30-km buffer area of potential coal seam gas development. Coal seam gas development occurs from the Gunnedah Basin underlying the Surat Basin. As the gas wells are drilled through the aquifers of the Surat Basin including the Pilliga Sandstone, it is important to develop improved understanding of the Pilliga Sandstone baseline water quality in the area. Figure 1A shows the broader region of the Great Artesian Basin in eastern Australia and the sedimentary basins including the Surat Basin that holds the Pilliga Sandstone unit and the underlying Gunnedah Basin from which coal seam gas development is planned. Figure 1B shows the extend of the coal seam gas project and the buffer zone in which water quality monitoring is intended. Regional extent of the aquifer with limited monitoring data makes spatial prediction of water quality parameters difficult.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.256504,-31.039432],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43677648,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7167e21aecc219a1b3a14d85-site-4","true_coordinates":[150.2565037,-30.97943189999999],"spread_km":6.7},{"name":"30-km buffer zone near potential coal seam gas development, Namoi region, NSW, Australia","kind":"region","role":"Spatial domain for groundwater monitoring network optimization and analysis","river_name":null,"geocode_query":"30 km buffer area near coal seam gas development, Namoi region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Specifically, we focus on the part of the Great Artesian Basin in northern New South Wales, Australia where Pilliga Sandstone is an economically important aquifer. The GAB aquifer, Pilliga Sandstone, is a major source of fresh groundwater. It is used for irrigation, stock and domestic water uses and provides valuable ecosystem services. It is a regional aquifer extending large areas and water quality measurements are available from sparsely distributed monitoring bores. Understanding baseline water quality indicators in the Pilliga Sandstone aquifer is critical, but prediction of such indicators at the aquifer scale is difficult, particularly resulting in high levels of uncertainty. Our method therefore focuses on a procedure with the goal of determining optimal locations for future observational boreholes that will minimize uncertainty in the spatial prediction of water quality parameters when used in conjunction with existing observation data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.627118,-30.545873],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Namoi River, New South Wales, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7167e21aecc219a1b3a14d85-site-5","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7}],"limitations":["Assumes continuous and stationary spatial variation in water quality parameters; abrupt changes or spatial discontinuities are not accounted for.","Spatial interpolation accuracy may be limited by sparse data and composite hydrostratigraphic units.","Optimization constrained to fixed number of bores (10) due to budget considerations; does not explore optimal number of bores for defined uncertainty targets.","Study area restricted to Pilliga Sandstone aquifer within a 30-km buffer zone, limiting generalization beyond this region."],"river_ids":[],"site_ids":["doc-7167e21aecc219a1b3a14d85-site-0","doc-7167e21aecc219a1b3a14d85-site-1","doc-7167e21aecc219a1b3a14d85-site-3","doc-7167e21aecc219a1b3a14d85-site-4","doc-7167e21aecc219a1b3a14d85-site-5"],"anchor":[149.57267,-30.568464],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040511570,5040524870],"main_basin_ids":[5040073410]},{"id":"doc-76975eafb3dee9e4c03953df","title":"Inter-basin surface water transfers database for public water supplies in conterminous United States, 1986–2015","display_title":"Inter-basin Water Transfers in CONUS, 1986–2015","doi":"https://doi.org/10.1038/s41597-023-02148-5","theme":"Water management","color":"#eeabac","year":2023,"publication_date":"2023-05-06","authors":["G. Rebecca Dobbs","Ning Liu","Peter V. Caldwell","Chelcy Ford Miniat","Ge Sun","Kai Duan","Paul V. Bolstad"],"summary":"This study presents an updated, systematic geodatabase of inter-basin surface water transfers (IBTs) connected with public water supplies across the conterminous United States (CONUS) from 1986 to 2015. It includes volumetric data and high spatial resolution points of withdrawal and delivery. The database addresses limitations of previous surveys by USGS in the 1980s and improves the spatial and temporal detail of IBT data, facilitating hydrologic modeling and research into water resource management under changing climate and population demands.","finding":"The new CONUS IBT database includes 627 transfer steps with volumetric data for 599 steps, representing a total average annual transferred volume of approximately 119,909 million m³ per year. The spatial resolution includes HUC12 hydrologic unit boundaries, enabling finer scale hydrologic modeling and understanding of complex water transfer systems such as those in Colorado and California.","finding_evidence":{"passage_id":"p006-002","page":6,"quote":"Each record (row) of the table represents a single step; multi-step transfers thus occupy multiple rows of the table and steps can therefore be modelled individually from that data. Fields in this table include TransferID, StepID, FromHUC12, ToHUC12, and fields for annual volumes from 1986 through 2015, in Mm3 water yr−1 . Lastly are several fields for key metadata content about the step, including type of year (i.e., calendar or water year, and how that water year is defined if available) reflected in the data, general and specific data source information to allow users to find the original data we used, comments where needed, an indicator of reservoirs at the beginning and/or end of a step, and transfer type (to surface, into ground, or calculated losses). The StepVolH12 table contains 627 records, 599 of which we acquired data for from at least some years in the 1986–2015 range. For the remaining 28 records, we retained a record toward future data availability.","verified_in_page":true},"topics":["inter-basin water transfers","public water supply","water resource management","hydrologic modeling","surface water volume","hydrologic units","water infrastructure","climate change impact"],"study_period":"1986–2015","locations":[{"name":"CONUS (conterminous United States)","kind":"region","role":"study area encompassing all inter-basin transfers investigated","river_name":null,"geocode_query":"conterminous United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Here we present the outcome of a systematic investigation into inter-basin surface water transfers connected with public water supplies in the conterminous United States (CONUS), 1986 to 2015. The present open-access geodatabase includes transfer volumes collected, evaluated, and compiled from disparate sources. We provide an updated snapshot of CONUS IBTs at a higher spatial resolution of points of withdrawal and delivery than previous datasets. This paper puts the national inter-basin transfer data in context, and shows how we acquired, structured, and validated the locations and volumes of surface water transfers in public water systems. Background & Summary Human societies broadly, through time and on every inhabited continent, have moved or otherwise managed water in order to support urban populations, moisten dry places, modify watery landscapes, make water available during dry times, ease the transport of people or goods, or improve access to aquatic resource species1–19.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Colorado","kind":"region","role":"example area with complex IBT systems needing HUC12 resolution, crossing the Continental Divide","river_name":null,"geocode_query":"Colorado, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"As we wished to use the data in finer resolution hydrologic modelling than was possible with the USGS IBT data, we established a default scale of HUC8 for the boundary the water must cross to be considered an IBT, while recognizing that in some instances we would need to employ a finer scale in order to fully resolve the IBT connections. For example, in Colorado many significant water transfers begin by collecting snowmelt from mountain stream headwaters in multiple HUC12s west of the Continental Divide, interconnecting at various points, and ending in a complex array of HUC12s east of the Continental Divide (Fig. 2). California likewise has complex and interlocking systems best understood at the HUC12 scale. Furthermore, for every transfer in the database we identified origin and destination watersheds at the HUC12 scale, to support high-resolution modelling. We next established criteria regarding water type, use type, and endpoints.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-108.905995,39.216132],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":7040585120,"main_basin_id":7040008710,"basin_region":"North America and Caribbean","basin_area_km2":67936.1,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-1"},{"name":"California","kind":"region","role":"complex interlocking IBT systems including State Water Project and Central Valley Project, demonstrated with San Luis Reservoir system","river_name":null,"geocode_query":"California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"Scientific Data | (2023) 10:255 | https://doi.org/10.1038/s41597-023-02148-5 8 www.nature.com/scientificdata/ www.nature.com/scientificdata In this example, the San Luis Reservoir is an “off-stream” storage facility that functions as a “regulator”48 for both of California’s two massive and intertwined north-to-south water transfer systems, the State Water Project (SWP) (state) and the Central Valley Project (CVP) (federal). The reservoir and associated IBTs make fall-winter-spring water from northern California available for summer irrigation demand in central and southern California as well as year-round demand in California’s southern cities and to some extent buffers the effects of drought years through storage and release cycles. Beginning from this understanding of the system, we sought out SWP and CVP data.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Greater Los Angeles","kind":"landscape","role":"example of a complex IBT delivery area with multiple sources and agencies distributing transferred water","river_name":null,"geocode_query":"Greater Los Angeles, California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Represented schematically (via straight arrows from HUC centroid to HUC centroid, for each step), the topology of a complex system requires from two to dozens of arrows and associated pairs of HUC12s, worked out through detailed analysis of available source materials. Topologies were especially important for understanding complex systems that were interconnected with other systems. Greater Los Angeles, for example, receives water from multiple major aqueducts carrying water from the east, the northeast, and the north, much of which is distributed to different agencies, some of which is combined in different reservoirs and treatment plants, and some of those agencies also redistribute both wholesale and retail water to additional agencies, making for quite a complicated topology. Sources for flow data also had to be researched extensively.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-118.2848067,33.8726016],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Greater Los Angeles, CA, USA","basin_id":7040008720,"main_basin_id":7040008720,"basin_region":"North America and Caribbean","basin_area_km2":177193.6,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-3"},{"name":"San Luis Reservoir, California","kind":"facility","role":"key off-stream storage and regulator facility linking major IBT systems of California (State Water Project and Central Valley Project)","river_name":null,"geocode_query":"San Luis Reservoir, California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"Scientific Data | (2023) 10:255 | https://doi.org/10.1038/s41597-023-02148-5 8 www.nature.com/scientificdata/ www.nature.com/scientificdata In this example, the San Luis Reservoir is an “off-stream” storage facility that functions as a “regulator”48 for both of California’s two massive and intertwined north-to-south water transfer systems, the State Water Project (SWP) (state) and the Central Valley Project (CVP) (federal). The reservoir and associated IBTs make fall-winter-spring water from northern California available for summer irrigation demand in central and southern California as well as year-round demand in California’s southern cities and to some extent buffers the effects of drought years through storage and release cycles. Beginning from this understanding of the system, we sought out SWP and CVP data.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-121.1137544,37.056069],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"San Luis Reservoir, California, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-4"},{"name":"O’Neill Forebay, California","kind":"facility","role":"component of California’s joint federal-state water transfer complex; point of water entry and release related to San Luis Reservoir system","river_name":null,"geocode_query":"O'Neill Forebay, California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"From the reservoir, certain water is delivered to Santa Clara and Hollister, CA (18012.03xx and 18012.0401), removing this water from circulation in the joint-use complex. Remaining water in the San Luis Reservoir is released back to the O’Neill Forebay as needed, again via the San Luis plant but this time generating electricity as it falls (18012.0501). Water released from the Forebay to the Delta-Mendota Canal likewise generates electricity as it falls through the O’Neill plant (18012.0601). Water released from the Forebay to the California Aqueduct (18011.0201) is measured at the Dos Amigos pump to the south. As a final check, we followed our compiled data through the system arithmetically for several random years. Given that our transfer data did not quantify volumes retained for storage, the inflows and outflows of the system balanced each other within reasonable limits and validated our understanding of this complex system.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-121.0478433,37.0824598],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"O'Neill Forebay, California 95322, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-5"},{"name":"Tulsa, Oklahoma","kind":"settlement","role":"example of city with an IBT serving a population over 200,000, included in study","river_name":null,"geocode_query":"Tulsa, Oklahoma, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Our final criterion considered the population served by a Public Water System (PWS). If a city was not connected to a complex system of IBTs such as those mentioned in Colorado and California, we limited our defined IBTs to cities whose PWSs served populations of at least 200,000 based on the 2017 Safe Drinking Water Information System (SDWIS; https://www.epa.gov/ground-water-and-drinking-water/ safe-drinking-water-information-system-sdwis-federal-reporting) data from the US Environmental Protection Agency (EPA), and the PWS received surface water from across a HUC8 boundary as discussed previously. Cities of this size are often important centers of function and culture for less urbanized states or regions, or they may form important parts of larger metropolitan conglomerations. In either context, they are likely to have a greater per capita water demand due to development trajectories than small cities and towns, and they often sell water to smaller nearby PWSs as well.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-95.98950099999999,36.1551375],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tulsa, OK, USA","basin_id":7040686450,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":414787.8,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-6"},{"name":"Cincinnati, Ohio","kind":"settlement","role":"example of city supplied by an IBT crossing HUC8 boundaries serving population >200,000","river_name":null,"geocode_query":"Cincinnati, Ohio, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-006","page":3,"quote":"Our final criterion considered the population served by a Public Water System (PWS). If a city was not connected to a complex system of IBTs such as those mentioned in Colorado and California, we limited our defined IBTs to cities whose PWSs served populations of at least 200,000 based on the 2017 Safe Drinking Water Information System (SDWIS; https://www.epa.gov/ground-water-and-drinking-water/ safe-drinking-water-information-system-sdwis-federal-reporting) data from the US Environmental Protection Agency (EPA), and the PWS received surface water from across a HUC8 boundary as discussed previously. Cities of this size are often important centers of function and culture for less urbanized states or regions, or they may form important parts of larger metropolitan conglomerations. In either context, they are likely to have a greater per capita water demand due to development trajectories than small cities and towns, and they often sell water to smaller nearby PWSs as well.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-84.5120196,39.1031182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cincinnati, OH, USA","basin_id":7040612640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":528052.3,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-7"},{"name":"Ohio Region (Water Resource Region WRR 5)","kind":"region","role":"origin region of some inter-basin transfers, part of the USGS hydrologic unit code system","river_name":null,"geocode_query":"Ohio Region, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Below we discuss methods of data acquisition, structure and characteristics of data records, and validation of the database. 10 17 03 11 04 13 07 12 18 15 05 16 14 02 08 01 09 455 10 Km 04 200K 00 m 01 75 50 Km 01 60 20 Km 03 15 0 Km 06 3.25 .5 Km HUC2 = 05 Ohio Region HUC 05 contains 14 HUC4s H UC4 = 0512 Wabash HUC 0512 contains 2 HUC6s HUC 051201 contains 15 HUC8s HUC 05120113 contains 46 HUC12s HUC2 or Water Resource Region 01 New England Region 02 Mid Atlantic Region 03 South Atlantic-Gulf Region 04 Great Lakes Region 05 Ohio Region 06 Tennessee Region 07 Upper Mississippi Region 08 Lower Mississippi Region 09 Souris-Red-Rainy Region 10 Missouri Region 11 Arkansas-White-Red Region 12 Texas-Gulf Region 13 Rio Grande Region 14 Upper Colorado Region 15 Lower Colorado Region 16 Great Basin Region 17 Pacific Northwest Region 18 California Region HUC6 = 051201 Wabash HUC8 = 05120113 Lower Wabash HUC12 = 051201130402 Little Bonpas Creek Fig. 1 Representation of USGS Hydrologic Unit Code system (in CONUS).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"California Aqueduct, California","kind":"aquifer","role":"component of the State Water Project contributing water to the San Luis Reservoir system","river_name":"California Aqueduct","geocode_query":"California Aqueduct, California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"From the former source, we used pumping plant data to derive amounts of water flowing into the system from the California Aqueduct (i.e., Banks Pumping Plant minus South Bay Pumping Plant). From the latter source we derived the amount of water in the Delta-Mendota Canal arriving at the system, and the amount actually entering the system (the O’Neill Pumping Plant data category). These two project data sources provided no information about the workings of the regulator system itself, nor any clear data on amounts of water leaving the system. We therefore turned to further examination of textual descriptions and map sources. In doing so we found that the San Luis Reservoir is only one part of the joint-use complex. One reflection of this is the existence of two additional data series covering the joint federal-state operation of the system.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-121.0953786108395,37.2672335700945],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Governor Edmund G Brown California Aqueduct, California, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-76975eafb3dee9e4c03953df-site-9"}],"limitations":["Coordinates of locations are not specified in the study and thus latitude/longitude are null.","Some locations and infrastructures are represented only at the hydrologic unit centroid scale (HUC12), not the exact infrastructure coordinates, to preserve sensitive location information.","Dataset covers only the period 1986–2015; transfers outside this time are not included.","Data completeness depends on availability from multiple agencies and some entries have missing data for some years (indicated as NoData)."],"river_ids":["ne-247","ne-248","ne-292"],"site_ids":["doc-76975eafb3dee9e4c03953df-site-1","doc-76975eafb3dee9e4c03953df-site-3","doc-76975eafb3dee9e4c03953df-site-4","doc-76975eafb3dee9e4c03953df-site-5","doc-76975eafb3dee9e4c03953df-site-6","doc-76975eafb3dee9e4c03953df-site-7","doc-76975eafb3dee9e4c03953df-site-9"],"anchor":[-118.2848067,33.8726016],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[7040008720,7040013550,7040585120,7040612640,7040686450],"main_basin_ids":[7040008710,7040008720,7040013550,7040047060]},{"id":"doc-79c3afec9f1815e91afe0385","title":"A review of inorganic contaminants in Australian marine mammals, birds and turtles","display_title":"Inorganic Contaminants in Australian Marine Fauna","doi":"https://doi.org/10.1071/en23057","theme":"Flow & ecology","color":"#80edc2","year":2023,"publication_date":"2023-09-11","authors":["Chad V. Jarolimek","Josh J. King","Simon C. Apte","Jane Hall","Anil Gautam","Megan L. Gillmore","Chris Doyle"],"summary":"This review compiles and analyzes published data on trace element concentrations, including metals and metalloids, in Australian marine mammals, birds, and turtles. Data were sourced from opportunistic sampling such as strandings and bycatch, revealing large gaps in geographic, temporal, and species coverage. The review highlights contaminant accumulation patterns related to tissue type, diet, and age, comparing findings with international data. It also discusses analytical quality control and provides recommendations for future research and monitoring strategies.","finding":"Mercury concentrations in Australian marine fauna follow global trends with highest levels in liver tissues of apex predators, significant biomagnification with diet, and increased concentrations with age. Notably, the Indo-Pacific bottlenose dolphin (Tursiops aduncus) had some of the highest reported liver mercury concentrations worldwide, which may result from natural background levels, anthropogenic inputs, and specific feeding habits in coastal industrialized areas.","finding_evidence":{"passage_id":"p014-002","page":14,"quote":"Tursiops truncatus and T. australis had similarly, albeit slightly lower mercury liver concentrations, which were approximately an order of magnitude higher than that of Delphinus delphis from the same location (Lavery et al. 2008; Monk et al. 2014), which has been attributed to differences in diets of the Tursiops spp. and Delphinus spp. (Lavery et al. 2008). While T. aduncus had the highest reported liver mercury concentrations, it also had the greatest range of reported concentrations (Fig. 3). The authors speculated that the high concentrations observed may result from a combination of naturally high background mercury concentrations, additional anthropogenic mercury inputs and the feeding strategies of T.aduncus which have a varied diet and generally feed on bottom- or near-bottom-dwelling cephalopods and fish (Butterfield and Gaylard 2005). They also noted that further research is needed to explain why T. aduncus hepatic mercury concentrations are elevated.","verified_in_page":true},"topics":["marine contaminants","bioaccumulation","mercury","trace elements","marine mammals","ecotoxicology","analytical chemistry","dietary exposure"],"study_period":"1974–2022","locations":[{"name":"Western Australia","kind":"region","role":"Study area including major whale harvest site and marine mammal sampling","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Cetaceans were by far the most sampled fauna, with almost 40% of samples coming from a single season Western Australia (WA) whale harvest in the 1970s (Canella and Kitchener 1992). Ignoring this data spike, sample numbers for marine fauna have generally increased over the last five decades, highlighting the increased awareness of using marine organisms as sentinels of environmental health. Dugongs are perhaps the exception to this trend, with sample numbers peaking in the 1990s, with no dugong trace element data reported since 2000. As illustrated in Fig. 1, the national distribution of fauna types sampled followed the general habitats that the organisms Table 1. Reported number of samples analysed for inorganic contaminants over time.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia","kind":"region","role":"Study area with dolphin specimen archive and samples with elevated mercury concentrations","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"The species with the highest sample numbers was the sperm whale (Physeter macrocephalus) (n = 416), due to a large number of samples collected during a 1976 whale harvest conducted in WA, which were principally analysed for mercury concentrations in muscle tissue (Canella and Kitchener 1992). Since 1982, most samples were from species such as the common dolphin (Delphinus delphis) (n = 151), Indo-Pacific bottlenose dolphin (Tursiops aduncus) (n = 88) and the false killer whale (Pseudorca crassidens) (n = 38). Most dolphin specimens were obtained from the South Australian Museum, which has been collecting and archiving dolphin specimens that have died from entanglements, intentional killing, disease and natural causes in South Australia (SA) since 1988 (Butterfield and Gaylard 2005). Discrepancies in sample numbers between species, locations and how studies have categorised age make comparison between studies difficult.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Tasmania","kind":"region","role":"Stranding location for cetacean samples and seabird sampling","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"occupy (e.g. dugongs and turtles in Queensland (QLD)) or common stranding locations (e.g. cetaceans in Tasmania). Contaminant concentrations measured in various marine species and their tissue types were extremely variable, with concentration ranges typically spanning several orders of magnitude. Consequently, where possible, data were summarised in this report in the form of ranges. Full data sets as presented in the original publications are collated in the Supplementary material. Details of methodology and QC information play an important role in supporting the reliability and comparability of analytical data. Desirable QC includes details on methodology, calibration standards (traceability), blanks (to check that the analytical signal is not affected by contamination), precision checks (replicate measurements), accuracy checks using reference materials and spike recoveries.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Territory","kind":"region","role":"Region with data gaps for cetacean contaminant data and dugong sampling","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"This has resulted in large gaps in geographical, temporal and species coverage data. For instance, little or no data are available for cetaceans in New South Wales or the Northern Territory, and out of 14 endemic species of dolphins, data only exist for seven species. The aforementioned data gaps make it hard to identify statistically significant trends, a problem compounded by data being reported in the form of ranges without raw data. Trace element concentrations measured in various marine species and their tissue types are extremely variable, with ranges typically spanning several orders of magnitude, but are generally comparable with international data. Trends in contaminant concentrations with tissue type follow generally accepted patterns of behaviour for higher organisms, with the highest mercury concentrations in liver and cadmium in kidney tissues.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland","kind":"region","role":"Sampling area for dugongs and green turtles, seagrass contaminant studies","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"(r = 0.975, P < 0.001), spleen (r = 0.799), brain (r = 0.711) and hair (r = 0.760) (Bacher 1985). This is likely influenced by diet, as well as age (Julshamn and Grahl-Nielsen 2000; Gerpe et al. 2009). Age related mercury bioaccumulation is a well-known effect in marine mammals and piscivorous fish owing to detoxification, storage and low excretion rates (Rainbow 2018). The observed increase in liver mercury concentration with age could be a result of formation of insoluble mercuric selenide (HgSe), which can account for over 50% total mercury (Wagemann et al. 2000), and its long body half-life, e.g. up to 500 days in ringed seals (Ewald et al. 2019). Sirenians: dugongs Trace element data for dugongs were sourced from four published papers (Denton et al. 1980; Denton and Breck 1981; Marsh 1989; Haynes et al. 2005) and one report from the Great Barrier Reef Marine Park Authority (Gladstone 1996).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales","kind":"region","role":"Region with notably little cetacean contaminant data, urban/industrial center (Sydney) of interest","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p020-000","page":20,"quote":"site away from anthropogenic point sources, and coastal populations closer to identified point sources of contaminants. A separate study of seagrass from similar locations revealed a similar trend with seagrass cobalt concentrations an order of magnitude greater at the anthropogenically impacted coastal sites compared to the baseline site (Thomas et al. 2020). Copper and zinc were occasionally measured across studies. In cetaceans there was a relatively small variation between individuals of the same species due to homeostatic control of these essential elements (Weijs et al. 2016; Rainbow 2018); a trend also observed in fairy prions (P. turtur) (Brothers and Brown 1987). A comprehensive review by Rainbow (2018) highlighted that marine mammal livers and kidneys tend to contain 3–30 mg kg−1 wet weight copper in a metabolically available form to meet the essential needs of the animal, with little evidence of detoxified storage.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Spencer Gulf","kind":"region","role":"Nearshore industrially impacted gulf with elevated hepatic mercury in dolphins","river_name":null,"geocode_query":"Spencer Gulf, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-002","page":14,"quote":"Tursiops truncatus and T. australis had similarly, albeit slightly lower mercury liver concentrations, which were approximately an order of magnitude higher than that of Delphinus delphis from the same location (Lavery et al. 2008; Monk et al. 2014), which has been attributed to differences in diets of the Tursiops spp. and Delphinus spp. (Lavery et al. 2008). While T. aduncus had the highest reported liver mercury concentrations, it also had the greatest range of reported concentrations (Fig. 3). The authors speculated that the high concentrations observed may result from a combination of naturally high background mercury concentrations, additional anthropogenic mercury inputs and the feeding strategies of T.aduncus which have a varied diet and generally feed on bottom- or near-bottom-dwelling cephalopods and fish (Butterfield and Gaylard 2005). They also noted that further research is needed to explain why T. aduncus hepatic mercury concentrations are elevated.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.7938782,-32.782483],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50288809,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Spencer Gulf, Cultana SA 5601, Australia","basin_id":5040074330,"main_basin_id":5040074330,"basin_region":"Australasia","basin_area_km2":100682.8,"hydrology_source":"geofabric","site_id":"doc-79c3afec9f1815e91afe0385-site-6"},{"name":"Macquarie Island","kind":"region","role":"Penguin species contaminant sampling site, used for temporal trend analysis","river_name":null,"geocode_query":"Macquarie Island, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"Bacher (1985) found a correlation between liver and hair mercury concentrations in Australian fur seals (r = 0.69, P < 0.005), while Aubail et al. (2013) identified liver–skin Hg in liver Cd in kidney 0.001 Tursiops truncatus Delphinus delphis Stenella attenuata Tursiops aduncus Tursiops australis Not specied Orcinus orca Pseudorca crassidens Globicephala spp. Peponocephala electra Kogia breviceps Physeter macrocephalus Mesoplodon spp. Caperea marginata Arctocephalus pusillus Thalassarche cauta Diomedea epomophora Diomedea exulans Dugong dugon Chelonia mydas Caretta caretta Eretmochelys imbricata Lepidochelys olivacea 0.01 0.1 1 10 n = 20 n = 39 n = 70 n = 37 n = 2 n = 1 n = 59 n = 6 n = 3 n = 2 n = 1 n = 1 n = 1 n = 1 n = 1 n = 16 n = 4 n = 29 n = 29 n = 22 n = 22 n = 78 n = 48 n = 45 n = 64 n = 5 n = 6 n = 2 n = 3 n = 1 n = 9 n = 9 n = 1 n = 1 n = 2 n = 2 n = 5 n = 3 n = 3 n = 1 n = 38 n = 37 Tissue concentration (mg kg−1 wet weight) 100 1000 10 000 Fig. 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[158.8556149,-54.62081149999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Macquarie Island, Australia","basin_id":5040087540,"main_basin_id":5040087540,"basin_region":"Australasia","basin_area_km2":980.2,"hydrology_source":"hydrobasins","site_id":"doc-79c3afec9f1815e91afe0385-site-7"},{"name":"Torres Strait","kind":"region","role":"Site of dugong contaminant sampling and observed potential dietary exposure to cadmium and cobalt","river_name":null,"geocode_query":"Torres Strait, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"the Supplementary material. Possibly due to their herbivorous diets, a broader range of elements have been targeted for analysis in dugong studies, including both essential (iron, copper and zinc) and non-essential (cadmium, mercury and lead) elements. High concentrations of iron in dugong liver (from 120 to approximately 45 000 mg kg−1 wet weight) were observed, such that haemosiderin deposits were detected in some individuals; a result observed in mammals in cases of prolonged dietary iron ingestion (Denton et al. 1980; Mazzaro et al. 2004; Rosen and Worthy 2018). Dietary related high concentrations of iron storage in the liver of dugongs is well known in the literature, and likely of little pathological significance (Woolford et al. 2015). Comparatively high concentrations of copper have been measured in dugong livers (up to 472 mg kg−1 wet weight) compared to other marine mammals (e.g. dolphin maximum Cu of 85 mg kg−1 wet weight, Table 4).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.43156025,-10.02025055],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Torres Strait Islands","hydrology_source":"none","site_id":"doc-79c3afec9f1815e91afe0385-site-8"}],"limitations":["Data rely largely on opportunistic sample collection leading to incomplete species and geographic coverage","Coordinates not provided for specific sampling sites or regions","Data reported often as ranges without raw detail, limiting statistical analysis","Use of indicator tissues (feathers, skin) to estimate organ contaminant concentrations remains uncertain","Temporal coverage uneven with large gaps for some decades and taxa"],"river_ids":[],"site_ids":["doc-79c3afec9f1815e91afe0385-site-6","doc-79c3afec9f1815e91afe0385-site-7","doc-79c3afec9f1815e91afe0385-site-8"],"anchor":[137.7938782,-32.782483],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040074330,5040087540],"main_basin_ids":[5040074330,5040087540]},{"id":"doc-7b5617bfd880b4585fccc80f","title":"Towards developing comparable optical and SAR remote sensing inundation mapping with hydrodynamic modelling","display_title":"Inundation Mapping in the Fitzroy Catchment","doi":"https://doi.org/10.1080/01431161.2023.2211714","theme":"Floodplains","color":"#e9bd77","year":2023,"publication_date":"2023-05-03","authors":["Catherine Ticehurst","Fazlul Karim"],"summary":"The study compares flood inundation maps derived from multiple optical (Landsat, MODIS, Sentinel-2, VIIRS) and SAR (Sentinel-1, NovaSAR-1) remote sensing sensors with hydrodynamic modelling in the Fitzroy floodplain, Western Australia. Optical remote sensing showed better agreement with hydrodynamic model outputs, especially during peak floods, than SAR data, which struggled with flooded vegetation. Landsat, MODIS, and Sentinel-2 can be used interchangeably around peak flood times with reasonable confidence.","finding":"Optical remote sensing data (Landsat, MODIS, Sentinel-2) provide moderate to substantial agreement with hydrodynamic modelled flood extent in the Fitzroy floodplain, especially during peak flood events, while SAR sensors (Sentinel-1 and NovaSAR-1) show poorer agreement due to challenges detecting inundation among vegetation.","finding_evidence":{"passage_id":"p020-001","page":20,"quote":"Among the satellite data, the optical remote sensing data produced a moderate to substantial agreement when compared to the HD model results, with Landsat and MODIS performing the best with mean F1 scores of 0.65 and 0.61, respectively. In contrast, Sentinel-1 and NovaSAR1 SAR data produced relatively poor agreements with HD model results (mean F1 scores of 0.31 and 0.35 respectively) and the optical remote sensing data (mean F1 scores ranging from 0.34 to 0.46), particularly within scattered vegetation adjacent to the river channel, with better results obtained in open water on the floodplain and in the river channel. This study also showed that Landsat, MODIS, and Sentinel-2 all compare well to the HD model around the peak periods of a flood event with mean F1 scores of 0.80, 0.70 and 0.72, respectively.","verified_in_page":true},"topics":["hydrodynamic modelling","remote sensing","flood inundation mapping","synthetic aperture radar","optical satellite imagery","floodplain hydrology","flood monitoring","data comparison"],"study_period":null,"locations":[{"name":"Fitzroy floodplain, Western Australia","kind":"basin","role":"study basin for remote sensing and hydrodynamic model comparison","river_name":null,"geocode_query":"Fitzroy floodplain, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"This paper presents results from a study comparing near-coincident flood inundation maps derived from optical (Landsat, MODIS, Sentinel-2 and VIIRS) and Synthetic Aperture Radar (SAR) (Sentinel-1 and NovaSAR-1) remote sensing imagery. We also compare all inundation maps derived from remote sensing data with near-coincident hydrodynamic (HD) modelling. The study was conducted for the Fitzroy floodplain in Western Australia, a large, complex and remotely located river basin. The results show that optical remote sensing data have average F1 scores ranging from 0.57 to 0.65 when compared to the HD model results, with Landsat and MODIS performing best. Sentinel-1 and NovaSAR-1 SAR have a poor agreement (average F1 score of 0.31 and 0.35 respectively) when compared to the HD model results, particularly within scattered vegetation adjacent to the river channel, with better results in open water on the floodplain and in the river.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.568887,-18.134162],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102209614,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-0","true_coordinates":[125.568887,-18.194162],"spread_km":6.7},{"name":"Fitzroy River, Western Australia","kind":"river","role":"major river system within the study basin","river_name":"Fitzroy River","geocode_query":"Fitzroy River, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"sides of the river in its mid-to-lower reaches. The topography of the floodplain is complex, having several braided channels of varying width and depth along the main channel of the river. The Margaret River and Christmas Creek are two major tributaries that have significant effects on the floodplain flow regime and inundation. At Fitzroy Crossing, the river can swell to extend 15 km across the floodplain during floods. The floodplains of the Fitzroy catchment are underlain by alluvial aquifers, which are recharged during flood events (Karim et al. 2018). The Fitzroy catchment experiences a semi-arid climate with a mean annual rainfall of 550 mm. The rainfall is characterized by a highly distinctive wet season (November to April) and dry season (May to October). Rivers and creeks in the Fitzroy catchment are mostly ephemeral except the Fitzroy River downstream of Fitzroy Crossing where flow varies between perennial and intermittent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.138927,-18.421948],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":102217189,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-1","true_coordinates":[125.138927,-18.481948],"spread_km":6.7},{"name":"Margaret River, Western Australia","kind":"river","role":"major tributary affecting floodplain flow regime in study area","river_name":"Margaret River","geocode_query":"Margaret River, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"sides of the river in its mid-to-lower reaches. The topography of the floodplain is complex, having several braided channels of varying width and depth along the main channel of the river. The Margaret River and Christmas Creek are two major tributaries that have significant effects on the floodplain flow regime and inundation. At Fitzroy Crossing, the river can swell to extend 15 km across the floodplain during floods. The floodplains of the Fitzroy catchment are underlain by alluvial aquifers, which are recharged during flood events (Karim et al. 2018). The Fitzroy catchment experiences a semi-arid climate with a mean annual rainfall of 550 mm. The rainfall is characterized by a highly distinctive wet season (November to April) and dry season (May to October). Rivers and creeks in the Fitzroy catchment are mostly ephemeral except the Fitzroy River downstream of Fitzroy Crossing where flow varies between perennial and intermittent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.062967,-33.893547],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417189,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Margaret River WA 6285, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-2","true_coordinates":[115.0629667,-33.9535468],"spread_km":6.7},{"name":"Christmas Creek, Western Australia","kind":"river","role":"major tributary influencing floodplain flow and inundation","river_name":"Christmas Creek","geocode_query":"Christmas Creek, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"sides of the river in its mid-to-lower reaches. The topography of the floodplain is complex, having several braided channels of varying width and depth along the main channel of the river. The Margaret River and Christmas Creek are two major tributaries that have significant effects on the floodplain flow regime and inundation. At Fitzroy Crossing, the river can swell to extend 15 km across the floodplain during floods. The floodplains of the Fitzroy catchment are underlain by alluvial aquifers, which are recharged during flood events (Karim et al. 2018). The Fitzroy catchment experiences a semi-arid climate with a mean annual rainfall of 550 mm. The rainfall is characterized by a highly distinctive wet season (November to April) and dry season (May to October). Rivers and creeks in the Fitzroy catchment are mostly ephemeral except the Fitzroy River downstream of Fitzroy Crossing where flow varies between perennial and intermittent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.9179,-18.879009],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217184,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Christmas Creek, St George Ranges WA 6728, Australia","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-3"},{"name":"Looma, Western Australia","kind":"settlement","role":"gauging site used for hydrodynamic model calibration and flood monitoring","river_name":null,"geocode_query":"Looma, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Water levels at Fitzroy Barrage, Fitzroy Crossing, Looma and Noonkanbah gauging sites were used to calibrate model parameters (e.g. roughness coefficient and eddy viscosity). Streamflow data were used to identify relatively recent flood events in the Fitzroy floodplain that were suitable for HD modelling and captured by earth observation sensors. Based on the availability of satellite data, we have selected five floods to evaluate modelled inundation. The magnitude of the selected floods varies from 1 in 5 to 1 in 25 years exceedance probability (Table 2). Table 2. Characteristics of the recent flood events in the Fitzroy floodplain (ARI = annual recurrence interval). Start Date End Date Duration (days) Maximum Flow (m3 /s) Maximum Flow Date ARI 15/02/2001 14/03/2001 28 6080.83 21/02/2001 10 20/02/2002 7/03/2002 16 15456.89 24/02/2002 25 25/02/2011 29/03/2011 33 14138.57 16/03/2011 24 8/02/2017 22/02/2017 15 6662.36 17/02/2017 16 3/03/2020 8/03/2020 6 5076.32 4/03/2020 5 2916 C.","verified_in_page":true},"resolution_status":"resolved","coordinates":[124.1493803,-18.0398016],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217185,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Looma, Camballin WA 6728, Australia","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-4"},{"name":"Fitzroy Barrage, Western Australia","kind":"facility","role":"gauging site for model calibration and hydrodynamic boundary condition","river_name":null,"geocode_query":"Fitzroy Barrage, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Water levels at Fitzroy Barrage, Fitzroy Crossing, Looma and Noonkanbah gauging sites were used to calibrate model parameters (e.g. roughness coefficient and eddy viscosity). Streamflow data were used to identify relatively recent flood events in the Fitzroy floodplain that were suitable for HD modelling and captured by earth observation sensors. Based on the availability of satellite data, we have selected five floods to evaluate modelled inundation. The magnitude of the selected floods varies from 1 in 5 to 1 in 25 years exceedance probability (Table 2). Table 2. Characteristics of the recent flood events in the Fitzroy floodplain (ARI = annual recurrence interval). Start Date End Date Duration (days) Maximum Flow (m3 /s) Maximum Flow Date ARI 15/02/2001 14/03/2001 28 6080.83 21/02/2001 10 20/02/2002 7/03/2002 16 15456.89 24/02/2002 25 25/02/2011 29/03/2011 33 14138.57 16/03/2011 24 8/02/2017 22/02/2017 15 6662.36 17/02/2017 16 3/03/2020 8/03/2020 6 5076.32 4/03/2020 5 2916 C.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Noonkanbah, Western Australia","kind":"settlement","role":"reference location downstream area with high agreement of remote sensing and hydrodynamic model inundation","river_name":null,"geocode_query":"Noonkanbah, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-000","page":17,"quote":"The mean UA for the MODIS – Landsat and MODIS – Sentinel-2 image pairs are lower than the mean PA, indicating that MODIS is under-mapping water more than the other sensor (Table 6). The same result is also seen in the VIIRS – Sentinel-2 comparison. This is largely due to the different spatial resolution of the sensors and the fine spatial inundation extent of the receding flood water. The MODIS – VIIRS image pairs have similar mean UA (0.80) and mean PA (0.81) meaning they are mapping similar extents. Similar to the MODIS – SAR image pairs, the VIIRS – SAR image pairs have a low to moderate mean F1 score (lower than 0.48). All the image pairs containing SAR have a much lower mean PA than mean UA indicating SAR is under-mapping water extent compared to the other sensors. Figure 6 summarizes the spatial agreement of the MODIS water maps with the other remote sensing water maps (Landsat, SAR, Sentinel-2 and VIIRS) for the common dates.","verified_in_page":true},"resolution_status":"resolved","coordinates":[124.8256,-18.509939],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217187,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Noonkanbah, Mount Hardman WA 6765, Australia","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-7b5617bfd880b4585fccc80f-site-6"}],"limitations":["Some locations and floodplain areas have no explicit latitude and longitude provided, so coordinates are null.","The study basin area includes large complex floodplains, and the spatial resolution limits differ among sensors, influencing direct spatial comparisons.","SAR data acquisition was less frequent and sometimes not coincident with optical imagery acquisitions."],"river_ids":["ne-491"],"site_ids":["doc-7b5617bfd880b4585fccc80f-site-0","doc-7b5617bfd880b4585fccc80f-site-1","doc-7b5617bfd880b4585fccc80f-site-2","doc-7b5617bfd880b4585fccc80f-site-3","doc-7b5617bfd880b4585fccc80f-site-4","doc-7b5617bfd880b4585fccc80f-site-6"],"anchor":[124.8256,-18.509939],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040055960,5040059440],"main_basin_ids":[5040055960,5040059440]},{"id":"doc-8aafb002d7b45027e1281e5f","title":"Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model","display_title":"ML-Enhanced Prediction of Eucalypt Growth in Tasmania","doi":"https://doi.org/10.3389/ffgc.2023.1181049","theme":"Land & ecosystems","color":"#c8df98","year":2023,"publication_date":"2023-07-20","authors":["Peter Taylor","Auro C. Almeida","Ernst Kemmerer","Rafael Olivares de Salles Abreu"],"summary":"This study developed and demonstrated a machine learning (ML) approach combined with the 3-PG forest process-based model to predict growth of Eucalyptus nitens plantations over 63,000 ha in north-west Tasmania, Australia. ML models were trained using soil and terrain data to estimate soil fertility rating (FR), a critical parameter in 3-PG, allowing improved and spatially explicit predictions of plantation productivity, validated using permanent sample plots.","finding":"The ML-derived soil fertility rating (FR) spatial predictions integrated into the 3-PG model achieved high accuracy in predicting observed forest growth metrics such as tree diameter at breast height (R² = 0.97) and stand volume (R² = 0.96) across 161 permanent sample plots, demonstrating the utility of combining interpretable ML with process-based models for spatially explicit forest growth predictions.","finding_evidence":{"passage_id":"p001-004","page":1,"quote":"The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R 2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R 2 stand volume = 0.96, RMSE = 23.1 m3 ha−1 ) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas. KEYWORDS interpretable machine learning, forestry modeling, 3-PG model, Eucalyptus nitens, soil fertility rating, plantation growth and yield, SHapley Additive exPlanation (SHAP) Frontiers in Forests and Global Change 01 frontiersin.org","verified_in_page":true},"topics":["machine learning","forest growth modeling","soil fertility","eucalypt plantations","spatial prediction","3-PG model","soil and terrain data","interpretable machine learning"],"study_period":null,"locations":[{"name":"north-west Tasmania","kind":"region","role":"study area of eucalyptus nitens plantations and soil fertility mapping for forest growth modeling","river_name":null,"geocode_query":"north-west Tasmania, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Additionally, we show how interpretable machine learning libraries related to game theory, specifically the SHapley Additive exPlanations (SHAP) method (Lundberg and Lee, 2017), can be used to interpret these predictions. The proposed ML-based method for FR estimation has the advantage of being derivable from available soil data, can be used by plot scale (point) and/or spatial (grid) 3-PG, provides transparency of the terrain and soil attributes that are influencing the predictions, and can generate spatial FR maps that can assist interpretation of plantation productivity. 2. Materials and methods 2.1. Study area The study area covers over 63,000 ha of Eucalyptus nitens plantation in the north-west of Tasmania, Australia (Figure 1). The elevation ranges from approximately 200 to 750 m above sea level (m.a.s.l.), and the study area is predominantly on a basaltic plateau (Onfray et al., 2015).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tasmania","kind":"region","role":"regional context for soils, climate, and forest plantation study","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Additionally, we show how interpretable machine learning libraries related to game theory, specifically the SHapley Additive exPlanations (SHAP) method (Lundberg and Lee, 2017), can be used to interpret these predictions. The proposed ML-based method for FR estimation has the advantage of being derivable from available soil data, can be used by plot scale (point) and/or spatial (grid) 3-PG, provides transparency of the terrain and soil attributes that are influencing the predictions, and can generate spatial FR maps that can assist interpretation of plantation productivity. 2. Materials and methods 2.1. Study area The study area covers over 63,000 ha of Eucalyptus nitens plantation in the north-west of Tasmania, Australia (Figure 1). The elevation ranges from approximately 200 to 750 m above sea level (m.a.s.l.), and the study area is predominantly on a basaltic plateau (Onfray et al., 2015).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Surrey Hills, Northwest Tasmania","kind":"other","role":"historical region referenced as origin of industrial-scale eucalypt plantations in Tasmania","river_name":null,"geocode_query":"Surrey Hills, Northwest Tasmania, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p019-003","page":19,"quote":"Surrey Hills, Northwest Tasmania – the Birthplace of Industrial- Scale Eucalypt Plantations in Australia, 19. Paul, K. I., Booth, T. H., Jovanovic, T., Sands, P. J., and Morris, J. D. (2007). Calibration of the forest growth model 3-PG to eucalypt plantations growing in low rainfall regions of Australia. For. Ecol. Manag. 243, 237–247. doi: 10.1016/j.foreco.2007.03.029 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830. doi: 10.48550/arXiv.1201.0490 Pérez-Cruzado, C., Muñoz-Sáez, F., Basurco, F., Riesco, G., and RodríguezSoalleiro, R. (2011). Combining empirical models and the process-based model 3- PG to predict Eucalyptus nitens plantations growth in Spain. For. Ecol. Manag. 262, 1067–1077. doi: 10.1016/j.foreco.2011.05.045 Pinkard, E. A., and Neilsen, W. A. (2003).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Small sample size (161 plots) limiting robustness and generalizability of ML models","Method does not account for temporal variation in soil fertility, treating soil properties as static","Lack of direct soil moisture measurements for validating model water balance","Spatial scale limited to north-west Tasmania; performance and variable importance may vary at larger or other spatial scales","Certain uncertainties in chemical soil attribute layers due to spatial variability and measurement uncertainty"],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[],"main_basin_ids":[]},{"id":"doc-8b7ad931daabe42c4f0137c2","title":"Nationwide Subsidence in Bahrain Island: Drivers and Implications for Relative Sea-Level Rise","display_title":"Bahrain Island Subsidence and Sea-Level Impacts","doi":"https://doi.org/10.1109/jstars.2023.3286471","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-01-01","authors":["Jonathan C.L. Normand","Essam Heggy","Pascal Castellazzi"],"summary":"This study investigates a significant nationwide land subsidence over Bahrain's main island, specifically the 7×12 km central depression coinciding with the Awali oil field. Using satellites SAR interferometry data from 2007 to 2021, subsidence rates up to 20 mm/year were detected, strongly correlated with increased oil production and aquifer depletion. This subsidence amplifies relative sea-level rise and coastal retreat risks on the low-lying island. Shoreline analysis from 1985 to 2021 indicates mostly stable coastlines except a 5 m/year retreat observed on the southwestern coast during 2003-2014. The research highlights land subsidence as a critical factor alongside global sea-level rise in coastal vulnerability assessments for Bahrain and similar Gulf islands.","finding":"Bahrain's main island is experiencing a widespread subsidence of up to 20 mm/year over a 7×12 km area centered on the Awali oil field, primarily driven by oil extraction and aquifer depletion, substantially increasing relative sea-level rise and coastal shoreline retreat vulnerability beyond sea-level rise alone.","finding_evidence":{"passage_id":"p005-000","page":5,"quote":"5 1. SBAS-InSAR Using 94 C-band Sentinel-1A scenes acquired in the ascending orbit direction between 20160603 and 20191221, we produce 940 interferograms with a spatial resolution of 140 meters (Table I). To improve the reliability of our results, we mask areas of low interferometric coherence. Then, we integrate selected interferograms (Table II) with the Small BAseline Subset Algorithm (SBAS) [2] to retrieve LOS time series displacement velocity and its acceleration, as expressed in (1). We apply the same SBAS-InSAR methodology to 131 Sentinel-1A scenes acquired along a descending orbit direction during 20170321-20220130. D = K1 (t − t0 ) + 1 2 K2 (t − t0 ) 2 + 1 6 K3 (t − t0 ) 3 (1) Where D is the displacement at time t, K1 is the linear term, in mm/year, corresponding to the displacement velocity; K2 is the quadratic term, in mm/year2 , corresponding to the displacement acceleration; K3 is the cubic term, in mm/year3 , corresponding to the change in displacement acceleration.","verified_in_page":true},"topics":["land subsidence","remote sensing","oil extraction","groundwater depletion","sea level rise","coastal erosion","satellite interferometry","small island vulnerability"],"study_period":"2007-2021","locations":[{"name":"Bahrain main island","kind":"region","role":"primary study area with nationwide subsidence and coastal impact assessment","river_name":null,"geocode_query":"Bahrain main island, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Jonathan C.L. Normand, Essam Heggy, Member, IEEE and Pascal Castellazzi Abstract—The IPCC-AR6 report suggests that the sea-level in the Persian Gulf is expected to rise by 2.1-4.9mm/year by 2100, which is lower than the global projections of 4-14mm/year. However, a central nationwide ground subsidence in Bahrain's low-lying island can aggravate these figures. The island is only 14-kilometers-wide, yet we observe a 7×12-kilometer ground subsidence with vertical rates up to 20mm/year for 2016-2021. To characterize the subsidence, we use the following methods: (i) SBAS-InSAR with 94 and 131 C-band Sentinel1A orbital radar acquisitions in ascending and descending directions for 2016-2021, respectively, and (ii) Stacking-InSAR with eight L-band ALOS-1-PALSAR acquisitions for 2007-2010 and six ALOS-2- PALSAR-ScanSAR acquisitions for 2016-2021. Although the observed subsidence velocities are non-linear, we estimate a subsidence of up to 1.5-meters by 2100 by extrapolating the average rates.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Awali oil field","kind":"field_site","role":"center of observed subsidence related to oil extraction","river_name":null,"geocode_query":"Awali oil field, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The latter is crucial for understanding the potential upcoming changes in the Exclusive Economic Zone (EEZ) constituting maritime borders [44], affecting the sovereignty over oil and gas fields. Bahrain is a representative case study for all of the above. Fig. 1. Volume of crude oil produced in Bahrain, mainly from the Awali oil field, between January 2009 and December 2019 (open database) [45]. The production had increased by 47% between mid-2010 and mid-2011. III. STUDY AREA A. Geology and Oil production A salt anticline in the N-S direction (see Fig. 2) hosts the Awali onshore oilfield, which has been exploited since 1933 [46]. The base of the salt swell is ~5.5 kilometers deep, and the fold is 48 kilometers long and 18 kilometers wide, dipping 2° on the East flank and 4° on the West flank [46, 47]. The crest of the episodic This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Malkiya beach","kind":"landscape","role":"undisturbed shoreline transect with stable shoreline for 1985-2021","river_name":null,"geocode_query":"Malkiya beach, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 B. Shoreline evolution Fig. 9 shows the shoreline change at three locations in Bahrain: Malkiya beach (transect #6) and South-West beach #1 and #2 (transects #4 and #2). We compare our shoreline observations extracted with the COASTSAT toolkit for each transect with the ones from Luijendijk, et al. [73]. At Transect #2 of South-West beach #2, we observe a shoreline recession of 5m/year (±9m RMSE) for 2003-2014, compared to 3.5m/year (±8.5m RMSE) reported by Luijendijk, et al. [73]. However, linear trends could not be established for our other COASTSAT analyses and those conducted by Luijendijk, et al. [73] at South-West beach #1 and Malkiya beach due to an inadequate coefficient of determination (R2=0). Nevertheless, we still measure the data span for each transect by calculating the 90% central range R90c between the 5th and 95th percentiles. Our study and Luijendijk, et al. [73] show similar ranges of R90c for each transect.","verified_in_page":true},"resolution_status":"resolved","coordinates":[50.479608,26.090899],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Malkiya Beach, Malkiya, Bahrain","basin_id":2040073580,"main_basin_id":2040073580,"basin_region":"Europe and Middle East","basin_area_km2":99750.9,"hydrology_source":"hydrobasins","site_id":"doc-8b7ad931daabe42c4f0137c2-site-2"},{"name":"South-West beach #1 (transect #4)","kind":"landscape","role":"undisturbed shoreline transect with stable shoreline for 1985-2021","river_name":null,"geocode_query":"South-West beach, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 B. Shoreline evolution Fig. 9 shows the shoreline change at three locations in Bahrain: Malkiya beach (transect #6) and South-West beach #1 and #2 (transects #4 and #2). We compare our shoreline observations extracted with the COASTSAT toolkit for each transect with the ones from Luijendijk, et al. [73]. At Transect #2 of South-West beach #2, we observe a shoreline recession of 5m/year (±9m RMSE) for 2003-2014, compared to 3.5m/year (±8.5m RMSE) reported by Luijendijk, et al. [73]. However, linear trends could not be established for our other COASTSAT analyses and those conducted by Luijendijk, et al. [73] at South-West beach #1 and Malkiya beach due to an inadequate coefficient of determination (R2=0). Nevertheless, we still measure the data span for each transect by calculating the 90% central range R90c between the 5th and 95th percentiles. Our study and Luijendijk, et al. [73] show similar ranges of R90c for each transect.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"South-West beach #2 (transect #2)","kind":"landscape","role":"shoreline transect showing retreat up to 5m/year during 2003-2014","river_name":null,"geocode_query":"South-West beach, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 B. Shoreline evolution Fig. 9 shows the shoreline change at three locations in Bahrain: Malkiya beach (transect #6) and South-West beach #1 and #2 (transects #4 and #2). We compare our shoreline observations extracted with the COASTSAT toolkit for each transect with the ones from Luijendijk, et al. [73]. At Transect #2 of South-West beach #2, we observe a shoreline recession of 5m/year (±9m RMSE) for 2003-2014, compared to 3.5m/year (±8.5m RMSE) reported by Luijendijk, et al. [73]. However, linear trends could not be established for our other COASTSAT analyses and those conducted by Luijendijk, et al. [73] at South-West beach #1 and Malkiya beach due to an inadequate coefficient of determination (R2=0). Nevertheless, we still measure the data span for each transect by calculating the 90% central range R90c between the 5th and 95th percentiles. Our study and Luijendijk, et al. [73] show similar ranges of R90c for each transect.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Manama","kind":"settlement","role":"location of tide gauge measurements for calibrated sea-level rise","river_name":null,"geocode_query":"Manama, Bahrain","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"For Bahrain, we combine the tide gauge rate located in the city of Manama [38] with the corresponding VLM [38, 39] (see details in Appendix section IX.A) to obtain a corrected absolute SLR rate ranging from 2.77(±0.56) mm/year to 2.95(±0.55) mm/year. Assuming a steady sediment transport flow (i.e., absence of beach accretion or erosion), this corrected rate would result in a cumulative SLR of 21.3-22.7cm by the year 2100. Moreover, this absolute SLR would result in an average shoreline retreat of 85mm/year in present-day conditions, and 60-140 mm/year after 2100 under the RCPs scenari (see details in Appendix section IX.B). Al-Jeneid, et al. [29] also modeled the shoreline retreat in Bahrain, resulting from the predicted SLR in 2100, and mapped future potential coastal flooding areas; Hassan and Hassaan [40] performed the same analysis for the coast of Kuwait (NorthWest of the Persian Gulf).","verified_in_page":true},"resolution_status":"resolved","coordinates":[50.5875935,26.2235305],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Manama, Bahrain","basin_id":2040073580,"main_basin_id":2040073580,"basin_region":"Europe and Middle East","basin_area_km2":99750.9,"hydrology_source":"hydrobasins","site_id":"doc-8b7ad931daabe42c4f0137c2-site-5"}],"limitations":["InSAR ground deformation model is relative and not calibrated with GPS due to lack of publicly available GPS data over Bahrain","Medium confidence in SBAS-InSAR time series fit suggests possible non-linearity in subsidence rates and some measurement uncertainty","Presence of smoke and associated atmospheric effects introduce noise impacting InSAR accuracy near industrial sites","Radio Frequency Interference (RFI) affected some SAR acquisitions requiring processing to remove artifacts","Older SAR datasets have fewer interferograms limiting robustness of early-period subsidence detection","InSAR is challenging over northern Bahrain due to RFI and urban complexity, suggesting subsidence there may be underestimated","Uncertainty in isolating shoreline retreat causes due to interaction of sediment transport, reclamation, and relative sea-level rise effects"],"river_ids":[],"site_ids":["doc-8b7ad931daabe42c4f0137c2-site-2","doc-8b7ad931daabe42c4f0137c2-site-5"],"anchor":[50.5875935,26.2235305],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[2040073580],"main_basin_ids":[2040073580]},{"id":"doc-8c48ff401f499f24a8754fee","title":"Characterisation of meteorological drought at sub-catchment scale in Afghanistan using station-observed climate data","display_title":"Meteorological Drought in Afghanistan Sub-catchments","doi":"https://doi.org/10.1371/journal.pone.0280522","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-02-06","authors":["Yun Chen","David Penton","Fazlul Karim","Santosh Aryal","Shahriar Wahid","Peter Taylor","Susan M. Cuddy"],"summary":"This study characterises meteorological drought in Afghanistan at a detailed sub-catchment scale using the Standardized Precipitation Evapotranspiration Index (SPEI) derived from observed precipitation and temperature data from 1980 to 2017. It maps drought occurrence, frequency, duration, and severity across 189 sub-catchments, revealing significant spatiotemporal variability and identifying the 2000s as the driest decade, with the peak drought in the 2001 water year affecting most of the country severely.","finding":"Afghanistan experienced its worst recorded meteorological drought in 2001, with over 53% of sub-catchments suffering the worst drought in history and about 58% experiencing very frequent to extremely frequent drought months; the drought persisted from 1999 through 2002 and has since recurred with severe droughts in 2004, 2006, 2008, and 2011, with an increasing trend in drought duration and severity spreading from southeast to north and central regions.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"duration and associated severity (as an indicator of intensity) from both SPEI-1 (as a proxy of monthly drought) and SPEI-3 (as a proxy of seasonal drought). Drought intensity has a similar meaning to drought severity; as such only severity was assessed. Details of the drought characteristics that were estimated using SPEI to quantitatively describe the spatiotemporal variation of drought at sub-catchment scale in Afghanistan are listed in Table 2. Results and discussion Results Drought occurrence and frequency. The broad overview of temporal distribution and spatial extent of drought occurrence at monthly, seasonal and yearly scale is presented in Fig 4. During the period 1980–2017, the results of SPEI-1, SPEI-3 and SPEI-12 (Fig 4) show that the 2000s are the driest 10 years among the nearly four decades in this study with the most prolonged drought period running from 1999 to 2004 (highlighted in the frames).","verified_in_page":true},"topics":["meteorological drought","drought indices","standardized precipitation evapotranspiration index","climate variability","hydrology","spatial analysis","climate change impacts","water resources management"],"study_period":"1980-2017","locations":[{"name":"Afghanistan","kind":"region","role":"main study area and country under investigation for meteorological drought at sub-catchment scale","river_name":null,"geocode_query":"Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"investigate agricultural drought from 1979 to 2018 for four countries including Afghanistan. The system contains a set of drought indices (including SPI and SPEI) to produce seasonal outlook on potential drought hazards. However, some of the above-mentioned pioneer studies used out-of-date long-term historical data; some used gridded global datasets at a coarse resolution of 0.5 ̊ (or ~ 50km) [20, 36, 38]; and some were conducted only at river basin scale [36, 37]. A more detailed review of relevant applications in Afghanistan and its adjacent countries can be found from Qutbudin et al. [38]. More investigations on national scale meteorological drought specifically calibrated to Afghanistan using ground observations are needed, at a scale commensurate with the availability of the ground observation data. For this purpose, we proposed to adopt a sub-catchment conceptualisation as would be commonly used by the catchment modelling community.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Helmand Basin","kind":"basin","role":"major river basin within Afghanistan subdivided into sub-catchments studied for drought","river_name":"Helmand River","geocode_query":"Helmand Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are five major river basins, Helmand, Harirod-Murghab, Kabul, Northern and Panj Amu, with areas varying from 71,995 km2 (Northern Basin) to 327,660 km2 (Helmand Basin). They can be further divided into 41 catchments consisting of 189 sub-catchments with an average area PLOS ONE Station-observed characteristics of meteorological drought at sub-catchment scale in Afghanistan PLOS ONE | https://doi.org/10.1371/journal.pone.0280522 February 6, 2023 4 / 20","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Harirod-Murghab Basin","kind":"basin","role":"major river basin within Afghanistan subdivided into sub-catchments studied for drought","river_name":null,"geocode_query":"Harirod-Murghab Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are five major river basins, Helmand, Harirod-Murghab, Kabul, Northern and Panj Amu, with areas varying from 71,995 km2 (Northern Basin) to 327,660 km2 (Helmand Basin). They can be further divided into 41 catchments consisting of 189 sub-catchments with an average area PLOS ONE Station-observed characteristics of meteorological drought at sub-catchment scale in Afghanistan PLOS ONE | https://doi.org/10.1371/journal.pone.0280522 February 6, 2023 4 / 20","verified_in_page":true},"resolution_status":"resolved","coordinates":[63.3918347,35.4055703],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085500,"main_basin_id":2040085500,"basin_region":"Europe and Middle East","basin_area_km2":461141.1,"hydrology_source":"hydrobasins","site_id":"doc-8c48ff401f499f24a8754fee-site-2"},{"name":"Kabul Basin","kind":"basin","role":"major river basin within Afghanistan subdivided into sub-catchments studied for drought","river_name":"Kabul River","geocode_query":"Kabul Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are five major river basins, Helmand, Harirod-Murghab, Kabul, Northern and Panj Amu, with areas varying from 71,995 km2 (Northern Basin) to 327,660 km2 (Helmand Basin). They can be further divided into 41 catchments consisting of 189 sub-catchments with an average area PLOS ONE Station-observed characteristics of meteorological drought at sub-catchment scale in Afghanistan PLOS ONE | https://doi.org/10.1371/journal.pone.0280522 February 6, 2023 4 / 20","verified_in_page":true},"resolution_status":"resolved","coordinates":[69.207486,34.5553494],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040648310,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":92570.8,"hydrology_source":"hydrobasins","site_id":"doc-8c48ff401f499f24a8754fee-site-3"},{"name":"Northern Basin","kind":"basin","role":"major river basin within Afghanistan subdivided into sub-catchments studied for drought","river_name":null,"geocode_query":"Northern Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are five major river basins, Helmand, Harirod-Murghab, Kabul, Northern and Panj Amu, with areas varying from 71,995 km2 (Northern Basin) to 327,660 km2 (Helmand Basin). They can be further divided into 41 catchments consisting of 189 sub-catchments with an average area PLOS ONE Station-observed characteristics of meteorological drought at sub-catchment scale in Afghanistan PLOS ONE | https://doi.org/10.1371/journal.pone.0280522 February 6, 2023 4 / 20","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Panj Amu Basin","kind":"basin","role":"major river basin within Afghanistan subdivided into sub-catchments studied for drought","river_name":null,"geocode_query":"Panj Amu Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There are five major river basins, Helmand, Harirod-Murghab, Kabul, Northern and Panj Amu, with areas varying from 71,995 km2 (Northern Basin) to 327,660 km2 (Helmand Basin). They can be further divided into 41 catchments consisting of 189 sub-catchments with an average area PLOS ONE Station-observed characteristics of meteorological drought at sub-catchment scale in Afghanistan PLOS ONE | https://doi.org/10.1371/journal.pone.0280522 February 6, 2023 4 / 20","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.441246,37.086528],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040545690,"main_basin_id":4040050220,"basin_region":"Central and South-East Asia","basin_area_km2":121256.0,"hydrology_source":"hydrobasins","site_id":"doc-8c48ff401f499f24a8754fee-site-5","true_coordinates":[68.4412456,37.1465281],"spread_km":6.7},{"name":"Southeastern Afghanistan","kind":"region","role":"area identified as persistently severely drought impacted with increasing drought duration and severity","river_name":null,"geocode_query":"Southeastern Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"Meteorological drought has also been increasing as a consequence of reduced spring precipitation and increased evaporation rates across the sub-catchments in the north and central regions. The increase in frequency, duration and severity of drought events during the extensive drought periods between 1999 and 2002 are mostly driven by a significant decrease in precipitation in addition to the highest PET. Progressive reduction of rainfall could have accelerated the other droughts between 2004 and 2011. This is in agreement with previous studies in Afghanistan [36, 37, 50, 51]. The southern parts of the country have been strongly affected by climatic changes, especially in winter; and the northwest and the southwest of the country have been more affected by droughts due to alternations in rainfall and temperature [38].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Northern Afghanistan","kind":"region","role":"region observed to be severely drought impacted in 2011 and part of increasing drought trend","river_name":null,"geocode_query":"Northern Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-001","page":14,"quote":"The droughts characterised in the present study are also consistent with the findings of other studies conducted in nearby countries and regions such as India [52], South Asia [39], central Southwest Asia [53] and Asian Least Developed Countries [54]. The onset of the 2001 drought was in 1999 with a significant decrease in rainfall, reached its peak in 2001 due to the combination of extremely low rainfall and high temperature (or PET), and persisted until 2002 when heavy snow began falling. This resembles patterns in its neighbouring countries including Pakistan [55, 56] and Iran [57, 58]. As shown in Fig 8, the most recent drought in 2011, especially severe in northern and eastern Afghanistan, affected 182 sub-catchments accounting for 93% of the area of the country. Drought is known to have direct impacts on livelihoods and the economy and has already pushed 12 million people in 14 of the country’s 34 provinces into food insecurity and poverty [59].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Central Afghanistan","kind":"region","role":"region showing increasing drought duration and severity over study period","river_name":null,"geocode_query":"Central Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"Meteorological drought has also been increasing as a consequence of reduced spring precipitation and increased evaporation rates across the sub-catchments in the north and central regions. The increase in frequency, duration and severity of drought events during the extensive drought periods between 1999 and 2002 are mostly driven by a significant decrease in precipitation in addition to the highest PET. Progressive reduction of rainfall could have accelerated the other droughts between 2004 and 2011. This is in agreement with previous studies in Afghanistan [36, 37, 50, 51]. The southern parts of the country have been strongly affected by climatic changes, especially in winter; and the northwest and the southwest of the country have been more affected by droughts due to alternations in rainfall and temperature [38].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Study period ends in 2017, recent droughts post-2017 not included","Spatial density of meteorological stations varies, causing interpolation uncertainty especially in Helmand Basin","Snowfall and snowmelt were not considered, possible underestimation of hydrological drought impacts","Station data may have gaps and measurement errors despite correction attempts"],"river_ids":["ne-461"],"site_ids":["doc-8c48ff401f499f24a8754fee-site-2","doc-8c48ff401f499f24a8754fee-site-3","doc-8c48ff401f499f24a8754fee-site-5"],"anchor":[68.441246,37.086528],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040085500,4040545690,4040648310],"main_basin_ids":[2040085500,4040033640,4040050220]},{"id":"doc-99dbd4c083bb5a2c8840bef5","title":"Global assessment of spatiotemporal changes of frequency of terrestrial wind speed","display_title":"Global Changes in Terrestrial Wind Speed Frequency","doi":"https://doi.org/10.1088/1748-9326/acc9d5","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-04-01","authors":["Yanan Zhao","Shijing Liang","Yi Liu","Tim R. McVicar","César Azorín-Molina","Lihong Zhou","Robert Dunn","Sónia Jerez","Yingzuo Qin","Xinrong Yang","Jiayu Xu","Zhenzhong Zeng"],"summary":"This study analyzes global hourly near-surface wind speed data from 1511 meteorological stations over 1981–2021 to assess changes in wind speed frequency distributions and their impact on wind energy potential. The analysis reveals a decline in strong wind frequency contributing predominantly to the 'stilling' period (1981–2010) and an increase in light winds contributing to a reversal since 2011, though the rise in wind energy potential is modest compared to the wind speed recovery. Continental variations and implications for wind power generation are also detailed.","finding":"During 1981–2010, the decreasing frequency of strong surface wind speeds (≥5.0 m/s) contributed 220.37% to the decline in global mean wind speed (stilling effect). Following 2011, the increase in frequency of slight winds (0.1–2.9 m/s) contributed 64.07% to the reversal and increase in mean wind speed, while strengthened strong winds contributed 73.38% to the trend reversal in 2010. Despite the reversal, the increase in wind power potential was modest (2.67 TWh/yr) compared to the decline (−10.02 TWh/yr) during stilling.","finding_evidence":{"passage_id":"p002-003","page":2,"quote":"We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s−1 , the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s−1 < SWS < 2.9 m s−1 ) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s−1 ) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr−1 , p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr−1 , p < 0.05) during 2011–2021. 1. Introduction Wind energy is a key component of the energy market and a potential way for climate mitigation (IEA 2020).","verified_in_page":true},"topics":["wind-speed","wind-energy","climate-trends","frequency-distribution","surface-wind","power-generation","temporal-trends","spatial-analysis"],"study_period":"1981–2021","locations":[{"name":"Global","kind":"region","role":"study area","river_name":null,"geocode_query":"Global","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s−1 , the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s−1 < SWS < 2.9 m s−1 ) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s−1 ) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr−1 , p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr−1 , p < 0.05) during 2011–2021. 1. Introduction Wind energy is a key component of the energy market and a potential way for climate mitigation (IEA 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North America","kind":"region","role":"continental scale analysis region","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Wind speed changes We found that global SWS in the corrected HadISD showed a continuous decline during 1980–2010 with a rate of −0.08 m s−1 decade−1 (p < 0.001). The turning year was near 2010, and then SWS increased at a rate of 0.096 m s−1 decade−1 (p < 0.001, figure 1(a)). At the continental scale, the changes in SWS from 1980 to around 2010 varied and turning points marking the different SWS trends are found in most continents except for Africa. In North America, Europe and Asia, a turning point marking the declining trend transferring to an increasing trend can be identified (figures 1(b)–(d)). The declining SWS trends in America and Europe were close, with −0.118 m s−1 decade−1 and −0.112 m s−1 decade−1 (both p < 0.001), respectively. The slowing down of SWS was faster in Asia, with a trend of −0.136 m s−1 decade−1 (p < 0.001). Moreover, the turning point indicating the wind speed reversal occurred early in 2000 in Asia, for example, in southwestern China (Xiaomeiet al 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.306813,54.577923],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-99dbd4c083bb5a2c8840bef5-site-1","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Europe","kind":"region","role":"continental scale analysis region","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Wind speed changes We found that global SWS in the corrected HadISD showed a continuous decline during 1980–2010 with a rate of −0.08 m s−1 decade−1 (p < 0.001). The turning year was near 2010, and then SWS increased at a rate of 0.096 m s−1 decade−1 (p < 0.001, figure 1(a)). At the continental scale, the changes in SWS from 1980 to around 2010 varied and turning points marking the different SWS trends are found in most continents except for Africa. In North America, Europe and Asia, a turning point marking the declining trend transferring to an increasing trend can be identified (figures 1(b)–(d)). The declining SWS trends in America and Europe were close, with −0.118 m s−1 decade−1 and −0.112 m s−1 decade−1 (both p < 0.001), respectively. The slowing down of SWS was faster in Asia, with a trend of −0.136 m s−1 decade−1 (p < 0.001). Moreover, the turning point indicating the wind speed reversal occurred early in 2000 in Asia, for example, in southwestern China (Xiaomeiet al 2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Asia","kind":"region","role":"continental scale analysis region; early wind speed reversal observed in southwestern China","river_name":null,"geocode_query":"Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Wind speed changes We found that global SWS in the corrected HadISD showed a continuous decline during 1980–2010 with a rate of −0.08 m s−1 decade−1 (p < 0.001). The turning year was near 2010, and then SWS increased at a rate of 0.096 m s−1 decade−1 (p < 0.001, figure 1(a)). At the continental scale, the changes in SWS from 1980 to around 2010 varied and turning points marking the different SWS trends are found in most continents except for Africa. In North America, Europe and Asia, a turning point marking the declining trend transferring to an increasing trend can be identified (figures 1(b)–(d)). The declining SWS trends in America and Europe were close, with −0.118 m s−1 decade−1 and −0.112 m s−1 decade−1 (both p < 0.001), respectively. The slowing down of SWS was faster in Asia, with a trend of −0.136 m s−1 decade−1 (p < 0.001). Moreover, the turning point indicating the wind speed reversal occurred early in 2000 in Asia, for example, in southwestern China (Xiaomeiet al 2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"South America","kind":"region","role":"continental scale analysis region","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"While in Europe and North America, the turning points occurred later in 2012 and 2010, respectively. From 2011 to 2021, the reversed increasing trends of SWS in North America and Europe were not significant, being respectively 0.084 m s−1 decade−1 (p > 0.05), 0.056 m s−1 decade−1 (p > 0.05), while the trend in Asia is 0.091 m s−1 decade−1 (p < 0.001). The asymmetry between the SWS weakening trend and the SWS reversal trend is consistent with the previous studies (Wu et al 2018, Zeng et al 2019, Deng et al 2021, Liu et al 2022). Exploring the trends of SWS aids in the comprehensive analysis of SWS frequency variation. On the other hand, SWS in South America, Africa, and Australia did not show the first weakening and then reversing trend. The SWS in South America has a nonsignificant trend before 1990 (p > 0.05), then following a continuous increasing trend with a rate of 0.14 m s−1 decade−1 (p < 0.05) till 2021 (figure 1(e)).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Africa","kind":"region","role":"continental scale analysis region","river_name":null,"geocode_query":"Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"While in Europe and North America, the turning points occurred later in 2012 and 2010, respectively. From 2011 to 2021, the reversed increasing trends of SWS in North America and Europe were not significant, being respectively 0.084 m s−1 decade−1 (p > 0.05), 0.056 m s−1 decade−1 (p > 0.05), while the trend in Asia is 0.091 m s−1 decade−1 (p < 0.001). The asymmetry between the SWS weakening trend and the SWS reversal trend is consistent with the previous studies (Wu et al 2018, Zeng et al 2019, Deng et al 2021, Liu et al 2022). Exploring the trends of SWS aids in the comprehensive analysis of SWS frequency variation. On the other hand, SWS in South America, Africa, and Australia did not show the first weakening and then reversing trend. The SWS in South America has a nonsignificant trend before 1990 (p > 0.05), then following a continuous increasing trend with a rate of 0.14 m s−1 decade−1 (p < 0.05) till 2021 (figure 1(e)).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Australia","kind":"region","role":"continental scale analysis region","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"While in Europe and North America, the turning points occurred later in 2012 and 2010, respectively. From 2011 to 2021, the reversed increasing trends of SWS in North America and Europe were not significant, being respectively 0.084 m s−1 decade−1 (p > 0.05), 0.056 m s−1 decade−1 (p > 0.05), while the trend in Asia is 0.091 m s−1 decade−1 (p < 0.001). The asymmetry between the SWS weakening trend and the SWS reversal trend is consistent with the previous studies (Wu et al 2018, Zeng et al 2019, Deng et al 2021, Liu et al 2022). Exploring the trends of SWS aids in the comprehensive analysis of SWS frequency variation. On the other hand, SWS in South America, Africa, and Australia did not show the first weakening and then reversing trend. The SWS in South America has a nonsignificant trend before 1990 (p > 0.05), then following a continuous increasing trend with a rate of 0.14 m s−1 decade−1 (p < 0.05) till 2021 (figure 1(e)).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Southwestern China","kind":"region","role":"region where early turning point of wind speed reversal observed","river_name":null,"geocode_query":"Southwestern China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Wind speed changes We found that global SWS in the corrected HadISD showed a continuous decline during 1980–2010 with a rate of −0.08 m s−1 decade−1 (p < 0.001). The turning year was near 2010, and then SWS increased at a rate of 0.096 m s−1 decade−1 (p < 0.001, figure 1(a)). At the continental scale, the changes in SWS from 1980 to around 2010 varied and turning points marking the different SWS trends are found in most continents except for Africa. In North America, Europe and Asia, a turning point marking the declining trend transferring to an increasing trend can be identified (figures 1(b)–(d)). The declining SWS trends in America and Europe were close, with −0.118 m s−1 decade−1 and −0.112 m s−1 decade−1 (both p < 0.001), respectively. The slowing down of SWS was faster in Asia, with a trend of −0.136 m s−1 decade−1 (p < 0.001). Moreover, the turning point indicating the wind speed reversal occurred early in 2000 in Asia, for example, in southwestern China (Xiaomeiet al 2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Geographic coordinates for stations are not individually listed and the analysis is global and continental scale, limiting fine regional/local precision.","The study assumes a constant wind profile power-law exponent (alpha=1/7) globally, which may not capture local atmospheric stability/topographical effects.","Wind gusts and higher temporal resolution wind speed extremes are not included due to dataset limitations.","Wake effects and air density variations affecting turbine power curves are not included, so power estimates may overestimate actual generation.","Observational data quality and station distribution vary, with fewer stations and more uncertainty in the Southern Hemisphere."],"river_ids":[],"site_ids":["doc-99dbd4c083bb5a2c8840bef5-site-1"],"anchor":[-105.306813,54.577923],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[7040021740],"main_basin_ids":[7040021740]},{"id":"doc-ada39fa399a9d0ed0caaf684","title":"Evaluating and optimising performance of multi‐species call recognisers for ecoacoustic restoration monitoring","display_title":"Multi-Species Frog Call Recognition in MDB","doi":"https://doi.org/10.1002/ece3.10309","theme":"Land & ecosystems","color":"#c8df98","year":2023,"publication_date":"2023-08-01","authors":["Simon Linke","Daniella Teixeira","Katie Turlington"],"summary":"This study presents the development, optimization, and evaluation of multi-species call recognisers for eight frog species in the Koondrook-Pericoota wetland complex within the Murray–Darling Basin, Australia. The recognisers were constructed using a template-matching algorithm on a large annotated dataset collected between 2018 and 2020 to monitor ecosystem restoration effects, particularly responses to environmental watering. The approach focused on careful training data representativeness, amplitude, and score cut-offs to optimize detection performance. Most species' recognisers showed high accuracy, enabling automated acoustic monitoring as an effective ecological restoration assessment tool.","finding":"Call recognisers for most frog species in the Koondrook-Pericoota wetland complex performed well, with receiver operating characteristic (ROC) values generally above 0.7, allowing clear detection of ecological responses to environmental watering events while reducing false positives from ambient noises.","finding_evidence":{"passage_id":"p005-000","page":5,"quote":"LINKE et al. | 5 of 11 positive detection files were reviewed until the source categories of these false positive detections were considered consistent. 3 | RESULTS 3.1 | Recogniser performance evaluation Recognisers performed well for most species (Table 3). Performance was high (ROC > 0.8) for templates of L. dumerilii, L. fletcheri and L. peronii. Performance was also high for N. sudelli and L. raniformis but the sample sizes of their evaluation files were relatively small (Table 2). Performance was moderately high (ROC > 0.7) for most templates of C. signifera and C. parinsignifera. Conversely, performance was poor for L. tasmaniensis (ROC < 0.6), for which most templates showed low precision and moderate recall. The L. fletcherii recogniser comprised two templates from two sites. All templates performed well. The first template had very high precision with only two false positive detections in one sound file.","verified_in_page":true},"topics":["bioacoustics","species detection","ecological restoration","acoustic monitoring","receiver operating characteristic","frogs","template matching","ecosystem monitoring"],"study_period":"February 2018 to February 2020","locations":[{"name":"Koondrook-Pericoota wetland complex, Murray–Darling Basin, New South Wales, Australia","kind":"region","role":"Primary study area where acoustic data were collected and call recognisers were developed and evaluated for frog species detection to monitor ecosystem restoration effects","river_name":null,"geocode_query":"Koondrook-Pericoota wetland complex, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-007","page":2,"quote":"As a case study, we tested this protocol on the calls of eight sympatric frog species from the Koondrook-Pericoota wetland complex in the Murray–Darling Basin, Australia. 2 | METHODS 2.1 | Overview and computational strategy To build and evaluate the recognisers for eight target frog species, we used a large database of annotated calls from the study area in Koondrook-Perricoota (KP) forest—an extensive forest of river","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.510526,-31.078265],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-ada39fa399a9d0ed0caaf684-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Murray River near Deniliquin and Echuca, New South Wales, Australia","kind":"region","role":"Landscapes associated with study area, providing environmental context and reference for acoustic data collection sites","river_name":"Murray River","geocode_query":"Murray River near Deniliquin and Echuca, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"LINKE et al. | 3 of 11 red gums along the Murray river near the towns of Deniliquin and Echuca (Appendix 1). The Forestry Corporation of the Australian state of New South Wales—the body commissioning the study—had previously annotated 831 files from 20 sites and found varying levels of presence for the different species. From these files, we extracted between 100 and 200 reference calls per species, from all sites where the species was detected (Figure 1). Following manual inspections for call clarity and variation, we used these reference calls to build approximately 5–10 candidate recognisers per species (i.e. one template equals one recogniser per species). Some recognisers were based on templates of the same reference call but were created using different amplification settings, which is modifiable in monitoR.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.7511231,-36.1404431],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43676965,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Echuca VIC 3564, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ada39fa399a9d0ed0caaf684-site-1"},{"name":"Sites S and H, Koondrook-Pericoota Forest, New South Wales, Australia","kind":"field_site","role":"Specific sites within the study region from which frog call templates were selected and recognisers constructed","river_name":null,"geocode_query":"Koondrook-Pericoota Forest, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The C. parinsignifera recognisers were constructed from three templates, stemming from two sites. The template from the first site performed poorly, with an ROC value of 0.671. This template detected over 6700 calls in 58 sound files where the species was absent (low precision). The other templates were more precise and performed moderately well, with ROC values around 0.7–0.73. The L. tasmaniensis recognisers performed poorly, with ROC values of 0.562, 0.596 and 0.582. One template performed moderately well for survey recall (0.822) but had poor precision and a very high number of detections in sound files where the species was absent. The other templates performed worse. Two other species had limited validation data. The L. raniformis recogniser comprised three templates from site R. All performed highly, with two templates having no false positives (precision of 1.00), and one template having no false negatives (recall of 1.00).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Site R, Koondrook-Pericoota Forest, New South Wales, Australia","kind":"field_site","role":"Site providing data for Litoria raniformis call templates and evaluation where recogniser precision and recall were high","river_name":null,"geocode_query":"Koondrook-Pericoota Forest, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The C. parinsignifera recognisers were constructed from three templates, stemming from two sites. The template from the first site performed poorly, with an ROC value of 0.671. This template detected over 6700 calls in 58 sound files where the species was absent (low precision). The other templates were more precise and performed moderately well, with ROC values around 0.7–0.73. The L. tasmaniensis recognisers performed poorly, with ROC values of 0.562, 0.596 and 0.582. One template performed moderately well for survey recall (0.822) but had poor precision and a very high number of detections in sound files where the species was absent. The other templates performed worse. Two other species had limited validation data. The L. raniformis recogniser comprised three templates from site R. All performed highly, with two templates having no false positives (precision of 1.00), and one template having no false negatives (recall of 1.00).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Site M, Koondrook-Pericoota Forest, New South Wales, Australia","kind":"field_site","role":"Site providing data for Neobatrachus sudelli call templates and evaluation showing high recogniser performance","river_name":null,"geocode_query":"Koondrook-Pericoota Forest, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The C. parinsignifera recognisers were constructed from three templates, stemming from two sites. The template from the first site performed poorly, with an ROC value of 0.671. This template detected over 6700 calls in 58 sound files where the species was absent (low precision). The other templates were more precise and performed moderately well, with ROC values around 0.7–0.73. The L. tasmaniensis recognisers performed poorly, with ROC values of 0.562, 0.596 and 0.582. One template performed moderately well for survey recall (0.822) but had poor precision and a very high number of detections in sound files where the species was absent. The other templates performed worse. Two other species had limited validation data. The L. raniformis recogniser comprised three templates from site R. All performed highly, with two templates having no false positives (precision of 1.00), and one template having no false negatives (recall of 1.00).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Exact coordinates of study sites were not provided in the text.","Representativeness of call templates limited to sites within Koondrook-Pericoota complex, transferability to other areas was recommended for further research.","Performance metrics for some species based on small evaluation sample sizes (e.g., Litoria raniformis and Neobatrachus sudelli) and should be interpreted 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-3","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-b4f35c18bbf9dda7a71d91cc","title":"Spatial patterns of climate change and associated climate hazards in Northwest China","display_title":"Spatial Patterns of Climate Change in Northwest China","doi":"https://doi.org/10.1038/s41598-023-37349-w","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-06-27","authors":["Haojing Chi","Yanhong Wu","Hongxing Zheng","Bing Zhang","Zhonghua Sun","Jiaheng Yan","Yongkang Ren","Linan Guo"],"summary":"This study systematically investigates the spatial patterns of climate change and associated climate hazards in Northwest China (NWC) over the period 1979–2018 using high-resolution reanalysis climate data. It identifies warming and wetting trends in the region with significant spatial variability, classifies five climate change zones, and assesses changes in climate extremes such as heatwaves, cold waves, and rainstorms that affect the vulnerable ecosystems and socioeconomic systems.","finding":"Northwest China is undergoing a notable warming and wetting transition with an average temperature increase of about 0.49 °C per decade and a precipitation increase of 22.8 mm per decade. The largest area (41%) is under a warm-wetting climate change, primarily in the arid or semi-arid northwestern NWC, which is increasingly impacted by intensifying heatwaves and rainstorms.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"We find that NWC overall is under a warming and wetting transition in climate with change rate of temperature and precipitation around 0.49 °C/10a and 22.8 mm/10a respectively. Characteristics of climate change over the NWC however vary considerably in space. According to significance of long-term trends in both temperature and aridity index for each 0.1° × 0.1° grids, five types of climate change are identified across NWC, including warm-wetting, warm-drying, warm without wetting, wetting without warming and unchanging. The warm-wetting zone accounts for the largest proportion of the region (41%) and mainly locates in the arid or semi-arid northwestern NWC. Our findings show most region of NWC is under impacts of intensifying heatwave and rainstorm due to significant increases in high temperature extremes and precipitation extremes. The warming but without wetting zone is found under a more severe impact of heatwave, particularly for areas near northern Mount.","verified_in_page":true},"topics":["regional climate variability","climate extremes","temperature trends","precipitation trends","climate hazards","heatwaves","rainstorms","aridity index"],"study_period":"1979-2018","locations":[{"name":"Northwest China","kind":"region","role":"study area encompassing the climate change spatial pattern and hazard assessment","river_name":null,"geocode_query":"Northwest China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"This research bases on the gridded climate reanalysis dataset in NWC, focusing on the period from 1979 to 2018. This research is expected to further extend and expand the understanding of climate change and the related climate hazards in NWC, which could contribute to developing site-specific climate adaptation strategies in mitigating the impacts of climatic hazards. Study area and data Study area. Northwest China (NWC) locates at the hinterland of Eurasia and the northeastern Tibetan Plateau (73° 15ʹ –111° 15ʹ E, 31° 32ʹ–49° 10ʹ N) with total area of 3.1 million km2 . Approximately 34% areas of NWC lies at an altitude above 3000 m, declining from northwest to southeast. Geomorphologically, the region mainly consists of four mountain ranges (i.e., the Mount. Altai in the north, the Mount. Tianshan in the west, the Mount. Kunlun in the south, and the Mount. Qilian in the middle) and three basins (i.e., the Junggar Basin, Tarim Basin, and Tsaidam Basin) between the mountain ranges.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Junggar Basin","kind":"basin","role":"site of highest temperature increase and notable climate change intensification","river_name":null,"geocode_query":"Junggar Basin, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The most pronounced increase is found at the Junggar Basin and Turpan Basin, which has an increasing rate in temperature higher than 0.6 °C/10a. Largely consistent with the trends in temperature, annual potential evapotranspiration increased over most of NWC at a rate around 20.2 mm/10a. Relatively smaller portion of NWC (e.g., the Tarim Basin and that near the Mount. Altai) is found having a decreased PET. This is because the contributions of temperature to changes in PET could be offset by changes in other climate variables54. Annual precipitation averaged across NWC increases at a rate of around 22.8 mm/10a for the period 1979–2018. The trends in precipitation however show much stronger spatial difference as compared against that of temperature. The arid or semi-arid northwestern NWC (mean annual P below 500 mm) is with an increasing trend in precipitation at a rate around 23.7 mm/10a. Qinghai, Mount. Kunlun and Mount.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Turpan Basin","kind":"basin","role":"site with high warming trend and pronounced changes in temperature extremes","river_name":null,"geocode_query":"Turpan Basin, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"The most pronounced increase is found at the Junggar Basin and Turpan Basin, which has an increasing rate in temperature higher than 0.6 °C/10a. Largely consistent with the trends in temperature, annual potential evapotranspiration increased over most of NWC at a rate around 20.2 mm/10a. Relatively smaller portion of NWC (e.g., the Tarim Basin and that near the Mount. Altai) is found having a decreased PET. This is because the contributions of temperature to changes in PET could be offset by changes in other climate variables54. Annual precipitation averaged across NWC increases at a rate of around 22.8 mm/10a for the period 1979–2018. The trends in precipitation however show much stronger spatial difference as compared against that of temperature. The arid or semi-arid northwestern NWC (mean annual P below 500 mm) is with an increasing trend in precipitation at a rate around 23.7 mm/10a. Qinghai, Mount. Kunlun and Mount.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.1895399,42.9512999],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040050410,"main_basin_id":4040050410,"basin_region":"Central and South-East Asia","basin_area_km2":144280.6,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-2"},{"name":"Tarim Basin","kind":"basin","role":"area showing wetting trend and pronounced increases in temperature extremes and precipitation extremes","river_name":null,"geocode_query":"Tarim Basin, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Figure 4 shows the long-term trends detected by the MK approach for the four extreme temperature indices (i.e., T1Dmax, HD30, T1Dmin and FD0) derived from the gridded reanalysis dataset for the period 1979 to 2018. The intensity and frequency of extremely high temperature demonstrate significant increase trends in most areas. The increasing rate of T1Dmax and HD30 are 0.5 °C/10a and 4.7 days/10a respectively. Meanwhile, under a warming climate, T1Dmin increases as well but in a lower rate (0.2 °C/10a) than that of T1Dmax and is found within smaller spatial domain (64.8%), along with shortening frost days (−3.9 days/10a). It is noteworthy that the intensity of both extremely high and low temperatures increases in the Tarim Basin, and the extreme nature of climate are more pronounced in this region. Changes in the temperature extremes vary across the NWC and among the zones with different characteristics of climate change (Table 4).","verified_in_page":true},"resolution_status":"resolved","coordinates":[82.262558,37.496713],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040416430,"main_basin_id":4040050210,"basin_region":"Central and South-East Asia","basin_area_km2":213833.2,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-3"},{"name":"Mount Altai","kind":"landscape","role":"mountain range experiencing significant wetting trend and cold wave hazard","river_name":null,"geocode_query":"Mount Altai, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 Vol:.(1234567890) Scientific Reports | (2023) 13:10418 | https://doi.org/10.1038/s41598-023-37349-w www.nature.com/scientificreports/ together with a declining trend (Fig. 5b). The location with high severity of cold wave mainly locates at mountain area (e.g., Mount. Altai, the Mount. Tianshan). Climate hazards related to changes in precipitation extremes. The long-term trends in precipitation extremes (i.e., P1dmax and P5dmax) for the period 1979 to 2018 are shown in Fig. 6. There are general upward trends in both two precipitation extremes for most areas of NWC (86.5%). The regionwide mean change rates of P1dmax and P5dmax are around 0.5 mm/10a and 0.7 mm/10a respectively. The increases of precipitation intensity are most notably in mountain ranges (e.g., Mount. Altai, Mount. Kunlun and Mount. Tianshan), where the increase rate in annual precipitation could be greater than 2 mm/10a.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.14023,47.84563989999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Altay Prefecture, Xinjiang, China","basin_id":3040496490,"main_basin_id":3040001840,"basin_region":"Siberia","basin_area_km2":696392.9,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-4"},{"name":"Mount Kunlun","kind":"landscape","role":"mountain range with strong wetting trends and increased precipitation extremes","river_name":null,"geocode_query":"Mount Kunlun, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The spatial difference of trends in the precipitation extremes are summarized in Table 5 for each climate change zones. The results show that precipitation extremes (P1dmax and P5dmax) of the wetting zones (WW and NW) increase at higher rates than other zones. The increasing rate of P1dmax and P5dmax is the highest in the NW (wetting without warming) zone, reaching to 0.77 mm/10a and 1.25 mm/10a respectively. Meanwhile, the drying zone (WD) is experiencing weakening precipitation extremes. For climate hazard related to rainstorm, Fig. 7 shows that most of NWC is under low or moderate severity of impact as assessed by its long-term mean of P1dmax. The area with high hazard severity mainly locates in southern Mount. Qinling. This is mainly because that a larger proportion of NWC belongs to arid or semi-arid climate zone and is with low precipitation (Figs. 1, 3). However, the hazard severity is intensifying broadly due to the increase in the precipitation extremes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[87.8147493,47.3327833],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kun Lun Jie, Xin Jiang Wei Wu Er Zi Zhi Qu, China","basin_id":3040496490,"main_basin_id":3040001840,"basin_region":"Siberia","basin_area_km2":696392.9,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-5"},{"name":"Mount Tianshan","kind":"landscape","role":"mountain range with prominent increases in precipitation extremes and ecological significance","river_name":null,"geocode_query":"Mount Tianshan, Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The spatial difference of trends in the precipitation extremes are summarized in Table 5 for each climate change zones. The results show that precipitation extremes (P1dmax and P5dmax) of the wetting zones (WW and NW) increase at higher rates than other zones. The increasing rate of P1dmax and P5dmax is the highest in the NW (wetting without warming) zone, reaching to 0.77 mm/10a and 1.25 mm/10a respectively. Meanwhile, the drying zone (WD) is experiencing weakening precipitation extremes. For climate hazard related to rainstorm, Fig. 7 shows that most of NWC is under low or moderate severity of impact as assessed by its long-term mean of P1dmax. The area with high hazard severity mainly locates in southern Mount. Qinling. This is mainly because that a larger proportion of NWC belongs to arid or semi-arid climate zone and is with low precipitation (Figs. 1, 3). However, the hazard severity is intensifying broadly due to the increase in the precipitation extremes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[80,42],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tien Shan, Wensu County, Aksu Prefecture, China, 843102","basin_id":4040438030,"main_basin_id":4040050210,"basin_region":"Central and South-East Asia","basin_area_km2":98577.8,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-6"},{"name":"Mount Qilian","kind":"landscape","role":"mountain range in central NWC between basins related to climate variability analysis","river_name":null,"geocode_query":"Mount Qilian, Qinghai, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"This research bases on the gridded climate reanalysis dataset in NWC, focusing on the period from 1979 to 2018. This research is expected to further extend and expand the understanding of climate change and the related climate hazards in NWC, which could contribute to developing site-specific climate adaptation strategies in mitigating the impacts of climatic hazards. Study area and data Study area. Northwest China (NWC) locates at the hinterland of Eurasia and the northeastern Tibetan Plateau (73° 15ʹ –111° 15ʹ E, 31° 32ʹ–49° 10ʹ N) with total area of 3.1 million km2 . Approximately 34% areas of NWC lies at an altitude above 3000 m, declining from northwest to southeast. Geomorphologically, the region mainly consists of four mountain ranges (i.e., the Mount. Altai in the north, the Mount. Tianshan in the west, the Mount. Kunlun in the south, and the Mount. Qilian in the middle) and three basins (i.e., the Junggar Basin, Tarim Basin, and Tsaidam Basin) between the mountain ranges.","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.25305,38.1770899],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qilian County, Haibei, Qinghai, China","basin_id":4040050320,"main_basin_id":4040050320,"basin_region":"Central and South-East Asia","basin_area_km2":71257.7,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-7"},{"name":"Northern Loess Plateau","kind":"landscape","role":"area near warming without wetting zone with severe heatwave impacts","river_name":null,"geocode_query":"Northern Loess Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"We find that NWC overall is under a warming and wetting transition in climate with change rate of temperature and precipitation around 0.49 °C/10a and 22.8 mm/10a respectively. Characteristics of climate change over the NWC however vary considerably in space. According to significance of long-term trends in both temperature and aridity index for each 0.1° × 0.1° grids, five types of climate change are identified across NWC, including warm-wetting, warm-drying, warm without wetting, wetting without warming and unchanging. The warm-wetting zone accounts for the largest proportion of the region (41%) and mainly locates in the arid or semi-arid northwestern NWC. Our findings show most region of NWC is under impacts of intensifying heatwave and rainstorm due to significant increases in high temperature extremes and precipitation extremes. The warming but without wetting zone is found under a more severe impact of heatwave, particularly for areas near northern Mount.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Northern Mount Qinling","kind":"landscape","role":"zone with severe heatwave impacts and warming trends","river_name":null,"geocode_query":"Northern Mount Qinling, Shaanxi, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"The warming regions (WW, WD and WN) largely are experiencing higher T1Dmax and T1Dmin, longer HD30 and shorter FD30. Changes rates of temperature extremes in the WW zone are generally lower than those of the WD zone (Table 4), which implicates cooling effects from wetting. The cooling effects of a wetting climate is more clearly shown in trends of temperature extremes in the NW zone, which contrastively is experiencing lower T1Dmax and T1Dmin, and shorter FD30 though with slightly longer HD30 (Table 4). A large proportion of NWC is exposed to the high severity of heatwave impacts (Fig. 5a). Heatwave occurred at least once every 5 years over most NWC. The Tarim Basin, Junggar Basin, Turpan Basin, northern Mount. A-erh-chin, northern Loess Plateau and northern Mount. Qinling are areas with more severe heatwave impacts. The severity of heatwave is found increasing for most of the high severity zone, except for a small portion in western Tarim Basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[108.783509,33.85144],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qinling, Chang'An, Xi'An, China, 710112","basin_id":4040748300,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":165305.9,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-9"},{"name":"Shaanxi Province","kind":"region","role":"eastern NWC province experiencing warming and drying trends and affected by heatwave and drought","river_name":null,"geocode_query":"Shaanxi, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Three types of climate hazards in NWC are investigated in this study, including heatwave, cold wave and rainstorm. The heatwave could trigger secondary hazards like drought in NWC, which profoundly affect socioeconomical development in the region. For example, drought accompanied with the persistent high temperature in June 2022 has affected 3227 km2 of farmland in Shaanxi Province, directly causing around $35 million economic losses. The severity of cold wave hazard in NWC though is found declining under a warming climate, the warmer winter may delay the fulfillment of chilling requirements and thus lead to later onset of spring phases62, which needs further exploration. In NWC, rainstorm could induce secondary natural disasters such as flash floods, landslides and mudslides due to low vegetation cover ratio and loose surface composition in the region.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Gansu Province","kind":"region","role":"eastern NWC province experiencing warming and drying trends","river_name":null,"geocode_query":"Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"The wetting trend in northwestern NWC however implies only a relative increase in water quantity but not a fundamental change of its dry nature. The eastern NWC, including Shaanxi and eastern Gansu, however is experiencing warming and drying change in climate as also noticed by other researchers29,57. It is important to note that though there is general warm-wetting trend in NWC, substantial climate variation is also observed. For example, in eastern NWC, warming trends have been evident since the early 1960s, Figure 6. Long-term changes in precipitation extremes across Northwest China. Table 5. Long-term trends in precipitation extremes for different climate change zones. Zones P1dmax(mm/10a) P5dmax(mm/10a) Warm-wetting (WW) 0.57 0.89 Warm-drying (WD) −0.11 −0.63 Warming without wetting (WN) 0.38 0.26 Wetting without warming (NW) 0.77 1.25 Unchanging (NN) 0.27 0.12 Entire NWC 0.51 0.67","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Xinjiang Uygur Autonomous Region","kind":"region","role":"northwestern NWC with extensive warm-wetting climate and main area of wetting trend","river_name":null,"geocode_query":"Xinjiang, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The area with increasing impacts of rainstorm accounts for 82.8% of NWC. Particularly, it is noted that eastern NWC is with moderate or high severity of rainstorm hazard. The intensifying impacts of rainstorm of in eastern NWC have been demonstrated by disasters observed in recent years55. Discussions Dominant characteristics of climate change in NWC. This study extends and expands upon the previous research on investigating climate change in NWC by using the gridded reanalysis dataset, which covers a larger spatial extent than the previous research that bases on limited ground-based observations. It is found that the warming trend is universal across NWC but the wetting trend limits to the arid or semi-arid area (with mean annual precipitation less than 400 mm) in northwestern NWC. Warm-wetting transition in climate is particularly notable in the arid or semi-arid area in northwestern NWC (e.g., Xinjiang), which is consistent with that reported by Shi et al.56.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yellow River basin","kind":"landscape","role":"population concentration area and socioeconomically important basin within NWC","river_name":"Yellow River","geocode_query":"Yellow River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"3 Vol.:(0123456789) Scientific Reports | (2023) 13:10418 | https://doi.org/10.1038/s41598-023-37349-w www.nature.com/scientificreports/ NWC administratively consists of six provinces including Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. This region is rich in land and mineral resources with population of 103.52 million. The population is mainly concentrated in the Yellow River basin, accounting for about 60% of that in NWC. The economy and population of NWC are growing rapidly. As of 2021, the gross domestic product (GDP) of NWC reached to $106.48 billion. Under a changing climate, the region is experiencing considerable negative impacts from natural disasters. Hydroclimatic hazards like heatwaves, cold waves, droughts and floods pose a great stress on regional socioeconomic system and ecosystem as well. Data. The dataset used herein in classifying the regional climate change and assessing the related climate hazards is the China Meteorological Forcing Dataset (CMFD).","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.535897,36.519334],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yellow River, Changqing District, Jinan, China, 251116","basin_id":4040007850,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":77127.5,"hydrology_source":"hydrobasins","site_id":"doc-b4f35c18bbf9dda7a71d91cc-site-13","true_coordinates":[116.610556,36.519334],"spread_km":6.7}],"limitations":["Data uncertainties exist due to limited meteorological station density affecting dataset quality in parts of southeastern Tarim Basin and southwestern NWC.","Only three climate hazards were assessed (heatwave, cold wave and rainstorm); other hazards like wildfire, sandstorm, and drought were not included, limiting the full picture of climate risk.","Criteria for defining climate hazards may need adaptation when applied beyond NWC due to regional climate differences.","Coordinates for locations are not provided or unambiguous, thus latitude and longitude are null."],"river_ids":[],"site_ids":["doc-b4f35c18bbf9dda7a71d91cc-site-2","doc-b4f35c18bbf9dda7a71d91cc-site-3","doc-b4f35c18bbf9dda7a71d91cc-site-4","doc-b4f35c18bbf9dda7a71d91cc-site-5","doc-b4f35c18bbf9dda7a71d91cc-site-6","doc-b4f35c18bbf9dda7a71d91cc-site-7","doc-b4f35c18bbf9dda7a71d91cc-site-9","doc-b4f35c18bbf9dda7a71d91cc-site-13"],"anchor":[100.25305,38.1770899],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[3040496490,4040007850,4040050320,4040050410,4040416430,4040438030,4040748300],"main_basin_ids":[3040001840,4040007850,4040009880,4040050210,4040050320,4040050410]},{"id":"doc-b85ca423f98a0f220ce46e93","title":"Applicability of Chronic Multiple Linear Regression Models for Predicting Zinc Toxicity in Australian and New Zealand Freshwaters","display_title":"Predicting Zinc Toxicity in Australian & NZ Waters","doi":"https://doi.org/10.1002/etc.5722","theme":"Water management","color":"#eeabac","year":2023,"publication_date":"2023-07-21","authors":["Jennifer L. Stauber","Jennifer Gadd","Gwilym A. V. Price","Anthony M. Evans","Aleicia Holland","Anathea Albert","Graeme E. Batley","Monique T. Binet","Lisa A. Golding","Christopher W. Hickey","Andrew J. Harford","Dianne F. Jolley","Darren J. Koppel","Kitty McKnight","Lucas G. Morais","Adam C. Ryan","Karen Thompson","Eric van Genderen","Rick A. van Dam","Michael St. J. Warne"],"summary":"This study evaluated the applicability of chronic bioavailability multiple linear regression models (MLRs) for predicting zinc toxicity in various uncontaminated natural freshwaters across Australia and New Zealand. Using water chemistry data including pH, hardness, and dissolved organic carbon, chronic toxicity tests were conducted with local species: the green alga Raphidocelis subcapitata and native cladocerans Ceriodaphnia cf. dubia (Australia) and Daphnia thomsoni (New Zealand). The study found that existing MLRs based on EC50 values more accurately predicted zinc toxicity within a factor of 2 for cladocerans across most waters, whereas EC10-based models had more variability. Algal toxicity predictions were more challenging, with some models underperforming. The results support using validated bioavailability models incorporating key modifiers to improve zinc water quality guideline derivation in Australia and New Zealand.","finding":"EC50 multiple linear regression models better predict chronic zinc toxicity to cladocerans in Australian and New Zealand freshwaters within a factor of 2 of observed values, supporting their use over EC10 models for bioavailability normalization in guideline development.","finding_evidence":{"passage_id":"p003-006","page":3,"quote":"All natural water samples were analyzed by Hill Laboratories (which is accredited by International Accreditation New Zealand) for pH, anions and cations, dissolved (0.45‐μm filtered) metals (arsenic, cadmium, chromium, copper, lead, nickel, zinc, aluminium, iron), dissolved and total nutrients, and DOC (as dissolved nonpurgeable organic carbon). Methods of analysis are given in the Supporting Information, Table S5, and are in most cases consistent with those used for Australian waters. 2616 Environmental Toxicology and Chemistry, 2023;42:2614–2629—Stauber et al. © 2023 The Authors wileyonlinelibrary.com/ETC 15528618, 2023, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/etc.5722, Wiley Online Library on [24/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License","verified_in_page":true},"topics":["metal bioavailability","zinc toxicity","chronic toxicity","multiple linear regression","freshwater ecology","water quality guidelines","species sensitivity","dissolved organic carbon"],"study_period":null,"locations":[{"name":"Woronora River","kind":"river","role":"Study water source for Australian natural waters in ecotoxicity testing","river_name":null,"geocode_query":"Woronora River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.00058,-33.990302],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054736,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Woronora River, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-0","true_coordinates":[151.000580183185,-34.05030152297743],"spread_km":6.7},{"name":"Jingellic Creek","kind":"river","role":"Study water source for Australian natural waters in ecotoxicity testing","river_name":null,"geocode_query":"Jingellic Creek, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.69000011000003,-35.93722218199997],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43668000,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-1"},{"name":"Ovens River","kind":"river","role":"Study water source covering variety of TMFs for Australian ecotoxicity testing","river_name":"Ovens River","geocode_query":"Ovens River, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.781601,-36.652358],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676905,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-2","true_coordinates":[146.85638899200012,-36.65235757999994],"spread_km":6.7},{"name":"Teatree Creek","kind":"river","role":"Study water source representing Australian natural waters with varied chemistry for toxicity tests","river_name":null,"geocode_query":"Teatree Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.9330922085406,-25.24167260097011],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12128386,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tea Tree Creek, Good Night QLD 4671, Australia","basin_id":5040069960,"main_basin_id":5040069960,"basin_region":"Australasia","basin_area_km2":33211.9,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-3"},{"name":"Waterpark Creek","kind":"river","role":"Australian natural water collected for zinc toxicity testing including low pH environment","river_name":null,"geocode_query":"Waterpark Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.7337826715548,-22.94204663719996],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12126888,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Water Park Creek, Queensland 4703, Australia","basin_id":5040068460,"main_basin_id":5040068460,"basin_region":"Australasia","basin_area_km2":23350.6,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-4"},{"name":"Limestone Creek","kind":"river","role":"Australian natural water selected for chronic zinc toxicity testing with variable water chemistry","river_name":null,"geocode_query":"Limestone Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.561068,-23.2843309],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12126637,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone Creek QLD 4701, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-5"},{"name":"Magela Creek","kind":"river","role":"Australian natural water for ecotoxicity testing covering range of TMFs for zinc toxicity assessments","river_name":null,"geocode_query":"Magela Creek, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Natural surface freshwaters were collected from eight unimpacted waterways (i.e., not affected by point sources or diffuse pollution from intensive agriculture or urban land use) across Australia, selected to cover a range of climatic, geographical, and state jurisdictions, as well as a range of TMFs. These waters included Woronora River and Jingellic Creek from New South Wales; Ovens River from Victoria; Teatree Creek, Waterpark Creek, and Limestone Creek from Queensland; Magela Creek from Northern Territory; and Blackwood River from Western Australia (Supporting Information, Figure S2). Co‐ordinates of the sample locations are given in the Supporting Information, Table S3. Approximately 25 L of water were collected from each site in 5‐L high‐density polyethylene containers that had been previously acid washed and rinsed in ultrapure water (18 MΩ.cm, Milli‐Q®; Millipore) and site water.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.765371,-12.53404],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102215402,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Magela Creek, Kakadu NT 0822, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-6","true_coordinates":[132.8185952,-12.5040405],"spread_km":6.7},{"name":"Blackwood River","kind":"river","role":"Australian water sampled for toxicity testing; high hardness sample excluded from some tests due to tolerance limits","river_name":"Blackwood River","geocode_query":"Blackwood River, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Physicochemical and chemical parameters in freshwaters collected for ecotoxicity testing Australian freshwater samples. The database was used to help select eight natural waters from unimpacted sites with a range of TMFs: from pH 5.6 to 7.5, hardness <1 (i.e., less than the limit of detection [LOD]) to 412 mg CaCO3/L, and DOC <0.5 (i.e.,less than the LOD) to 27 mg C/L, with Ca:Mg ratios of 0.23–1.5 (Supporting Information, Table S13). All waters had low concentrations of dissolved metals and nutrients relevant to Australian and New Zealand water quality guidelines (ANZG, 2018; Supporting Information, Table S12–S14). Physicochemical and chemical parameters for each natural water including concentrations of major ions, metals (total and dissolved), and nutrients at the time of collection are given in the Supporting Information, Tables S12−S15. Major ions and major metals (dissolved aluminum, iron, and manganese) varied substantially across sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[116.316742,-33.938002],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":70413243,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-b85ca423f98a0f220ce46e93-site-7"},{"name":"Okutua Stream","kind":"river","role":"New Zealand natural water with low pH, collected and mixed for toxicity testing representative of NZ waters","river_name":null,"geocode_query":"Okutua Stream, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-003","page":7,"quote":"The pH of Okutua Stream was 4.8, and the water had very low hardness (2.6 mg CaCO3/L, outside the range that would be acceptable for Daphnia survival and reproduction). The water was therefore mixed with the Waihou River water in a 1:1 ratio for ecotoxicity testing. All waters had relatively low concentrations of nutrients (e.g., nitrate <0.002−0.77 mg/L, dissolved reactive phosphorus less than 0.004−0.064 mg/L) and metals (<1 μg/L), with the exception of the filtered Okutua/Waihou River mixture, which contained 22 μg zinc/L, possibly because of contamination of the water sample during the filtration process due to the high acidity of the Okutua Stream water. The Okutua/Waihou mixture also had a relatively high dissolved aluminum concentration (0.22 mg/L), likely to be in the form of colloidal Al(OH)3.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Waihou River","kind":"river","role":"New Zealand river with moderate water chemistry, used for toxicity testing including water mixture with Okutua Stream","river_name":"Waihou River","geocode_query":"Waihou River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-003","page":7,"quote":"The pH of Okutua Stream was 4.8, and the water had very low hardness (2.6 mg CaCO3/L, outside the range that would be acceptable for Daphnia survival and reproduction). The water was therefore mixed with the Waihou River water in a 1:1 ratio for ecotoxicity testing. All waters had relatively low concentrations of nutrients (e.g., nitrate <0.002−0.77 mg/L, dissolved reactive phosphorus less than 0.004−0.064 mg/L) and metals (<1 μg/L), with the exception of the filtered Okutua/Waihou River mixture, which contained 22 μg zinc/L, possibly because of contamination of the water sample during the filtration process due to the high acidity of the Okutua Stream water. The Okutua/Waihou mixture also had a relatively high dissolved aluminum concentration (0.22 mg/L), likely to be in the form of colloidal Al(OH)3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[175.766058,-37.618153],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-b85ca423f98a0f220ce46e93-site-9"},{"name":"Hoteo River","kind":"river","role":"Representative New Zealand river sampled for chronic zinc toxicity testing","river_name":"Hoteo River","geocode_query":"Hoteo River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Although the natural waters differed somewhat in their water chemistries (Supporting Information, Table S15), there were no significant differences between the natural waters for the zinc EC10 or EC50 values for C. cf. dubia reproduction. Observed EC values for two waters only were compared with those predicted using several MLR models (reproduction) for C. cf. dubia and D. magna (Table 1), with correction for C. cf. dubia species‐specific sensitivity (y‐intercept). The C. dubia and D. magna EC10 MLRs (DeForest et al., 2023), as well as the Gadd and Hickey (2023) D. magna MLR, only predicted the toxicity of zinc (EC10) in the Woronora River sample within a factor of 2, but within a factor of 3 for both waters (Table 5). Only the CCME EC10 D. magna MLR predicted the toxicity of zinc within a factor of 2 of observed EC10 values for both waters. The DeForest et al. (2023) EC20 MLRs also predicted zinc toxicity within a factor of 2 for both waters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[174.516594510213,-36.35386397572678],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hoteo River, Auckland, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-b85ca423f98a0f220ce46e93-site-10"},{"name":"Clutha River","kind":"river","role":"New Zealand river with relatively low DOC but higher hardness used in toxicity assessment","river_name":"Clutha River","geocode_query":"Clutha River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Although the natural waters differed somewhat in their water chemistries (Supporting Information, Table S15), there were no significant differences between the natural waters for the zinc EC10 or EC50 values for C. cf. dubia reproduction. Observed EC values for two waters only were compared with those predicted using several MLR models (reproduction) for C. cf. dubia and D. magna (Table 1), with correction for C. cf. dubia species‐specific sensitivity (y‐intercept). The C. dubia and D. magna EC10 MLRs (DeForest et al., 2023), as well as the Gadd and Hickey (2023) D. magna MLR, only predicted the toxicity of zinc (EC10) in the Woronora River sample within a factor of 2, but within a factor of 3 for both waters (Table 5). Only the CCME EC10 D. magna MLR predicted the toxicity of zinc within a factor of 2 of observed EC10 values for both waters. The DeForest et al. (2023) EC20 MLRs also predicted zinc toxicity within a factor of 2 for both waters.","verified_in_page":true},"resolution_status":"resolved","coordinates":[169.355039,-45.312824],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040082270,"main_basin_id":5040082270,"basin_region":"Australasia","basin_area_km2":152497.7,"hydrology_source":"hydrobasins","site_id":"doc-b85ca423f98a0f220ce46e93-site-11"},{"name":"Mahurangi River","kind":"river","role":"New Zealand river with highest hardness among sites, sampled for toxicity testing","river_name":"Mahurangi River","geocode_query":"Mahurangi River, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Measured dissolved zinc concentrations in the waters were within 20% of nominal concentrations in the reference, Waihou, and Clutha River samples. However, zinc concentrations in the Okutua/Waihou water samples, and at the lowest zinc concentrations of the Hoteo River and Mahurangi River tests, were >20% higher than the nominal zinc concentrations (Supporting Information, Table S21). Therefoe, measured dissolved zinc concentrations were used in all data analyses. Zinc was toxic to R. subcapitata in all five zinc‐spiked natural waters tested, with EC10 and EC50 values ranging from 6.3 and 67 μg/L in the Waihou River water to 43 and 133 μg/L in the Okutua/Waihou Rivers water mix (Table 6).","verified_in_page":true},"resolution_status":"resolved","coordinates":[174.6609825867941,-36.4143376902222],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mahurangi River, Auckland, New Zealand","basin_id":5040085040,"main_basin_id":5040085040,"basin_region":"Australasia","basin_area_km2":116650.0,"hydrology_source":"hydrobasins","site_id":"doc-b85ca423f98a0f220ce46e93-site-12"}],"limitations":["Limited DOC data for some Australian regions such as Queensland, South Australia, and Tasmania","No acclimation of cladocerans in some tests may limit reproducibility and representativeness of reproduction endpoints","pH variability during tests in low buffering capacity waters (e.g., Clutha River) introduces uncertainty in predictions","Limited number of natural water samples (only 5 New Zealand and 8 Australian) restrict statistical power and site representativeness","No direct coordinates given for most sampling sites limiting spatial precision","DOC quality and composition differences complicate direct application of Northern Hemisphere models"],"river_ids":["43639557","43639567","43639570","43640241","43640344","43643199","43643227","43643345","43643771","43644030","43644470","43644997","43645007","43645094","43645429","43645755","43645787","43645803","43645925","43646976","43646999","43647042","43647060","43647075","43647242","43647646","43648328","43648338","43648350","43648354","43648403","43648454","43648456","43649191","43649379","43650481","43660233","43660234","43660236","43660242","43660251","43660252","43660256","ne-1129","ne-1397","ne-1405"],"site_ids":["doc-b85ca423f98a0f220ce46e93-site-0","doc-b85ca423f98a0f220ce46e93-site-1","doc-b85ca423f98a0f220ce46e93-site-2","doc-b85ca423f98a0f220ce46e93-site-3","doc-b85ca423f98a0f220ce46e93-site-4","doc-b85ca423f98a0f220ce46e93-site-5","doc-b85ca423f98a0f220ce46e93-site-6","doc-b85ca423f98a0f220ce46e93-site-7","doc-b85ca423f98a0f220ce46e93-site-9","doc-b85ca423f98a0f220ce46e93-site-10","doc-b85ca423f98a0f220ce46e93-site-11","doc-b85ca423f98a0f220ce46e93-site-12"],"anchor":[151.9330922085406,-25.24167260097011],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040055960,5040061670,5040068460,5040069590,5040069960,5040070710,5040082270,5040085040,5040591050],"main_basin_ids":[5040055960,5040061670,5040068460,5040069590,5040069960,5040070710,5040073410,5040082270,5040085040]},{"id":"doc-b8e45f30eeb78052c40762a8","title":"Spatiotemporal changes in temperature projections over Bangladesh using multi-model ensemble data","display_title":"Projected Temperature Rise in Bangladesh","doi":"https://doi.org/10.3389/fenvs.2022.1074974","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-01-06","authors":["H. M. Touhidul Islam","Mohammad Kamruzzaman","Shamsuddin Shahid","Mohammed Mainuddin","Edris Alam","Abu Reza Md. Towfiqul Islam","Jatish Chnadra Biswas","Md. Azharul Islam"],"summary":"This study investigates future monthly, seasonal, and annual minimum and maximum temperature changes over Bangladesh using multimodel ensemble data from 40 CMIP5 General Circulation Models under RCP4.5 and RCP8.5 emission scenarios. Employing the SimCLIM statistical downscaling model, projections indicate increased temperatures across all time scales, with northern and northwestern Bangladesh facing the highest rise, especially in winter, while the southeastern coastal region shows the least increase. Nighttime temperatures are projected to increase faster than daytime temperatures, leading to a decreased diurnal temperature range. These spatiotemporal projections aid long-term climate adaptation planning in Bangladesh.","finding":"Northern and northwestern Bangladesh will experience the greatest rise in both maximum and minimum temperatures, especially in winter months, while the southeastern coastal region will experience the least increase.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"The mean annual Tmax over Bangladesh would increase by 0.61°C and 1.75°C in the near future and 0.91°C and 3.85°C in the far future, while the mean annual Tmin would rise by 0.65°C and 1.85°C in the near future and 0.96°C and 4.07°C in the far future, for RCP4.5 and RCP8.5, respectively. The northern and northwestern parts of the country would experience the highest rise in Tmax and Tmin, which have traditionally been exposed to temperature extremes. In contrast, the southeastern coastal region would experience the least rise in temperature. A higher increase in Tmin than Tmax was detected for all timescales, signifying a future decrease in the diurnal temperature range (DTR). The highest increase in Tmax and Tmin will be in winter compared to other seasons for both the periods and RCPs. The spatial variability of Tmax and Tmin changes can be useful for the long-term planning of the country.","verified_in_page":true},"topics":["temperature projection","climate modeling","temperature extremes","climate variability","climate adaptation","regional climate change","statistical downscaling","multi-model ensemble"],"study_period":"1986–2100","locations":[{"name":"Bangladesh","kind":"region","role":"study area for climate projections","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"The main objectives of this research are i) to investigate the monthly, seasonal, and annual future Tmax and Tmin trends across Bangladesh for the near (2021–2060) and the far (2061–2100) periods based on the MME of 40 CMIP5 GCMs; ii) to examine the spatiotemporal variability and changes in future Tmax and Tmin over the country. The novelty of this study is that this is the first study using all available CMIP5 models to project temperature over Bangladesh. Furthermore, a pattern-scaling bias-correction technique based on the SimCLIM climate model has been adopted for the first time for temperature downscaling and projection in Bangladesh. The findings of this study will help design and develop targeted CC adaptation or mitigation strategies in Bangladesh. 2 Methods 2.1 Study area Bangladesh is a flat topographical country in Southeast Asia, consisting of low-lying alluvial plains in the deltas of Asia’s three largest rivers known as the Ganges-Brahmaputra-Meghna.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Rangpur, Bangladesh","kind":"settlement","role":"location with highest projected increase in Tmax and Tmin in northern Bangladesh","river_name":null,"geocode_query":"Rangpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"For MayJuly, the greater Tmax increase was in the western region (the greatest is in Rajshahi) and the lower in the northeastern region (the lowest was in Sylhet). The results indicate a higher Tmax rise in the near and lower in the far period for RCP4.5. In contrast, a higher Tmax trend was observed in the far future than near future for RCP8.5. Figure 5 presents the annual and seasonal change (SS) in Tmax for the near and far future periods at 30 stations across Bangladesh for RCP4.5 and RCP8.5. The highest annual Tmax increase was by 0.13°C/ decade in near and 0.03°C/decade in far futures for RCP4.5. The projected increases for RCP8.5 were 0.51° C/decade in the near and 0.66° C/decade in the far period. Among the seasons, winter exhibited the highest increase at a rate of 0.16° C/decade in near and 0.04° C/ decade in far futures for RCP4.5. For RCP8.5, increases were 0.62° C/ decade in the near and 0.80° C/decade in the far periods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.261086,25.809394],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rangpur, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-1","true_coordinates":[89.26108649999999,25.7493938],"spread_km":6.7},{"name":"Mymensingh, Bangladesh","kind":"settlement","role":"location with high projected temperature increase in northern Bangladesh","river_name":null,"geocode_query":"Mymensingh, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"For MayJuly, the greater Tmax increase was in the western region (the greatest is in Rajshahi) and the lower in the northeastern region (the lowest was in Sylhet). The results indicate a higher Tmax rise in the near and lower in the far period for RCP4.5. In contrast, a higher Tmax trend was observed in the far future than near future for RCP8.5. Figure 5 presents the annual and seasonal change (SS) in Tmax for the near and far future periods at 30 stations across Bangladesh for RCP4.5 and RCP8.5. The highest annual Tmax increase was by 0.13°C/ decade in near and 0.03°C/decade in far futures for RCP4.5. The projected increases for RCP8.5 were 0.51° C/decade in the near and 0.66° C/decade in the far period. Among the seasons, winter exhibited the highest increase at a rate of 0.16° C/decade in near and 0.04° C/ decade in far futures for RCP4.5. For RCP8.5, increases were 0.62° C/ decade in the near and 0.80° C/decade in the far periods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.360639,24.775953],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mymensingh, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-2","true_coordinates":[90.41785399999999,24.7459528],"spread_km":6.7},{"name":"Sylhet, Bangladesh","kind":"settlement","role":"location with significant projected temperature rise in northeastern Bangladesh","river_name":null,"geocode_query":"Sylhet, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"RCP8.5 are presented in Figure 6, 7, respectively. The MK test revealed a significant trend in Tmin at all 30 stations in both future periods and RCPs. SS revealed the highest Tmin rise in February at a rate of 0.03–0.17° C/decade for RCP4.5 and 0.48–0.85° C/decade for RCP8.5 in both futures. The lowest Tmin rise was projected in July and August for both RCPs. The highest rise was for January-April at Srimangal, Sylhet, and Mymensingh, in the northern or northeastern region, and for May-June at Ishwardi and Rajshahi stations, in the western region, FIGURE 3 Spatial distribution of monthly (Jan-Dec) Tmax (o C) trends (SS) for near (2021–2060) and far (2061–2100) futures periods over Bangladesh considering 30 meteorological stations for RCP4.5. Frontiers in Environmental Science 07 frontiersin.org Islam et al. 10.3389/fenvs.2022.1074974","verified_in_page":true},"resolution_status":"resolved","coordinates":[91.86000790000001,24.9047808],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sylhet, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-3"},{"name":"Rajshahi, Bangladesh","kind":"settlement","role":"location with high Tmax increase projections in western Bangladesh","river_name":null,"geocode_query":"Rajshahi, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"For MayJuly, the greater Tmax increase was in the western region (the greatest is in Rajshahi) and the lower in the northeastern region (the lowest was in Sylhet). The results indicate a higher Tmax rise in the near and lower in the far period for RCP4.5. In contrast, a higher Tmax trend was observed in the far future than near future for RCP8.5. Figure 5 presents the annual and seasonal change (SS) in Tmax for the near and far future periods at 30 stations across Bangladesh for RCP4.5 and RCP8.5. The highest annual Tmax increase was by 0.13°C/ decade in near and 0.03°C/decade in far futures for RCP4.5. The projected increases for RCP8.5 were 0.51° C/decade in the near and 0.66° C/decade in the far period. Among the seasons, winter exhibited the highest increase at a rate of 0.16° C/decade in near and 0.04° C/ decade in far futures for RCP4.5. For RCP8.5, increases were 0.62° C/ decade in the near and 0.80° C/decade in the far periods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.60036649999999,24.3746497],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rajshahi, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-4"},{"name":"Bogura, Bangladesh","kind":"settlement","role":"location showing highest annual Tmax increase in northern/northwestern Bangladesh","river_name":null,"geocode_query":"Bogura, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"For MayJuly, the greater Tmax increase was in the western region (the greatest is in Rajshahi) and the lower in the northeastern region (the lowest was in Sylhet). The results indicate a higher Tmax rise in the near and lower in the far period for RCP4.5. In contrast, a higher Tmax trend was observed in the far future than near future for RCP8.5. Figure 5 presents the annual and seasonal change (SS) in Tmax for the near and far future periods at 30 stations across Bangladesh for RCP4.5 and RCP8.5. The highest annual Tmax increase was by 0.13°C/ decade in near and 0.03°C/decade in far futures for RCP4.5. The projected increases for RCP8.5 were 0.51° C/decade in the near and 0.66° C/decade in the far period. Among the seasons, winter exhibited the highest increase at a rate of 0.16° C/decade in near and 0.04° C/ decade in far futures for RCP4.5. For RCP8.5, increases were 0.62° C/ decade in the near and 0.80° C/decade in the far periods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.3730275,24.8525537],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bogura, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-5"},{"name":"Ishwardi, Bangladesh","kind":"settlement","role":"location with significant Tmin increase projections in western Bangladesh","river_name":null,"geocode_query":"Ishwardi, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"RCP8.5 are presented in Figure 6, 7, respectively. The MK test revealed a significant trend in Tmin at all 30 stations in both future periods and RCPs. SS revealed the highest Tmin rise in February at a rate of 0.03–0.17° C/decade for RCP4.5 and 0.48–0.85° C/decade for RCP8.5 in both futures. The lowest Tmin rise was projected in July and August for both RCPs. The highest rise was for January-April at Srimangal, Sylhet, and Mymensingh, in the northern or northeastern region, and for May-June at Ishwardi and Rajshahi stations, in the western region, FIGURE 3 Spatial distribution of monthly (Jan-Dec) Tmax (o C) trends (SS) for near (2021–2060) and far (2061–2100) futures periods over Bangladesh considering 30 meteorological stations for RCP4.5. Frontiers in Environmental Science 07 frontiersin.org Islam et al. 10.3389/fenvs.2022.1074974","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.064321,24.183724],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ishwardi, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-6","true_coordinates":[89.06432149999999,24.123724],"spread_km":6.7},{"name":"Dinajpur, Bangladesh","kind":"settlement","role":"extreme northwestern region with lower Tmax increase projections","river_name":null,"geocode_query":"Dinajpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"The spatial distribution of projected annual and seasonal Tmax changes is exhibited in Figure 12. It shows the degree of the Tmax change varies greatly across Bangladesh for both futures and RCPs. A noticeable increase in annual Tmax was detected at the Rangpur, in the north, for RCP4.5. In contrast, the rising Tmax area was extended from the central, northern, and western regions except for Dinajpur (extreme northwestern part) for RCP8.5. An analogous to the annual spatial pattern was observed for the monsoon season for both periods and RCPs. The pre-monsoon season showed a higher increase at the Rajshahi, Rangpur, Bogura, Ishwardi, and Jessore stations in northwestern and western regions. Overall, the winter exhibited the highest Tmax increase for both periods and RCPs. A higher increase in winter Tmax was also observed over the whole of Bangladesh, except in the extreme northwest and some parts of the south and southeast.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.63317579999999,25.6279123],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dinajpur District, Bangladesh","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-7"},{"name":"Barind region, northwestern Bangladesh","kind":"region","role":"drought-prone zone with notable temperature increase and vulnerability","river_name":null,"geocode_query":"Barind region, northwestern Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p019-000","page":19,"quote":"The projected monsoon Tmax and Tmin showed a relatively lower increase than in other seasons. Similar to annual and winter, the pre-monsoon and post-monsoon season exhibits greater changes in Tmax and Tmin. Central and northwestern regions were predicted to have the most significant annual and seasonal temperature changes. The northwestern portion of Bangladesh, particularly the Barind region, is considered a drought-prone zone. The annual average rainfall over the region is about 1,400 and 1,550 mm (national average 2,200 mm), with an uneven distribution within the seasons (Shahid and Khairulmaini 2009). Due to the effects of FIGURE 14 Spatial distribution of projected change in annual and seasonal Tmin (°C) for near (2021–2060) and far (2061–2100) future periods in Bangladesh for RCP8.5. Frontiers in Environmental Science 19 frontiersin.org Islam et al. 10.3389/fenvs.2022.1074974","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Srimongal, Bangladesh","kind":"settlement","role":"location with high projected Tmin increase in northeastern Bangladesh during pre- and post-monsoon and winter","river_name":null,"geocode_query":"Srimongal, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"mean of 0.78°C (2.24° C) and 0.96°C–1.32°C (4.05°C–5.60°C) in far future, with a mean of 1.16° C (4.92°C) for RCP4.5 (RCP8.5). In contrast, a relatively less increase in Tmin was projected for June–August (Figures 10C,D; Table 2). Nevertheless, Tmin was projected to rise higher in the far future than near future for both RCPs. RCP4.5 projected an increase in annual Tmin over Bangladesh by 0.57–0.71° C (1.63° C–2.03°C), with an average of 0.65°C (1.85° C), in the near future, and by 0.85–1.06° C (3.59°C–4.46°C), with an average of 0.96°C (4.07°C), in the far future (RCP8.5) (Figures 11C,D; Table 2). Nevertheless, on a seasonal timescale, winter exhibited the highest increase in Tmin and monsoon revealed the lowest for both periods and RCPs. The projected incrase in winter Tmin was 0.64–0.86°C (1.83°C–2.46° C) with an average of 0.76°C (2.19°C) in the near period, and 0.95°C–1.28°C (4.02°C–5.41°C) with an average of 1.14°C (4.781°C) in the far future for RCP4.5 (RCP8.5).","verified_in_page":true},"resolution_status":"resolved","coordinates":[91.7314423,24.3094552],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sreemangal, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-9"},{"name":"Dhaka, Bangladesh","kind":"settlement","role":"location with higher winter Tmin increase in central Bangladesh","river_name":null,"geocode_query":"Dhaka, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"mean of 0.78°C (2.24° C) and 0.96°C–1.32°C (4.05°C–5.60°C) in far future, with a mean of 1.16° C (4.92°C) for RCP4.5 (RCP8.5). In contrast, a relatively less increase in Tmin was projected for June–August (Figures 10C,D; Table 2). Nevertheless, Tmin was projected to rise higher in the far future than near future for both RCPs. RCP4.5 projected an increase in annual Tmin over Bangladesh by 0.57–0.71° C (1.63° C–2.03°C), with an average of 0.65°C (1.85° C), in the near future, and by 0.85–1.06° C (3.59°C–4.46°C), with an average of 0.96°C (4.07°C), in the far future (RCP8.5) (Figures 11C,D; Table 2). Nevertheless, on a seasonal timescale, winter exhibited the highest increase in Tmin and monsoon revealed the lowest for both periods and RCPs. The projected incrase in winter Tmin was 0.64–0.86°C (1.83°C–2.46° C) with an average of 0.76°C (2.19°C) in the near period, and 0.95°C–1.28°C (4.02°C–5.41°C) with an average of 1.14°C (4.781°C) in the far future for RCP4.5 (RCP8.5).","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.415238,23.864093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dhaka, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-10","true_coordinates":[90.4152376,23.804093],"spread_km":6.7},{"name":"Faridpur, Bangladesh","kind":"settlement","role":"location with higher winter Tmin increase in central Bangladesh","river_name":null,"geocode_query":"Faridpur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"mean of 0.78°C (2.24° C) and 0.96°C–1.32°C (4.05°C–5.60°C) in far future, with a mean of 1.16° C (4.92°C) for RCP4.5 (RCP8.5). In contrast, a relatively less increase in Tmin was projected for June–August (Figures 10C,D; Table 2). Nevertheless, Tmin was projected to rise higher in the far future than near future for both RCPs. RCP4.5 projected an increase in annual Tmin over Bangladesh by 0.57–0.71° C (1.63° C–2.03°C), with an average of 0.65°C (1.85° C), in the near future, and by 0.85–1.06° C (3.59°C–4.46°C), with an average of 0.96°C (4.07°C), in the far future (RCP8.5) (Figures 11C,D; Table 2). Nevertheless, on a seasonal timescale, winter exhibited the highest increase in Tmin and monsoon revealed the lowest for both periods and RCPs. The projected incrase in winter Tmin was 0.64–0.86°C (1.83°C–2.46° C) with an average of 0.76°C (2.19°C) in the near period, and 0.95°C–1.28°C (4.02°C–5.41°C) with an average of 1.14°C (4.781°C) in the far future for RCP4.5 (RCP8.5).","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.8386531,23.6057352],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Faridpur, Bangladesh","basin_id":4040975150,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":4180.4,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-11"},{"name":"Madaripur, Bangladesh","kind":"settlement","role":"location with higher projected Tmin increase in winter in central Bangladesh","river_name":null,"geocode_query":"Madaripur, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"mean of 0.78°C (2.24° C) and 0.96°C–1.32°C (4.05°C–5.60°C) in far future, with a mean of 1.16° C (4.92°C) for RCP4.5 (RCP8.5). In contrast, a relatively less increase in Tmin was projected for June–August (Figures 10C,D; Table 2). Nevertheless, Tmin was projected to rise higher in the far future than near future for both RCPs. RCP4.5 projected an increase in annual Tmin over Bangladesh by 0.57–0.71° C (1.63° C–2.03°C), with an average of 0.65°C (1.85° C), in the near future, and by 0.85–1.06° C (3.59°C–4.46°C), with an average of 0.96°C (4.07°C), in the far future (RCP8.5) (Figures 11C,D; Table 2). Nevertheless, on a seasonal timescale, winter exhibited the highest increase in Tmin and monsoon revealed the lowest for both periods and RCPs. The projected incrase in winter Tmin was 0.64–0.86°C (1.83°C–2.46° C) with an average of 0.76°C (2.19°C) in the near period, and 0.95°C–1.28°C (4.02°C–5.41°C) with an average of 1.14°C (4.781°C) in the far future for RCP4.5 (RCP8.5).","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.1869644,23.2393346],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Madaripur District, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-b8e45f30eeb78052c40762a8-site-12"}],"limitations":["Use of CMIP5 models; CMIP6 models could improve projections","Some coarse resolution issues of GCMs addressed by downscaling but unresolved uncertainties remain","Lack of explicit detailed coordinates for stations restricts precise geolocation","Statistical downscaling method limitations such as bull's-eye effect in IDW interpolation"],"river_ids":[],"site_ids":["doc-b8e45f30eeb78052c40762a8-site-1","doc-b8e45f30eeb78052c40762a8-site-2","doc-b8e45f30eeb78052c40762a8-site-3","doc-b8e45f30eeb78052c40762a8-site-4","doc-b8e45f30eeb78052c40762a8-site-5","doc-b8e45f30eeb78052c40762a8-site-6","doc-b8e45f30eeb78052c40762a8-site-7","doc-b8e45f30eeb78052c40762a8-site-9","doc-b8e45f30eeb78052c40762a8-site-10","doc-b8e45f30eeb78052c40762a8-site-11","doc-b8e45f30eeb78052c40762a8-site-12"],"anchor":[90.360639,24.775953],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040960200,4040960480,4040975150,4040975160],"main_basin_ids":[4040025450,4040025460]},{"id":"doc-bae5fe8f46ea60c8f598572d","title":"State of the Climate in 2022","display_title":"Global Climate Trends and Extremes in 2022","doi":"https://doi.org/10.1175/2023bamsstateoftheclimate.1","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-09-01","authors":["J. Blunden","T. Boyer","E. Bartow-Gillies"],"summary":"The 2022 global climate was influenced by a \"triple dip\" La Niña event persisting in the tropical Pacific from 2020 through 2022, the first in the 21st century, causing widespread climate anomalies. Despite the cooling La Niña influence, 2022 was one of the six warmest years in the instrumental record, with significant temperature anomalies, especially in Europe and parts of Asia. Extreme heatwaves impacted several regions worldwide, while regional droughts and floods also caused substantial impacts. Global atmospheric greenhouse gases (CO2, CH4, N2O) continued to increase reaching new record highs, contributing strongly to radiative forcing. Arctic and Antarctic regions saw record atmospheric and oceanic changes, including unprecedented stratospheric water vapor injection from the Hunga Tonga volcano. Tropical cyclone activity was near or below average globally, with several destructive storms (Hurricanes Fiona and Ian in the North Atlantic). These findings summarize the state of essential climate variables and related extreme events for 2022.","finding":"A \"triple dip\" La Niña event between 2020 and 2022 notably influenced global climate patterns, with 2022 registering as one of the six warmest years on record despite the cooling influence of La Niña.","finding_evidence":{"passage_id":"p034-001","page":34,"quote":"Exceptional heatwaves occurred across the globe in 2022, boosted by above-average temperatures that continue their relentless long-term rise. In Europe, the “unweather”—an Old English term for weather so severe that it appears to come from a different climate or world—shattered records across the continent during the summer months, while rivers and reservoirs fell to critically low levels. Meanwhile China experienced its hottest summer on record and at Wuhan, the Yangtze River reached record-low values. The extreme high summer temperatures over Europe resulted in unprecedented melting of glaciers in the Alps, with over 6% of their volume lost in Switzerland this year alone, a record loss. Globally, 2022 was the 35th consecutive year of glacier mass loss and the 14th consecutive year of exceptional loss (more than 500 mm water equivalent).","verified_in_page":true},"topics":["temperature","greenhouse gases","radiative forcing","climate variability","extreme heat","drought","precipitation","tropical cyclones"],"study_period":null,"locations":[{"name":"Europe","kind":"region","role":"climate anomaly and record temperature area","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record. While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"New Zealand","kind":"region","role":"record high average temperature area and marine heatwave location","river_name":null,"geocode_query":"New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"In East Antarctica, a heatwave event led to a new all-time record-high temperature of −9.4°C—44°C above the March average—on 18 March at Dome C. This was followed by the collapse of the critically unstable Conger Ice Shelf. More than 100 daily low sea-ice extent and sea-ice area records were set in 2022, including two new all-time annual record lows in net sea-ice extent and area in February. Across the world’s oceans, global mean sea level was record high for the 11th consecutive year, reaching 101.2 mm above the 1993 average when satellite altimetry measurements began, an increase of 3.3±0.7 over 2021. Globally-averaged ocean heat content was also record high in 2022, while the global sea-surface temperature was the sixth highest on record, equal with 2018. Approximately 58% of the ocean surface experienced at least one marine heatwave in 2022. In the Bay of Plenty, New Zealand’s longest continuous marine heatwave was recorded.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Australia","kind":"region","role":"widespread warming and heat records; record temperature and drought areas","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record. While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"China","kind":"region","role":"record warm temperatures area; drought and flood impacts","river_name":null,"geocode_query":"China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record. While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sioux Station","kind":"station","role":"station with record temperature in Europe","river_name":null,"geocode_query":"France Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C for the first time ever. China experienced its second-warmest year and warmest summer on record. In the Southern Hemisphere, the average temperature across New Zealand reached a record high for the second year in a row. While Australia’s annual temperature was slightly below the 1991–2020 average, Onslow Airport in Western Australia reached 50.7°C on 13 January, equaling Australia's highest temperature on record. While fewer in number and locations than record-high temperatures, record cold was also observed during the year. Southern Africa had its coldest August on record, with minimum temperatures as much as 5°C below normal over Angola, western Zambia, and northern Namibia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"India","kind":"region","role":"monsoon rainfall area; extreme heat events in South Asia","river_name":null,"geocode_query":"India","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"This ongoing 2.5-year drought is the most extensive and persistent drought event in decades, and led to crop failure, millions of livestock deaths, water scarcity, and inflated prices for staple food items. In South Asia, Pakistan received around three times its normal volume of monsoon precipitation in August, with some regions receiving up to eight times their expected monthly totals. Resulting floods affected over 30 million people, caused over 1700 fatalities, led to major crop and property losses, and was recorded as one of the world’s costliest natural disasters of all time. Near Rio de Janeiro, Brazil, Petrópolis received 530 mm in 24 hours on 15 February, about 2.5 times the monthly February average, leading to the worst disaster in the city since 1931 with over 230 fatalities. On 14–15 January, the Hunga Tonga-Hunga Ha'apai submarine volcano in the South Pacific erupted multiple times.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Antarctica","kind":"region","role":"region with record snowfall and surface mass balance; volcanic stratospheric water vapor injection study location","river_name":null,"geocode_query":"Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"In the high northern latitudes, the annual surface-air temperature across the Arctic was the fifth highest in the 123-year record. The seasonal Arctic minimum sea-ice extent, typically reached in September, was the 11th-smallest in the 43-year record; however, the amount of multiyear ice—ice that survives at least one summer melt season—remaining in the Arctic continued to decline. Since 2012, the Arctic has been nearly devoid of ice more than four years old. In Antarctica, an unusually large amount of snow and ice fell over the continent in 2022 due to several landfalling atmospheric rivers, which contributed to the highest annual surface mass balance, 15% to 16% above the 1991–2020 normal, since the start of two reanalyses records dating to 1980. It was the second-warmest year on record for all five of the long-term staffed weather stations on the Antarctic Peninsula.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Arctic","kind":"region","role":"region of fifth highest annual air temperature on record; multiple climate observations including precipitation and sea ice","river_name":null,"geocode_query":"Arctic","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"In the high northern latitudes, the annual surface-air temperature across the Arctic was the fifth highest in the 123-year record. The seasonal Arctic minimum sea-ice extent, typically reached in September, was the 11th-smallest in the 43-year record; however, the amount of multiyear ice—ice that survives at least one summer melt season—remaining in the Arctic continued to decline. Since 2012, the Arctic has been nearly devoid of ice more than four years old. In Antarctica, an unusually large amount of snow and ice fell over the continent in 2022 due to several landfalling atmospheric rivers, which contributed to the highest annual surface mass balance, 15% to 16% above the 1991–2020 normal, since the start of two reanalyses records dating to 1980. It was the second-warmest year on record for all five of the long-term staffed weather stations on the Antarctic Peninsula.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-100.113952,76.310578],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arctic","basin_id":8040047650,"main_basin_id":8040047650,"basin_region":"North American Arctic","basin_area_km2":106895.7,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-7","true_coordinates":[-100.1139521,76.2505778],"spread_km":6.7},{"name":"Bavarian Alps around Oberstdorf, Germany","kind":"landscape","role":"climate impact and example of heat event","river_name":null,"geocode_query":"Bavarian Alps, Bavaria, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"September 2023 | State of the Climate in 2022 Si Cover Credit: Climate Change - Skiing and snowboarding during Winter Holidays in the Bavarian Alps around Oberstdorf. Oberstdorf, Germany Matthias Manuel / Alamy Stock Photo How to cite this document: Special Supplement to the Bulletin of the American Meteorological Society, Vol. 104, No. 9, September 2023 https://doi.org/10.1175/2023BAMSStateoftheClimate.1. Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2022 is based on contributions from scientists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instruments located on land, water, ice, and in space. Citing the complete report: Blunden, J., T. Boyer, and E. Bartow-Gillies, Eds., 2023: “State of the Climate in 2022”. Bull. Amer. Meteor. Soc., 104 (9), Si–S501 https://doi.org/10.1175/2023BAMSStateoftheClimate.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[10.9381827,47.5175925],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bavarian Alps, 82467 Garmisch-Partenkirchen, Germany","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-8"},{"name":"Mauna Loa Observatory, Hawaii, USA","kind":"facility","role":"long-term atmospheric CO2 monitoring site disrupted by volcanic eruption in 2022","river_name":null,"geocode_query":"Mauna Loa Observatory, Hawaii, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p020-000","page":20,"quote":"September 2023 | State of the Climate in 2022 1. Introduction S6 Another instance of volcanic activity, though not of the scale of HTHH, but with significant effects on the climate observing system, was the eruption of Mauna Loa in late November 2022. This eruption and subsequent lava flow shut down access and power to the NOAA Mauna Loa Observatory (featured on the cover of Chapter 8, Datasets), interrupting one of the longest time series for a variety of atmospheric variables, including atmospheric carbon dioxide (CO2 ) levels. After a 10-day interruption, NOAA’s CO2 measurements were transferred to the University of Hawaii’s Maunakea Observatories. The Mauna Loa CO2 time series is an invaluable monitor of the changes in our climate system (as detailed in Chapter 2). This serves as a reminder of the importance of long-term continuous time series in our understanding of Earth’s climate system and the importance of continuing such time series.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-155.5764306,19.5363065],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"44-2441 Mauna Loa Access Road, Waimea, HI 96743, USA","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-9"},{"name":"Bay of Plenty, New Zealand","kind":"region","role":"location of longest continuous marine heatwave on record","river_name":null,"geocode_query":"Bay of Plenty, Bay of Plenty Region, New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-005","page":4,"quote":"In East Antarctica, a heatwave event led to a new all-time record-high temperature of −9.4°C—44°C above the March average—on 18 March at Dome C. This was followed by the collapse of the critically unstable Conger Ice Shelf. More than 100 daily low sea-ice extent and sea-ice area records were set in 2022, including two new all-time annual record lows in net sea-ice extent and area in February. Across the world’s oceans, global mean sea level was record high for the 11th consecutive year, reaching 101.2 mm above the 1993 average when satellite altimetry measurements began, an increase of 3.3±0.7 over 2021. Globally-averaged ocean heat content was also record high in 2022, while the global sea-surface temperature was the sixth highest on record, equal with 2018. Approximately 58% of the ocean surface experienced at least one marine heatwave in 2022. In the Bay of Plenty, New Zealand’s longest continuous marine heatwave was recorded.","verified_in_page":true},"resolution_status":"resolved","coordinates":[176.7416374,-37.4233917],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bay Of Plenty","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-10"},{"name":"Eastern Pacific Ocean","kind":"region","role":"location of the triple dip La Niña SST anomaly","river_name":null,"geocode_query":"Eastern Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-001","page":34,"quote":"Exceptional heatwaves occurred across the globe in 2022, boosted by above-average temperatures that continue their relentless long-term rise. In Europe, the “unweather”—an Old English term for weather so severe that it appears to come from a different climate or world—shattered records across the continent during the summer months, while rivers and reservoirs fell to critically low levels. Meanwhile China experienced its hottest summer on record and at Wuhan, the Yangtze River reached record-low values. The extreme high summer temperatures over Europe resulted in unprecedented melting of glaciers in the Alps, with over 6% of their volume lost in Switzerland this year alone, a record loss. Globally, 2022 was the 35th consecutive year of glacier mass loss and the 14th consecutive year of exceptional loss (more than 500 mm water equivalent).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.508523,-8.723195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-11","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Western Pacific Ocean","kind":"region","role":"region of SST and ocean heat content anomalies associated with La Niña","river_name":null,"geocode_query":"Western Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-002","page":34,"quote":"Ice cover on lakes was almost nine days shorter than average, the fourth shortest since 1980; the five shortest ice seasons have all occurred since 2016. The average temperature anomaly for more than 1950 lakes across the Northern Hemisphere was the second highest since the beginning of the record in 1995. Drought conditions were pervasive, occurring across Europe (linked to the extreme summer temperatures), as well as the American West, China, and most of Southern Hemisphere South America. Globally, record-high areas of land experiencing extreme drought (6.2%) were reached in August 2022; overall, 29% of land experienced moderate or worse categories of drought. Low values of terrestrial water storage also occurred in Europe and parts of China (linked to the heatwaves), but La Niña influenced high values in southeastern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.538879,-8.731233],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-12","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Indian Ocean","kind":"region","role":"region with anomalous SST and salinity associated with negative Indian Ocean dipole event in 2022","river_name":null,"geocode_query":"Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-002","page":34,"quote":"Ice cover on lakes was almost nine days shorter than average, the fourth shortest since 1980; the five shortest ice seasons have all occurred since 2016. The average temperature anomaly for more than 1950 lakes across the Northern Hemisphere was the second highest since the beginning of the record in 1995. Drought conditions were pervasive, occurring across Europe (linked to the extreme summer temperatures), as well as the American West, China, and most of Southern Hemisphere South America. Globally, record-high areas of land experiencing extreme drought (6.2%) were reached in August 2022; overall, 29% of land experienced moderate or worse categories of drought. Low values of terrestrial water storage also occurred in Europe and parts of China (linked to the heatwaves), but La Niña influenced high values in southeastern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.868289,-33.08901],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indian Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-13","true_coordinates":[81.826172,-33.137551],"spread_km":6.7},{"name":"North Atlantic Ocean","kind":"region","role":"region with above-average SST and ocean heat content in 2022","river_name":null,"geocode_query":"North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-002","page":34,"quote":"Ice cover on lakes was almost nine days shorter than average, the fourth shortest since 1980; the five shortest ice seasons have all occurred since 2016. The average temperature anomaly for more than 1950 lakes across the Northern Hemisphere was the second highest since the beginning of the record in 1995. Drought conditions were pervasive, occurring across Europe (linked to the extreme summer temperatures), as well as the American West, China, and most of Southern Hemisphere South America. Globally, record-high areas of land experiencing extreme drought (6.2%) were reached in August 2022; overall, 29% of land experienced moderate or worse categories of drought. Low values of terrestrial water storage also occurred in Europe and parts of China (linked to the heatwaves), but La Niña influenced high values in southeastern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-39.536818,35.746512],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Atlantic Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-14","true_coordinates":[-39.462891,35.746512],"spread_km":6.7},{"name":"North Pacific Ocean","kind":"region","role":"region with warm center and cooler edges, part of Pacific decadal oscillation pattern","river_name":null,"geocode_query":"North Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-002","page":34,"quote":"Ice cover on lakes was almost nine days shorter than average, the fourth shortest since 1980; the five shortest ice seasons have all occurred since 2016. The average temperature anomaly for more than 1950 lakes across the Northern Hemisphere was the second highest since the beginning of the record in 1995. Drought conditions were pervasive, occurring across Europe (linked to the extreme summer temperatures), as well as the American West, China, and most of Southern Hemisphere South America. Globally, record-high areas of land experiencing extreme drought (6.2%) were reached in August 2022; overall, 29% of land experienced moderate or worse categories of drought. Low values of terrestrial water storage also occurred in Europe and parts of China (linked to the heatwaves), but La Niña influenced high values in southeastern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.561101,-8.753195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-15","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Amazon River basin, Brazil","kind":"region","role":"region with record floods and drought impacts in 2022","river_name":null,"geocode_query":"Amazon River basin, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p104-001","page":104,"quote":"Briefly, EESC and the ODGI are separated into two categories, midlatitude and Antarctic, because transport processes cause widely different reactive halogen abundances in these stratospheric regions. ODGI is derived from a simple scaling of EESC such that an ODGI of 100 represents the peak EESC value, and an ODGI of 0 represents the value of EESC in 1980 (Hoffmann and Montzka 2009). Using the ODGI, recovery of the stratospheric ozone layer is expected to reach 1980 levels in 2049 for the midlatitudes and 2076 in the Antarctic (Fig. 2.59). Fig. 2.58. Global mean abundances (mole fractions) at Earth’s surface (ppt = nmol mol−1 in dry air) for a suite of halogenated gases, most of which deplete stratospheric ozone. See Table 2.8 for the 2022 global mean mole fractions of these and other gases. All compounds, except hydrofluorocarbon (HFC)-23, are mid-year global means taken from https://gml.noaa.gov/aftp/data/hats/.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.702388,-1.601702],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon River","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-16","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Petrópolis, Rio de Janeiro, Brazil","kind":"settlement","role":"site of extreme rainfall disaster in 2022","river_name":null,"geocode_query":"Petrópolis, Rio de Janeiro, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p406-001","page":406,"quote":"Integrated Drought Index (IDI) maps for central South America during (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. (Source: CEMADEN.) Brought to you by NOAA Central Library | Unauthenticated | Downloaded 10/27/23 07:10 PM UTC","verified_in_page":true},"resolution_status":"resolved","coordinates":[-43.17950829999999,-22.5120883],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Petrópolis - Cascatinha, Petrópolis - Rio de Janeiro, Brazil","basin_id":6040013750,"main_basin_id":6040013750,"basin_region":"South America","basin_area_km2":57233.3,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-17"},{"name":"Central Chile","kind":"region","role":"location of record-breaking drought in 2022","river_name":null,"geocode_query":"Central Chile","latitude":null,"longitude":null,"evidence":{"passage_id":"p081-001","page":81,"quote":"A persistent lack of precipitation in large areas of Europe from winter to summer, together with warmer-thanusual conditions and a sequence of heatwaves (sections 2b4, 7f) triggered a severe-to-extreme drought (Plate 2.1q). At its peak, the drought affected more than two-thirds of Europe, becoming one of the worst historical droughts in France, Spain, Germany, and Italy. In northern Italy, the Po River and canals in Venice reached record-low levels. The drought did not extend into northern Europe, where wet conditions across Fennoscandia continued through 2022. In northern Africa, previous moderate drought intensified to extreme drought along the Mediterranean coast from Morocco to Tunisia (Plate 2.1q). Most of the Middle East from eastern Turkey to Pakistan also saw an intensification of drought to severe or extreme conditions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-69.9587257,-18.3603039],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central, Arica, Arica y Parinacota, Chile","basin_id":6040029050,"main_basin_id":6040029050,"basin_region":"South America","basin_area_km2":40638.6,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-18"},{"name":"Svalbard, Norway","kind":"region","role":"location with monthly record warm June, seasonal temperature observations and glacier mass balance loss","river_name":null,"geocode_query":"Svalbard, Norway","latitude":null,"longitude":null,"evidence":{"passage_id":"p058-000","page":58,"quote":"September 2023 | State of the Climate in 2022 2. Global Climate S44 On Svalbard, the mean loss of the four reporting glaciers was −1102 mm w.e. The negative mass balances were due to several summer heat events (see section 5b, Sidebar 5.1), which led to many glaciers and ice caps losing all or most of their snow cover, further accelerating mass loss (Fig. 2.16). In Alberta and British Columbia, Canada, and in Alaska and Washington, United States, 19 glaciers had a negative mass balance, averaging −965 mm w.e. The Alberta, British Columbia, and Washington regions experienced several prolonged heatwaves as they did in 2021. Daily glacier ablation in this region was noted as increasing by 30%–40% during heatwave periods (Pelto et al. 2022). In South America, mass balance data, reported from five Andean glaciers in Ecuador, Argentina, and Chile were negative, with a mean of −1465 mm w.e.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Hudson Bay, Canada","kind":"region","role":"area with below-normal air temperature anomaly in 2022","river_name":null,"geocode_query":"Hudson Bay, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p294-001","page":294,"quote":"This was characterized by above-normal air temperatures in the Eurasian Arctic and cold departures over the North American high latitudes, associated with prevailing positive Arctic Oscillation/North Atlantic Oscillation conditions during much of winter. A large region of ≥3°C positive anomalies was concentrated over the central Arctic extending south to western Siberia and stretching across northern Eurasia. This region of above-average temperatures was associated with southerly flow off the Eurasian continent from a large, below-normal sea-level pressure (SLP) pattern (≤−5 hPa) over the Barents and Kara Seas combined with broad, above-normal SLP spanning central Siberia into the North American Arctic (Fig. 5.3a). Contrasting winter cold temperature anomalies (≤−2°C) were noted across high-latitude North America, extending from northeastern Alaska southeastward over Hudson Bay and Labrador Sea to the east (Fig. 5.2a).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-102.388114,52.796798],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hudson Bay, SK, Canada","basin_id":7040192560,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":586387.2,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-20","true_coordinates":[-102.3881142,52.856798],"spread_km":6.7},{"name":"Arctic Ocean","kind":"region","role":"region showing sea-surface temperature anomalies in summer 2022","river_name":null,"geocode_query":"Arctic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p301-002","page":301,"quote":"In the Arctic Ocean overall, the OISSTv2 product exhibits a cold bias (i.e., underestimates SST) of up to 0.5°C compared to ship-based measurements (Stroh et al. 2015). The OISSTv2 product uses a simplified linear relationship with sea-ice concentration to infer SST under sea ice (Reynolds et al. 2007), which means SSTs may be too cool by up to 0.2°C where there is sea-ice cover. There is an updated product (version 2.1) that employs a different method than OISSTv2 for setting a proxy SST in sea-ice-covered regions, applied only after January 2016 (in addition to some other differences that are not specific to the polar regions). See Huang et al. (2021) for a description. In our examination of trends in the Arctic Ocean, we require a product that estimates SST in the presence of sea ice using a consistent method for the duration of the data record. Otherwise, estimated trends might be artifacts of the change in methodology part way through the record.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-60.4620976,65.2481624],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Arctic Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-21"},{"name":"East Antarctica","kind":"region","role":"region of unprecedented heatwave in March 2022 and surface melt and snowfall maxima","river_name":null,"geocode_query":"East Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p342-003","page":342,"quote":"Combined with the large deficit of protective offshore sea ice in the Weddell Sea, which exposed the fast ice to open ocean swells, this contributed to the disintegration of a large area of decade-old fast ice in the remnant Larsen B embayment, as discussed in Sidebar 6.2. The breakup of fast ice was followed by rapid retreat and even collapse of upstream glaciers to which the fast ice had been attached. In East Antarctica, there was an unprecedented heatwave and coastal surface melt event in March due to a strong atmospheric river, which is detailed in Sidebar 6.1. Across a large portion of the high-elevation East Antarctic plateau, temperatures exceeded the previous March record maxima for several consecutive days, and a new all-time record-high temperature of −9.4°C (44°C above the March average) was set at Dome C II automated weather station on 18 March.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Ross Sea, Antarctica","kind":"region","role":"region with variable sea-ice extent anomalies including recent positive anomalies associated with Amundsen Sea Low","river_name":null,"geocode_query":"Ross Sea, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p359-002","page":359,"quote":"Regional patterns of sea-ice coverage in January–March 2022 largely continued those observed in the latter part of 2021 (Reid et al. 2022), with strong negative SIE anomalies being 1–2 std. dev. below average in the Ross, Bellingshausen, and Weddell Seas (Fig. 6.11b). The negative net SIE anomaly (Fig. 6.11a) at this time was dominated by sea-ice loss in the Ross Sea which saw SIE anomalies of >2 std. dev. below average from late January onward (Fig. 6.11b). This major loss event was driven by wind anomalies associated with an intense and persistent Amundsen Sea Low (ASL; see section 6b, Fig. 6.3a) that had strengthened during September–December 2021 (Fig. 6.3g in Clem et al. 2022), resulting in strong northward ice advection from the Ross Sea into the Amundsen Sea.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-166.3074289,-74.5486518],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ross Sea","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-23"},{"name":"Amundsen Sea, Antarctica","kind":"region","role":"area of positive SMB anomaly and sea-ice retreat, also atmospheric circulation anomaly site","river_name":null,"geocode_query":"Amundsen Sea, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p351-004","page":351,"quote":"They also coincide with the deep and persistent Amundsen Sea Low (ASL) that was present for nearly all of 2022 and a strong blocking high pressure south of New Zealand and Tasmania from January through October (section 6b). The ASL produced warm northerly flow across the Peninsula and West Antarctic (section 6f), while the blocking high in the far southwest Pacific/southeast Indian Oceans, reflecting the pattern seen during the East Antarctic heatwave in March (see Sidebar 6.1), delivered warm northerly flow to large portions of East Antarctica for much of the year. Despite the magnitude of the 2022 SMB, portions of Dronning Maud Land and the Ross Sea region feature anomalously low SMB (<50% of the climatological mean). Regions with high SMB are coincident with regions experiencing anomalously high numbers of ARs (Fig. 6.5d), favored by the aforementioned blocking high/low pressure anomaly couplets shown in Fig. 6.3, triggering poleward heat and moisture transport.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Wilkes Land, Antarctica","kind":"region","role":"region of strong SMB increase and record surface melt event in March 2022","river_name":null,"geocode_query":"Wilkes Land, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p347-001","page":347,"quote":"The Dome C II automatic weather station (AWS) located high on the plateau at 3250 m a.s.l. (shown in green), captured the evolution of the heatwave at a high temporal resolution. For the first half of March, temperatures were near the Dome C II AWS March climatological mean of −53.4°C and ranged from around −45°C to −60°C (Fig. SB6.1b). Fig. SB6.1. (a) Mean 2-m temperature anomaly (°C) for 14–20 Mar 2022 as compared to the 1991–2020 Mar mean (Source: ERA5) and (b) UW-Madison Dome C II automated weather station (AWS; 75.106°S, 123.347°E) 10-min quality-controlled temperature observations for Mar 2022 (°C; red line). The black horizontal dashed line denotes the Dome C II March climatological mean temperature (−53.4°C).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Larsen B embayment, Antarctica","kind":"region","role":"location of fast-ice breakout and rapid glacial retreat in 2022","river_name":null,"geocode_query":"Larsen B embayment, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p363-002","page":363,"quote":"SB6.4. (a)–(c) Landsat 8 true-color images of the Larsen A and Larsen B embayments spanning the period of the breakup of the 11-year-old Larsen B fast ice. (d) Enhanced-color hand-held aerial photograph of the Crane Glacier fjord looking southwestward, acquired by British Antarctic Survey. Brought to you by NOAA Central Library | Unauthenticated | Downloaded 10/27/23 07:10 PM UTC","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"New Caledonia","kind":"region","role":"region impacted by Severe Tropical Cyclone Dovi in early 2022","river_name":null,"geocode_query":"New Caledonia","latitude":null,"longitude":null,"evidence":{"passage_id":"p473-004","page":473,"quote":"At its peak, Dovi had one-minute sustained winds of 175 km h−1 (94 kt) and a lowest central pressure of 940 hPa. Tropical Cyclone Dovi caused heavy rainfall and flash flooding across Vanuatu. Several rivers overflowed, causing damage to housing and crops in nearby areas, and mudslides destroyed several bridges in Port Vila, Vanuatu’s capital. In southern New Caledonia, Dovi caused all flights from the country to be cancelled for two days and businesses and schools to be closed. The heavy rainfall led to landslides and mudslides and resulted in some evacuations in the capital Nouméa. As Dovi weakened and tracked west of Norfolk Island and across northern New Zealand, the storm brought strong winds and heavy rainfall to both regions. In total, there was approximately $80 million (U.S. dollars) of damage across the four regions according to AON's Global Catastrophe Recap, with one fatality recorded in New Caledonia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southwestern Pacific Ocean","kind":"region","role":"region with above-normal chlorophyll, rainfall, and SST in 2022, tropical cyclone genesis region","river_name":null,"geocode_query":"Southwestern Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p470-002","page":470,"quote":"In the second quarter, the below-normal air temperatures expanded westward across the date line, in line with expanding negative sea-surface temperature anomalies. These cooler air temperatures persisted along the central and western equator during the third quarter while Fig. 7.61. Islands (highlighted in blue) in the Oceania region. (Source: SPREP.) Brought to you by NOAA Central Library | Unauthenticated | Downloaded 10/27/23 07:10 PM UTC","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.569235,-8.783195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-28","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"New Zealand","kind":"region","role":"region with warmest year recorded in 2022 influenced by ocean and atmospheric patterns","river_name":null,"geocode_query":"New Zealand","latitude":null,"longitude":null,"evidence":{"passage_id":"p478-000","page":478,"quote":"September 2023 | State of the Climate in 2022 7. Regional Climates S464 Various climate drivers contributed to the exceptional warmth, with La Niña being the primary driver. In New Zealand, La Niña is typically linked with higher-than-normal air pressure near and to the east of the country with lower pressures to the north. These circulation anomalies were observed in 2022, which led to more northeasterly winds than usual and were likely a major cause of the above-average air temperatures. Throughout 2022, coastal sea-surface temperatures (SSTs) were either above or well above average every month, with January, November, and December having the most anomalously warm SSTs. This resulted in a protracted marine heatwave (MHW), where SSTs are above the 90th percentile as derived from a 30-year climatology, with ocean temperatures staying unseasonably warm over thousands of kilometers for much of the year.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Atlantic Ocean","kind":"other","role":"ocean basin with high SST and ocean heat content showing warming trends in 2022","river_name":null,"geocode_query":"Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p166-002","page":166,"quote":"The Indian Ocean dipole (IOD) was negative in 2022, with positive SST anomalies in the east and negative anomalies in the west. As expected with a negative IOD, upper-ocean heat content and sea-surface height were anomalously high in the east and lower (although still above the climatological means) in the west. With warm water in the east came an eastward shift in precipitation, yielding anomalously low SSS there and anomalously high SSS in the west. In the Atlantic, SST, upper-ocean heat content, and sea level were all above average over much of the basin in 2022, with especially high values off the east coast of North America. The only location with both cold SST and low ocean heat content anomalies was southeast of Greenland, potentially a fingerprint of slowing meridional overturning circulation. Sea-surface salinity values were anomalously high in salty regions and anomalously low around the ITCZ, off the Amazon, and in the subpolar North Atlantic.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North Pacific Ocean","kind":"other","role":"ocean basin with distinct spatial warming and cooling patterns in 2022","river_name":null,"geocode_query":"North Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p166-002","page":166,"quote":"The Indian Ocean dipole (IOD) was negative in 2022, with positive SST anomalies in the east and negative anomalies in the west. As expected with a negative IOD, upper-ocean heat content and sea-surface height were anomalously high in the east and lower (although still above the climatological means) in the west. With warm water in the east came an eastward shift in precipitation, yielding anomalously low SSS there and anomalously high SSS in the west. In the Atlantic, SST, upper-ocean heat content, and sea level were all above average over much of the basin in 2022, with especially high values off the east coast of North America. The only location with both cold SST and low ocean heat content anomalies was southeast of Greenland, potentially a fingerprint of slowing meridional overturning circulation. Sea-surface salinity values were anomalously high in salty regions and anomalously low around the ITCZ, off the Amazon, and in the subpolar North Atlantic.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Indian Ocean","kind":"other","role":"ocean basin with negative Indian Ocean dipole and associated SST and salinity anomaly patterns in 2022","river_name":null,"geocode_query":"Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p166-002","page":166,"quote":"The Indian Ocean dipole (IOD) was negative in 2022, with positive SST anomalies in the east and negative anomalies in the west. As expected with a negative IOD, upper-ocean heat content and sea-surface height were anomalously high in the east and lower (although still above the climatological means) in the west. With warm water in the east came an eastward shift in precipitation, yielding anomalously low SSS there and anomalously high SSS in the west. In the Atlantic, SST, upper-ocean heat content, and sea level were all above average over much of the basin in 2022, with especially high values off the east coast of North America. The only location with both cold SST and low ocean heat content anomalies was southeast of Greenland, potentially a fingerprint of slowing meridional overturning circulation. Sea-surface salinity values were anomalously high in salty regions and anomalously low around the ITCZ, off the Amazon, and in the subpolar North Atlantic.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Tibetan Plateau","kind":"region","role":"high-elevation site of permafrost and active layer thickness monitoring","river_name":null,"geocode_query":"Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p053-003","page":53,"quote":"ALT increased by 19.6 cm decade–1 over the past 40 years in the Qinghai-Tibet Plateau (Fig. 2.13) and has increased by a few meters in the past 20 years at several sites in the European Alps. Fig. 2.12. Permafrost temperatures (°C) measured in boreholes in the European Alps and the Nordic countries at a depth of (a) ~10 m (monthly means) and (b) 20 m (annual means). (Sources: Switzerland: Swiss Permafrost Monitoring Network; Norway: Norwegian Meteorological Institute and the Norwegian Permafrost Database; France: updated from Magnin et al. 2015; Italy: updated from Pogliotti et al. 2015.) Brought to you by NOAA Central Library | Unauthenticated | Downloaded 10/27/23 07:10 PM UTC","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Greenland","kind":"region","role":"ice sheet mass balance, surface melt, and temperature anomaly region","river_name":null,"geocode_query":"Greenland","latitude":null,"longitude":null,"evidence":{"passage_id":"p307-000","page":307,"quote":"September 2023 | State of the Climate in 2022 5. The Arctic S293 f. Greenland Ice Sheet —K. Poinar, K. D. Mankoff, T. A. Moon, B. D. Loomis, X. Fettweis, R. S. Fausto, T. L. Mote, C. D. Jensen, A. Wehrlé, and M. Tedesco The Greenland Ice Sheet (GrIS) contributes directly to global sea levels when ice melts or breaks off into the ocean, increasing coastal erosion and flooding. Currently, the equivalent of ~7.4 m of eustatic sea level is contained in the GrIS (Morlighem et al. 2017). The GrIS has lost ice mass every mass balance year (1 September of the preceding year through 31 August) since 1998 (Mouginot et al. 2019). In 2022 (September 2021–August 2022), the Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) satellite mission measured a GrIS mass loss of −165±18 Gt, the equivalent of ~0.5 mm eustatic sea level rise (Fig. 5.15). This loss was 95 Gt (37%) less than the 2002–22 average of −260±11 Gt.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"North America","kind":"region","role":"regional climate study including drought and temperature anomalies","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p391-000","page":391,"quote":"September 2023 | State of the Climate in 2022 7. Regional Climates S377 2. UNITED STATES —K. Gleason, C. Fenimore, R. R. Heim Jr., and A. Smith The annual average temperature for the contiguous United States (CONUS) in 2022 was 11.9°C, which was 0.1°C above the 1991–2020 average and equal with 1953 as the 18th-warmest year in the 128-year record (Fig. 7.3a). Below-average temperatures were concentrated across the Upper Midwest while above-average temperatures were scattered across parts of California, Texas, Florida, and New England (Fig. 7.4a). Based on a linear regression of data from 1895 to 2022, the annual CONUS temperature is increasing at an average rate of 0.09°C decade−1 (0.27°C decade −1 since 1970). Average precipitation for the nation totaled 722 mm, which is 91% of the 1991–2020 average (Fig. 7.3). However, the annual precipitation total has been increasing at an average rate of 5 mm decade−1 since 1895 (2 mm decade−1 since 1970).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.344656,54.555961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-35","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Central America","kind":"region","role":"regional climate study including severe weather and temperature anomalies","river_name":null,"geocode_query":"Central America","latitude":null,"longitude":null,"evidence":{"passage_id":"p396-000","page":396,"quote":"September 2023 | State of the Climate in 2022 7. Regional Climates S382 c. Central America and the Caribbean —A. Sánchez-Lugo, Ed. 1. CENTRAL AMERICA —H. G. Hidalgo, J. A. Amador, E. J. Alfaro, B. Calderón, and N. Mora For this region, nine stations from five countries were analyzed (see Fig. 7.8 for data and station list). The station distribution is representative of the relevant seasonal and intraseasonal regimes of precipitation (Amador 1998; Magaña et al. 1999; Amador et al. 2016a,b), wind (Amador 2008), and temperature (Hidalgo et al. 2019) on the Caribbean and Pacific slopes of Central America (CA). Precipitation and temperature records for the stations analyzed and regional winds were provided either by CA National Weather Services (CA-NWS), NOAA, or the University of Costa Rica. Anomalies are reported using a 1991–2020 base period and were calculated from CA-NWS data. The methodologies used for all variables can be found in Amador et al. (2011). Fig. 7.8.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-85.549085,12.799013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central America","basin_id":7040050930,"main_basin_id":7040050930,"basin_region":"North America and Caribbean","basin_area_km2":382831.5,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-36","true_coordinates":[-85.60236429999999,12.7690126],"spread_km":6.7},{"name":"Mexico","kind":"region","role":"regional climate study with notable tropical cyclone landfalls and drought","river_name":null,"geocode_query":"Mexico","latitude":null,"longitude":null,"evidence":{"passage_id":"p394-000","page":394,"quote":"September 2023 | State of the Climate in 2022 7. Regional Climates S380 3. MEXICO —R. Pascual Ramírez and A. E. Martínez Mexico’s mean annual temperature for 2022 was the eighth highest since national records began in 1950 (Fig. 7.6a). The national precipitation total for 2022 was 743.6 mm, which is slightly below the 1991–2020 average and ranked in the middle of the 73-year record (Fig. 7.6b). Precipitation was below average across the northeast, central region, and the northern coast of the Gulf of Mexico. Conversely, the northwest, southern Baja California Peninsula, and the Yucatan Peninsula had above-average annual rainfall through the year (Fig. 7.7b). (i) Temperature The 2022 national average annual temperature was 22.0°C, which was 0.6°C above the 1991–2020 average (Fig 7.6a), marking the eighth-warmest year in the 73-year record.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Caribbean","kind":"region","role":"tropical cyclone, temperature, and rainfall climate impacts region","river_name":null,"geocode_query":"Caribbean","latitude":null,"longitude":null,"evidence":{"passage_id":"p398-000","page":398,"quote":"September 2023 | State of the Climate in 2022 7. Regional Climates S384 Other rain-producing systems caused landslides and flooding that killed 110 people: 2 in Panamá, 10 in Costa Rica, 7 in Nicaragua, 27 in El Salvador, 12 in Honduras, and 52 in Guatemala. Lightning strikes caused 13 fatalities in the region during the season (three in Costa Rica, three in Nicaragua, one in El Salvador, four in Honduras, and two in Guatemala). Please refer to sections 4g2 and 4g3 for more information on these tropical cyclones. 2. CARIBBEAN —T. S. Stephenson, M. A. Taylor, A. Trotman, C. J. Van Meerbeeck, J. Spence-Hemmings, L. Clarke, J. Campbell, and K. Kerr (i) Temperature The Caribbean had relatively small positive temperature anomalies in 2022 compared to the previous four years (2018–21, as analyzed from European Centre for Medium-Range Weather Forecasts Reanalysis version 5 [ERA5] reanalysis data).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-78.656894,21.409114],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Caribbean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-38","true_coordinates":[-78.6568942,21.4691137],"spread_km":6.7},{"name":"North Indian Ocean","kind":"region","role":"regional basin climate and tropical cyclone variability","river_name":null,"geocode_query":"North Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p262-000","page":262,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S248 5. NORTH INDIAN OCEAN —C. J. Schreck (i) Seasonal activity The North Indian Ocean (NIO) TC season typically occurs between April and December, with two peaks of activity: May–June and October–December, due to the presence of the monsoon trough over tropical waters of the NIO during these periods. Tropical cyclone genesis typically occurs in the Arabian Sea and the Bay of Bengal between 8°N and 15°N. The Bay of Bengal, on average, experiences four times more TCs than the Arabian Sea (Dube et al. 1997). The 2022 NIO TC season had seven named storms according to JTWC, which was above the IBTrACS–JTWC 1991–2020 climatology of 5.5. Two storms were in the Arabian Sea with the rest in the Bay of Bengal. Only one, Sitrang, became a cyclone, and none became major cyclones. These values were below the climatological averages of 2.2 cyclones and 1.1 major cyclones (Fig. 4.35 ).","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.784055,-33.08901],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indian Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-39","true_coordinates":[81.826172,-33.137551],"spread_km":6.7},{"name":"South Indian Ocean","kind":"region","role":"tropical cyclone activity region including orphan landfalls and impacts","river_name":null,"geocode_query":"South Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p264-000","page":264,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S250 (ii) Individual tropical cyclones and impacts The North Indian Ocean cyclone season had an early start with an unnamed tropical storm on 4 March. The strongest storm of the season was Severe Cyclone Asani in May. Asani’s winds briefly reached Category 1 strength on the SSHWS on two occasions over the Bay of Bengal. However, Asani weakened to a tropical depression before making landfall in eastern India, which limited its impacts. Tropical cyclones are uncommon during the Indian summer monsoon season, but two tropical storms formed in August: one in the Bay of Bengal and one in the Arabian Sea. Each only lasted for about 24 hours. Only two other years (1979 and 1982) have observed a North Indian Ocean storm during August. This was the first time on record there were multiple storms in August.","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.758025,-33.156092],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Indian Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-40","true_coordinates":[81.826172,-33.137551],"spread_km":6.7},{"name":"Australian Basin","kind":"region","role":"tropical cyclone and temperature anomalies basin on southern hemisphere","river_name":null,"geocode_query":"Australian basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p267-000","page":267,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S253 7. AUSTRALIAN BASIN —B. C. Trewin (i) Seasonal activity The 2021/22 TC season was near normal in the broader Australian basin (areas south of the equator and between 90°E and 160°E4 , which includes Australian, Papua New Guinean, and Indonesian areas of responsibility). The season produced 12 TCs in a season with weak to moderate La Niña conditions. The 1991–2020 IBTrACS-JTWC seasonal averages for the basin are 10.2 named storms, 5.0 TCs, and 2.5 major TCs, which compares with the 2021/22 IBTrACS-based counts of 12, 4, and 1, respectively (Fig. 4.39; Table 4.2), and is consistent with La Niña conditions. There were seven TCs in the western sector5 of the broader Australian region during 2021/22, two in the northern sector, and three in the eastern sector.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southwest Pacific Basin","kind":"region","role":"tropical cyclone activity and ENSO influence region","river_name":null,"geocode_query":"Southwest Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p470-001","page":470,"quote":"The year began with a mature La Niña event that persisted into the second quarter of the year, before atmospheric and oceanic indicators in the Pacific briefly eased. La Niña strengthened again towards the end of 2022. Below-normal sea-surface temperatures (SSTs) were present across the central and western equatorial Pacific during the first half of 2022 before the cool regions contracted, although rainfall patterns remained persistently La Niña-like. Signs that La Niña would strengthen later in the year were present in the cloud and pressure patterns as early as June, with trade winds re-strengthening during August. The ocean lagged the atmospheric indicators but began to cool during the third quarter of the year. Air temperatures and rainfall patterns were both typical of a La Niña event throughout 2022. Air temperatures at the start of the year were below normal along the central to eastern equatorial region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.561101,-8.813195],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-bae5fe8f46ea60c8f598572d-site-42","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Western North Pacific Basin","kind":"region","role":"tropical cyclone activity region with below-normal activity and major storms","river_name":null,"geocode_query":"Western North Pacific","latitude":null,"longitude":null,"evidence":{"passage_id":"p259-000","page":259,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S245 Chaba and Aere, both of which formed in June, were the only active storms in July. TC activity increased in August and was notable for having only four named storms (bottom quartile: 5): Tropical Storms Meari and Ma-On, Typhoon Tokage, and Super Typhoon Hinnamnor. September was the most active month of the season and the only month in 2022 with an above-average level of TC activity, with the occurrence of six typhoons (upper quartile: 4): Muifa, Merbok, Kulap, and Roke, with Nanmadol and Noru reaching super-typhoon intensity (upper quartile: 2). Seven storms formed in October, including four tropical storms, 21W, Sonca, Haitang, and Banyan, and two typhoons, Nesat and Nalgae (named storms, upper quartile: 5; typhoons, bottom quartile: 2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-117.627752,44.044072],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western United States, USA","basin_id":7040388740,"main_basin_id":7040014930,"basin_region":"North America and Caribbean","basin_area_km2":191669.7,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-43","true_coordinates":[-117.6277515,43.9840719],"spread_km":6.7},{"name":"Yangtze River basin","kind":"basin","role":"region of record-breaking hot summer and drought with seasonal hydrologic impacts in China","river_name":null,"geocode_query":"Yangtze River basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p457-002","page":457,"quote":"Unusually high temperatures led to serious hydrological drought, agricultural failure, and ecological damage causing a direct economic loss of 32.8 billion yuan ($4.75 billion U.S. dollars) and affecting more than 38 million people. The highest above-average temperatures were mainly located in the YRB in July and August (Figs. SB7.3a,b). Relative to the 90th percentile of temperature records in the period of 1991–2020, there were 30% more extreme high-temperature days (EHDs) during July and August across the YRB (slashed region in Fig. SB7.3a). The above-normal temperatures, averaged over the YRB, lasted about 80 days, with a maximum anomaly of +5.3°C on 22 August (Fig. SB7.3b). There were 35 EHDs from June to August, with 31 of those in July and August. The detrended normalized July–August mean temperature averaged over the YRB (referred to as YT afterwards) in 2022 was more than two standard deviations, which was the largest since 1979 (Fig. SB7.3c).","verified_in_page":true},"resolution_status":"resolved","coordinates":[112.650141,29.780415],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-bae5fe8f46ea60c8f598572d-site-44","true_coordinates":[112.650141,29.720415],"spread_km":6.7},{"name":"Pacific Ocean","kind":"other","role":"ocean basin with La Niña and tropical Pacific intraseasonal activity","river_name":null,"geocode_query":"Pacific Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p232-000","page":232,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S218 c. Tropical intraseasonal activity —A. Allgood and C. J. Schreck Organized tropical intraseasonal activity is modulated by several different modes of coherent atmospheric variability, most notably the Madden-Julian Oscillation (MJO; Madden and Julian 1971, 1972, 1994; Zhang 2005). The MJO is characterized by eastward-propagating envelopes of large-scale anomalous enhanced and suppressed convection that typically circumnavigate the globe in a 30–60-day period. MJO-related convective anomalies are similar in spatial extent to those generated by the atmospheric response to the El Niño–Southern Oscillation (ENSO), but the latter signal remains largely stationary and lasts for at least several months.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Indian Ocean","kind":"other","role":"ocean basin with negative Indian Ocean dipole and teleconnections impacting tropical convection","river_name":null,"geocode_query":"Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p242-000","page":242,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S228 f. Indian Ocean dipole —L. Chen and J.-J. Luo The Indian Ocean dipole (IOD) is the dominant interannual mode in the tropical Indian Ocean (IO), characterized by a zonal dipole of sea-surface temperature (SST) anomalies in the equatorial IO (Saji et al. 1999; Luo et al. 2010, 2012). The IOD originates from local air–sea interaction processes in the tropical IO and/or the tropical El Niño–Southern Oscillation (ENSO) forcing (Luo et al. 2007, 2010). The dipole usually develops in austral autumn and winter, matures in spring, and terminates rapidly in early austral summer. A negative IOD event is characterized by anomalously high SSTs in the eastern IO and below-average SSTs in the western IO and vice versa for a positive IOD.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"North Atlantic","kind":"other","role":"ocean basin with tropical cyclone activity and monsoon influence","river_name":null,"geocode_query":"North Atlantic Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p248-002","page":248,"quote":"The continuing La Niña event typically reduces wind shear and increases mid-level moisture across the tropical Atlantic Ocean, both of which enhance tropical cyclone development. (iv) Atmospheric conditions Climatologically, the ASO peak in Atlantic hurricane activity largely reflects the June–September peak in the West African monsoon. The inter-related circulation features of an enhanced monsoon act to further increase hurricane activity, while those of an anomalously weak monsoon act to suppress it (Gray 1990; Hastenrath 1990; Landsea et al. 1992; Bell and Chelliah 2006; Bell et al. 2018, 2020). The association on multi-decadal time scales between the AMO and Atlantic hurricane activity largely exists because of their common relationship to the West African monsoon (Bell and Chelliah 2006) and reduced vertical shear due to weaker easterly trade winds in the MDR (Goldenberg et al. 2001).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Bay of Bengal","kind":"other","role":"location of tropical cyclone activity and seasonal precipitation","river_name":null,"geocode_query":"Bay of Bengal, Indian Ocean","latitude":null,"longitude":null,"evidence":{"passage_id":"p262-000","page":262,"quote":"September 2023 | State of the Climate in 2022 4. The Tropics S248 5. NORTH INDIAN OCEAN —C. J. Schreck (i) Seasonal activity The North Indian Ocean (NIO) TC season typically occurs between April and December, with two peaks of activity: May–June and October–December, due to the presence of the monsoon trough over tropical waters of the NIO during these periods. Tropical cyclone genesis typically occurs in the Arabian Sea and the Bay of Bengal between 8°N and 15°N. The Bay of Bengal, on average, experiences four times more TCs than the Arabian Sea (Dube et al. 1997). The 2022 NIO TC season had seven named storms according to JTWC, which was above the IBTrACS–JTWC 1991–2020 climatology of 5.5. Two storms were in the Arabian Sea with the rest in the Bay of Bengal. Only one, Sitrang, became a cyclone, and none became major cyclones. These values were below the climatological averages of 2.2 cyclones and 1.1 major cyclones (Fig. 4.35 ).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Many coordinates are not specified due to lack of exact coordinate information.","The study period is often not precisely specified, sometimes given as season or year only.","Some large regional scale entities are broadly defined without precise fixed boundaries.","Some observational datasets with known biases or updates under investigation are used, which may affect anomaly calculations.","Some data products are reanalyses or multi-sensor merged products, including indirect or model-based estimates, that have known uncertainties.","Tropical cyclone counts and intensity have some discrepancies between different data agencies and records.","Satellite data products have temporal and sensor coverage limitations, as well as atmospheric disturbance impacts (e.g., volcanic aerosols).","Not all impacts or observations can be causally attributed due to complexity of climate system interactions."],"river_ids":[],"site_ids":["doc-bae5fe8f46ea60c8f598572d-site-7","doc-bae5fe8f46ea60c8f598572d-site-8","doc-bae5fe8f46ea60c8f598572d-site-9","doc-bae5fe8f46ea60c8f598572d-site-10","doc-bae5fe8f46ea60c8f598572d-site-11","doc-bae5fe8f46ea60c8f598572d-site-12","doc-bae5fe8f46ea60c8f598572d-site-13","doc-bae5fe8f46ea60c8f598572d-site-14","doc-bae5fe8f46ea60c8f598572d-site-15","doc-bae5fe8f46ea60c8f598572d-site-16","doc-bae5fe8f46ea60c8f598572d-site-17","doc-bae5fe8f46ea60c8f598572d-site-18","doc-bae5fe8f46ea60c8f598572d-site-20","doc-bae5fe8f46ea60c8f598572d-site-21","doc-bae5fe8f46ea60c8f598572d-site-23","doc-bae5fe8f46ea60c8f598572d-site-28","doc-bae5fe8f46ea60c8f598572d-site-35","doc-bae5fe8f46ea60c8f598572d-site-36","doc-bae5fe8f46ea60c8f598572d-site-38","doc-bae5fe8f46ea60c8f598572d-site-39","doc-bae5fe8f46ea60c8f598572d-site-40","doc-bae5fe8f46ea60c8f598572d-site-42","doc-bae5fe8f46ea60c8f598572d-site-43","doc-bae5fe8f46ea60c8f598572d-site-44"],"anchor":[-61.702388,-1.601702],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[2040539930,4040783120,6040013750,6040029050,6040280400,7040021740,7040050930,7040192560,7040388740,8040047650],"main_basin_ids":[2040008490,4040009880,6040007000,6040013750,6040029050,7040014930,7040021740,7040022240,7040050930,8040047650]},{"id":"doc-cf90c6adaedaf60327261bbf","title":"Caves demonstrate decrease in rainfall recharge of southwest Australian groundwater is unprecedented for the last 800 years","display_title":"Unprecedented Decline in Rainfall Recharge in SW Australia","doi":"https://doi.org/10.1038/s43247-023-00858-7","theme":"Groundwater","color":"#a8b9ff","year":2023,"publication_date":"2023-06-09","authors":["Stacey C. Priestley","Pauline C. Treble","Alan D. Griffiths","Andy Baker","Nerilie J. Abram","Karina Meredith"],"summary":"This study uses isotope analyses of speleothems and cave drip waters from four caves in southwest Australia to reveal a significant reduction in rainfall recharge to groundwater since the late 1960s. This reduction, traced using oxygen isotope ratios (δ18O) in stalagmites and drip waters, is attributed to decreased rainfall causing a decline in preferential karst flow paths, leading to diminished groundwater recharge. Analysis indicates this decline in recharge is unprecedented in the last 800 years, raising concerns over groundwater-dependent ecosystems and water security in a region relying heavily on groundwater.","finding":"The increase in speleothem and drip water δ18O values during recent decades reflects a reduction in preferential-flow rainfall recharge to groundwater, which corresponds to a 43% decrease in the seasonal duration of soil moisture saturation and an overall unprecedented decline in rainfall recharge over the past 800 years.","finding_evidence":{"passage_id":"p009-004","page":9,"quote":"In this study, the δ18O of drip waters and calcite deposited from them in caves located in the vadose zone, and where recharge can be identified unambiguously as being from rainfall, have provided clear evidence of decreased rainfall recharge to groundwater resources. We confirm that no replicated uptick events are seen in the last 800 years in the stalagmite record from the southwest Australian region27 suggesting that the current decoupling of rainfall to groundwater recharge is unprecedented for the last eight centuries. This has important Fig. 7 Recent uptick in δ18O values indicates a disconnection in rainfall recharge (reduced preferential flow) to groundwater that is unprecedented in the last 800 years.","verified_in_page":true},"topics":["groundwater recharge","isotopic geochemistry","karst hydrology","climate change impact","paleoclimate reconstruction","hydroclimate variability","soil moisture dynamics","aquifer recharge mechanisms"],"study_period":null,"locations":[{"name":"Leeuwin-Naturaliste National Park, Western Australia","kind":"region","role":"study region and location of all investigated caves","river_name":null,"geocode_query":"Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Climate models indicate, with high confidence, that rainfall in this region is likely to decline further throughout the 21st century18 which will continue to impact water availability in southwest Australia21,22. This will have a consequent impact on water-dependent ecosystems24 and water security for this region that currently depends on groundwater to meet 75% of its water demand21. In this study, we constructed a composite speleothem δ18O record using seven speleothems from four caves across a 30-km transect in southwest Australia (Fig. 1; see Supplementary Fig. 1 for speleothem images). All caves are located in the LeeuwinNaturaliste National Park and are developed in the Late Quaternary aeolian calcarenite that outcrops along the coast25. All caves are located in the vadose zone and receive recharge from rainfall only.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.0272203,-33.539165],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412624,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Leeuwin-Naturaliste National Park, Cape Naturaliste Rd, Dunsborough WA 6281, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-0"},{"name":"Golgotha Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","kind":"field_site","role":"primary cave for modern drip water and speleothem δ18O monitoring and analysis, extensive monitoring since 2005","river_name":null,"geocode_query":"Golgotha Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":-34.1,"longitude":115.1,"evidence":{"passage_id":"p002-004","page":2,"quote":"Climate models indicate, with high confidence, that rainfall in this region is likely to decline further throughout the 21st century18 which will continue to impact water availability in southwest Australia21,22. This will have a consequent impact on water-dependent ecosystems24 and water security for this region that currently depends on groundwater to meet 75% of its water demand21. In this study, we constructed a composite speleothem δ18O record using seven speleothems from four caves across a 30-km transect in southwest Australia (Fig. 1; see Supplementary Fig. 1 for speleothem images). All caves are located in the LeeuwinNaturaliste National Park and are developed in the Late Quaternary aeolian calcarenite that outcrops along the coast25. All caves are located in the vadose zone and receive recharge from rainfall only.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.1,-34.1],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70412654,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-1"},{"name":"Labyrinth Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","kind":"field_site","role":"cave site for speleothem sampling and δ18O record development","river_name":null,"geocode_query":"Labyrinth Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":-34.3,"longitude":115.1,"evidence":{"passage_id":"p004-005","page":4,"quote":"Sample code Site information Sample information Climate statistics Source previously published data Name Lat (°S), lon (°E) Altitude (m ASL) Soil thickness (m) Limestone thickness (m) Cave T (°C) Distance from entrance (m) MAT (°C) Rainfall (mm) Actual evapotranspiration (mm) CRY-S1 Crystal C. 34.1, 115.0 100 Unknown, likely similar to Golgotha C. 40 Unknown 140 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology49 δ18O, this study GL-S1, GL-S2, GL-S3, GL-S4 Golgotha C. 34.1, 115.1 70 Variable 0.3 – 3 m 30 (GL-S1, GL-S4) 40 (GL-S2, GL-S3) 14.5–14.8 60 (GL-S1, GL-S4) 90 (GL-S2, GL-S3) 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology δ18O27 LAB-S1 Labyrinth C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 35 15.9–16.5 unknown 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology51 δ18O, this study MND-S1 Moondyne C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 15–20 15.9–16.550 60 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology50 δ18O30 Locations are rounded to the nearest tenth of a degree, as exact locations are not disclosed for cave conservation purposes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.1,-34.36],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70412650,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-2","true_coordinates":[115.1,-34.3],"spread_km":6.7},{"name":"Moondyne Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","kind":"field_site","role":"cave site for speleothem sampling and isotope analysis","river_name":null,"geocode_query":"Moondyne Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":-34.3,"longitude":115.1,"evidence":{"passage_id":"p004-005","page":4,"quote":"Sample code Site information Sample information Climate statistics Source previously published data Name Lat (°S), lon (°E) Altitude (m ASL) Soil thickness (m) Limestone thickness (m) Cave T (°C) Distance from entrance (m) MAT (°C) Rainfall (mm) Actual evapotranspiration (mm) CRY-S1 Crystal C. 34.1, 115.0 100 Unknown, likely similar to Golgotha C. 40 Unknown 140 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology49 δ18O, this study GL-S1, GL-S2, GL-S3, GL-S4 Golgotha C. 34.1, 115.1 70 Variable 0.3 – 3 m 30 (GL-S1, GL-S4) 40 (GL-S2, GL-S3) 14.5–14.8 60 (GL-S1, GL-S4) 90 (GL-S2, GL-S3) 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology δ18O27 LAB-S1 Labyrinth C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 35 15.9–16.5 unknown 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology51 δ18O, this study MND-S1 Moondyne C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 15–20 15.9–16.550 60 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology50 δ18O30 Locations are rounded to the nearest tenth of a degree, as exact locations are not disclosed for cave conservation purposes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.1,-34.24],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70412650,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-3","true_coordinates":[115.1,-34.3],"spread_km":6.7},{"name":"Crystal Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","kind":"field_site","role":"site of speleothem CRY-S1 used for δ18O record","river_name":null,"geocode_query":"Crystal Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":-34.1,"longitude":115.0,"evidence":{"passage_id":"p004-005","page":4,"quote":"Sample code Site information Sample information Climate statistics Source previously published data Name Lat (°S), lon (°E) Altitude (m ASL) Soil thickness (m) Limestone thickness (m) Cave T (°C) Distance from entrance (m) MAT (°C) Rainfall (mm) Actual evapotranspiration (mm) CRY-S1 Crystal C. 34.1, 115.0 100 Unknown, likely similar to Golgotha C. 40 Unknown 140 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology49 δ18O, this study GL-S1, GL-S2, GL-S3, GL-S4 Golgotha C. 34.1, 115.1 70 Variable 0.3 – 3 m 30 (GL-S1, GL-S4) 40 (GL-S2, GL-S3) 14.5–14.8 60 (GL-S1, GL-S4) 90 (GL-S2, GL-S3) 15.6 ± 0.5 1101 ± 157 730 ± 42 Chronology δ18O27 LAB-S1 Labyrinth C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 35 15.9–16.5 unknown 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology51 δ18O, this study MND-S1 Moondyne C. 34.3, 115.1 65 Unknown, likely variable as for Golgotha C. 15–20 15.9–16.550 60 16.6 ± 0.4 1095 ± 164 609 ± 47 Chronology50 δ18O30 Locations are rounded to the nearest tenth of a degree, as exact locations are not disclosed for cave conservation purposes.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.0,-34.1],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":70412615,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-4"},{"name":"Perth Basin, Western Australia, Australia","kind":"region","role":"regional groundwater basin affected by rainfall decline and groundwater recharge reduction","river_name":null,"geocode_query":"Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In response to anthropogenic changes in stratospheric ozone and then atmospheric greenhouse gases18, rainfall has decreased by 10–25% overall across the region since the 1960s19. This has resulted in an annual rainfall decrease of between 100 and 230 mm since 1971–2010 compared to 1911–1970 (Fig. 1). Several local studies have since recognised the effect of reduced rainfall and increased abstraction on water resources within southwest Australia, as direct rainfall recharge has generally been found to be the major recharge mechanism20,21. For example, the Rottnest Island freshwater lens (Fig. 1) has decreased in size by 40% primarily due to the rainfall decrease20. Likewise, water table decline within the Perth Basin has been identified as a result of decreasing rainfall21,22. However, quantifying the amount of water table decline attributable to decreased rainfall input is complicated by increasing groundwater extraction and land use changes over the same time period23.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.837493,-31.895018],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Perth WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-5","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Lake Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","kind":"field_site","role":"location with overlapping water table data monitoring rainfall recharge dynamics","river_name":null,"geocode_query":"Lake Cave, Leeuwin-Naturaliste National Park, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"This is also supported by water table measurements at Lake Cave (2000–2022; Fig. 1 for location) overlapping the period of drip discharge monitoring (2005–2022; Golgotha Cave) in Fig. 6. For example, higher rainfall in 2013 (Fig. 6a) resulted in a rapid rise in discharge for drips with a greater preferential-flow influence (Fig. 6b) and this coincided with the local water table also rising approximately 0.5 m (Fig. 6c). In comparison, the discharge from diffuse-flow dominated drips showed little to no response (Fig. 6b). We also compared the isotopic value of drip water and groundwater, and this further supports that rainfall recharge to groundwater beneath these caves occurs primarily via preferential flow. i.e., Fig. 5 shows that the distribution mode of preferentialflow-influenced drip water δ18O values (−4.67 ± 0.06‰VMSOW; campaign data) closely agrees with that of Holocene groundwater δ18O values (mode: −4.67 ± 0.03‰).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Rottnest Island, Western Australia, Australia","kind":"region","role":"comparison site demonstrating 40% reduction in freshwater lens volume due to rainfall decline","river_name":null,"geocode_query":"Rottnest Island, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In response to anthropogenic changes in stratospheric ozone and then atmospheric greenhouse gases18, rainfall has decreased by 10–25% overall across the region since the 1960s19. This has resulted in an annual rainfall decrease of between 100 and 230 mm since 1971–2010 compared to 1911–1970 (Fig. 1). Several local studies have since recognised the effect of reduced rainfall and increased abstraction on water resources within southwest Australia, as direct rainfall recharge has generally been found to be the major recharge mechanism20,21. For example, the Rottnest Island freshwater lens (Fig. 1) has decreased in size by 40% primarily due to the rainfall decrease20. Likewise, water table decline within the Perth Basin has been identified as a result of decreasing rainfall21,22. However, quantifying the amount of water table decline attributable to decreased rainfall input is complicated by increasing groundwater extraction and land use changes over the same time period23.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.5398997,-31.9965027],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417873,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rottnest Island WA 6161, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-7"},{"name":"Cape Leeuwin - Cape Naturaliste, Western Australia, Australia","kind":"region","role":"coastal limestone belt hosting study caves across a 30-km transect","river_name":null,"geocode_query":"Cape Leeuwin and Cape Naturaliste, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Climate models indicate, with high confidence, that rainfall in this region is likely to decline further throughout the 21st century18 which will continue to impact water availability in southwest Australia21,22. This will have a consequent impact on water-dependent ecosystems24 and water security for this region that currently depends on groundwater to meet 75% of its water demand21. In this study, we constructed a composite speleothem δ18O record using seven speleothems from four caves across a 30-km transect in southwest Australia (Fig. 1; see Supplementary Fig. 1 for speleothem images). All caves are located in the LeeuwinNaturaliste National Park and are developed in the Late Quaternary aeolian calcarenite that outcrops along the coast25. All caves are located in the vadose zone and receive recharge from rainfall only.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.0166667,-33.5333333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412624,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cape Naturaliste, Naturaliste WA 6281, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-cf90c6adaedaf60327261bbf-site-8"}],"limitations":["Exact cave coordinates not disclosed to protect conservation, coordinates given are approximate","Study region covers multiple caves within a 30-km transect but precise spatial variation is limited by data availability","Temporal coverage for some speleothems ceases before present, limiting continuous record length in some locations"],"river_ids":[],"site_ids":["doc-cf90c6adaedaf60327261bbf-site-0","doc-cf90c6adaedaf60327261bbf-site-1","doc-cf90c6adaedaf60327261bbf-site-2","doc-cf90c6adaedaf60327261bbf-site-3","doc-cf90c6adaedaf60327261bbf-site-4","doc-cf90c6adaedaf60327261bbf-site-5","doc-cf90c6adaedaf60327261bbf-site-7","doc-cf90c6adaedaf60327261bbf-site-8"],"anchor":[115.1,-34.24],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040055960,5040056460,5040056470],"main_basin_ids":[5040055960,5040056460,5040056470]},{"id":"doc-d2521a40c450420a0f730f5b","title":"Estimation of groundwater storage loss for the Indian Ganga Basin using multiple lines of evidence","display_title":"Groundwater Storage Decline in Indian Ganga Basin","doi":"https://doi.org/10.1038/s41598-023-28615-y","theme":"Groundwater","color":"#a8b9ff","year":2023,"publication_date":"2023-01-31","authors":["Sreekanth Janardhanan","Akhilesh S. Nair","J. Indu","Dan Pagendam","G. S. Kaushika"],"summary":"This study quantifies long-term groundwater storage loss in the Indian Ganga Basin regional aquifer using three complementary approaches: trend analysis of groundwater levels from 2851 monitoring bores (1996–2017), GRACE satellite-derived groundwater storage anomalies (2003–2016), and probabilistic numerical groundwater modeling that incorporates hydrogeological uncertainties. Results consistently indicate significant groundwater depletion across the basin, with highest rates of storage loss occurring in western basin states.","finding":"Groundwater storage in the Ganga Basin aquifer is declining significantly, with median rates of decline estimated as −2.6 cm year⁻¹ from observed groundwater levels, −3.2 cm year⁻¹ from numerical modeling, and −1.7 cm year⁻¹ from GRACE satellite data. The highest declining trends occur in the western basin states of Rajasthan (−14±4 cm year⁻¹), Haryana (−7.5±6.5 cm year⁻¹), and Delhi (−7.2±4.8 cm year⁻¹), while eastern states show lower but still significant decline.","finding_evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"topics":["groundwater storage","groundwater depletion","remote sensing","numerical groundwater modeling","trend analysis","hydrogeology","GRACE satellite data","aquifer heterogeneity"],"study_period":"1996-2017 (groundwater levels); 2003-2016 (GRACE and modeling)","locations":[{"name":"Ganga Basin","kind":"basin","role":"Study area for groundwater storage assessment","river_name":null,"geocode_query":"Ganga Basin, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 Vol.:(0123456789) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports Estimation of groundwater storage loss for the Indian Ganga Basin using multiple lines of evidence Sreekanth Janardhanan1*, Akhilesh S. Nair2 , J. Indu3,4, Dan Pagendam5 & G. S. Kaushika6 We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land Data Assimilation System (GLDAS) data sets and numerical modelling of long-term groundwater storage changes underpinned by over 50,000 groundwater level observations and uncertainty analysis.","verified_in_page":true},"resolution_status":"resolved","coordinates":[80.9175072,26.9549557],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-d2521a40c450420a0f730f5b-site-0"},{"name":"Rajasthan, India","kind":"region","role":"Western Ganga Basin state with highest groundwater storage decline","river_name":null,"geocode_query":"Rajasthan, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Haryana, India","kind":"region","role":"Western Ganga Basin state showing substantial groundwater depletion","river_name":null,"geocode_query":"Haryana, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Delhi, India","kind":"settlement","role":"Urban area within western Ganga Basin with significant groundwater storage decline","river_name":null,"geocode_query":"Delhi, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Uttar Pradesh, India","kind":"region","role":"Eastern Ganga Basin state with moderate groundwater storage decline","river_name":null,"geocode_query":"Uttar Pradesh, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Bihar, India","kind":"region","role":"Eastern Ganga Basin state with lower groundwater storage decline","river_name":null,"geocode_query":"Bihar, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"West Bengal, India","kind":"region","role":"Eastern Ganga Basin state showing lowest groundwater storage decline among studied states","river_name":null,"geocode_query":"West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"6 Vol:.(1234567890) Scientific Reports | (2023) 13:1797 | https://doi.org/10.1038/s41598-023-28615-y www.nature.com/scientificreports/ flow that is stored in the aquifer and released out resulting in groundwater level changes. The difference between these two components over long period of time represents the rate of storage loss (or build up). The annual average rate of storage loss was computed as − 3.2±1.0 cm year−1. This estimated value indicates that the average storage in the aquifer across the basin is declining. Groundwater balance and rate of storage decline was also analysed for the aquifer at the state level considering 6 major states within the basin. It was observed that groundwater storage loss is highest in the western states of the basin with the steepest average declining rate of − 14±4.0 cm year−1 in the state of Rajasthan. The states of Haryana and Delhi have storage declines at − 7.5±6.5 and − 7.2±4.8 cm year−1 respectively.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Terai Plains, Nepal","kind":"landscape","role":"Source of groundwater inflow across northern boundary into Ganga Basin aquifer","river_name":null,"geocode_query":"Terai Plains, Nepal","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"These are not natural groundwater boundaries and hence head dependent flux boundary conditions were used to represent them in the model to simulate the predominant inflow of groundwater from the Terai plains of Nepal into the broader Ganga Basin in India as well as the predominant outflow of groundwater into to northwest region of Bangladesh. Model boundary on the west coincides with the boundary of the Ganga Basin. In the south the model boundary aligns with the extent of the alluvial aquifer. Model calibration and uncertainty analysis. A recently developed tool called Iterative Ensemble Smoother approach26 was used for model calibration and uncertainty analysis using the PEST+ +software suite. The PEST-IES offers an alternative approach for using the Jacobian matrix derived empirically from an ensemble of random parameter sets using the formulation proposed by Chen and Oliver27.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Exact latitude and longitude coordinates for study area and states were not provided, set as null.","Spatial resolution limits detailed sub-district scale assessment.","River flow data were not available, limiting representation of surface water-groundwater interactions.","Model relies on estimated groundwater abstraction at district scale which may have uncertainties.","GRACE satellite data provide coarse spatial resolution relative to aquifer heterogeneity."],"river_ids":[],"site_ids":["doc-d2521a40c450420a0f730f5b-site-0"],"anchor":[80.9175072,26.9549557],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040960480],"main_basin_ids":[4040025450]},{"id":"doc-d51769e2230d9ab99ac4c228","title":"High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections","display_title":"Global 1-km Köppen-Geiger Climate Maps 1901–2099","doi":"https://doi.org/10.1038/s41597-023-02549-6","theme":"Climate","color":"#98d4f1","year":2023,"publication_date":"2023-10-23","authors":["Hylke E. Beck","Tim R. McVicar","Noemi Vergopolan","Alexis Berg","Nicholas J. Lutsko","Ambroise Dufour","Zhenzhong Zeng","Xin Jiang","Albert I. J. M. van Dijk","Diego G. Miralles"],"summary":"This study presents Version 2 of high-resolution global Köppen-Geiger climate classification maps at 1 km resolution covering historical periods from 1901 to 2020 and future projections up to 2099. It uses carefully selected CMIP6 climate models with realistic CO2-induced warming, downscaled and bias-corrected for seven socio-economic scenarios (SSPs). The maps classify global land climates into five major and 30 sub-classes based on temperature and precipitation, offering detailed spatio-temporal distributions useful for ecological and climate impact studies.","finding":"The study found that from 1991–2020 to 2071–2099, the projected global land surface transitioning to a different major Köppen-Geiger class varies from 5% under low emissions SSP1-2.6, 8% under SSP2-4.5, to 13% under high emissions SSP5-8.5, with substantial biome shifts consistent with warming scenarios.","finding_evidence":{"passage_id":"p004-000","page":4,"quote":"Scientific Data | (2023) 10:724 | https://doi.org/10.1038/s41597-023-02549-6 4 www.nature.com/scientificdata/ www.nature.com/scientificdata middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. Under SSP2-4.5, the global land surface area (excluding Antarctica) with favorable climatic conditions for tropical, arid, temperate, cold, and polar vegetation is expected to undergo net changes of+8%,+4%, −3%, −2%, and −33%, respectively, from 1991–2020 to 2071–2099. Furthermore, we estimate that 2.6 million km2 (roughly the area of Argentina, the world’s eighth largest country) of the global land surface will transition from polar (E) to cold (D) between 1991–2020 and 2071–2099 under scenario SSP2-4.5, while 2.4 million km2 will transition from cold (D) to temperate (C), 1.1 million km2 will transition from cold (D) to arid (B), and 2.8 million km2 will transition from temperate (C) to tropical (A).","verified_in_page":true},"topics":["climate-classification","climate-projections","temperature-change","precipitation-change","model-evaluation","climate-impacts","spatial-downscaling","uncertainty-assessment"],"study_period":"1901-2099","locations":[{"name":"European Alps","kind":"region","role":"illustrative study region showing high-resolution Köppen-Geiger classification","river_name":null,"geocode_query":"Alps, Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Additionally, the new version covers six approximately 30-year periods (1901–1930, 1931–1960, 1961–1990, 1991–2020, 2041–2070, and 2071–2099), while V1 covered just two periods of different lengths (1980–2016 and 2071–2100). Figure 1 presents the newly derived Köppen-Geiger map for 1991–2020 and regional maps for the Alps (Europe) and the central Rocky Mountains (North America), illustrating the high level of detail. The classification accuracy, defined as the percentage of correctly classified classes using observations from meteorological stations worldwide as reference, ranged from 79.2% to 86.4% (Table 2). Recent studies found that a large number of CMIP6 climate models exhibit unrealistic CO2-induced warming rates27–30. To identify and exclude these models, we conducted a comprehensive assessment of CMIP6 climate models (see section “Constraining CMIP6 projections”).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.656782,46.947398],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alps","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-d51769e2230d9ab99ac4c228-site-0","true_coordinates":[9.6567822,46.8873983],"spread_km":6.7},{"name":"Central Rocky Mountains","kind":"region","role":"illustrative study region showing high-resolution Köppen-Geiger classification","river_name":null,"geocode_query":"Rocky Mountains, North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Additionally, the new version covers six approximately 30-year periods (1901–1930, 1931–1960, 1961–1990, 1991–2020, 2041–2070, and 2071–2099), while V1 covered just two periods of different lengths (1980–2016 and 2071–2100). Figure 1 presents the newly derived Köppen-Geiger map for 1991–2020 and regional maps for the Alps (Europe) and the central Rocky Mountains (North America), illustrating the high level of detail. The classification accuracy, defined as the percentage of correctly classified classes using observations from meteorological stations worldwide as reference, ranged from 79.2% to 86.4% (Table 2). Recent studies found that a large number of CMIP6 climate models exhibit unrealistic CO2-induced warming rates27–30. To identify and exclude these models, we conducted a comprehensive assessment of CMIP6 climate models (see section “Constraining CMIP6 projections”).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-94.7674486,35.8053663],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Rocky Mountain, OK, USA","basin_id":7040686450,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":414787.8,"hydrology_source":"hydrobasins","site_id":"doc-d51769e2230d9ab99ac4c228-site-1"},{"name":"Global land surface excluding Antarctica","kind":"region","role":"primary global study area for Köppen-Geiger climate classification and projected changes","river_name":null,"geocode_query":"Global land surface excluding Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen. Background & Summary The Köppen-Geiger classification remains1 , to this day, one of the most well-known and widely used climate classification systems.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Argentina","kind":"region","role":"used as area size reference for biome transition estimates","river_name":null,"geocode_query":"Argentina, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Scientific Data | (2023) 10:724 | https://doi.org/10.1038/s41597-023-02549-6 4 www.nature.com/scientificdata/ www.nature.com/scientificdata middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. Under SSP2-4.5, the global land surface area (excluding Antarctica) with favorable climatic conditions for tropical, arid, temperate, cold, and polar vegetation is expected to undergo net changes of+8%,+4%, −3%, −2%, and −33%, respectively, from 1991–2020 to 2071–2099. Furthermore, we estimate that 2.6 million km2 (roughly the area of Argentina, the world’s eighth largest country) of the global land surface will transition from polar (E) to cold (D) between 1991–2020 and 2071–2099 under scenario SSP2-4.5, while 2.4 million km2 will transition from cold (D) to temperate (C), 1.1 million km2 will transition from cold (D) to arid (B), and 2.8 million km2 will transition from temperate (C) to tropical (A).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Excludes Antarctica from global land surface analysis","Confidence levels may be overestimated in mountainous, coastal, and heterogeneous regions due to coarse original climate model resolution","Confidence intervals in early 20th-century historical maps may be overestimated due to data uncertainty","Selected CMIP6 model subset may still slightly overestimate future warming","Model genealogy not accounted for, possibly overrepresenting some biases","Potential biome changes not guaranteed to align with Köppen-Geiger classification shifts due to ecological and anthropogenic factors","Future map confidence reflects model uncertainty within scenarios, not uncertainty across all scenarios","Projection uncertainties influenced by limited model numbers for some SSP scenarios"],"river_ids":[],"site_ids":["doc-d51769e2230d9ab99ac4c228-site-0","doc-d51769e2230d9ab99ac4c228-site-1"],"anchor":[-94.7674486,35.8053663],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[2040023010,7040686450],"main_basin_ids":[2040023010,7040047060]},{"id":"doc-da1aca9c1d1f9f4fc1b89fb1","title":"An overview of groundwater response to a changing climate in the Murray-Darling Basin, Australia: potential implications for the basin system and opportunities for management","display_title":"Groundwater Response to Climate Change in MDB","doi":"https://doi.org/10.1007/s10040-023-02723-5","theme":"Groundwater","color":"#a8b9ff","year":2023,"publication_date":"2023-10-31","authors":["Rebecca Doble","Glen Walker","Russell S. Crosbie","Joseph H. A. Guillaume","Tanya M. Doody"],"summary":"This paper reviews the impacts of climate change on groundwater resources in the Murray-Darling Basin (MDB), Australia, a mostly semiarid basin heavily reliant on groundwater for irrigation, ecosystems, and town water supply. It synthesizes observed declines in groundwater levels, predicted reductions in recharge under future climates, changing surface water-groundwater connectivity, and risks to groundwater-dependent ecosystems (GDEs). The study highlights management challenges and opportunities, including adaptive and active groundwater management, improved monitoring, integrated surface-groundwater modeling, and incorporation of social and cultural values for sustainable water governance.","finding":"Groundwater levels in the MDB have been declining across many alluvial aquifers over the last 50 years, driven by reduced recharge due to less rainfall and possibly historical groundwater extraction, with projections indicating further declines under future drying climate scenarios.","finding_evidence":{"passage_id":"p005-001","page":5,"quote":"It fosters smarter, more cost-effective management of salt within the MDB, including salt interception operations and combined groundwater and river flow management. The implementation of these salinity management plans has resulted in improvements to river and dryland salinity. Salinity is recognised as being largely under control, but is still considered to be a risk to the basin which requires ongoing active management. Biophysical challenges and opportunities for groundwater in the MDB Trends in climate, groundwater use and their impacts on groundwater levels Historical changes in groundwater level Groundwater levels have been observed to be falling in the eight main alluvial groundwater systems in the MDB that represent almost 75% of groundwater use (Fu et al. 2022). Results from the statistical analysis of 910 bores in NSW by Fu et al. (2022) showed an overall decreasing trend in watertable levels across alluvial aquifers during the last 50 years (1971–2021).","verified_in_page":true},"topics":["groundwater recharge","climate change impacts","hydrogeology","groundwater levels","groundwater-dependent ecosystems","adaptive management","water resource management","surface water-groundwater connectivity"],"study_period":"1971–2021","locations":[{"name":"Murray-Darling Basin","kind":"basin","role":"primary study area","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Site description and historical groundwater management in the Murray‐Darling Basin The MDB covers an area of 1.06 million km2 in the southeast of Australia (Fig. 1), including sections of Queensland (Qld), New South Wales (NSW), Victoria (Vic) and South Australia (SA) and all of the Australian Capital Territory (ACT). The basin includes Köppen–Geiger climate types ranging from oceanic (Cfb) in the southern upland parts of the basin (11% of total area) and subhumid (Cfa) in the northern upland regions (26% of total area), to semiarid (Bsk and Bsh) and desert (arid) (Bwk and Bwh) in the western lowland parts of the basin (56 and 7% of total area respectively; Fig. 2). The river network drains from the Great Dividing Range, which stretches along the eastern and south-eastern boundary of the MDB, through the hotter and drier parts of inland Australia before discharging to the ocean in the Mediterranean climate (Csb) in South Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.498154,-31.082505],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Namoi River","kind":"river","role":"study river for groundwater impact on baseflow","river_name":"Namoi River","geocode_query":"Namoi River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"The nature of these varies with linked surface- water/groundwater determinations on regulated highly connected NSW streams; cease-to-pump rules common to surface and groundwater in unregulated perennial streams in NSW and Victoria; protection of baseflow in the Adelaide Fold Belt and protection of spring flow in Queensland. Outside of these plans, the expectation is that the SDL, together with annual allocations and local management rules should avoid adverse impacts on stream flow. This assumption allows groundwater and surface-water plans to be mostly developed in isolation. Historical increases in extraction have led to about 17% reduction in the median stream availability in the Namoi and about 6% in the Lachlan rivers (Walker 2022). Rassam et al. (2017) have shown how a reduction of this magnitude can affect low flows for the regulated Namoi.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.043889,-30.574167],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43674288,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-1","true_coordinates":[150.04388901700008,-30.634166583999956],"spread_km":6.7},{"name":"Lachlan River","kind":"river","role":"study river for groundwater impact on baseflow","river_name":"Lachlan River","geocode_query":"Lachlan River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-003","page":10,"quote":"The nature of these varies with linked surface- water/groundwater determinations on regulated highly connected NSW streams; cease-to-pump rules common to surface and groundwater in unregulated perennial streams in NSW and Victoria; protection of baseflow in the Adelaide Fold Belt and protection of spring flow in Queensland. Outside of these plans, the expectation is that the SDL, together with annual allocations and local management rules should avoid adverse impacts on stream flow. This assumption allows groundwater and surface-water plans to be mostly developed in isolation. Historical increases in extraction have led to about 17% reduction in the median stream availability in the Namoi and about 6% in the Lachlan rivers (Walker 2022). Rassam et al. (2017) have shown how a reduction of this magnitude can affect low flows for the regulated Namoi.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.496189,-33.322023],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677700,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-2","true_coordinates":[147.49618934400007,-33.26202250799997],"spread_km":6.7},{"name":"Darling River","kind":"river","role":"study river affected by flow reductions relevant to climate and groundwater interactions","river_name":"Darling River","geocode_query":"Darling River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The River Murray drains the southern part of the catchment, including the Loddon, Campaspe, Murrumbidgee, Goulburn-Broken and Lachlan rivers. The southern basin has winter-dominant rainfall, and higher and more reliable streamflow (Walker 2022). This, and the proximity to large cities in the southeast of Australia, has led to the earlier development of the southern basin and larger areas of irrigated agriculture. The Darling River drains the northern basin, and tributaries include the Paroo, Warrego and Maranoa Rivers (draining desert and semiarid regions), and Condamine-BaloneCulgoa, Gwydir, Namoi and Macquarie rivers (draining semiarid and subtropical regions). The northern basin is located in hot semiarid and subtropical climates, with summer dominant rainfall and higher seasonal variation, resulting in lower and more variable streamflow and higher susceptibility to drought (Bureau of Meteorology 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.57638898200003,-30.31611102599993],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43664391,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040533450,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":20319.1,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-3"},{"name":"River Murray","kind":"river","role":"major river draining southern MDB, focus area for groundwater and salinity management","river_name":"Murray River","geocode_query":"River Murray, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The River Murray drains the southern part of the catchment, including the Loddon, Campaspe, Murrumbidgee, Goulburn-Broken and Lachlan rivers. The southern basin has winter-dominant rainfall, and higher and more reliable streamflow (Walker 2022). This, and the proximity to large cities in the southeast of Australia, has led to the earlier development of the southern basin and larger areas of irrigated agriculture. The Darling River drains the northern basin, and tributaries include the Paroo, Warrego and Maranoa Rivers (draining desert and semiarid regions), and Condamine-BaloneCulgoa, Gwydir, Namoi and Macquarie rivers (draining semiarid and subtropical regions). The northern basin is located in hot semiarid and subtropical climates, with summer dominant rainfall and higher seasonal variation, resulting in lower and more variable streamflow and higher susceptibility to drought (Bureau of Meteorology 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.609624,-35.293959],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676891,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-4","true_coordinates":[143.5663889650001,-35.34249995499994],"spread_km":6.7},{"name":"Gwydir River","kind":"river","role":"northern tributary river system referenced for groundwater level trends","river_name":"Gwydir River","geocode_query":"Gwydir River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"It fosters smarter, more cost-effective management of salt within the MDB, including salt interception operations and combined groundwater and river flow management. The implementation of these salinity management plans has resulted in improvements to river and dryland salinity. Salinity is recognised as being largely under control, but is still considered to be a risk to the basin which requires ongoing active management. Biophysical challenges and opportunities for groundwater in the MDB Trends in climate, groundwater use and their impacts on groundwater levels Historical changes in groundwater level Groundwater levels have been observed to be falling in the eight main alluvial groundwater systems in the MDB that represent almost 75% of groundwater use (Fu et al. 2022). Results from the statistical analysis of 910 bores in NSW by Fu et al. (2022) showed an overall decreasing trend in watertable levels across alluvial aquifers during the last 50 years (1971–2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.95694458000003,-29.901944355999944],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43675469,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-5"},{"name":"Condamine-Balonne-Culgoa Rivers","kind":"river","role":"northern tributary rivers in semiarid and subtropical climates","river_name":null,"geocode_query":"Condamine-Balonne-Culgoa Rivers, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The River Murray drains the southern part of the catchment, including the Loddon, Campaspe, Murrumbidgee, Goulburn-Broken and Lachlan rivers. The southern basin has winter-dominant rainfall, and higher and more reliable streamflow (Walker 2022). This, and the proximity to large cities in the southeast of Australia, has led to the earlier development of the southern basin and larger areas of irrigated agriculture. The Darling River drains the northern basin, and tributaries include the Paroo, Warrego and Maranoa Rivers (draining desert and semiarid regions), and Condamine-BaloneCulgoa, Gwydir, Namoi and Macquarie rivers (draining semiarid and subtropical regions). The northern basin is located in hot semiarid and subtropical climates, with summer dominant rainfall and higher seasonal variation, resulting in lower and more variable streamflow and higher susceptibility to drought (Bureau of Meteorology 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.194737,-26.897087],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677463,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Condamine QLD 4416, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-6","true_coordinates":[150.1364566,-26.9270865],"spread_km":6.7},{"name":"Great Dividing Range","kind":"landscape","role":"eastern and south-eastern basin boundary and recharge source area","river_name":null,"geocode_query":"Great Dividing Range, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Site description and historical groundwater management in the Murray‐Darling Basin The MDB covers an area of 1.06 million km2 in the southeast of Australia (Fig. 1), including sections of Queensland (Qld), New South Wales (NSW), Victoria (Vic) and South Australia (SA) and all of the Australian Capital Territory (ACT). The basin includes Köppen–Geiger climate types ranging from oceanic (Cfb) in the southern upland parts of the basin (11% of total area) and subhumid (Cfa) in the northern upland regions (26% of total area), to semiarid (Bsk and Bsh) and desert (arid) (Bwk and Bwh) in the western lowland parts of the basin (56 and 7% of total area respectively; Fig. 2). The river network drains from the Great Dividing Range, which stretches along the eastern and south-eastern boundary of the MDB, through the hotter and drier parts of inland Australia before discharging to the ocean in the Mediterranean climate (Csb) in South Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.0,-25.06],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12124900,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Great Dividing Range, Caldervale QLD 4478, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-7","true_coordinates":[147,-25],"spread_km":6.7},{"name":"Adelaide Fold Belt","kind":"region","role":"area of fractured rock aquifers and groundwater management focus","river_name":null,"geocode_query":"Adelaide Fold Belt, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Productivity is relatively low in the Lachlan Fold Belt (Foster and Gray 2000), moderate in the western Adelaide Fold Belt (Habermehl 2020) and generally higher in parts of the upper New England and eastern Adelaide Fold belts. Major alluvial deposits consist of deposits of sands and gravels from the main river tributaries and exist in both upper paleovalleys, which are spatially defined by floodplain channels, and lower deep leads, which are more generally widespread reaches (Taylor et al. 2021). Key aquifer units are shown in Fig. 1b. The alluvial deposits are generally intermediate to regional groundwater systems with medium to high productivity and good quality water. These systems are often recharged by losing river systems (Lamontagne et al. 2014) in addition to diffuse recharge from rainfall (Crosbie et al. 2010b) and overbank flood recharge (Doble et al. 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.543529,-34.965908],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide SA, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-8","true_coordinates":[138.6007456,-34.9284989],"spread_km":6.7},{"name":"Lachlan Fold Belt","kind":"region","role":"region with relatively low aquifer productivity mentioned for groundwater extraction context","river_name":null,"geocode_query":"Lachlan Fold Belt, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Productivity is relatively low in the Lachlan Fold Belt (Foster and Gray 2000), moderate in the western Adelaide Fold Belt (Habermehl 2020) and generally higher in parts of the upper New England and eastern Adelaide Fold belts. Major alluvial deposits consist of deposits of sands and gravels from the main river tributaries and exist in both upper paleovalleys, which are spatially defined by floodplain channels, and lower deep leads, which are more generally widespread reaches (Taylor et al. 2021). Key aquifer units are shown in Fig. 1b. The alluvial deposits are generally intermediate to regional groundwater systems with medium to high productivity and good quality water. These systems are often recharged by losing river systems (Lamontagne et al. 2014) in addition to diffuse recharge from rainfall (Crosbie et al. 2010b) and overbank flood recharge (Doble et al. 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.691375,-33.830919],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666222,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lachlan River, New South Wales, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-9","true_coordinates":[146.6913748976105,-33.89091850027836],"spread_km":6.7},{"name":"Murray Basin","kind":"region","role":"location of porous rock aquifers and groundwater salinity concerns","river_name":null,"geocode_query":"Murray Basin, South Australia and Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Porous rock aquifers are located within the arid and semiarid western Murray Basin (Fig. 1a), where stored groundwater is thought to be a result of paleorecharge from an era when the climate was wetter (Commander 2004). Water resources are generally saline in the central and western parts of the Murray Geological Basin, with fresher areas in the eastern region closer to recharge Fig. 1 Murray-Darling Basin system, including a simplified geological regions, b major alluvial groundwater systems and c location of the basin across the five states and territory in Australia. States: SA – South Australia, Qld – Queensland, NSW – New South Wales, ACT – Australian Capital Territory, Vic – Victoria","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.762027,-35.46926],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-10","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Grassland floodplains in South Australia","kind":"landscape","role":"area of salt interception schemes and salinity management","river_name":null,"geocode_query":"Floodplains, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"As a response, groundwater extraction networks to intercept saline groundwater prior to reaching the river valley (salt interception schemes), have been constructed in South Australia, NSW and Victoria (Middlemis et al. 2005; Munday et al. 2005). Water management Groundwater in Australia is managed by state or territory jurisdictions, under regulation and frameworks developed by the federal government. A more detailed overview of national groundwater management and legislation can be gained from Nelson et al. (2020), Cook et al. (2022), Rinaudo et al. (2020), Walker et al. (2021b). A brief overview is presented in the following. The 1994 National Water Reform Framework Agreement instigated water allocation systems in Australia at a national scale and formed an agreement between the federal government and all states and territories (Nelson et al. 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Namoi region","kind":"region","role":"site of groundwater modeling studies and application of deep learning for groundwater predictions","river_name":null,"geocode_query":"Namoi region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"As defined by Richardson et al. (2011), GDEs are ‘natural ecosystems that require access to groundwater to meet all or some of their water requirements on a permanent or intermittent basis, so as to maintain their communities of plants and animals, ecosystem processes and ecosystem services’. GDEs include springs, which demonstrate a surface expression of groundwater, cave and aquifer aquatic ecosystems and riparian ecosystems, where groundwater contributes to the hydrological environment and estuarine and marine ecosystems relying on submarine discharge of groundwater (Doody et al. 2017; Eamus et al. 2006; Golder Associates Pty Ltd 2021; Hatton and Evans 1998; Richardson et al. 2011).","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.640566,-30.495113],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Namoi River, New South Wales, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-12","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7},{"name":"Barwon-Darling system","kind":"river","role":"river system where environmental watering has mitigated some groundwater level effects","river_name":"Barwon-Darling River","geocode_query":"Barwon-Darling River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"Groundwater extraction from these systems can result in a reduction in evapotranspiration and decline in the condition of terrestrial GDEs at the end of valleys (Walker et al. 2020b). The decreased connectivity of pools along streams can be somewhat mitigated through environmental watering in regulated systems, which occurred in 2018 and 2019 for over 1,000 km of the Barwon-Darling system (Eco Logical Australia 2020; MDBA 2018). Replacing the source of water for the terrestrial GDEs at the end of valleys requires engineered flooding (Bond et al. 2014). Integrated water management plans exist for a small proportion of the MDB, and accounts for a small proportion of extraction.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.6985569472199,-29.49591194652932],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43668025,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barwon River, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-13"},{"name":"Coleambally Irrigation Area","kind":"field_site","role":"location investigated for managed aquifer recharge and water banking potential","river_name":null,"geocode_query":"Coleambally Irrigation Area, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-000","page":14,"quote":"Hydrogeology Journal 1 3 concept of “water banking” (Gonzalez et al. 2020; Page et al. 2022). MAR schemes supplement natural recharge, addressing imbalances between desired surface and groundwater storages that cannot be achieved through conjunctive use or to compensate for limitations of existing management. This can allow full utilisation of existing water allocations, support resilience in times of drought, or provide a tool to improve reliability, flexibility and efficiency of existing water delivery systems (Harvey et al. 2023). Page et al. (2022) concluded that the current Basin Plan supports water banking, but that existing water accounting systems would need to be adjusted and there is potential for demonstration sites to facilitate policy development while providing critical experience to support learning and wider adoption.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.891321,-34.949081],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43663510,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coleambally Irrigation Area, NSW, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-14","true_coordinates":[145.8913207,-34.8890807],"spread_km":6.7},{"name":"NSW Groundwater Strategy study area","kind":"region","role":"location of projected recharge impacts under drying climate and local recharge sensitivity assessment","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"Hydrogeology Journal 1 3 world (and within the MDB), information on changes to localised recharge are far less common. The changes in recharge due to losing streams and overbank flooding are dependent upon changes in river flows rather than climate directly and so require the additional step of modelling river flows before climate change impacts on recharge can be assessed. One example of predicting changes in localised recharge due to climate change covering a large proportion of the MDB was conducted for the NSW Groundwater Strategy (DPE 2022). This used a single dry future climate scenario to investigate potential risks to groundwater resources rather than a ‘best’ estimate of the future climate (Crosbie et al. 2022b). The sensitivity of the changes in recharge to changes in rainfall can be assessed as the slope of a scatterplot in a similar way that ‘elasticity’ is often used in rainfall-runoff modelling (Fig. 5).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Eastern uplands of MDB","kind":"landscape","role":"area of fractured rock aquifers with diffuse recharge from higher rainfall","river_name":null,"geocode_query":"Eastern uplands, Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Hydrogeology Journal 1 3 Three broad groups of aquifers important for water resources are present in the MDB: fractured rock aquifers in the eastern upland and far western areas of the basin, alluvial deposits associated with the major tributary floodplains and porous rock aquifers in the northern and western parts of the basin (Fig. 1a; Hart et al. 2020). Deeper geology of the MDB is described in Kingham (1998), and includes the Great Artesian Basin (GAB). Fractured rock aquifers occur throughout the Great Dividing Range in Queensland, New South Wales, the ACT and Victoria, and in the Mount Lofty Ranges of South Australia. The aquifers are generally local to intermediate in scale (defined by Tóth 1963), of moderate productivity and contribute to baseflow in upland rivers. Recharge is predominantly diffuse due to higher rainfall in these regions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Murrumbidgee River","kind":"river","role":"southern basin tributary river with winter rainfall influence","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"The River Murray drains the southern part of the catchment, including the Loddon, Campaspe, Murrumbidgee, Goulburn-Broken and Lachlan rivers. The southern basin has winter-dominant rainfall, and higher and more reliable streamflow (Walker 2022). This, and the proximity to large cities in the southeast of Australia, has led to the earlier development of the southern basin and larger areas of irrigated agriculture. The Darling River drains the northern basin, and tributaries include the Paroo, Warrego and Maranoa Rivers (draining desert and semiarid regions), and Condamine-BaloneCulgoa, Gwydir, Namoi and Macquarie rivers (draining semiarid and subtropical regions). The northern basin is located in hot semiarid and subtropical climates, with summer dominant rainfall and higher seasonal variation, resulting in lower and more variable streamflow and higher susceptibility to drought (Bureau of Meteorology 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.431539,-35.069972],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677696,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-da1aca9c1d1f9f4fc1b89fb1-site-17","true_coordinates":[147.36802785600003,-35.09997214699996],"spread_km":6.7},{"name":"Victoria portion of MDB","kind":"region","role":"area of groundwater extraction trends and adaptive allocations","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"The reporting over the last decade shows that there has been an increasing trend in groundwater extraction from 2012–2013 to 2018–2019, but this variance in groundwater extraction was able to be explained by responses to rainfall variation, where rates of irrigation are increased to offset lower seasonal rainfall, rather than growth in new licences (Walker 2022). There was no significant increase in groundwater extraction for the non-Victorian portion of the MDB for the period 2003–2004 to 2020–2021. Although there has been an increasing trend in groundwater extraction within the Victorian portion of the MDB, groundwater thresholds were able to be maintained with reductions in seasonal allocations (Walker 2023). Taken together, these results suggest only a very minor potential increase in near future groundwater extraction volumes based on historical trends. The overall lack of increase in groundwater extraction is due to a range of causes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"New South Wales portion of MDB","kind":"region","role":"area with groundwater extraction limits under Basin Plan","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"The reporting over the last decade shows that there has been an increasing trend in groundwater extraction from 2012–2013 to 2018–2019, but this variance in groundwater extraction was able to be explained by responses to rainfall variation, where rates of irrigation are increased to offset lower seasonal rainfall, rather than growth in new licences (Walker 2022). There was no significant increase in groundwater extraction for the non-Victorian portion of the MDB for the period 2003–2004 to 2020–2021. Although there has been an increasing trend in groundwater extraction within the Victorian portion of the MDB, groundwater thresholds were able to be maintained with reductions in seasonal allocations (Walker 2023). Taken together, these results suggest only a very minor potential increase in near future groundwater extraction volumes based on historical trends. The overall lack of increase in groundwater extraction is due to a range of causes.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Australian Capital Territory (ACT)","kind":"region","role":"state/territory part of MDB coverage","river_name":null,"geocode_query":"Australian Capital Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Site description and historical groundwater management in the Murray‐Darling Basin The MDB covers an area of 1.06 million km2 in the southeast of Australia (Fig. 1), including sections of Queensland (Qld), New South Wales (NSW), Victoria (Vic) and South Australia (SA) and all of the Australian Capital Territory (ACT). The basin includes Köppen–Geiger climate types ranging from oceanic (Cfb) in the southern upland parts of the basin (11% of total area) and subhumid (Cfa) in the northern upland regions (26% of total area), to semiarid (Bsk and Bsh) and desert (arid) (Bwk and Bwh) in the western lowland parts of the basin (56 and 7% of total area respectively; Fig. 2). The river network drains from the Great Dividing Range, which stretches along the eastern and south-eastern boundary of the MDB, through the hotter and drier parts of inland Australia before discharging to the ocean in the Mediterranean climate (Csb) in South Australia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Queensland portion of MDB","kind":"region","role":"state portion of MDB covering northern basin tributaries","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Site description and historical groundwater management in the Murray‐Darling Basin The MDB covers an area of 1.06 million km2 in the southeast of Australia (Fig. 1), including sections of Queensland (Qld), New South Wales (NSW), Victoria (Vic) and South Australia (SA) and all of the Australian Capital Territory (ACT). The basin includes Köppen–Geiger climate types ranging from oceanic (Cfb) in the southern upland parts of the basin (11% of total area) and subhumid (Cfa) in the northern upland regions (26% of total area), to semiarid (Bsk and Bsh) and desert (arid) (Bwk and Bwh) in the western lowland parts of the basin (56 and 7% of total area respectively; Fig. 2). The river network drains from the Great Dividing Range, which stretches along the eastern and south-eastern boundary of the MDB, through the hotter and drier parts of inland Australia before discharging to the ocean in the Mediterranean climate (Csb) in South Australia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"South Australia portion of MDB","kind":"region","role":"state portion including downstream end of basin and saline groundwater management","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Site description and historical groundwater management in the Murray‐Darling Basin The MDB covers an area of 1.06 million km2 in the southeast of Australia (Fig. 1), including sections of Queensland (Qld), New South Wales (NSW), Victoria (Vic) and South Australia (SA) and all of the Australian Capital Territory (ACT). The basin includes Köppen–Geiger climate types ranging from oceanic (Cfb) in the southern upland parts of the basin (11% of total area) and subhumid (Cfa) in the northern upland regions (26% of total area), to semiarid (Bsk and Bsh) and desert (arid) (Bwk and Bwh) in the western lowland parts of the basin (56 and 7% of total area respectively; Fig. 2). The river network drains from the Great Dividing Range, which stretches along the eastern and south-eastern boundary of the MDB, through the hotter and drier parts of inland Australia before discharging to the ocean in the Mediterranean climate (Csb) in South Australia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Broad regional geographies without precise site coordinates","No explicit numeric latitude/longitude data available for locations mentioned","Limited temporal resolution beyond 1971–2021 for groundwater level trends","Uncertainty inherent in climate model projections and recharge modeling noted in the 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040069590,5040073420,5040511570,5040524870,5040533450,5040591010,5040591050],"main_basin_ids":[5040069590,5040073410,5040073420]},{"id":"doc-fd0cdd372fc23449c9178731","title":"The floodplain inundation history of the Murray-Darling Basin through two-monthly maximum water depth maps","display_title":"Floodplain Inundation History via Depth Maps","doi":"https://doi.org/10.1038/s41597-023-02559-4","theme":"Floodplains","color":"#e9bd77","year":2023,"publication_date":"2023-09-23","authors":["Dave Penton","Jin Teng","Catherine Ticehurst","Steve Marvanek","Andrew Freebairn","Cherry May R. Mateo","Jai Vaze","Ang Yang","Fathaha Khanam","Ashmita Sengupta","Carmel Pollino"],"summary":"This study developed a comprehensive timeseries dataset of floodplain inundation depth across the entire Murray-Darling Basin (MDB), Australia (~1 million km²), spanning 1988 to 2022 at two-monthly intervals with approximately 30 m spatial resolution. Using remotely sensed imagery and the Floodwater Depth Estimation Tool (FwDET) applied to 23 hydrologically defined subregions, the dataset provides maximum floodwater depth maps validated against hydrodynamic models with reasonable error (mean absolute error ~0.49 m). The data supports system-wide flood pattern analysis and ecological research, aiding flood management, ecosystem services valuation, and informed decision-making related to MDB floodplain ecosystems.","finding":"FwDET-derived flood depth estimates for the MDB systematically underpredicted flood depths with an average bias of 0.32 m, but exhibited reasonable accuracy overall, with a median mean absolute error of 0.49 m when compared to selected hydrodynamic model outputs across diverse MDB floodplains.","finding_evidence":{"passage_id":"p007-004","page":7,"quote":"In other words, if the mean error were subtracted from the outputs, 75% of values were within 0.09m of the benchmark model. As shown in Table 1, the benchmark versus predicted RMSE ranged from 0.48m to 1.29m and MAE ranged from 0.36 m to 0.87 m across the flood events. The median MAE was 0.49 m with an IQR of 0.39 m to 0.54 m (established through statistical bootstrapping). The MAE demonstrated reasonable accuracy and reliability for a dataset of this spatial and temporal coverage. A greater level of accuracy would be required for emergency management or the engineering of flood protection levees (e.g. +/− 0.15m). However, the level of accuracy demonstrated is sufficient for understanding ecosystem processes, regional planning and decision-making, especially when used to compare scenarios, infer correlations or develop causative hypotheses.","verified_in_page":true},"topics":["remote sensing","hydrodynamic modeling","flood depth estimation","digital elevation model","floodplain ecology","water resource management","spatial analysis","dataset validation"],"study_period":"1988-2022","locations":[{"name":"Murray-Darling Basin","kind":"basin","role":"primary study area for floodplain inundation depth mapping and analysis","river_name":null,"geocode_query":"Murray-Darling Basin, South-Eastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Scientific Data | (2023) 10:652 | https://doi.org/10.1038/s41597-023-02559-4 1 www.nature.com/scientificdata The floodplain inundation history of the Murray-Darling Basin through two-monthly maximum water depth maps David J. Penton 1 ✉, JinTeng1, CatherineTicehurst1, Steve Marvanek1, Andrew Freebairn1, Cherry Mateo1, JaiVaze1, AngYang1, Fathaha Khanam2, Ashmita Sengupta1 & Carmel Pollino1 With growing concerns over water management in rivers worldwide, researchers are seeking innovative solutions to monitor and understand changing flood patterns. In a noteworthy advancement, stakeholders interested in the changing flood patterns of the Murray Darling Basin (MDB) in Australia, covering an area of 1 million km2, can now access a consistent timeseries of water depth maps for the entire basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.486943,-31.088672],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Lower Balonne System floodplains","kind":"field_site","role":"validation site for flood depth estimates using hydrodynamic models","river_name":null,"geocode_query":"Lower Balonne System, Queensland and New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Scientific Data | (2023) 10:652 | https://doi.org/10.1038/s41597-023-02559-4 6 www.nature.com/scientificdata/ www.nature.com/scientificdata Selection of hydrodynamic models for benchmarking. After we had conducted a thorough review of 13 hydrodynamic models for the MDB16, we carefully selected three representative hydrodynamic models38–40 that covered different parts of the MDB, as illustrated in Fig. 3. The first model was chosen to represent the floodplains of the Lower Balonne System (LBS) in Queensland and New South Wales, while the second model represented the floodplains of the Gunbower-Koondrook-Perricoota Forest (GKPF) in New South Wales and Victoria. The third model encompassed the floodplains downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The selection process was guided by several factors, including input data quality, validation against gauge height measurements, peer review of the model setup, and availability to the project team.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Gunbower-Koondrook-Perricoota Forest floodplains","kind":"field_site","role":"validation site for flood depth estimates using hydrodynamic models","river_name":null,"geocode_query":"Gunbower Forest, Murray-Darling Basin, New South Wales and Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Scientific Data | (2023) 10:652 | https://doi.org/10.1038/s41597-023-02559-4 6 www.nature.com/scientificdata/ www.nature.com/scientificdata Selection of hydrodynamic models for benchmarking. After we had conducted a thorough review of 13 hydrodynamic models for the MDB16, we carefully selected three representative hydrodynamic models38–40 that covered different parts of the MDB, as illustrated in Fig. 3. The first model was chosen to represent the floodplains of the Lower Balonne System (LBS) in Queensland and New South Wales, while the second model represented the floodplains of the Gunbower-Koondrook-Perricoota Forest (GKPF) in New South Wales and Victoria. The third model encompassed the floodplains downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The selection process was guided by several factors, including input data quality, validation against gauge height measurements, peer review of the model setup, and availability to the project team.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"River Murray reaches between Lock 6 and Lock 3","kind":"reach","role":"validation site for flood depth estimates using hydrodynamic models","river_name":"Murray River","geocode_query":"Murray River between Lock 6 and Lock 3, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Scientific Data | (2023) 10:652 | https://doi.org/10.1038/s41597-023-02559-4 6 www.nature.com/scientificdata/ www.nature.com/scientificdata Selection of hydrodynamic models for benchmarking. After we had conducted a thorough review of 13 hydrodynamic models for the MDB16, we carefully selected three representative hydrodynamic models38–40 that covered different parts of the MDB, as illustrated in Fig. 3. The first model was chosen to represent the floodplains of the Lower Balonne System (LBS) in Queensland and New South Wales, while the second model represented the floodplains of the Gunbower-Koondrook-Perricoota Forest (GKPF) in New South Wales and Victoria. The third model encompassed the floodplains downstream of Lock 6 and upstream of Lock 3 on the River Murray in South Australia (SA). The selection process was guided by several factors, including input data quality, validation against gauge height measurements, peer review of the model setup, and availability to the project team.","verified_in_page":true},"resolution_status":"resolved","coordinates":[135.755965,-33.5679236],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":61127579,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock SA 5633, Australia","basin_id":5040074330,"main_basin_id":5040074330,"basin_region":"Australasia","basin_area_km2":100682.8,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-3"},{"name":"St George gauge site","kind":"station","role":"hydrological gauge used to characterize flood events for validation in the Lower Balonne region","river_name":null,"geocode_query":"St George, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"This resulted in flood dynamics in the Lower Balonne floodplain that were intricate because multiple upstream river systems contributed to the overbank flooding. The peak heights of the three events at St George gauge (422201) were 10.6 m on 1996-01-20, 12.8m on 2011-01-08 and 13.5m on 2012-02-07 with estimated discharges of 1872 m3 s−1 , 2964 m3 s−1 , and 3963 m3 s−1 . The floodwaters remained at major flood levels (overbank) for a number of weeks. The SA and GKPF models simulated flooding during the November 2016 period. Earlier, in September, the Murray-Darling Basin experienced its wettest September on record with average aerial rainfall of 119mm, which was 249% above the long-term average44. This continued through to November and marked a shift from drought to flood. The heavy rainfall was widespread and affected both the upper catchments along the eastern mountain range of the MDB (i.e. Great Dividing Range) and the broader Basin area.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.580104,-27.976845],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670095,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"St George QLD 4487, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-4","true_coordinates":[148.5801042,-28.0368451],"spread_km":6.7},{"name":"Lake Victoria","kind":"other","role":"permanently inundated lake affecting bathymetry and flood depth accuracy","river_name":null,"geocode_query":"Lake Victoria, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-006","page":7,"quote":"The bias is likely to be similar, so to improve the accuracy of depth estimates, it is recommended to apply a bias correction by adding 0.32m to the predictions. This adjustment aims to address any systematic deviations and align the estimates more closely with the true values. Moreover, to assess the level of uncertainty associated with the predictions, it is possible to estimate uncertainty ranges using Laplace or Cauchy probability distributions. These distributions provide a means to quantify the likelihood of different outcomes and can help in understanding the potential variability in the estimated depths. Review of outputs. The outputs were reviewed by the project team and external stakeholders to identify limitations and issues. Four issues were identified: accuracy around permanently inundated lakes, water classification in dense forest, persistent cloud cover and misclassified water pixels. In areas with estimated bathymetry, especially around permanently inundated lakes (e.g.","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.2841298,-33.9763408],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660956,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Victoria, Rufus NSW 2648, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-5"},{"name":"Koondrook-Perricoota Forest and Barmah Forest","kind":"field_site","role":"densely forested areas where dense canopy affects water classification accuracy in optical remote sensing","river_name":null,"geocode_query":"Koondrook-Perricoota Forest, Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"Scientific Data | (2023) 10:652 | https://doi.org/10.1038/s41597-023-02559-4 8 www.nature.com/scientificdata/ www.nature.com/scientificdata errors are likely to occur. Where the adjustment values are greater than zero, we recommend future research to improve the estimation of bathymetry. Future research could involve collecting sonar bathymetry for permanently inundated regions and collecting LiDAR for ephemerally inundated regions when dry. It may also be possible to infer bathymetry with remote sensing using approaches modified from those applied to coastal regions47. In the areas of Koondrook-Perricoota Forest, and some areas around Barmah Forest, the forest density appears to affect the water classification algorithms. Areas with a closed canopy may be classified as dry by optical remote sensing when they are actually inundated. This has the consequence that the depth product underpredicts inundation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.1260831,-35.642752],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Koondrook VIC 3580, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-6"},{"name":"Gunbower Forest south of Koondrook-Perricoota Forest","kind":"field_site","role":"area without water classification issues compared to nearby forests, highlighting localized effects of forest density","river_name":null,"geocode_query":"Gunbower Forest, Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The area south of Koondrook-Perricoota Forest – Gunbower Forest does not appear to have the same issue, so it appears the effect is localised to very densely forested areas of the Murray. Future research could investigate if there are alternate algorithms, remotely sensed indices or thresholds that perform better for water classification, or whether higher resolution optical sensors can improve classification accuracy. Since the product uses optical remote sensing inputs, persistent cloud cover affects the flood extents used to calculate flood depth. The percentage of cloud cover in a two-monthly image of the MDB ranges from 0.1% up to 38% (which was in the 1990’s when only one Landsat sensor was operating), with an average value of 3%. A two-month period almost always has a cloud-free satellite overpass and major floods in the MDB take months to travel downstream. However, the flood peak is unlikely to be captured perfectly.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.1410565,-35.6478213],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677639,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"942R+VC Gunbower Forest, Koondrook VIC 3580, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-fd0cdd372fc23449c9178731-site-7"}],"limitations":["DEM vertical accuracy varies spatially; LiDAR provides higher precision than SRTM, with some areas at 5-15m RMSE (p005-001).","Optical remote sensing cannot penetrate cloud; persistent cloud cover can lead to missing or inaccurate flood detection in some periods (p008-001).","Water classification errors occur in densely forested canopies, causing underestimation of inundation extent and depth (p008-000).","Bathymetry estimates in permanently inundated lakes can be inaccurate, causing errors in flood depth, especially near lake edges and centers (p007-006).","The FwDET model underpredicts depths by a mean bias of 0.32 m; bias correction is recommended for more accurate depths (p007-004).","Some artefacts exist from satellite swath edges, cloud shadows and Landsat 7 scan line errors affecting pixel classification and depth estimation (p008-002).","Hydrodynamic models used for validation are themselves approximations; additional field validation could improve confidence in results (p009-001).","Results are best suited for system-wide analyses over time rather than emergency flood response or fine-scale engineering applications requiring +/- 0.15 m accuracy 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040073410,5040074330,5040511570,5040591010,5040591050],"main_basin_ids":[5040073410,5040074330]},{"id":"doc-0376203208e62f8aa78f4a41","title":"The Potential of Hydrogeodesy to Address Water‐Related and Sustainability Challenges","display_title":"Hydrogeodesy: Monitoring Global Water Changes","doi":"https://doi.org/10.1029/2023wr037020","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-11-01","authors":["Fernando Jaramillo","Saeid Aminjafari","Pascal Castellazzi","Ayan Santos Fleischmann","Etienne Fluet‐Chouinard","Hossein Hashemi","Clara Hübinger","Hilary R. Martens","Fabrice Papa","Tilo Schöne","Angelica Tarpanelli","Vili Virkki","Lan Wang‐Erlandsson","Rodrigo Abarca-del-Río","A. A. Borsa","Georgia Destouni","Giuliano Di Baldassarre","Michele‐Lee Moore","José A Posada-Marín","Shimon Wdowinski","Susanna Werth","George H. Allen","Donald F. Argus","Omid Elmi","Luciana Fenoglio-Marc","Frédéric Frappart","Xander Huggins","Zahra Kalantari","Simon Munier","Sebastián Palomino‐Ángel","Abigail Robinson","Kristian Rubiano","Gabriela Siles","Marc Simard","Chunqiao Song","Christopher Spence","Mohammad J. Tourian","Yoshihide Wada","Chao Wang","Jida Wang","Fangfang Yao","Wouter R. Berghuijs","Jean‐François Crétaux","J. S. Famiglietti","Alice César Fassoni‐Andrade","Jessica V. Fayne","Félix Girard","Matti Kummu","Kristine M. Larson","Martin Marañon","Daniel Medeiros Moreira","Karina Nielsen","Tamlin M. Pavelsky","Francisco J. Peña","J. T. Reager","Maria Cristina Rulli","Juan F. Salazar"],"summary":"This study reviews hydrogeodesy, a scientific field employing satellite geodetic technologies such as Altimetry, Interferometric Synthetic Aperture Radar (InSAR), Gravimetry (GRACE), and Global Navigation Satellite Systems (GNSS), to monitor Earth's water resources. Through meta-analysis of over 3,000 articles and expert assessment, it highlights hydrogeodesy's rapid growth, key technological advancements, and its broad applications including monitoring lakes, glaciers, groundwater, and wetlands, though wetlands remain understudied. The review also discusses hydrogeodesy's potential to address critical hydrological questions, support water management, and contribute to global sustainability frameworks such as the Planetary Boundaries, emphasizing the need for integration with artificial intelligence and multidisciplinary approaches. The work calls for enhanced education, data accessibility, and coordination to unlock hydrogeodesy's full potential.","finding":"Hydrogeodesy technologies, particularly GRACE gravimetry, Altimetry, InSAR, and GNSS, have significantly advanced the monitoring of terrestrial water storage, surface water, groundwater, and cryospheric changes at global to regional scales, with the potential to solve key hydrological problems and inform sustainability science and water management.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"the various areas of expertise among coauthors concerning the four technologies and a wide range of related space missions. The rating ranged from scores 1 (low) to 5 (high) for the potential of Hydrogeodesy to contribute to resolving each UPH. Depending on their areas of expertise, the coauthors also elucidated the potential of hydrogeodetic technologies to help advance the science to help solve the 23 UPH and the limitations that would need to be overcome to do so. Finally, we discussed the potential of Hydrogeodesy in relation to global sustainability frameworks, with special emphasis on the Planetary Boundaries framework that aims to delimit a biophysically safe operating space for humanity (Rockström et al., 2009). 4. Results and Discussion 4.1.","verified_in_page":true},"topics":["remote sensing","terrestrial water storage","groundwater storage","wetlands","hydrological modeling","climate change impact","water management","sustainability science"],"study_period":null,"locations":[{"name":"Amazon Basin","kind":"basin","role":"Region monitored for surface water levels and storage using altimetry and InSAR data","river_name":null,"geocode_query":"Amazon Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"et al. (2017), who estimated absolute water storage in the Congo River floodplains by computing water depths and storage volumes by integrating InSAR and Altimetry. They later compared it with large‐scale estimates of total water storage as obtained from gravimetric measurements of the GRACE satellite. 4.2. Hydrogeodesy Is Published Mostly in Remote Sensing and Multidisciplinary Journals Regarding the journals hosting these publications, 33%–59% of articles are published in Multidisciplinary journals, depending on the technology, and thus are not necessarily solely directed to the Hydrological or Remote Sensing audiences (Figure 4). This is evident across all four technologies.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.719969,-1.644128],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-0","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Congo River Basin","kind":"basin","role":"Region examined using satellite observations for hydrological monitoring and integration of geodetic data","river_name":"Congo River","geocode_query":"Congo River Basin, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"et al. (2017), who estimated absolute water storage in the Congo River floodplains by computing water depths and storage volumes by integrating InSAR and Altimetry. They later compared it with large‐scale estimates of total water storage as obtained from gravimetric measurements of the GRACE satellite. 4.2. Hydrogeodesy Is Published Mostly in Remote Sensing and Multidisciplinary Journals Regarding the journals hosting these publications, 33%–59% of articles are published in Multidisciplinary journals, depending on the technology, and thus are not necessarily solely directed to the Hydrological or Remote Sensing audiences (Figure 4). This is evident across all four technologies.","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.744221,-1.898349],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":1041213640,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":894488.8,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-1","true_coordinates":[18.77950841330257,-1.946890029824763],"spread_km":6.7},{"name":"San Joaquin Valley, California, USA","kind":"region","role":"Study area for groundwater depletion and subsidence using InSAR and GNSS observations","river_name":null,"geocode_query":"San Joaquin Valley, California, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Such studies can relate ground and infrastructure subsidence to human groundwater withdrawal. For example, poroelastic deformation in aquifers has been used to determine water use/withdrawal rates in the San Joaquin Valley in California and Mexico City using InSAR data (Khorrami et al., 2023; Levy et al., 2020; Manuel Pardo et al., 2013; Ojha et al., 2018; Smith et al., 2017), and to a lesser extent using GNSS observations. However, in most regions worldwide, GNSS data do not provide sufficient spatial coverage for the deformation signal to resolve water use/withdrawal rates accurately. High‐resolution deformation data can be combined with groundwater level data to calculate storage coefficients and groundwater heads in compacting aquifer layers (e.g., Chen et al., 2016).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-121.8677352,37.9832553],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"San Joaquin Valley, Pittsburg, CA 94509, USA","basin_id":7040013550,"main_basin_id":7040013550,"basin_region":"North America and Caribbean","basin_area_km2":156294.6,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-2"},{"name":"Mexico City, Mexico","kind":"settlement","role":"Urban area studied for groundwater-related subsidence using InSAR techniques","river_name":null,"geocode_query":"Mexico City, Mexico","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Such studies can relate ground and infrastructure subsidence to human groundwater withdrawal. For example, poroelastic deformation in aquifers has been used to determine water use/withdrawal rates in the San Joaquin Valley in California and Mexico City using InSAR data (Khorrami et al., 2023; Levy et al., 2020; Manuel Pardo et al., 2013; Ojha et al., 2018; Smith et al., 2017), and to a lesser extent using GNSS observations. However, in most regions worldwide, GNSS data do not provide sufficient spatial coverage for the deformation signal to resolve water use/withdrawal rates accurately. High‐resolution deformation data can be combined with groundwater level data to calculate storage coefficients and groundwater heads in compacting aquifer layers (e.g., Chen et al., 2016).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-99.133208,19.372608],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mexico City, CDMX, Mexico","basin_id":7040049990,"main_basin_id":7040049990,"basin_region":"North America and Caribbean","basin_area_km2":153217.8,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-3","true_coordinates":[-99.133208,19.4326077],"spread_km":6.7},{"name":"Qinghai-Tibet Plateau, China","kind":"region","role":"Monitored for soil moisture and permafrost thaw using InSAR and gravimetry","river_name":null,"geocode_query":"Qinghai-Tibet Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p034-008","page":34,"quote":"Environmental Research Letters, 15(10), 104083. https://doi.org/10.1088/1748‐9326/abb55c Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., et al. (2019). Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges. Water Resources Research, 55(9), 7564–7586. https://doi.org/10.1029/2018WR024618 Li, X., Long, D., Scanlon, B. R., Mann, M. E., Li, X., Tian, F., et al. (2022). Climate change threatens terrestrial water storage over the Tibetan Plateau. Nature Climate Change, 12(9), 801–807. https://doi.org/10.1038/s41558‐022‐01443‐0 Water Resources Research 10.1029/2023WR037020 JARAMILLO ET AL. 34 of 38 19447973, 2024, 11, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR037020 by Helmholtz-Zentrum Potsdam GFZ, Wiley Online Library on [08/01/2025].","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Lake Mead, Nevada, USA","kind":"facility","role":"Reservoir studied for relations between water storage changes and ground deformation using InSAR and GNSS","river_name":null,"geocode_query":"Lake Mead, Nevada, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-002","page":15,"quote":"InSAR and GNSS are also used to determine the geomorphological changes occurring by waterstorage changes in managed reservoirs. For instance, ground deformation around Lake Mead and vertical displacements of the Hoover Dam (United States) have been found to relate to water storage changes in the reservoir (Cavalié et al., 2007; Darvishi et al., 2021). InSAR and GNSS technologies are usually combined with geomorphological modeling to understand the dynamics of elastic deformation necessary to guarantee the stability of water‐related infrastructure such as dams and ancillary structures (e.g., Neelmeijer et al., 2018). 4.6. The Potential of Hydrogeodesy to Help Solving Key Hydrological Problems The International Association of Hydrological Studies (IAHS) has outlined the main topics of focus for the Hydrological Community during the last three decades.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-114.4144415,36.1435231],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Mead, United States","basin_id":7040707710,"main_basin_id":7040008710,"basin_region":"North America and Caribbean","basin_area_km2":117076.9,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-5"},{"name":"Three Gorges Dam, China","kind":"facility","role":"Hydroelectric dam whose reservoir storage variations and ground deformation have been monitored by geodetic techniques","river_name":null,"geocode_query":"Three Gorges Dam, Hubei, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-002","page":15,"quote":"InSAR and GNSS are also used to determine the geomorphological changes occurring by waterstorage changes in managed reservoirs. For instance, ground deformation around Lake Mead and vertical displacements of the Hoover Dam (United States) have been found to relate to water storage changes in the reservoir (Cavalié et al., 2007; Darvishi et al., 2021). InSAR and GNSS technologies are usually combined with geomorphological modeling to understand the dynamics of elastic deformation necessary to guarantee the stability of water‐related infrastructure such as dams and ancillary structures (e.g., Neelmeijer et al., 2018). 4.6. The Potential of Hydrogeodesy to Help Solving Key Hydrological Problems The International Association of Hydrological Studies (IAHS) has outlined the main topics of focus for the Hydrological Community during the last three decades.","verified_in_page":true},"resolution_status":"resolved","coordinates":[111.0037277,30.8233072],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"China, 湖北省宜昌市秭归县R2F3+8FH 邮政编码: 443131","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-6"},{"name":"Lake Poopó, Bolivia","kind":"other","role":"Example of remote sensing-derived bathymetry studies using hydrogeodetic data","river_name":null,"geocode_query":"Lake Poopó, Bolivia","latitude":null,"longitude":null,"evidence":{"passage_id":"p030-004","page":30,"quote":"Remote Sensing, 6(1), 407–420. https://doi.org/10.3390/rs6010407 Bai, L., Jiang, L., Zhao, Y., Li, Z., Cao, G., Zhao, C., et al. (2022). Quantifying the influence of long‐term overexploitation on deep groundwater resources across Cangzhou in the North China Plain using InSAR measurements. Journal of Hydrology, 605, 127368. https://doi.org/10.1016/j. jhydrol.2021.127368 Barnett, J., Evans, L. S., Gross, C., Kiem, A. S., Kingsford, R. T., Palutikof, J. P., et al. (2015). From barriers to limitsto climate change adaptation: Path dependency and the speed of change. Ecology and Society, 20(3), art5. https://doi.org/10.5751/es‐07698‐200305 Basu, N. B., Van Meter, K. J., Byrnes, D. K., Van Cappellen, P., Brouwer, R., Jacobsen, B. H., et al. (2022). Managing nitrogen legacies to accelerate water quality improvement. Nature Geoscience, 15(2), 97–105. https://doi.org/10.1038/s41561‐021‐00889‐9 Bhanja, S. N., Mukherjee, A., & Rodell, M. (2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-67.04645909999999,-18.7942434],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Poopó Lake, Bolivia","basin_id":6040040470,"main_basin_id":6040040470,"basin_region":"South America","basin_area_km2":326937.3,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-7"},{"name":"Baltic Sea","kind":"other","role":"Region studied for ice and sea surface topography using altimetry","river_name":null,"geocode_query":"Baltic Sea, Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"Ice sheets/caps and glaciers have been monitored mainly by Altimetry since the 1980s, with the first studies focusing on the Antarctic and Greenland ice sheets and sea ice in the Baltic Sea (Scott et al., 1994). Many altimetric missions track ice topography, determine ice surface elevations, map the boundaries of ice shelves, and identify icebergs and ice‐surface features (Mcintyre, 1991). In ice‐free regions, the water‐related words most targeted by Hydrogeodesy are lakes (15%) and watersheds (15%). Regarding the first, although radar altimeters were designed to measure the global sea level, altimetric sensors now track water level changes in numerous lakes, reservoirs, and ponds worldwide due to their improved spatial coverage and along‐track resolution. While most applications focused on large water bodies only (Birkett et al., 2011; Crétaux et al., 2011, 2016; Y.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Hudson Bay, Canada","kind":"region","role":"Example of permafrost and freeze-thaw dynamics studied with InSAR","river_name":null,"geocode_query":"Hudson Bay, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"To create a DEM, InSAR employs two or more radar acquisitions collected from slightly different viewing geometries—either from antennas separated by a boom (e.g., Farr et al., 2007) or by shifting the platform's orbit (e.g., Krieger et al., 2007). Changes in surface elevation, commonly called ground deformation in the geomorphology literature, can be retrieved with up to millimetric accuracy (Wdowinski et al., 2004). These changes have been related to changes in soil moisture (Mira et al., 2022; Ranjbar et al., 2021), groundwater changes (Levy et al., 2020; Wu et al., 2022), fluvial sediment (Higgins et al., 2014), water mass changes in lakes or reservoirs (Cavalié et al., 2007; Darvishi et al., 2021; Doin et al., 2015), snow topography (Guneriussen et al., 2001; Molan et al., 2018; Oveisgharan & Zebker, 2007), permafrost thaw (Chen et al., 2020; Short et al., 2014; C. Wang et al., 2018), and ice flow (Fatland & Lingle, 2002; Forster et al., 2003; Palmer et al., 2009).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-102.388114,52.916798],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hudson Bay, SK, Canada","basin_id":7040192560,"main_basin_id":7040022240,"basin_region":"North America and Caribbean","basin_area_km2":586387.2,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-9","true_coordinates":[-102.3881142,52.856798],"spread_km":6.7},{"name":"Western United States","kind":"region","role":"Region studied for water storage changes monitored with GNSS, InSAR and satellites","river_name":null,"geocode_query":"Western United States, USA","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-005","page":13,"quote":"Terrestrial water storage changes estimated from GNSS include water cycling through the Himalayas (Y. Fu & Freymueller, 2012), seasonal water changes in the western USA (Argus et al., 2014; Y. Fu et al., 2015), multiannual cycles of drought and recovery (Adusumilli et al., 2019; Argus et al., 2017; Borsa, Agnew, & Cayan, 2014; Borsa, Moholdt, et al., 2014), and the impact of individual storms (Milliner et al., 2018). Recent progress in InSAR processing for yielding large‐scale deformation maps in global reference frames that are combined and validated with GNSS observations can provide high‐resolution maps of the loading response as recently achieved in Mexico City (Khorrami et al., 2023) and are promising for further applications of this approach in regions worldwide where GNSS networks are sparse or absent. GNSS observations are also used to measure water levels of rivers and lakes and inundation dynamics (Holden & Larson, 2021; Zeiger et al., 2021), especially in the tropics.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-97.197897,40.453485],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western, NE 68464, USA","basin_id":7040569640,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":1381472.0,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-10","true_coordinates":[-97.19789659999999,40.3934846],"spread_km":6.7},{"name":"Lake Turkana, Kenya","kind":"other","role":"Lake whose water level dynamics were modeled using multi-source satellite data","river_name":null,"geocode_query":"Lake Turkana, Kenya","latitude":null,"longitude":null,"evidence":{"passage_id":"p038-000","page":38,"quote":"Velicogna, I., Mohajerani, Y., Geruo, A., Landerer, F., Mouginot, J., Noel, B., et al. (2020). Continuity of ice sheet mass loss in Greenland and Antarctica from the GRACE and GRACE follow‐on missions. Geophysical Research Letters, 47(8). https://doi.org/10.1029/2020GL087291 Velpuri, N. M., Senay, G. B., & Asante, K. O. (2012). A multi‐source satellite data approach for modelling lake Turkana water level: Calibration and validation using satellite altimetry data. Hydrology and Earth System Sciences, 16(1), 1–18. https://doi.org/10.5194/hess‐16‐1‐2012 Verpoorter, C., Kutser, T., Seekell, D. A., & Tranvik, L. J. (2014). A global inventory of lakes based on high‐resolution satellite imagery. Geophysical Research Letters, 41(18), 6396–6402. https://doi.org/10.1002/2014GL060641 Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., et al. (2010). Global threats to human water security and river biodiversity.","verified_in_page":true},"resolution_status":"resolved","coordinates":[36.0022946,3.6267946],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Turkana","basin_id":1040040260,"main_basin_id":1040040260,"basin_region":"Africa","basin_area_km2":149329.1,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-11"},{"name":"Brazil","kind":"region","role":"Country applying hydrogeodesy for reservoir and water management and research","river_name":null,"geocode_query":"Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p017-003","page":17,"quote":"Special attention is paid to Cryosat‐2, which can accurately monitor river profiles and slopes due to its short inter‐track distances, benefiting hydraulic applications and river discharge estimation (Schneider et al., 2018). Given the role of surface water bodies and rivers in providing water storage during drought periods and conveying and storing water during flood events, their observation is critical, and the role of water elevation in this regard allows mitigation of the impacts of these hydrological extremes. Concerning water resource management, monitoring reservoirs by measuring water height with altimeters (and changing spatial extent from optical sensors) allows their volume to be quantified when combined with water extension changes obtained by optical and radar imagery (Tourian et al., 2022).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Greenland Ice Sheet, Greenland","kind":"other","role":"Ice sheet monitored using altimetry and gravimetry for mass balance and climate change studies","river_name":null,"geocode_query":"Greenland Ice Sheet, Greenland","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"Ice sheets/caps and glaciers have been monitored mainly by Altimetry since the 1980s, with the first studies focusing on the Antarctic and Greenland ice sheets and sea ice in the Baltic Sea (Scott et al., 1994). Many altimetric missions track ice topography, determine ice surface elevations, map the boundaries of ice shelves, and identify icebergs and ice‐surface features (Mcintyre, 1991). In ice‐free regions, the water‐related words most targeted by Hydrogeodesy are lakes (15%) and watersheds (15%). Regarding the first, although radar altimeters were designed to measure the global sea level, altimetric sensors now track water level changes in numerous lakes, reservoirs, and ponds worldwide due to their improved spatial coverage and along‐track resolution. While most applications focused on large water bodies only (Birkett et al., 2011; Crétaux et al., 2011, 2016; Y.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Antarctic Ice Sheet, Antarctica","kind":"other","role":"Polar ice monitored via satellite altimetry and gravimetry to assess mass loss and dynamics","river_name":null,"geocode_query":"Antarctic Ice Sheet, Antarctica","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"Ice sheets/caps and glaciers have been monitored mainly by Altimetry since the 1980s, with the first studies focusing on the Antarctic and Greenland ice sheets and sea ice in the Baltic Sea (Scott et al., 1994). Many altimetric missions track ice topography, determine ice surface elevations, map the boundaries of ice shelves, and identify icebergs and ice‐surface features (Mcintyre, 1991). In ice‐free regions, the water‐related words most targeted by Hydrogeodesy are lakes (15%) and watersheds (15%). Regarding the first, although radar altimeters were designed to measure the global sea level, altimetric sensors now track water level changes in numerous lakes, reservoirs, and ponds worldwide due to their improved spatial coverage and along‐track resolution. While most applications focused on large water bodies only (Birkett et al., 2011; Crétaux et al., 2011, 2016; Y.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Patagonia","kind":"region","role":"Region of accelerated glacier loss monitored by hydrogeodetic techniques","river_name":null,"geocode_query":"Patagonia, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-005","page":14,"quote":"These regions include Greenland, Patagonia, West Antarctica and the Antarctic Peninsula, where ice‐cap and glacier loss are accelerating; and California (United States), Northern India, the Middle East, Caspian and Aral Sea regions, and Eastern China, where drought and groundwater depletion are reducing water availability for human use (Rodell et al., 2018); or for instance, northwestern South America, where water availability is decreasing due to an increasing frequency of El Niño Southern Oscillation events (Bolanos et al., 2021). Hydrogeodetic studies addressing hydrological problems beyond technical developments, such as those aiming to understand hydrological and geomorphological processes or attribute changes to human or climatic impacts, are Figure 6. Sankey diagram of the main objective behind hydrogeodetic studies. The article objectives were tagged based on random samples of articles for each technology or their combination (n = 120).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-68.9062689,-41.8101472],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Patagonia","basin_id":6040040500,"main_basin_id":6040040500,"basin_region":"South America","basin_area_km2":131645.8,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-15"},{"name":"Northern India","kind":"region","role":"Monitored region for drought and groundwater depletion via satellite hydrogeodesy","river_name":null,"geocode_query":"Northern India, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-005","page":14,"quote":"These regions include Greenland, Patagonia, West Antarctica and the Antarctic Peninsula, where ice‐cap and glacier loss are accelerating; and California (United States), Northern India, the Middle East, Caspian and Aral Sea regions, and Eastern China, where drought and groundwater depletion are reducing water availability for human use (Rodell et al., 2018); or for instance, northwestern South America, where water availability is decreasing due to an increasing frequency of El Niño Southern Oscillation events (Bolanos et al., 2021). Hydrogeodetic studies addressing hydrological problems beyond technical developments, such as those aiming to understand hydrological and geomorphological processes or attribute changes to human or climatic impacts, are Figure 6. Sankey diagram of the main objective behind hydrogeodetic studies. The article objectives were tagged based on random samples of articles for each technology or their combination (n = 120).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Middle East","kind":"region","role":"Region facing drought and groundwater depletion monitored by hydrogeodesy","river_name":null,"geocode_query":"Middle East","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-005","page":14,"quote":"These regions include Greenland, Patagonia, West Antarctica and the Antarctic Peninsula, where ice‐cap and glacier loss are accelerating; and California (United States), Northern India, the Middle East, Caspian and Aral Sea regions, and Eastern China, where drought and groundwater depletion are reducing water availability for human use (Rodell et al., 2018); or for instance, northwestern South America, where water availability is decreasing due to an increasing frequency of El Niño Southern Oscillation events (Bolanos et al., 2021). Hydrogeodetic studies addressing hydrological problems beyond technical developments, such as those aiming to understand hydrological and geomorphological processes or attribute changes to human or climatic impacts, are Figure 6. Sankey diagram of the main objective behind hydrogeodetic studies. The article objectives were tagged based on random samples of articles for each technology or their combination (n = 120).","verified_in_page":true},"resolution_status":"resolved","coordinates":[42.55096,29.358528],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Middle East","basin_id":2040085850,"main_basin_id":2040085850,"basin_region":"Europe and Middle East","basin_area_km2":78300.3,"hydrology_source":"hydrobasins","site_id":"doc-0376203208e62f8aa78f4a41-site-17","true_coordinates":[42.5509603,29.2985278],"spread_km":6.7},{"name":"Caspian Sea region","kind":"region","role":"Area experiencing groundwater depletion estimated using satellite data","river_name":null,"geocode_query":"Caspian Sea region, Eurasia","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-005","page":14,"quote":"These regions include Greenland, Patagonia, West Antarctica and the Antarctic Peninsula, where ice‐cap and glacier loss are accelerating; and California (United States), Northern India, the Middle East, Caspian and Aral Sea regions, and Eastern China, where drought and groundwater depletion are reducing water availability for human use (Rodell et al., 2018); or for instance, northwestern South America, where water availability is decreasing due to an increasing frequency of El Niño Southern Oscillation events (Bolanos et al., 2021). Hydrogeodetic studies addressing hydrological problems beyond technical developments, such as those aiming to understand hydrological and geomorphological processes or attribute changes to human or climatic impacts, are Figure 6. Sankey diagram of the main objective behind hydrogeodetic studies. The article objectives were tagged based on random samples of articles for each technology or their combination (n = 120).","verified_in_page":true},"resolution_status":"resolved","coordinates":[50.66894500000001,41.934977],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Caspian Sea","hydrology_source":"none","site_id":"doc-0376203208e62f8aa78f4a41-site-18"},{"name":"Eastern China","kind":"region","role":"Region where groundwater depletion has been tracked with gravimetry and geodesy","river_name":null,"geocode_query":"Eastern China, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p014-005","page":14,"quote":"These regions include Greenland, Patagonia, West Antarctica and the Antarctic Peninsula, where ice‐cap and glacier loss are accelerating; and California (United States), Northern India, the Middle East, Caspian and Aral Sea regions, and Eastern China, where drought and groundwater depletion are reducing water availability for human use (Rodell et al., 2018); or for instance, northwestern South America, where water availability is decreasing due to an increasing frequency of El Niño Southern Oscillation events (Bolanos et al., 2021). Hydrogeodetic studies addressing hydrological problems beyond technical developments, such as those aiming to understand hydrological and geomorphological processes or attribute changes to human or climatic impacts, are Figure 6. Sankey diagram of the main objective behind hydrogeodetic studies. The article objectives were tagged based on random samples of articles for each technology or their combination (n = 120).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Limited time series length of hydrogeodetic data sets due to relatively recent satellite missions","Coherence and signal availability issues in InSAR over densely vegetated or less urbanized areas, limiting wetland and groundwater deformation applications","Low spatial resolution of GRACE gravimetry restricts monitoring of small aquifers and small-scale hydrological processes","Limited availability of in situ ground data to calibrate and validate hydrogeodetic estimates in poorly gauged or remote areas","Need for algorithm improvement and standardization across different sensors and missions to maximize data utility","Integration challenges among different hydrogeodetic technologies and with hydrological models","Technical knowledge and data access barriers for hydrologists limiting broader application of hydrogeodesy","Challenges in teaching hydrogeodesy effectively due to interdisciplinary knowledge requirements and resource constraints"],"river_ids":[],"site_ids":["doc-0376203208e62f8aa78f4a41-site-0","doc-0376203208e62f8aa78f4a41-site-1","doc-0376203208e62f8aa78f4a41-site-2","doc-0376203208e62f8aa78f4a41-site-3","doc-0376203208e62f8aa78f4a41-site-5","doc-0376203208e62f8aa78f4a41-site-6","doc-0376203208e62f8aa78f4a41-site-7","doc-0376203208e62f8aa78f4a41-site-9","doc-0376203208e62f8aa78f4a41-site-10","doc-0376203208e62f8aa78f4a41-site-11","doc-0376203208e62f8aa78f4a41-site-15","doc-0376203208e62f8aa78f4a41-site-17","doc-0376203208e62f8aa78f4a41-site-18"],"anchor":[-67.04645909999999,-18.7942434],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[1040040260,1041213640,2040085850,4040783120,6040040470,6040040500,6040280400,7040013550,7040049990,7040192560,7040569640,7040707710],"main_basin_ids":[1040020040,1040040260,2040085850,4040009880,6040007000,6040040470,6040040500,7040008710,7040013550,7040022240,7040047060,7040049990]},{"id":"doc-0aa79fe6c062959d1cccf3d5","title":"Combined effects of urbanization and climate variability on water and carbon balances in a rice paddy-dominated basin in southern China","display_title":"Urbanization and Climate Effects on Basin Water-Carbon Cycles","doi":"https://doi.org/10.1088/1748-9326/ad8a73","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-10-23","authors":["D. L. Chen","Ning Liu","Guojing Gan","Yuanbo Liu","Mengsheng Qin","Qingzhou Zheng","Ge Sun","Lu Hao"],"summary":"This study examined the combined effects of urbanization and climate variability on water and carbon balances in the Qinhuai River Basin (QRB), a rice paddy-dominated watershed in southern China, from 2001 to 2018. By improving a water supply stress index model with local eddy flux and satellite data, the study quantified trends in evapotranspiration, water yield, and gross primary productivity related to land use changes including impervious surface area increases and rice paddy loss amid climate warming. Urbanization-induced expansion of impervious surfaces dominated reductions in ecosystem productivity and evapotranspiration, driving increased runoff despite rising precipitation and potential evapotranspiration. These findings provide crucial insights for urban watershed management and nature-based solution design to mitigate urban environmental impacts.","finding":"From 2001 to 2018 in the Qinhuai River Basin, urbanization increased impervious surface area by 1.9 times and urban areas by 96%, causing significant decreases in evapotranspiration (−4.6 mm yr−1) and gross primary productivity (−10.4 gC m−2 yr−1), while water yield increased significantly (+28.6 mm yr−1). This indicates that impervious surface expansion, rather than general land use classification shifts, primarily drove decreased evapotranspiration and productivity and increased runoff, offsetting positive effects of climate warming.","finding_evidence":{"passage_id":"p002-003","page":2,"quote":"We found a significant downward trend in both ET (−4.6 mm yr−1 , p < 0.05) and gross primary productivity (GPP) (−10.4 gC m−2 yr−1 , p < 0.05) but a significant upward trend in water yield (Q) (+28.6 mm yr−1 , p < 0.05). These ecosystem function changes coincided with a 96% increase in urban areas, 1.9-fold increase in ISA, and a 37% reduction in rice paddy fields. The mean annual watershed GPP decreased from 1048 gC m−2 to 998 gC m−2 while the annual Q increased from 284 mm to 669 mm from 2001 to 2018. Scenario modeling experiments suggested that the negative impacts of loss of rice paddy fields and increase in ISA on ET and GPP overwhelmed the positive impacts of climate warming. The reduction in GPP and increase in Q were largely attributed to the increases in ISA, not necessarily due to changes in land use types (e.g. urban area). The expansion of urban area, increase in ISA and reduction in leaf area index, and increase in precipitation explained the increase in Q.","verified_in_page":true},"topics":["urbanization","evapotranspiration","gross primary productivity","impervious surface area","water yield","land use change","climate variability","ecosystem productivity"],"study_period":"2001–2018","locations":[{"name":"Qinhuai River Basin","kind":"basin","role":"primary study watershed","river_name":"Qinhuai River","geocode_query":"Qinhuai River Basin, Jiangsu Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Environ. Res. Lett. 19 (2024) 124042 D Chen et al WaSSI model by incorporating local eddy flux measurements, (2) quantify the spatial and temporal distributions of water including ET and water yield (Q), and carbon fluxes (specifically, gross primary production, GPP) under a changing environment from 2001 to 2018, and (3) quantify the combined and individual effects of climate variability and urbanization including ISA on water-carbon coupling. 2. Methods and data 2.1. Study area The QRB with a catchment area of 2631 km2 is located in the southwest of Jiangsu Province in southern China (118◦ 39′–119◦ 19′E, 31◦ 34′–32◦ 10′N) (figure 1). The watershed consists of a diverse topography, mainly plains and hills with elevation ranging from 0 m asl to 410 m asl. The Qinhuai River is 110 km long, and it flows through Lishui, Jurong cities, and finally Nanjing City before it pours into the Yangtze River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.725833,32.0175],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-0aa79fe6c062959d1cccf3d5-site-0","true_coordinates":[118.7258333,32.0775],"spread_km":6.7},{"name":"Qinhuai River","kind":"river","role":"main river in study basin","river_name":"Qinhuai River","geocode_query":"Qinhuai River, Jiangsu Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Environ. Res. Lett. 19 (2024) 124042 D Chen et al WaSSI model by incorporating local eddy flux measurements, (2) quantify the spatial and temporal distributions of water including ET and water yield (Q), and carbon fluxes (specifically, gross primary production, GPP) under a changing environment from 2001 to 2018, and (3) quantify the combined and individual effects of climate variability and urbanization including ISA on water-carbon coupling. 2. Methods and data 2.1. Study area The QRB with a catchment area of 2631 km2 is located in the southwest of Jiangsu Province in southern China (118◦ 39′–119◦ 19′E, 31◦ 34′–32◦ 10′N) (figure 1). The watershed consists of a diverse topography, mainly plains and hills with elevation ranging from 0 m asl to 410 m asl. The Qinhuai River is 110 km long, and it flows through Lishui, Jurong cities, and finally Nanjing City before it pours into the Yangtze River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.725833,32.1375],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qinhuai He, Jianye District, Nanjing, China, 210011","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-0aa79fe6c062959d1cccf3d5-site-1","true_coordinates":[118.7258333,32.0775],"spread_km":6.7},{"name":"Nanjing","kind":"settlement","role":"major urban area in basin with increased impervious surface","river_name":null,"geocode_query":"Nanjing, Jiangsu Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Environ. Res. Lett. 19 (2024) 124042 D Chen et al Figure 2. Validation of the revised WaSSI model. (a) A comparison of measured and modeled water yield (Q) for the Qinhuai River Basin (QRB) from 2001 to 2013, (b) inter-annual trends in measured and modeled Q for the QRB from 2001 to 2013, (c) scatted plot comparing WaSSI modeled ET and PML_V2 model (2001–2018), and (d) a comparison of annual evapotranspiration (ET) estimated based on three methods. Figure 3. WaSSI model validation based on two remotely sensed products, VPM and PML_V2 for the Qinhuai River Basin over 2001–2018. (a) Scatter plot comparing monthly GPP estimates by the WaSSI model, VPM, and PML_V2 models, and (b) mean annual GPP trend. of runoff coefficient (Q/P) were consistent with Q, showing larger values in urban areas and smaller ones in paddy field and farmland-dominated watersheds. Numerically Q/P values ranged from 0.2 to 0.5. In contrast, Q/P ratios were more than 0.6 in urban areas with the maximum value was 0.91.","verified_in_page":true},"resolution_status":"resolved","coordinates":[118.79647,32.0583799],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nanjing, Jiangsu, China","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-0aa79fe6c062959d1cccf3d5-site-2"},{"name":"Lishui","kind":"settlement","role":"urban area in basin with increased impervious surface","river_name":null,"geocode_query":"Lishui, Jiangsu Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Environ. Res. Lett. 19 (2024) 124042 D Chen et al Figure 2. Validation of the revised WaSSI model. (a) A comparison of measured and modeled water yield (Q) for the Qinhuai River Basin (QRB) from 2001 to 2013, (b) inter-annual trends in measured and modeled Q for the QRB from 2001 to 2013, (c) scatted plot comparing WaSSI modeled ET and PML_V2 model (2001–2018), and (d) a comparison of annual evapotranspiration (ET) estimated based on three methods. Figure 3. WaSSI model validation based on two remotely sensed products, VPM and PML_V2 for the Qinhuai River Basin over 2001–2018. (a) Scatter plot comparing monthly GPP estimates by the WaSSI model, VPM, and PML_V2 models, and (b) mean annual GPP trend. of runoff coefficient (Q/P) were consistent with Q, showing larger values in urban areas and smaller ones in paddy field and farmland-dominated watersheds. Numerically Q/P values ranged from 0.2 to 0.5. In contrast, Q/P ratios were more than 0.6 in urban areas with the maximum value was 0.91.","verified_in_page":true},"resolution_status":"resolved","coordinates":[119.0284,31.6509999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lishui District, Nanjing, Jiangsu, China","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-0aa79fe6c062959d1cccf3d5-site-3"},{"name":"Jurong","kind":"settlement","role":"urban area in basin with increased impervious surface","river_name":null,"geocode_query":"Jurong, Jiangsu Province, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Environ. Res. Lett. 19 (2024) 124042 D Chen et al Figure 2. Validation of the revised WaSSI model. (a) A comparison of measured and modeled water yield (Q) for the Qinhuai River Basin (QRB) from 2001 to 2013, (b) inter-annual trends in measured and modeled Q for the QRB from 2001 to 2013, (c) scatted plot comparing WaSSI modeled ET and PML_V2 model (2001–2018), and (d) a comparison of annual evapotranspiration (ET) estimated based on three methods. Figure 3. WaSSI model validation based on two remotely sensed products, VPM and PML_V2 for the Qinhuai River Basin over 2001–2018. (a) Scatter plot comparing monthly GPP estimates by the WaSSI model, VPM, and PML_V2 models, and (b) mean annual GPP trend. of runoff coefficient (Q/P) were consistent with Q, showing larger values in urban areas and smaller ones in paddy field and farmland-dominated watersheds. Numerically Q/P values ranged from 0.2 to 0.5. In contrast, Q/P ratios were more than 0.6 in urban areas with the maximum value was 0.91.","verified_in_page":true},"resolution_status":"resolved","coordinates":[119.16864,31.9448199],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jurong, Zhenjiang, Jiangsu, China","basin_id":4040694350,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":77129.0,"hydrology_source":"hydrobasins","site_id":"doc-0aa79fe6c062959d1cccf3d5-site-4"},{"name":"Yangtze River Delta","kind":"region","role":"larger rapidly urbanizing region containing study basin","river_name":null,"geocode_query":"Yangtze River Delta, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"Existing remote sensing driven, plant physiological processbased models such as CEVSA (Wang et al 2009), BEPS (Kang et al 2022), IBIS (Lu et al 2019), and integrated models constructed by integrating existing hydrological models and ecological models such as DLEM (Tian et al 2010), water supply stress index (WaSSI) (Sun et al 2011), and RHESSys model (Chen et al 2020) are available for comprehensive ecosystem service assessments. These models have their own advantages and disadvantages, and users apply these models according to their own objectives and modeling resource (Sun et al 2023b). The Yangtze River Delta area in southern China has been rapidly urbanized during the past three decades resulting in great loss in rice paddies and other lands.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates for study areas are not reported explicitly, latitude and longitude set to null.","Quantitative validation of evapotranspiration and carbon fluxes at watershed scale limited by scarce eddy flux and in situ data in urban areas.","Model structural uncertainties and reliance on remote sensing based products with some discrepancies in spatial distributions."],"river_ids":[],"site_ids":["doc-0aa79fe6c062959d1cccf3d5-site-0","doc-0aa79fe6c062959d1cccf3d5-site-1","doc-0aa79fe6c062959d1cccf3d5-site-2","doc-0aa79fe6c062959d1cccf3d5-site-3","doc-0aa79fe6c062959d1cccf3d5-site-4"],"anchor":[118.79647,32.0583799],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[4040694350],"main_basin_ids":[4040009880]},{"id":"doc-0d2190a7a37743a7031ab2e2","title":"Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia","display_title":"Data Assimilation Improves Rainfall‐Runoff Modeling","doi":"https://doi.org/10.1029/2023wr036595","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-06-01","authors":["Julien Lerat","Francis H. S. Chiew","David Robertson","Vazken Andréassian","Hongxing Zheng"],"summary":"This paper introduces the DAISI method, combining data assimilation and polynomial updates to improve rainfall-runoff model structures. Applied to the GR2M model across 201 catchments in Southeastern Australia, DAISI improves model performance metrics and enhances model responsiveness to climate inputs, supporting more reliable future streamflow predictions under climate change.","finding":"Applying DAISI to the GR2M rainfall-runoff model significantly improved the simulation of streamflow across 201 Southeastern Australian catchments, showing better performance metrics (KGE, NSElog, flow duration curve bias) and increased elasticity of runoff to rainfall, indicating more robust climate change scenario predictions.","finding_evidence":{"passage_id":"p019-001","page":19,"quote":"For example, KGE increases from 0.82 and 0.65 for the two GR2M configurationsto 0.87 for DAISI when calibrating on Period 1 and evaluating on Period 2, and from 0.86 and 0.74 to 0.92 when calibrating on Period 2 and evaluating on Period 1. Similar metric improvements are seen for both NSElog and FB metrics with DAISI reaching systematically higher performance. These improvements, especially when evaluating the model outside of the calibration period, suggest that the updated model is a robust alternative to GR2M. At the same time, the modeled rainfall elasticity εP is generally higher for the updated model compared to both GR2M configurations. For example, εP increases from 1.75 for GR2M‐kge and 1.93 for GR2M‐bc02 to 1.98 for the updated model when calibrating on Period 1 and evaluating on Period 2.","verified_in_page":true},"topics":["hydrological-model-structure","data-assimilation","rainfall-runoff-model","climate-change-impact","model-calibration","statistical-modeling","performance-evaluation","regional-hydrology"],"study_period":"1980-2018","locations":[{"name":"South-Eastern Australia","kind":"region","role":"study region where DAISI method was applied to 201 catchments for rainfall-runoff modeling under climate change scenarios","river_name":null,"geocode_query":"South-Eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"updated model structure using the GR2M‐kge parameters and update coefficients fitted for the same calibration period. 3.3. Catchment Data Set The DAISI approach was tested on a set of 201 catchments located in South‐Eastern Australia as shown in Figure 2. The hydro‐climatic catchment characteristics are provided in Table 2. The data was extracted from the data sets collated by Lerat et al. (2020) including rainfall and potential‐evapotranspiration data obtained from the Bureau of Meteorology Australian Water Outlook website (Frost et al., 2016) and streamflow data obtained from the Bureau of Meteorology Water Data Online website (Bureau of Meteorology, 2019). The data was collected from 1980 to 2018, split into two sub‐periods 1980–1999 (Period 1) and 1999–2018 (Period 2). In addition, two sub‐groups of stations including stations located in Western Victoria (WVIC) and Northern New South Wales (NNSW) are located in Figure 2 to support the presentation of results in Section 4.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Jamieson River at Gerrang Bridge, Victoria, Australia","kind":"station","role":"example catchment used to illustrate the DAISI method workflow and performance assessment, part of the 201 catchments studied in Southeastern Australia","river_name":"Jamieson River","geocode_query":"Jamieson River Gerrang Bridge Victoria Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"one of the largest reservoirs in Australia with a maximum storage capacity of 3,334 Mm3 . Lake Eildon is a key piece of infrastructure which supports irrigation and environmental flows along the Goulburn and Murray Rivers. 4. Results 4.1. Example of DAISI Workflow Applied to the Jamieson River at Gerrang Bridge This section follows the three steps of the DAISI workflows applied to the example catchment. The parameters and diagnostic metrics for this catchment are provided in Table 3. 4.1.1. Step 1: Data Assimilation Figure 3 illustrates Step 1 of DAISI by showing as orange lines the ensemble time series resulting from the ES data assimilation algorithm applied to the GR2M model calibrated using the KGE objective function (GR2M‐kge) over the first period (1980–1999). Each plot in this figure corresponds to the four states listed in Table 1. In addition, the figure shows the original GR2M‐kge simulations in green along with the observed streamflow data as black dotted lines in Figure 3d.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.2539038527682,-37.31586696734373],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665714,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jamieson River, Victoria 3723, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-0d2190a7a37743a7031ab2e2-site-1"},{"name":"Western Victoria (WVIC), Victoria, Australia","kind":"region","role":"sub-region with distinct regional patterns of model structure updates identified by DAISI with significant performance improvements and altered evapotranspiration representation","river_name":null,"geocode_query":"Western Victoria Victoria Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p020-001","page":20,"quote":"Even if the performance improvement is modest for certain metrics (e.g., KGE metric when comparing against GR2M‐kge), the number of catchments where DAISI is worse than GR2M remains limited which suggests that the updated model does not introduce major structural trade‐offs (e.g., favoring a certain type of catchments against another). Overall, the updated model combines the strength of both GR2M configurations by equaling or exceeding their combined maximum for all performance metrics. This is an important result as it suggests that the DAISI structural updates surpass the performance obtained from alternative objective functions. The fourth evaluation metric is the elasticity of modeled runoff to rainfall shown in Figure 7d. The median elasticity is 2.51 for GR2M‐kge which increasesto 2.73 for the updated model. Pairwise comparisons confirm this result with updated model elasticity being significantly higher than GR2M‐kge in 71% of the site/periods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.4696,-37.667583],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21557393,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western District of Victoria, VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-0d2190a7a37743a7031ab2e2-site-2","true_coordinates":[142.4039296,-37.6975835],"spread_km":6.7},{"name":"Northern New South Wales (NNSW), New South Wales, Australia","kind":"region","role":"sub-region with distinct DAISI-identified model structural updates differing from other regions especially in evapotranspiration modeling, showing improved performance","river_name":null,"geocode_query":"Northern New South Wales New South Wales Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p020-001","page":20,"quote":"Even if the performance improvement is modest for certain metrics (e.g., KGE metric when comparing against GR2M‐kge), the number of catchments where DAISI is worse than GR2M remains limited which suggests that the updated model does not introduce major structural trade‐offs (e.g., favoring a certain type of catchments against another). Overall, the updated model combines the strength of both GR2M configurations by equaling or exceeding their combined maximum for all performance metrics. This is an important result as it suggests that the DAISI structural updates surpass the performance obtained from alternative objective functions. The fourth evaluation metric is the elasticity of modeled runoff to rainfall shown in Figure 7d. The median elasticity is 2.51 for GR2M‐kge which increasesto 2.73 for the updated model. Pairwise comparisons confirm this result with updated model elasticity being significantly higher than GR2M‐kge in 71% of the site/periods.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Central region of Southeastern Australia","kind":"region","role":"sub-region where original GR2M model performed well, resulting in negligible DAISI structural update magnitude","river_name":null,"geocode_query":"Central region South-Eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p021-001","page":21,"quote":"Improvement of rainfall‐runoff model performance in the WVIC region is important because this region has been reported to suffer from strong rainfall‐runoff non‐stationarity with long lasting effects from recent drought (Peterson, Saft et al., 2021). Conversely, KGE values for the catchments located in the center of the domain (Eastern Victoria) are comparable between GR2M and the updated model (white dots). A closer inspection of Figures 8a and 8b reveals that GR2M reaches its highest KGE values in these catchments (dark blue points in Figure 8a). As GR2M simulations are of high quality there, it is difficult for DAISI to improve performance significantly. Nonetheless, it is important to note that DAISI does not degrade performance in this region. The spatial distribution of performance differences for NSElog and FB metrics resembles the one of KGE as can be seen in Figures 8f and 8i.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["DAISI requires an existing rainfall-runoff model structure to apply updates; fully data-driven structure generation was less effective","Current DAISI version uses simplified ensemble smoother data assimilation which assumes linearity and Gaussian errors, potentially limiting accuracy","Estimated model update coefficients lack physical constraints potentially leading to non-physical state values requiring bounding in model operation","DAISI currently relies on fixed model parameters from previous calibration, parameter uncertainty handling is limited","Application shown only at monthly timestep and single model (GR2M); application to daily scales and more complex models remains to be tested","No assimilation of additional observed variables beyond streamflow (e.g., evapotranspiration) applied though suggested as future improvement","Computational and methodological complexity may increase with more advanced assimilation or fitting approaches"],"river_ids":["43640270","43644300","43644762","43644911","43645042","43645074","43645128","43645166","43645220","43645264","43645300","43645327","43645348","43645409","43645430","43647166"],"site_ids":["doc-0d2190a7a37743a7031ab2e2-site-1","doc-0d2190a7a37743a7031ab2e2-site-2"],"anchor":[146.2539038527682,-37.31586696734373],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040070710,5040591050],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-160e238b70299d3c86f5346c","title":"Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe","display_title":"Global Evaluation of Reanalysis Soil Moisture","doi":"https://doi.org/10.5194/hess-28-1999-2024","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-05-07","authors":["Yanchen Zheng","Gemma Coxon","Ross Woods","Daniel Power","Miguel Angel Rico-Ramirez","David McJannet","Rafael Rosolem","Jianzhu Li","Ping Feng"],"summary":"This study evaluates eight widely used reanalysis soil moisture products by comparing them against harmonized cosmic ray neutron sensor (CRNS) measurements from 135 sites across the UK, mainland Europe, USA, and Australia. The assessment considers climate, soil properties, land cover, and topography impacts on product performance, providing recommendations for suitable product selection and insights for future improvements.","finding":"All reanalysis products generally show good temporal correlation with CRNS soil moisture data (median R over 0.7) but perform poorly in capturing soil moisture anomalies (Rano as low as 0.46 for GLDAS). Products tend to overestimate soil moisture in arid climates and underestimate in humid regions and grasslands, with poorer performance in steep terrain and sites with low bulk density and high soil organic carbon, notably in the UK.","finding_evidence":{"passage_id":"p016-002","page":16,"quote":"All reanalysis products generally exhibit good agreement in terms of temporal correlation with the median of R values over 0.7, whereas the lower performance with Rano values is detected, indicating the weaker ability of capturing the soil moisture anomalies. In particular, GLDAS has the lowest Rano values (0.46) across all sites. Low correlations of reanalysis products are observed in cropland or shrubland with seasonally varying vegetation cover. As for the bias, reanalysis soil moisture products tend to overestimate data in arid regions and underestimate data in humid regions as well as grassland. It is also notable that reanalysis products exhibit worse performance in steep terrain. Performances of reanalysis soil moisture products differ among regions, climate, soil properties, land cover and topography conditions. CFSv2 exhibits good performance across the UK and Europe. ERA5-Land performs well in Europe and Australia. SMAP L4 is suitable to be used in Australia and USA.","verified_in_page":true},"topics":["soil moisture","remote sensing","reanalysis","cosmic ray neutron sensor","soil properties","climate influence","land cover impact","topography influence"],"study_period":null,"locations":[{"name":"UK","kind":"region","role":"CRNS network and study area","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"mainland Europe","kind":"region","role":"CRNS network and study area","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"USA","kind":"region","role":"CRNS network and study area","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[62.64088,66.692107],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid","basin_id":2040043720,"main_basin_id":2040043720,"basin_region":"Europe and Middle East","basin_area_km2":312695.6,"hydrology_source":"hydrobasins","site_id":"doc-160e238b70299d3c86f5346c-site-2","true_coordinates":[62.64088,66.632107],"spread_km":6.7},{"name":"Australia","kind":"region","role":"CRNS network and study area","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"COSMOS-UK network","kind":"facility","role":"Source of harmonized CRNS data for UK sites","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"COSMOS-Europe network","kind":"facility","role":"Source of harmonized CRNS data for mainland European sites","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"COSMOS USA network","kind":"facility","role":"Source of harmonized CRNS data for USA sites","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"CosmOz Australia network","kind":"facility","role":"Source of harmonized CRNS data for Australian sites","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"Y. Zheng et al.: Evaluation of reanalysis soil moisture products 2001 against a global dataset of harmonised CRNS measurements. We analyse the reanalysis products with contrasting climate, soil properties, land cover and topography to provide insights into explaining the differences in performance. Finally, we provide recommendations to researchers for selecting suitable reanalysis soil moisture products. 2 Data description 2.1 CRNS measurements In this paper, we collected CRNS data from numerous networks globally and ensured the data were processed in a harmonised way to serve as the reference for evaluating reanalysis products. The geographical locations of CRNS sites collected in this study are shown in Fig. 1. A total of 180 CRNS data were collected from COSMOS-UK (51 sites) (Stanley et al., 2021), COSMOS-Europe (66 sites) (Bogena et al., 2022), COSMOS USA (45 sites) (Zreda et al., 2012) and the Australian CosmOz network (18 sites) (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"WADDN site","kind":"field_site","role":"CRNS measurement site in UK grassland illustrating high temporal correlation of soil moisture","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The performance of Rano is generally lower than the correlation of original and seasonal soil moisture time series. No clear spatial pattern is observed in the USA and Australia in terms of MSE and ubRMSE, while several sites with high MSE and ubRMSE values are notable in the UK (Fig. S7). Soil moisture time series comparison between reanalysis soil moisture products and CRNS measurements for four representative sites is presented in Fig. 4. Site WADDN in the UK is a grassland site from the COSMOS-UK network. The soil moisture values from reanalysis products for this site closely follow the temporal trend of the CRNS measurements, exhibiting high temporal correlation with the R = 0.85 (Fig. 4a). In contrast, the UK site RDMER is selected to demonstrate the variations in time series data for sites with low bulk density and high organic soils (Fig. 4b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"RDMER site","kind":"field_site","role":"CRNS measurement site in UK with low bulk density, high organic soils showing poor anomaly capture and soil moisture variability","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"The performance of Rano is generally lower than the correlation of original and seasonal soil moisture time series. No clear spatial pattern is observed in the USA and Australia in terms of MSE and ubRMSE, while several sites with high MSE and ubRMSE values are notable in the UK (Fig. S7). Soil moisture time series comparison between reanalysis soil moisture products and CRNS measurements for four representative sites is presented in Fig. 4. Site WADDN in the UK is a grassland site from the COSMOS-UK network. The soil moisture values from reanalysis products for this site closely follow the temporal trend of the CRNS measurements, exhibiting high temporal correlation with the R = 0.85 (Fig. 4a). In contrast, the UK site RDMER is selected to demonstrate the variations in time series data for sites with low bulk density and high organic soils (Fig. 4b).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Hauser Farm South site","kind":"field_site","role":"USA CRNS site showing reanalysis products’ failure to dry out accurately in dry periods","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"This also indicates that it is challenging to capture the temporal soil moisture variations at hourly scale for sites with low bulk density and high organic soils. This might be related to the fact that organic soils contain hydrogen and also that the variations of organic soil content could lead to uncertainties in soil moisture calibration (Dimitrova-Petrova et al., 2021; Peng et al., 2021; Bogena et al., 2013). Figure 4c and d show the time series comparison of two US sites with low average R values. The time series from reanalysis soil moisture products for site Hauser Farm South generally capture the temporal variations against CRNS observations. Yet, the reanalysis soil moisture products do not dry out as much as CRNS measurements during the dry periods. Accordingly, the reanalysis soil moisture products fail to represent the accurate soil moisture condition in the sites located in the dry regions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Rosemount site","kind":"field_site","role":"USA CRNS site showing poor temporal correlation during growing season (after snow removal) often in cropland/shrubland","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-004","page":8,"quote":"Site Rosemount from the USA is affected by snow cover in winter; thus the CRNS measurements during the snow events are discarded. Since the metrics are only calculated when CRNS observations and reanalysis soil moisture products are both available, the low average R value indicates poor temHydrol. Earth Syst. Sci., 28, 1999–2022, 2024 https://doi.org/10.5194/hess-28-1999-2024","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Bias estimates are limited by spatial and vertical scale mismatches between CRNS measurements and reanalysis grid cells.","Evaluation results are regionally biased toward areas with CRNS data availability, mainly the UK, Europe, USA, and Australia.","The number of grid cells representing Europe is limited and uneven, reducing representativeness.","SMAP L4 evaluation period is shorter than other products (from 2015 onwards).","CRNS measurement uncertainties remain for low bulk density and high organic soil sites, especially in humid regions."],"river_ids":["ne-93"],"site_ids":["doc-160e238b70299d3c86f5346c-site-2"],"anchor":[62.64088,66.692107],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[2040043720],"main_basin_ids":[2040043720]},{"id":"doc-19cfd46191d732d39e0e3af3","title":"Cavitron extraction of xylem water suggests cryogenic extraction biases vary across species but are independent of tree water stress","display_title":"Tree Species Cryogenic Extraction Biases in Australia","doi":"https://doi.org/10.1002/hyp.15099","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-02-01","authors":["Clément Duvert","Adrià Barbeta","Lindsay B. Hutley","Leidy Rodríguez","Dylan J. Irvine","Andrew Taylor"],"summary":"This study conducted in tropical northern Australia at Elsey National Park investigates isotopic biases in plant stem water extraction methods across seven native tree species experiencing a range of water stress. Using the Cavitron centrifugation technique to extract xylem water as a reference and comparing it to cryogenic vacuum distillation (CVD) results, the researchers found significant deuterium biases in CVD-extracted bulk stem water that vary by species but are independent of tree water stress and stem water content. The xylem water isotopic signature aligned closely with source water from groundwater and soil, supporting the reliability of Cavitron extraction under field conditions. The findings imply anatomical factors likely drive species-specific bias variations, emphasizing the need for species- and site-specific corrections when using CVD for plant water source studies.","finding":"Cryogenic vacuum distillation-extracted bulk stem water exhibits significant deuterium isotopic bias compared to Cavitron-extracted xylem water, and this bias varies significantly among seven native Australian tree species but is independent of water stress indicators such as predawn leaf water potential and relative stem water content.","finding_evidence":{"passage_id":"p004-003","page":4,"quote":"In addition, we included sampling site as a random effect in the mixed-effects model. 3 | RESULTS 3.1 | Isotopic biases between xylem water and bulk stem water The δD of CVD-derived bulk stem water was significantly and consistently more depleted than that of Cavitron-derived xylem water (Figure 1), with a median δD bias of \u000114.9‰ and values ranging between \u000120.4‰ and \u0001 8.6‰ (Kolmogorov–Smirnov; p < 0.01). In contrast, there was no significant δ18O bias between CVD and Cavitron data, with δ18O biases centred around 0.3‰ and ranging from \u00011.7‰ to 0.9‰ (Kolmogorov–Smirnov; p = 0.945). 3.2 | Deuterium offsets between stem water and source water We found large differences in the relationships between xylem water, bulk stem water and source water (Figure 2). While Cavitron-extracted xylem water aligned well with the source water line (Kolmogorov–Smirnov; p = 0.945), CVD-extracted water plotted well below the source water line (Kolmogorov–Smirnov; p < 0.01).","verified_in_page":true},"topics":["plant-water relations","xylem water isotopes","stable isotopes","water stress","tree species differences","isotopic bias","water extraction methods","groundwater isotopes"],"study_period":null,"locations":[{"name":"Elsey National Park, Northern Territory","kind":"region","role":"Primary study site with diverse vegetation along groundwater depth gradient where isotopic water extractions and comparisons were conducted","river_name":null,"geocode_query":"Elsey National Park, Northern Territory, Australia","latitude":-15.0,"longitude":133.19,"evidence":{"passage_id":"p002-004","page":2,"quote":"Does the CVD-induced deuterium bias occur consistently across tree species, regardless of water stress conditions and xylem water isotopic composition? We provide a detailed operating procedure for our Cavitron xylem water extraction (see Supporting information S1). Following the recommendations of Millar et al. (2022), we hope that this procedure can contribute to the establishment of a unified protocol for use by other research groups. 2 | METHODS 2.1 | Site description Stem, soil and groundwater samples were collected at Elsey National Park, a site located in northern Australia, about 400 km southeast of Darwin (15.00 S, 133.19 E). This region experiences a wet–dry tropical climate (Aw according to the Köppen–Geiger classification), with an average annual rainfall of 1017 mm for the period 2004–2022 (Bureau of Meteorology's Cave Creek Station #14650), >95% of which falls between November and April.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.19,-15.0],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":120769452,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-19cfd46191d732d39e0e3af3-site-0"},{"name":"Cave Creek Station, Northern Territory, Australia","kind":"station","role":"Climate data source providing rainfall records for study site climate characterization","river_name":null,"geocode_query":"Cave Creek Station, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Does the CVD-induced deuterium bias occur consistently across tree species, regardless of water stress conditions and xylem water isotopic composition? We provide a detailed operating procedure for our Cavitron xylem water extraction (see Supporting information S1). Following the recommendations of Millar et al. (2022), we hope that this procedure can contribute to the establishment of a unified protocol for use by other research groups. 2 | METHODS 2.1 | Site description Stem, soil and groundwater samples were collected at Elsey National Park, a site located in northern Australia, about 400 km southeast of Darwin (15.00 S, 133.19 E). This region experiences a wet–dry tropical climate (Aw according to the Köppen–Geiger classification), with an average annual rainfall of 1017 mm for the period 2004–2022 (Bureau of Meteorology's Cave Creek Station #14650), >95% of which falls between November and April.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Mataranka area, Northern Territory, Australia","kind":"region","role":"Referenced location related to aquifer groundwater flow and source characterization relevant to region's hydrogeology","river_name":null,"geocode_query":"Mataranka Northern Territory Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"Water availability from the aquifer in the Tindall limestone south of the Roper River (Report No. 34/2004D). Department of Infrastructure Planning and Environment. https:// territorystories.nt.gov.au/10070/673850/0/0 Karp, D. (2008). Surface and groundwater interaction in the Mataranka area (Technical Report No. 17/2008D). Department of Natural Resources Environment the Arts and Sport. https://territorystories.nt.gov.au/ 10070/674028/0 Kübert, A., Dubbert, M., Bamberger, I., Kühnhammer, K., Beyer, M., van Haren, J., Bailey, K., Hu, J., Meredith, L. K., Nemiah Ladd, S., & Werner, C. (2023). Tracing plant source water dynamics during drought by continuous transpiration measurements: An in-situ stable isotope approach. Plant, Cell & Environment, 46(1), 133–149. https:// doi.org/10.1111/pce.14475 Kühnhammer, K., Dahlmann, A., Iraheta, A., Gerchow, M., Birkel, C., Marshall, J. D., & Beyer, M. (2022).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.0667202,-14.9230049],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769443,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mataranka NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-19cfd46191d732d39e0e3af3-site-2"},{"name":"Tindall Limestone south of Roper River, Northern Territory, Australia","kind":"region","role":"Regional groundwater aquifer connected to the study area providing hydrogeological context","river_name":"Roper River","geocode_query":"Tindall limestone south of Roper River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-004","page":9,"quote":"Water availability from the aquifer in the Tindall limestone south of the Roper River (Report No. 34/2004D). Department of Infrastructure Planning and Environment. https:// territorystories.nt.gov.au/10070/673850/0/0 Karp, D. (2008). Surface and groundwater interaction in the Mataranka area (Technical Report No. 17/2008D). Department of Natural Resources Environment the Arts and Sport. https://territorystories.nt.gov.au/ 10070/674028/0 Kübert, A., Dubbert, M., Bamberger, I., Kühnhammer, K., Beyer, M., van Haren, J., Bailey, K., Hu, J., Meredith, L. K., Nemiah Ladd, S., & Werner, C. (2023). Tracing plant source water dynamics during drought by continuous transpiration measurements: An in-situ stable isotope approach. Plant, Cell & Environment, 46(1), 133–149. https:// doi.org/10.1111/pce.14475 Kühnhammer, K., Dahlmann, A., Iraheta, A., Gerchow, M., Birkel, C., Marshall, J. D., & Beyer, M. (2022).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.299639,-14.946286],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Roper River, Elsey NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-19cfd46191d732d39e0e3af3-site-3","true_coordinates":[133.2375381085312,-14.94628617529888],"spread_km":6.7}],"limitations":["Low replication per species (1-2 trees) limits the detection of species-specific patterns except for Hakea arborescens with eight replicates","Exact volumes of water extracted by CVD not recorded, causing some uncertainty in bias magnitude related to extraction volume","Coordinates for some referenced locations such as Mataranka and aquifer details not explicitly provided","Latitude and longitude data for Cave Creek Station and some groundwater bore sites are not reported","Study conducted only in late dry season months (October 2021 and 2022), limiting seasonal generalization"],"river_ids":["ne-1081"],"site_ids":["doc-19cfd46191d732d39e0e3af3-site-0","doc-19cfd46191d732d39e0e3af3-site-2","doc-19cfd46191d732d39e0e3af3-site-3"],"anchor":[133.19,-15.0],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040064130],"main_basin_ids":[5040064130]},{"id":"doc-2241cdae3d526c3ec4eb1b72","title":"An underground drip water monitoring network to characterize rainfall recharge of groundwater at different geologies, environments, and climates across Australia","display_title":"Australia's National Groundwater Recharge Network","doi":"https://doi.org/10.5194/gi-13-117-2024","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-05-02","authors":["Andy Baker","Margaret Shanafield","Wendy Timms","Martin Sogaard Andersen","Stacey Priestley","Marilu Melo Zurita"],"summary":"This study presents the National Groundwater Recharge Observing System (NGROS), a pioneering sensor network in Australia designed to directly monitor groundwater recharge events at an event scale across diverse Australian geologies, environments, and climates. By deploying automated drip-counting loggers in underground spaces such as caves, mines, and tunnels located in the vadose zone, NGROS measures water percolation representing potential recharge, allowing quantification of rainfall recharge thresholds and temporal-spatial variability of recharge. The initial network includes 14 sites spanning a wide climatic gradient from arid to temperate zones, covering various geological settings including karstified limestones, basalt lava caves, and metasedimentary mines. The system produces high temporal resolution data capturing individual recharge events, providing unprecedented insight into groundwater recharge dynamics critical for sustainable water resource management in Australia.","finding":"The NGROS successfully determines rainfall recharge thresholds at an event timescale across diverse Australian geological formations and climates, demonstrating recharge responses within 24 hours of rainfall events and revealing temporal variability in rainfall thresholds needed to generate recharge.","finding_evidence":{"passage_id":"p008-004","page":8,"quote":"Published applications of the use of the loggers for their original application of monitoring individual cave drip waters are global in extent, e.g., in China (Hu et al., 2008), the USA (Onac et al., 2008; Wortham et al., 2021), and Europe (Suric et al., 2017). ́ 3 Initial results from the first year of monitoring As examples of the types of data and interpretations that are possible, we present 1 month of data from three contrasting sites from the first year of monitoring. At the Byaduk Lava Cave (VIC), Fig. 4 shows five recharge events that occurred in October 2022. This included four events that occurred over a 10 d period. At this site, recharge occurs within 24 h of rainfall. This allows event-scale determination of the amount of rainfall necessary to generate recharge. Because rainfall data are at daily resolution and individual rain events can occur over 2 recording days, we calculate the 48 h total rainfall recharge thresholds.","verified_in_page":true},"topics":["groundwater recharge","hydroclimate variability","vadose zone","karst hydrogeology","rainfall thresholds","sensor networks","aquifer recharge","water resource management"],"study_period":"2022 onwards","locations":[{"name":"Burra Mine Adit, Ngadjuri Country, South Australia, Australia","kind":"facility","role":"NGROS monitoring site in arid steppe climate with Cambrian sedimentary geology and disused copper mine underground space for drip measurement","river_name":null,"geocode_query":"Burra Mine Adit, South Australia, Australia","latitude":-33.7,"longitude":138.9,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Baker et al.: An underground drip water monitoring network 121 Figure 2. Example logger deployment. (a) Durham Lead. The logger is situated on the floor, recording water dripping from the mine roof. (b) Capricorn Caves. The logger is situated beneath a flowstone, an actively forming calcium carbonate speleothem formed by flowing water. Water flows along the cave walls and drips onto the logger. (c) Walhalla Long Tunnel. Two loggers are visible, both recording water dripping from the mine roof. 2.1.1 Burra Mine Adit, SA, Ngadjuri Country (33.7° S, 138.9° E; ∼ 500 m a.s.l. (above sea level)) This short, hand-dug adit forms part of a copper mine complex which at its peak was the largest copper mine in Australia, and it is now part of a National Heritage area. The mine lies within the Adelaide geosyncline, a thick Neoproterozoic to Cambrian sedimentary succession (Preiss, 2000).","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.9,-33.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43660572,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-0"},{"name":"Stawell Mine, Djab Wurrung Country, Victoria, Australia","kind":"facility","role":"Operating gold mine NGROS monitoring site with Cambrian slate/schist geology and access tunnels 100 m underground for drip measurement","river_name":null,"geocode_query":"Stawell Mine, Victoria, Australia","latitude":-37.6,"longitude":142.8,"evidence":{"passage_id":"p005-001","page":5,"quote":"At the surface, there are loamy soils over rock (Hall et al., 2009), and a layer of fine loam was apparent at the edges of the adit entrance. The depth of the adit is approximately 5 m below ground where the shaft opens into the engine house (Lee Wallis, personal communication, 2022), dug through flaky sandstone. To take advantage of the deepest portion of the adit, five loggers were placed approximately 2 m apart along the sides of the adit beginning from the engine house end. Burra sits within a landscape of temperate grasslands. The local climate has a mean annual temperature of 14.8 °C, mean annual rainfall of 424 mm, and modelled pan evaporation of 2211 mm. The climate according to the Köppen– Geiger system is BSk (arid steppe, cold).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.8,-37.6],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21562736,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-1"},{"name":"Naracoorte Caves, Meintangk Country, South Australia, Australia","kind":"facility","role":"Tourist karst caves NGROS site in Miocene karstified limestone for drip monitoring and groundwater recharge assessment","river_name":null,"geocode_query":"Naracoorte Caves, South Australia, Australia","latitude":-37.0,"longitude":140.8,"evidence":{"passage_id":"p005-002","page":5,"quote":"The site is currently under the management of the National Trust of South Australia. 2.1.2 Stawell Mine, Stawell, VIC, Djab Wurrung Country (37.6° S, 142.8° E; ∼ 250 m a.s.l.) This mine adit is in an operating gold mine with large blasted and excavated access tunnels. The loggers are placed in an access tunnel at a level approximately 100 m below surface, suspended approximately 30 cm down from the fractured rock roof that is supported with rock-bolts and some minor roof areas with reinforcing mesh. Sections of the tunnel roof with shotcrete have been avoided. Gold-quartz veins here are associated with altered Cambrian slate or schist and metavolcanics adjacent to a Devonian intrusive body (Miller et al., 2006). The local climate has a mean annual temperature of 14.2 °C, mean annual rainfall of 480 mm, and modelled pan evaporation of 1680 mm. The climate is at the northern limit of Cfb (warm summer, no dry season, temperate), close to a transition to BSk (arid steppe, cold).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.7968,-37.09463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21562702,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Naracoorte Caves, Joanna SA 5271, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-2","true_coordinates":[140.7968,-37.0346299],"spread_km":6.7},{"name":"Montacute Mine, Kaurna Country, South Australia, Australia","kind":"facility","role":"Disused metal mine NGROS monitoring adit in Cambrian sandstone for drip water recharge monitoring","river_name":null,"geocode_query":"Montacute Mine, South Australia, Australia","latitude":-34.9,"longitude":138.7,"evidence":{"passage_id":"p006-001","page":6,"quote":"The outline map is from Wikimedia Commons, shared under the CC3.0 licence. steep, wooded hillside dominated by native vegetation adjacent to a dolomite quarry and quartzite cliffs. Five loggers were placed at 1–2 m intervals beneath active drip sites starting approximately 10 m from the entrance to the adit. The local climate has a mean annual temperature of 14.7 °C, mean annual rainfall of 612 mm, and modelled pan evaporation of 2090 mm. The climate is CSb (temperate, dry summer, warm summer). 2.1.5 Phosphate Mine, NSW, Wiradjuri Country (32.6° S, 148.9° E; 340 m a.s.l.) With native box grassland vegetation cover and midDevonian marmorized reef limestone geology, this site is located near the top of a shallow (∼ 40 m above the nearby river alluvium) ridgeline formed by the reef limestone. The local climate has a mean annual temperature of 17.2 °C, mean annual rainfall of 620 mm, and synthetic pan evaporation of 2231 mm. The climate is Cfa (temperate, no dry season, hot summer).","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.7,-34.9],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":50289745,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-3"},{"name":"Phosphate Mine, Wiradjuri Country, New South Wales, Australia","kind":"facility","role":"Disused phosphate mine in Devonian karstified limestone with drip loggers monitoring groundwater recharge","river_name":null,"geocode_query":"Phosphate Mine, New South Wales, Australia","latitude":-32.6,"longitude":148.9,"evidence":{"passage_id":"p006-001","page":6,"quote":"The outline map is from Wikimedia Commons, shared under the CC3.0 licence. steep, wooded hillside dominated by native vegetation adjacent to a dolomite quarry and quartzite cliffs. Five loggers were placed at 1–2 m intervals beneath active drip sites starting approximately 10 m from the entrance to the adit. The local climate has a mean annual temperature of 14.7 °C, mean annual rainfall of 612 mm, and modelled pan evaporation of 2090 mm. The climate is CSb (temperate, dry summer, warm summer). 2.1.5 Phosphate Mine, NSW, Wiradjuri Country (32.6° S, 148.9° E; 340 m a.s.l.) With native box grassland vegetation cover and midDevonian marmorized reef limestone geology, this site is located near the top of a shallow (∼ 40 m above the nearby river alluvium) ridgeline formed by the reef limestone. The local climate has a mean annual temperature of 17.2 °C, mean annual rainfall of 620 mm, and synthetic pan evaporation of 2231 mm. The climate is Cfa (temperate, no dry season, hot summer).","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.9,-32.6],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43670975,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-4"},{"name":"Old Sleep’s Hill Rail Tunnel, Kaurna Country, South Australia, Australia","kind":"facility","role":"Disused railway tunnel used for NGROS with sandstone geology monitoring vadose zone recharge via drips","river_name":null,"geocode_query":"Old Sleep’s Hill Rail Tunnel, South Australia, Australia","latitude":-35.0,"longitude":138.6,"evidence":{"passage_id":"p006-002","page":6,"quote":"The only source of recharge is from precipitation onto the ridge above the site. This hand-dug heritage mine was developed for the possible extraction of mineralized bat guano accumulated in karst passages. Loggers have been placed in the lowest mine passages, closest to the groundwater. This site complements existing long-term cave-based groundwater recharge monitoring at Cathedral Cave in the same formation (Jex et al., 2012), groundwater monitoring bores established at the site, a soil moisture network at the site (Berthelin et al., 2020), the nearby (∼ 10 km) UNSW Wellington Field Station (Rutlidge et al., 2023), and a newly established Critical Zone Observatory. This site is managed by the Government of New South Wales and the Dubbo Regional Council. 2.1.6 Old Sleep’s Hill Rail Tunnel, SA, Kaurna Country (35.0° S, 138.6° E; 115 m a.s.l.) This disused, heritage-listed train tunnel is located on private land in the Adelaide metropolitan region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.6,-35.0],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":50289546,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-5"},{"name":"Byaduk Lava Cave, Djab Wurrung Country, Victoria, Australia","kind":"facility","role":"Basalt lava cave NGROS site with drip count sensors capturing deep drainage and rapid recharge signals","river_name":null,"geocode_query":"Byaduk Lava Cave, Victoria, Australia","latitude":-37.9,"longitude":142.0,"evidence":{"passage_id":"p007-000","page":7,"quote":"A. Baker et al.: An underground drip water monitoring network 123 native soil and rock and largely consists of a conservation park for the remnant grey box eucalypt forest. Loggers were placed about 10 m inside the tunnel at locations where drips were observed. The local climate has a mean annual temperature of 16.8 °C, mean annual rainfall of 700 mm, and modelled pan evaporation of 2170 mm. The climate is CSb (temperate, dry summer, warm summer). This site is privately owned and managed. 2.1.7 Byaduk Lava Cave, VIC, Djab Wurrung Country (37.9° S, 142.0° E; ∼ 150 m a.s.l.) This basalt lava cave is in late-Quaternary-age basalts that derive from the volcanic eruption of Mount Napier ca. 36 000 years ago, the most recent active volcanism in Australia. The local climate has a mean annual temperature of 13.1 °C, mean annual rainfall of 747 mm, and modelled pan evaporation of 1600 mm. The climate is CSb (temperate, dry summer, warm summer).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.0,-37.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21557264,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-6"},{"name":"Tantanoola Cave, Bungandidj Country, South Australia, Australia","kind":"facility","role":"Miocene karstified dolomite cave monitored for groundwater recharge via drip sensors","river_name":null,"geocode_query":"Tantanoola Cave, South Australia, Australia","latitude":-37.7,"longitude":140.5,"evidence":{"passage_id":"p007-001","page":7,"quote":"The overlying vegetation is native shrub and open woodland with organic loam soil (tenosol) (Corangamite Catchment Management Authority et al., 2023). The site is managed by the Government of Victoria through Parks Victoria and is located upstream of the Budj Bim Cultural Landscape, listed by UNESCO in 2019 (UNESCO World Heritage Convention, 2023). 2.1.8 Tantanoola Cave, SA, Bungandidj Country (37.7° S, 140.5° E; ∼ 50 m a.s.l.) This karst cave was formed in Miocene-age bryozoan dolomite, with sandy soils at the surface. The cave is set into a cliff face which is believed to have been exposed to wave action during a period of higher sea levels (Department for Environment and Heritage, Government of South Australia, 2008). Above the cave is a hilltop of native vegetation that was highly disturbed by agricultural activities prior to the protection of the area as a conservation park in 1972.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.5,-37.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21556931,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-7"},{"name":"Durham Lead, Clarendon Forest, Wadawurrung Country, Victoria, Australia","kind":"facility","role":"Historic gold mine adit NGROS drip logger site in Ordovician metasediments","river_name":null,"geocode_query":"Durham Lead, Victoria, Australia","latitude":-37.7,"longitude":143.9,"evidence":{"passage_id":"p007-002","page":7,"quote":"The local climate has a mean annual temperature of 14.2 °C, mean annual rainfall of 778 mm, and modelled pan evaporation of 1692 mm. This site is managed by the National Parks and Wildlife Service of the Government of South Australia. 2.1.9 Durham Lead, Clarendon Forest, VIC, Wadawurrung Country (37.7° S, 143.9° E; ∼ 360 m a.s.l.) This historic hand-dug adit located on private property is relatively short and of small diameter, and it is situated in gold deposits associated with Devonian quartz mineralization of Ordovician sedimentary rocks. The local climate has a mean annual temperature of 13.0 °C, mean annual rainfall of 779 mm, and modelled pan evaporation of 1678 mm. Native woodland and sodic clayey subsoil (sodosol) overlie this adit.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.9,-37.7],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21558216,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-8"},{"name":"Capricorn Caves, Darumbal Country, Queensland, Australia","kind":"facility","role":"Devonian karstified reef limestone caves monitored by NGROS sensors to assess recharge dynamics","river_name":null,"geocode_query":"Capricorn Caves, Queensland, Australia","latitude":-23.16,"longitude":150.5,"evidence":{"passage_id":"p007-003","page":7,"quote":"The climate is Cfb (temperate, no dry season, warm summer). 2.1.10 Capricorn Caves, QLD, Darumbal Country (23.16° S, 150.5° E; ∼ 270 m a.s.l.) This natural cave site is in an isolated ca. 50 m high limestone hill with 390 Myr old Devonian reef limestone geology. It has a native dry rainforest vegetation cover. The local climate has a mean annual temperature of 23.4 °C, mean annual rainfall of 815 mm, and modelled pan evaporation of 2525 mm. The climate is on the boundary of Cfa (temperate, no dry season, hot summer) and tropical savannah climate classifications (Aw). Loggers have been placed in the lowest cave passages, closest to the groundwater. The site is privately owned and managed. 2.1.11 Daylesford Ajax South, VIC, Dja Dja Wurrung Country (37.3° S, 144.1° E; ∼ 550 m a.s.l.) This historic hand-dug adit, located on private property, is relatively short and of small diameter and is associated with the gold deposits of the Castlemaine Supergroup (Lower Ordovician) sediments.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.5,-23.16],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":12126663,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-9"},{"name":"Daylesford Ajax South, Dja Dja Wurrung Country, Victoria, Australia","kind":"facility","role":"Historic gold mine NGROS site in Ordovician metasediments with recharge drip monitoring","river_name":null,"geocode_query":"Daylesford Ajax South, Victoria, Australia","latitude":-37.3,"longitude":144.1,"evidence":{"passage_id":"p007-004","page":7,"quote":"The local climate has a mean annual temperature of 11.7 °C, mean annual rainfall of 879 mm, and modelled pan evaporation of 1728 mm. chromosol soils, and scattered tall trees and grass overlie the adit. The climate is Cfb (temperate, no dry season, warm summer). 2.1.12 Willi Willi, NSW, Djangadi Country (30.9° S, 152.5° E; 270 m a.s.l.) Daylight Cave is situated in a national park comprising cleared dry and subtropical rainforest with remnant vegetation. The site was burnt over by a containment (control) fire during the 2019–20 Australian wildfires. The limestone geology is Permian in age and comprises calcareous mudstone, crinoidal limestone, and reef limestones. The ∼ 60 km belt of limestone has been highly folded and faulted post-deposition, leading to the limestone retaining low primary porosity and becoming highly fractured.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.1,-37.3],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43662707,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-10"},{"name":"Willi Willi, Djangadi Country, New South Wales, Australia","kind":"facility","role":"Permian karstified limestone cave NGROS recharge monitoring site with adjacent soil moisture network","river_name":null,"geocode_query":"Willi Willi, New South Wales, Australia","latitude":-30.9,"longitude":152.5,"evidence":{"passage_id":"p007-005","page":7,"quote":"Adjacent to and overlying the cave is a soil moisture network comprising 20 randomly distributed soil moisture probes (an extension of the network described in Berthelin et al., 2020). Loggers are placed in the upper and lower levels of this cave to allow comparison between potential recharge events and soil moisture characteristics. The local climate has a mean annual temperature of 18.7 °C, mean annual rainfall of 1218 mm, and modelled pan evaporation of 1879 mm. The climate is Cfa (temperate, no dry season, hot summer). The site is managed by the Government of New South Wales through the NSW National Parks and Wildlife Service. 2.1.13 Harrie Wood Cave, NSW, Ngarigo Country (35.7° S, 148.5° E; ∼ 980 m a.s.l.) This former tourist cave is situated in Silurian limestone that has low primary porosity and is highly fractured, and it is a long-term hydrological monitoring site (Markowska et al., https://doi.org/10.5194/gi-13-117-2024 Geosci. Instrum. Method. Data Syst., 13, 117–129, 2024","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.4607174,-30.9304145],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26969117,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Willi Willi NSW 2440, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-11"},{"name":"Harrie Wood Cave, Ngarigo Country, New South Wales, Australia","kind":"facility","role":"Silurian karst cave NGROS site in sub-alpine region with long-term drip water hydrological monitoring","river_name":null,"geocode_query":"Harrie Wood Cave, New South Wales, Australia","latitude":-35.7,"longitude":148.5,"evidence":{"passage_id":"p008-000","page":8,"quote":"124 A. Baker et al.: An underground drip water monitoring network 2015; Chapman et al., 2024). Located in the Kosciuszko National Park, the local vegetation comprises sub-alpine open snow gum woodland, although above the cave the steep slopes limit the extent of soil cover and vegetation. The site was burnt over in the 2019–2020 Australian wildfires. This site provides data from a sub-alpine location that experiences seasonal snowfall, a site that is recovering from wildfire, and a site from which stalagmite paleo-climate records have been obtained (Tadros et al., 2022). Loggers are located on a depth transect from the cave entrance to a depth approximately 50 m below land surface. The local climate has a mean annual temperature of 11.1 °C (recorded at the on-site weather station), mean annual rainfall of 1260 mm, and modelled pan evaporation of 1492 mm. The climate is Cfb (temperate, no dry season, warm summer).","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.4863427,-35.720005],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43669927,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Harrie Wood Cave Access, Yarrangobilly NSW 2720, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-12"},{"name":"Walhalla Extended Long Tunnel Mine, Gunaikurnai Country, Victoria, Australia","kind":"facility","role":"Historic gold mine with drip logger sensors monitoring groundwater recharge in Devonian metasediments","river_name":null,"geocode_query":"Walhalla Extended Long Tunnel Mine, Victoria, Australia","latitude":-37.9,"longitude":146.5,"evidence":{"passage_id":"p008-001","page":8,"quote":"The site is managed by the Government of New South Wales. 2.1.14 Walhalla Extended Long Tunnel Mine, VIC, Gunaikurnai Country (37.9° S 146.5° E; ∼ 400 m a.s.l.) Walhalla is a large-scale historic gold mine, with one level managed as a tourist site: lower levels of the historic mine are flooded with groundwater. The gold deposits are associated with Devonian-age dykes found within the Devonian metasedimentary rocks of the eastern Victorian gold province. The local climate has a mean annual temperature of 13.2 °C, mean annual rainfall of 1290 mm, and modelled pan evaporation of 1565 mm. Native forest with tall trees and thick understorey occurs on the slopes with thin soils (chromosol) above the mine. The climate is Cfb (temperate, no dry season, warm summer). The site is a Crown land reserve and is managed by the Victoria Government through the Walhalla Board of Management. 2.2 Technical description of loggers The NGROS uses commercially available Stalagmate © loggers as shown in Fig. 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.5,-37.9],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":21560127,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-2241cdae3d526c3ec4eb1b72-site-13"}],"limitations":["Rainfall data at daily resolution limits precise timing of recharge events","Some site locations have incomplete or broad regional coordinates","Limited to Australian sites initially, lacking tropical north coverage where data are sparse","The monitoring measures timing and thresholds but does not directly quantify recharge volume","Uncertainties exist in rainfall inputs used from gridded climate products"],"river_ids":[],"site_ids":["doc-2241cdae3d526c3ec4eb1b72-site-0","doc-2241cdae3d526c3ec4eb1b72-site-1","doc-2241cdae3d526c3ec4eb1b72-site-2","doc-2241cdae3d526c3ec4eb1b72-site-3","doc-2241cdae3d526c3ec4eb1b72-site-4","doc-2241cdae3d526c3ec4eb1b72-site-5","doc-2241cdae3d526c3ec4eb1b72-site-6","doc-2241cdae3d526c3ec4eb1b72-site-7","doc-2241cdae3d526c3ec4eb1b72-site-8","doc-2241cdae3d526c3ec4eb1b72-site-9","doc-2241cdae3d526c3ec4eb1b72-site-10","doc-2241cdae3d526c3ec4eb1b72-site-11","doc-2241cdae3d526c3ec4eb1b72-site-12","doc-2241cdae3d526c3ec4eb1b72-site-13"],"anchor":[143.9,-37.7],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040069590,5040070710,5040073410,5040073420,5040524870,5040591050],"main_basin_ids":[5040069590,5040070710,5040073410,5040073420]},{"id":"doc-320267191a5f61e8e5ab4723","title":"Drought and radiation explain fluctuations in Amazon rainforest greenness during the 2015–2016 drought","display_title":"Drought and Radiation Control Amazon Greenness","doi":"https://doi.org/10.5194/bg-21-2273-2024","theme":"Climate","color":"#98d4f1","year":2024,"publication_date":"2024-05-08","authors":["Yi Y. Liu","Albert I. J. M. van Dijk","Patrick Meir","Tim R. McVicar"],"summary":"The study analyzed the Amazon rainforest's greenness response during the 2015–2016 drought using satellite vegetation indices (NDVI and EVI) alongside terrestrial water storage, land surface temperature, and vapor pressure deficit data. It found that greenness anomalies were governed by exceeding normal hydrological and thermal limits. A southward progression of drought stress regions was identified over the drought period. The approach explained over 70% of the spatial-temporal patterns in canopy greenness, highlighting the combined roles of drought impact and radiation in vegetation dynamics.","finding":"Regions where terrestrial water storage, land surface temperature, or vapor pressure deficit exceeded their non-drought year ranges during the 2015–2016 Amazon drought exhibited negative vegetation index anomalies, regardless of radiation anomalies, explaining more than 70% of observed greenness fluctuation patterns.","finding_evidence":{"passage_id":"p013-004","page":13,"quote":"We found a gradual southwards shift in these regions from (1) the northeastern Amazon during August to October 2015, mainly due to high temperatures and high atmospheric moisture demand, to (2) the north–central Amazon during November 2015 to February 2016 where soil water deficit, high temperatures, and high atmospheric moisture demand co-existed simultaneously, and to (3) the southern Amazon in July 2016, caused by high temperatures and high atmospheric moisture demand again. Within these regions, most grid cells were characterized by negative greenness anomalies determined from the MODIS vegetation index. Outside of these regions, greenness anomalies and radiation anomalies were generally in phase, which is expected to occur under normal conditions.","verified_in_page":true},"topics":["vegetation-indices","drought","terrestrial-water-storage","land-surface-temperature","vapor-pressure-deficit","photosynthetically-active-radiation","remote-sensing","climate-impacts"],"study_period":"2003-2016","locations":[{"name":"Amazon rainforest","kind":"region","role":"study region and landscape experiencing drought and canopy greenness change","river_name":null,"geocode_query":"Amazon rainforest, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Biogeosciences, 21, 2273–2295, 2024 https://doi.org/10.5194/bg-21-2273-2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Drought and radiation explain fluctuations in Amazon rainforest greenness during the 2015–2016 drought Yi Y. Liu1 , Albert I. J. M. van Dijk2 , Patrick Meir3,4, and Tim R. McVicar5 1School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia 2Fenner School of Environment & Society, Australian National University, Canberra, Australian Capital Territory, 0200, Australia 3Research School of Biology, Australian National University, Canberra, Australian Capital Territory, 0200, Australia 4School of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh, EH93FF, UK 5CSIRO Environment, G.P.O. Box 1700, Canberra, Australian Capital Territory, 2601, Australia Correspondence: Yi Y.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-62.21588,-3.525305],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon Rainforest - Codajás, Amazonas, 69450-000, Brazil","basin_id":6040280410,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":2216665.3,"hydrology_source":"hydrobasins","site_id":"doc-320267191a5f61e8e5ab4723-site-0","true_coordinates":[-62.21588049999999,-3.4653053],"spread_km":6.7},{"name":"north-eastern Amazon","kind":"region","role":"initial drought-affected region with high temperature and moisture demand exceeding normal ranges during August to October 2015","river_name":null,"geocode_query":"northeastern Amazon, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"During Stage III, only a small area with TWS < TWSMin occurred in the northeast. During Stage IV, LST and VPD were “beyond non-drought years’ extreme values” in the south of the Amazon. Thus, there was a gradual southwards movement of the regions “exceeding normal ranges”, from the northeast during August to October 2015, to the central–north during November 2015 to February 2016, and finally to the south by July 2016. Spatial distributions of predicted VI anomaly direction (derived from Approach #1, #2A, #2B, and #2C), as well as MODIS-observed VI anomaly direction for the four stages from August 2015 through July 2016, are shown in Fig. 8. Their spatial agreements ( %) are shown in Table 2. When compared with Approach #1, all three #2 approaches have better spatial agreement with MODIS observations, with the best performance derived from Approach #2C.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"north–central Amazon","kind":"region","role":"region with concurrent soil water deficit, high temperatures, and moisture demand exceeding normal ranges during November 2015 to February 2016","river_name":null,"geocode_query":"north-central Amazon, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"During Stage III, only a small area with TWS < TWSMin occurred in the northeast. During Stage IV, LST and VPD were “beyond non-drought years’ extreme values” in the south of the Amazon. Thus, there was a gradual southwards movement of the regions “exceeding normal ranges”, from the northeast during August to October 2015, to the central–north during November 2015 to February 2016, and finally to the south by July 2016. Spatial distributions of predicted VI anomaly direction (derived from Approach #1, #2A, #2B, and #2C), as well as MODIS-observed VI anomaly direction for the four stages from August 2015 through July 2016, are shown in Fig. 8. Their spatial agreements ( %) are shown in Table 2. When compared with Approach #1, all three #2 approaches have better spatial agreement with MODIS observations, with the best performance derived from Approach #2C.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"southern Amazon","kind":"region","role":"region experiencing drought conditions with high temperature and moisture demand exceeding normal ranges during July 2016","river_name":null,"geocode_query":"southern Amazon, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"During Stage III, only a small area with TWS < TWSMin occurred in the northeast. During Stage IV, LST and VPD were “beyond non-drought years’ extreme values” in the south of the Amazon. Thus, there was a gradual southwards movement of the regions “exceeding normal ranges”, from the northeast during August to October 2015, to the central–north during November 2015 to February 2016, and finally to the south by July 2016. Spatial distributions of predicted VI anomaly direction (derived from Approach #1, #2A, #2B, and #2C), as well as MODIS-observed VI anomaly direction for the four stages from August 2015 through July 2016, are shown in Fig. 8. Their spatial agreements ( %) are shown in Table 2. When compared with Approach #1, all three #2 approaches have better spatial agreement with MODIS observations, with the best performance derived from Approach #2C.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"grid cell centered at 2.5° S, 60.5° W","kind":"station","role":"site with field measurements confirming atmospheric moisture demand as main driver of drought stress","river_name":null,"geocode_query":"2.5 S, 60.5 W, Amazon rainforest, South America","latitude":-2.5,"longitude":-60.5,"evidence":{"passage_id":"p011-003","page":11,"quote":"These results indicate that the atmospheric demand could be the main driver for decreasing plants’ LXSMs. We examined the anomalies of TWS, LST, and VPD over Fontes’ grid cell for the same period (August to December 2015) (Fig. 9). Strong positive anomalies in LST and VPD agree with the field measurements in Fontes et al. (2018). Moreover, TWS from August to November 2015 was higher than in the same months of non-drought years, suggesting that sufficient soil water was available during this period. Our “exceeding normal ranges”-based method developed herein can help resolve the debate around greenness anomalies in the dry season (July to September) of the 2005 drought (Saleska et al., 2007; Samanta et al., 2010). When we examined the MODIS-observed VI anomalies from May to October over the southern Amazon, both 2005 and 2010 witnessed a two-stage process: positive VI anomalies followed by negative VI anomalies (Fig. 10a and d).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-62.21588,-3.405305],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon Rainforest - Codajás, Amazonas, 69450-000, Brazil","basin_id":6040280410,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":2216665.3,"hydrology_source":"hydrobasins","site_id":"doc-320267191a5f61e8e5ab4723-site-4","true_coordinates":[-62.21588049999999,-3.4653053],"spread_km":6.7}],"limitations":["Uncertainties exist in satellite-derived vegetation indices due to atmospheric and sensor-based effects.","Simplified tolerance thresholds based on non-drought year ranges may not precisely correspond to physiological limits.","Local factors influencing greenness may not be fully captured by the datasets used.","The non-drought range is based on a relatively short observational period relative to vegetation lifespan and might not represent long-term norms."],"river_ids":[],"site_ids":["doc-320267191a5f61e8e5ab4723-site-0","doc-320267191a5f61e8e5ab4723-site-4"],"anchor":[-62.21588,-3.525305],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[6040280410],"main_basin_ids":[6040007000]},{"id":"doc-37da843d3659b6adde0dbe74","title":"Effect Of Plant Spacing And Variety On Growth, Yield And Quality Of Maize In Southern Coastal Region In Bangladesh","display_title":"Maize Growth and Yield in Coastal Bangladesh","doi":"https://doi.org/10.3329/sja.v22i1.72864","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-07-27","authors":["S Akther","AK Hasan","A Kader","RW Bell","MA Hossen","M Mainuddin"],"summary":"This study investigated the influence of plant spacing and variety on maize growth, yield, and quality in the southern coastal region of Bangladesh. Field experiments were conducted in the Khatail area of Dacope upazila, Khulna district during two consecutive rabi seasons (2019-2020 and 2020-2021). The experiment tested four plant spacings and three maize varieties to evaluate their effects on growth parameters, grain yield, carbohydrate and protein content under local coastal conditions, including salinity stress.","finding":"The combination of BARI hybrid Maize-16 variety planted at 45 cm × 20 cm spacing (S4V1) produced the highest grain yield of 8.43 t ha-1 due to higher plant population density, while the widest spacing (60 cm × 20 cm) with the same variety (S1V1) resulted in the highest carbohydrate content (74.61%) but lower grain yield.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"EFFECT OF PLANT SPACING AND VARIETY OF MAIZE 201 CONCLUSION From the study, it may be concluded that, yield contributing characters, yield and grain quality of maize can be effectively manipulated by different plant spacing and variety. The highest rows cob-1 , kernel row-1 and 100 grain weight were observed from S1V1 treatment combination but S4V1 (BARI hybrid Maize-16 planted with 45 cm × 20 cm plant spacing) produced significantly the highest grain yield (8.43 t ha-1 ) due to higher number of plant population. The highest carbohydrate content (74.61%) was recorded from S1V1 and the maximum protein content (7.98 %) found from S1V3 treatment combination. ACKNOWLEDGEMENT The authors thankfully acknowledged the Krishi Gobeshona Foundation and Australian Centre for International Agricultural Research (KGF-ACIAR) for supporting to conduct the present study. REFERENCES Abuzar, M.R., Sadozai, G.U., Baloch, M.S., Baloch, A.A., Shah, I.H., Javaid, T. and Hussain, N. (2011).","verified_in_page":true},"topics":["plant spacing","crop yield","maize variety","grain quality","salinity tolerance","crop management","agroecology","field experiment"],"study_period":"2019-2021","locations":[{"name":"Khatail, Dacope upazila, Khulna district, Bangladesh","kind":"field_site","role":"Experimental site for maize growth, yield and quality assessment under coastal conditions","river_name":null,"geocode_query":"Khatail, Dacope upazila, Khulna, Bangladesh","latitude":22.95,"longitude":89.85,"evidence":{"passage_id":"p002-003","page":2,"quote":"So, the present study was conducted to find out the effect different plant spacing and variety on yield and quality (carbohydrate and protein content) of maize in coastal area of Bangladesh. MATERIALS AND METHODS Research Location The field experiments were conducted at Khatail under the Dacope upazila of Khulna district during the Rabi seasons (December-April) of 2019-2020 and 2020-2021. The experimental site is located at 22°57 ́ N latitude and 89° 51 ́ E longitude within Polder number 32. This site belongs to non-calcareous dark grey floodplain soil under","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.85,22.95],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-37da843d3659b6adde0dbe74-site-0"},{"name":"Southern coastal region, Bangladesh","kind":"region","role":"General study area context representing maize cultivation in coastal Bangladesh","river_name":null,"geocode_query":"Southern coastal region, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"SAARC J. Agric., 22(1): 191-202 (2024) DOI: https://doi.org/10.3329/sja.v22i1.72864 EFFECT OF PLANT SPACING AND VARIETY ON GROWTH, YIELD AND QUALITY OF MAIZE IN SOUTHERN COASTAL REGION IN BANGLADESH S. Akther*1 , A. K. Hasan2 , A. Kader2 , R. W. Bell3 , M. A. Hossen4 and M. Mainuddin5 1Bangladesh Agricultural Research Institute (BARI), Gazipur 1701, Bangladesh 2 Department of Agronomy, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh 3 Murdoch University, Western Australia, 6150. 4Ministry of Land, Dhaka, Bangladesh. 5 Water Security Program, CSIRO, Environment, Canberra ACT 2601, Australia ABSTRACT An experiment was conducted at the farmer’s field of Dacope upazila under Khulna district during the two consecutive rabi seasons of 2019-20 and 2020-21 to find out the effect of plant spacing and variety on yield and quality of maize in coastal region.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates are approximate based on text descriptions; exact geospatial data not provided.","Paper text includes some broad regional descriptions without precise local coordinates.","Study focused on one experimental site; results may have limited generalizability beyond similar agroecological zones."],"river_ids":[],"site_ids":["doc-37da843d3659b6adde0dbe74-site-0"],"anchor":[89.85,22.95],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-3ed7035c0460efa5235edad3","title":"Predictive analysis of groundwater balance and assessment of safe yield using a probabilistic groundwater model for the Dead Sea Basin","display_title":"Groundwater Balance and Safe Yield in Dead Sea Basin","doi":"https://doi.org/10.3389/frwa.2024.1380877","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-05-22","authors":["Dima Al Atawneh","Sreekanth Janardhanan","Nick Cartwright","Edoardo Bertone","Rebecca Doble"],"summary":"This study developed a probabilistic MODFLOW groundwater model to analyze groundwater balance components and estimate safe yield in the Dead Sea Basin aquifer in Jordan. The model accounted for uncertainties in recharge, hydraulic properties, and pumping. Results indicate an annual recharge of about 5.1% of precipitation, with safe yield estimates around 8 mm/year. Current groundwater extraction slightly exceeds safe yield, contributing to observed groundwater level declines. Climate change scenarios predict further groundwater level reductions by the century's end, though smaller than depletion from extraction. The study highlights the importance of considering natural groundwater discharge in sustainable management and the need for ongoing monitoring and adaptive strategies.","finding":"The model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The average annual safe yield is estimated as approximately 8.0 mm corresponding to 80% of the calibrated recharge value, but current groundwater extraction slightly exceeds this, contributing to declining groundwater levels indicative of unsustainable use.","finding_evidence":{"passage_id":"p001-004","page":1,"quote":"Results and discussion: The model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The eect of groundwater recharge and discharge components were evaluated in the context of estimation of safe yield of the aquifer. The average annual safe yield is estimated as ∼8.0 mm corresponding to the 80% of the calibrated recharge value. Simulated groundwater levels matched well with the declining trends in observed water levels which are indicative of unsustainable use. Long-term simulation of groundwater levels indicated that current conditions would result in large drawdown in groundwater levels by the end of the century. Simulation of climate change scenarios using projected estimates of rainfall and evaporation indicates that climate change scenarios would further exacerbate groundwater levels by relatively small amounts.","verified_in_page":true},"topics":["groundwater recharge","groundwater levels","probabilistic modeling","safe yield estimation","climate change impacts","hydraulic conductivity","water balance","aquifer sustainability"],"study_period":"2009-2014 (model calibration)","locations":[{"name":"Dead Sea Basin aquifer, Jordan","kind":"basin","role":"primary study site and groundwater aquifer system under analysis for groundwater balance, recharge, discharge, and safe yield estimation","river_name":null,"geocode_query":"Dead Sea Basin, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"The present study aims to address these knowledge gaps by developing a transient groundwater model (MODFLOW) to simulate the long-term groundwater balance over the present-day and far-future (end of century) periods under changing (rainfall and evapotranspiration) and non-changing climates. The modeling accounts for predictive uncertainties resulting from complex geology and data sparsity by using a model calibration and uncertainty analysis approach based on an ensemble of plausible model parameters and inputs to assess prediction uncertainty. The study evaluates the estimation of safe yield with the insights gained from water balance analysis and uncertainties in the relevant water balance components. 2 Methodology Figure 1 provides an overview of the modeling approach adopted in this study.","verified_in_page":true},"resolution_status":"resolved","coordinates":[35.4731895,31.5590287],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-0"},{"name":"Wadi Mujib catchment, Jordan","kind":"basin","role":"major catchment within Dead Sea Basin contributing surface and groundwater flow and recharge","river_name":"Wadi Mujib","geocode_query":"Wadi Mujib catchment, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"It consists of two major catchments the Wadi Al-Mujib catchment (4,500 km2 ) and the Wadi Wala catchment of about (2,100 km2 ; MWI, 2020). The area selected for modeling covers an approximate area of 6,500 km2 in the southeastern part of the basin and was selected due to its exposure to water scarcity. Practices such as high water abstraction and agricultural activities coupled with climate change had placed a burden on the aquifer (Hdoush et al., 2022). The area is multi-terrain accompanied by steep slopes toward the western side of the model domain, with variant contour elevations ranging from 1,200 m asl to −420 m bsl (MWI, 2020). The location of the DSB aquifer between the Mediterranean climate on the northwestern side and the arid to semi-arid climate in the southern eastern regions, led to climate diversity and great variation in rainfall rate (Abu-Allaban et al., 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[35.82482748724647,31.4205347836573],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-1"},{"name":"Wadi Wala catchment, Jordan","kind":"basin","role":"second major catchment forming part of the Dead Sea Basin groundwater system","river_name":"Wadi Wala","geocode_query":"Wadi Wala catchment, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"It consists of two major catchments the Wadi Al-Mujib catchment (4,500 km2 ) and the Wadi Wala catchment of about (2,100 km2 ; MWI, 2020). The area selected for modeling covers an approximate area of 6,500 km2 in the southeastern part of the basin and was selected due to its exposure to water scarcity. Practices such as high water abstraction and agricultural activities coupled with climate change had placed a burden on the aquifer (Hdoush et al., 2022). The area is multi-terrain accompanied by steep slopes toward the western side of the model domain, with variant contour elevations ranging from 1,200 m asl to −420 m bsl (MWI, 2020). The location of the DSB aquifer between the Mediterranean climate on the northwestern side and the arid to semi-arid climate in the southern eastern regions, led to climate diversity and great variation in rainfall rate (Abu-Allaban et al., 2015).","verified_in_page":true},"resolution_status":"resolved","coordinates":[35.69692383766819,31.51619676081708],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-2"},{"name":"Mujib Reserve, Jordan","kind":"region","role":"unique ecological zone dependent on groundwater discharge from the Dead Sea Basin aquifer, indicator of aquifer health and biodiversity","river_name":null,"geocode_query":"Mujib Reserve, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"The mean annual rainfall is <200 mm and the eastern parts are the driest with an annual precipitation not exceeding 70 mm (Abu-Allaban et al., 2015). Figure 3 indicates a declining trend of 0.63 mm/yr for the historical record between 1986 and 2014. Groundwater largely follows the direction of the NW-SE drainage (Lu et al., 2020). The basin supplies water for drinking and agriculture and has a unique ecosystem that contains attractive biodiversity represented by Mujib Reserve, which is the lowest natural reserve in the world at an altitude down to 400 m below sea level. The basin springs form the main source of Zara-Mae’n desalination station, and Mae’n thermal springs. Both the thermal springs and the desalination station are outside the modeled aquifer system without any impact on groundwater density properties and temperature (Schäffer and Sass, 2014). The impact of groundwater over-exploitation and climate change on the spring discharges is therefore a matter of concern.","verified_in_page":true},"resolution_status":"resolved","coordinates":[35.5682383,31.4703651],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"FHC9+47X, Al Judayyidah, Jordan","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-3"},{"name":"Zara-Mae’n desalination station, Jordan","kind":"facility","role":"uses spring water sourced from basin groundwater for desalination; affected by groundwater availability","river_name":null,"geocode_query":"Zara-Mae'n desalination station, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"The mean annual rainfall is <200 mm and the eastern parts are the driest with an annual precipitation not exceeding 70 mm (Abu-Allaban et al., 2015). Figure 3 indicates a declining trend of 0.63 mm/yr for the historical record between 1986 and 2014. Groundwater largely follows the direction of the NW-SE drainage (Lu et al., 2020). The basin supplies water for drinking and agriculture and has a unique ecosystem that contains attractive biodiversity represented by Mujib Reserve, which is the lowest natural reserve in the world at an altitude down to 400 m below sea level. The basin springs form the main source of Zara-Mae’n desalination station, and Mae’n thermal springs. Both the thermal springs and the desalination station are outside the modeled aquifer system without any impact on groundwater density properties and temperature (Schäffer and Sass, 2014). The impact of groundwater over-exploitation and climate change on the spring discharges is therefore a matter of concern.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Mae’n thermal springs, Jordan","kind":"facility","role":"thermal springs discharging groundwater from the basin indicating groundwater quantity and quality conditions","river_name":null,"geocode_query":"Mae'n thermal springs, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"The mean annual rainfall is <200 mm and the eastern parts are the driest with an annual precipitation not exceeding 70 mm (Abu-Allaban et al., 2015). Figure 3 indicates a declining trend of 0.63 mm/yr for the historical record between 1986 and 2014. Groundwater largely follows the direction of the NW-SE drainage (Lu et al., 2020). The basin supplies water for drinking and agriculture and has a unique ecosystem that contains attractive biodiversity represented by Mujib Reserve, which is the lowest natural reserve in the world at an altitude down to 400 m below sea level. The basin springs form the main source of Zara-Mae’n desalination station, and Mae’n thermal springs. Both the thermal springs and the desalination station are outside the modeled aquifer system without any impact on groundwater density properties and temperature (Schäffer and Sass, 2014). The impact of groundwater over-exploitation and climate change on the spring discharges is therefore a matter of concern.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Azraq Basin, Jordan","kind":"basin","role":"adjacent groundwater basin influencing boundary flow conditions of the Dead Sea Basin model domain","river_name":null,"geocode_query":"Azraq Basin, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Al Atawneh et al. 10.3389/frwa.2024.1380877 FIGURE 1 Overview of the modeling methodology, the calibration setup (A), and future predictions (B). faults, mainly the Siwaqa fault and the E-W fault, with a small dextral sliding motion along them (Abed, 2017). The underlain layers of clay loams at the north are characterized by moderate infiltration rates, and the stony sandy loams at the south have high infiltration rates (Salman et al., 2014). The groundwater model with appointed boundary conditions was built (Figure 2C) for the upper aquifer system only. The locations of the model boundaries were assigned following Abdulla and Al-Assa’D (2006). The model was influenced by conditions outside the model domain: (1) springs and waterfalls in the western side, (2) the adjacent Azraq Basin, Azraq Wetlands Reserve and Oasis located east of the model domain, and (3) other waterfalls and lakes north of the model boundaries.","verified_in_page":true},"resolution_status":"resolved","coordinates":[36.8143061,31.8349316],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-6"},{"name":"Azraq Wetlands Reserve, Jordan","kind":"region","role":"adjacent natural reserve east of model domain affecting boundary conditions and groundwater flow","river_name":null,"geocode_query":"Azraq Wetlands Reserve, Jordan","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Al Atawneh et al. 10.3389/frwa.2024.1380877 FIGURE 1 Overview of the modeling methodology, the calibration setup (A), and future predictions (B). faults, mainly the Siwaqa fault and the E-W fault, with a small dextral sliding motion along them (Abed, 2017). The underlain layers of clay loams at the north are characterized by moderate infiltration rates, and the stony sandy loams at the south have high infiltration rates (Salman et al., 2014). The groundwater model with appointed boundary conditions was built (Figure 2C) for the upper aquifer system only. The locations of the model boundaries were assigned following Abdulla and Al-Assa’D (2006). The model was influenced by conditions outside the model domain: (1) springs and waterfalls in the western side, (2) the adjacent Azraq Basin, Azraq Wetlands Reserve and Oasis located east of the model domain, and (3) other waterfalls and lakes north of the model boundaries.","verified_in_page":true},"resolution_status":"resolved","coordinates":[36.8205744,31.8337795],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Azraq Wetlands Reserve, الأزرق الجنوبي، Azraq, Jordan","basin_id":2040085720,"main_basin_id":2040085720,"basin_region":"Europe and Middle East","basin_area_km2":229408.5,"hydrology_source":"hydrobasins","site_id":"doc-3ed7035c0460efa5235edad3-site-7"}],"limitations":["Limited groundwater level monitoring data with 18 observation wells constrains precision of model calibration and validation.","Lack of detailed measurement of natural groundwater discharge at springs and drains introduces uncertainty in groundwater balance estimation.","Future changes in groundwater extraction and land use were not modeled, limiting assessment of anthropogenic impacts beyond current conditions.","Climate change projections are based on downscaled CMIP6 GCM ensemble with inherent uncertainties and limited spatial resolution at regional scale.","Model represents aquifer as a single layer, which may oversimplify complex heterogeneity of hydrogeology."],"river_ids":[],"site_ids":["doc-3ed7035c0460efa5235edad3-site-0","doc-3ed7035c0460efa5235edad3-site-1","doc-3ed7035c0460efa5235edad3-site-2","doc-3ed7035c0460efa5235edad3-site-3","doc-3ed7035c0460efa5235edad3-site-6","doc-3ed7035c0460efa5235edad3-site-7"],"anchor":[35.5682383,31.4703651],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[2040085720],"main_basin_ids":[2040085720]},{"id":"doc-42d07c9d6faf5098c3c23ea0","title":"Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia","display_title":"Deep Learning vs Conceptual Models for Australian Monthly Rainfall–Runoff","doi":"https://doi.org/10.5194/hess-28-1191-2024","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-03-13","authors":["Stephanie R. Clark","Julien Lerat","Jean-Michel Perraud","Peter Fitch"],"summary":"This study compares deep learning (LSTM) and conceptual (WAPABA) monthly rainfall–runoff models across 496 catchments in Australia, spanning diverse climate regions and catchment sizes. The models were calibrated on data from 1950–1995 and tested on 1996–2020, a period characterized by drought. The LSTM matched or outperformed the conceptual model in 69% of catchments, especially in larger catchments and under new climatic conditions, indicating promise for deep learning in hydrology despite smaller monthly datasets compared to daily models.","finding":"LSTM models matched or exceeded WAPABA conceptual model performance at a monthly scale in 69% of Australian catchments, with the largest advantages seen in larger catchments and improved generalisation to drier testing periods.","finding_evidence":{"passage_id":"p011-003","page":11,"quote":"Figure 8 quantifies the proportion of catchments with similar or better prediction performance by either WAPABA or LSTM (on the testing data). “Similarity” is defined here as an absolute normalised difference in NSE of less than 0.05 between WAPABA and LSTM predictions, meaning the sum of squared errors of the WAPABA and LSTMs at an individual catchment differ by no more than 5 %. The LSTMs produce similar or higher NSE values for 69 % of the catchments when tested on data not seen during the training process (and 89 % of the catchments during training, not shown).","verified_in_page":true},"topics":["monthly rainfall–runoff modelling","deep learning","conceptual models","hydrological prediction","generalisation under changing conditions","catchment size influence","model comparison","climate variability"],"study_period":"1950–2020","locations":[{"name":"Australia","kind":"region","role":"study area containing 496 varied catchments used for model comparison","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"The three labelled catchments, which will be used as examples in the study, represent a wet catchment (111005 in Northern Queensland), a temperate catchment (204014 in New South Wales), and a dry catchment (609012 in Western Australia). part of the AWRA-L landscape model developed jointly by CSIRO and the Bureau of Meteorology (Frost et al., 2018). Rainfall, temperature, and evapotranspiration are averaged from daily grids (5 km × 5 km) over each of the catchments. The runoff records begin between January 1950 and September 1982 and end between October 2016 and June 2020. The number of runoff observations per catchment ranges from 425 to 846 with a median dataset size of 613 observations. The rainfall and potential evapotranspiration data cover the period from 1911 to 2020 continuously. The resulting dataset consists of a set of 496 time series ranging from 37 to 70 years in length, with a median record length of 51 years.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Queensland, Australia","kind":"region","role":"location of a wet catchment example (Mulgrave River at The Fisheries, 111005)","river_name":"Mulgrave River","geocode_query":"Northern Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"A comparison of model performance is then made in relation to various catchment and time series characteristics (e.g. catchment size, flow level, record length) to determine if an association exists between these properties and the relative performance of the conceptual and machine learning models. 3.1 Example prediction results As a sample of the modelling output, Fig. 3 shows the WAPABA and LSTM runoff predictions along with the corresponding observed runoff for the three stations highlighted in Fig. 1 (over the testing period). These hydrographs are representative of a wet catchment in Northern Queensland (Mulgrave River at The Fisheries, 111005), a temperate catchment in New South Wales (Mann River at Mitchell, 204014), and a dry intermittent catchment in Western Australia (Blackwood River at Winnejup, 609012). NSE values of each of the predictions are noted. The WAPABA and LSTM predictions both match the observed data reasonably well in the three catchments.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.798891,-19.249674],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652762,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Queensland, QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-42d07c9d6faf5098c3c23ea0-site-1","true_coordinates":[146.7615238,-19.2982152],"spread_km":6.7},{"name":"New South Wales, Australia","kind":"region","role":"location of a temperate catchment example (Mann River at Mitchell, 204014)","river_name":"Mann River","geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"A comparison of model performance is then made in relation to various catchment and time series characteristics (e.g. catchment size, flow level, record length) to determine if an association exists between these properties and the relative performance of the conceptual and machine learning models. 3.1 Example prediction results As a sample of the modelling output, Fig. 3 shows the WAPABA and LSTM runoff predictions along with the corresponding observed runoff for the three stations highlighted in Fig. 1 (over the testing period). These hydrographs are representative of a wet catchment in Northern Queensland (Mulgrave River at The Fisheries, 111005), a temperate catchment in New South Wales (Mann River at Mitchell, 204014), and a dry intermittent catchment in Western Australia (Blackwood River at Winnejup, 609012). NSE values of each of the predictions are noted. The WAPABA and LSTM predictions both match the observed data reasonably well in the three catchments.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia, Australia","kind":"region","role":"location of a dry intermittent catchment example (Blackwood River at Winnejup, 609012)","river_name":"Blackwood River","geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-004","page":7,"quote":"A comparison of model performance is then made in relation to various catchment and time series characteristics (e.g. catchment size, flow level, record length) to determine if an association exists between these properties and the relative performance of the conceptual and machine learning models. 3.1 Example prediction results As a sample of the modelling output, Fig. 3 shows the WAPABA and LSTM runoff predictions along with the corresponding observed runoff for the three stations highlighted in Fig. 1 (over the testing period). These hydrographs are representative of a wet catchment in Northern Queensland (Mulgrave River at The Fisheries, 111005), a temperate catchment in New South Wales (Mann River at Mitchell, 204014), and a dry intermittent catchment in Western Australia (Blackwood River at Winnejup, 609012). NSE values of each of the predictions are noted. The WAPABA and LSTM predictions both match the observed data reasonably well in the three catchments.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Performance evaluation based on monthly data may mask subperiod variability.","Only one conceptual model (WAPABA) was used as benchmark, limiting broader conceptual model comparisons.","Coordinates for catchments and stations were not provided, only regional references included.","Study focused on single-layer LSTM without catchment-specific hyperparameter tuning.","Reduced data size with monthly aggregation; results may differ with daily data."],"river_ids":[],"site_ids":["doc-42d07c9d6faf5098c3c23ea0-site-1"],"anchor":[146.798891,-19.249674],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040067860],"main_basin_ids":[5040067860]},{"id":"doc-45d6243bb3fe24631ab0f8ad","title":"Shorebird habitat selection and foraging behaviour have important implications for management at an internationally important non‐breeding wetland","display_title":"Shorebird Habitat and Foraging in the Coorong","doi":"https://doi.org/10.1002/2688-8319.12316","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-01-01","authors":["Micha V. Jackson","Rowan Mott","Steven Delean","Brayden J. Hunt","Justin D. Brookes","Phillip Cassey","Thomas A. A. Prowse"],"summary":"This study investigates habitat selection and foraging behaviour of shorebirds in the Coorong wetland, South Australia, an internationally important non-breeding site. It finds that shorebird abundance is strongly correlated with the extent of potential habitat area (bare mud/sand plus shallow water) more than prey density or salinity. Shorebird step rate inversely relates to prey density, suggesting step rate as a useful indicator of prey availability. The study provides management implications for maintaining optimal water levels and habitat extent to support shorebird populations, highlighting the risk of ecological traps if prey abundance and disturbance are not also managed.","finding":"Potential habitat area (combined bare mud/sand and shallow water area) is the most important predictor of shorebird abundance, while shorebird step rate inversely correlates with prey density and can be used as a proxy indicator of prey availability.","finding_evidence":{"passage_id":"p008-000","page":8,"quote":"8 of 13 | JACKSON et al. predictor of subsite-level shorebird abundance that we measured was the area of potential habitat, defined as area of bare mud/sand plus area of shallow water over a ~500 m long shoreline (Tables SI.1 and SI.3). This finding held even though subsites with the highest densities of preferred prey items (Figure SI.3B–D) had the lowest area of potential habitat (Figure SI.2B). For both Red-necked Stint and Red-capped Plover, subsite-level abundance increased with both average and standardised habitat area. While the former effect identifies abundance differences between subsites due to consistent differences in the area of habitat they provide, the latter effect suggests the abundance of these shorebird species also responded to subsite-level variation in habitat area during the study period.","verified_in_page":true},"topics":["shorebird-habitat-selection","foraging-behaviour","wetland-management","habitat-quality","macroinvertebrate-prey","environmental-flows","water-level-management","ecological-traps"],"study_period":"April 2021 to March 2022","locations":[{"name":"Coorong wetland","kind":"wetland","role":"Main study site and internationally important non-breeding shorebird habitat","river_name":null,"geocode_query":"Coorong South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This area regularly supports 100,000 to 300,000 waterbirds (Kingsford & Porter, 2009; Paton et al., 2021) and provides critical foraging habitat for migratory shorebirds (Clemens et al., 2016). The Coorong is immediately adjacent to the coastline and comprises two saltwater lagoons connected by a narrow channel around the midpoint of the wetland (Figure 1). At its northern end the Coorong is connected to the ocean through the mouth of the River Murray (Figure 1). Freshwater flows enter primarily through a series of barrages in the Northern Coorong and also through freshwater discharge from a regulator that connects Morella Basin, a freshwater reservoir, with the South Lagoon (Figure 1). Though situated in the Coorong National Park (established 1966), the wetland has experienced a long-term decline in ecological condition (Brookes et al., 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.629516,-35.99637],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-0","true_coordinates":[139.5553549,-35.9963695],"spread_km":6.7},{"name":"Northern Coorong subsite NC1","kind":"station","role":"Northernmost subsite sampled for shorebird abundance and foraging in study","river_name":null,"geocode_query":"Northern Coorong South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 of 13 | JACKSON et al. 0.5 km in length. We conducted seven sampling trips across April, June, August, October and December 2021, and February and March 2022. Surveys at the northernmost subsite (NC1; Figure 1) did not commence until October 2021. 2.2 | Relative subsite abundance counts and foraging observations We visited each subsite twice during each of the seven sampling periods to record shorebird abundance, shorebird foraging behaviour and biophysical variables (described in Section 2.3) that we hypothesised may influence subsite-level abundance. During each sampling period, we sampled each subsite once in the morning before 12 pm and once in the afternoon after 12 pm on a different day to reflect possible diel variation in shorebird activity (SantiagoQuesada et al., 2012).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Salt Creek","kind":"station","role":"Subsite where freshwater is discharged into the South Lagoon, sampled for shorebirds and habitat variables","river_name":null,"geocode_query":"Salt Creek South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"To reflect recent shorebird distribution, we then selected two subsites in each stratum that had high average counts of Red-necked Stint Calidris ruficollis (the most abundant migratory shorebird in the Coorong) in the January census between 2010 and 2020 to represent high-use habitat, and one subsite with a low but non-zero average count of this species over the same period to represent low-use habitat. We included one additional subsite from the shoreline at Salt Creek, where freshwater is discharged into the South Lagoon (Figure 1). At each subsite, we used physical features of the shoreline to demarcate an area of interest for the subsite approximately FIGURE 1 Location of shorebird habitat quality sampling subsites (yellow dots) at the Coorong wetland in South Australia. NC = Northern Coorong, SL = Coorong South Lagoon, SC = Salt Creek.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.650867,-36.06382],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Salt Creek SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-2","true_coordinates":[139.6508666,-36.1238196],"spread_km":6.7},{"name":"South Lagoon, Coorong","kind":"wetland","role":"Southern stratum of Coorong wetlands included in study representing degraded ecological condition","river_name":null,"geocode_query":"South Lagoon Coorong South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This area regularly supports 100,000 to 300,000 waterbirds (Kingsford & Porter, 2009; Paton et al., 2021) and provides critical foraging habitat for migratory shorebirds (Clemens et al., 2016). The Coorong is immediately adjacent to the coastline and comprises two saltwater lagoons connected by a narrow channel around the midpoint of the wetland (Figure 1). At its northern end the Coorong is connected to the ocean through the mouth of the River Murray (Figure 1). Freshwater flows enter primarily through a series of barrages in the Northern Coorong and also through freshwater discharge from a regulator that connects Morella Basin, a freshwater reservoir, with the South Lagoon (Figure 1). Though situated in the Coorong National Park (established 1966), the wetland has experienced a long-term decline in ecological condition (Brookes et al., 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.555355,-35.936369],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-3","true_coordinates":[139.5553549,-35.9963695],"spread_km":6.7},{"name":"Parnka Point","kind":"region","role":"Area around midpoint of Coorong separating Northern Coorong and South Lagoon, near high shorebird abundance","river_name":null,"geocode_query":"Parnka Point South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-003","page":6,"quote":"Total shorebird abundance was dominated by Red-necked Stints, which accounted for nearly 75% of all shorebirds counted (Figure 2; figure colours selected using R package colorBlindness; Ou, 2021). Additional species with the highest average counts were Red-capped Plover (12.5% of total), Red-necked Avocet (5% of total) and Banded Stilt (3% of total; Figure 2; Figure SI.1). Given its dominance amongst the migratory shorebirds (Figure 2), Red-necked Stint was the only migratory shorebird whose subsite-level abundance was modelled. We also modelled Red-capped Plover abundance because it had the highest average abundance amongst non-migratory shorebirds. Total shorebird abundance was highest at the central subsites clustered around Parnka Point (subsites NC3, SL1 and SL2; Figure SI.1) where salinity and potential habitat area were highest (Figure SI.2), but macroinvertebrate density was low (Figure SI.3). Abundances were higher in the morning than the afternoon (Figure SI.4).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.3961,-35.84172],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Parnka Point, Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-4","true_coordinates":[139.3961,-35.9017199],"spread_km":6.7},{"name":"Morella Basin","kind":"facility","role":"Freshwater reservoir connected to South Lagoon providing freshwater input through a regulator","river_name":null,"geocode_query":"Morella Basin South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This area regularly supports 100,000 to 300,000 waterbirds (Kingsford & Porter, 2009; Paton et al., 2021) and provides critical foraging habitat for migratory shorebirds (Clemens et al., 2016). The Coorong is immediately adjacent to the coastline and comprises two saltwater lagoons connected by a narrow channel around the midpoint of the wetland (Figure 1). At its northern end the Coorong is connected to the ocean through the mouth of the River Murray (Figure 1). Freshwater flows enter primarily through a series of barrages in the Northern Coorong and also through freshwater discharge from a regulator that connects Morella Basin, a freshwater reservoir, with the South Lagoon (Figure 1). Though situated in the Coorong National Park (established 1966), the wetland has experienced a long-term decline in ecological condition (Brookes et al., 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.6860325,-36.1435265],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556922,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Morella Basin, Salt Creek SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-5"},{"name":"Murray Mouth","kind":"other","role":"Connection point between Coorong and ocean, influences tidal water levels and seawater inflow","river_name":null,"geocode_query":"Murray Mouth South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This area regularly supports 100,000 to 300,000 waterbirds (Kingsford & Porter, 2009; Paton et al., 2021) and provides critical foraging habitat for migratory shorebirds (Clemens et al., 2016). The Coorong is immediately adjacent to the coastline and comprises two saltwater lagoons connected by a narrow channel around the midpoint of the wetland (Figure 1). At its northern end the Coorong is connected to the ocean through the mouth of the River Murray (Figure 1). Freshwater flows enter primarily through a series of barrages in the Northern Coorong and also through freshwater discharge from a regulator that connects Morella Basin, a freshwater reservoir, with the South Lagoon (Figure 1). Though situated in the Coorong National Park (established 1966), the wetland has experienced a long-term decline in ecological condition (Brookes et al., 2023).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Coorong National Park","kind":"region","role":"Protected area encompassing the Coorong wetland study sites","river_name":null,"geocode_query":"Coorong National Park South Australia Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"This area regularly supports 100,000 to 300,000 waterbirds (Kingsford & Porter, 2009; Paton et al., 2021) and provides critical foraging habitat for migratory shorebirds (Clemens et al., 2016). The Coorong is immediately adjacent to the coastline and comprises two saltwater lagoons connected by a narrow channel around the midpoint of the wetland (Figure 1). At its northern end the Coorong is connected to the ocean through the mouth of the River Murray (Figure 1). Freshwater flows enter primarily through a series of barrages in the Northern Coorong and also through freshwater discharge from a regulator that connects Morella Basin, a freshwater reservoir, with the South Lagoon (Figure 1). Though situated in the Coorong National Park (established 1966), the wetland has experienced a long-term decline in ecological condition (Brookes et al., 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.481194,-35.99637],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43660390,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coorong SA 5264, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-45d6243bb3fe24631ab0f8ad-site-7","true_coordinates":[139.5553549,-35.9963695],"spread_km":6.7}],"limitations":["No precise latitude/longitude coordinates given for subsites, only geographic names available","Prey sampling conducted only at waterline, may not represent full prey distribution","Step rate used as proxy for prey density but feeding success not directly measured","Potential ecological trap scenario discussed but prey energetic sufficiency not directly quantified"],"river_ids":[],"site_ids":["doc-45d6243bb3fe24631ab0f8ad-site-0","doc-45d6243bb3fe24631ab0f8ad-site-2","doc-45d6243bb3fe24631ab0f8ad-site-3","doc-45d6243bb3fe24631ab0f8ad-site-4","doc-45d6243bb3fe24631ab0f8ad-site-5","doc-45d6243bb3fe24631ab0f8ad-site-7"],"anchor":[139.555355,-35.936369],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-45e8457573d801864f39ab6c","title":"Extensive Tracking of Nomadic Waterbird Movements Reveals an Inland Flyway","display_title":"Inland Flyway Movement of Nomadic Waterbirds","doi":"https://doi.org/10.1002/ece3.70668","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-12-01","authors":["Heather M. McGinness","Micha V. Jackson","Luke R. Lloyd‐Jones","Freya Robinson","Art Langston","Louis G. O’Neill","Shoshana Rapley","Melissa Piper","Micah Davies","Jessica Hodgson","John M. Martin","Richard T. Kingsford","Kate Brandis","Veronica Doerr","Ralph Mac Nally"],"summary":"This study used GPS satellite telemetry to track 73 straw-necked ibis and 42 royal spoonbills over seven years across Australia's Murray–Darling Basin (MDB) and surrounding areas. It identified long-distance movement routes and a common inland flyway spanning over 2000 km, characterized by flat, low-elevation open landscapes with moderate to high rainfall. The Great Dividing Range acts as an eastern barrier to movement. Understanding these routes provides key insights for conservation management, enabling prioritized environmental watering and threat mitigation across multiple jurisdictions to sustain waterbird populations.","finding":"An inland flyway of over 2000 km was identified connecting key breeding sites across the Murray–Darling Basin from southwest to northeast, characterized by flat, open landscapes under 350 m elevation and mid to high rainfall, and bounded eastward by the Great Dividing Range which limits waterbird movement.","finding_evidence":{"passage_id":"p001-004","page":1,"quote":"We used these data to identify long-distance movements and to demarcate and characterise movement routes. We identified common routes used by both species, including a ‘flyway’ over 2000km long, spanning Australia's Murray–Darling Basin from the south-west to the north-east. This flyway connects important breeding sites and is characterised by flat, open/unforested areas with low elevations of <350m and mid to high rainfall. The flyway corresponds to an area west of Australia's Great Dividing Range, which appears to act as a low-permeability barrier to the movement of both species. Identification of an inland flyway for waterbirds in Australia provides important context for multi-jurisdictional cooperation and strategic management. Where resources are limited, water and wetland management efforts (e.g., environmental watering) should be preferentially located within this route.","verified_in_page":true},"topics":["movement ecology","environmental water management","species conservation","habitat connectivity","wetland ecology","satellite telemetry","nomadism","inland flyways"],"study_period":"2016-2023","locations":[{"name":"Murray–Darling Basin","kind":"basin","role":"primary study region encompassing waterbird movement routes and breeding sites","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"We used these data to identify long-distance movements and to demarcate and characterise movement routes. We identified common routes used by both species, including a ‘flyway’ over 2000km long, spanning Australia's Murray–Darling Basin from the south-west to the north-east. This flyway connects important breeding sites and is characterised by flat, open/unforested areas with low elevations of <350m and mid to high rainfall. The flyway corresponds to an area west of Australia's Great Dividing Range, which appears to act as a low-permeability barrier to the movement of both species. Identification of an inland flyway for waterbirds in Australia provides important context for multi-jurisdictional cooperation and strategic management. Where resources are limited, water and wetland management efforts (e.g., environmental watering) should be preferentially located within this route.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.477298,-31.096543],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Gwydir Wetlands, New South Wales, Australia","kind":"wetland","role":"important breeding site connected by identified flyway","river_name":null,"geocode_query":"Gwydir Wetlands, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.3513072097811,-29.34742222729355],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670583,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gwydir River, New South Wales 2400, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-1"},{"name":"Narran Lakes, New South Wales, Australia","kind":"wetland","role":"important breeding site connected by identified flyway","river_name":null,"geocode_query":"Narran Lakes, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.3140865,-29.8077507],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667498,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Narran Lake NSW 2839, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-2"},{"name":"Macquarie Marshes, New South Wales, Australia","kind":"wetland","role":"important breeding site connected by identified flyway and within movement core use areas","river_name":null,"geocode_query":"Macquarie Marshes, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.632165,-30.765984],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677087,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Macquarie Marshes NSW 2831, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-3","true_coordinates":[147.6321653,-30.705984],"spread_km":6.7},{"name":"Lake Cowal, New South Wales, Australia","kind":"wetland","role":"important breeding site connected by identified flyway and part of environmental water management efforts","river_name":null,"geocode_query":"Lake Cowal, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"McGinness (heather.mcginness@csiro.au) Received: 5 September 2024 | Revised: 4 November 2024 | Accepted: 17 November 2024 Funding: The original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder's Office (CEWH/ CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH Monitoring, Evaluation and Research project (2019–2024) and the CEWO Environmental Watering Knowledge and Research project (2015–2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors and the Goulburn-Broken Catchment Management Authority (Keith Ward).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.3566433,-33.6924152],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667649,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Cowal NSW 2671, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-4"},{"name":"Booligal Wetlands, New South Wales, Australia","kind":"wetland","role":"important breeding site connected by identified flyway","river_name":null,"geocode_query":"Booligal Wetlands, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.8856425,-33.8685758],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43663505,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Booligal NSW 2711, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-5"},{"name":"Lowbidgee Floodplain, New South Wales, Australia","kind":"field_site","role":"significant aggregate-nesting waterbird breeding site connected by identified flyway","river_name":null,"geocode_query":"Lowbidgee Floodplain, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Kerang Lakes, Victoria, Australia","kind":"wetland","role":"breeding site connected by straw-necked ibis movement route","river_name":null,"geocode_query":"Kerang Lakes, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.9202005,-35.7350156],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43676949,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kerang VIC 3579, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-7"},{"name":"Barmah-Millewa Forest, Victoria/New South Wales, Australia","kind":"landscape","role":"wetland forest complex connected by straw-necked ibis movement route","river_name":null,"geocode_query":"Barmah-Millewa Forest, Victoria New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. For both species, the KED50 shows a common movement route that connects several important aggregate-nesting waterbird breeding sites, including (from north to south): the Gwydir Wetlands, Narran Lakes, the Macquarie Marshes, Lake Cowal, Booligal Wetlands and the Lowbidgee Floodplain (Figure 3; breeding site locations from McGinness, Brooks, and Hale 2023). For SNI, it also connects the Kerang Lakes, Barmah-Millewa Forest, Lake Mulwala, Kamarooka North Swamps and the Corop Wetlands Complex (Figure 3). For RSB, it also includes Upper Darling River (Figure 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.0494831,-35.7893356],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677227,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Millewa Rd, New South Wales 2710, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-8"},{"name":"Lake Mulwala, Victoria/New South Wales, Australia","kind":"wetland","role":"breeding site in straw-necked ibis movement network","river_name":null,"geocode_query":"Lake Mulwala, Victoria New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"When overlayed, about 50% of the SNI KED50 overlapped with the RSB KED50 and about 28% of the RSB KED50 overlapped with the SNI KED50. 3.2 | Characterising Movement Routes Long-distance movements by both species as represented by the KED50 areas predominantly occurred in north-easterly or south-westerly directions. 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These characteristics are associated with a combination of known preferred foraging and roosting habitats, food availability and wind conditions for these species, as described by previous work using this dataset (McGinness, Langston, and Brooks 2020; McGinness et al. 2024b, 2024a; Nicol, Lloyd-Jones, and McGinness 2024).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.057333,-24.97],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12124900,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Great Dividing Range, Caldervale QLD 4478, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-45e8457573d801864f39ab6c-site-13","true_coordinates":[147,-25],"spread_km":6.7}],"limitations":["Broad regional geography for 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040069590,5040524870,5040591010,5040591050],"main_basin_ids":[5040069590,5040073410]},{"id":"doc-4c7dc9587a3543ce275aa0f6","title":"Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners","display_title":"Effective acoustic monitoring for conservation","doi":"https://doi.org/10.1111/csp2.13132","theme":"Other science","color":"#d1b5e7","year":2024,"publication_date":"2024-05-10","authors":["Daniella Teixeira","Paul Roe","Berndt J. van Rensburg","Simon Linke","Paul G. McDonald","David Tucker","Susan Fuller"],"summary":"This paper outlines key principles and recommendations for designing effective ecological monitoring programs using passive acoustic sensors. It emphasizes the importance of clear objectives, appropriate partnerships, targeted sound measurements, robust field survey designs, optimized recording schedules, sound data processing strategies, and data storage considerations to yield high quality information useful in conservation and management.","finding":"Effective acoustic monitoring is not simply about recording sounds; success depends critically on planning, including objectives, sensor deployment, and data processing, to ensure data collected are informative for conservation management.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in-field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access.","verified_in_page":true},"topics":["ecological monitoring","acoustic sensors","biodiversity monitoring","data analysis","conservation planning","machine learning","adaptive management","bioacoustics"],"study_period":null,"locations":[{"name":"Queensland University of Technology, Brisbane, Queensland, Australia","kind":"facility","role":"Lead institution for the Australian Acoustic Observatory","river_name":null,"geocode_query":"Queensland University of Technology, Brisbane, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"By working toward common goals, partnerships ensure that programs address knowledge gaps that are important to all entities and, where relevant, contribute to policy or management decisions (Lindenmayer & Likens, 2018). Partners can also help determine whether similar or larger monitoring programs already exist, which may influence the design of the new program (e.g., collection of standard metadata that a larger program requires). For some programs, a small core team may be sufficient, but larger partnerships are increasingly needed as large-scale, long-term programs become more feasible. The Australian Acoustic Observatory is a good example of the importance of partnerships in large monitoring programs (Roe et al., 2021). The program currently monitors 90 sites (four solar-powered sensors per site; www. frontierlabs.com.au) across state land and private conservation properties.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.0274859,-27.4778241],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2 George St, Brisbane City QLD 4000, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-4c7dc9587a3543ce275aa0f6-site-0"},{"name":"Australian Acoustic Observatory, Australia","kind":"region","role":"Case example of large-scale acoustic monitoring program discussed","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"By working toward common goals, partnerships ensure that programs address knowledge gaps that are important to all entities and, where relevant, contribute to policy or management decisions (Lindenmayer & Likens, 2018). Partners can also help determine whether similar or larger monitoring programs already exist, which may influence the design of the new program (e.g., collection of standard metadata that a larger program requires). For some programs, a small core team may be sufficient, but larger partnerships are increasingly needed as large-scale, long-term programs become more feasible. The Australian Acoustic Observatory is a good example of the importance of partnerships in large monitoring programs (Roe et al., 2021). The program currently monitors 90 sites (four solar-powered sensors per site; www. frontierlabs.com.au) across state land and private conservation properties.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Brazilian Amazon, Brazil","kind":"region","role":"Empirical study site referenced for bird acoustic monitoring","river_name":null,"geocode_query":"Brazilian Amazon, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Likewise, Francomano et al. (2021) assessed temporal soundscape variability at eight sites from four continents and found that the representativeness of acoustic indices was improved more by increasing the number of subsamples analyzed than by increasing subsample duration. This also holds true for underwater sounds (Linke et al., 2020). Studies like these emphasize that all recording schedules are not equal, and their configurations need to be explicitly considered in line with monitoring questions. Where the acoustic dynamics of your study system are not understood, some acoustic data should be analyzed early (e.g., after one season) alongside concurrent on-ground data to inform an ongoing optimal schedule. Importantly, recording time and duration should be sufficient to maximize species detection probability, which should be informed by existing data, pilot data or, at least, expert knowledge.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"North America","kind":"region","role":"Region for empirical datasets assessing bird species richness sampling effort","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Given the diversity of monitoring objectives and questions, it is difficult to provide general recommendations for recording schedules, but studies in various ecosystems provide some insight. For example, from simulations and two empirical datasets of bird species richness in North America, Wood et al. (2021) found that increasing sampling effort on fewer days performed better than distributing effort over more days. Franklin et al. (2021) compared four postdawn survey schedules for assessing bird assemblages in an Australian montane dry sclerophyll forest, and found the optimal method comprised five 20-min samples from each of two survey days. In the eastern Brazilian Amazon, Metcalf, Barlow, et al. (2022) found that increasing temporal coverage and decreasing sample length improved predictions of bird alpha and gamma diversity.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.358507,54.525961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-4c7dc9587a3543ce275aa0f6-site-3","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Australian montane dry sclerophyll forest, Australia","kind":"landscape","role":"Study site for testing recording schedules on bird assemblages","river_name":null,"geocode_query":"montane dry sclerophyll forest, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Likewise, Francomano et al. (2021) assessed temporal soundscape variability at eight sites from four continents and found that the representativeness of acoustic indices was improved more by increasing the number of subsamples analyzed than by increasing subsample duration. This also holds true for underwater sounds (Linke et al., 2020). Studies like these emphasize that all recording schedules are not equal, and their configurations need to be explicitly considered in line with monitoring questions. Where the acoustic dynamics of your study system are not understood, some acoustic data should be analyzed early (e.g., after one season) alongside concurrent on-ground data to inform an ongoing optimal schedule. Importantly, recording time and duration should be sufficient to maximize species detection probability, which should be informed by existing data, pilot data or, at least, expert knowledge.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["No explicit latitude and longitude coordinates provided for study locations.","Study sites and geographic locations are broadly defined regions rather than precise field sites.","Descriptions of locations mostly relate to programmatic or literature examples rather than original data collection in a specific locale."],"river_ids":[],"site_ids":["doc-4c7dc9587a3543ce275aa0f6-site-0","doc-4c7dc9587a3543ce275aa0f6-site-3"],"anchor":[153.0274859,-27.4778241],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040069970,7040021740],"main_basin_ids":[5040069970,7040021740]},{"id":"doc-55dbfb0d121d21c0c7a1ce3a","title":"Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: not all droughts are created equal","display_title":"Southern Amazon Droughts: Solar Radiation & Water Storage","doi":"https://doi.org/10.1088/1748-9326/ad281e","theme":"Climate","color":"#98d4f1","year":2024,"publication_date":"2024-02-09","authors":["Shuang Liu","Tim R. McVicar","Xue Wu","Xin Cao","Yi Liu"],"summary":"This study investigates hydro-climatological drivers of dry-season land surface temperature (LST) anomalies during the 2005, 2010, and 2015 southern Amazon droughts. Using satellite data and linear regression, it finds that anomalies in precipitation, incoming solar radiation, and terrestrial water storage explain the thermal anomalies with differing contributions across the droughts. The study reveals contrasting hydro-climatological conditions preceding each drought, especially in wet-to-dry transition precipitation and biomass-burning aerosols, which influence solar radiation and water storage dynamics. These differences affect drought severity and spatial patterns of concurrent thermal and water stress, important for understanding drought impacts and predicting rainforest responses under climate change.","finding":"The contrasting dry-season incoming solar radiation and terrestrial water storage anomalies across the 2005, 2010, and 2015 southern Amazon droughts are due to different hydro-climatological conditions in the preceding wet-to-dry transition season; specifically, lower precipitation and increased fires/aerosols in 2005 and 2010 reduced solar radiation and terrestrial water storage, whereas higher precipitation in 2015 led to reduced fires, fewer aerosols, increased solar radiation, and higher terrestrial water storage.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"To understand this phenomenon, we investigated the anomalies of relevant hydro-climatological variables during the dry season (August and September) and the antecedent wet-todry transition season (May to July) of three droughts. Six variables were examined, namely P, TWSA, SW↓, AOD, COT and active fires (see table 1). Monthly regional averages of these six variables and their anomalies from May to September are compared in figure 5. Regional average precipitation decreases from May to August in droughts and non-drought years alike (figure 5(a)). However, precipitation in 2005 and 2010 was constantly lower than the average of non-drought years consecutively for these five months (figure 5(b)). Although negative Pano were also observed in August and September of 2015 (with the August 2015 Pano being near-zero), the study region mainly experienced above-average precipitation (positive anomalies) during the wet-to-dry transition season (May to July) in 2015.","verified_in_page":true},"topics":["drought","solar radiation","terrestrial water storage","land surface temperature","precipitation","aerosols","biomass burning","hydro-climatology"],"study_period":"2003-2015","locations":[{"name":"southeast Amazon","kind":"region","role":"region notably affected by concurrent thermal and water stress during 2005 drought","river_name":null,"geocode_query":"southeast Amazon, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LSTmax) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSAmin). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LSTmax and TWSA < TWSAmin) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-46.209155,-20.393182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southeast Region, Brazil","basin_id":6040639920,"main_basin_id":6040011780,"basin_region":"South America","basin_area_km2":107020.1,"hydrology_source":"hydrobasins","site_id":"doc-55dbfb0d121d21c0c7a1ce3a-site-0","true_coordinates":[-46.2091547,-20.3331817],"spread_km":6.7},{"name":"southern Amazon","kind":"region","role":"region notably affected by concurrent thermal and water stress during 2010 drought","river_name":null,"geocode_query":"southern Amazon, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LSTmax) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSAmin). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LSTmax and TWSA < TWSAmin) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"central and southeast Amazon","kind":"region","role":"region of large-scale dry-season thermal stress with little water stress in 2015 drought","river_name":null,"geocode_query":"central and southeast Amazon, Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydro-climatological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest. © 2024 The Author(s). Published by IOP Publishing Ltd","verified_in_page":true},"resolution_status":"resolved","coordinates":[-46.209155,-20.273182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southeast Region, Brazil","basin_id":6040639920,"main_basin_id":6040011780,"basin_region":"South America","basin_area_km2":107020.1,"hydrology_source":"hydrobasins","site_id":"doc-55dbfb0d121d21c0c7a1ce3a-site-2","true_coordinates":[-46.2091547,-20.3331817],"spread_km":6.7}],"limitations":["The study area is defined broadly as southern Amazon; no precise coordinates for subregions given.","Some data have spatial resolution aggregation; exact spatial boundaries of observed anomalies may be generalized.","Coordinates were not explicitly provided, only geocode queries based on named regions."],"river_ids":[],"site_ids":["doc-55dbfb0d121d21c0c7a1ce3a-site-0","doc-55dbfb0d121d21c0c7a1ce3a-site-2"],"anchor":[-46.209155,-20.393182],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[6040639920],"main_basin_ids":[6040011780]},{"id":"doc-5947a3a9799a482ca69c95a2","title":"Porewater exchange and iron transformation in a coastal groundwater system: a field investigation, driving mechanisms analysis, and conceptual model","display_title":"Iron Transformation in Coastal Groundwater, Deception Bay","doi":"https://doi.org/10.3389/fmars.2024.1385517","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-04-11","authors":["Wenran Cao","Harald Hofmann","Guanxi Yan","Alexander Scheuermann"],"summary":"This study presents a field investigation at a sandy beach on the northern end of Deception Bay, Southeast Queensland, Australia, focusing on porewater exchange and iron (Fe) transformation in a coastal groundwater system. It reveals that tidal forces drive significant groundwater-seawater mixing forming an upper saline plume (USP) with high dissolved oxygen, creating reactive conditions for Fe oxidation and precipitation along the groundwater-seawater interface, resulting in an iron curtain that adsorbs dissolved phosphate. The study develops a conceptual model illustrating the mobilization of Fe from inland acid sulfate soils through groundwater transport to coastal waters, contributing to understanding hydrogeochemical processes and land-ocean connectivity at this site.","finding":"The field datasets provide concrete evidence that tidal-induced circulation creates an upper saline plume fostering highly oxidizing conditions at the groundwater-seawater interface beneath the USP, leading to oxidative precipitation of Fe(II) as Fe oxides which accumulate forming an iron curtain acting as a geochemical barrier that retains dissolved phosphate in sediments.","finding_evidence":{"passage_id":"p012-001","page":12,"quote":"The dynamic process of groundwater-seawater mixing created a USP, a highly reactive zone that determined the speciation and solubility of Fe transporting through the FDT. Consequently, a substantial increase in DO concentration was observed, which created a localized hotspot with increased Eh. This indicated an oxidizing environment for Fe(II)-rich groundwater beneath and led to the oxidative precipitation of Fe(II) at the groundwater-seawater interface. Therefore, the sediment analysis revealed a spatial distribution of precipitated Fe with a strip-like pattern along this interface from land to the ocean. These regions with intensive accumulation of Fe oxides can be referred to as the iron curtain, acting as a geochemical barrier to retain dissolved phosphate by changing the seepage properties of coastal sediment.","verified_in_page":true},"topics":["porewater exchange","iron speciation","groundwater-seawater mixing","oxidation-reduction potential","phosphate sorption","coastal aquifers","tide-induced circulation","submarine groundwater discharge"],"study_period":"May 10-14 2021","locations":[{"name":"Deception Bay, Moreton Bay Region, Queensland, Australia","kind":"field_site","role":"primary study site with sandy beach and intertidal area for field investigation of groundwater-seawater mixing and Fe transformation","river_name":null,"geocode_query":"Deception Bay, Moreton Bay Region, Queensland, Australia","latitude":-27.08417,"longitude":153.13528,"evidence":{"passage_id":"p002-006","page":2,"quote":"In addition, this study contributes to an in-depth understanding of the evolution of coastal aquifers induced by these coupled processes and presents corresponding environmental functionalities in landocean connectivity through groundwater. 2 Field site The field site (see Figure 1A) is a tidal beach with a mild slope (Dh/DL = 0.015) on the northern end of Deception Bay (27° 5′ 3′′ S, 153° 8′ 7′′ E, a part of Moreton Bay Region), Southeast Queensland, Australia. Moreton Bay is a sheltered coastal system protected by large sand islands (Moreton Island (Mulgumpin) and Stradbroke Island (Minjerribah)). The southern and western sides of Moreton Bay are strongly influenced by major river estuaries, which transport sediments into the bay, producing intertidal mudflats and mangrove swamps. The northern and eastern parts of the Bay have predominantly sandy beaches with occasional meadows.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.0172547,-27.1899784],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130391,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Deception Bay QLD 4508, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-5947a3a9799a482ca69c95a2-site-0"},{"name":"Moreton Bay, Queensland, Australia","kind":"region","role":"broader coastal bay system encompassing the study field site, protected by Moreton and Stradbroke Islands","river_name":null,"geocode_query":"Moreton Bay, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"In addition, this study contributes to an in-depth understanding of the evolution of coastal aquifers induced by these coupled processes and presents corresponding environmental functionalities in landocean connectivity through groundwater. 2 Field site The field site (see Figure 1A) is a tidal beach with a mild slope (Dh/DL = 0.015) on the northern end of Deception Bay (27° 5′ 3′′ S, 153° 8′ 7′′ E, a part of Moreton Bay Region), Southeast Queensland, Australia. Moreton Bay is a sheltered coastal system protected by large sand islands (Moreton Island (Mulgumpin) and Stradbroke Island (Minjerribah)). The southern and western sides of Moreton Bay are strongly influenced by major river estuaries, which transport sediments into the bay, producing intertidal mudflats and mangrove swamps. The northern and eastern parts of the Bay have predominantly sandy beaches with occasional meadows.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.920592,-27.034608],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130201,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"City of Moreton Bay, QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-5947a3a9799a482ca69c95a2-site-1","true_coordinates":[152.9205918,-27.0946084],"spread_km":6.7},{"name":"Scarborough Boat Harbour, Redcliffe, Queensland, Australia","kind":"station","role":"site for tidal data measurement (~10 km from field site) to characterize tidal forcing during study period","river_name":null,"geocode_query":"Scarborough Boat Harbour, Redcliffe, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"K = 1 C rg m n3 (1 − n) 2 d2 (1) where C is the shape factor (C = 180–270 for granular soil, such as sand), r is the water density (kg/m3 ), g is the gravitational acceleration (m/s2 ), m is the water viscosity (Pa•s), n is porosity, d is the effective diameter (m), equivalent to the spatial mean diameter of pore hydraulic conduits (i.e., effective seepage flow paths). 4 Results and discussion 4.1 Tide-induced circulation and porewater exchange Tidal data was collected from the gauge located at Scarborough Boat Harbour in northernmost Redcliffe, Queensland, Australia, which is approximately 10 km away from the studied site. The dataset covered a semi-diurnal tide from the 10th to the 14th of May 2021. Figure 4 shows the field measurements in 2021 that occurred during spring tide. At HT, the water levels reached as high as 1.20 m, whereas they dropped to –0.80 m at LT.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"South Bribie Island, Queensland, Australia","kind":"region","role":"reference location for comparative dissolved Fe(II) concentrations in groundwater to contextualize findings","river_name":null,"geocode_query":"South Bribie Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-002","page":11,"quote":"Consequently, Fe(III) is more predominant than Fe(II), albeit still relatively low in concentration due to its low solubility in water bodies (Zhang et al., 2022). As shown in Figure 6A1, the transient peak of Fe(II) concentration at the groundwater-seawater interface reached 2.52 mg/L, which was eleven times higher than the value of 0.23 mg/L (data on 27 August 2018) observed in the borehole groundwater at a depth of 19.8 m in South Bribie Island, Queensland, Australia. Thus, the elevated concentration of Fe(II) in porewater is likely derived from a dissolved form of Fe(II) linked to local groundwater systems. According to earlier studies, Sandstone Point is situated on a coastal plain with the underlying Landsborough Sandstone, characterized by quartz sand and consisting of Fe-rich minerals (Cox and Preda, 2005; Ahern et al., 2006b). Moreover, the intertidal area off Sandstone Point was subject to sampling in 2000.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1325243,-26.9861003],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130574,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bribie Island, Queensland 4507, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-5947a3a9799a482ca69c95a2-site-3"},{"name":"Moreton Island (Mulgumpin), Queensland, Australia","kind":"region","role":"large sand island protecting Moreton Bay referenced in site description","river_name":null,"geocode_query":"Moreton Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The field site is on the northern end of Deception Bay (27°5′3′′S, 153°8′7′′E), a part of the Moreton Bay Region protected by Moreton Island (Mulgumpin) and North Stradbroke Island (Minjerribah). (B) The sandy intertidal area of the field site with the nearby buildings and major vegetation; (C) Snapshot of red-brown sandstone substrate exposed in the supratidal zone of the field site, and the outcropping sediments were identified to contain Fe minerals in earlier studies. Cao et al. 10.3389/fmars.2024.1385517 Frontiers in Marine Science 03 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.3739274,-27.179931],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130834,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Moreton Island QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-5947a3a9799a482ca69c95a2-site-4"},{"name":"Stradbroke Island (Minjerribah), Queensland, Australia","kind":"region","role":"large sand island protecting Moreton Bay referenced in site description","river_name":null,"geocode_query":"Stradbroke Island, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"The field site is on the northern end of Deception Bay (27°5′3′′S, 153°8′7′′E), a part of the Moreton Bay Region protected by Moreton Island (Mulgumpin) and North Stradbroke Island (Minjerribah). (B) The sandy intertidal area of the field site with the nearby buildings and major vegetation; (C) Snapshot of red-brown sandstone substrate exposed in the supratidal zone of the field site, and the outcropping sediments were identified to contain Fe minerals in earlier studies. Cao et al. 10.3389/fmars.2024.1385517 Frontiers in Marine Science 03 frontiersin.org","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.40401,-27.502317],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130865,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Stradbroke Island, Queensland 4183, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-5947a3a9799a482ca69c95a2-site-5","true_coordinates":[153.4626077,-27.5323174],"spread_km":6.7}],"limitations":["Some regional geographies such as Moreton Bay lack precise coordinates and are broadly defined.","The porewater sampling was not synchronized across all ports and times; temporal interpolation was applied to address this.","Biogeochemical processes and microbial activities affecting iron cycling were not directly measured and introduce complexity beyond the scope of study.","The stated coordinates for Deception Bay are provided as DMS converted to decimal but may have inherent uncertainty due to original data formatting."],"river_ids":[],"site_ids":["doc-5947a3a9799a482ca69c95a2-site-0","doc-5947a3a9799a482ca69c95a2-site-1","doc-5947a3a9799a482ca69c95a2-site-3","doc-5947a3a9799a482ca69c95a2-site-4","doc-5947a3a9799a482ca69c95a2-site-5"],"anchor":[153.0172547,-27.1899784],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[5040069970],"main_basin_ids":[5040069970]},{"id":"doc-5ac09c888cc8e04b73a31c6c","title":"Estimating the costs of managed aquifer recharge under uncertainty with examples for town water supply in regional Australia","display_title":"Costs and Sensitivities of Managed Aquifer Recharge","doi":"https://doi.org/10.1007/s40899-024-01095-2","theme":"Water management","color":"#eeabac","year":2024,"publication_date":"2024-04-23","authors":["Dennis Gonzalez","Joseph H. A. Guillaume","Luk Peeters","Paul R. Wyrwoll","Joanne Vanderzalm","Declan Page"],"summary":"This study develops a probabilistic, time-varying financial cost model for managed aquifer recharge (MAR) schemes using well injection and infiltration basin methods to improve town water supplies under drought conditions. The model incorporates recharge, storage losses, and recovery efficiencies over a 50-year horizon to estimate levelised costs and perform global sensitivity analyses. Six regional towns in Australia's Murray-Darling Basin (MDB) with histories of drought were used as conceptual case studies to demonstrate applicability of the framework for estimating MAR costs under diverse hydrogeological and operational conditions.","finding":"Levelised costs of recovered water ranged from $0.07–$0.24 per cubic meter for infiltration basin schemes and $0.32–$1.88 per cubic meter for well injection schemes in 2021 Australian dollars. Storage losses, expressed as recovery efficiencies, had a substantial impact on costs, with recovered water costing up to twice that of recharged water under low recovery efficiency scenarios. Global sensitivity analyses revealed that dominant cost drivers vary with scheme scale and operating conditions, with discount rate and pre-treatment operating costs especially influential for injection systems.","finding_evidence":{"passage_id":"p016-002","page":16,"quote":"This study aimed to address current gaps by presenting a set of transferable distributions of financial costs for well injection and infiltration basin schemes covering a range of operating scales and conditions. Three key advancements were made. Firstly, a time-varying financial cost assessment framework based on water balance time series of recharge, storage and recovery was developed. Secondly, the effects of aquifer storage losses on costs of recovered water were evaluated. Thirdly, a probabilistic approach to account for uncertainty in input parameters was applied analysing disaggregated capital and operating expenses and conducting global sensitivity analyses to highlight the most important factors for reducing uncertainty. Six case study examples of town water supplies in Australia’s Murray-Darling Basin were used to demonstrate application of the methods and interpretation of results.","verified_in_page":true},"topics":["aquifer recharge","cost assessment","hydro-economic modelling","water security","drought resilience","sensitivity analysis","regional water supply","managed aquifer recharge"],"study_period":null,"locations":[{"name":"Murray-Darling Basin, New South Wales, Australia","kind":"region","role":"Primary study region and site of case studies","river_name":null,"geocode_query":"Murray-Darling Basin, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Historical underinvestment in water supply infrastructure and inadequate maintenance has heightened risks of water availability and water quality shortfalls (Armstrong and Gellatly, 2008). Managed aquifer recharge (MAR) is the intentional, managed recharge of aquifers for later use or environmental benefit (Dillon 2005). MAR can be operated according to different objectives, e.g. balancing seasonal demand and supply or facilitating water recycling. MAR can also be used for increasing long-term water security and drought resilience as seen in various parts of the United States (Megdal and Dillon 2015; Scanlon et al. 2016) and Perth’s potable water supply (Gao et al. 2014). Studies have indicated significant potential for MAR in Australia’s Murray-Darling Basin (MDB) (Fuentes and Vervoort 2020; Gonzalez et al. 2020, 2021; Khan et al. 2008; Page et al. 2021), and northern Australia (Evans et al. 2017; Vanderzalm et al. 2022) and key irrigation regions around Australia (Page et al. 2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.469568,-31.105835],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Tamworth, New South Wales, Australia","kind":"settlement","role":"Case study town for well injection MAR scheme with limited aquifer storage","river_name":null,"geocode_query":"Tamworth, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The aquifers in both areas are narrow and shallow deposits of unconsolidated sediments in comparison to the alluvium lower in some of the other river valleys (e.g. Moree, Gunnedah, Narromine). This combined with relatively shallow water tables, meant the volumetric storage potential for MAR is likely to be limited to around 1 and 3 MCM for Tamworth and Bathurst respectively (see Supplementary 1). These volumes could be achieved over four years of recharge at 0.25 and 0.75 MCM/y. Tamworth and Bathurst have annual average potable water demands of 8.8 and 6 MCM respectively (NSW DPE 2022). MAR schemes recovering water at these capacities would meet 9% and 42% for up to 4 years of annual demand shortfalls due to 30% reductions in town water allocations as occurred during 2018-20 drought conditions at Tamworth and Bathurst respectively (WaterNSW 2022). Groundwater salinity in both areas is fresh so storage losses due to water quality are likely to be low.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.9293323,-31.09003839999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43675456,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tamworth NSW 2340, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-1"},{"name":"Bathurst, New South Wales, Australia","kind":"settlement","role":"Case study town for well injection MAR scheme with limited aquifer storage","river_name":null,"geocode_query":"Bathurst, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The aquifers in both areas are narrow and shallow deposits of unconsolidated sediments in comparison to the alluvium lower in some of the other river valleys (e.g. Moree, Gunnedah, Narromine). This combined with relatively shallow water tables, meant the volumetric storage potential for MAR is likely to be limited to around 1 and 3 MCM for Tamworth and Bathurst respectively (see Supplementary 1). These volumes could be achieved over four years of recharge at 0.25 and 0.75 MCM/y. Tamworth and Bathurst have annual average potable water demands of 8.8 and 6 MCM respectively (NSW DPE 2022). MAR schemes recovering water at these capacities would meet 9% and 42% for up to 4 years of annual demand shortfalls due to 30% reductions in town water allocations as occurred during 2018-20 drought conditions at Tamworth and Bathurst respectively (WaterNSW 2022). Groundwater salinity in both areas is fresh so storage losses due to water quality are likely to be low.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.578693,-33.4111857],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43673231,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bathurst NSW 2795, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-2"},{"name":"Gunnedah, New South Wales, Australia","kind":"settlement","role":"Case study town for infiltration basin MAR scheme with moderate aquifer storage","river_name":null,"geocode_query":"Gunnedah, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-005","page":16,"quote":"MAR scheme with these capacities would meet 73% to well over 100% of annual demand shortfalls for 4–5 years if town water allocations were reduced by 30% during drought, as occurred in Dubbo during 2018-20 (WaterNSW 2022). In the case of Narromine, a large-scale scheme could be used to distribute benefits to other users and the environment or augment potable water supplies for longer periods. Gunnedah has variable yields (6–47 L/s) and fresh to brackish groundwater so indicative levelised costs of recovered water could be around $0.10–0.17/m3 for a 1 MCM/y infiltration basin scheme depending on actual infiltration rates and RE (Fig. 5b) with capital costs of around $700,000- 900,000 (Fig. 6b). Moree has both moderate to high yielding bores (15–30 L/s) and generally fresh groundwater so indicative costs could be expected to be at the middle to lower end for a 2 MCM/y infiltration basin scheme around $0.09/m3 with a capital cost of around $1.2 million.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.256504,-30.919432],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677648,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gunnedah NSW 2380, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-3","true_coordinates":[150.2565037,-30.97943189999999],"spread_km":6.7},{"name":"Moree, New South Wales, Australia","kind":"settlement","role":"Case study town for infiltration basin MAR scheme with moderate-high aquifer yields","river_name":null,"geocode_query":"Moree, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-005","page":16,"quote":"MAR scheme with these capacities would meet 73% to well over 100% of annual demand shortfalls for 4–5 years if town water allocations were reduced by 30% during drought, as occurred in Dubbo during 2018-20 (WaterNSW 2022). In the case of Narromine, a large-scale scheme could be used to distribute benefits to other users and the environment or augment potable water supplies for longer periods. Gunnedah has variable yields (6–47 L/s) and fresh to brackish groundwater so indicative levelised costs of recovered water could be around $0.10–0.17/m3 for a 1 MCM/y infiltration basin scheme depending on actual infiltration rates and RE (Fig. 5b) with capital costs of around $700,000- 900,000 (Fig. 6b). Moree has both moderate to high yielding bores (15–30 L/s) and generally fresh groundwater so indicative costs could be expected to be at the middle to lower end for a 2 MCM/y infiltration basin scheme around $0.09/m3 with a capital cost of around $1.2 million.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.8415676,-29.465303],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677258,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Moree NSW 2400, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-4"},{"name":"Dubbo, New South Wales, Australia","kind":"settlement","role":"Case study town for infiltration basin MAR scheme with moderate aquifer storage","river_name":null,"geocode_query":"Dubbo, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-005","page":16,"quote":"MAR scheme with these capacities would meet 73% to well over 100% of annual demand shortfalls for 4–5 years if town water allocations were reduced by 30% during drought, as occurred in Dubbo during 2018-20 (WaterNSW 2022). In the case of Narromine, a large-scale scheme could be used to distribute benefits to other users and the environment or augment potable water supplies for longer periods. Gunnedah has variable yields (6–47 L/s) and fresh to brackish groundwater so indicative levelised costs of recovered water could be around $0.10–0.17/m3 for a 1 MCM/y infiltration basin scheme depending on actual infiltration rates and RE (Fig. 5b) with capital costs of around $700,000- 900,000 (Fig. 6b). Moree has both moderate to high yielding bores (15–30 L/s) and generally fresh groundwater so indicative costs could be expected to be at the middle to lower end for a 2 MCM/y infiltration basin scheme around $0.09/m3 with a capital cost of around $1.2 million.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.614418,-32.2443908],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670199,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dubbo NSW 2830, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-5"},{"name":"Narromine, New South Wales, Australia","kind":"settlement","role":"Case study town for infiltration basin MAR scheme with large aquifer storage capacity","river_name":null,"geocode_query":"Narromine, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-005","page":16,"quote":"MAR scheme with these capacities would meet 73% to well over 100% of annual demand shortfalls for 4–5 years if town water allocations were reduced by 30% during drought, as occurred in Dubbo during 2018-20 (WaterNSW 2022). In the case of Narromine, a large-scale scheme could be used to distribute benefits to other users and the environment or augment potable water supplies for longer periods. Gunnedah has variable yields (6–47 L/s) and fresh to brackish groundwater so indicative levelised costs of recovered water could be around $0.10–0.17/m3 for a 1 MCM/y infiltration basin scheme depending on actual infiltration rates and RE (Fig. 5b) with capital costs of around $700,000- 900,000 (Fig. 6b). Moree has both moderate to high yielding bores (15–30 L/s) and generally fresh groundwater so indicative costs could be expected to be at the middle to lower end for a 2 MCM/y infiltration basin scheme around $0.09/m3 with a capital cost of around $1.2 million.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.2394429,-32.2328429],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677272,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Narromine NSW 2821, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-6"},{"name":"Lower Murray River at Lock 1, Blanchetown, South Australia, Australia","kind":"station","role":"Hydrological data source for triggering MAR operations in model","river_name":"Murray River","geocode_query":"Lock 1, Blanchetown, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"Sustainable Water Resources Management Variable* Lower bound Upper bound Rationale References Operational trigger 11 100 m3 /s 120 m3 /s Recharge/recovery triggered by river discharge threshold using a 50-year time series of average annual flow at Lock 1 at Blanchetown on the Lower Murray River (1972–2021) (BOM 2022) Well injection rate2 5 L/s 33 L/s Four ranges within these bounds tested; bounds for each range were proportionally equal (upper bound=lower bound+30%) Infiltration rate2 0.2 m/d 1 m/d Four infiltration rate ranges between 0.2–1.0 m/d representing low to high permeability aquifers; bounds for each range were proportionally equal (upper bound=lower bound+30%) Days operation per year3 245 275 Scheme operations cease for maintenance for 3–4 months of any year Recovery efficiency4 80% 100% Four ranges between 80–100%; bounds for each range were proportionally equal (upper bound=lower bound+5%); range typical of proportion of recharged water able to be recovered at acceptable quality from fresh to brackish aquifers (Clark et al. 2015; Miotlinski et al. 2014; Pavelic et al. 2006) Basin scraping5 $3/m3 $4/m3 Scraping cost per unit volume cut, assumes 2 m average depth for basin construction with 3:1 batter slope, scraping of 0.1 m across basin area for each year of recharge to maintain permeability, costs include freight, fuel, taxes, operator based on approximate contractor quote prices (Footstool Earthmoving pers. comm. 22 August 2023) Injection well maintenance5 9% 11% Well redevelopment assumed every 5 years at cost of around 10% injection well construction (ICRC 2010) Basin instrumentation6 $4100/ha + $5000 Includes one 5TE soil moisture sensor at 3 depths for each ha basin ($900 each), one Level TROLL 400 water level logger per ha basin ($1400 each), one telemetered weather station with evaporation IC-SNIP-A41-SL ($5000 one per site), total cost varies according to basin sizing, effective service life of 25 years assumed Soil moisture sensor (Decagon Devices); level logger (InSitu); weather station (Instrument Choice); (ATO 2022) Injection well contruction6 $ 40,000 $ 60,000 Cost per well including mobilisation, based on real costs assuming 200 mm diameter, 50 m deep injection well at $1000/m±20% (Vanderzalm et al. 2022) Injection pump and instrumentation6 $ 160,000 $ 240,000 Estimate based on real costs per well from previous assessments±20%; effective service life of 25 years assumed (Vanderzalm et al. 2022) Observation bore drilling6 $20,000 $30,000 Costs per 75 mm diameter, 50 m deep observation bore, one for each MCM annual recharge capacity at $500/m±20% (Vanderzalm et al. 2022) Monitoring7 $400 $600 Based on real laboratory analytical costs of $500±20% per sample for a suite including basic physical/chemical parameters, nutrients, metals, herbicide/pesticide screen, pathogen indicator, sampled every 50,000 m3 recharged (Page et al. 2017) Opportunity cost of water8 $0.02/m3 $0.09/m3 Based on volume weighted average trade prices from 2008 to 2021 in years when average annual discharge at Lock 1 was >100 m3 /s (Supp 1) (BOM 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.6165099,-34.3501999],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677314,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 1, Blanchetown SA 5357, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-5ac09c888cc8e04b73a31c6c-site-7"}],"limitations":["No exact geographic coordinates provided for all case study towns.","Study region and cases focused on Murray-Darling Basin, may limit transferability to different hydrogeological environments.","Aquifer hydraulic properties and recharge limits assumed uniform and not constrained by local heterogeneity.","Estimated costs do not include post-recovery water treatment for direct potable use.","Water market opportunity costs simplified using historical average trade prices above flow 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040073410,5040524870,5040591010],"main_basin_ids":[5040073410]},{"id":"doc-5aedc8b900ef00992f3f3959","title":"Stochastic metapopulation dynamics of a threatened amphibian to improve water delivery","display_title":"Improving Bell Frog Persistence by Water Delivery","doi":"https://doi.org/10.1002/ecs2.4741","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-01-01","authors":["Rupert Mathwin","Skye Wassens","Matthew S. Gibbs","Jeanne Young","Qifeng Ye","Frédérik Saltré","Corey J. A. Bradshaw"],"summary":"This study develops a spatially explicit stochastic metapopulation model to evaluate environmental water delivery strategies for conserving the threatened southern bell frog (Litoria raniformis) along a 70 km regulated reach of the Murray River in southern Australia. By simulating hydrology, frog population dynamics, and dispersal among 23 wetlands, the study compares watering frequency and spatial distribution of interventions to optimize frog persistence under historical and severe drought conditions.","finding":"More frequent environmental watering and spreading intervention sites across the river reach significantly increase the probability of southern bell frog persistence compared to grouping sites or applying moderate watering frequency, especially under severe drought.","finding_evidence":{"passage_id":"p007-001","page":7,"quote":"Each parameter set ran for 50 iterations using the first 50 sets of simulated hydrology (60 years). We then applied a boosted regression tree emulator implemented using the dismo library in R (Hijmans et al., 2021) to quantify the influence of each parameter on the sum of surviving wetlands, setting the error distribution family as Poisson, the bag fraction to 0.75, learning rate to 0.001, tolerance to 0.0001, and tree complexity to 2, with 200,000 maximum trees. RESULTS The mean generation time of L. raniformis in the model was 2.1 years, and each 60-year iteration spanned approximately 28.5 generations. The greatest improvement in reach-wide probability of persistence occurred when intervention sites were spread (intervention at wetlands 4, 8, 13, and 21) and watering intervention frequency was high (≤ one dry year in every three years) (Figure 3).","verified_in_page":true},"topics":["stochastic modeling","metapopulation dynamics","environmental water delivery","amphibian conservation","hydroecology","population persistence","dispersal ecology","climate impact"],"study_period":"1926-2009 hydrology data; 60-year simulation period","locations":[{"name":"Murray River between Lock 3 and Lock 2","kind":"reach","role":"main study system for amphibian metapopulation and environmental watering modeling","river_name":"Murray River","geocode_query":"Murray River between Lock 3 and Lock 2, New South Wales and South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We hypothesized that programs with more frequent environmental water delivery would result in higher mean probability of persistence, and that spreading intervention sites more broadly across the reach would also spread population rescue and reinforcement, resulting in higher mean probability of persistence. We also hypothesized that during drought conditions, the probability of persistence within the reach depends on persistence at intervention sites. MATERIALS AND METHODS Study reach The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated for domestic and agricultural use. Our focus was 70 km of the Murray River channel between two main-channel weirs (“locks”) that dissect the river: Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E). This reach connects laterally to 23 wetlands with historical records of L. raniformis (Figure 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.523154,-35.293959],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676891,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-0","true_coordinates":[143.5663889650001,-35.34249995499994],"spread_km":6.7},{"name":"Murray–Darling Basin","kind":"region","role":"broader river basin context for the study reach and water regulation impact","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"We hypothesized that programs with more frequent environmental water delivery would result in higher mean probability of persistence, and that spreading intervention sites more broadly across the reach would also spread population rescue and reinforcement, resulting in higher mean probability of persistence. We also hypothesized that during drought conditions, the probability of persistence within the reach depends on persistence at intervention sites. MATERIALS AND METHODS Study reach The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated for domestic and agricultural use. Our focus was 70 km of the Murray River channel between two main-channel weirs (“locks”) that dissect the river: Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E). This reach connects laterally to 23 wetlands with historical records of L. raniformis (Figure 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.464032,-31.116211],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Wetlands 4, 8, 13, 21 in study reach","kind":"wetland","role":"intervention breeding sites in spatially spread watering program for Litoria raniformis within the study reach","river_name":"Murray River","geocode_query":"Wetlands 4, 8, 13, 21 near Murray River between Lock 3 and Lock 2, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"The first treatment used historical river conditions to simulate 60 consecutive years (described above). The second treatment reused this hydrology but replaced the 30th–53rd years with 23 years of historical drought conditions (from 1990 to 2012). We did this to examine the effects of environmental watering through a severe drought. We used the 10,000 starting metapopulations to populate each of the two hydrological scenarios that were repeated for each of the five intervention programs: (1) control (no intervention), (2) environmental-watering sites were spread across the 23 available wetlands (wetlands 4, 8, 13, and 21) and received water independent of river height and at high frequency that ensured no more than one dry year occurred every three years, ECOSPHERE 5 of 14 21508925, 2024, 1, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4741 by Readcube (Labtiva Inc.), Wiley Online Library on [07/01/2024].","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.358056,-34.247778],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677304,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-2","true_coordinates":[140.3580556060001,-34.187777722999954],"spread_km":6.7},{"name":"Wetlands 11, 13, 14, 16 in study reach","kind":"wetland","role":"intervention breeding sites in spatially grouped watering program for Litoria raniformis within the study reach","river_name":"Murray River","geocode_query":"Wetlands 11, 13, 14, 16 near Murray River between Lock 3 and Lock 2, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-005","page":5,"quote":"The first treatment used historical river conditions to simulate 60 consecutive years (described above). The second treatment reused this hydrology but replaced the 30th–53rd years with 23 years of historical drought conditions (from 1990 to 2012). We did this to examine the effects of environmental watering through a severe drought. We used the 10,000 starting metapopulations to populate each of the two hydrological scenarios that were repeated for each of the five intervention programs: (1) control (no intervention), (2) environmental-watering sites were spread across the 23 available wetlands (wetlands 4, 8, 13, and 21) and received water independent of river height and at high frequency that ensured no more than one dry year occurred every three years, ECOSPHERE 5 of 14 21508925, 2024, 1, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4741 by Readcube (Labtiva Inc.), Wiley Online Library on [07/01/2024].","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.358056,-34.127778],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677304,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-3","true_coordinates":[140.3580556060001,-34.187777722999954],"spread_km":6.7},{"name":"Lock 3","kind":"facility","role":"upstream boundary weir of study reach providing hydrological reference","river_name":"Murray River","geocode_query":"Lock 3, Murray River, South Australia, Australia","latitude":-34.187,"longitude":140.341,"evidence":{"passage_id":"p002-004","page":2,"quote":"We hypothesized that programs with more frequent environmental water delivery would result in higher mean probability of persistence, and that spreading intervention sites more broadly across the reach would also spread population rescue and reinforcement, resulting in higher mean probability of persistence. We also hypothesized that during drought conditions, the probability of persistence within the reach depends on persistence at intervention sites. MATERIALS AND METHODS Study reach The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated for domestic and agricultural use. Our focus was 70 km of the Murray River channel between two main-channel weirs (“locks”) that dissect the river: Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E). This reach connects laterally to 23 wetlands with historical records of L. raniformis (Figure 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.367883,-34.113563],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677304,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 3 Rd, Overland Corner SA 5330, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-4","true_coordinates":[140.367883,-34.1735634],"spread_km":6.7},{"name":"Lock 2","kind":"facility","role":"downstream boundary weir of study reach providing hydrological reference","river_name":"Murray River","geocode_query":"Lock 2, Murray River, South Australia, Australia","latitude":-34.677,"longitude":139.914,"evidence":{"passage_id":"p002-004","page":2,"quote":"We hypothesized that programs with more frequent environmental water delivery would result in higher mean probability of persistence, and that spreading intervention sites more broadly across the reach would also spread population rescue and reinforcement, resulting in higher mean probability of persistence. We also hypothesized that during drought conditions, the probability of persistence within the reach depends on persistence at intervention sites. MATERIALS AND METHODS Study reach The Murray–Darling Basin contains Australia’s longest river system and is heavily regulated for domestic and agricultural use. Our focus was 70 km of the Murray River channel between two main-channel weirs (“locks”) that dissect the river: Lock 3 (34\u0001110 16.9500 S, 140\u0001210 29.6500 E) and Lock 2 (34\u000140 39.3100 S, 139\u0001550 52.8100 E). This reach connects laterally to 23 wetlands with historical records of L. raniformis (Figure 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.93116,-34.01774],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677315,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lock 2, Qualco SA 5322, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-5aedc8b900ef00992f3f3959-site-5","true_coordinates":[139.93116,-34.0777399],"spread_km":6.7}],"limitations":["Exact coordinates of wetlands and intervention sites not provided; lat/lon set to null for these.","Hydrological data extend only until 2009; subsequent flow variation not included.","Population and dispersal parameters informed by surrogate or related species data due to sparse site-specific 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040073410,5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-776f9604c683a99fde2bcef3","title":"Balancing livestock production and environmental outcomes in northern Australia’s tropical savanna under global change","display_title":"Balancing livestock and environment in N. Australian savanna","doi":"https://doi.org/10.1088/1748-9326/ad6f2d","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-08-14","authors":["Rebecca K. Runting","Darran King","Martin Nolan","Javier Navarro Garcia","Raymundo Marcos-Martínez","Jonathan R. Rhodes","Lei Gao","Ian Watson","Andrew Ash","April E. Reside","Jorge G. Álvarez‐Romero","Jessie A. Wells","Euan G. Ritchie","Michalis Hadjikakou","Don A. Driscoll","Jeffery D. Connor","Jonathan Garber","Brett A. Bryan"],"summary":"This study developed an integrated spatio-temporal model to assess how different livestock management scenarios influence environmental and agricultural outcomes in northern Australia's tropical savanna to 2050 under various climate and global economic change scenarios. The model considered sustainability indicators including greenhouse gas emissions, biodiversity, land condition, water intake, profit, and beef production. It found that maintaining historical stocking rates is environmentally unsustainable due to climate-driven land degradation. However, strategic management combining reduced stocking below carrying capacity and early dry season prescribed burning improves profits, reduces emissions by 23%, prevents land degradation, and sustains biodiversity better than current practices. Supplementation with macroalgae offers further emission reductions but is currently economically challenging. The study highlights the need for balancing production and conservation and informs land management strategies for tropical savannas globally.","finding":"Combining safe stocking rates below carrying capacity with prescribed early dry season burning in northern Australia's tropical savanna can more than double landholder profits, reduce greenhouse gas emissions by 23%, prevent land degradation, and improve biodiversity outcomes compared to maintaining historical stocking rates.","finding_evidence":{"passage_id":"p013-002","page":13,"quote":"Cumulative impacts on sustainability indicators Our model shows that continuing historical grazing management is not environmentally sustainable, but combinations of management actions can improve the balance between production and environmental outcomes, even under changing climatic and economic conditions. In the ‘Balanced’ management scenario, combining prescribed burning with stocking below the carrying capacity of pastures prevents land degradation, reduces GHG emissions by 23%, supports higher species richness (increases the biodiversity index by 50%), and more than doubles baseline profits (compared to the baseline in M3, table 4). In fact, this was the most profitable management scenario across all global outlooks that included 12","verified_in_page":true},"topics":["climate change impacts","livestock management","greenhouse gas emissions","biodiversity conservation","fire management","rangeland sustainability","economic modelling","spatial modelling"],"study_period":"2023-2050","locations":[{"name":"Northern Australia tropical savanna","kind":"region","role":"primary study area encompassing large tracts of mostly intact savanna grasslands and open woodlands, subject of integrated modeling study on livestock and environment balance","river_name":null,"geocode_query":"Northern Australia tropical savanna, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"These conditions support large tracts of savanna grasslands and open woodlands, covering ∼2 million km2 , forming one of the largest areas of mostly intact ecosystems in the world (Woinarski et al 2007, Beyeret al 2020). Species richness generally increases with rainfall (Mokany et al 2022) and there is a steady rate of discovery of new species (Tingley et al 2019). Since colonisation, fire regimes have shifted from diverse fine-scale patterns of burning (a result of traditional fire management by Aboriginal peoples) to a regime dominated by large wildfires in the late dry season, with many areas experiencing fires every 1–2 years on average (Edwards et al 2021). Beef production from rainfed native pastures is the dominant agricultural land use in the region, occupying ∼60% of the land area (figure 1) with some individual grazing enterprises exceeding 1 million ha.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Queensland, Australia","kind":"region","role":"part of study area; eastern and south-eastern subregions have better grazing conditions and higher cattle production, but larger climate change-driven declines","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Aside from the spatial patterns, there was also considerable uncertainty across all scenarios and objectives from variations in key parameters (table 3), but general trends were still identifiable (figure 6). The parameters that contributed the most to this variation were the frequency and severity of fire (for GHG emissions and biodiversity), the safe pasture utilisation rate (for beef production) and future increases in technological innovation (for profit) (figure S26). 10","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia, Australia","kind":"region","role":"part of study area; one of three Australian jurisdictions included in northern Australia savanna grazing study","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Grazing has been implicated in the widespread declines of many birds, mammals, and reptiles across northern Australia, through alterations of the vegetation composition, ground cover and grass seed availability (Kuttet al 2012, Neilly et al 2021). Given the large land areas and low productivity, management strategies must be relatively low cost and easy to implement. Landholders’ ability to impose management solutions can be constrained by land tenure arrangements. With the exception of small areas of freehold in the south-east, most of the study area is pastoral leasehold land (the land is owned by the Crown) and certain conditions of the lease need to be met (such as grazing livestock). The study area includes three Australian jurisdictions (Western Australia, the Northern Territory, Queensland) and lease conditions differ in each jurisdiction. 2.2.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Territory, Australia","kind":"region","role":"part of study area; one of three Australian jurisdictions included in northern Australia savanna grazing study","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Grazing has been implicated in the widespread declines of many birds, mammals, and reptiles across northern Australia, through alterations of the vegetation composition, ground cover and grass seed availability (Kuttet al 2012, Neilly et al 2021). Given the large land areas and low productivity, management strategies must be relatively low cost and easy to implement. Landholders’ ability to impose management solutions can be constrained by land tenure arrangements. With the exception of small areas of freehold in the south-east, most of the study area is pastoral leasehold land (the land is owned by the Crown) and certain conditions of the lease need to be met (such as grazing livestock). The study area includes three Australian jurisdictions (Western Australia, the Northern Territory, Queensland) and lease conditions differ in each jurisdiction. 2.2.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Interim Biogeographic Regionalisation for Australia (IBRA) northern region","kind":"region","role":"definition of study area boundary at ~1km resolution to match modeling grid across northern Australia's savannas","river_name":null,"geocode_query":"IBRA northern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"Environ. Res. Lett. 19 (2024) 104014 R K Runting et al Figure 1. The northern Australian study region. The area depicted was defined by the Interim Biogeographic Regionalisation for Australia (IBRA) (Australian Government 2012) at 0.01 decimal degrees (∼1 km2 ) to match the resolution of our model. Panel (a) shows the dominant land uses of the region (ABARES 2016), (b) and (c) show the mean daily maximum temperature (◦C) and mean annual rainfall (respectively) from 1987–2010, and (d) shows the probability of fire in a given year from 1988–2014, as described in the supplementary information. This study focuses on land managed for grazing (non-hatched areas in (b)–(d)), which comprised 689 562 pixels.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Study area broad regional extent with no fine-scale spatial heterogeneity addressed explicitly; lack of precise location coordinates limits precise geographic contextualization.","Some model parameters rely on historical approximations and expert elicitation introducing uncertainties in predictions.","Macroalgae supplementation effects and costs in extensive grazing systems remain uncertain, limiting definitive conclusions for that intervention.","Climate extremes such as droughts and floods were not explicitly included in model projections, potentially underestimating risks.","Hydrological ecosystem service impacts from grazing were not fully integrated in this study, suggesting incomplete environmental assessment."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[],"main_basin_ids":[]},{"id":"doc-85df920f0a4e3ff9beda5375","title":"Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model","display_title":"Downscaling GRACE Water Storage in Cambrian Limestone Aquifer","doi":"https://doi.org/10.1038/s41598-024-60366-2","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-05-02","authors":["Ikechukwu Kalu","Christopher E. Ndehedehe","Vagner G. Ferreira","Sreekanth Janardhanan","Matthew Currell","Mark J. Kennard"],"summary":"This study presents a novel statistical downscaling approach using support vector machine regression to enhance the spatial resolution of GRACE terrestrial water storage change (ΔTWS) data from 1.0°×1.0° to 0.05°×0.05° over the Cambrian Limestone Aquifer (CLA), a large hydrogeological basin in northern Australia. The downscaled data were validated using in-situ groundwater level data from 12 monitoring bores and were compared with water budget estimates derived from the Australian Water Outlook (AWO) hydrological model. The method improves the characterization of local-scale hydrological dynamics and can support better freshwater reporting and management at catchment scales.","finding":"The downscaled GRACE ΔTWS estimates over the Cambrian Limestone Aquifer show significant spatio-temporal consistency with in-situ groundwater storage changes and water budget model estimates, effectively capturing local-scale hydrological variations not resolvable at original GRACE resolution.","finding_evidence":{"passage_id":"p013-001","page":13,"quote":"We observed that the downscaled GRACE PC1, which contained most of the downscaled signals was significantly correlated with the in-situ GWS changes, the water budget, and the original GRACE estimates (Fig. 6a,d,g). This is because it contains a large chunk of the variance proportion compared to PC2 and PC3. This shows that PC2 and PC3 cannot be relied upon to depict the spatio-temporal changes of TWS over our study region and period. Figure 6a,d,g shows a significant spatio-temporal consistency between the PC1 (strongest variability PC) and the other products used for validation. The coherency across board makes it statistically significant. Therefore, we can safely report that our downscaled estimate can be relied upon for making significant estimates for other regions in Australia, where similar hydro-climatic conditions exist.","verified_in_page":true},"topics":["statistical downscaling","terrestrial water storage","groundwater monitoring","hydrological modeling","gravity recovery and climate experiment","machine learning regression","water budget"],"study_period":"April 2002 to June 2017","locations":[{"name":"Cambrian Limestone Aquifer","kind":"aquifer","role":"study area for statistical downscaling of GRACE terrestrial water storage changes","river_name":null,"geocode_query":"Cambrian Limestone Aquifer, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0°× 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r= 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Daly Basin","kind":"basin","role":"regional sub-basin within the CLA with high terrestrial water storage variability and significant groundwater recharge","river_name":null,"geocode_query":"Daly Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-005","page":10,"quote":"We examined the eigenvectors for both datasets and the eigenvalues associated with each principal component. The first three PCA modes which gave a cumulative variance of 96.3% and 96.9% for the original and downscaled products, respectively, were adopted as meaningful signals representing most of the total TWS variability of CLA for both scenarios. The first PCA mode which explains 90.0% and 89.5% of variance for the respective original and downscaled variance (Fig. 5), depicts the annual variability of TWS changes over our test bed. This mode shows that the strongest annual variability (+ve) over the CLA is prominent over the Daly basin (northernmost section of the CLA). These strong spatial loadings in the north of the CLA are largely precipitation-driven, contributing to the relatively high annual recharge rates in the region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.657245,-13.734953],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102212844,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-85df920f0a4e3ff9beda5375-site-1","true_coordinates":[130.7107425,-13.7649526],"spread_km":6.7},{"name":"Wiso Basin","kind":"basin","role":"geological sub-basin within the CLA influencing intra-annual variations in water storage","river_name":null,"geocode_query":"Wiso Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"The second and third PCA modes (Supporting information 6 and 7) explains 7% and 2.3%, respectively, for the original GRACE and 4.5% and 3.2%, respectively for the downscaled products. We categorize them as greater intra-annual variations as they depicted more consistent inter-annual variations. However, due to their minimal variance, they maintained little relationship with the in-situ ΔGWS and may not provide a comprehensive characterization of TWS dynamics over the CLA. Most of these greater intra-annual signals are coming from eastern Wiso Basin and the North-western Georgina Basin. This variability is likely to be caused by ephemeral surface water bodies, seasonal flows and/or soil moisture in the region32,67. It is safe to conclude that the Daly basin witnesses more consistent variability in total water storage regardless of its relatively smaller size (Fig. 5, Supporting information 6, 7).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Georgina Basin","kind":"basin","role":"geological sub-basin within the CLA influencing intra-annual water storage variations","river_name":null,"geocode_query":"Georgina Basin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"The second and third PCA modes (Supporting information 6 and 7) explains 7% and 2.3%, respectively, for the original GRACE and 4.5% and 3.2%, respectively for the downscaled products. We categorize them as greater intra-annual variations as they depicted more consistent inter-annual variations. However, due to their minimal variance, they maintained little relationship with the in-situ ΔGWS and may not provide a comprehensive characterization of TWS dynamics over the CLA. Most of these greater intra-annual signals are coming from eastern Wiso Basin and the North-western Georgina Basin. This variability is likely to be caused by ephemeral surface water bodies, seasonal flows and/or soil moisture in the region32,67. It is safe to conclude that the Daly basin witnesses more consistent variability in total water storage regardless of its relatively smaller size (Fig. 5, Supporting information 6, 7).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Alice Springs","kind":"settlement","role":"reference location south of the Cambrian Limestone Aquifer region","river_name":null,"geocode_query":"Alice Springs, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"5 Vol.:(0123456789) Scientific Reports | (2024) 14:10113 | https://doi.org/10.1038/s41598-024-60366-2 www.nature.com/scientificreports/ shown in Table 2 provides context to their quality and efficiency in serving as a validation tool for the downscaled GRACE product. Case study The region of interest is an extensive carbonate aquifer—the Cambrian Limestone Aquifer (CLA) underlying a large portion of Australia’s Northern Territory, to the north of Alice Springs and south of Katherine32; Supporting Information 1a. The CLA comprises three geological sub-basins; Daly, Wiso and Georgina, within which groundwater flows are inter-connected. The CLA was selected as a suitable test location for our downscaling operation because, due to the significant gradient in its climate parameters (rainfall and ET) from south to north, it may be difficult to capture the variations across the CLA with the original GRACE products, thus justifying the need for effective downscaling.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.816217,-23.708461],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-85df920f0a4e3ff9beda5375-site-4","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Katherine","kind":"settlement","role":"reference location north of the Cambrian Limestone Aquifer region","river_name":null,"geocode_query":"Katherine, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"5 Vol.:(0123456789) Scientific Reports | (2024) 14:10113 | https://doi.org/10.1038/s41598-024-60366-2 www.nature.com/scientificreports/ shown in Table 2 provides context to their quality and efficiency in serving as a validation tool for the downscaled GRACE product. Case study The region of interest is an extensive carbonate aquifer—the Cambrian Limestone Aquifer (CLA) underlying a large portion of Australia’s Northern Territory, to the north of Alice Springs and south of Katherine32; Supporting Information 1a. The CLA comprises three geological sub-basins; Daly, Wiso and Georgina, within which groundwater flows are inter-connected. The CLA was selected as a suitable test location for our downscaling operation because, due to the significant gradient in its climate parameters (rainfall and ET) from south to north, it may be difficult to capture the variations across the CLA with the original GRACE products, thus justifying the need for effective downscaling.","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.269862,-14.391968],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217145,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Katherine NT 0850, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-85df920f0a4e3ff9beda5375-site-5","true_coordinates":[132.2698623,-14.4519683],"spread_km":6.7},{"name":"Northern Territory","kind":"region","role":"administrative region encompassing the Cambrian Limestone Aquifer and study area","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"5 Vol.:(0123456789) Scientific Reports | (2024) 14:10113 | https://doi.org/10.1038/s41598-024-60366-2 www.nature.com/scientificreports/ shown in Table 2 provides context to their quality and efficiency in serving as a validation tool for the downscaled GRACE product. Case study The region of interest is an extensive carbonate aquifer—the Cambrian Limestone Aquifer (CLA) underlying a large portion of Australia’s Northern Territory, to the north of Alice Springs and south of Katherine32; Supporting Information 1a. The CLA comprises three geological sub-basins; Daly, Wiso and Georgina, within which groundwater flows are inter-connected. The CLA was selected as a suitable test location for our downscaling operation because, due to the significant gradient in its climate parameters (rainfall and ET) from south to north, it may be difficult to capture the variations across the CLA with the original GRACE products, thus justifying the need for effective downscaling.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Australia","kind":"region","role":"Country encompassing the study area and AWO model domain","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0°× 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r= 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."}],"limitations":["Limited groundwater monitoring bores with sparse spatial distribution introduces uncertainty in validation.","Uncertainties in the evapotranspiration dataset from the Australian Water Outlook impact water budget closure and downscaling accuracy, especially in summer months.","Conversion of groundwater levels to storage volumes requires assumptions on storage coefficients, which may not capture localized hydrogeological variability.","Some data gaps were interpolated linearly, potentially introducing error given non-linear groundwater dynamics.","The weak correlation between water budget estimates and downscaled GRACE ΔTWS is partly due to uncertainties inherent in hydrological flux estimates especially ET."],"river_ids":["ne-469"],"site_ids":["doc-85df920f0a4e3ff9beda5375-site-1","doc-85df920f0a4e3ff9beda5375-site-4","doc-85df920f0a4e3ff9beda5375-site-5"],"anchor":[132.269862,-14.391968],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[5040061670,5040491370],"main_basin_ids":[5040061670,5040087590]},{"id":"doc-9de698f871aac8958b5eac7a","title":"Using natural gas content of groundwater to improve the understanding of complex thermo-mineral spring systems","display_title":"Gas Tracers Reveal Complex Springs Flow","doi":"https://doi.org/10.1016/j.jhydrol.2024.130956","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-02-28","authors":["Margaux Dupuy","Émilie Garel","Eliot Chatton","Thierry Labasque","Alexandra Mattéi","Sébastien Santoni","Virginie Vergnaud","Luc Aquilina","Frédéric Huneau"],"summary":"This study investigates the complex thermomineral springs in eastern Corsica, France, a geologically diverse, non-tectonically active zone. By analyzing dissolved gas compositions (CO2, CH4, H2S, N2, He) alongside geochemical and isotopic data, the study identifies distinct groundwater flow patterns and processes. Three gas-rich spring types (CH4 & H2S-rich, N2-rich, CO2-rich) are characterized, revealing that groundwater diversity arises from deep crustal gas influences, biotic activity, and interactions with local geology. The approach advances understanding of hydrogeological functioning beyond conventional methods and informs sustainable management of mineral waters under environmental pressures.","finding":"Using dissolved gas content as hydrogeological tracers allows distinction of local from regional groundwater flow paths and uncovers both abiotic and biotic processes affecting mineral water diversity in a non-active tectonic zone.","finding_evidence":{"passage_id":"p045-003","page":45,"quote":"This paper answers the primary objectives yet further demonstrates that using dissolved gas as a tracer of groundwater flow paths allows a deeper interpretation of surface geochemical and isotopic observations, distinguishes local from regional flow paths, and provides information about processes at the origin of groundwater diversity. The combination of tools presented in this paper (i.e., geochemical, dissolved gas, and isotopic tools) allows the establishment of a reliable regional groundwater flow scheme for thermo-mineral waters in a non-active zone. This scheme is essential to improve thermo-mineral water management, and protection to ensure their sustainable quality in front of increasing anthropogenic and climatic pressures.","verified_in_page":true},"topics":["hydrogeology","thermo-mineral waters","water-rock interactions","water stable isotopes","dissolved gases","groundwater flow paths","abiotic processes","biotic processes"],"study_period":null,"locations":[{"name":"Eastern Corsica","kind":"region","role":"study area with complex geological and structural settings supporting investigation of thermo-mineral springs flow and gas compositions","river_name":null,"geocode_query":"Eastern Corsica, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"From field 27 investigations in a geologically and structurally complex area of Eastern Corsica (France), 28 three types of gas contents are identified: (type 1) CH4 & H2S-rich, (type 2) N2-rich and 29 (type 3) CO2-rich. The study of these dissolved gases highlights that the wide geochemical 30 diversity of thermo-mineral waters observed here is not only related to the mineralogical 31 composition of the local aquifer but also involves strong and cumulative interactions 32 along deep regional circulation pathways. This approach also reveals a common deep 33 crustal gaseous influence characterised by N2 production, which interacts during up flow 34 with groundwater and then with the local metamorphic or sedimentary rock matrix. The 35 groundwater’s isotopic and geochemical contents are then altered by local lithologies 36 encountered through both abiotic and biotic interactions.","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.012893,41.979604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-0","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7},{"name":"Pietrapola springs","kind":"field_site","role":"thermal springs emerging from granitic monzogranite basement, representing geothermal recharge at >800 m altitude","river_name":null,"geocode_query":"Pietrapola, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.3009,41.989572],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pietrapola, 20243 Isolaccio-di-Fiumorbo, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-1"},{"name":"Caldaniccia spring","kind":"field_site","role":"thermal spring emerging from tonalogranodiorite basement in Travu Valley, indicative of deep geothermal water flow","river_name":null,"geocode_query":"Caldaniccia, Travu Valley, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Travu Valley","kind":"region","role":"valley hosting springs like Caldaniccia and Vignola, showing diverse water types and mineralization, reflecting interaction of aquifers","river_name":null,"geocode_query":"Travu Valley, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.082857,42.009604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-3","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7},{"name":"Vignola spring","kind":"field_site","role":"cold spring exhibiting Na-Cl water type with long water-rock interaction","river_name":null,"geocode_query":"Vignola, Travu Valley, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Fontanella spring","kind":"field_site","role":"cold spring at interface of lithologies showing high mineralization and Na-Cl water type","river_name":null,"geocode_query":"Fontanella, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.082857,42.069604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-5","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7},{"name":"Acqua Acetosa spring","kind":"field_site","role":"highly mineralized spring at interface between Eocene Flyschs and basement sediments with Ca-Na-HCO3 type indicating complex flow","river_name":null,"geocode_query":"Acqua Acetosa, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"7 193 groundwaters and results are summarised below. 194 The Pietrapola and Caldaniccia sites show springs flowing out respectively from 195 potassium feldspar-rich- monzogranite and tonalograniodiorite. Thereby a high-altitude 196 recharge (>800 m asl) supplies the springs emerging through major faults providing 197 thermal-alkaline-water, fully equilibrated with rock. Their low mineralisation (<300 198 μS/cm) associated with a Na-HCO3 water type and high SiO2 content confirms that this 199 water is provided by the hydrolysis of Hercynian granitoids at depth (Dupuy et al., 2021). 200 All other springs are cold resulting from a local recharge at a shallower depth. Acqua 201 acetosa, Vignola and Fontanella flow out at the interface between Eocene Flyschs and 202 Hercynian basement or Neogene sediments (Table 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.012893,42.099604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-6","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7},{"name":"Puzzichellu springs group","kind":"field_site","role":"group of springs along Saint Antoine fault emerging from sandstones and organic matter-rich marls, reflecting geochemical mixing and biotic activity","river_name":null,"geocode_query":"Puzzichellu, Aghione, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"These 3 springs are highly 203 mineralised (from 2100 and up to 12 000 μS/cm2), contain high levels of chlorine, boron 204 and fluorine and display respectively a Ca-Na-HCO3 for Acqua acetosa and a Na-Cl water 205 type for the latter two. Their high mineralisation comes partly from the dissolution of 206 interstitial-residual-brines (Aquilina et al., 2011). This geochemical diversity translates to 207 the importance of tectonics in the complexity of the flow pattern involved at the interface 208 of the different geological units (Martín-Loeches et al., 2020). 209 The Puzzichellu springs group flows out along the Saint Antoine fault, aligned on a 210 N20-N30 axis (Fig. 2), where the Puzzichellu and the Campo-Favajo springs emerge from 211 sandstones including rhyolite conglomerates covered by deposits of organic matter-rich 212 marls (Caritg et al., 2009a, 2009b). This group is characterised by Na-HCO3 , Ca-Na-HCO3 213 and Ca-SO4- HCO3 water types.","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.408987,42.120416],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Puzzichello, 20270 Aghione, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-7"},{"name":"Saint Antoine fault","kind":"other","role":"regional fault controlling location of Puzzichellu springs and influencing groundwater pathways","river_name":null,"geocode_query":"Saint Antoine fault, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"These 3 springs are highly 203 mineralised (from 2100 and up to 12 000 μS/cm2), contain high levels of chlorine, boron 204 and fluorine and display respectively a Ca-Na-HCO3 for Acqua acetosa and a Na-Cl water 205 type for the latter two. Their high mineralisation comes partly from the dissolution of 206 interstitial-residual-brines (Aquilina et al., 2011). This geochemical diversity translates to 207 the importance of tectonics in the complexity of the flow pattern involved at the interface 208 of the different geological units (Martín-Loeches et al., 2020). 209 The Puzzichellu springs group flows out along the Saint Antoine fault, aligned on a 210 N20-N30 axis (Fig. 2), where the Puzzichellu and the Campo-Favajo springs emerge from 211 sandstones including rhyolite conglomerates covered by deposits of organic matter-rich 212 marls (Caritg et al., 2009a, 2009b). This group is characterised by Na-HCO3 , Ca-Na-HCO3 213 and Ca-SO4- HCO3 water types.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Castagniccia region","kind":"region","role":"area with Alpine metamorphic units contributing to complex groundwater flow and springs emergence, hosting lustrous schists with diverse metamorphic facies","river_name":null,"geocode_query":"Castagniccia, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"These 130 observations are confirmed by the ombrothermal diagrams displaying precipitation and 131 temperature observed on 3 cities of the study area, at Corte at 429 m asl (inland), Bastia 132 and Solenzara, two lowland stations respectively at 1 and 12 m asl . The inland part is 133 characterised by alpine climate conditions with high snowfall in winter and storm rain 134 events during the driest months from May to September for inland stations (Fig. 1) (Mattei 135 et al., 2021; Rome and Giorgetti, 2007). While lowland stations saw their driest months 136 from April to September (Fig. 1). The study area is drained by four main rivers flowing 137 from West to East: the Solenzara River and the Travu River in the south part, the 138 Tavignanu River and the Golu River in the northern part (Fig. 2). 139 Geological and hydrogeological overview 140 The study area is located at the interface of 4 main geological units on the island (Fig. 141 2) (Caritg, 2009a, 2009b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.433934299999999,42.3385081],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Castagniccia, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-9"},{"name":"Aleria plain","kind":"region","role":"Neogene sedimentary basin with thick deposits and low local aquifers, overlain by Eocene flyschs and sedimentary units, affected by faults and influencing deep aquifer flows","river_name":null,"geocode_query":"Aleria Plain, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"These 130 observations are confirmed by the ombrothermal diagrams displaying precipitation and 131 temperature observed on 3 cities of the study area, at Corte at 429 m asl (inland), Bastia 132 and Solenzara, two lowland stations respectively at 1 and 12 m asl . The inland part is 133 characterised by alpine climate conditions with high snowfall in winter and storm rain 134 events during the driest months from May to September for inland stations (Fig. 1) (Mattei 135 et al., 2021; Rome and Giorgetti, 2007). While lowland stations saw their driest months 136 from April to September (Fig. 1). The study area is drained by four main rivers flowing 137 from West to East: the Solenzara River and the Travu River in the south part, the 138 Tavignanu River and the Golu River in the northern part (Fig. 2). 139 Geological and hydrogeological overview 140 The study area is located at the interface of 4 main geological units on the island (Fig. 141 2) (Caritg, 2009a, 2009b).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.5134613,42.1147718],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"20270 Aléria, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-10"},{"name":"Corsica Island","kind":"region","role":"Mediterranean island featuring diverse geological units and mineral springs, site of comprehensive hydrogeological and geochemical study","river_name":null,"geocode_query":"Corsica Island, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-000","page":6,"quote":"4 84 Halihan, 2018). Apart from atmospheric origin, the dissolved CO2 has various origins such 85 as deep-seated mantle (magmatic influence), thermogenic or biogenic (both occurring in 86 sediments). Well known as anthropogenic emission (i.e. landfill, agricultural activities, 87 etc.), the greenhouse gas CH4 has multiple natural origins such as volcanic and, wetlands 88 and or could be also the evidence of biotic processes from an anaerobic environment 89 where microbial fermentation of organic matter occurs. The last major gas found is N2 , 90 which may have an atmospheric origin, a magmatic origin, or can be caused by the 91 intrusion of deep fluid. According to their potential origin and to their geochemical and 92 isotopic composition, groundwater samples collected at the surface can provide 93 information about fluid origin, up-flow paths and reservoir interactions with geological 94 matrixes (D’amore et al., 1987).","verified_in_page":true},"resolution_status":"resolved","coordinates":[8.942928,42.069604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-11","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7},{"name":"Solenzara River","kind":"river","role":"one of the four main rivers draining the eastern part of Corsica study area, contextual hydrologic feature","river_name":"Solenzara River","geocode_query":"Solenzara River, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"5 120 Localisation and hydroclimatology 121 In the Western Mediterranean Sea, Corsica Island (France) has a contrasted topography 122 (up to 2706 m. above sea level – asl.). The investigated area, located on the eastern part of 123 the island extends over 1,500 km2. In the west of the study area, a mountainous zone, of 124 1034 km2, rises to 2136 m, while in the east, the coastal part is a lowland plain extending 125 over 466 km2. Despite the typical Mediterranean climate of the island, the mean 126 temperatures and precipitation vary significantly with the altitude (Fig. 1). According to 127 Bruno et al. (2001) average annual precipitation increases from about 600 mm near the 128 coast to more than 1600 mm at 1500 m asl, while the mean annual temperature decreases 129 from 16°C near the coast to 10°C at 1500 m asl (Mattei et al., 2021; Bruno et al., 2001).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.372282,41.836585],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"20145 Sari-Solenzara, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-12"},{"name":"Travu River","kind":"river","role":"river draining south part of eastern Corsica, region hosting several springs of hydrogeological interest","river_name":"Travu River","geocode_query":"Travu River, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"5 120 Localisation and hydroclimatology 121 In the Western Mediterranean Sea, Corsica Island (France) has a contrasted topography 122 (up to 2706 m. above sea level – asl.). The investigated area, located on the eastern part of 123 the island extends over 1,500 km2. In the west of the study area, a mountainous zone, of 124 1034 km2, rises to 2136 m, while in the east, the coastal part is a lowland plain extending 125 over 466 km2. Despite the typical Mediterranean climate of the island, the mean 126 temperatures and precipitation vary significantly with the altitude (Fig. 1). According to 127 Bruno et al. (2001) average annual precipitation increases from about 600 mm near the 128 coast to more than 1600 mm at 1500 m asl, while the mean annual temperature decreases 129 from 16°C near the coast to 10°C at 1500 m asl (Mattei et al., 2021; Bruno et al., 2001).","verified_in_page":true},"resolution_status":"resolved","coordinates":[9.1845005,41.848028],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Travo, 20132 Zicavo, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-13"},{"name":"Tavignanu River","kind":"river","role":"river flowing in northern study area of eastern Corsica, part of regional hydrological context","river_name":"Tavignanu River","geocode_query":"Tavignanu River, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"5 120 Localisation and hydroclimatology 121 In the Western Mediterranean Sea, Corsica Island (France) has a contrasted topography 122 (up to 2706 m. above sea level – asl.). The investigated area, located on the eastern part of 123 the island extends over 1,500 km2. In the west of the study area, a mountainous zone, of 124 1034 km2, rises to 2136 m, while in the east, the coastal part is a lowland plain extending 125 over 466 km2. Despite the typical Mediterranean climate of the island, the mean 126 temperatures and precipitation vary significantly with the altitude (Fig. 1). According to 127 Bruno et al. (2001) average annual precipitation increases from about 600 mm near the 128 coast to more than 1600 mm at 1500 m asl, while the mean annual temperature decreases 129 from 16°C near the coast to 10°C at 1500 m asl (Mattei et al., 2021; Bruno et al., 2001).","verified_in_page":true},"resolution_status":"resolved","coordinates":[8.9428551,42.2538662],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Tavignano, 20250 Corte, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-14"},{"name":"Golu River","kind":"river","role":"river in northern eastern Corsica study area forming part of drainage system influencing groundwater flow","river_name":"Golu River","geocode_query":"Golu River, Corsica, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"5 120 Localisation and hydroclimatology 121 In the Western Mediterranean Sea, Corsica Island (France) has a contrasted topography 122 (up to 2706 m. above sea level – asl.). The investigated area, located on the eastern part of 123 the island extends over 1,500 km2. In the west of the study area, a mountainous zone, of 124 1034 km2, rises to 2136 m, while in the east, the coastal part is a lowland plain extending 125 over 466 km2. Despite the typical Mediterranean climate of the island, the mean 126 temperatures and precipitation vary significantly with the altitude (Fig. 1). According to 127 Bruno et al. (2001) average annual precipitation increases from about 600 mm near the 128 coast to more than 1600 mm at 1500 m asl, while the mean annual temperature decreases 129 from 16°C near the coast to 10°C at 1500 m asl (Mattei et al., 2021; Bruno et al., 2001).","verified_in_page":true},"resolution_status":"resolved","coordinates":[8.942928,42.009604],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Corsica, France","basin_id":2040047500,"main_basin_id":2040047500,"basin_region":"Europe and Middle East","basin_area_km2":33396.8,"hydrology_source":"hydrobasins","site_id":"doc-9de698f871aac8958b5eac7a-site-15","true_coordinates":[9.012892599999999,42.0396042],"spread_km":6.7}],"limitations":["Need for rapid (<48h) dissolved gas sample analysis to avoid atmospheric contamination or artificial degassing bias.","Sampling and transport require careful handling to prevent gas consumption/production by biotic activity or pressure changes.","High diversity and contrasting dissolved gas signatures require cautious data interpretation.","Methodology applicability and effectiveness for springs with lower geochemical and gas diversity need further evaluation.","Limited or absent exact latitude/longitude coordinates for study sites and regional locations in the dataset."],"river_ids":[],"site_ids":["doc-9de698f871aac8958b5eac7a-site-0","doc-9de698f871aac8958b5eac7a-site-1","doc-9de698f871aac8958b5eac7a-site-3","doc-9de698f871aac8958b5eac7a-site-5","doc-9de698f871aac8958b5eac7a-site-6","doc-9de698f871aac8958b5eac7a-site-7","doc-9de698f871aac8958b5eac7a-site-9","doc-9de698f871aac8958b5eac7a-site-10","doc-9de698f871aac8958b5eac7a-site-11","doc-9de698f871aac8958b5eac7a-site-12","doc-9de698f871aac8958b5eac7a-site-13","doc-9de698f871aac8958b5eac7a-site-14","doc-9de698f871aac8958b5eac7a-site-15"],"anchor":[9.082857,42.069604],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[2040047500],"main_basin_ids":[2040047500]},{"id":"doc-9ee3ebfaf3573695f4e0f1b8","title":"Stability Analysis for Seed Yield of Linseed (Linum Usitatissimum) Genotype in Saline Affected Soils of South-Western Coastal Region of Bangladesh","display_title":"Linseed Yield Stability in Saline Soils of Bangladesh","doi":"https://doi.org/10.59324/ejeba.2024.1(5).09","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-11-01","authors":["Hosna Kohinoor","Md. Shalim Uddin","Md. Jamilur Rahman","Mohammed Mainuddin","Abul Kashem Chowdhury"],"summary":"This study assessed seed yield stability and adaptability of ten linseed genotypes cultivated in saline affected soils across four locations in South-Western coastal Bangladesh over three winter seasons (2020-2023). Using AMMI and GGE biplot analyses, it identified genotypes with superior yield and stability suitable for saline conditions, notably BD-10698 (G6).","finding":"The linseed genotype BD-10698 (G6) exhibited high seed yield, wide adaptation, and excellent stability over all tested saline environments in South-Western coastal Bangladesh, making it a suitable variety for cultivation in these saline affected soils.","finding_evidence":{"passage_id":"p015-003","page":15,"quote":"Considering the results of biplot analyses and stability parameters like ASV, SIPC, CV, and EV, genotypes G1, G2, G4 and G6 were identified as superior genotypes. The genotypes, G6 exhibited high seed yield, wide adaptation and excellent stable performance over all four tested locations. Thus BD-10698 (G6) can be selected as a variety for cultivation in the saline affected soils of South-Western coastal region of Bangladesh. Genotypes BD-10708 (G2) and Lin-1403 (G4) showed excellent yield performance but medium stability, while Lin-1203 (G9) showed excellent stability but low yield performance. So, BD10707, Lin-1403 and Lin-1203 can be utilized to develop salt tolerant linseed variety through required breeding program. Acknowledgement The first author would like to acknowledge the financial support from Krishi Gobeshana Foundation (KGF), Bangladesh Agricultural Research Council, Dhaka, Bangladesh and she would also like to thank Mr. Md.","verified_in_page":true},"topics":["genotype-environment interaction","crop yield stability","salinity tolerance","plant breeding","multi-environment trials","AMMI analysis","GGE biplot","linseed"],"study_period":"2020-21 to 2022-23 (Rabi seasons)","locations":[{"name":"Amtali Upazila, Barguna District, Bangladesh","kind":"region","role":"study region representing saline soils under AEZ-13","river_name":null,"geocode_query":"Amtali Upazila, Barguna, Bangladesh","latitude":22.129,"longitude":90.229,"evidence":{"passage_id":"p003-002","page":3,"quote":"The genotypes were obtained from the Genetic Resource Center of Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh. Neela is a nationally released variety and was included in the experiment as a standard material. The materials were tested at two locations of Amtali Upazila under Barguna district and at another two locations of Kalapara Upazila under Patuakhali district). The locations of the experiment are representative of saline soils in the South-West Costal region under AEZ-13 of Bangladesh. The experiment was conducted in three consecutive Rabi (winter) season (December to April) of 2020-21, 2021-22 and 2022-23. Each year and every location was considered as a separate environment, constituting altogether 12 test environments as described in Table 2. Experimental, Design, Layout and Data Collection In all locations and seasons, the experiment was laid out in a randomized complete block design with 3 replications.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.174457,22.163126],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amtali, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-9ee3ebfaf3573695f4e0f1b8-site-0","true_coordinates":[90.23055169999999,22.1331256],"spread_km":6.7},{"name":"Kalapara Upazila, Patuakhali District, Bangladesh","kind":"region","role":"study region representing saline soils under AEZ-13","river_name":null,"geocode_query":"Kalapara Upazila, Patuakhali, Bangladesh","latitude":21.986,"longitude":90.242,"evidence":{"passage_id":"p003-002","page":3,"quote":"The genotypes were obtained from the Genetic Resource Center of Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh. Neela is a nationally released variety and was included in the experiment as a standard material. The materials were tested at two locations of Amtali Upazila under Barguna district and at another two locations of Kalapara Upazila under Patuakhali district). The locations of the experiment are representative of saline soils in the South-West Costal region under AEZ-13 of Bangladesh. The experiment was conducted in three consecutive Rabi (winter) season (December to April) of 2020-21, 2021-22 and 2022-23. Each year and every location was considered as a separate environment, constituting altogether 12 test environments as described in Table 2. Experimental, Design, Layout and Data Collection In all locations and seasons, the experiment was laid out in a randomized complete block design with 3 replications.","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.23105559999999,21.9851561],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kalapara Upazila, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-9ee3ebfaf3573695f4e0f1b8-site-1"},{"name":"Uttar Kalayanpur (location in Amtali Upazila, Barguna District)","kind":"field_site","role":"experimental trial site for linseed genotypes","river_name":null,"geocode_query":"Uttar Kalayanpur, Amtali, Barguna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Geographic Position, Temperature, Rainfall, and Soil pH and Soil Salinity of Four Experimental Locations Description of location Upzila: Amtali, District : Barguna Upzila : Kalapara, District : Patuakhali Latitude Longitude Altitude 22.129 ̊N 90.229 ̊E 1.9 m 21.986 ̊N 90.242 ̊E 3.5 m During cropping period (December-April) Uttar Kalayanpur Dokkhin Kalayanpur Diaramkhola Nayapara Temperature (Min-Max) 15.4- 31.1°C 15.4- 31.1°C 18.3-29.5 °C 18.3-29.5 °C Rainfall 13.47 mm 13.47 mm 14.36 mm 14.36 mm Rainy days 2.03 days 2.03 days 3.34 days 3.34 days Soil pH (0-10 cm deep) 5.7-6.9 6.0-7.1 35.7-6.8 5.7-7.0 Salinity (0-10 cm deep) 3.8-11.9 dS m-1 9.0-15.4 dS m-1 8.0-14.8 dS m-1 8.5-15.3 dSm-1 Statistical Analysis of Data Combined Analysis of Variance: The data on seed yield of 10 genotypes across 12 test environments were analyzed through combined analysis of variance according to Gomez & Gomez (1983) to determine the effects of genotypes (G), environments (E), and GE interactions effects.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Dakkhin Kalayanpur (location in Amtali Upazila, Barguna District)","kind":"field_site","role":"experimental trial site for linseed genotypes","river_name":null,"geocode_query":"Dakkhin Kalayanpur, Amtali, Barguna, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Geographic Position, Temperature, Rainfall, and Soil pH and Soil Salinity of Four Experimental Locations Description of location Upzila: Amtali, District : Barguna Upzila : Kalapara, District : Patuakhali Latitude Longitude Altitude 22.129 ̊N 90.229 ̊E 1.9 m 21.986 ̊N 90.242 ̊E 3.5 m During cropping period (December-April) Uttar Kalayanpur Dokkhin Kalayanpur Diaramkhola Nayapara Temperature (Min-Max) 15.4- 31.1°C 15.4- 31.1°C 18.3-29.5 °C 18.3-29.5 °C Rainfall 13.47 mm 13.47 mm 14.36 mm 14.36 mm Rainy days 2.03 days 2.03 days 3.34 days 3.34 days Soil pH (0-10 cm deep) 5.7-6.9 6.0-7.1 35.7-6.8 5.7-7.0 Salinity (0-10 cm deep) 3.8-11.9 dS m-1 9.0-15.4 dS m-1 8.0-14.8 dS m-1 8.5-15.3 dSm-1 Statistical Analysis of Data Combined Analysis of Variance: The data on seed yield of 10 genotypes across 12 test environments were analyzed through combined analysis of variance according to Gomez & Gomez (1983) to determine the effects of genotypes (G), environments (E), and GE interactions effects.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Diaramkhola (location in Kalapara Upazila, Patuakhali District)","kind":"field_site","role":"experimental trial site for linseed genotypes","river_name":null,"geocode_query":"Diaramkhola, Kalapara, Patuakhali, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Geographic Position, Temperature, Rainfall, and Soil pH and Soil Salinity of Four Experimental Locations Description of location Upzila: Amtali, District : Barguna Upzila : Kalapara, District : Patuakhali Latitude Longitude Altitude 22.129 ̊N 90.229 ̊E 1.9 m 21.986 ̊N 90.242 ̊E 3.5 m During cropping period (December-April) Uttar Kalayanpur Dokkhin Kalayanpur Diaramkhola Nayapara Temperature (Min-Max) 15.4- 31.1°C 15.4- 31.1°C 18.3-29.5 °C 18.3-29.5 °C Rainfall 13.47 mm 13.47 mm 14.36 mm 14.36 mm Rainy days 2.03 days 2.03 days 3.34 days 3.34 days Soil pH (0-10 cm deep) 5.7-6.9 6.0-7.1 35.7-6.8 5.7-7.0 Salinity (0-10 cm deep) 3.8-11.9 dS m-1 9.0-15.4 dS m-1 8.0-14.8 dS m-1 8.5-15.3 dSm-1 Statistical Analysis of Data Combined Analysis of Variance: The data on seed yield of 10 genotypes across 12 test environments were analyzed through combined analysis of variance according to Gomez & Gomez (1983) to determine the effects of genotypes (G), environments (E), and GE interactions effects.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Nayapara (location in Kalapara Upazila, Patuakhali District)","kind":"field_site","role":"experimental trial site for linseed genotypes","river_name":null,"geocode_query":"Nayapara, Kalapara, Patuakhali, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Geographic Position, Temperature, Rainfall, and Soil pH and Soil Salinity of Four Experimental Locations Description of location Upzila: Amtali, District : Barguna Upzila : Kalapara, District : Patuakhali Latitude Longitude Altitude 22.129 ̊N 90.229 ̊E 1.9 m 21.986 ̊N 90.242 ̊E 3.5 m During cropping period (December-April) Uttar Kalayanpur Dokkhin Kalayanpur Diaramkhola Nayapara Temperature (Min-Max) 15.4- 31.1°C 15.4- 31.1°C 18.3-29.5 °C 18.3-29.5 °C Rainfall 13.47 mm 13.47 mm 14.36 mm 14.36 mm Rainy days 2.03 days 2.03 days 3.34 days 3.34 days Soil pH (0-10 cm deep) 5.7-6.9 6.0-7.1 35.7-6.8 5.7-7.0 Salinity (0-10 cm deep) 3.8-11.9 dS m-1 9.0-15.4 dS m-1 8.0-14.8 dS m-1 8.5-15.3 dSm-1 Statistical Analysis of Data Combined Analysis of Variance: The data on seed yield of 10 genotypes across 12 test environments were analyzed through combined analysis of variance according to Gomez & Gomez (1983) to determine the effects of genotypes (G), environments (E), and GE interactions effects.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Coordinates for specific trial sites not provided; latitude and longitude for field sites unknown.","Study focused on a regional saline zone limiting geographic generalization."],"river_ids":[],"site_ids":["doc-9ee3ebfaf3573695f4e0f1b8-site-0","doc-9ee3ebfaf3573695f4e0f1b8-site-1"],"anchor":[90.23105559999999,21.9851561],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-ae345b776ae14aef67b4df66","title":"Global Climate","display_title":"Global Climate in 2023: Record Warmth and Impacts","doi":"https://doi.org/10.1175/bams-d-24-0116.1","theme":"Climate","color":"#98d4f1","year":2024,"publication_date":"2024-08-01","authors":["R. J. H. Dunn","J. Blannin","N. Gobron","J. B. Miller","K. M. Willett","Melanie Ades","Robert Adler","Mihai Alexe","Richard P. Allan","John Anderson","Orlane Anneville","Yasuyuki Aono","Anthony Arguez","Dolores Armenteras Pascual","Carlo Arosio","Elizabeth Asher","John A. Augustine","Cesar Azorin-Molina","Oscar M. Baez-Villanueva","J. Barichivich","Hylke E. Beck","Nicolas Bellouin","Angela Benedetti","Stephen Blenkinsop","Olivier Bock","Xavier Bodin","Olivier Bonte","Michael G. Bosilovich","Olivier Boucher","Stefan A. Buehler","Michael P. Byrne","Diego Campos","Fabrizio Cappucci","Laura Carrea","Kai-Lan Chang","Hanne H. Christiansen","John R. Christy","Eui-Seok Chung","Laura M. Ciasto","Scott Clingan","Melanie Coldewey-Egbers","Owen R. Cooper","Richard C. Cornes","Curt Covey","Jean-Francois Crétaux","Theresa Crimmins","Molly Crotwell","Joshua Culpepper","Diego Cusicanqui","Sean Davis","Richard A. M. de Jeu","Doug Degenstein","Reynald Delaloye","Elizabeth DiGangi","Martin T. Dokulil","Markus G. Donat","Wouter A. Dorigo","Diane Duchemin","Hilary Dugan","Imke Durre","Geoff Dutton","Gregory Duveiller","Thomas W. Estilow","Nicole Estrella","David Fereday","Vitali E. Fioletov","Johannes Flemming","Michael J. Foster","Thomas Frederikse","Stacey M. Frith","Lucien Froidevaux","Martin Füllekrug","Judith Garforth","Jay Garg","Sophie Godin-Beekmann","Steven Goodman","Atsushi Goto","Alice Grimm","Alexander Gruber","Guojun Gu","Mauro Guglielmin","Sebastian Hahn","Leopold Haimberger","Brad D. Hall","Merritt E. Harlan","I. Harris","Deborah L. Hemming","Martin Hirschi","Shu-peng (Ben) Ho","Robert Holzworth","Radley M. Horton","Filip Hrbáček","Guojie Hu","Dale Hurst","Antje Inness","Ketil Isaksen","Viju O. John","P. D. Jones","Robert Junod","Andreas Kääb","Johannes W. Kaiser","Viktor Kaufmann","Andreas Kellerer-Pirklbauer","Sergey Khaykin","Richard Kidd","Tyler V. King","Zak Kipling","Akash Koppa","Benjamin M. Kraemer","R. Sofia La Fuente","Alo Laas","Xin Lan","Kathleen O. Lantz","Jeff Lapierre","David A. Lavers","Thierry Leblanc","Eric Leibensperger","Chris Lennard","Ben Liley","Yakun Liu","Y. T. Eunice Lo","Norman G. Loeb","Diego Loyola","Florence Magnin","Shin-Ichiro Matsuzaki","Tom Matthews","Michael Mayer","Michael McCarthy","Tim R. McVicar","Carl A. Mears","Annette Menzel","Christopher J. Merchant","Leo-Juhani Merio","Michael F. Meyer","Diego G. Miralles","Stephan A. Montzka","Colin Morice","Isamu Morino","Ivan Mrekaj","Jens Mühle","D. Nance","Julien P. Nicolas","Jeannette Noetzli","Ben Noll","John O’Keefe","Tim J. Osborn","Mark Parrington","Cécile Pellet","Mauri S. Pelto","Kyle Petersen","Coda Phillips","Don Pierson","Izidine Pinto","Stephen Po-Chedley","Paolo Pogliotti","Lorenzo Polvani","Wolfgang Preimesberger","Colin Price","William J. Randel","Colin Raymond","Samuel Rémy","Lucrezia Ricciardulli","Andrew D. Richardson","David A. Robinson","Matthew Rodell","Nemesio Rodriguez-Fernandez","Cassandra D. W. Rogers","P. Rohini","Karen Rosenlof","Alexei Rozanov","Jozef Rozkošný","Olga O. Rusanovskaya","This Rutishauser","C. T. Sabeerali","Ryan Said","Tetsu Sakai","Ahira Sánchez-Lugo","Parnchai Sawaengphokhai","Verena Schenzinger","Robert W. Schlegel","Martin Schmid","Sonia I. Seneviratne","Fumi Sezaki","Xi Shao","Sapna Sharma","Lei Shi","Svetlana V. Shimaraeva","Ryuichiro Shinohara","Eugene A. Silow","Adrian J. Simmons","Sharon L. Smith","Brian J. Soden","Viktoria Sofieva","Logan Soldo","O. P. Sreejith","Paul W. Stackhouse","Ryan Stauffer","Wolfgang Steinbrecht","Andrea K. Steiner","Paul C. Stoy","Pietro Stradiotti","Dmitry A. Streletskiy","Ghassan Taha","Stephen J. Thackeray","Emmanuel Thibert","Maxim A. Timofeyev","Kleareti Tourpali","Emma Tronquo","Mari R. Tye","Ronald van der A","Robin van der Schalie","Gerard van der Schrier","Arnold J. H. van Vliet","Piet Verburg","Jean-Paul Vernier","Isaac J. Vimont","Katrina Virts","Sebastián Vivero","Holger Vömel","Russell S. Vose","Ray H. J. Wang","Taran Warnock","Mark Weber","David N. Wiese","Jeannette D. Wild","Earle Williams","Takmeng Wong","Richard Iestyn Woolway","Xungang Yin","Zhenzhong Zeng","Lin Zhao","Feng Zhong","Jerry R. Ziemke","Markus Ziese","Ruxandra M. Zotta","Cheng-Zhi Zou"],"summary":"This comprehensive study presents a detailed assessment of global climate indicators in 2023. That year was the warmest on record since the mid-1800s across seven global temperature datasets. A transition from a prolonged La Niña to a strong El Niño contributed to exceptional warmth, record numbers of warm days, and widespread hydrological and ecological impacts. Notable changes include glacier mass loss, increased atmospheric greenhouse gas concentrations, altered precipitation extremes, diminished snow and ice cover, and shifts in terrestrial vegetation phenology. The study relies on a global observational network and satellite products, analyzing temperature, cryosphere status, hydrological cycles, atmospheric circulation, radiation budgets, atmospheric composition, and terrestrial surface properties.","finding":"Globally, 2023 was the warmest year on record since temperature records began, with widespread and intense temperature extremes, driven in part by the transition from La Niña to El Niño conditions.","finding_evidence":{"passage_id":"p010-000","page":10,"quote":"August 2024 | State of the Climate in 2023 2. Global Climate S21 2. Global Climate R. J. H. Dunn, J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett, Eds. a. Overview —R. J. H. Dunn, J. Blannin, N. Gobron, J. B. Miller, and K. M. Willett Globally, 2023 was the warmest year since records began in the mid-1800s to mid-1900s, according to all seven global temperature datasets. The prolonged La Niña that began in 2020 faded at the start of 2023 and was replaced by a strong El Niño by the end of the year. The change to El Niño conditions contributed to exceptionally high temperatures worldwide, especially in the latter part of the year as the El Niño strengthened. The pervasive warmth was highlighted by widespread and intense temperature extremes, with record numbers of warm days globally and the third-highest land fraction experiencing record numbers of warm days. Also, globally averaged lake surface temperatures in the warm season were the highest since records began in 1995.","verified_in_page":true},"topics":["global-surface-temperature","lake-surface-temperature","tropospheric-temperature","permafrost-temperature","glacier-mass-balance","precipitation-extremes","sea-level-pressure","greenhouse-gas-concentration"],"study_period":"2023","locations":[{"name":"Japan","kind":"region","role":"location with earliest recorded cherry tree full bloom in over 1200 years in 2023","river_name":null,"geocode_query":"Kyoto, Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p100-001","page":100,"quote":"For the Arashiyama district of Kyoto, these are updated with daily observations at train stations that are recorded in newspapers and on websites by railway passengers. In 2023, the FBD was the earliest on record for Arashiyama at 12 days earlier than the baseline (2000–20 mean; Table 2.13; Fig. 2.84e). Monitoring data on lake water concentrations of the photosynthetic pigment chlorophyll-a were available to estimate spring phytoplankton phenology (nine Northern Hemisphere and one Southern Hemisphere lakes are reported; Fig. 2.85). The seasonal timing was estimated for start of season (SOSL; Park et al. 2016), day of maximum concentration (DOML), and center of gravity (COGL), which is an estimate of the mid-point of the plankton bloom (Edwards and Richardson 2004). The lake basins showed great interannual variation and mixed phenological behavior in 2023 relative to 2000–20.","verified_in_page":true},"resolution_status":"resolved","coordinates":[135.7681489,35.011564],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kyoto, Japan","basin_id":4040039450,"main_basin_id":4040039450,"basin_region":"Central and South-East Asia","basin_area_km2":295817.7,"hydrology_source":"hydrobasins","site_id":"doc-ae345b776ae14aef67b4df66-site-0"},{"name":"Eastern Africa","kind":"region","role":"region with floods and increased vegetation growth due to El Niño and precipitation events in 2023","river_name":null,"geocode_query":"Eastern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p102-002","page":102,"quote":"A2.7). In northern Australia, the patterns are likely due to above-average rainfall (sections 2d4, 7h4) driving vegetation growth. In the Horn of Africa, predominantly positive annual (Plate 2.1ah) and high monthly (Appendix Fig. A2.8) VOD anomalies in November and December coincided with heavy rains associated with El Niño (section 2d4) and with the switch of the Indian Ocean dipole from negative to positive in September 2023 (see section 4f). The remarkable increases in VOD across Ethiopia, Somalia, and Kenya (Appendix Fig. A2.7) were likely due to favorable growing conditions caused by large precipitation amounts and were also captured in other satellite-borne vegetation indicators (FEWS NET 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[37.349196,1.987709],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Africa","basin_id":1041145090,"main_basin_id":1040008100,"basin_region":"Africa","basin_area_km2":230636.1,"hydrology_source":"hydrobasins","site_id":"doc-ae345b776ae14aef67b4df66-site-1","true_coordinates":[37.2972044,1.957709],"spread_km":6.7}],"limitations":["Climate data derived from multiple observational and satellite sources with inherent measurement uncertainties","Sparse coverage in some regions for variables like humidity, soil moisture, and phenology","Permafrost and glaciers monitoring limited to regions with available boreholes and observational sites","Reanalysis datasets have limitations especially prior to 1990 and show divergences among models","Temporal gaps in some observational time series due to equipment damage or natural events (e.g., volcanic eruption damage at Mauna Loa Observatory)","Vegetation indices can be influenced by land use and atmospheric conditions not fully captured by satellite products","Short-term variability due to natural oscillations (ENSO, IOD, QBO) can complicate interpretation of longer-term trends","Wildfire emissions data influenced by detection uncertainties and variation in fire types"],"river_ids":[],"site_ids":["doc-ae345b776ae14aef67b4df66-site-0","doc-ae345b776ae14aef67b4df66-site-1"],"anchor":[135.7681489,35.011564],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-5","basin_ids":[1041145090,4040039450],"main_basin_ids":[1040008100,4040039450]},{"id":"doc-b5b31fe520d83b41cfb5ed63","title":"A hydrogeomorphic dataset for characterizing catchment hydrological behavior across the Tibetan Plateau","display_title":"Hydrogeomorphic Dataset of Tibetan Plateau Catchments","doi":"https://doi.org/10.5194/essd-16-1651-2024","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-04-03","authors":["Yuhan Guo","Hongxing Zheng","Yuting Yang","Yanfang Sang","Congcong Wen"],"summary":"This study presents the first comprehensive hydrogeomorphic dataset of 18,440 catchments across the Tibetan Plateau. It includes 18 hydrogeomorphic metrics and the width-function-based instantaneous unit hydrograph (WFIUH) for 13,456 catchments. The dataset facilitates understanding of hydrologic and geomorphic interactions in a largely pristine environment and supports hydrological modeling, particularly for flash-flood events. Validation using a hydrological model confirms the practical value of the derived WFIUH despite inherent uncertainties arising from DEM resolution and flow velocity estimation methods.","finding":"Peak flow of the width-function-based instantaneous unit hydrograph (WFIUH) is positively related to catchment slope and curvature but negatively related to catchment area, perimeter, length, and circularity, while time to peak shows opposite relationships.","finding_evidence":{"passage_id":"p008-002","page":8,"quote":"Regions that have high Qp but short Tp and Tc are usually characterized by elongated catchments, a pinnate river network, higher slopes, and lower roughness. Figure 8 further shows the relationships of Qp, Tp and Tc with the 18 hydrogeomorphic metrics in our TPHGD dataset. It is found that Qp is positively related to slope, horizontal curvature and absolute vertical curvature. Qp is negatively related to catchment area, perimeter, length and circularity. The relationships of Tp with the hydrogeomorphic metrics are similar to those of Qp but in an oppositive direction. This suggests that a catchment with a larger, more circular shape may exhibit a more gradual rising limb in its hydrograph. Tc has a strong negative correlation with catchment slope as a steeper land surface can result in faster flow, hence shortening the traveling time of water flow. Unsurprisingly, Tc is Earth Syst. Sci. Data, 16, 1651–1665, 2024 https://doi.org/10.5194/essd-16-1651-2024","verified_in_page":true},"topics":["catchment hydrology","geomorphology","hydrological modeling","hydrogeomorphic metrics","instantaneous unit hydrograph","digital elevation model","flash flood risk","flow velocity estimation"],"study_period":null,"locations":[{"name":"Tibetan Plateau","kind":"region","role":"primary study region with 18,440 catchments analyzed for hydrogeomorphic metrics and WFIUH derivation","river_name":null,"geocode_query":"Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"This dataset is expected to contribute to a better understanding of hydrogeomorphic processes and to facilitate hydrological modeling of catchments across the Tibetan Plateau. 2 Study area and data The Tibetan Plateau (TP) is situated between 26 to 40° N and 73 to 105° E and has a mean elevation of more than 4500 m, occupying about 2.5 × 106 km2 . The TP is the highest and most extensive highland in the world. In addition to having the largest cryospheric extent outside the polar region, the TP also serves as the source region for all major rivers in Asia. Consequently, it has been widely acknowledged as the driving force behind both regional and global environmental change (Kang et al., 2010). The Mekong River, the Yellow River, the Yangtze River, the Yarlung Tsampo (Brahmaputra), the Indus and the Karnali all originate on the Tibetan Plateau and support hundreds of millions of people downstream.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yellow River basin, Tibetan Plateau, China","kind":"region","role":"sub-region of the Tibetan Plateau where catchments were studied and WFIUH analyzed","river_name":"Yellow River","geocode_query":"Yellow River basin, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"1658 Y. Guo et al.: A hydrogeomorphic dataset for the Tibetan Plateau Figure 4. Spatial patterns of hydrogeomorphic metrics across the Tibetan Plateau. As shown in Fig. 7, the relative magnitude of peak flow for most catchments ranges from 0.05 to 0.15, with the mean and median being 0.121 and 0.103, respectively. The smaller relative magnitude of the peak flow value represents a more uniform hydrograph distribution and a less obvious peak. The higher the Qp, the more susceptible the catchment is to flash floods. Catchments with a higher Qp are found in the northwestern or northern edge of the inland region (IR). Catchments in the central IR have relatively lower Qp due to their smaller slopes. Catchments in the upper Yellow River basin (YLRB) and Yangtze River basin (YZRB) have correspondingly smaller Qp than catchments in the lower YLRB and YZRB. Catchments in the southwest basin (SWB) have the highest Qp as compared to catchments in the other three regions.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Yangtze River basin, Tibetan Plateau, China","kind":"region","role":"sub-region of the Tibetan Plateau where catchments were studied and WFIUH analyzed","river_name":"Yangtze River","geocode_query":"Yangtze River basin, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"1658 Y. Guo et al.: A hydrogeomorphic dataset for the Tibetan Plateau Figure 4. Spatial patterns of hydrogeomorphic metrics across the Tibetan Plateau. As shown in Fig. 7, the relative magnitude of peak flow for most catchments ranges from 0.05 to 0.15, with the mean and median being 0.121 and 0.103, respectively. The smaller relative magnitude of the peak flow value represents a more uniform hydrograph distribution and a less obvious peak. The higher the Qp, the more susceptible the catchment is to flash floods. Catchments with a higher Qp are found in the northwestern or northern edge of the inland region (IR). Catchments in the central IR have relatively lower Qp due to their smaller slopes. Catchments in the upper Yellow River basin (YLRB) and Yangtze River basin (YZRB) have correspondingly smaller Qp than catchments in the lower YLRB and YZRB. Catchments in the southwest basin (SWB) have the highest Qp as compared to catchments in the other three regions.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Inland-region basin, Tibetan Plateau, China","kind":"region","role":"sub-region of the Tibetan Plateau notable for endorheic basins; catchments analyzed for hydrogeomorphic metrics and WFIUH","river_name":null,"geocode_query":"Inland-region basin, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Due to the harsh and complex natural environment, the Tibetan Plateau is a typical ungauged area in China. Within the boundary of China, the Tibetan Plateau can be roughly divided into several basins, namely the inland-region basin, the Yellow River basin, the Yangtze River basin and the southwest basin (Fig. 1). In this study, the hydrogeomorphic dataset we developed covers 18 440 catchments across the Tibetan Plateau. The boundaries of the catchments are determined according to the HydroBASINS dataset, where the 12th-level catchments are considered (https://www.hydrosheds.org/products/ hydrobasins, last access: 19 March 2024). To successfully derive the width function for each catchment, the flow direction raster map must first be produced. The Tibetan Plateau has numerous endorheic basins (mainly in the inland region in Fig. 1), and the algorithms applied to determine flow directions of endorheic and exorheic basins can be slightly different (e.g., Prusevich et al., 2022).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southwest basin, Tibetan Plateau, China","kind":"region","role":"sub-region of the Tibetan Plateau where catchments were studied and WFIUH analyzed, noted for highest peak flows","river_name":null,"geocode_query":"Southwest basin, Tibetan Plateau, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"1658 Y. Guo et al.: A hydrogeomorphic dataset for the Tibetan Plateau Figure 4. Spatial patterns of hydrogeomorphic metrics across the Tibetan Plateau. As shown in Fig. 7, the relative magnitude of peak flow for most catchments ranges from 0.05 to 0.15, with the mean and median being 0.121 and 0.103, respectively. The smaller relative magnitude of the peak flow value represents a more uniform hydrograph distribution and a less obvious peak. The higher the Qp, the more susceptible the catchment is to flash floods. Catchments with a higher Qp are found in the northwestern or northern edge of the inland region (IR). Catchments in the central IR have relatively lower Qp due to their smaller slopes. Catchments in the upper Yellow River basin (YLRB) and Yangtze River basin (YZRB) have correspondingly smaller Qp than catchments in the lower YLRB and YZRB. Catchments in the southwest basin (SWB) have the highest Qp as compared to catchments in the other three regions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Nu River, Tibet, China","kind":"river","role":"catchments in this river area exhibit high peak flow values, indicating flood risk","river_name":"Nu River","geocode_query":"Nu River, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[98.854015,25.193671],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":4040023060,"main_basin_id":4040023060,"basin_region":"Central and South-East Asia","basin_area_km2":266292.4,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-5"},{"name":"Jinsha River, Tibet, China","kind":"river","role":"catchments in this river area exhibit high peak flow values, indicating flood risk","river_name":"Jinsha River","geocode_query":"Jinsha River, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[99.183526,28.455871],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jinsha River, Deqen, Diqing Tibetan Autonomous Prefecture, China, 674599","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-6"},{"name":"Lancang River, Tibet, China","kind":"river","role":"catchments in this river area exhibit high peak flow values, indicating flood risk","river_name":"Lancang River","geocode_query":"Lancang River, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[98.368304,29.520732],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lancang River, Markam County, Chamdo, China, 854526","basin_id":4041043580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":231768.3,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-7"},{"name":"Palong Zangbo area, Tibet, China","kind":"landscape","role":"area with catchments showing high peak flow, contributing to flood risk analysis","river_name":null,"geocode_query":"Palong Zangbo area, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[96.07059473606614,29.68357098669975],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Palong Zangbo, China","basin_id":4040838850,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":257277.4,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-8"},{"name":"Middle and lower reaches of the Yarlung Zangbo River, Tibet, China","kind":"river","role":"region with catchments exhibiting higher peak flow and flash flood risk","river_name":"Yarlung Zangbo River","geocode_query":"Middle and lower reaches of the Yarlung Zangbo River, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.111549,29.345982],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yarlung Zangbo River, Rinbung County, Shigatse, China, 851318","basin_id":4040838850,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":257277.4,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-9"},{"name":"Shiquan River area, Tibet, China","kind":"river","role":"area with catchments showing relatively higher peak flows in WFIUH analysis","river_name":"Shiquan River","geocode_query":"Shiquan River area, Tibet, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-001","page":8,"quote":"Particularly, in the Nu River, Jinsha River, Lancang River and the Palong Zangbo area, Qp can reach 0.2 and above, which explains well the high flood risks in those regions. In the middle and lower reaches of the Yarlung Zangbo River and the Shiquan River areas, the relative magnitude of peak flow is also higher than that of the whole Tibetan Plateau. The time to peak in most catchments mainly ranges from 2 to 15 h, with mean and median Tp around 10.3 and 9.0 h, respectively. The spatial distribution of Tp across the Tibetan Plateau is similar to that of Qp, suggesting that the shorter the Tp is, the higher the Qp is. Catchment concentration time, however, varies from 10 to 35 h, with mean and median Tc around 18.3 and 16.5 h, respectively. Catchments with longer Tc are found in the central TP and YLRB. Catchments in the northwest IR and in the middle and lower reaches of the Yarlung Zangbo River, Palong Zangbo River and Nu-JinshaLancang basins all have relatively short Tc (< 20 h).","verified_in_page":true},"resolution_status":"resolved","coordinates":[81.273936,32.069444],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040607420,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":72730.3,"hydrology_source":"hydrobasins","site_id":"doc-b5b31fe520d83b41cfb5ed63-site-10"}],"limitations":["DEM resolution (90 m) limits accuracy for small or complex catchments","Uncertainty in flow velocity estimation due to use of Manning's formula and land cover roughness classification","Model structure and input data uncertainties contribute to WFIUH simulation uncertainty","Spatial distribution of width function types lacks clear aggregation pattern, limiting spatial extrapolation"],"river_ids":["43643177","43645137","ne-208","ne-222","ne-301","ne-890"],"site_ids":["doc-b5b31fe520d83b41cfb5ed63-site-5","doc-b5b31fe520d83b41cfb5ed63-site-6","doc-b5b31fe520d83b41cfb5ed63-site-7","doc-b5b31fe520d83b41cfb5ed63-site-8","doc-b5b31fe520d83b41cfb5ed63-site-9","doc-b5b31fe520d83b41cfb5ed63-site-10"],"anchor":[96.07059473606614,29.68357098669975],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040023060,4040607420,4040783120,4040838850,4041043580],"main_basin_ids":[4040009880,4040017020,4040023060,4040025450,4040033640]},{"id":"doc-b9399155a9cea2dc0e226e7e","title":"Global and local perspectives on food security and food systems","display_title":"Global Perspectives on Food System Challenges","doi":"https://doi.org/10.1038/s43247-024-01398-4","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-04-30","authors":["Jessica Fanzo","Bart de Steenhuijsen Piters","Ariel Soto","Arlette Saint Ville","Mohammed Mainuddin","Jane Battersby"],"summary":"This viewpoint article discusses the challenges and opportunities for transforming food systems globally, with detailed regional insights from South America, the Caribbean Islands, Bangladesh, and South Africa. It emphasizes the need for equitable access to healthy diets, tackling power imbalances in food policy, building food system resilience, and tailored governmental support to meet local needs under climate, social, and economic stressors.","finding":"Food system resilience requires enhancing agency, buffering, connectivity, and diversity, while regional disparities in government support, infrastructure, and socio-economic conditions significantly affect food security outcomes.","finding_evidence":{"passage_id":"p001-005","page":1,"quote":"Bart de Steenhuijsen Piters: A practical framework to enhance food systems resilience Resilience as a concept has been known for over a hundred years in many scientific fields, but its application to food systems is relatively recent. Given the complex nature of food systems, resilience is still difficult to define, but it is linked to the ability of food systems to deliver desired outcomes in the face of social, economic, and environmental shocks and stressors. Recently, a practical ABCD framework has been developed for decisionmakers to identify and connect critical capacities of food resiliency2 . In short, the framework suggests (1) ensuring the Agency and the means and capacities of populations to mitigate risks and respond to shocks, (2) creating Buffers and resources to rely on in the face of shocks and stressors, (3) boosting Connectivity between market actors and segments, and (4) enhancing Diversity from production to consumption and across spatial scales (Fig. 1).","verified_in_page":true},"topics":["food security","food systems","nutrition","climate change adaptation","agricultural policy","governance","food supply chains","social equity"],"study_period":null,"locations":[{"name":"South America","kind":"region","role":"Regional focus for agriculture and food system challenges and potential for collaboration","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Effective and resilient balancing of objectives and alignment among key actors in the governance of a food system is perhaps the most important challenge of the coming decade. Ariel Soto-Caro: South American agriculture needs support and guidance to meet the food demand of its population South American countries like Argentina, Chile, Ecuador, and Peru have become critical players in the global agricultural market, exporting products such as blueberries, grapes, and broccoli to the United States and China. However, these countries often compete instead of fostering collaboration, striving to offer their crops at the most competitive prices. This unequal dynamic presents various challenges for South American agriculture that deserve attention. Landscape of soybean fields. Budimir Jevtic/ Stock.adobe.com. Decades ago, Uruguay, Argentina, and Chile recognized the potential of exporting agricultural products to economic powerhouses in the Northern Hemisphere.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Argentina","kind":"region","role":"Country discussed for agricultural export dynamics and government support challenges","river_name":null,"geocode_query":"Argentina, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Effective and resilient balancing of objectives and alignment among key actors in the governance of a food system is perhaps the most important challenge of the coming decade. Ariel Soto-Caro: South American agriculture needs support and guidance to meet the food demand of its population South American countries like Argentina, Chile, Ecuador, and Peru have become critical players in the global agricultural market, exporting products such as blueberries, grapes, and broccoli to the United States and China. However, these countries often compete instead of fostering collaboration, striving to offer their crops at the most competitive prices. This unequal dynamic presents various challenges for South American agriculture that deserve attention. Landscape of soybean fields. Budimir Jevtic/ Stock.adobe.com. Decades ago, Uruguay, Argentina, and Chile recognized the potential of exporting agricultural products to economic powerhouses in the Northern Hemisphere.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Chile","kind":"region","role":"Country providing examples of agricultural exports and food system challenges in South America","river_name":null,"geocode_query":"Chile, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Effective and resilient balancing of objectives and alignment among key actors in the governance of a food system is perhaps the most important challenge of the coming decade. Ariel Soto-Caro: South American agriculture needs support and guidance to meet the food demand of its population South American countries like Argentina, Chile, Ecuador, and Peru have become critical players in the global agricultural market, exporting products such as blueberries, grapes, and broccoli to the United States and China. However, these countries often compete instead of fostering collaboration, striving to offer their crops at the most competitive prices. This unequal dynamic presents various challenges for South American agriculture that deserve attention. Landscape of soybean fields. Budimir Jevtic/ Stock.adobe.com. Decades ago, Uruguay, Argentina, and Chile recognized the potential of exporting agricultural products to economic powerhouses in the Northern Hemisphere.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Ecuador","kind":"region","role":"Mentioned as a critical player in global agricultural exports from South America","river_name":null,"geocode_query":"Ecuador, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Effective and resilient balancing of objectives and alignment among key actors in the governance of a food system is perhaps the most important challenge of the coming decade. Ariel Soto-Caro: South American agriculture needs support and guidance to meet the food demand of its population South American countries like Argentina, Chile, Ecuador, and Peru have become critical players in the global agricultural market, exporting products such as blueberries, grapes, and broccoli to the United States and China. However, these countries often compete instead of fostering collaboration, striving to offer their crops at the most competitive prices. This unequal dynamic presents various challenges for South American agriculture that deserve attention. Landscape of soybean fields. Budimir Jevtic/ Stock.adobe.com. Decades ago, Uruguay, Argentina, and Chile recognized the potential of exporting agricultural products to economic powerhouses in the Northern Hemisphere.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Peru","kind":"region","role":"South American country noted for its role in agricultural exports","river_name":null,"geocode_query":"Peru, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"Effective and resilient balancing of objectives and alignment among key actors in the governance of a food system is perhaps the most important challenge of the coming decade. Ariel Soto-Caro: South American agriculture needs support and guidance to meet the food demand of its population South American countries like Argentina, Chile, Ecuador, and Peru have become critical players in the global agricultural market, exporting products such as blueberries, grapes, and broccoli to the United States and China. However, these countries often compete instead of fostering collaboration, striving to offer their crops at the most competitive prices. This unequal dynamic presents various challenges for South American agriculture that deserve attention. Landscape of soybean fields. Budimir Jevtic/ Stock.adobe.com. Decades ago, Uruguay, Argentina, and Chile recognized the potential of exporting agricultural products to economic powerhouses in the Northern Hemisphere.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Uruguay","kind":"region","role":"Country recognizing export potential of agricultural products decades ago","river_name":null,"geocode_query":"Uruguay, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"This realization encouraged small and large-scale farmers to improve the quality of their products and professionalize their exports to meet the demands of discerning markets, particularly those importing agricultural goods during off-seasons. However, supporting farmers is not a straightforward task. Climate change has forced farmers to adapt to increasingly frequent weather shocks. While a wealth of literature attempts to determine if farmers are responding effectively to these challenges, the question remains whetherthe authorities provide the necessary financial and technical assistance in countries where government support is critical. This disparity underscores a significant difference in how developed and developing countries address such challenges. Moreover, social issues such as insecurity, crime, and corruption further complicate agricultural endeavors, problems that are not typical concerns in developed countries’ agriculture.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-53.769928,-27.157647],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"HydroBASINS v1.c","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid","basin_id":6040016760,"main_basin_id":6040016760,"basin_region":"South America","basin_area_km2":266133.3,"hydrology_source":"hydrobasins","site_id":"doc-b9399155a9cea2dc0e226e7e-site-5"},{"name":"Caribbean Small Islands Developing States","kind":"region","role":"Focus area for food and nutritional insecurity with dominant food import reliance and climate vulnerability","river_name":null,"geocode_query":"Caribbean Small Islands Developing States","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Agricultural governance is often maintained at a status quo level, lacking proactive planning for the sustainable development of farmers, who ultimately provide food security. There is a pressing need for more proactive policies that consider local and foreign market needs, adequately address climate change impacts, and encourage the growth of a more modernized agricultural industry. Arlette Saint Ville: Imports and unhealthy choices increase food and nutritional insecurity in Caribbean islands Caribbean Small Islands Developing States are the world’s oldest colonies where food and nutrition insecurity is a persistent problem. Historically, food imports dominated domestic food systems, and they still do. Local fresh food producers are under-resourced and declining. Mostly rain-fed, small-scale farms of less than two hectares are highly vulnerable to climate variability and have low adaptive capacity to cope with rapid onset impacts from storms, droughts, and floods.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-78.601059,21.499114],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Caribbean","hydrology_source":"none","site_id":"doc-b9399155a9cea2dc0e226e7e-site-6","true_coordinates":[-78.6568942,21.4691137],"spread_km":6.7},{"name":"Trinidad and Tobago","kind":"region","role":"Example location for research on unhealthy food consumption patterns and policy needs in Caribbean Small Islands","river_name":null,"geocode_query":"Trinidad and Tobago, Caribbean","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Moreover, the production is also decreasing due to land degradation. Although the food trade increases food availability for many net-importing Caribbean Small Islands Developing Nations, it also reduces food system resilience as policymakers wield little influence over food prices, volume, and nutritional quality. As a food systems researcher in Trinidad and Tobago, I often observe secondary school students consuming breakfast that consists of energy-dense, fried street foods and ultraprocessed snacks such as chips or sweet biscuits. These are frequently combined with sugary beverages. Cheap, convenient, and unhealthy foods are increasingly common as breakfast options for children, leading to slower cognitive development and childhood obesity. Obese children are more likely to become obese adults with an increased probability of non-communicable diseases.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Bangladesh","kind":"region","role":"Case study country highlighting transformation from food insecurity to self-sufficiency in rice production and ongoing challenges","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Mohammed Mainuddin: Targeted actions can boost Bangladesh’s food security In the fifty years since its independence, Bangladesh, one of the most densely populated countries in the world, has transformed from a country constantly threatened by famine to a state that is food secure and self-sufficient in rice production, the main staple. The widespread adoption of dry season groundwater irrigation, selection of modern high-yielding rice crop varieties, and higher application of fertilizers have facilitated the transition. The increased crop harvest and storage capacity have also enhanced food resilience. However, conversion of agricultural land to urban and industrial areas, unsustainable use of groundwater for irrigation, climate change, and natural disasters, such as cyclones and floods, threaten food production and security and question self-sufficiency. Rice. Egdao/stock.adobe.com. There are many opportunities to help overcome these challenges.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"southern coastal region of Bangladesh","kind":"region","role":"Area with potential for dry season crop production facing salinity and water limitations","river_name":null,"geocode_query":"southern coastal region, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Many crops, including rice, can be grown in the southern coastal region during the dry season between December and April when water for irrigation is limited, high salinity poses a constant threat, and land is often left fallow or uncultivated. Crop production can be achieved by planting short-duration and highyielding rice in the monsoon season between July and November, which facilitates sowing sunflower, watermelon, maize, and zero-tillage potatoes. During the dry season, the farmers can increase the freshwater supply for crop irrigation by re-excavating canals and improving the management of polder infrastructure. There is also potential to use groundwater for sustainable irrigation through its conjunctive usewith surface water or through managed aquifer recharge in the northwest and central regions. Scaling up local knowledge to the regional level requires understanding the suitability of crop varieties in different localities.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"northwest and central regions of Bangladesh","kind":"region","role":"Regions discussed for sustainable groundwater irrigation potential","river_name":null,"geocode_query":"northwest and central regions, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Many crops, including rice, can be grown in the southern coastal region during the dry season between December and April when water for irrigation is limited, high salinity poses a constant threat, and land is often left fallow or uncultivated. Crop production can be achieved by planting short-duration and highyielding rice in the monsoon season between July and November, which facilitates sowing sunflower, watermelon, maize, and zero-tillage potatoes. During the dry season, the farmers can increase the freshwater supply for crop irrigation by re-excavating canals and improving the management of polder infrastructure. There is also potential to use groundwater for sustainable irrigation through its conjunctive usewith surface water or through managed aquifer recharge in the northwest and central regions. Scaling up local knowledge to the regional level requires understanding the suitability of crop varieties in different localities.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Dhaka","kind":"settlement","role":"Capital city of Bangladesh identified as a major market requiring improved transportation links","river_name":null,"geocode_query":"Dhaka, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"For example, shorter season varieties can help overcome seasonal food shortages among poor farmers before the northern region’s main harvest. Transportation options to connect farmers with major markets in the capital city of Dhaka need to be further developed. The volatility of rice prices must be addressed, as stable prices could improve farmers’ confidence and promote production. While rice production is sufficient in Bangladesh, the country still must import wheat, pulses, and oils. As per capita gross domestic product has increased, the average Bangladeshi diet has become more diverse, although it is still dominated by rice. A further increase in dietary diversity, including more vegetables, fruits, fish, and meat intake, will reduce the demand for rice and wheat and provide greater nutritional security. Despite the region’s many challenges, it is possible to sustain food security by implementing the appropriate measures outlined in the Delta Plan 21005 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[90.349659,23.804093],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dhaka, Bangladesh","basin_id":4040975160,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":85401.0,"hydrology_source":"hydrobasins","site_id":"doc-b9399155a9cea2dc0e226e7e-site-11","true_coordinates":[90.4152376,23.804093],"spread_km":6.7},{"name":"South Africa","kind":"region","role":"Country illustrating urban food insecurity issues, poverty, and multidimensional barriers to healthy diets","river_name":null,"geocode_query":"South Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"of food alone. It is essential to know how multidimensional poverty shapes food access in these settings. In low income urban neighborhoods in South Africa many households depend on state social grants, which fall well below the official food poverty line8 . In urban areas of South Africa, as in many places worldwide, healthier foods are more expensive than less healthy options9 . In the context of income poverty, the food basket is one of the few flexible costs: food budgets are worked out only after fixed rent costs, servicing debt, and school uniforms have been paid, and diet quality is sacrificed to meet essential expenditures10. Hard financial choices are made daily, and healthy diets are sacrificed to meet other needs.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"urban low-income neighborhoods in South Africa","kind":"landscape","role":"Study context for food access challenges influenced by poverty and infrastructure deficits","river_name":null,"geocode_query":"urban low-income neighborhoods, South Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"of food alone. It is essential to know how multidimensional poverty shapes food access in these settings. In low income urban neighborhoods in South Africa many households depend on state social grants, which fall well below the official food poverty line8 . In urban areas of South Africa, as in many places worldwide, healthier foods are more expensive than less healthy options9 . In the context of income poverty, the food basket is one of the few flexible costs: food budgets are worked out only after fixed rent costs, servicing debt, and school uniforms have been paid, and diet quality is sacrificed to meet essential expenditures10. Hard financial choices are made daily, and healthy diets are sacrificed to meet other needs.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Broad regional geographies limit precision of spatial context","Coordinates for many locations are not provided or convertible from text","Information derived mainly from cited policy and research reports rather than direct field data"],"river_ids":["ne-445"],"site_ids":["doc-b9399155a9cea2dc0e226e7e-site-5","doc-b9399155a9cea2dc0e226e7e-site-6","doc-b9399155a9cea2dc0e226e7e-site-11"],"anchor":[-78.601059,21.499114],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[4040975160,6040016760],"main_basin_ids":[4040025450,6040016760]},{"id":"doc-ce04a5c8203f11ab20d91d00","title":"Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation‐Based Recharge Through Downscaling","display_title":"Downscaling GRACE to Estimate Recharge Perth Basin","doi":"https://doi.org/10.1029/2024wr037360","theme":"Groundwater","color":"#a8b9ff","year":2024,"publication_date":"2024-12-01","authors":["Ikechukwu Kalu","Christopher E. Ndehedehe","Vagner G. Ferreira","Sreekanth Janardhanan","Matthew Currell","Russell S. Crosbie","Mark J. Kennard"],"summary":"This study developed a machine learning framework using random forest regression to downscale GRACE satellite groundwater storage anomaly data from 0.5° to 0.05°, enabling recharge estimation over the small Gnangara groundwater system nested within the larger Perth Basin in Western Australia. Recharge was estimated by integrating downscaled GRACE data with groundwater budgets accounting for baseflow, groundwater evaporation, and extraction. Validation with in-situ monitoring bores showed reasonable correlations, particularly for shallow aquifers. The study revealed that shallow (superficial) aquifers respond more directly to precipitation-based recharge compared to deeper (Leederville and Yarragadee) aquifers, and showed how climate variability and pumping influence groundwater storage and recharge patterns in the region.","finding":"Downscaled GRACE groundwater storage anomalies can reliably estimate precipitation-based groundwater recharge in small and complex aquifers such as the Gnangara system, with recharge response decreasing with aquifer depth.","finding_evidence":{"passage_id":"p022-004","page":22,"quote":"The Perth basin was used as a testing site for the downscaling procedure, and recharge was estimated by accounting for groundwater discharge through baseflow, groundwater evaporation and extraction. For both basins, a significant relationship between the GRACE, WGHM, and in‐situ recharge estimates was noticed throughout the study period. Our recharge estimates show that 32% and 10% of precipitation over the Perth basin and Gnangara system recharges their respective groundwater systems. While the precipitation‐recharge response for the Perth Basin falls within the average, based on past projections (Meredith et al., 2012), the Gnangara system is observed to lose a large chunk of its precipitation through natural means thereby affecting its recharge intensity. For example, much of the precipitation water over the Gnangara system flows out horizontally through the aquifer to rivers and the ocean.","verified_in_page":true},"topics":["groundwater recharge","remote sensing","machine learning","aquifer depth response","groundwater storage anomalies","climate variability","water budget estimation","satellite data downscaling"],"study_period":"2003–2016","locations":[{"name":"Perth Basin","kind":"basin","role":"Testing site for ground water recharge estimation and machine learning downscaling of GRACE data","river_name":null,"geocode_query":"Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"The major aim of this study is the estimation of groundwater recharge across different aquifer depths using downscaled GRACE ΔGWS. Specific objectives include (a) apply machine learning downscaling of GRACE‐ GWS for recharge estimation, (b) verify the reliability of an integrated groundwater recharge model (groundwater budget equation + numerical models) based on GRACE and the WGHM, (c) explore the different recharge rates and characteristics of aquifers with varying water table depths, and (d) Validating our downscaled groundwater recharge model using in‐situ monitoring bores. 2. Study Regions (Testing and Calibration) In this study, two nested groundwater systems in Western Australia were studied, with different spatial scales: the Perth Basin (∼100,000 km2 ) and the Gnangara system (∼2,200 km2 ), which is an important sub‐region within the broader Perth sedimentary basin system (Figure 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.800439,-31.921399],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-0","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Gnangara groundwater system","kind":"basin","role":"Calibration site for downscaled recharge estimation; important groundwater source supplying 35-50% of Perth drinking water","river_name":null,"geocode_query":"Gnangara groundwater system, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation‐based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.865707,-31.715704],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70413000,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-1","true_coordinates":[115.8657072,-31.7757036],"spread_km":6.7},{"name":"Perth","kind":"settlement","role":"Major city reliant on the Gnangara groundwater system for drinking water","river_name":null,"geocode_query":"Perth, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"The Perth Basin is highly developed, with ∼850 GL of groundwater used every year for industry, agriculture, mining, and household watersupply (Priestley et al., 2020). Within this, the Gnangara system is the largest source of groundwater in southwest Australia, supplying an estimated 35%–50% of drinking water to the city of Perth (population: ∼2 million). The spatial scale of the Perth Basin allows for GRACE‐based groundwater storage assessment; however, this is not the case with the Gnangara system whose spatial extent is too small for a direct assessment. We therefore tested our downscaling procedure on the Perth Basin to improve the signals of the GRACE‐ΔGWS and minimize uncertainty in key water budget terms, allowing more confident predictions of unknown variables–for example, recharge. With the improved resolution, recharge over the Gnangara system was assessed. 2.1.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.792039,-31.961818],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Perth WA, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-2","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Gnangara and Jandabup Lakes","kind":"wetland","role":"Sites with groundwater evaporation losses, related to shallow water table depths","river_name":null,"geocode_query":"Gnangara Lake, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Water, Western Australia, 2014). The lithology of the deposits varies across the basin with sand and limestone deposits spread through the central and southern portion of the Perth basin and at the coast. This aquifer's major source of recharge is precipitation‐based infiltration, with percolation rateslargely dependent on water table depth (Meredith et al., 2012), amount of precipitation, topography, and land cover. Discharge is predominantly to lakes and wetlands through baseflow (Appleyard & Cook, 2009), drainage systems through extraction, and groundwater evaporation. Lossesfrom groundwater evaporation are mostly significant from wetlands with shallow water tables for example, Gnangara and Jandabup Lakes (Appleyard & Cook, 2009). The Leederville aquifer is a generally confined aquifer across the Central and Southern part of the Perth basin. It is composed of interbedded sandstones, shale, and siltstones deposited across the Early Cretaceous epoch.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.8684422,-31.7878568],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413000,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Gnangara, Gnangara WA 6077, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-3"},{"name":"Moora and Gingin","kind":"settlement","role":"Location of groundwater recharge into Leederville aquifer via direct percolation in deep valleys","river_name":null,"geocode_query":"Moora and Gingin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"thickness ranges from 50 to 300 m in the Southern region and 600 m in the Central region. The major source of recharge is precipitation‐based infiltration through the overlying Gnangara system, where the confining beds are absent (Meredith et al., 2012). Additionally, there is a direct percolation of water into the Leederville aquifer in the deep valleys between Moora and Gingin (Priestley et al., 2020). In this aquifer, groundwater flows from the north‐east to the south‐west in the Central Perth Basin, and from areas of preferential recharge with groundwater discharge thought to occur to the ocean through the Superficial aquifer (Figure S1 in Supporting Information S1). Discharge here is predominantly through groundwater extraction. The Yarragadee aquifer is a deep, multi‐layered, generally confined aquifer consisting of sandstones with minor shale horizons, which were deposited during the late Jurassic.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.9043668,-31.3475158],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412863,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gingin WA 6503, Australia","basin_id":5040056470,"main_basin_id":5040056470,"basin_region":"Australasia","basin_area_km2":51776.1,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-4"},{"name":"Northern Perth Basin","kind":"region","role":"Area where Yarragadee aquifer outcrops, a major hotspot for precipitation-based recharge","river_name":null,"geocode_query":"Northern Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"The Leederville formation overlies the Yarragadee aquifer in most areas across the Perth basin extent, and the Yarragadee aquifer is confined by the overlying South Perth shale or the shale beds of the lower part of the Leederville formation (Priestley et al., 2020). The Yarragadee formation is the largest aquifer in the Perth Basin, with an estimated thickness up to ∼2,000 m. The unconfined portions of the aquifer (i.e., where it outcrops–in the Northern Perth Basin and small areas of sub‐crop below the Gnangara system) are major hotspots for precipitation‐based recharge. Downward leakage and percolation from the overlying superficial and Leederville formations also contribute to its recharge more widely over the basin Figure 2.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.852492,-31.867384],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Perth WA 6006, Australia","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-5","true_coordinates":[115.852492,-31.9273844],"spread_km":6.7},{"name":"Southwest Australia","kind":"region","role":"Regional context of aquifer system studied, affected by declining precipitation and recharge under climate change","river_name":null,"geocode_query":"Southwestern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation‐Based Recharge Through Downscaling Ikechukwu Kalu1,2 , Christopher E. Ndehedehe1,2, Vagner G. Ferreira3 , Sreekanth Janardhanan4 , Matthew Currell2,5, Russell S. Crosbie6 , and Mark J. Kennard1,2 1 School of Environment & Science, Griffith University, Nathan, QLD, Australia, 2 Australian Rivers Institute, Griffith University, Nathan, QLD, Australia, 3 School of Earth Sciences and Engineering, Hohai University, Nanjing, China, 4 CSIRO Environment, Dutton Park, QLD, Australia, 5 School of Engineering and Built Environment, Griffith University, Nathan, QLD, Australia, 6 CSIRO Environment, Adelaide, SA, Australia Abstract The Gnangara groundwater system is a highly productive water resource in southwestern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.970784,-34.042332],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-ce04a5c8203f11ab20d91d00-site-6","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7}],"limitations":["GRACE satellite data has coarse native resolution limiting small basin analysis without downscaling.","Estimations of groundwater storage changes from deep bores may reflect pressure loading rather than actual storage addition.","Uncertainties exist in specific yield and storage coefficients, especially for deep and heterogeneous aquifers.","Model estimates (WGHM, baseflow) have inherent uncertainties and simplify complex hydrogeologic conditions.","Recharge estimation complexities increase for deep aquifers due to lagged responses and indirect recharge processes.","Groundwater evaporation estimation is difficult and subject to assumptions from ET products.","Validation is limited by spatial coverage of in-situ bore measurements and resolution mismatch.","Downscaling introduces high frequency noise requiring de-noising procedures."],"river_ids":[],"site_ids":["doc-ce04a5c8203f11ab20d91d00-site-0","doc-ce04a5c8203f11ab20d91d00-site-1","doc-ce04a5c8203f11ab20d91d00-site-2","doc-ce04a5c8203f11ab20d91d00-site-3","doc-ce04a5c8203f11ab20d91d00-site-4","doc-ce04a5c8203f11ab20d91d00-site-5","doc-ce04a5c8203f11ab20d91d00-site-6"],"anchor":[115.792039,-31.961818],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040055960,5040056460,5040056470],"main_basin_ids":[5040055960,5040056460,5040056470]},{"id":"doc-ddab1ba68dcc6e3a8a51ddb4","title":"Habitat use by nomadic ibis and spoonbills post-dispersal from breeding sites","display_title":"Post-Breeding Habitat Use of Nomadic Waterbirds","doi":"https://doi.org/10.1007/s10980-024-01982-1","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-11-05","authors":["Heather M. McGinness","Luke R. Lloyd‐Jones","Freya Robinson","Art Langston","Louis G. O’Neill","Shoshana Rapley","Micha V. Jackson","Jessica Hodgson","Melissa Piper","Micah Davies","John M. Martin","Richard T. Kingsford","Kate Brandis","Veronica Doerr","Ralph Mac Nally"],"summary":"This study analyzed a 7-year GPS satellite telemetry dataset from 141 individual straw-necked ibis and royal spoonbills originating from breeding sites in the Murray-Darling Basin, Australia. It quantified their habitat use and selection post-dispersal from breeding events, revealing distinct species-level differences in habitat preference and spatial-temporal use. The findings inform conservation management, especially environmental water provisioning, emphasizing the differing habitat needs and vulnerabilities of these nomadic waterbird species.","finding":"Royal spoonbills use fewer and more restricted aquatic habitats, showing strong preference for reservoirs, marshes, and permanent wetlands, making them more vulnerable to habitat change from water regime alterations compared to straw-necked ibis, which utilize a broader range of aquatic and terrestrial habitats, including modified pastures and woodlands.","finding_evidence":{"passage_id":"p011-003","page":11,"quote":"Historically, this has been difficult to quantify for nomadic species, particularly those in remote areas and outside of known breeding locations when individuals disperse hundreds to thousands of kilometres. Advances in satellite tracking have enabled data collection over vast distances and in remote areas. Consequently, we have been able to characterise non-breeding habitat preferences for two such nomadic waterbirds. This knowledge has potential to inform identification and prioritisation of potential sites for environmental watering and other management, to support a) juvenile survival to breeding age (recruitment), b) adult recovery from breeding and c) adult survival between breeding events. While ibis and spoonbill species in Australia share breeding sites and nest in mixed aggregations, their non-breeding habitats differ.","verified_in_page":true},"topics":["satellite telemetry","habitat selection","waterbird ecology","environmental water management","species conservation","wetland ecosystems","nomadic species","landscape ecology"],"study_period":"2016-2024","locations":[{"name":"Murray-Darling Basin, Australia","kind":"basin","role":"study region encompassing breeding sites and post-breeding dispersal areas of tracked waterbirds","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Here we use a GPS satellite telemetry dataset of 141 individuals spanning seven years to quantify non-breeding habitat requirements for two wading waterbird species of the Threskiornithidae family: straw-necked ibis Threskiornis spinicollis and royal spoonbill Platalea regia. We hypothesised that topography, land-use, vegetation, and water indices would predict habitat selection, and that habitat selection and habitat availability would differ between strawnecked ibis and royal spoonbill. Methods Transmitter deployment We tracked 141 birds (straw-necked ibis=89, royal spoonbill=52) using satellite telemetry with tracking durations ranging from 30 – 2,000 days per bird and spanning October 2016 – January 2024. Individuals were captured and fitted with transmitters in eight wetlands of the Murray-Darling Basin (MDB), which spans approximately 14% of Australia’s landmass and is subject to intensive water and wetland management (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.46089,-31.127296],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-ddab1ba68dcc6e3a8a51ddb4-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Eight wetlands within the Murray-Darling Basin, Australia","kind":"wetland","role":"sites where straw-necked ibis and royal spoonbill were captured and fitted with GPS transmitters","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Here we use a GPS satellite telemetry dataset of 141 individuals spanning seven years to quantify non-breeding habitat requirements for two wading waterbird species of the Threskiornithidae family: straw-necked ibis Threskiornis spinicollis and royal spoonbill Platalea regia. We hypothesised that topography, land-use, vegetation, and water indices would predict habitat selection, and that habitat selection and habitat availability would differ between strawnecked ibis and royal spoonbill. Methods Transmitter deployment We tracked 141 birds (straw-necked ibis=89, royal spoonbill=52) using satellite telemetry with tracking durations ranging from 30 – 2,000 days per bird and spanning October 2016 – January 2024. Individuals were captured and fitted with transmitters in eight wetlands of the Murray-Darling Basin (MDB), which spans approximately 14% of Australia’s landmass and is subject to intensive water and wetland management (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.809347,-35.455223],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ddab1ba68dcc6e3a8a51ddb4-site-1","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Lake Cowal Conservation Centre, New South Wales, Australia","kind":"facility","role":"co-investment and research support partner in study","river_name":null,"geocode_query":"Lake Cowal Conservation Centre, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder’s Office (CEWH/CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH Monitoring, Evaluation and Research project (2019–2024) and the CEWO Environmental Watering Knowledge and Research project (2015–2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre, and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors, and the Goulburn-Broken Catchment Management Authority (Keith Ward). Data availability The repository contains the necessary files required to run the scripts and recreate the analyses.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.4513996,-33.6837943],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667659,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"419 Uncle Bill's Road, Lake Cowal NSW 2671, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ddab1ba68dcc6e3a8a51ddb4-site-2"},{"name":"Royal Botanic Gardens, Sydney, New South Wales, Australia","kind":"facility","role":"research support and co-investment partner","river_name":null,"geocode_query":"Royal Botanic Gardens, Sydney, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder’s Office (CEWH/CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH Monitoring, Evaluation and Research project (2019–2024) and the CEWO Environmental Watering Knowledge and Research project (2015–2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre, and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors, and the Goulburn-Broken Catchment Management Authority (Keith Ward). Data availability The repository contains the necessary files required to run the scripts and recreate the analyses.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Goulburn-Broken Catchment Management Authority, Victoria, Australia","kind":"region","role":"support and co-investment partner of the research","river_name":null,"geocode_query":"Goulburn-Broken Catchment Management Authority, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"The original research that formed the basis of this article was co-funded by the Commonwealth Environmental Water Holder’s Office (CEWH/CEWO) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) through the CEWH Monitoring, Evaluation and Research project (2019–2024) and the CEWO Environmental Watering Knowledge and Research project (2015–2018), administered through the Commonwealth Environmental Water Office within the Department of Climate Change, Energy, the Environment and Water and its precursors. The research also benefited from co-investment by the Lake Cowal Conservation Centre, and from in-kind support from the Royal Botanic Garden Sydney (John Martin), NSW Department of Planning and Environment and its precursors, and the Goulburn-Broken Catchment Management Authority (Keith Ward). Data availability The repository contains the necessary files required to run the scripts and recreate the analyses.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.3973314,-36.3825998],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43676973,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"168 Welsford St, Shepparton VIC 3630, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-ddab1ba68dcc6e3a8a51ddb4-site-4"}],"limitations":["Some environmental covariate datasets (e.g. Water Observations from Space, Multi Index Method surface water dataset) have known errors or coarse resolution affecting habitat availability accuracy, especially under emergent vegetation or for water depth estimates (~±500 mm error).","GPS 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-e4d970df46b7e3761189b6bb","title":"pH and thiosulfate dependent microbial sulfur oxidation strategies across diverse environments","display_title":"pH and Thiosulfate Control Microbial Sulfur Oxidation","doi":"https://doi.org/10.3389/fmicb.2024.1426584","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-07-19","authors":["Lauren E. Twible","Kelly Whaley-Martin","Lin-Xing Chen","Tara Colenbrander Nelson","James L.S. Arrey","Chad V. Jarolimek","Josh J. King","Lisa Ramilo","Helga Sonnenberg","Jillian F. Banfield","Simon C. Apte","Lesley A. Warren"],"summary":"This study examined microbial sulfur oxidation in waters of four base metal mine tailings impoundments (TIs) across Canada over four years (2016-2019). Using 16S rRNA and metagenomic analyses coupled with sulfur geochemistry and physicochemical measurements, the research identified nine major sulfur oxidizing bacterial (SOB) genera. SOB metabolic pathways (complete and incomplete sox, rdsr, and S4I) were linked to environmental pH and thiosulfate concentrations. A distinct pH-dependent partitioning was found where complete sox (csox) dominant bacteria predominated at lower pHs (~5 to 6.5), driving acid generation, while non-csox dominant bacteria prevailed at circumneutral pHs (~6.5 to 8.5) with higher thiosulfate and limited acidity. Comparative analysis with global mining and natural environments demonstrated a specialized mining microbiome with elevated csox SOB abundances sustained by continual sulfur inputs. These findings indicate that thiosulfate availability and pH critically influence sulfur oxidation strategies under oxic conditions in mining environments, offering opportunities for biological management via pH and thiosulfate control.","finding":"A distinct pH-dependence governs the prevalence of sulfur oxidizing bacterial pathways in mine tailings impoundments: complete sox (csox) dominant SOB drive acidity generation under lower pH (~5–6.5) conditions with thiosulfate consumption, while non-csox dominant SOB dominate at circumneutral pHs (~6.5–8.5), resulting in higher thiosulfate concentrations and limited acidity generation.","finding_evidence":{"passage_id":"p002-002","page":2,"quote":"Here, we identified TI water column SOB community composition, metagenomics derived metabolic repertoires, physicochemistry, and aqueous sulfur concentration and speciation in four Canadian base metal mine, circumneutral-alkaline TIs over four years (2016 – 2019). Identification and examination of genomes from nine SOB genera occurring in these TI waters revealed two pH partitioned, metabolically distinct groups, which differentially influenced acid generation and sulfur speciation. Complete sox (csox) dominant SOB (e.g., Halothiobacillus spp., Thiomonas spp.) drove acidity generation and S2O3 2- consumption via the csox pathway at lower pH (pH ~5 to ~6.5). At circumneutral pH conditions (pH ~6.5 to ~8.5), the presence of non-csox dominant SOB (hosting the incomplete sox, rdsr, and/or other S oxidation reactions; e.g. Thiobacillus spp., Sulfuriferula spp.) were associated with higher [S2O3 2-] and limited acidity generation.","verified_in_page":true},"topics":["microbial ecology","biogeochemistry","sulfur cycle","acid mine drainage","metagenomics","microbial metabolism","geochemistry","environmental microbiology"],"study_period":"2016-2019","locations":[{"name":"Mine 1 Tailings Impoundment, Flin Flon, Manitoba, Canada","kind":"facility","role":"primary study site providing tailings impoundment water samples for microbial, geochemical, and physicochemical analyses","river_name":null,"geocode_query":"Mine 1 Tailings Impoundment, Flin Flon, Manitoba, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Twible et al. 10.3389/fmicb.2024.1426584 Frontiers in Microbiology 03 frontiersin.org geochemistry, physicochemistry, and genus level community structure and function data from the four base metal mine TIs were examined. To determine if the findings of this study were site specific or reflective of broader environmental trends, comparisons were made to published studies on other mines and industrial environments, as well as natural environments. 2 Materials and methods 2.1 Site descriptions Four base metal mine TI waters were sampled from 2016 to 2019 resulting in a total of 42 water samples (see Supplementary Figure S1). These four mines are located across central and eastern Canada and consist of: Mine 1 in Flin Flon, Manitoba (Cu, Zn, Au, Ag), Mine 2 in Sudbury, Ontario (Ni, Cu, Co, Pt, Pd), Mine 3 in Snow Lake, Manitoba (Cu, Zn, Se, Te, Ag), and Mine 4 in Baie Verte, Newfoundland (Cu, Au).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Mine 2 Tailings Impoundment, Sudbury, Ontario, Canada","kind":"facility","role":"primary study site; largest and deepest tailings impoundment sampled for microbial and geochemical studies","river_name":null,"geocode_query":"Mine 2 Tailings Impoundment, Sudbury, Ontario, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Twible et al. 10.3389/fmicb.2024.1426584 Frontiers in Microbiology 03 frontiersin.org geochemistry, physicochemistry, and genus level community structure and function data from the four base metal mine TIs were examined. To determine if the findings of this study were site specific or reflective of broader environmental trends, comparisons were made to published studies on other mines and industrial environments, as well as natural environments. 2 Materials and methods 2.1 Site descriptions Four base metal mine TI waters were sampled from 2016 to 2019 resulting in a total of 42 water samples (see Supplementary Figure S1). These four mines are located across central and eastern Canada and consist of: Mine 1 in Flin Flon, Manitoba (Cu, Zn, Au, Ag), Mine 2 in Sudbury, Ontario (Ni, Cu, Co, Pt, Pd), Mine 3 in Snow Lake, Manitoba (Cu, Zn, Se, Te, Ag), and Mine 4 in Baie Verte, Newfoundland (Cu, Au).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Mine 3 Tailings Impoundment, Snow Lake, Manitoba, Canada","kind":"facility","role":"primary study site; tailings impoundment water sampled for microbial and geochemical analyses","river_name":null,"geocode_query":"Mine 3 Tailings Impoundment, Snow Lake, Manitoba, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Twible et al. 10.3389/fmicb.2024.1426584 Frontiers in Microbiology 03 frontiersin.org geochemistry, physicochemistry, and genus level community structure and function data from the four base metal mine TIs were examined. To determine if the findings of this study were site specific or reflective of broader environmental trends, comparisons were made to published studies on other mines and industrial environments, as well as natural environments. 2 Materials and methods 2.1 Site descriptions Four base metal mine TI waters were sampled from 2016 to 2019 resulting in a total of 42 water samples (see Supplementary Figure S1). These four mines are located across central and eastern Canada and consist of: Mine 1 in Flin Flon, Manitoba (Cu, Zn, Au, Ag), Mine 2 in Sudbury, Ontario (Ni, Cu, Co, Pt, Pd), Mine 3 in Snow Lake, Manitoba (Cu, Zn, Se, Te, Ag), and Mine 4 in Baie Verte, Newfoundland (Cu, Au).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Mine 4 Tailings Impoundment, Baie Verte, Newfoundland, Canada","kind":"facility","role":"primary study site; youngest and smallest tailings impoundment sampled for analyses of sulfur chemistry and microbial communities","river_name":null,"geocode_query":"Mine 4 Tailings Impoundment, Baie Verte, Newfoundland, Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Twible et al. 10.3389/fmicb.2024.1426584 Frontiers in Microbiology 03 frontiersin.org geochemistry, physicochemistry, and genus level community structure and function data from the four base metal mine TIs were examined. To determine if the findings of this study were site specific or reflective of broader environmental trends, comparisons were made to published studies on other mines and industrial environments, as well as natural environments. 2 Materials and methods 2.1 Site descriptions Four base metal mine TI waters were sampled from 2016 to 2019 resulting in a total of 42 water samples (see Supplementary Figure S1). These four mines are located across central and eastern Canada and consist of: Mine 1 in Flin Flon, Manitoba (Cu, Zn, Au, Ag), Mine 2 in Sudbury, Ontario (Ni, Cu, Co, Pt, Pd), Mine 3 in Snow Lake, Manitoba (Cu, Zn, Se, Te, Ag), and Mine 4 in Baie Verte, Newfoundland (Cu, Au).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Canada","kind":"region","role":"national region encompassing the four mine tailings impoundment study sites","river_name":null,"geocode_query":"Canada","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Twible et al. 10.3389/fmicb.2024.1426584 Frontiers in Microbiology 03 frontiersin.org geochemistry, physicochemistry, and genus level community structure and function data from the four base metal mine TIs were examined. To determine if the findings of this study were site specific or reflective of broader environmental trends, comparisons were made to published studies on other mines and industrial environments, as well as natural environments. 2 Materials and methods 2.1 Site descriptions Four base metal mine TI waters were sampled from 2016 to 2019 resulting in a total of 42 water samples (see Supplementary Figure S1). These four mines are located across central and eastern Canada and consist of: Mine 1 in Flin Flon, Manitoba (Cu, Zn, Au, Ag), Mine 2 in Sudbury, Ontario (Ni, Cu, Co, Pt, Pd), Mine 3 in Snow Lake, Manitoba (Cu, Zn, Se, Te, Ag), and Mine 4 in Baie Verte, Newfoundland (Cu, Au).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates for mine sites and tailings impoundments were not explicitly provided; thus latitude and longitude are null.","Some broader comparative literature data incorporate various sample types (solid, water, biofilm) which may affect direct comparability.","Geographic specificity limited to Canadian mine tailings impoundments and literature synthesis; finer spatial heterogeneity within sites not resolved."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[],"main_basin_ids":[]},{"id":"doc-ec340eeec7395eba2df0edd5","title":"A generalised ecohydrological landscape classification for assessing ecosystem risk in Australia due to an altering water regime","display_title":"Ecohydrological Landscape Classification in Eastern Australia","doi":"https://doi.org/10.5194/hess-28-1957-2024","theme":"Land & ecosystems","color":"#c8df98","year":2024,"publication_date":"2024-05-06","authors":["Alexander Herr","Linda E. Merrin","Patrick J. Mitchell","Anthony P. O'Grady","Kate L. Holland","Richard E. Mount","David A. Post","Chris R. Pavey","Ashley D. Sparrow"],"summary":"This study developed a generalised ecohydrological landscape classification to assess ecosystem risks from altering water regimes in three distinct regions of eastern Australia (Namoi, Maranoa–Balonne–Condamine, and Galilee). The classification integrates existing spatial datasets with hydrological connectivity features, prioritizing hydrological attributes to causally link landscape components to water pathways affected by coal and coal seam gas resource developments. The approach enables spatially explicit risk assessments by expert elicitation and quantitative modeling, demonstrating applicability across different landscapes with varied data availability.","finding":"The ecohydrological landscape classification enables spatially explicit linkage of hydrological changes to ecosystems, providing a consistent framework for assessing ecological risks from coal resource developments across diverse Australian regions.","finding_evidence":{"passage_id":"p016-001","page":16,"quote":"Using our landscape classification as a crucial input, the modelling delineated impacted areas within each region, based on a zone of potential hydrological change. This is the area in the landscape where hydrological modelling identified an expected change to surface water and groundwater from future resource extraction. Risk levels across a landscape group are a result of aggregating individual risks associated with each ecological variable and categorising the risks into three levels based on their percentile spreads (for details, see Herr et al., 2018b). For the Namoi region, for example, dryland remnant vegetation, human-modified ecosystems, nonfloodplain and upland riverine ecosystems, and rainforests will not experience impacts, whereas floodplain and lowland ecosystems and streams or floodplain and lowland ecosystems will potentially experience impacts (Herr et al., 2018a).","verified_in_page":true},"topics":["ecohydrology","landscape classification","ecosystem risk assessment","hydrological connectivity","groundwater-dependent ecosystems","surface water hydrology","coal resource impacts","regional scale assessment"],"study_period":null,"locations":[{"name":"Namoi region","kind":"region","role":"study region for ecohydrological landscape classification and risk assessment","river_name":null,"geocode_query":"Namoi region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1960 A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk Figure 1. Visualisation of the workflow for developing our ecological landscape classification (non-patterned area – identifies the focus of this paper) and its application in an ecological risk assessment, which we briefly summarise to show the classification’s applicability (inside patterned rectangle – described in Sect. 4). The outcome of combining the landscape classification with hydrological modelling and risk modelling is the map of risk (identified by the lightly patterned red circle). Hydrological features are descriptors that have a hydrology component in their character. Ecohydrological elements are unique, identifiable building blocks of the landscape that contain similar (hydrological) features. 2.1.1 Namoi region The Namoi region covers approximately 35 700 km2 in eastern Australia, is located within New South Wales and forms one catchment of the Murray–Darling Basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.602886,-30.454406],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Namoi River, New South Wales, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-0","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7},{"name":"Namoi River catchment","kind":"basin","role":"main river catchment within Namoi region with key aquatic ecosystems","river_name":"Namoi River","geocode_query":"Namoi River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"The long-term mean annual rainfall varies from 600 to 1100 mm and potential evapotranspiration (PET) varies from 1200 to 1400 mm. It contains six operational coal mines (one underground mine and five open-cut mines), nine potential future coal mines and one potential CSG development. The nine potential future coal mines consist of two underground (one combined open-cut and underground) and seven open-cut mines. The region covers most of the Namoi River catchment, with the Namoi River being the main river within the region. It also contains two major aquifer systems: the Namoi alluvial aquifer and the Pilliga sandstone aquifer (Fig. 2). The main land use within the region is agriculture, both dryland and irrigated cropping, and livestock grazing, as well as forestry.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.542454,-30.454406],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-1","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7},{"name":"Pilliga sandstone aquifer","kind":"aquifer","role":"major aquifer system in Namoi region","river_name":null,"geocode_query":"Pilliga sandstone aquifer, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"The long-term mean annual rainfall varies from 600 to 1100 mm and potential evapotranspiration (PET) varies from 1200 to 1400 mm. It contains six operational coal mines (one underground mine and five open-cut mines), nine potential future coal mines and one potential CSG development. The nine potential future coal mines consist of two underground (one combined open-cut and underground) and seven open-cut mines. The region covers most of the Namoi River catchment, with the Namoi River being the main river within the region. It also contains two major aquifer systems: the Namoi alluvial aquifer and the Pilliga sandstone aquifer (Fig. 2). The main land use within the region is agriculture, both dryland and irrigated cropping, and livestock grazing, as well as forestry.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.4157758,-31.0161824],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43672827,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sandstone Caves Walking Track, Dandry NSW 2357, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-2"},{"name":"Liverpool and Kaputar ranges","kind":"landscape","role":"diverse ecosystems in Namoi region encompassing mountainous landscapes","river_name":null,"geocode_query":"Liverpool and Kaputar ranges, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"There is also a diverse range of landscapes and ecosystems within the region, including the Liverpool and Kaputar ranges, the Liverpool Plains floodplains, and the Darling Riverine plains in the west of the region; open box woodlands on the slopes; and temperate and subalpine forests in the east of the region. A range of aquatic habitats occur downstream of Narrabri, with large areas of anabranches and billabong wetlands. The Pilliga Nature Reserve, in the upper catchment of Bohena Creek, and the Pilliga State Forest form the largest remaining area of dry sclerophyll forest west of the Great Diving Range in New South Wales (Welsh et al., 2014). 2.1.2 Galilee region The Galilee region covers approximately 612 300 km2 and is located mostly within Queensland, Australia. PET far exceeds rainfall, particularly in the summer months. Yearly rainfall ranges from 300 to 700 mm and PET from 2200 to 2900 mm. There are 17 proposed coal resource developments in the Galilee region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.1635868,-30.2769635],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674466,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kaputar NSW 2390, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-3"},{"name":"Liverpool Plains floodplains","kind":"landscape","role":"floodplain ecosystem within Namoi region relevant to water dependency analysis","river_name":null,"geocode_query":"Liverpool Plains floodplains, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"There is also a diverse range of landscapes and ecosystems within the region, including the Liverpool and Kaputar ranges, the Liverpool Plains floodplains, and the Darling Riverine plains in the west of the region; open box woodlands on the slopes; and temperate and subalpine forests in the east of the region. A range of aquatic habitats occur downstream of Narrabri, with large areas of anabranches and billabong wetlands. The Pilliga Nature Reserve, in the upper catchment of Bohena Creek, and the Pilliga State Forest form the largest remaining area of dry sclerophyll forest west of the Great Diving Range in New South Wales (Welsh et al., 2014). 2.1.2 Galilee region The Galilee region covers approximately 612 300 km2 and is located mostly within Queensland, Australia. PET far exceeds rainfall, particularly in the summer months. Yearly rainfall ranges from 300 to 700 mm and PET from 2200 to 2900 mm. There are 17 proposed coal resource developments in the Galilee region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.4892292,-31.6431494],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674817,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Liverpool Plains, NSW, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-4"},{"name":"Darling Riverine Plains","kind":"landscape","role":"western landscape within Namoi region with distinctive ecological characteristics","river_name":null,"geocode_query":"Darling Riverine Plains, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"There is also a diverse range of landscapes and ecosystems within the region, including the Liverpool and Kaputar ranges, the Liverpool Plains floodplains, and the Darling Riverine plains in the west of the region; open box woodlands on the slopes; and temperate and subalpine forests in the east of the region. A range of aquatic habitats occur downstream of Narrabri, with large areas of anabranches and billabong wetlands. The Pilliga Nature Reserve, in the upper catchment of Bohena Creek, and the Pilliga State Forest form the largest remaining area of dry sclerophyll forest west of the Great Diving Range in New South Wales (Welsh et al., 2014). 2.1.2 Galilee region The Galilee region covers approximately 612 300 km2 and is located mostly within Queensland, Australia. PET far exceeds rainfall, particularly in the summer months. Yearly rainfall ranges from 300 to 700 mm and PET from 2200 to 2900 mm. There are 17 proposed coal resource developments in the Galilee region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.083787,-32.060557],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662686,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darling River, New South Wales, Australia","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-5","true_coordinates":[144.08378715163,-32.00055714361058],"spread_km":6.7},{"name":"Pilliga Nature Reserve","kind":"landscape","role":"large protected dry sclerophyll forest area in upper catchment of Bohena Creek in Namoi","river_name":null,"geocode_query":"Pilliga Nature Reserve, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"There is also a diverse range of landscapes and ecosystems within the region, including the Liverpool and Kaputar ranges, the Liverpool Plains floodplains, and the Darling Riverine plains in the west of the region; open box woodlands on the slopes; and temperate and subalpine forests in the east of the region. A range of aquatic habitats occur downstream of Narrabri, with large areas of anabranches and billabong wetlands. The Pilliga Nature Reserve, in the upper catchment of Bohena Creek, and the Pilliga State Forest form the largest remaining area of dry sclerophyll forest west of the Great Diving Range in New South Wales (Welsh et al., 2014). 2.1.2 Galilee region The Galilee region covers approximately 612 300 km2 and is located mostly within Queensland, Australia. PET far exceeds rainfall, particularly in the summer months. Yearly rainfall ranges from 300 to 700 mm and PET from 2200 to 2900 mm. There are 17 proposed coal resource developments in the Galilee region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.4160625,-31.0116875],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43672934,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"XCQ8+8C Pilliga Nature Reserve, Dandry NSW 2357, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-6"},{"name":"Pilliga State Forest","kind":"landscape","role":"largest remaining dry sclerophyll forest west of Great Dividing Range in New South Wales","river_name":null,"geocode_query":"Pilliga State Forest, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"There is also a diverse range of landscapes and ecosystems within the region, including the Liverpool and Kaputar ranges, the Liverpool Plains floodplains, and the Darling Riverine plains in the west of the region; open box woodlands on the slopes; and temperate and subalpine forests in the east of the region. A range of aquatic habitats occur downstream of Narrabri, with large areas of anabranches and billabong wetlands. The Pilliga Nature Reserve, in the upper catchment of Bohena Creek, and the Pilliga State Forest form the largest remaining area of dry sclerophyll forest west of the Great Diving Range in New South Wales (Welsh et al., 2014). 2.1.2 Galilee region The Galilee region covers approximately 612 300 km2 and is located mostly within Queensland, Australia. PET far exceeds rainfall, particularly in the summer months. Yearly rainfall ranges from 300 to 700 mm and PET from 2200 to 2900 mm. There are 17 proposed coal resource developments in the Galilee region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.888562,-30.291763],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43670847,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pilliga NSW 2388, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-7","true_coordinates":[148.8885619,-30.3517626],"spread_km":6.7},{"name":"Galilee region","kind":"region","role":"study region with multiple proposed coal resource developments and varied hydrological and climatic conditions","river_name":null,"geocode_query":"Galilee region, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.9581354,-22.4349671],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111007203,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Galilee, Aramac QLD 4726, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-8"},{"name":"Bulloo catchment","kind":"basin","role":"major drainage basin with headwaters in Galilee region","river_name":null,"geocode_query":"Bulloo catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.0215415,-27.8166507],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111006237,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-9"},{"name":"Burdekin catchment","kind":"basin","role":"major drainage basin with headwaters in Galilee region","river_name":null,"geocode_query":"Burdekin catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.1915504,-19.8873977],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":16653120,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040068450,"main_basin_id":5040068450,"basin_region":"Australasia","basin_area_km2":130431.0,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-10"},{"name":"Cooper Creek catchment","kind":"basin","role":"largest drainage catchment in Galilee region, with ephemeral streams and groundwater importance","river_name":null,"geocode_query":"Cooper Creek catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.231948,-26.987703],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111005645,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-11","true_coordinates":[140.2902441016662,-26.95770346527561],"spread_km":6.7},{"name":"Diamantina catchment","kind":"basin","role":"large drainage basin within Galilee region","river_name":null,"geocode_query":"Diamantina catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.6911374,-24.8426236],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":115718493,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040455970,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":248581.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-12"},{"name":"Flinders catchment","kind":"basin","role":"major drainage basin with headwaters in Galilee region","river_name":null,"geocode_query":"Flinders catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.434214,-20.95843],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122860716,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040065600,"main_basin_id":5040065600,"basin_region":"Australasia","basin_area_km2":107785.3,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-13","true_coordinates":[144.4342138,-21.0184301],"spread_km":6.7},{"name":"Paroo catchment","kind":"basin","role":"major drainage basin within Galilee region","river_name":null,"geocode_query":"Paroo catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.846805,-27.936939],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665440,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524780,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":15576.0,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-14","true_coordinates":[145.8468053,-27.996939],"spread_km":6.7},{"name":"Warrego catchment","kind":"basin","role":"major drainage basin with headwaters in Galilee region","river_name":null,"geocode_query":"Warrego catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.59798,-26.584133],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673349,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-15","true_coordinates":[149.5979804,-26.6441333],"spread_km":6.7},{"name":"Great Artesian Basin","kind":"aquifer","role":"key groundwater source for livestock, towns, and industry in Galilee and Maranoa–Balonne–Condamine regions","river_name":null,"geocode_query":"Great Artesian Basin, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"These include three open-cut coal mines, two underground coal mines, five combined open-cut and underground coal mines, four coal mines of currently unknown type, and three CSG projects (Fig. 3). The Galilee region includes the headwaters of seven major drainage catchments. These catchments are the Bulloo, Burdekin, Cooper Creek, Diamantina, Flinders, Paroo and Warrego. The largest of these catchments within the region are Cooper Creek and Diamantina. Groundwater within the region is a very important resource, as most of the streams are ephemeral. Groundwater is used for town water, agriculture and industry. Most groundwater in the region is extracted from the Great Artesian Basin. The region covers a range of environments, including the mountains of the Great Dividing Range in the east through to semi-arid and arid areas in the central and western parts of the region. The main land use in the region is livestock grazing on native vegetation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.9789104,-26.4863157],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43676862,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2 Cambridge St, Mitchell QLD 4465, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-16"},{"name":"Maranoa–Balonne–Condamine region","kind":"region","role":"study region covering southeast Queensland and part of Murray–Darling Basin with multiple coal and coal seam gas developments","river_name":null,"geocode_query":"Maranoa–Balonne–Condamine region, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"There is no intensive agriculture in the region and a low human population density, largely due to the low and unpredictable rainfall (Evans et al., 2014). 2.1.3 Maranoa–Balonne–Condamine region The Maranoa–Balonne–Condamine region covers approximately 130 000 km2 and is located mostly within south-east Queensland with about half of this area within the Murray– Darling Basin. From east to west, average annual rainfall decreases from 800 to 420 mm, while PET increases from 1500 to 2370 mm. The region overlies the Surat Basin and Hydrol. Earth Syst. Sci., 28, 1957–1979, 2024 https://doi.org/10.5194/hess-28-1957-2024","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.0071922,-26.5712075],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43668750,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Maranoa, QLD, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-17"},{"name":"Condamine–Balonne River catchment","kind":"basin","role":"river system and its headwaters within Maranoa–Balonne–Condamine region","river_name":"Condamine–Balonne River","geocode_query":"Condamine–Balonne River catchment, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk 1961 Figure 2. Namoi region study area, showing the potential coal resource development sites. has five open-cut coal mines and five CSG projects, as well as two proposed open-cut coal mines (Fig. 4). The region contains the headwaters of the Condamine– Balonne, Moonie, Weir, Maranoa and Dawson rivers. Most of the rivers within the region are ephemeral. Therefore, groundwater is an important water source and is used for stock and domestic purposes as well as, in some cases, town water supply. The Great Artesian Basin is the main source of groundwater used within the region (Welsh et al., 2015). The main land use within the region is grazing on natural vegetation, with dryland cropping and production forestry also being major land uses. The main vegetation type within the region is grassy woodlands, with river red gums, coolabah and river oak common riparian species.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.078177,-26.897087],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43677463,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-18","true_coordinates":[150.1364566,-26.9270865],"spread_km":6.7},{"name":"Moonie River","kind":"river","role":"river with headwaters in Maranoa–Balonne–Condamine region","river_name":"Moonie River","geocode_query":"Moonie River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk 1961 Figure 2. Namoi region study area, showing the potential coal resource development sites. has five open-cut coal mines and five CSG projects, as well as two proposed open-cut coal mines (Fig. 4). The region contains the headwaters of the Condamine– Balonne, Moonie, Weir, Maranoa and Dawson rivers. Most of the rivers within the region are ephemeral. Therefore, groundwater is an important water source and is used for stock and domestic purposes as well as, in some cases, town water supply. The Great Artesian Basin is the main source of groundwater used within the region (Welsh et al., 2015). The main land use within the region is grazing on natural vegetation, with dryland cropping and production forestry also being major land uses. The main vegetation type within the region is grassy woodlands, with river red gums, coolabah and river oak common riparian species.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.54583345800006,-27.893611005999958],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677044,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-19"},{"name":"Weir River","kind":"river","role":"river with headwaters in Maranoa–Balonne–Condamine region","river_name":"Weir River","geocode_query":"Weir River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk 1961 Figure 2. Namoi region study area, showing the potential coal resource development sites. has five open-cut coal mines and five CSG projects, as well as two proposed open-cut coal mines (Fig. 4). The region contains the headwaters of the Condamine– Balonne, Moonie, Weir, Maranoa and Dawson rivers. Most of the rivers within the region are ephemeral. Therefore, groundwater is an important water source and is used for stock and domestic purposes as well as, in some cases, town water supply. The Great Artesian Basin is the main source of groundwater used within the region (Welsh et al., 2015). The main land use within the region is grazing on natural vegetation, with dryland cropping and production forestry also being major land uses. The main vegetation type within the region is grassy woodlands, with river red gums, coolabah and river oak common riparian species.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.34916679700007,-27.899722116999953],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677402,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-20"},{"name":"Maranoa River","kind":"river","role":"river with headwaters in Maranoa–Balonne–Condamine region","river_name":"Maranoa River","geocode_query":"Maranoa River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk 1961 Figure 2. Namoi region study area, showing the potential coal resource development sites. has five open-cut coal mines and five CSG projects, as well as two proposed open-cut coal mines (Fig. 4). The region contains the headwaters of the Condamine– Balonne, Moonie, Weir, Maranoa and Dawson rivers. Most of the rivers within the region are ephemeral. Therefore, groundwater is an important water source and is used for stock and domestic purposes as well as, in some cases, town water supply. The Great Artesian Basin is the main source of groundwater used within the region (Welsh et al., 2015). The main land use within the region is grazing on natural vegetation, with dryland cropping and production forestry also being major land uses. The main vegetation type within the region is grassy woodlands, with river red gums, coolabah and river oak common riparian species.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.87305566700002,-25.916944323999928],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677014,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-21"},{"name":"Dawson River","kind":"river","role":"river with headwaters in Maranoa–Balonne–Condamine region","river_name":"Dawson River","geocode_query":"Dawson River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"A. Herr et al.: A generalised ecohydrological landscape classification for assessing ecosystem risk 1961 Figure 2. Namoi region study area, showing the potential coal resource development sites. has five open-cut coal mines and five CSG projects, as well as two proposed open-cut coal mines (Fig. 4). The region contains the headwaters of the Condamine– Balonne, Moonie, Weir, Maranoa and Dawson rivers. Most of the rivers within the region are ephemeral. Therefore, groundwater is an important water source and is used for stock and domestic purposes as well as, in some cases, town water supply. The Great Artesian Basin is the main source of groundwater used within the region (Welsh et al., 2015). The main land use within the region is grazing on natural vegetation, with dryland cropping and production forestry also being major land uses. The main vegetation type within the region is grassy woodlands, with river red gums, coolabah and river oak common riparian species.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.054859512889,-24.96708088380403],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12131015,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dawson River, Queensland 4719, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-22"},{"name":"Balonne River Floodplain","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"Balonne River Floodplain, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.0507872,-28.7240353],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Balonne River, Dirranbandi QLD 4486, Australia","basin_id":5040524780,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":15576.0,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-23"},{"name":"Boggomoss Springs","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"Boggomoss Springs, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Dalrymple and Blackfellow Creeks","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"Dalrymple and Blackfellow Creeks, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.2685538825644,-27.84665758116294],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12128979,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Blackfellow Creek, Queensland 4343, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-25"},{"name":"Lake Broadwater","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"Lake Broadwater, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.0966326,-27.3504402],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43675630,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Broadwater, Nandi QLD 4405, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-26"},{"name":"Palm Tree and Robinson Creeks","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"Palm Tree and Robinson Creeks, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"The Gums Lagoon","kind":"wetland","role":"wetland of national significance within Maranoa–Balonne–Condamine region","river_name":null,"geocode_query":"The Gums Lagoon, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"There are also six wetlands of national significance within the region: Balonne River Floodplain, Boggomoss Springs, Dalrymple and Blackfellow Creeks, Lake Broadwater, Palm Tree and Robinson Creeks, and The Gums Lagoon (Welsh et al., 2015). 2.2 Landscape classification development – overview and rationale The purpose of this ecohydrological landscape classification is to characterise the landscape based on patterns in land use, ecology, geomorphology and hydrology and, from these, to develop landscape classes of water-dependent, remnant and human-modified features. We chose these features because these three types represent a generally applicable delineation for classification development in our different regions. For example, in Australia, remnant vegetation (our remnant features) describes all vegetation where there was no clearing or regrowth of (semi-) native vegetation, resulting in a vegetation community that resembles its predecessor’s structure.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.1833378,-27.3280567],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674332,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"The Gums QLD 4406, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-ec340eeec7395eba2df0edd5-site-28"}],"limitations":["Regional differences require distinct datasets limiting direct national application","Simplified discrete boundaries may not capture ecotonal gradients and transitions","Wetlands mapping incomplete in large areas of Australia","Separation of groundwater-dependent and surface-water-dependent wetlands may be uncertain","Limited knowledge of groundwater–surface water interactions especially in extensive forested areas","Data gaps in groundwater-dependent ecosystem mappings and lack of ground-truthing","Lack of hydrological gauging data limits risk quantification in some lowland riverine classes"],"river_ids":["43639706","43639717","43639755","43639874","43640134","43640138","43640140","43640141","43640144","43640151","43640155","43640162","43642806","43643251","43643457","43645069","43645592","43645678","43645958","43646184","43646358","43646599","43646657","43646672","43646759","43647172","43647173","43647323","43647468","43647564","43647632","43647799","43647975","43647983","43648037","43648040","43648046","43648109","43648111","43648127","43648152","43648192","43648223","43648274","43648384","43648522","43648545","43648563","43648606","43648751","43648883","43648892","43648954","43649286","43649512","43649596","43649597","43649614","43649617","43649621","43649643","43649674","43649742","43649839","43649845","43649851","43649852","43649866","43649869","43650016","43650110","43652911","43652982","43653696","43657748","43658611","43659559","43659569","43659571","43659573","43659575","43659576","43659577","43659615","43659617","43659619","43659621","43659622","43659623","43659624","43659625","43659626","43659627","43659931","43659932","43659934","43659935"],"site_ids":["doc-ec340eeec7395eba2df0edd5-site-0","doc-ec340eeec7395eba2df0edd5-site-1","doc-ec340eeec7395eba2df0edd5-site-2","doc-ec340eeec7395eba2df0edd5-site-3","doc-ec340eeec7395eba2df0edd5-site-4","doc-ec340eeec7395eba2df0edd5-site-5","doc-ec340eeec7395eba2df0edd5-site-6","doc-ec340eeec7395eba2df0edd5-site-7","doc-ec340eeec7395eba2df0edd5-site-8","doc-ec340eeec7395eba2df0edd5-site-9","doc-ec340eeec7395eba2df0edd5-site-10","doc-ec340eeec7395eba2df0edd5-site-11","doc-ec340eeec7395eba2df0edd5-site-12","doc-ec340eeec7395eba2df0edd5-site-13","doc-ec340eeec7395eba2df0edd5-site-14","doc-ec340eeec7395eba2df0edd5-site-15","doc-ec340eeec7395eba2df0edd5-site-16","doc-ec340eeec7395eba2df0edd5-site-17","doc-ec340eeec7395eba2df0edd5-site-18","doc-ec340eeec7395eba2df0edd5-site-19","doc-ec340eeec7395eba2df0edd5-site-20","doc-ec340eeec7395eba2df0edd5-site-21","doc-ec340eeec7395eba2df0edd5-site-22","doc-ec340eeec7395eba2df0edd5-site-23","doc-ec340eeec7395eba2df0edd5-site-25","doc-ec340eeec7395eba2df0edd5-site-26","doc-ec340eeec7395eba2df0edd5-site-28"],"anchor":[148.0507872,-28.7240353],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040065600,5040068450,5040069590,5040069970,5040455970,5040491510,5040511570,5040524780,5040524870,5040591010],"main_basin_ids":[5040065600,5040068450,5040069590,5040069970,5040073410,5040087590]},{"id":"doc-ec4059bea4ba3cfabdc79e06","title":"Large scale water yield assessment for sparsely monitored river basins: A case study for Afghanistan","display_title":"Water Yield Assessment in Afghanistan Basins","doi":"https://doi.org/10.1371/journal.pwat.0000165","theme":"Flow & ecology","color":"#80edc2","year":2024,"publication_date":"2024-04-16","authors":["Fazlul Karim","David J. Penton","Santosh K. Aryal","Shahriar Wahid","Yun Chen","Peter Taylor","Susan M. Cuddy"],"summary":"This study assesses water yield across Afghanistan's five major river basins—Helmand, Harirod-Murghab, Kabul, Northern, and Panj-Amu—using GR4J and GR4JSG precipitation-runoff models calibrated on 2008–2015 observed streamflow and validated on 2016–2020 data. The analysis covers 207 subcatchments subdivided into functional units based on elevation to capture snow and glacier melt contributions. Results indicate large spatial and temporal variability with northeastern mountainous regions, particularly the Hindu Kush, being primary water sources. The overall average water yield is 72.1 mm/year, equivalent to 46.3 billion cubic meters, with 38% of flow contributed from neighboring Tajikistan and Pakistan. Seasonal flow concentration occurs mostly from March to June, highlighting critical periods for water resource planning.","finding":"The northeastern mountainous areas of Afghanistan, especially parts of the Panj-Amu and Kabul basins within the Hindu Kush mountains, are the primary water sources for the country, with modeled mean annual water yield reaching up to 248 mm, while lower yields occur in basins like Helmand due to drier climates. About 38% of the water yield originates from neighboring countries, emphasizing the transboundary nature of water resources.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"mm in a subcatchment in Helmand Basin to 248 mm in the Panj-Amu Basin, with an average of 72.1 mm for the entire country. Area averaged water yields in the five river basins are 36, 83, 97, 69 and 174 mm in the Helmand, Harirod-Murghab, Kabul, Northern and Panj-Amu basins respectively. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) for the entire country. In addition, about 28.9 BCM (about 38% of the total water yield from five river basins) generates annually in the subcatchments of neighbouring Tajikistan and Pakistan primarily from melting snow and glaciers of the Hindu-Kush mountains. Proportion of this water flows to Afghanistan through Panj and Konar rivers.","verified_in_page":true},"topics":["rainfall-runoff modeling","water yield","hydrological modeling","transboundary rivers","snowmelt hydrology","mountain hydrology","water resource variability","climate impacts"],"study_period":"2008-2020","locations":[{"name":"Afghanistan","kind":"region","role":"national study area encompassing all basins","river_name":null,"geocode_query":"Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"The climate is mostly semi-arid, and the population depends on rainfall and irrigated agriculture to produce food. Majority of the rural population is farmers who live precariously from small plots of land. River flow from melting of snow and glaciers recharges alluvial aquifers and these aquifers provide reliable sources of groundwater for agriculture and domestic use [9]. Afghanistan’s water resources face pressure to support its growing population and continued economic development while the warming climate depletes its glaciers [10–12]. Managing the growing water demand under decreasing water source is a huge challenge requiring accurate estimation of water yield potential across the country. Studies on water availability across the river basins in Afghanistan are very limited. While there are some studies on local scale [13] and basin scale [14], there is no study on national scale water yield assessment.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Helmand River Basin","kind":"basin","role":"largest river basin in Afghanistan, characterized by dry climate and lowest average water yield","river_name":"Helmand River","geocode_query":"Helmand River Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"resolved","coordinates":[63.21203547604946,31.07427932504247],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040085530,"main_basin_id":2040085530,"basin_region":"Europe and Middle East","basin_area_km2":213875.9,"hydrology_source":"hydrobasins","site_id":"doc-ec4059bea4ba3cfabdc79e06-site-1"},{"name":"Harirod-Murghab River Basin","kind":"basin","role":"northwestern basin contributing moderate water yield to national totals","river_name":"Harirod River","geocode_query":"Harirod-Murghab River Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"resolved","coordinates":[72.37727880098377,38.126203641877],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040545690,"main_basin_id":4040050220,"basin_region":"Central and South-East Asia","basin_area_km2":121256.0,"hydrology_source":"hydrobasins","site_id":"doc-ec4059bea4ba3cfabdc79e06-site-2"},{"name":"Kabul River Basin","kind":"basin","role":"southeastern basin with significant contribution from snow and glacier melt, linked to transboundary flows from Pakistan","river_name":"Kabul River","geocode_query":"Kabul River Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"resolved","coordinates":[70.73822620104086,34.28376366825792],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040648310,"main_basin_id":4040033640,"basin_region":"Central and South-East Asia","basin_area_km2":92570.8,"hydrology_source":"hydrobasins","site_id":"doc-ec4059bea4ba3cfabdc79e06-site-3"},{"name":"Northern River Basin","kind":"basin","role":"northern basin with lowest total water yield among basins studied","river_name":null,"geocode_query":"Northern River Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Panj-Amu River Basin","kind":"basin","role":"primary high water yielding basin in northeastern Afghanistan with substantial contribution from Tajikistan glaciers","river_name":"Panj-Amu River","geocode_query":"Panj-Amu River Basin, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-000","page":11,"quote":"mm in a subcatchment in Helmand Basin to 248 mm in the Panj-Amu Basin, with an average of 72.1 mm for the entire country. Area averaged water yields in the five river basins are 36, 83, 97, 69 and 174 mm in the Helmand, Harirod-Murghab, Kabul, Northern and Panj-Amu basins respectively. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) for the entire country. In addition, about 28.9 BCM (about 38% of the total water yield from five river basins) generates annually in the subcatchments of neighbouring Tajikistan and Pakistan primarily from melting snow and glaciers of the Hindu-Kush mountains. Proportion of this water flows to Afghanistan through Panj and Konar rivers.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.441246,37.206528],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040545690,"main_basin_id":4040050220,"basin_region":"Central and South-East Asia","basin_area_km2":121256.0,"hydrology_source":"hydrobasins","site_id":"doc-ec4059bea4ba3cfabdc79e06-site-5","true_coordinates":[68.4412456,37.1465281],"spread_km":6.7},{"name":"Hindu Kush Mountains","kind":"landscape","role":"major mountainous region driving snow and glacier melt, primary headwater source feeding northeastern basins","river_name":null,"geocode_query":"Hindu Kush Mountains, Afghanistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-001","page":10,"quote":"Gauge in Basin Calibration [2008–2015] Validation [2016–2020] Correlation coefficient PBias (%) NSE Daily NSE Monthly Correlation coefficient PBias (%) NSE Daily NSE Monthly Pul-i-Bangi (PARB) 0.61 −6.41 0.36 0.49 0.58 23.6 0.24 0.46 Rabat-i-Bala (NRB) 0.66 −0.04 0.43 0.58 0.54 −3.9 0.30 0.41 Tang-i-Sayedan (KRB) 0.73 −3.00 0.53 0.70 0.84 10.07 0.69 0.87 Cheghcheran (HMRB) 0.80 −2.93 0.64 0.79 0.79 −4.95 0.60 0.73 Adraskan (HRB) 0.63 −2.74 0.40 0.47 0.63 −33.7 0.23 0.29 https://doi.org/10.1371/journal.pwat.0000165.t004 Fig 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[73.263333,36.952222],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hindu Kush","basin_id":4040545690,"main_basin_id":4040050220,"basin_region":"Central and South-East Asia","basin_area_km2":121256.0,"hydrology_source":"hydrobasins","site_id":"doc-ec4059bea4ba3cfabdc79e06-site-6","true_coordinates":[73.2633333,36.8922222],"spread_km":6.7},{"name":"Tajikistan","kind":"region","role":"neighboring country contributing approximately 26.7 BCM of runoff primarily from glaciers and snow to Panj-Amu Basin in Afghanistan","river_name":null,"geocode_query":"Tajikistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Pakistan","kind":"region","role":"neighboring country contributing about 1.9 BCM of flow primarily via the Kunar River to Kabul Basin in Afghanistan","river_name":null,"geocode_query":"Pakistan","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Across the region, mean annual water yield in the period of 2008 to 2020 is about 11.7 BCM (25% of the total water yield) in the Helmand basin which is the largest river basin in Afghanistan (51% of the country) (Table 5). The Panj-Amu basin is the second largest with an area of 92,041 km2 within Afghanistan (14.3% of the country) and 146,250 km2 in neighbouring Tajikistan. The water yield from Panj-Amu basin is about 42.7 BCM comprising 16.0 BCM within Afghanistan and 26.7 BCM in the neighbouring Tajikistan, mostly from glacier melting in the Hindu-Kush mountains. It is important to note that the water yield in the Panj-Amu basin largely depends on flow from neighbouring Tajikistan. Kabul Basin occupies about 11% of country’s land and it produces 6.9 BCM water within Afghanistan and 1.9 BCM in the neighbouring Pakistan, primarily through the Kunar River from melting glaciers and snow of the Hindu Kush mountains.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Limited and inconsistent observed hydrologic and meteorological data across Afghanistan.","Data gaps required interpolation and gap filling using neighboring stations, which may affect accuracy.","Model calibration and validation limited to five subcatchments due to data constraints, limiting spatial rigor.","Absence of data on water storages, diversions, and irrigation practices not included in modeling, which may impact flow estimations.","Limited in situ verification for transboundary water yield estimates especially from glacier melt contributions.","Potential bias in gauge data due to use of rating curves not accounting for flood flows.","Study period (2008-2020) may not fully represent changing conditions due to recent climate change and political instability affecting data continuity."],"river_ids":[],"site_ids":["doc-ec4059bea4ba3cfabdc79e06-site-1","doc-ec4059bea4ba3cfabdc79e06-site-2","doc-ec4059bea4ba3cfabdc79e06-site-3","doc-ec4059bea4ba3cfabdc79e06-site-5","doc-ec4059bea4ba3cfabdc79e06-site-6"],"anchor":[73.263333,36.952222],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[2040085530,4040545690,4040648310],"main_basin_ids":[2040085530,4040033640,4040050220]},{"id":"doc-0090cecff8273f85bcb325d4","title":"Forecasting agricultural drought: the Australian Agricultural Drought Indicators","display_title":"Australian Agricultural Drought Forecasting System","doi":"https://doi.org/10.5194/nhess-25-4053-2025","theme":"Climate","color":"#98d4f1","year":2025,"publication_date":"2025-10-21","authors":["Andrew Schepen","Andrew Bolt","Dorine Bruget","John Carter","Donald Gaydon","Mihir Gupta","Zvi Hochman","Neal Hughes","Chris Sharman","Peter Tan","Peter Taylor"],"summary":"This study evaluates the Australian Agricultural Drought Indicators (AADI) system, which integrates climate forecasts from ACCESS-S2 with biophysical and agro-economic models to forecast agricultural drought indicators such as pasture growth, crop yields, and farm profits across Australia at 5 km resolution. Using hindcast data from 1990–2018, they demonstrate that antecedent environmental conditions significantly enhance forecast skill, with farm profit forecasts showing notably high skill even 12 months ahead. The system provides reliable probabilistic forecasts suited for drought risk management and decision-making, improving early warning compared to traditional rainfall deficit indicators.","finding":"The AADI farm profit indicator forecasts have high skill up to 12 months ahead, with median CRPS skill scores of about 45%, substantially exceeding the skill of climate forecasts alone, demonstrating the predictive importance of antecedent environmental and economic conditions.","finding_evidence":{"passage_id":"p007-002","page":7,"quote":"However, regions of positive bias exist in the central east for forecasts issued in July of the current FY and April of the previous FY (up to 20 %). Skill increases markedly as the end of the financial year approaches, which is mainly due to an increasing proportion of observed data being integrated into the indicator. Even so, for July-issued forecasts, which have a lead time of 12 months, the median CRPS skill score is 45 % (23 %–64 % IDR). This level of skill is quite high when contrasted with the skill of climate forecasts, which typically have CRPS skill scores of less than 30 %, depending on the location and season, and typically not more than a few months ahead (see discussion in Sect. 5). We can infer that the high degree of skill in the farm profit indicator is primarily due to knowledge of the antecedent environmental and economic conditions rather than climate.","verified_in_page":true},"topics":["agricultural-drought","seasonal-forecasting","climate-forecast-calibration","crop-yield-modeling","economic-modeling","pasture-growth","ensemble-forecasting","drought-risk-management"],"study_period":"1990–2018","locations":[{"name":"Australia","kind":"region","role":"study area for drought forecasting and agricultural assessment","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"An example of farmpredict output is provided in Fig. 1, which shows the expected relative farm profit for the financial year 2018–2019, where the negative impact of the Tinderbox Drought (Devanand et al., 2024) on farm profits can be seen over south-eastern Australia. The question arises, then, whether farmpredict can be successfully driven by climate forecast ensembles to generate skilful forecasts of financial outcomes for farm businesses. Such an approach is not without precedent, as climate forecast ensembles have been used to generate seasonal outlooks of crop yields (e.g. Potgieter et al., 2022; Schepen et al., 2020a). Indeed, in addition to farm profit outlooks, drought analysts and policy planners can also be interested in the potential yield outlooks for winter and summer crops or pasture availability for livestock.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Queensland","kind":"region","role":"location of SILO climate data operational dataset and AussieGRASS model operational base","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Section 6 concludes the paper with the main findings. It is anticipated that the results of this study will support the operational AADI system by providing drought analysts with a level of confidence in the forecasts dependent on location, lead time and indicator, as well as by serving as a demonstration of a novel agricultural drought forecasting system driven by climate ensemble forecasts. 2 Data and models 2.1 SILO gridded climate data SILO is a gridded dataset of climate data, mostly constructed from real measurements, that is used as the observational data. It is interpolated and infilled to give continuous coverage across Australia at 5 km resolution (Jeffrey et al., 2001), which makes it highly suitable for large simulation studies. In addition, it is already integrated with the AussieGRASS and APSIM (Agricultural Production Systems sIMulator) simulation systems.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South-eastern Australia","kind":"region","role":"area affected by the Tinderbox Drought and site of notable farm profit drought impacts","river_name":null,"geocode_query":"South-eastern Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"An example of farmpredict output is provided in Fig. 1, which shows the expected relative farm profit for the financial year 2018–2019, where the negative impact of the Tinderbox Drought (Devanand et al., 2024) on farm profits can be seen over south-eastern Australia. The question arises, then, whether farmpredict can be successfully driven by climate forecast ensembles to generate skilful forecasts of financial outcomes for farm businesses. Such an approach is not without precedent, as climate forecast ensembles have been used to generate seasonal outlooks of crop yields (e.g. Potgieter et al., 2022; Schepen et al., 2020a). Indeed, in addition to farm profit outlooks, drought analysts and policy planners can also be interested in the potential yield outlooks for winter and summer crops or pasture availability for livestock.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Central Queensland","kind":"region","role":"region exhibiting biases in wheat yield forecasts and farm profit forecasts at some lead times","river_name":null,"geocode_query":"Central Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-005","page":7,"quote":"January is the latest forecast issue date because the crops are harvested by this time. Skill tends to be low for wheat around sowing time and, as to be expected, increases gradually as the season progresses. For forecasts issued in July, the median CRPS skill score is 31 % (14 %–56 % IDR), which indicates moderate association with historical simulations with 3–6 months’ lead time. By flowering and harvesting, skill is typically very high (80 %–90 %), indicating that the forecasts have converged and match the historical simulations well. Biases overall are small (within ±5 %), except for the April-issued forecasts, where positive biases appear in central Queensland, and negative biases appear in south-western Western Australia. Reliability is high for forecasts issued in April, July and October.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.728529,-23.384134],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111006239,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Queensland, QLD, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-0090cecff8273f85bcb325d4-site-3","true_coordinates":[144.7851531,-23.4141339],"spread_km":6.7},{"name":"South-western Western Australia","kind":"region","role":"region exhibiting negative bias in wheat yield forecasts at some lead times","river_name":null,"geocode_query":"South-western Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-005","page":7,"quote":"January is the latest forecast issue date because the crops are harvested by this time. Skill tends to be low for wheat around sowing time and, as to be expected, increases gradually as the season progresses. For forecasts issued in July, the median CRPS skill score is 31 % (14 %–56 % IDR), which indicates moderate association with historical simulations with 3–6 months’ lead time. By flowering and harvesting, skill is typically very high (80 %–90 %), indicating that the forecasts have converged and match the historical simulations well. Biases overall are small (within ±5 %), except for the April-issued forecasts, where positive biases appear in central Queensland, and negative biases appear in south-western Western Australia. Reliability is high for forecasts issued in April, July and October.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.984768,-34.093092],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70413103,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South West, WA, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-0090cecff8273f85bcb325d4-site-4","true_coordinates":[116.0413889,-34.0556829],"spread_km":6.7},{"name":"Queensland and New South Wales","kind":"region","role":"regions where sorghum summer cropping is modeled and hindcasted","river_name":null,"geocode_query":"Queensland and New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"4060 A. Schepen et al.: Forecasting agricultural drought Figure 3. Summary of CRPS skill scores of the climate forecasts over the climate hindcast period 1981–2018. The three rings depict the median (middle ring) and interdecile range (inner ring = 10th percentile; outer ring = 90th percentile). Each ring segment represents a forecast issue month from January to December in a clockwise direction. Target climate variables are in rows, and the lead times (months ahead) are in columns. 4.4 Summer crops (Sorghum) Sorghum crops are limited to regions in Queensland and New South Wales, often sown as an opportunistic crop in between regular wheat planting. Sorghum has a wide window of sowing opportunity from September to January, with the crop cycle taking 4–5 months to complete. The assessment of sorghum hindcasting skill is presented in Fig. 6. Overall skill, in terms of the CRPS skill score, is only evident after crop emergence, that is for forecast issue dates in January and April.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Australia (national scale)","kind":"region","role":"used for national average time series analysis of farm profit and drought progression","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-006","page":11,"quote":"In fact, the evolution of the drought indices through 2017 to 2019 shows that the farm profit indicator provided advanced indications of high farm profit with 12 months’ lead time in 2017 before leading the rainfall indicator into drought conditions in 2018. Whilst the focus in this study is on drought, the performance of the drought indicators in normal and “good” years is also relevant, for example, in the context of false alarms. We can summarise that in 1994, 1997 and 2017, the farm profit indicator provided advance information for high farm profits compared to the rainfall indicator; however, for 2012, rainfall remained high following the 2010–2011 high rainfall, and the farm profit indicator was relatively late in identifying high profits, partially because the profit indicator “rehttps://doi.org/10.5194/nhess-25-4053-2025 Nat. Hazards Earth Syst. Sci., 25, 4053–4070, 2025","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinate data for specific grid cells or cropping zones were not provided, precluding precise geolocation at finer than regional scale.","The reliability assessment uses model pseudo-observations and does not directly measure absolute skill against on-the-ground agricultural outcomes.","Biases in sorghum and pasture growth modeling in some regions require further investigation and system improvements.","The temporal coverage of hindcasts ends in 2018, limiting analysis of more recent drought events."],"river_ids":[],"site_ids":["doc-0090cecff8273f85bcb325d4-site-3","doc-0090cecff8273f85bcb325d4-site-4"],"anchor":[115.984768,-34.093092],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040055960,5040491510],"main_basin_ids":[5040055960,5040087590]},{"id":"doc-03c3c6412ce4d0b1fa4b0dee","title":"Resilience investments under climate change: a regional case study in Southeast Australia","display_title":"Resilience Investments in Bega Valley, Southeast Australia","doi":"https://doi.org/10.3389/fenvs.2025.1504043","theme":"Water management","color":"#eeabac","year":2025,"publication_date":"2025-05-06","authors":["Paul J. Box","Stefanos Xenarios","Russell M. Wise","Peter Heinmiller","Chandrama Sarker","Petina L. Pert","Seona Meharg","Gemma Edwards","Russell Gorddard","Sofia Azevedo","Dianne Flett","Nic Mesic","Maryam Ahmad","John Marinopoulos","Greg Parish","Jacqui Montgomery"],"summary":"This study develops and applies a discursive, place-based, and cross-scale approach to enable resilient investment planning under climate change in the Bega Valley Local Government Area (LGA) in southeastern Australia. It involves identifying values and vulnerabilities to multiple natural hazards, prioritizing critical infrastructure, and evaluating bundles of interventions under climate and socioeconomic scenarios. The focus is on transport infrastructure but is applicable to nature-based solutions (NbS) and other critical infrastructure. Participatory workshops engage local governments, community stakeholders, utilities, and businesses to collectively assess multi-hazard exposure and vulnerabilities, explore interventions, and formulate resilient investment cases. Economic assessments estimate benefits, costs, and potential co-benefits. The approach aims to overcome institutional and technical barriers faced by local governments in disaster resilience investment planning.","finding":"The approach enabled the Bega Valley Shire Council (BVSC) to identify priority hazard-prone areas and intervention bundles that could yield significant economic benefits—estimated at over AUD 600 million across two key areas—while addressing infrastructure vulnerabilities and enhancing regional resilience through coordinated, place-based investment planning.","finding_evidence":{"passage_id":"p012-001","page":12,"quote":"This potential increase in residential property values was conducted through the benefit transfer method, presuming similar conditions for properties to which we could confidently attribute these benefits. The benefits derived from the avoided reconstruction costs due to the enhanced resilience of the relevant assets were estimated at AUD 280 million by calculating the avoided road damage and repair costs. Additional benefits included approximately 1,280 direct jobs, primarily in the construction sector, contributing AUD 167 million in Gross Value Added from these employment opportunities. Conversely, the capital and operational cost of all the interventions could reach AUD 400 million and AUD 84 million, respectively.","verified_in_page":true},"topics":["infrastructure resilience","climate adaptation","risk assessment","disaster risk reduction","multi-criteria decision analysis","nature-based solutions","investment planning","community engagement"],"study_period":"2023-2024","locations":[{"name":"Bega Valley Local Government Area","kind":"region","role":"main study area; focus of resilience investment planning and multi-hazard vulnerability assessment","river_name":null,"geocode_query":"Bega Valley Local Government Area, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"implications for natural capital and nature-based solutions are identified and reviewed. 2 Materials and methods 2.1 The case study of Bega Valley Shire The Bega Valley Local Government Area (LGA) is situated in the southeastern part of New South Wales (NSW) state. It is the largest LGA in NSW, with a territorial area of 6,280 km2 . The Bega Valley LGA encompasses the largest mountainous range in the country, and is therefore characterised with escarpments, plateaus, and coastal slopes and plains (Brierley and Fryirs, 2000). The range also delineates an extensive network of water basins in eastern Australia. This network encompasses the Bega River, which has a catchment area of 1,040 km2 , covering a significant portion of the Bega Valley LGA. The river drains the upland areas and ultimately discharges into the southern Pacific Ocean after merging with other tributaries.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.721241,-36.65061],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29052296,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bega Valley, NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-03c3c6412ce4d0b1fa4b0dee-site-0","true_coordinates":[149.6564494,-36.6806101],"spread_km":6.7},{"name":"Bega River catchment","kind":"basin","role":"hydrological network in the Bega Valley LGA, relevant to flood hazards and environmental context","river_name":"Bega River","geocode_query":"Bega River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"implications for natural capital and nature-based solutions are identified and reviewed. 2 Materials and methods 2.1 The case study of Bega Valley Shire The Bega Valley Local Government Area (LGA) is situated in the southeastern part of New South Wales (NSW) state. It is the largest LGA in NSW, with a territorial area of 6,280 km2 . The Bega Valley LGA encompasses the largest mountainous range in the country, and is therefore characterised with escarpments, plateaus, and coastal slopes and plains (Brierley and Fryirs, 2000). The range also delineates an extensive network of water basins in eastern Australia. This network encompasses the Bega River, which has a catchment area of 1,040 km2 , covering a significant portion of the Bega Valley LGA. The river drains the upland areas and ultimately discharges into the southern Pacific Ocean after merging with other tributaries.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.8699158512596,-36.69968171021743],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":29052553,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-03c3c6412ce4d0b1fa4b0dee-site-1"},{"name":"Bega town","kind":"settlement","role":"major service hub in the Bega Valley LGA; focus of transport infrastructure and community vulnerability assessments","river_name":null,"geocode_query":"Bega town, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"The region receives an annual rainfall of 750–1,000 mm/year, proportionally increasing in the fall and winter seasons (BOM, 2024). However, acute precipitation events have been recorded every season in the last 4–5 years, largely due to the La Nina phenomenon, which has caused wetter conditions in eastern Australia since 2020. The recurrent flood incidents have become more pronounced in the lowlands and the coastline area, which stretches for 225 km and hosts several tourism activities and seasonal accommodations. The Bega Valley has a relatively low (~35,000) and dispersed population compared to other LGAs in NSW state. Two-thirds of the population live in four towns, where Bega town is the major service hub with a population of 4,368 (in 2021), and the rest of the population is distributed over 12 villages and smaller settlements in rural areas, with much of the population located in small settlements on the coastal fringe.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.8432479,-36.6742897],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29052537,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bega NSW 2550, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-03c3c6412ce4d0b1fa4b0dee-site-2"},{"name":"Southeast coast of Australia","kind":"region","role":"regional context for the study area and the location of Bega Valley Shire","river_name":null,"geocode_query":"Southeast coast, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"By doing so, our approach seeks to overcome the seven barriers mentioned above by moving away from traditional asset management practices and redefining how resilience interventions are identified and implemented to foster place-based resilience and scalability. We present the case study of Bega Valley Shire, situated along the southeast coast of Australia. This region exemplifies many large heterogeneous coastal areas across Australia facing increasing levels of exposure and vulnerability to intensifying natural hazards due to climate change, while also struggling to secure adequate funding and finance for resilience investments. Although the Shire Council is responsible for managing a range of asset types—including natural, social, economic, and built infrastructure—to ensure the reliable supply of critical services, this project could only concentrate on one asset class due to its scope and funding limitations.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Southern Pacific Ocean coast within Bega Valley LGA","kind":"region","role":"exposure zone for hazards including estuarine inundation and coastal flooding affecting vulnerabilities and intervention planning","river_name":null,"geocode_query":"Southern Pacific Ocean coast, Bega Valley Shire, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"The region receives an annual rainfall of 750–1,000 mm/year, proportionally increasing in the fall and winter seasons (BOM, 2024). However, acute precipitation events have been recorded every season in the last 4–5 years, largely due to the La Nina phenomenon, which has caused wetter conditions in eastern Australia since 2020. The recurrent flood incidents have become more pronounced in the lowlands and the coastline area, which stretches for 225 km and hosts several tourism activities and seasonal accommodations. The Bega Valley has a relatively low (~35,000) and dispersed population compared to other LGAs in NSW state. Two-thirds of the population live in four towns, where Bega town is the major service hub with a population of 4,368 (in 2021), and the rest of the population is distributed over 12 villages and smaller settlements in rural areas, with much of the population located in small settlements on the coastal fringe.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.591658,-36.65061],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29052296,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bega Valley, NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-03c3c6412ce4d0b1fa4b0dee-site-4","true_coordinates":[149.6564494,-36.6806101],"spread_km":6.7}],"limitations":["Lack of precise coordinates for study locations; coordinates set to null","Economic benefit estimates rely on benefit transfer method with acknowledged low reliability","Hazard probability and climate scenarios downscaled with inherent uncertainties over 35-year horizon","Workshop participant understanding of complex climate data was restricted to simplified, qualitative categories","Study focused on transport infrastructure only, though approach claims transferability to other critical infrastructure types"],"river_ids":[],"site_ids":["doc-03c3c6412ce4d0b1fa4b0dee-site-0","doc-03c3c6412ce4d0b1fa4b0dee-site-1","doc-03c3c6412ce4d0b1fa4b0dee-site-2","doc-03c3c6412ce4d0b1fa4b0dee-site-4"],"anchor":[149.721241,-36.65061],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040070710],"main_basin_ids":[5040070710]},{"id":"doc-0523f8b0ed1080eac7b2df8e","title":"Advancing a Hydrological Model to Quantify Groundwater Discharge of Forest Groundwater Dependent Ecosystems","display_title":"Modeling Forest Groundwater Discharge in Australia","doi":"https://doi.org/10.1002/hyp.70203","theme":"Groundwater","color":"#a8b9ff","year":2025,"publication_date":"2025-07-01","authors":["Tanya M. Doody","Sicong Gao","Zunyi Xie","Anthony P. O'Grady","Richard G. Benyon"],"summary":"The study advances the ABCD hydrological model by integrating the Budyko Framework and GRACE satellite data (the ABCD-BF model) to estimate groundwater discharge by forest groundwater dependent ecosystems (GDEs). Using an extensive multi-year dataset of forest water use measurements from 18 plantation sites in the Green Triangle region of southeast South Australia and western Victoria, Australia, the study validates the model's ability to provide fine temporal scale groundwater discharge estimates. The model shows strong correlation with on-ground data, enabling quantification of groundwater use timing, duration, and volume, critical for sustainable groundwater management and GDE protection. The approach can be applied broadly utilizing remote sensing data, providing a new desktop tool for groundwater impact assessment worldwide.","finding":"The ABCD-BF model, validated with multi-year on-ground plantation forest data from the Green Triangle region, accurately estimates vegetation groundwater discharge with approximately half the sites showing correlation coefficients over 0.8 and low root mean square error (~0.46 mm/day), confirming its suitability for quantifying forest groundwater extraction.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Given the aim of the study was to accurately simulate forest groundwater discharge, an r>0.8 from simulation comparison of approximately half the on-ground sites (Figure 4b) with a low RMSE of 0.46mmday−1 provides evidence that the ABCD model exhibits an effective ability to simulate forest groundwater discharge for the study region. Comparison of on-ground and simulated timeseries groundwater discharge data (Figure 4c) reveals similar trends across the majority of field locations, providing an indication of timing, magnitude and duration of groundwater extraction over the study period for each site. 3.2 | Combining the ABCD Model With the Budyko Framework The Budyko Framework was employed to validate the assumption that including P, forest groundwater discharge, and soil water change would explain the relationship between water availability and water demand more accurately.","verified_in_page":true},"topics":["groundwater discharge","evapotranspiration","hydrological modeling","satellite remote sensing","water balance","forest hydrology","plant groundwater uptake","climate impact"],"study_period":"1999-2008","locations":[{"name":"Green Triangle Forestry Region","kind":"region","role":"study area and field data site for model validation of forest groundwater discharge","river_name":null,"geocode_query":"Green Triangle, South Australia and Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 of 16 Hydrological Processes, 2025 the ABCD model and Budyko Framework serves two functions: (1) provide an independent validation tool for ABCD model outputs to confirm sites were accessing groundwater by plotting above the Budyko water limit line (Creed et al. 2014) or alternatively, highlight sites below the water limit line indicating forest water use was lower than available water and sites did not access groundwater (Creed et al. 2014); (2) enable development of a method to estimate groundwater discharge at site scale as well as across larger catchment/river basin areas. The ABCD-BF model provides a methodological advancement beyond using the Budyko Framework as a validation technique. 2.2 | Field Collected Data The Green Triangle is a major commercial plantation forestry region in Australia which straddles the southeastern South Australian and western Victoria state borders (see region map, fig. 1 in Doody et al. 2023).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Southeast South Australia","kind":"region","role":"location of study plantation sites over karst aquifer studied for groundwater discharge","river_name":null,"geocode_query":"southeast South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 of 16 Hydrological Processes, 2025 the ABCD model and Budyko Framework serves two functions: (1) provide an independent validation tool for ABCD model outputs to confirm sites were accessing groundwater by plotting above the Budyko water limit line (Creed et al. 2014) or alternatively, highlight sites below the water limit line indicating forest water use was lower than available water and sites did not access groundwater (Creed et al. 2014); (2) enable development of a method to estimate groundwater discharge at site scale as well as across larger catchment/river basin areas. The ABCD-BF model provides a methodological advancement beyond using the Budyko Framework as a validation technique. 2.2 | Field Collected Data The Green Triangle is a major commercial plantation forestry region in Australia which straddles the southeastern South Australian and western Victoria state borders (see region map, fig. 1 in Doody et al. 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.283356,-36.753562],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21556923,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Limestone Coast, SA, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-0523f8b0ed1080eac7b2df8e-site-1","true_coordinates":[140.3481835,-36.7235617],"spread_km":6.7},{"name":"Western Victoria","kind":"region","role":"location of study plantation sites forming part of Green Triangle forestry region for groundwater discharge study","river_name":null,"geocode_query":"western Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 of 16 Hydrological Processes, 2025 the ABCD model and Budyko Framework serves two functions: (1) provide an independent validation tool for ABCD model outputs to confirm sites were accessing groundwater by plotting above the Budyko water limit line (Creed et al. 2014) or alternatively, highlight sites below the water limit line indicating forest water use was lower than available water and sites did not access groundwater (Creed et al. 2014); (2) enable development of a method to estimate groundwater discharge at site scale as well as across larger catchment/river basin areas. The ABCD-BF model provides a methodological advancement beyond using the Budyko Framework as a validation technique. 2.2 | Field Collected Data The Green Triangle is a major commercial plantation forestry region in Australia which straddles the southeastern South Australian and western Victoria state borders (see region map, fig. 1 in Doody et al. 2023).","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.338259,-37.667583],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21557393,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western District of Victoria, VIC, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-0523f8b0ed1080eac7b2df8e-site-2","true_coordinates":[142.4039296,-37.6975835],"spread_km":6.7},{"name":"Murray-Darling Basin","kind":"region","role":"funding region and potential broad scale application region of ABCD-BF model for groundwater management","river_name":null,"geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Doody (tanya.doody@csiro.au) Received: 3 January 2025 | Revised: 2 July 2025 | Accepted: 3 July 2025 Funding: This work was supported by the CSIRO, the South Australian Department of Water, Land and Biodiversity Conservation, the South East Catchment Water Management Board, ForestrySA, the Natural Heritage Trust, Auspine, Green Triangle Forest Products, Wood Products Research and Development Corporation, the Glenelg Hopkins Catchment Management Authority, the Green Triangle Regional Plantation Committee and the District Council of Grant. Funding for the pilot study to develop the ABCD-BF model was provided by the Murray-Darling Basin Authority. Keywords: Budyko framework | evapotranspiration | GRACE | hydroprobe | sap flow | soil moisture | terrestrial vegetation ABSTRACT Worldwide, groundwater dependent ecosystems (GDEs) support rare/endemic species, providing essential habitat and access to water when rivers, wetlands and springs are present.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.460256,-31.13869],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-0523f8b0ed1080eac7b2df8e-site-3","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7}],"limitations":["Study region coordinates and precise field site coordinates not provided.","Model parameter fixed values may limit dynamic response representation.","Spatial resolution of input remote sensing data (5 km) coarser than field measurement plots (20 m) possibly affecting accuracy.","Overestimation tendency in some seasonal groundwater discharge estimates noted.","Validation data limited to plantation forests, limits direct transferability to other GDE types without further 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040070710,5040591010],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-0714c02d6cfa10b2fe208f21","title":"A simplified drought indicator based on high-resolution GRACE terrestrial water storage anomalies","display_title":"High-Resolution GRACE Drought Index in Australia","doi":"https://doi.org/10.1016/j.jhydrol.2025.134035","theme":"Climate","color":"#98d4f1","year":2025,"publication_date":"2025-08-06","authors":["Ikechukwu Kalu","Christopher E. Ndehedehe","Vagner G. Ferreira","Sreekanth Janardhanan","Matthew Currell","Oluwafemi E. Adeyeri","Onuwa Okwuashi","Mark J. Kennard"],"summary":"This study develops a high-resolution drought index (dGdi) for Australia by downscaling GRACE terrestrial water storage anomalies with hydrological flux variables using machine learning. The index captures local drought conditions more accurately than original GRACE data and aligns well with existing drought indicators like SPEI, NDVI, and soil moisture indices across major Australian river basins, including the Murray-Darling Basin (MDB). The index detects both short-term agricultural drought and long-term drought evolutions, identifying the Canning Basin as at risk of long-term drought and ecological changes. The study also assesses influences of climate teleconnections such as ENSO, ONI, and IOD on regional drought patterns.","finding":"The downscaled GRACE drought index (dGdi) agrees well with in-situ groundwater measurements, standardized soil moisture indices (SSMI), SPEI, and NDVI, effectively capturing local-scale and long-term drought conditions across Australian river basins, particularly the MDB which experienced significant drought and recovery phases between 2003 and 2022.","finding_evidence":{"passage_id":"p007-004","page":7,"quote":"For best comparisons, a similar index for the groundwater readings were computed and used for this validation. The yellow vertical line in the figure marks the heavy rainfall event in southeast Australia, which contributed to the end of the Millennium Drought era. This event was significant not only for its regional impact but also for the contrast it provided with rainfall patterns in the Northern Territory, an area that did not experience the millennium drought. By correlating the observed shifts in the GRACE drought index with the timing of this heavy rainfall, we were able to assess the accuracy of the downscaled drought estimates in reflecting real-world hydrological changes. This validation approach, which uses the SSMI and in-situ groundwater measurements against the dGdi, ensures that our downscaled product remains consistent with observed hydrological conditions and at a similar spatial resolution.","verified_in_page":true},"topics":["drought monitoring","remote sensing","terrestrial water storage","machine learning","groundwater","climate teleconnections","hydrology","vegetation health"],"study_period":"2003–2022","locations":[{"name":"Canning Basin","kind":"basin","role":"study basin for drought assessment and long-term drought risk evaluation","river_name":null,"geocode_query":"Canning Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"To address this, we developed fine-scale drought indices based on Gravity Recovery and Climate Experiment (GRACE)-derived terrestrial water storage anomalies (TWSA) using a statistical downscaling approach. This was achieved by employing a Random Forest machine learning algorithm to integrate key water budget terms (i.e., precipitation, evapotranspiration, runoff and deep drainage) into the original GRACE grids to achieve a drought index at 5 km spatial resolution. The resulting downscaled GRACE drought index (dGdi) is effective for localized drought predictions, providing a comprehensive picture of hydrological and climatic conditions over major river basins in Australia. Application of this downscaled drought index over the Canning Basin, Western Australia, reveals long-term drought evolutions indicating that the region is at a risk of a permanent shift in ecosystem composition (e.g., dominance of drought-tolerant invasive species), land degradation and aquifer depletion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[124.2984526,-18.1588541],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102217185,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-0"},{"name":"Carpentaria Basin","kind":"basin","role":"study basin for drought evaluation and hydrological analysis","river_name":null,"geocode_query":"Carpentaria Basin, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-006","page":7,"quote":"However, these trends shifted after the heavy rains in 2009 and 2010 that provided a more reliable water balance, thus improving the correlation between GRACE and in-situ groundwater bores in Fig. 3. The regions not significantly affected by the Millenium drought maintained an average correlation of r = 0.40 between GRACE and in-situ monitoring bores over the entire study period (i.e., 2003 to 2022). For example, the Canning Basin (Fig. 3a), CLA (Fig. 3d), and the Perth Basin (Fig. 3f). A similar observation was observed between dGdi and SSMI, where correlation values were generally lower before drought episodes and higher after them, as seen in the Canning, Central Eromanga, CLA, MDB, and Perth Basins. However, over the MDB, the correlation between dGdI and SSMI strengthened after the heavy rains, while the correlation between dGdi and in-situ groundwater levels weakened during the same period.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.3375404,-18.1121389],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":122859408,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064140,"main_basin_id":5040064140,"basin_region":"Australasia","basin_area_km2":178643.3,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-1"},{"name":"Central Eromanga Basin","kind":"basin","role":"study basin for drought progression and vegetation response analysis","river_name":null,"geocode_query":"Central Eromanga Basin, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"Basins respond well to influences of rainfall translating to a predictable drought index for them. 5.2. Validation of downscaled GRACE drought index using SPEI and NDVI To better understand climate influences on drought patterns across our study region, we partitioned the period into 2003 to 2009 and 2010 to 2022, with each respective period representing the Millenium drought and the post-Millenium drought era. During the Millenium and post-Milllenium drought era, the strongest monthly relationship between the dGdi and NDVI was observed in the Central and Western Eromanga basins (Fig. 5c, 5 g). This strong relationship could be attributed to the region’s groundwater-dependent ecosystems and arid to semi-arid climate. Vegetation in these areas relies heavily on groundwater due to limited and highly variable rainfall, making its health, as reflected in NDVI, sensitive to changes in total water storage.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.26722,-26.728409],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111006834,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-2","true_coordinates":[143.2672195,-26.6684091],"spread_km":6.7},{"name":"Cooper–Lake Eyre (CLA) Basin","kind":"basin","role":"study basin with strong correlation between drought index and vegetation health","river_name":null,"geocode_query":"Cooper–Lake Eyre Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"And since the GRACE data captures groundwater, soil moisture, and surface water anomalies, it provides a more comprehensive indicator of hydrological drought than precipitation alone. Also, groundwater is relatively shallow in these basins, allowing plants to access stored subsurface water during dry periods (Toupin et al., 1997; Department of Climate Change, 2024). For the SPEI values, the Carpentaria, Central Eromanga, MDB, and Western Eromanga all showed strong relationships during the Millenium drought era; however, after the rains, they all recorded low relationships except the MDB (Fig. 5e). This is mostly because, unlike the other basins, the MDB experienced two clear episodes of water storage recharge and decline during our study period (Fig. 4e), increasing its sensitivity to precipitation and evaporation. This led to a higher relationship with the SPEI during and after the Millenium drought.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.252382,-28.126469],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":119370154,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491380,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":19208.6,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-3","true_coordinates":[137.2923893,-28.1750101],"spread_km":6.7},{"name":"Murray–Darling Basin (MDB)","kind":"basin","role":"major study basin for drought evolution and validation of drought index","river_name":"Murray River","geocode_query":"Murray-Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Drought Monitor (a data-informed expert assessment of drought intensity and distribution across the USA). They observed a strong relationship between the two indicators, even under the influence of intensive groundwater abstractions. Over the Australian landscape (Fig. 1), Leblanc et al. (2009) employed the GRACE data to quantify the severity of drought over the Murray Darling Basin (MDB) in southeast Australia during the Millenium Drought and reported an estimated 50 % groundwater loss between 2001 and 2007. Their study showed a significant correlation between in-situ groundwater monitoring data and GRACE data, both confirming the 6-year groundwater decline over the study region. Also, Yang et al. (2014) identified a strong decreasing trend over mainland Australia during the Millennium drought from 1997 to 2009 using a GRACE-based indicator.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.462153,-31.149981],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-4","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Perth Basin","kind":"basin","role":"study basin highlighting teleconnection climate influences and groundwater-dominated hydrology","river_name":null,"geocode_query":"Perth Basin, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"hydrological and ecosystem changes in the region. 5.5. Hotspots of climatic influence Fig. 9 presents the temporal relationship between the dGdi for all river basins and global teleconnection patterns from the Indian and Pacific Oceans. Specifically, we examined the influence of various teleconnections on the drought conditions across different basins to identify basins whose drought progressions are worsened by teleconnection impacts. While our experiment reveals low influence from the global indices in Australia’s drought progression, we identified some basins that are susceptible to these climate actions. For example, the Perth Basin witnessed a correlation of 0.41 and 0.43 from ENSO and ONI, respectively. This played a key role in the inconsistent patterns exhibited in the annual signals of dGdi, SPEI, NDVI and in-situ GWLs over the Perth Basin.","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.816225,-31.997362],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":70417278,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040056460,"main_basin_id":5040056460,"basin_region":"Australasia","basin_area_km2":128290.3,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-5","true_coordinates":[115.8616783,-31.9513993],"spread_km":6.7},{"name":"Western Eromanga Basin","kind":"basin","role":"study basin for drought and vegetation condition correlations","river_name":null,"geocode_query":"Western Eromanga Basin, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"Basins respond well to influences of rainfall translating to a predictable drought index for them. 5.2. Validation of downscaled GRACE drought index using SPEI and NDVI To better understand climate influences on drought patterns across our study region, we partitioned the period into 2003 to 2009 and 2010 to 2022, with each respective period representing the Millenium drought and the post-Millenium drought era. During the Millenium and post-Milllenium drought era, the strongest monthly relationship between the dGdi and NDVI was observed in the Central and Western Eromanga basins (Fig. 5c, 5 g). This strong relationship could be attributed to the region’s groundwater-dependent ecosystems and arid to semi-arid climate. Vegetation in these areas relies heavily on groundwater due to limited and highly variable rainfall, making its health, as reflected in NDVI, sensitive to changes in total water storage.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.26722,-26.608409],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111006834,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-0714c02d6cfa10b2fe208f21-site-6","true_coordinates":[143.2672195,-26.6684091],"spread_km":6.7}],"limitations":["Limited quality and spatial coverage of in-situ groundwater bores in some basins (e.g., Canning, Carpentaria, Central Eromanga, Western Eromanga) requiring data interpolation.","Use of groundwater level measurements rather than converted groundwater storage due to unavailable aquifer parameters.","Validation limited by point-based groundwater monitoring data which may not fully represent spatial heterogeneity.","Uncertainty in downscaled GRACE data inherent due to global nature and scale without ground truth 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-8","basin_ids":[5040056460,5040059440,5040064140,5040491380,5040491510,5040591010],"main_basin_ids":[5040056460,5040059440,5040064140,5040073410,5040087590]},{"id":"doc-10dfef3a6d8fa5173e0a15b9","title":"Water or Energy Supply Driven Change in Moisture Availability Switches the Slope Direction Between Shallow Soil Moisture and Actual Evapotranspiration: Interpretation Within the Context of the Complementary Relationship","display_title":"Water vs Energy Supply Effects on Soil Moisture and Evapotranspiration","doi":"https://doi.org/10.1029/2024wr039632","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-07-31","authors":["Thomas G. Van Niel","Tim R. McVicar","Nik Callow","Caitlin E. Moore","Jason Beringer","Dejun Cai"],"summary":"This study investigated how changes in water supply or energy supply influence ecosystem moisture availability (ma), actual evapotranspiration (E), and shallow soil moisture (S) dynamics. Using simulations and flux tower observations, the authors distinguished between water-driven and energy-driven changes, revealing a polarity switch in the relationships between these variables depending on which supply varied more. The study also assessed two complementary relationship (CR) model versions, finding that a dynamic Priestley–Taylor α parameter enhances model performance, especially under energy-driven conditions.","finding":"The relationship between shallow soil moisture (S) and actual evapotranspiration (E) switches polarity based on whether water supply or energy supply predominantly drives moisture availability changes, with a positive slope for water-driven conditions and a negative slope for energy-driven conditions.","finding_evidence":{"passage_id":"p014-000","page":14,"quote":"When 8‐daily observed S was plotted against observed E, the relationships that were proposed in the introduction and summarized in Table 2 were evident (Figure 8). Water‐driven Howard Springs had an overall positive (although non‐linear) slope (Figure 8a) and the energy‐driven Wombat site had an inverse slope (Figure 8d). Their proposed relationships were also observed where at Howard Springs ma and both E and S had a positive relationship, indicated by low ma (red markers) in the lower left‐hand corner and high ma (blue markers) in the upper right‐hand corner of the data space (Figure 8a). Observed ma and both E and S had an inverse relationship at Wombat, indicated by high ma (blue markers) in the lower right‐hand corner and low ma (yellow markers) in the upper left‐hand corner of the data space (Figure 8d).","verified_in_page":true},"topics":["actual evapotranspiration","shallow soil moisture","moisture availability","complementary relationship","energy supply","water supply","evaporation modeling","flux tower observations"],"study_period":null,"locations":[{"name":"Howard Springs","kind":"station","role":"Flux tower site with water-driven moisture availability","river_name":null,"geocode_query":"Howard Springs, Northern Territory, Australia","latitude":-12.495,"longitude":131.15,"evidence":{"passage_id":"p004-001","page":4,"quote":"The conversion factor from kg m− 2 s − 1 to mm d− 1 is (c · dt)/ρw, where c = 1,000 (mm m− 1 ) is the number of mm in a meter, dt = 86,400 (s d− 1 ) is the number of seconds per day, and ρw (kg m− 3 ) is the density of water. Latent heat flux (J s − 1 m− 2 = W m− 2 ) is derived by multiplying the latent heat of vaporization (J kg− 1 ) by the evaporation rate (λE) and will herein be used to express energy units. 2.2. Observed Water‐ and Energy‐Driven ma We further tested whether observed data from two sites representing real‐world water‐ and energy‐driven conditions replicated the simulated relationships, and specifically resulted in the correct signs of the relationships postulated in the introduction. Half‐hourly gap‐filled OzFlux Level 6 flux and meteorological data (Beringer et al., 2022) were acquired for Howard Springs, NT, Australia (AU‐How: 12.495°S, 131.150°E) and Wombat State Forest, Vic., Australia (AU‐Wom: 37.422°S, 144.094°E), Figure 2a.","verified_in_page":true},"resolution_status":"resolved","coordinates":[131.15,-12.495],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":false,"catchment_id":102213457,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-10dfef3a6d8fa5173e0a15b9-site-0"},{"name":"Wombat State Forest","kind":"station","role":"Flux tower site with energy-driven moisture availability","river_name":null,"geocode_query":"Wombat State Forest, Victoria, Australia","latitude":-37.422,"longitude":144.094,"evidence":{"passage_id":"p004-001","page":4,"quote":"The conversion factor from kg m− 2 s − 1 to mm d− 1 is (c · dt)/ρw, where c = 1,000 (mm m− 1 ) is the number of mm in a meter, dt = 86,400 (s d− 1 ) is the number of seconds per day, and ρw (kg m− 3 ) is the density of water. Latent heat flux (J s − 1 m− 2 = W m− 2 ) is derived by multiplying the latent heat of vaporization (J kg− 1 ) by the evaporation rate (λE) and will herein be used to express energy units. 2.2. Observed Water‐ and Energy‐Driven ma We further tested whether observed data from two sites representing real‐world water‐ and energy‐driven conditions replicated the simulated relationships, and specifically resulted in the correct signs of the relationships postulated in the introduction. Half‐hourly gap‐filled OzFlux Level 6 flux and meteorological data (Beringer et al., 2022) were acquired for Howard Springs, NT, Australia (AU‐How: 12.495°S, 131.150°E) and Wombat State Forest, Vic., Australia (AU‐Wom: 37.422°S, 144.094°E), Figure 2a.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.094,-37.422],"precision":"Paper-reported coordinate · found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43662587,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-10dfef3a6d8fa5173e0a15b9-site-1"}],"limitations":["Theoretical steady-state assumptions limit representation of non-saturated conditions","Use of shallow soil moisture (top 100 mm) does not capture full root zone dynamics","Coordinates provided only for flux tower sites; other locations or catchments not specified in detail"],"river_ids":[],"site_ids":["doc-10dfef3a6d8fa5173e0a15b9-site-0","doc-10dfef3a6d8fa5173e0a15b9-site-1"],"anchor":[144.094,-37.422],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[5040061670,5040591050],"main_basin_ids":[5040061670,5040073410]},{"id":"doc-12f5cdb8fd1506597cd33eee","title":"Measuring How Water‐Related Policies of the Global South Consider Gender: Insights From Trialling a New Policy Gender Index in Nepal","display_title":"Gender Inclusivity in Nepal's Water Policies","doi":"https://doi.org/10.1002/app5.70029","theme":"Water management","color":"#eeabac","year":2025,"publication_date":"2025-06-20","authors":["Susan Cuddy","Sanju Koirala","Shahriar Wahid","Dave Penton"],"summary":"This study introduces the Multi-Dimensional Index of Gender in Water Policy (MDI-GWP), a tool designed to evaluate how well gender considerations are incorporated in water-related policies. It was trialled on 16 federal water-related policies in Nepal across water resources management (WRM), agriculture, and water, sanitation and hygiene (WASH) sectors. The analysis revealed a progressive inclusion of gender in agriculture and WASH policies over the past 30 years, while WRM policies have become gender blind again. The index highlights a critical gap in intersectionality—policies often treat women as a homogenous group without accounting for differences such as caste or social status. The study recommends enhanced focus on intersectional identities, empowerment of marginalized women, revising user group membership criteria, and strengthening policy monitoring frameworks.","finding":"Nepal’s federal water-related policies in agriculture and WASH sectors show an improving trajectory in gender inclusivity, but WRM policies have reverted to being largely gender blind.","finding_evidence":{"passage_id":"p014-005","page":14,"quote":"MDI‐GWP was underpinned by gender theory, such as Kanenberg et al. (2019), and contextualised to water and development through a detailed literature review. This paper makes important contributions by integrating feminist perspectives into policymaking and introducing the MDI‐ GWP index. This index is a practical tool designed to measure various aspects of gender inclusivity in policies, providing policymakers and researchers with a useful method to include gender considerations in water‐related policies. The development of the MDI‐GWP index itself is a methodological advancement. MDI‐GWP was successfully trialled on 16 of Nepalʼs federal water‐related policies. Through applying MDI‐GWP, we identified intersectionality as a blind spot in Nepalʼs water‐related policies. We could also discriminate between progress in the agriculture, WRM and WASH sectors.","verified_in_page":true},"topics":["policy analysis","gender equality","water policy","feminist policy analysis","intersectionality","water resources management","agriculture policy","sanitation policy"],"study_period":"1992–2020","locations":[{"name":"Nepal","kind":"region","role":"study region for water-related gender policies","river_name":null,"geocode_query":"Nepal","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"increasing womenʼs share of decision‐making positions in the public service from 2.0% to 7.5% by 2030. While SDG 6 (WASH) does not include specific reference to gender, it does include a continued commitment to IWRM. 2 | Background to the Nepal Trial Nepalʼs deep engagement with international donors, development banks, the United Nations, and active gender‐based research community, make it a prime location to examine the influence of gender on policy development. Much of the international investment in the country, besides seeking to improve the livelihoods of its people, is directly related to water—its generation as glaciers and snow in the Hindu Kush, and its many rivers with their potential for production of hydropower within Nepal, and the industries and societies that they sustain as they make their way through Nepal to India and Bangladesh. Developing water‐related policies and implementing programs poses many challenges.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Kamala River Basin, Nepal","kind":"region","role":"region involved in water resources development and monitoring initiatives cited in context of the study","river_name":"Kamala River","geocode_query":"Kamala River Basin, Nepal","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-003","page":15,"quote":"Cahill, A. 2008. “Power Over, Power to, Power With: Shifting Perceptions of Power for Local Economic Development in the Philippines.” Asia Pacific Viewpoint 49, no. 3: 294–304. https://doi.org/10.1111/j.1467‐ 8373.2008.00378.x. Clement, F., D. Suhardiman, and L. Bharati. 2017. “IWRM Discourses, Institutional Holy Grail and Water Justice in Nepal.” Water Alternatives 10, no. 3: 870. https://www.water‐alternatives.org/index.php/alldoc/ articles/vol10/v10issue3/386‐a10‐3‐12/file. CSIRO and WECS. 2021. Water Resources Development Strategy for the Kamala River Basin, Nepal. CSIRO, Australia and Water and Energy Commission Secretariat, Government of Nepal. https://doi.org/10. 25919/131j‐5d64. Derman, B., and P. Prabhakaran. 2016. “Reflections on the Formulation and Implementation of IWRM in Southern Africa From a Gender Perspective.” Water Alternatives 9, no. 3. https://www.water‐alternatives. org/index.php/alldoc/articles/vol9/v9issue3/325‐a9‐3‐14/file. Devkota, R., L. P. Pant, H.","verified_in_page":true},"resolution_status":"resolved","coordinates":[86.15991489999999,26.6449457],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kamala River, Nepal","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-12f5cdb8fd1506597cd33eee-site-1"}],"limitations":["The study is based solely on policy document analysis and does not assess actual implementation outcomes.","Scoring of policies was conducted by a single gender expert, which may introduce subjective bias.","Detailed geocoordinates for study sites or regions were not provided.","The focus was on national (federal) policies; sub-national policies were not included, limiting full context of policy impact."],"river_ids":[],"site_ids":["doc-12f5cdb8fd1506597cd33eee-site-1"],"anchor":[86.15991489999999,26.6449457],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[4040960480],"main_basin_ids":[4040025450]},{"id":"doc-185e4baa3b5bb6a201c01086","title":"A Distributional Regression Network With Data Transformation for Calibrating Rainfall Forecasts","display_title":"Transforming Data Improves Rainfall Forecast Calibration","doi":"https://doi.org/10.1029/2025jh000635","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-06-01","authors":["Zeqing Huang","Andrew Schepen","James Bennett","David Robertson","Tongtiegang Zhao","Eun‐Soon Im","Quan J. Wang"],"summary":"This study develops a distributional regression neural network incorporating a log-sinh data transformation and a unified loss function to calibrate 10-day rainfall forecasts from ECMWF numeric weather predictions. Using data from 2011–2022 over 563 catchments in central Tasmania, Australia, the methodology accounts for the positive skewness and zero bound of rainfall. Five experiments isolate the effect of data transformation in inputs and outputs, demonstrating improved forecast skill, reduced bias, and enhanced reliability compared to baseline neural networks and established Bayesian methods.","finding":"Incorporating a fixed-parameter log-sinh transformation in both the input normalization stage and the calibrated forecast generation stage of the neural network significantly reduces forecast bias to below 0.5%, improves reliability scores above 0.9, and achieves mean skill improvements of 2% to 11% over raw forecasts. This method marginally outperforms advanced Bayesian calibration methods such as BJP and ELFs, highlighting the critical role of data transformation in rainfall forecast calibration.","finding_evidence":{"passage_id":"p011-002","page":11,"quote":"This suggests that the three methods exhibit limited efficacy in correcting biases across individual subareas but achieve overall unbiasedness due to the compensating errors: that is, due to positive and negative biases canceling each other. Figure 5 shows that the RB of raw and calibrated forecasts tends to vary by month. For raw forecasts, the RB exhibits a temporalshift, transitioning from negative values during the May‐August period to positive values from November through February. Since rainfall over these subareas is concentrated between May and October, this means that the raw ECMWF forecasts tend to underestimate rainfall during the wet season and overestimate it during the dry season. The calibrated forecasts generated by the NNbase exhibit an overall positive bias of 5.79%. In contrast, the TR‐NN‐TF, TR‐NN‐TP, NN‐TF and NN‐TP (Figures 5c–5f) effectively reduce the mean RB to Figure 3.","verified_in_page":true},"topics":["rainfall","forecast calibration","machine learning","data transformation","neural networks","uncertainty quantification","ensemble forecasting","forecast bias"],"study_period":"2011-2022","locations":[{"name":"central Tasmania","kind":"region","role":"primary study area encompassing 563 rainfall catchment subareas","river_name":null,"geocode_query":"central Tasmania, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"of normal predictive distributions and yPʹ( gi), which accounts for the positive skewness of rainfall observations (Wang et al., 2012). The distribution and transformation parameters are derived from the output layer by four link functions: ⎧⎪⎪⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎪⎪⎩ uP = tanh O1 ( 1 − tanh2 O1) − 1 2 σP = φ(O2)(1 − φ(O2)) − 1 εP = φ(O3) λP = 5φ(O4) (16) where φ(·) represents the sigmoid function φ(x) = ex (1 + ex ) − 1 , for which the return value ranges from 0 to 1. The four link functions are used to rescale the parameter ranges and facilitate convergence. For the TR‐NN‐TP and NN‐TP, the NN is trained to directly produce the two distribution parameters and the two transformation parameters that jointly characterize the predictive distributions for each target date.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Hydro Tasmania catchments","kind":"basin","role":"operational domain for rainfall observations and streamflow forecasting","river_name":null,"geocode_query":"Hydro Tasmania catchments, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"of normal predictive distributions and yPʹ( gi), which accounts for the positive skewness of rainfall observations (Wang et al., 2012). The distribution and transformation parameters are derived from the output layer by four link functions: ⎧⎪⎪⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎪⎪⎩ uP = tanh O1 ( 1 − tanh2 O1) − 1 2 σP = φ(O2)(1 − φ(O2)) − 1 εP = φ(O3) λP = 5φ(O4) (16) where φ(·) represents the sigmoid function φ(x) = ex (1 + ex ) − 1 , for which the return value ranges from 0 to 1. The four link functions are used to rescale the parameter ranges and facilitate convergence. For the TR‐NN‐TP and NN‐TP, the NN is trained to directly produce the two distribution parameters and the two transformation parameters that jointly characterize the predictive distributions for each target date.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Europe","kind":"region","role":"origin of rainfall forecasts from the ECMWF Ensemble Prediction System used as raw forecast inputs","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This paper highlights the role of data transformation in rainfall forecast calibration with neural networks. We develop a distributional regression network that accounts for the positive skewness and zero bound of rainfall by incorporating a normalizing transformation (log‐sinh) in both the input and output stages. A unified loss function is formulated based on the negative log‐likelihood function for parameter optimization. To test the role of data transformation, we conduct five calibration experiments: one that does not use transformation at all (the baseline) while the others use the log‐sinh transformation in different ways. All experiments are based on 10‐day rainfall forecasts from the European Centre for Medium‐range Weather Forecasts (ECMWF) from 2011 to 2022.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"}],"limitations":["Lack of precise latitude/longitude coordinates for catchment subareas in Tasmania","Large spatial aggregation of rainfall data limits finer-scale hydrological insights","Study limited to one NWP model (ECMWF) and Tasmania region, limiting global generalizability"],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[],"main_basin_ids":[]},{"id":"doc-33ea8cc1780238c4a7803628","title":"Computationally Efficient Hybrid Downscaling of Surf Zone Hydrodynamics: Methodology and Evaluation","display_title":"Hybrid Machine Learning for Coastal Wave Modeling","doi":"https://doi.org/10.1029/2024jh000523","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-05-28","authors":["Emilio R. Echevarría","Stephanie Contardo","Beatriz Pérez-Díaz","Ron Hoeke","Ben Leighton","Claire Trenham","Laura Cagigal","Fernando J. Méndez"],"summary":"This study presents a hybrid surf-zone modeling approach combining the numerical XBeach model with machine learning techniques (Deep Neural Networks, Radial Basis Function interpolators, and linear regression), aiming to efficiently and accurately predict nearshore wave and hydrodynamic parameters. The methodology involves selecting representative forcing conditions via the Maximum Dissimilarity Algorithm, running XBeach simulations for these cases, and building surrogate models to emulate XBeach output fields by decomposing data with Empirical Orthogonal Functions. The approach is applied and evaluated at two distinct Australian coastal sites: Secret Harbour, Western Australia, and Narrabeen, New South Wales, characterized by different tidal regimes and wave climates. Results demonstrate that the surrogate models, particularly the DNN and RBF, predict spatial wave heights, infragravity wave heights, mean wavelength, fraction of breaking waves, and depth-averaged currents with high accuracy and significantly reduced computational cost, enabling long-term hindcasts and rapid forecasts useful for coastal management and planning.","finding":"The hybrid surrogate model using Deep Neural Networks and Radial Basis Function interpolation can accurately predict spatial wave and hydrodynamic parameters in the surf zone with errors generally below 10%, while reducing computational costs by two orders of magnitude compared to full numerical simulations.","finding_evidence":{"passage_id":"p015-000","page":15,"quote":"decision‐making in coastal management and planning. Table 2 compares the estimated computational time required to generate a 40‐year deterministic simulation of XBeach outputs using both the fully numerical model and the surrogate model, which is a commonly used time frame to establish climatological baselines for extreme event analysis and coastal hazards assessment (e.g., Perez et al., 2017), except in certain areas such as those impacted by tropical cyclones (Haigh et al., 2014). Carrying out such long simulations with the fully dynamical numerical model can be quite computationally demanding, and the surrogate model can reduce this computational time by two orders of magnitude. Moreover, the versatility of this methodology extends beyond the specific outputs discussed herein; it can predict additional variables and can be adapted to other numerical models, such as SWAN, WAVEWATCHIII, and SCHISM‐WWM3 wave models, to develop corresponding surrogate models.","verified_in_page":true},"topics":["surf-zone hydrodynamics","machine learning","numerical modeling","empirical orthogonal functions","nearshore wave prediction","coastal hydrodynamics","hybrid modeling","computational efficiency"],"study_period":"1980–2024","locations":[{"name":"Secret Harbour Beach","kind":"region","role":"primary study domain for surf zone hydrodynamics model application","river_name":null,"geocode_query":"Secret Harbour Beach, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"Secret Harbour Beach, Western Australia Secret Harbour is located in south‐western Western Australia. It experiences a diurnal microtidal regime (mean tidal range <0.5 m) and a wave climate consisting of swell waves arriving remotely from the Southern Ocean (predominantly during winter) and locally generated wind waves due to strong sea breezes predominantly during summer; both wave sources approach the coastline from the south to south‐west and it is further partly sheltered from open ocean swell by a mostly continuous offshore reef located ∼6 km from the shore with a minimum depth of ∼5 m (Contardo et al., 2019; Segura et al., 2018). The model domain is defined with a rotated regular grid spanning approximately 9 km in the alongshore direction with a resolution of 15 m, and approximately 3 km with a resolution of 10 m in the cross‐shore direction. At this study site, in situ observations from previous studies are available (e.g., Contardo & Symonds, 2013).","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.759818,-32.4065698],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":70412796,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Secret Harbour WA 6173, Australia","basin_id":5040055960,"main_basin_id":5040055960,"basin_region":"Australasia","basin_area_km2":61996.0,"hydrology_source":"geofabric","site_id":"doc-33ea8cc1780238c4a7803628-site-0"},{"name":"Narrabeen Beach","kind":"region","role":"secondary study domain with contrasting tidal and wave regimes","river_name":null,"geocode_query":"Narrabeen Beach, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-004","page":4,"quote":"Narrabeen Beach, New South Wales Approximately 3,300 km away, Narrabeen Beach in central New South Wales is located on the opposite side of Australia from Secret Harbour (Figure 1); it experiencessemi‐diurnal tides, and while it isstill classified as micro‐ tidal (mean spring range of 1.3 m, e.g. see Turner et al., 2016), the range is roughly double that of Secret Harbour. Local wave climate is dominated by a mix of sources: trade‐wind swell associated with the subtropical high, Tasman Sea mid‐latitude cyclones and Australian East Coast Lows(Mortlock & Goodwin, 2015); wave incidence tends to be much less oblique than at Secret Harbour, but is also considerably more energetic and variable. The model domain consists of a regular grid which extends approximately 4.9 km in the alongshore direction and approximately 3.3 km in the cross‐shore; the lateral boundaries coincide with rocky headlands and adjacent reefs that delineate Narrabeen Beach.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.3033826,-33.7095423],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29055051,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Narrabeen Beach, New South Wales, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-33ea8cc1780238c4a7803628-site-1"}],"limitations":["Surrogate model training uses quasi-stationary simulations not accounting for morphological changes.","Bathymetry is held constant without incorporating morphological evolution affecting accuracy.","Intertidal wetting and drying introduce discontinuities that reduce EOF analysis effectiveness in model reconstruction.","Real-world accuracy depends on calibration quality of underlying XBeach numerical simulations.","EOF decomposition limits prediction of highly non-linear and discontinuous variables such as the fraction of breaking waves.","Predictive skill tested mostly against model output; surrogate accuracy relative to actual field conditions depends on numerical model validation."],"river_ids":[],"site_ids":["doc-33ea8cc1780238c4a7803628-site-0","doc-33ea8cc1780238c4a7803628-site-1"],"anchor":[115.759818,-32.4065698],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040055960,5040070710],"main_basin_ids":[5040055960,5040070710]},{"id":"doc-3d7c5f47837aaf44200f301f","title":"Salinity dynamics in the Sundarbans of Bangladesh: influence of climate, freshwater inflow, and sea level changes","display_title":"Salinity Trends in Bangladesh Sundarbans Mangrove","doi":"https://doi.org/10.1007/s10661-025-14667-2","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-10-20","authors":["Shahriar Wahid","Mohammed Mainuddin","Francis H. S. Chiew","Fazlul Karim","Shaikh Nahiduzzaman","Rubayat Alam","Md. Raqubul Hasib"],"summary":"This study investigates the salinity dynamics within the Bangladesh Sundarbans mangrove ecosystem over 26 years (1996–2022). By analysing spatial and temporal salinity patterns alongside climate variables, river discharge, and sea level changes, the study identifies increasing salinity trends, seasonal variations, and influences of freshwater inflow reductions and sea-level rise on the mangrove environment. It uses multi-station hydrometeorological and salinity monitoring data to provide location-specific assessments critical for ecosystem management and adaptation to climate change impacts.","finding":"Salinity has been consistently higher in the western Sundarbans with a significant upward trend since 2010, driven primarily by reduced freshwater inflow from the Ganges through the Gorai River and modulated by rainfall and temperature, while rising sea levels are contributing to water level increases at coastal monitoring points like Hiron Point.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"This study synthesised and analysed the best available data in the Bangladesh Sundarbans over the 26 years to quantify the characteristics, trends, and interrelationships of these variables spatially and temporally. The results reveal that salinity has been consistently higher in the western part of the Sundarbans, and extreme salinity levels have increased since 2010. There has been a statistically significant decrease in the freshwater inflow from the Ganges through the Gorai River (at the Gorai railway bridge location) since the early 1980s, which has led to increased downstream salinity. Strong statistical correlations enabled robust predictions of salinity as a function of rainfall and temperature. A rising water level trend at Hiron Point is attributed to the rise in sea levels. The absence of long-term measurement data prevents the exploration of salinity dynamics in the numerous creeks that crisscross the mangrove ecosystem.","verified_in_page":true},"topics":["salinity dynamics","freshwater inflow","climate change impacts","sea level rise","mangrove ecosystems","river discharge","statistical modeling","seasonal variation"],"study_period":"1996–2022","locations":[{"name":"Sundarbans","kind":"landscape","role":"main study area","river_name":null,"geocode_query":"Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Shared by southern Bangladesh and the southern part of West Bengal State of India, this is the largest single block of tidal halophytic mangrove forest in the world (Sarkar et al., 2019). The Sundarbans extend from 21°27′30′′ to 22°30′30′′ north latitude and 87°55′01′′ to 89°00′00′′ east longitude and cover an area of approximately 10,000 km2 (Fig. 1). Approximately 60% of the total Sundarbans lies in Bangladesh and 40% in India (Spalding et al., 2010). Unfortunately, acquiring consistent and longterm data from the Indian Sundarbans proved challenging within the scope of this study. Therefore, our analysis is limited to the Bangladesh Sundarbans, for which we compiled a robust dataset. The unique morphological feature of the Sundarbans is a conglomeration of mudflats, coastal dunes, creeks, island beaches, estuaries, river channels, inlets, and mangrove swamps, where the individual components are interdependent.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.7157113,25.7578022],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarban, Bangladesh","basin_id":4040960200,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":185347.6,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-0"},{"name":"Bangladesh Sundarbans","kind":"landscape","role":"primary focus of salinity and climate analysis","river_name":null,"geocode_query":"Bangladesh Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This study synthesised and analysed the best available data in the Bangladesh Sundarbans over the 26 years to quantify the characteristics, trends, and interrelationships of these variables spatially and temporally. The results reveal that salinity has been consistently higher in the western part of the Sundarbans, and extreme salinity levels have increased since 2010. There has been a statistically significant decrease in the freshwater inflow from the Ganges through the Gorai River (at the Gorai railway bridge location) since the early 1980s, which has led to increased downstream salinity. Strong statistical correlations enabled robust predictions of salinity as a function of rainfall and temperature. A rising water level trend at Hiron Point is attributed to the rise in sea levels. The absence of long-term measurement data prevents the exploration of salinity dynamics in the numerous creeks that crisscross the mangrove ecosystem.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.18333,21.889727],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarbans","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-1","true_coordinates":[89.1833304,21.9497274],"spread_km":6.7},{"name":"Gorai River","kind":"river","role":"main distributary providing freshwater inflow controlling salinity","river_name":"Gorai River","geocode_query":"Gorai River, Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This study synthesised and analysed the best available data in the Bangladesh Sundarbans over the 26 years to quantify the characteristics, trends, and interrelationships of these variables spatially and temporally. The results reveal that salinity has been consistently higher in the western part of the Sundarbans, and extreme salinity levels have increased since 2010. There has been a statistically significant decrease in the freshwater inflow from the Ganges through the Gorai River (at the Gorai railway bridge location) since the early 1980s, which has led to increased downstream salinity. Strong statistical correlations enabled robust predictions of salinity as a function of rainfall and temperature. A rising water level trend at Hiron Point is attributed to the rise in sea levels. The absence of long-term measurement data prevents the exploration of salinity dynamics in the numerous creeks that crisscross the mangrove ecosystem.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.43493576077148,23.45216245500611],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Madhumati River, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-2"},{"name":"Gorai Railway Bridge","kind":"station","role":"freshwater discharge monitoring station","river_name":"Gorai River","geocode_query":"Gorai Railway Bridge, Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This study synthesised and analysed the best available data in the Bangladesh Sundarbans over the 26 years to quantify the characteristics, trends, and interrelationships of these variables spatially and temporally. The results reveal that salinity has been consistently higher in the western part of the Sundarbans, and extreme salinity levels have increased since 2010. There has been a statistically significant decrease in the freshwater inflow from the Ganges through the Gorai River (at the Gorai railway bridge location) since the early 1980s, which has led to increased downstream salinity. Strong statistical correlations enabled robust predictions of salinity as a function of rainfall and temperature. A rising water level trend at Hiron Point is attributed to the rise in sea levels. The absence of long-term measurement data prevents the exploration of salinity dynamics in the numerous creeks that crisscross the mangrove ecosystem.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.18009359999999,23.8862421],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Garai Railway Bridge, Gorai Railway Bridge, Dhaka - Kushtia Hwy, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-3"},{"name":"Hiron Point","kind":"station","role":"coastal tidal water level and salinity monitoring station to assess sea level rise influence","river_name":null,"geocode_query":"Hiron Point, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"This study synthesised and analysed the best available data in the Bangladesh Sundarbans over the 26 years to quantify the characteristics, trends, and interrelationships of these variables spatially and temporally. The results reveal that salinity has been consistently higher in the western part of the Sundarbans, and extreme salinity levels have increased since 2010. There has been a statistically significant decrease in the freshwater inflow from the Ganges through the Gorai River (at the Gorai railway bridge location) since the early 1980s, which has led to increased downstream salinity. Strong statistical correlations enabled robust predictions of salinity as a function of rainfall and temperature. A rising water level trend at Hiron Point is attributed to the rise in sea levels. The absence of long-term measurement data prevents the exploration of salinity dynamics in the numerous creeks that crisscross the mangrove ecosystem.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Khulna","kind":"station","role":"Salinity and climate monitoring station further inland with lower salinity","river_name":null,"geocode_query":"Khulna, Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Results Salinity variation and trends We analysed the salinity data from the six locations from 2009 to 2022 to quantify the spatiotemporal trends and variations. The data was smoothed using a rolling mean with a 24-h window to account for daily fluctuations in tidal areas. Figure 1 shows the six salinity measurement locations of the Sundarbans: Khulna, Mongla, Nalianala, Kobadak, Kaikhali, and Hiron Point. Box plots were prepared to compare salinity variation across different stations (Fig. 2). The central line in each box represents the median salinity value for each station. The median shows the central tendency of salinity measurements at each location. The height of each box indicates the interquartile range, representing the middle 50% of the data (from the 25th to the 75th percentiles). A smaller box height suggests that the salinity values are closer to the median, while a taller box indicates more variability.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.540047,22.897329],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Khulna, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-5","true_coordinates":[89.54004719999999,22.8373287],"spread_km":6.7},{"name":"Mongla","kind":"station","role":"Salinity and climate monitoring station near freshwater inflow and tidal influence","river_name":null,"geocode_query":"Mongla, Khulna Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"Results Salinity variation and trends We analysed the salinity data from the six locations from 2009 to 2022 to quantify the spatiotemporal trends and variations. The data was smoothed using a rolling mean with a 24-h window to account for daily fluctuations in tidal areas. Figure 1 shows the six salinity measurement locations of the Sundarbans: Khulna, Mongla, Nalianala, Kobadak, Kaikhali, and Hiron Point. Box plots were prepared to compare salinity variation across different stations (Fig. 2). The central line in each box represents the median salinity value for each station. The median shows the central tendency of salinity measurements at each location. The height of each box indicates the interquartile range, representing the middle 50% of the data (from the 25th to the 75th percentiles). A smaller box height suggests that the salinity values are closer to the median, while a taller box indicates more variability.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.5901274,22.5304661],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mongla, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-6"},{"name":"Nalianala","kind":"station","role":"salinity monitoring station showing moderate salinity variability","river_name":null,"geocode_query":"Nalianala, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"Environ Monit Assess (2025) 197:1219 Page 9 of 24 1219 Vol.: (0123456789) (Fig. 2). The data shows a narrow interquartile range (IQR), indicating low variability in salinity across the years. This low variability suggests that Khulna experiences stable freshwater conditions, potentially due to effective river discharge during most seasons. Salinity levels at Kobadak exhibit a more comprehensive range, with a median value of around 17.6 ppt. The upper quartile reaches as high as 22.7 ppt, indicating higher salinity during specific periods, possibly in the dry season or when freshwater inflow is reduced. Kaikhali’s salinity distribution shows a similar pattern to Kobadak, with a slightly lower median value of around 13.6 ppt. The broader range of salinity levels suggests that Kaikhali experiences significant seasonal variations. Salinity at Nalianala has a median of 10.3 ppt, with the data showing a relatively consistent range.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.24802,21.949727],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarbans","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-7","true_coordinates":[89.1833304,21.9497274],"spread_km":6.7},{"name":"Kobadak","kind":"station","role":"salinity hotspot station exhibiting highest salinity and seasonal variability","river_name":null,"geocode_query":"Kobadak, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"Environ Monit Assess (2025) 197:1219 Page 9 of 24 1219 Vol.: (0123456789) (Fig. 2). The data shows a narrow interquartile range (IQR), indicating low variability in salinity across the years. This low variability suggests that Khulna experiences stable freshwater conditions, potentially due to effective river discharge during most seasons. Salinity levels at Kobadak exhibit a more comprehensive range, with a median value of around 17.6 ppt. The upper quartile reaches as high as 22.7 ppt, indicating higher salinity during specific periods, possibly in the dry season or when freshwater inflow is reduced. Kaikhali’s salinity distribution shows a similar pattern to Kobadak, with a slightly lower median value of around 13.6 ppt. The broader range of salinity levels suggests that Kaikhali experiences significant seasonal variations. Salinity at Nalianala has a median of 10.3 ppt, with the data showing a relatively consistent range.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.27497534751933,22.65992615304695],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kobadak River, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-8"},{"name":"Kaikhali","kind":"station","role":"salinity monitoring station with sharp seasonal salinity changes","river_name":null,"geocode_query":"Kaikhali, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-000","page":9,"quote":"Environ Monit Assess (2025) 197:1219 Page 9 of 24 1219 Vol.: (0123456789) (Fig. 2). The data shows a narrow interquartile range (IQR), indicating low variability in salinity across the years. This low variability suggests that Khulna experiences stable freshwater conditions, potentially due to effective river discharge during most seasons. Salinity levels at Kobadak exhibit a more comprehensive range, with a median value of around 17.6 ppt. The upper quartile reaches as high as 22.7 ppt, indicating higher salinity during specific periods, possibly in the dry season or when freshwater inflow is reduced. Kaikhali’s salinity distribution shows a similar pattern to Kobadak, with a slightly lower median value of around 13.6 ppt. The broader range of salinity levels suggests that Kaikhali experiences significant seasonal variations. Salinity at Nalianala has a median of 10.3 ppt, with the data showing a relatively consistent range.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.0657027,22.2238121],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kaikhali, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-9"},{"name":"Passur River","kind":"river","role":"downstream distributary receiving freshwater from Gorai and contributing to Sundarbans hydrology","river_name":"Passur River","geocode_query":"Passur River, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"It comprises a large alluvial basin floored with Quaternary sediment deposited by the GBM rivers and their numerous distributaries (Ghosh et al., 2015; Sarkar et al., 2019). The Sundarbans present a complex ecosystem developed by an intricate system of tidal rivers, mudflats, and salt-tolerant mangrove forests (Hossain, 2018; Sarkar et al., 2019). With a tidal amplitude of more than 5 m, tidal influence extends to 130 km inland. This, combined with the low elevation of less than 3 m above mean sea level across a large region, has formed one of the world’s most extensive coastal lowlands (Snead, 2010). The hydrology is dominated by freshwater flow from the Ganges, which exhibits very high seasonal variation. The Gorai River is the major distributary of the Ganges River and a significant source of freshwater to the channels and creeks of the Sundarbans. The Gorai River delivers freshwater to the Sundarbans’ mangrove forest through the Madhumati, Passur, and Sibsa rivers.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.50146579999999,21.7502997],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pasur River, Bangladesh","hydrology_source":"none","site_id":"doc-3d7c5f47837aaf44200f301f-site-10"},{"name":"Sibsa River","kind":"river","role":"distributary in Sundarbans receiving freshwater from Gorai","river_name":"Sibsa River","geocode_query":"Sibsa River, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"It comprises a large alluvial basin floored with Quaternary sediment deposited by the GBM rivers and their numerous distributaries (Ghosh et al., 2015; Sarkar et al., 2019). The Sundarbans present a complex ecosystem developed by an intricate system of tidal rivers, mudflats, and salt-tolerant mangrove forests (Hossain, 2018; Sarkar et al., 2019). With a tidal amplitude of more than 5 m, tidal influence extends to 130 km inland. This, combined with the low elevation of less than 3 m above mean sea level across a large region, has formed one of the world’s most extensive coastal lowlands (Snead, 2010). The hydrology is dominated by freshwater flow from the Ganges, which exhibits very high seasonal variation. The Gorai River is the major distributary of the Ganges River and a significant source of freshwater to the channels and creeks of the Sundarbans. The Gorai River delivers freshwater to the Sundarbans’ mangrove forest through the Madhumati, Passur, and Sibsa rivers.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.4741457568276,22.26727579805228],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Shibsa River, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-11"},{"name":"Madhumati River","kind":"river","role":"distributary receiving freshwater from Gorai influencing Sundarbans salinity","river_name":"Madhumati River","geocode_query":"Madhumati River, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"It comprises a large alluvial basin floored with Quaternary sediment deposited by the GBM rivers and their numerous distributaries (Ghosh et al., 2015; Sarkar et al., 2019). The Sundarbans present a complex ecosystem developed by an intricate system of tidal rivers, mudflats, and salt-tolerant mangrove forests (Hossain, 2018; Sarkar et al., 2019). With a tidal amplitude of more than 5 m, tidal influence extends to 130 km inland. This, combined with the low elevation of less than 3 m above mean sea level across a large region, has formed one of the world’s most extensive coastal lowlands (Snead, 2010). The hydrology is dominated by freshwater flow from the Ganges, which exhibits very high seasonal variation. The Gorai River is the major distributary of the Ganges River and a significant source of freshwater to the channels and creeks of the Sundarbans. The Gorai River delivers freshwater to the Sundarbans’ mangrove forest through the Madhumati, Passur, and Sibsa rivers.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.18333,22.009727],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarbans","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-12","true_coordinates":[89.1833304,21.9497274],"spread_km":6.7},{"name":"Bay of Bengal","kind":"region","role":"source of tidal saline water influencing salinity intrusion and sea level rise impact","river_name":null,"geocode_query":"Bay of Bengal","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Vol.: (0123456789) Environ Monit Assess (2025) 197:1219 https://doi.org/10.1007/s10661-025-14667-2 RESEARCH Salinity dynamics in the Sundarbans of Bangladesh: influence of climate, freshwater inflow, and sea level changes Shahriar Wahid · Mohammed Mainuddin · Francis Chiew · Fazlul Karim · Shaikh Nahiduzzaman · Rubayat Alam · Md. Raqubul Hasib Received: 6 February 2025 / Accepted: 26 September 2025 © Crown 2025 Abstract The Sundarbans mangrove ecosystem, the world’s largest contiguous mangrove forest, is under pressure from changing salinity due to climate change, reduced freshwater input from upstream rivers, and tidal dynamics in the Bay of Bengal. Despite its ecological and socio-economic significance, previous studies have not provided a comprehensive, location-specific assessment of salinity that explicitly links spatial heterogeneity with hydroclimatic drivers.","verified_in_page":true},"resolution_status":"resolved","coordinates":[87.5395855,13.531665],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bay of Bengal","hydrology_source":"none","site_id":"doc-3d7c5f47837aaf44200f301f-site-13"},{"name":"Baleswor River","kind":"river","role":"eastern boundary of Sundarbans draining to Bay of Bengal","river_name":"Baleswor River","geocode_query":"Baleswor River, Sundarbans, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"1219 Page 4 of 24 Environ Monit Assess (2025) 197:1219 Vol:. (1234567890) sustainability of the Sundarbans’ ecosystems. On the eastern side, the Baleswor River forms the Sundarbans’ eastern boundary before emptying into the Bay of Bengal. The freshwater flows down the rivers with the tidal ingress, creating a gradient of salinity that varies spatially and temporally, with salinity Fig. 1 Monitoring stations’ data used in the study","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.976072,22.4884777],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Baleshwari River, Zianagar, Bangladesh","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-3d7c5f47837aaf44200f301f-site-14"}],"limitations":["Coordinates of locations are not provided explicitly, latitude and longitude set to null.","Study area defined broadly as Sundarbans and Bangladesh Sundarbans, limiting granularity.","Lack of long-term salinity data for numerous small creeks within Sundarbans ecosystem limiting detailed spatial assessment.","Evapotranspiration was not included in analysis due to lack of long-term data for the region.","Data gaps in time series were filled by interpolation, possibly introducing uncertainty."],"river_ids":[],"site_ids":["doc-3d7c5f47837aaf44200f301f-site-0","doc-3d7c5f47837aaf44200f301f-site-1","doc-3d7c5f47837aaf44200f301f-site-2","doc-3d7c5f47837aaf44200f301f-site-3","doc-3d7c5f47837aaf44200f301f-site-5","doc-3d7c5f47837aaf44200f301f-site-6","doc-3d7c5f47837aaf44200f301f-site-7","doc-3d7c5f47837aaf44200f301f-site-8","doc-3d7c5f47837aaf44200f301f-site-9","doc-3d7c5f47837aaf44200f301f-site-10","doc-3d7c5f47837aaf44200f301f-site-11","doc-3d7c5f47837aaf44200f301f-site-12","doc-3d7c5f47837aaf44200f301f-site-13","doc-3d7c5f47837aaf44200f301f-site-14"],"anchor":[89.18333,22.009727],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460,4040960200],"main_basin_ids":[4040025450,4040025460]},{"id":"doc-4ec17be6b3b38836869dd471","title":"Unveiling hidden dynamics: fine-scale mapping of groundwater-dependent ecosystems using multi-source Earth observations","display_title":"Fine-scale Mapping of GDEs in Hexi Corridor, China","doi":"https://doi.org/10.1080/17538947.2025.2528636","theme":"Groundwater","color":"#a8b9ff","year":2025,"publication_date":"2025-07-14","authors":["Yu Liang","Chunyan Cao","Xiaoyu Zhu","Sicong Gao","Yongqiang Zhang","Xuanlong Ma"],"summary":"This study develops a fine-scale 30 m resolution mapping workflow for groundwater-dependent ecosystems (GDEs) in the Hexi Corridor dryland region of northwestern China. Using multi-seasonal Landsat-8 NDVI and NDWI, groundwater depth, and other Earth observation datasets, the authors classify ecosystems into groundwater-dependent and non-dependent categories including natural vegetation, irrigated cropland, and water bodies. The mapping reveals that 34.23% of the Hexi Corridor is GDE, mainly in shallow groundwater areas within a highly arid climate. Validation with land cover, evapotranspiration, and groundwater data confirms the classification accuracy. Analysis shows more rapid groundwater storage decline correlated with areas of high GDE occurrence, highlighting vulnerability and need for sustainable management.","finding":"Groundwater-dependent ecosystems cover 34.23% of the Hexi Corridor study area, primarily occurring where groundwater table depths are shallow (<30 m) and in strongly arid regions (aridity index <0.1), demonstrating their vulnerability to groundwater depletion in drylands.","finding_evidence":{"passage_id":"p020-001","page":20,"quote":"Their percentage declines sharply with depth, dropping below 30% at >30 m and below 10% at >45 m. 4.5. Interplay of climate aridity and groundwater availability in determining the occurrence of GDEs To further elucidate the interplay between groundwater availability and the climatic aridity on GDE distribution, heatmaps were generated to explore the relationships between GDE percentage, water table depth (WTD), and the aridity index (AI) (see Figure 15). These visualizations provide critical insights into the spatial dynamics of GDEs under different hydroclimatic conditions. As can be seen, GDE prevalence is highest in areas with strong water-limitation (AI < 0.1) and shallow groundwater table (WTD < 50 m) (Figure 15a). As WTD increases (groundwater depth becomes deeper) and AI increases (climate becomes wetter), the proportion of GDEs declines significantly (Figure 15a).","verified_in_page":true},"topics":["groundwater dependence","remote sensing","ecosystem mapping","aridity","land cover","evapotranspiration","groundwater depth","climate impacts"],"study_period":"2020","locations":[{"name":"Hexi Corridor","kind":"region","role":"primary study area where GDE mapping was conducted","river_name":null,"geocode_query":"Hexi Corridor, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Focusing on the Hexi Corridor of northwestern China – a dryland region where groundwater sustains both ecosystems and human activities – this study aims to: (1) Develop a comprehensive GDE classification framework encompassing both natural and artificial GDEs; (2) Map the 30 m resolution spatial distribution of GDEs using Landsat imagery; (3) Validate results using groundwater depth models, evapotranspiration datasets, and land cover classifications. By advancing fine-scale GDE mapping, this research enhances understanding of GDE dynamics and their vulnerability to groundwater depletion, offering critical insights for sustainable groundwater management in drylands. 2. Data 2.1. Overview of the study area The Hexi Corridor (93°23′ E-104°12′ E, 37°17′ N-42°48′ N) spans 1,000 km in northwestern China, covering 215,000 km2, bordered by the Qilian Mountains (south), Arjinshan Mountains (west), and multiple deserts (north/east) (Meng et al. 2013).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Qilian Mountains","kind":"region","role":"recharge area feeding rivers and groundwater sustaining GDEs in Hexi Corridor","river_name":null,"geocode_query":"Qilian Mountains, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Snowmelt and glacial runoff from the Qilian Mountains feed major rivers like the Hei, Shiyang, and Shule, crucial for sustaining both natural habitats and agricultural activities, thereby making the region as a whole heavily reliant on groundwater resources. The Hexi Corridor features a diverse landscape composed of plains, mountains, hills, and deserts (Yao et al. 2018). Average annual temperatures range from 6°C to 9°C, with extremes reaching −30°C in the winter to 40°C in the summer (Gao and Zhang 2016). Precipitation varies, being around 200 mm in the eastern area and decreasing to about 50 mm in the west, with the majority of rainfall occurring between June and September (Yao et al. 2018). Due relatively low precipitation and high potential evaporation rates, ranging from 2,000–3,000 mm per year, groundwater resources are essential for maintaining both natural ecosystems and human agricultural activities (Cao et al. 2024).","verified_in_page":true},"resolution_status":"resolved","coordinates":[100,38.5],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Qilian Shan, Sunan Yugur Autonomous County, Zhangye, China, 734404","basin_id":4040050320,"main_basin_id":4040050320,"basin_region":"Central and South-East Asia","basin_area_km2":71257.7,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-1"},{"name":"Arjinshan Mountains","kind":"region","role":"western mountainous boundary influencing regional hydrology","river_name":null,"geocode_query":"Arjinshan Mountains, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Focusing on the Hexi Corridor of northwestern China – a dryland region where groundwater sustains both ecosystems and human activities – this study aims to: (1) Develop a comprehensive GDE classification framework encompassing both natural and artificial GDEs; (2) Map the 30 m resolution spatial distribution of GDEs using Landsat imagery; (3) Validate results using groundwater depth models, evapotranspiration datasets, and land cover classifications. By advancing fine-scale GDE mapping, this research enhances understanding of GDE dynamics and their vulnerability to groundwater depletion, offering critical insights for sustainable groundwater management in drylands. 2. Data 2.1. Overview of the study area The Hexi Corridor (93°23′ E-104°12′ E, 37°17′ N-42°48′ N) spans 1,000 km in northwestern China, covering 215,000 km2, bordered by the Qilian Mountains (south), Arjinshan Mountains (west), and multiple deserts (north/east) (Meng et al. 2013).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shiyang River basin","kind":"basin","role":"major river basin within Hexi Corridor supplying water to GDEs","river_name":"Shiyang River","geocode_query":"Shiyang River basin, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Snowmelt and glacial runoff from the Qilian Mountains feed major rivers like the Hei, Shiyang, and Shule, crucial for sustaining both natural habitats and agricultural activities, thereby making the region as a whole heavily reliant on groundwater resources. The Hexi Corridor features a diverse landscape composed of plains, mountains, hills, and deserts (Yao et al. 2018). Average annual temperatures range from 6°C to 9°C, with extremes reaching −30°C in the winter to 40°C in the summer (Gao and Zhang 2016). Precipitation varies, being around 200 mm in the eastern area and decreasing to about 50 mm in the west, with the majority of rainfall occurring between June and September (Yao et al. 2018). Due relatively low precipitation and high potential evaporation rates, ranging from 2,000–3,000 mm per year, groundwater resources are essential for maintaining both natural ecosystems and human agricultural activities (Cao et al. 2024).","verified_in_page":true},"resolution_status":"resolved","coordinates":[102.7480577892036,38.1455101738089],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040050550,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":115686.7,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-3"},{"name":"Hei River basin","kind":"basin","role":"major river basin within Hexi Corridor supplying water to GDEs","river_name":"Hei River","geocode_query":"Hei River basin, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Snowmelt and glacial runoff from the Qilian Mountains feed major rivers like the Hei, Shiyang, and Shule, crucial for sustaining both natural habitats and agricultural activities, thereby making the region as a whole heavily reliant on groundwater resources. The Hexi Corridor features a diverse landscape composed of plains, mountains, hills, and deserts (Yao et al. 2018). Average annual temperatures range from 6°C to 9°C, with extremes reaching −30°C in the winter to 40°C in the summer (Gao and Zhang 2016). Precipitation varies, being around 200 mm in the eastern area and decreasing to about 50 mm in the west, with the majority of rainfall occurring between June and September (Yao et al. 2018). Due relatively low precipitation and high potential evaporation rates, ranging from 2,000–3,000 mm per year, groundwater resources are essential for maintaining both natural ecosystems and human agricultural activities (Cao et al. 2024).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Shule River basin","kind":"basin","role":"major river basin supporting riparian GDEs in Hexi Corridor","river_name":"Shule River","geocode_query":"Shule River basin, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Snowmelt and glacial runoff from the Qilian Mountains feed major rivers like the Hei, Shiyang, and Shule, crucial for sustaining both natural habitats and agricultural activities, thereby making the region as a whole heavily reliant on groundwater resources. The Hexi Corridor features a diverse landscape composed of plains, mountains, hills, and deserts (Yao et al. 2018). Average annual temperatures range from 6°C to 9°C, with extremes reaching −30°C in the winter to 40°C in the summer (Gao and Zhang 2016). Precipitation varies, being around 200 mm in the eastern area and decreasing to about 50 mm in the west, with the majority of rainfall occurring between June and September (Yao et al. 2018). Due relatively low precipitation and high potential evaporation rates, ranging from 2,000–3,000 mm per year, groundwater resources are essential for maintaining both natural ecosystems and human agricultural activities (Cao et al. 2024).","verified_in_page":true},"resolution_status":"resolved","coordinates":[93.76324829115023,40.40149594659295],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":4040050280,"main_basin_id":4040050280,"basin_region":"Central and South-East Asia","basin_area_km2":101428.2,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-5"},{"name":"Zhangye","kind":"settlement","role":"major city within study area, contextual location","river_name":null,"geocode_query":"Zhangye, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Groundwater depth data Groundwater depth data were obtained from (http://thredds-gfnl.usc.es/thredds/catalog/ GLO BALWTDFTP/catalog.html) (Fan, Li, and Miguez-Macho 2013). The dataset provides monthly and annual water table depths at a 30-second global resolution. By incorporating interactions among soil moisture, surface water (lakes, rivers, and floodplains), and groundwater, it improves upon earlier models with enhanced ecohydrological accuracy (Figure 3). Figure 1. Geographic Location of the Hexi Corridor. This map illustrates the Hexi Corridor in northwestern China, highlighting key geographical features. Major cities (e.g. Zhangye, Jiuquan), rivers (Hei, Shiyang, Shule), and surrounding mountain ranges (Qilian, Arjin) are labeled. The Gobi, Badain Jaran, and Tengger Deserts are also marked. The elevation shading represents topographic variation, with higher elevations in darker tones. 4 Y. LIANG ET AL.","verified_in_page":true},"resolution_status":"resolved","coordinates":[100.44981,38.9259199],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Zhangye, Gansu, China","basin_id":4040050320,"main_basin_id":4040050320,"basin_region":"Central and South-East Asia","basin_area_km2":71257.7,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-6"},{"name":"Jiuquan","kind":"settlement","role":"major city within study area, contextual location","river_name":null,"geocode_query":"Jiuquan, Gansu, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"Groundwater depth data Groundwater depth data were obtained from (http://thredds-gfnl.usc.es/thredds/catalog/ GLO BALWTDFTP/catalog.html) (Fan, Li, and Miguez-Macho 2013). The dataset provides monthly and annual water table depths at a 30-second global resolution. By incorporating interactions among soil moisture, surface water (lakes, rivers, and floodplains), and groundwater, it improves upon earlier models with enhanced ecohydrological accuracy (Figure 3). Figure 1. Geographic Location of the Hexi Corridor. This map illustrates the Hexi Corridor in northwestern China, highlighting key geographical features. Major cities (e.g. Zhangye, Jiuquan), rivers (Hei, Shiyang, Shule), and surrounding mountain ranges (Qilian, Arjin) are labeled. The Gobi, Badain Jaran, and Tengger Deserts are also marked. The elevation shading represents topographic variation, with higher elevations in darker tones. 4 Y. LIANG ET AL.","verified_in_page":true},"resolution_status":"resolved","coordinates":[98.49394,39.7325499],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jiuquan, Gansu, China","basin_id":4040050320,"main_basin_id":4040050320,"basin_region":"Central and South-East Asia","basin_area_km2":71257.7,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-7"},{"name":"Gobi Desert","kind":"region","role":"arid desert region bordering Hexi Corridor marking landscape context","river_name":null,"geocode_query":"Gobi Desert, Mongolia/China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Focusing on the Hexi Corridor of northwestern China – a dryland region where groundwater sustains both ecosystems and human activities – this study aims to: (1) Develop a comprehensive GDE classification framework encompassing both natural and artificial GDEs; (2) Map the 30 m resolution spatial distribution of GDEs using Landsat imagery; (3) Validate results using groundwater depth models, evapotranspiration datasets, and land cover classifications. By advancing fine-scale GDE mapping, this research enhances understanding of GDE dynamics and their vulnerability to groundwater depletion, offering critical insights for sustainable groundwater management in drylands. 2. Data 2.1. Overview of the study area The Hexi Corridor (93°23′ E-104°12′ E, 37°17′ N-42°48′ N) spans 1,000 km in northwestern China, covering 215,000 km2, bordered by the Qilian Mountains (south), Arjinshan Mountains (west), and multiple deserts (north/east) (Meng et al. 2013).","verified_in_page":true},"resolution_status":"resolved","coordinates":[105.0323635,42.7951536],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gobi Desert","basin_id":4040050380,"main_basin_id":4040050380,"basin_region":"Central and South-East Asia","basin_area_km2":46481.8,"hydrology_source":"hydrobasins","site_id":"doc-4ec17be6b3b38836869dd471-site-8"},{"name":"Badain Jaran Desert","kind":"region","role":"desert region bordering Hexi Corridor influencing arid environment","river_name":null,"geocode_query":"Badain Jaran Desert, Inner Mongolia, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Focusing on the Hexi Corridor of northwestern China – a dryland region where groundwater sustains both ecosystems and human activities – this study aims to: (1) Develop a comprehensive GDE classification framework encompassing both natural and artificial GDEs; (2) Map the 30 m resolution spatial distribution of GDEs using Landsat imagery; (3) Validate results using groundwater depth models, evapotranspiration datasets, and land cover classifications. By advancing fine-scale GDE mapping, this research enhances understanding of GDE dynamics and their vulnerability to groundwater depletion, offering critical insights for sustainable groundwater management in drylands. 2. Data 2.1. Overview of the study area The Hexi Corridor (93°23′ E-104°12′ E, 37°17′ N-42°48′ N) spans 1,000 km in northwestern China, covering 215,000 km2, bordered by the Qilian Mountains (south), Arjinshan Mountains (west), and multiple deserts (north/east) (Meng et al. 2013).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tengger Desert","kind":"region","role":"desert region bordering Hexi Corridor influencing arid environment","river_name":null,"geocode_query":"Tengger Desert, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"Focusing on the Hexi Corridor of northwestern China – a dryland region where groundwater sustains both ecosystems and human activities – this study aims to: (1) Develop a comprehensive GDE classification framework encompassing both natural and artificial GDEs; (2) Map the 30 m resolution spatial distribution of GDEs using Landsat imagery; (3) Validate results using groundwater depth models, evapotranspiration datasets, and land cover classifications. By advancing fine-scale GDE mapping, this research enhances understanding of GDE dynamics and their vulnerability to groundwater depletion, offering critical insights for sustainable groundwater management in drylands. 2. Data 2.1. Overview of the study area The Hexi Corridor (93°23′ E-104°12′ E, 37°17′ N-42°48′ N) spans 1,000 km in northwestern China, covering 215,000 km2, bordered by the Qilian Mountains (south), Arjinshan Mountains (west), and multiple deserts (north/east) (Meng et al. 2013).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Use of NDVI and NDWI proxies may not fully capture fine-scale groundwater-ecosystem interactions","30 m resolution pixels can contain mixed land cover types causing classification challenges","Lack of in situ field measurements limits validation rigor","Meteorological data used for seasonal delineation have coarse resolution (~0.1°)","Potential limitations in distinguishing natural GDEs from irrigated croplands without direct groundwater use data"],"river_ids":[],"site_ids":["doc-4ec17be6b3b38836869dd471-site-1","doc-4ec17be6b3b38836869dd471-site-3","doc-4ec17be6b3b38836869dd471-site-5","doc-4ec17be6b3b38836869dd471-site-6","doc-4ec17be6b3b38836869dd471-site-7","doc-4ec17be6b3b38836869dd471-site-8"],"anchor":[100.44981,38.9259199],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-8","basin_ids":[4040050280,4040050320,4040050380,4040050550],"main_basin_ids":[4040007850,4040050280,4040050320,4040050380]},{"id":"doc-6129f86fe16166d21c6484ff","title":"Time shift between precipitation and evaporation has more impact on annual streamflow variability than the elasticity of potential evaporation","display_title":"Impact of Climate Synchronicity on Streamflow Variability","doi":"https://doi.org/10.5194/hess-29-5477-2025","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-10-21","authors":["Vazken Andréassian","Guilherme Mendoza Guimarães","Alban de Lavenne","Julien Lerat"],"summary":"This study analyses the influence of the synchronicity between precipitation and potential evaporation on annual streamflow variability using data from 4122 catchments across nine countries on four continents. It finds that the timing relationship between precipitation and evaporation anomalies is a significant predictor of yearly streamflow variability, more so than potential evaporation anomalies alone.","finding":"Accounting for the synchronicity between precipitation and potential evaporation significantly improves the prediction of annual streamflow variability, explaining an additional 6% of variance on average globally, and is more significant than potential evaporation anomalies in many catchments.","finding_evidence":{"passage_id":"p005-002","page":5,"quote":"Streamflow anomaly is clearly negatively correlated to the synchronicity index anomaly (3) for all countries. This negative correlation indicates that years with a lower 3 (i.e., when precipitation and potential evaporation are more out of phase) yield greater streamflow. This observation is perfectly hydro-logical, and conforms to general observations previously identified by Pardé (1933a). In the case of Australia, where streamflow anomalies are clearly negatively correlated to the synchronicity index anomaly (3) on Fig. 4, it is interesting to mention the opposite conclusion of Potter et al. (2005) who wrote that “the inclusion of seasonally varying forcing alone was not sufficient to explain variability in the mean annual water balance”. This surprising conclusion may be an artefact of the index chosen by the authors to describe seasonality.","verified_in_page":true},"topics":["streamflow variability","hydrological elasticity","climate variability","precipitation timing","potential evaporation","hydrology","statistical modeling","catchment hydrology"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Brazil","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Denmark","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"Denmark","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"France","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"France","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Germany","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sweden","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"Sweden","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Switzerland","kind":"region","role":"Study region with catchment data and list of almost natural catchments","river_name":null,"geocode_query":"Switzerland","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"This identification was done by either asking the datasets authors, or, where the information was available, by setting a limit equal to 10 mm equivalent volume storage in dams). For Switzerland, the list of almost natural catchments published by Muelchi et al. (2022) was utilized. 2.3 Climatic inputs Where several precipitation products were available in the original dataset, we used the product recommended by dataset authors as being of the best quality, while avoiding precipitation data based exclusively on satellite estimates. Hydrol. Earth Syst. Sci., 29, 5477–5491, 2025 https://doi.org/10.5194/hess-29-5477-2025","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United Kingdom","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States","kind":"region","role":"Study region with catchment data","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5479 Table 1. Origin of the catchments used in this paper. Country Number of Number of catchments available Dataset Reference catchments selected in the original dataset Australia 546 561 Camels-AUS Fowler et al. (2025) Brazil 636 734 Cabra Almagro et al. (2021) Denmark 202 304 Camels-DK Liu et al. (2025) France 628 654 Camels-FR Delaigue et al. (2025) Germany 1094 1555 Camels-DE Loritz et al. (2024) Sweden 152 158 Selection by Göran Lindström de Lavenne et al. (2022) Switzerland 73 331 Camels-CH Höge et al. (2023) United Kingdom 136 670 Camels-UK Coxon et al. (2020) USA 655 672 Camels-US Addor et al. (2017) In the original datasets, potential evaporation was computed with a variety of different formulas (Makkink, Morton, FAO-56, Penman-Monteith, Hargreaves, Oudin, etc.).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Meurthe River at Raon-l'Étape","kind":"river","role":"Illustrative catchment example for elasticity computation","river_name":"Meurthe River","geocode_query":"Raon-l'Étape, Meurthe River, France","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"V. Andréassian et al.: Time shift between precipitation and evaporation impacts annual streamflow 5481 Figure 2. Two series of precipitation and potential evaporation at catchment scale: the part of precipitation that is the most easily accessible to evaporation is illustrated in hatched pattern. Finally, Eqs. (6) and (7) were solved on a catchment-bycatchment basis, i.e., we computed 4122 distinct regressions. Figure 3 illustrates this catchment-based computation using the example of the Meurthe River at Raon-l’Étape (727 km2 ). For this catchment, annual streamflow anomalies exhibit a well-defined dependency on both precipitation and synchronicity anomalies, with the dependency on potential evaporation anomaly being very weak. The visual impression of Fig. 3 is confirmed by the results of the linear regressions of Eqs. (6) and (7) in Table 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[6.8428476,48.4073283],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"88110 Raon-l'Étape, France","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-6129f86fe16166d21c6484ff-site-9"}],"limitations":["Study uses linear regression models; more complex models could improve coefficient realism.","Catchments with high humidity index (>2) show less sensitivity to synchronicity, limiting generality in wet climates.","Coordinates for catchments are not reported, so precise geolocation is unavailable.","Some elasticity coefficients are outside physical realistic ranges, indicating estimation sensitivity issues.","Study relies on catchments with minimal regulation and interannual memory, limiting applicability to regulated or memory-affected catchments."],"river_ids":[],"site_ids":["doc-6129f86fe16166d21c6484ff-site-9"],"anchor":[6.8428476,48.4073283],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[2040023010],"main_basin_ids":[2040023010]},{"id":"doc-6f1ae31040cfb2b2c4be289b","title":"Greenhouse gas emissions from dry season rice irrigation in Bangladesh","display_title":"GHG Emissions from Dry Season Rice Irrigation in Bangladesh","doi":"https://doi.org/10.1007/s11027-024-10197-3","theme":"Water management","color":"#eeabac","year":2025,"publication_date":"2025-01-18","authors":["M. Maniruzzaman","Jatish Chandra Biswas","Md. Belal Hossain","Mohammed Mainuddin","Debjit Roy","Mst. Shetara Yesmin","Palash Kumar Kundu","Md. Mozammel Haque"],"summary":"This study estimates greenhouse gas (GHG) emissions from irrigation of dry season rice in Bangladesh during 2019–2020. It considers irrigation devices (deep tubewells, shallow tubewells, low lift pumps), their power sources (diesel and electricity), lifting heads, and area coverage. Results identified that groundwater pumping dominates irrigation-related GHG emissions (~2.04 Mt CO2e), constituting about 90% of total irrigation emissions (2.27 Mt CO2e). Rajshahi Division is the chief emission hotspot, followed by Rangpur and Mymensingh Divisions, due to intensive groundwater use. The study advocates expanding surface water irrigation and adopting water- and energy-saving technologies like alternate wetting and drying to reduce emissions and sustain groundwater levels.","finding":"Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons CO2e per dry season, with groundwater pumps contributing about 2.04 million tons CO2e, and Rajshahi Division being the main hotspot due to intensive groundwater use.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"GHG emissions varied with locations, sources of irrigation, fuel and water sources used. Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons (Mt) CO2e DS−1, which is about only 4% of agricultural sector GHG emission. Groundwater pumps contributed the lion shares (2.04 Mt CO2e DS−1), and surface water pumps contributed only 0.23 Mt CO2e DS−1. Based on the GHG emissions, Rajshahi Division is the main hotspot followed by Rangpur and Mymensingh Divisions, because of intensive groundwater used in these areas. Current deep tubewells (DTWs), shallow tubewells (STWs) and low lift pumps (LLPs) area coverage is about 19.2%, 56.8% and 24.0% of the total cultivable areas of the country; but it contributes about 49.1%, 40.6% and 10.3% of emitted GHG, respectively. The results revealed that withdrawal of groundwater is an important source of GHG emission.","verified_in_page":true},"topics":["greenhouse gas emissions","groundwater","irrigation","energy use","rice cultivation","climate mitigation","water management","agricultural sustainability"],"study_period":"2019–2020","locations":[{"name":"Rajshahi Division","kind":"region","role":"GHG emission hotspot from intensive groundwater irrigation","river_name":null,"geocode_query":"Rajshahi Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"GHG emissions varied with locations, sources of irrigation, fuel and water sources used. Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons (Mt) CO2e DS−1, which is about only 4% of agricultural sector GHG emission. Groundwater pumps contributed the lion shares (2.04 Mt CO2e DS−1), and surface water pumps contributed only 0.23 Mt CO2e DS−1. Based on the GHG emissions, Rajshahi Division is the main hotspot followed by Rangpur and Mymensingh Divisions, because of intensive groundwater used in these areas. Current deep tubewells (DTWs), shallow tubewells (STWs) and low lift pumps (LLPs) area coverage is about 19.2%, 56.8% and 24.0% of the total cultivable areas of the country; but it contributes about 49.1%, 40.6% and 10.3% of emitted GHG, respectively. The results revealed that withdrawal of groundwater is an important source of GHG emission.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Rangpur Division","kind":"region","role":"Secondary GHG emission hotspot due to groundwater irrigation","river_name":null,"geocode_query":"Rangpur Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"GHG emissions varied with locations, sources of irrigation, fuel and water sources used. Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons (Mt) CO2e DS−1, which is about only 4% of agricultural sector GHG emission. Groundwater pumps contributed the lion shares (2.04 Mt CO2e DS−1), and surface water pumps contributed only 0.23 Mt CO2e DS−1. Based on the GHG emissions, Rajshahi Division is the main hotspot followed by Rangpur and Mymensingh Divisions, because of intensive groundwater used in these areas. Current deep tubewells (DTWs), shallow tubewells (STWs) and low lift pumps (LLPs) area coverage is about 19.2%, 56.8% and 24.0% of the total cultivable areas of the country; but it contributes about 49.1%, 40.6% and 10.3% of emitted GHG, respectively. The results revealed that withdrawal of groundwater is an important source of GHG emission.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Mymensingh Division","kind":"region","role":"Significant GHG emission source from groundwater irrigation","river_name":null,"geocode_query":"Mymensingh Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"GHG emissions varied with locations, sources of irrigation, fuel and water sources used. Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons (Mt) CO2e DS−1, which is about only 4% of agricultural sector GHG emission. Groundwater pumps contributed the lion shares (2.04 Mt CO2e DS−1), and surface water pumps contributed only 0.23 Mt CO2e DS−1. Based on the GHG emissions, Rajshahi Division is the main hotspot followed by Rangpur and Mymensingh Divisions, because of intensive groundwater used in these areas. Current deep tubewells (DTWs), shallow tubewells (STWs) and low lift pumps (LLPs) area coverage is about 19.2%, 56.8% and 24.0% of the total cultivable areas of the country; but it contributes about 49.1%, 40.6% and 10.3% of emitted GHG, respectively. The results revealed that withdrawal of groundwater is an important source of GHG emission.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Dhaka Division","kind":"region","role":"Area with moderate groundwater depletion and irrigation energy use","river_name":null,"geocode_query":"Dhaka Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 Page 8 of 20 Mitig Adapt Strateg Glob Change (2025) 30:8 3 Results 3.1 Groundwater level, pumping lift, and energy use rates Maximum and minimum groundwater levels varied in different locations of the country (Fig. 4). The depth to groundwater level (GWL) was within 5 m in most areas except in Dhaka (mostly urban area) and Rajshahi (Barind tract area with low rainfall) regions at the end of the monsoon season in October/November. After October and/or November, GWL starts declining with irrigation season water withdrawal and reaches its peak in March/ April. In most cases, the maximum depth to GWL was within 8 m, within the safe suction limit for STWs. The GWL declined in most areas, especially in peak irrigation period (Fig. 4). The depth to GWL was the highest in the Rajshahi division followed by Mymensingh and Dhaka divisions (influenced by urban use) and the lowest was found in Barishal division followed by Chattogram and Khulna divisions (Table 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Barishal Division","kind":"region","role":"Area with relatively low groundwater level and GHG emission","river_name":null,"geocode_query":"Barishal Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 Page 8 of 20 Mitig Adapt Strateg Glob Change (2025) 30:8 3 Results 3.1 Groundwater level, pumping lift, and energy use rates Maximum and minimum groundwater levels varied in different locations of the country (Fig. 4). The depth to groundwater level (GWL) was within 5 m in most areas except in Dhaka (mostly urban area) and Rajshahi (Barind tract area with low rainfall) regions at the end of the monsoon season in October/November. After October and/or November, GWL starts declining with irrigation season water withdrawal and reaches its peak in March/ April. In most cases, the maximum depth to GWL was within 8 m, within the safe suction limit for STWs. The GWL declined in most areas, especially in peak irrigation period (Fig. 4). The depth to GWL was the highest in the Rajshahi division followed by Mymensingh and Dhaka divisions (influenced by urban use) and the lowest was found in Barishal division followed by Chattogram and Khulna divisions (Table 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Sylhet Division","kind":"region","role":"Region with lower GHG emission rates for irrigation","river_name":null,"geocode_query":"Sylhet Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-000","page":8,"quote":"8 Page 8 of 20 Mitig Adapt Strateg Glob Change (2025) 30:8 3 Results 3.1 Groundwater level, pumping lift, and energy use rates Maximum and minimum groundwater levels varied in different locations of the country (Fig. 4). The depth to groundwater level (GWL) was within 5 m in most areas except in Dhaka (mostly urban area) and Rajshahi (Barind tract area with low rainfall) regions at the end of the monsoon season in October/November. After October and/or November, GWL starts declining with irrigation season water withdrawal and reaches its peak in March/ April. In most cases, the maximum depth to GWL was within 8 m, within the safe suction limit for STWs. The GWL declined in most areas, especially in peak irrigation period (Fig. 4). The depth to GWL was the highest in the Rajshahi division followed by Mymensingh and Dhaka divisions (influenced by urban use) and the lowest was found in Barishal division followed by Chattogram and Khulna divisions (Table 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Chattogram Division","kind":"region","role":"Area with low GHG emissions from surface water irrigation","river_name":null,"geocode_query":"Chattogram Division, Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"On the other hand, the highest amounts of GHG emission because of LLPs were observed in Chattogram division (61.23 Kt CO2e) followed by Dhaka (43.97 Kt CO2e) and Sylhet (38.88 Kt CO2e) divisions. The spatial distribution of GHG emissions, as shown in Fig. 5, indicates that north and north-western regions are hotspots of GHG emission because of DTWs and STWs i.e. for groundwater irrigation. South-east, north-east, and southern regions are less GHG emitting hotspots because of LLPs i.e. surface water irrigation.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"North-Western region of Bangladesh","kind":"region","role":"Study site for field measurements of irrigation water use and pump lifts","river_name":null,"geocode_query":"North-Western Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"Mitig Adapt Strateg Glob Change (2025) 30:8 Page 7 of 20 8 geographical location, climatic conditions, soil properties, growth duration, rice varieties and its transplanting time (Mainuddin et al. 2015, 2019). We have measured the field scale irrigation water supplied to the individual rice fields in north-west region for different mode of irrigation devices like electrical and diesel operated DTWs and STWs to estimate the actual water use for rice production for the whole crop growing season over consecutive two dry seasons of 2015–16 and 2016–17 and we have also monitored water distribution patterns in about 435 farmer’s fields (Mainuddin et al. 2020, 2021). District-wise irrigation water requirements data for different irrigation devices were also collected from literatures (Karim et al. 2009; Rashid et al. 2009; Sarkar and Ali 2010; Dey et al. 2013; Mainuddin et al. 2015, 2019; Maniruzzaman et al. 2019).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Bangladesh","kind":"region","role":"Country-scale assessment of irrigation-related GHG emissions for dry season rice","river_name":null,"geocode_query":"Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"GHG emissions varied with locations, sources of irrigation, fuel and water sources used. Irrigation water driven GHG emission in Bangladesh is about 2.27 million tons (Mt) CO2e DS−1, which is about only 4% of agricultural sector GHG emission. Groundwater pumps contributed the lion shares (2.04 Mt CO2e DS−1), and surface water pumps contributed only 0.23 Mt CO2e DS−1. Based on the GHG emissions, Rajshahi Division is the main hotspot followed by Rangpur and Mymensingh Divisions, because of intensive groundwater used in these areas. Current deep tubewells (DTWs), shallow tubewells (STWs) and low lift pumps (LLPs) area coverage is about 19.2%, 56.8% and 24.0% of the total cultivable areas of the country; but it contributes about 49.1%, 40.6% and 10.3% of emitted GHG, respectively. The results revealed that withdrawal of groundwater is an important source of GHG emission.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Study based on selected locations and limited primary data, results are indicative.","No specific latitude/longitude coordinates provided for regions or field sites.","Extrapolations assume similar conditions across unsampled locations."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[],"main_basin_ids":[]},{"id":"doc-6f7ec41aafac52d7ab597d6f","title":"Monitoring agricultural and economic drought: the Australian Agricultural Drought Indicators (AADI)","display_title":"Australian Agricultural Drought Indicators (AADI)","doi":"https://doi.org/10.5194/nhess-25-3461-2025","theme":"Climate","color":"#98d4f1","year":2025,"publication_date":"2025-09-17","authors":["Neal Hughes","Donald Gaydon","Mihir Gupta","Andrew Schepen","Peter Tan","Geoffrey Brent","Andrew Turner","Sean Bellew","Wei Ying Soh","Christopher Sharman","Peter Taylor","John Carter","Dorine Bruget","Zvi Hochman","Ross Searle","Yong Song","Patrick Mitchell","Yacob Beletse","Dean Holzworth","Laura Guillory","Connor Brodie","Jonathon McComb","Ramneek Singh"],"summary":"This paper presents the Australian Agricultural Drought Indicators (AADI), an integrated bio-physical and economic modeling system designed to monitor and forecast agricultural and economic drought impacts across Australia's dry-land broadacre farming regions. The system uses gridded climate data, soil maps, synthetic farm business data, crop and pasture growth models, and a farm profit micro-simulation model to produce outcome-based drought indicators including crop yields, pasture growth, and farm profits. Validation against extensive observational agricultural, economic, and socio-economic data demonstrates higher correlation of these outcome-based indicators with real-world drought impacts compared to traditional meteorological indicators such as rainfall percentiles.","finding":"The farm profit indicator that incorporates both climate and commodity price variability correlates strongly with actual farm profits, drought-related financial stress indicators, and regional socio-economic impacts, substantially outperforming meteorological indicators like rainfall percentiles.","finding_evidence":{"passage_id":"p013-001","page":13,"quote":"Farm profit Farm profit Pasture Pasture Annual (AADI) (with prices) (AADI) (TSDM) rainfall Annual FHA claims lodged +158.1 +326.4 ∗ +69.3 +120.7 +90.9 House transfers −12.3 −16.9∗ −12.6 −10.5 −12.7 Dwelling transfers −14.1∗ −10.5 −10.6 −6.5 −10.3 Personal insolvencies +7.7 +15.0 ∗ +4.7 +9.3 +8.1 Job numbers −3.0 −3.0∗ −0.8 −2.0 −0.0 Agricultural job numbers −8.2∗ −7.4 −2.2 −4.0 +1.6 Total employee income −1.0 −1.6 −1.7 −1.9∗ −1.8 Total business income −28.1 −53.5∗ −22.7 −39.8 −26.7 Total income −5.8 −11.3∗ −5.0 −7.1 −4.4 Small-business exits +9.2 ∗ +7.2 +4.3 +1.3 +3.5 Births −3.1∗ −1.2 −1.2 −2.2 +0.1 Deaths +2.6 ∗ +1.1 +0.0 −0.2 −0.1 Net internal migration −8.3 −4.7 +1.7 −15.5∗ −8.5 ∗ Largest effect and statistically significant at 1 % level. Overall, the results demonstrate the value of an outcomebased approach to drought. The indicators of the AADI have consistently higher correlation with a range of agricultural and economic outcomes.","verified_in_page":true},"topics":["drought-indicators","agricultural-economics","climate-impacts","crop-yields","pasture-growth","economic-impacts","regional-socioeconomics","farm-profit-models"],"study_period":"1990–2023","locations":[{"name":"Australia","kind":"region","role":"study area","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Drought events can have significant agricultural and economic impacts, and in many parts of the world their intensity appears to be increasing with climate change. However, drought measurement remains a highly contested space, with a multitude of indicators across both research and operational settings. This article presents a new drought monitoring and forecasting system: the Australian Agricultural Drought Indicators (AADI). Rather than use common meteorological indicators, AADI attempts to estimate specific agricultural and economic drought impacts. An integrated bio-physical and economic modelling system is developed, which translates gridded climate observations and forecasts into outcome-based indicators of crop yields, pasture growth and farm business profits. These indicators are validated against a range of ground-truth data drawn from survey and administrative sources.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"New South Wales, Australia","kind":"region","role":"regional validation and performance assessment area","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Victoria, Australia","kind":"region","role":"regional validation and performance assessment area","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.425295,-37.033997],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-6f7ec41aafac52d7ab597d6f-site-2","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7},{"name":"Queensland, Australia","kind":"region","role":"regional validation and performance assessment area","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia, Australia","kind":"region","role":"regional validation and performance assessment area","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia, Australia","kind":"region","role":"regional validation and performance assessment area with noted weaker model performance","river_name":null,"geocode_query":"Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Tasmania, Australia","kind":"region","role":"regional validation and performance assessment area","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Territory, Australia","kind":"region","role":"regional validation and performance assessment area with mixed indicator performance","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The inclusion of commodity price variability leads to a significant improvement in skill, relative to the farm returns (climate only) indicator (mean regional correlation of 0.46), although the “climate only” indicator still yields a nontrivial improvement over a simple rainfall percentile (mean regional correlation of 0.25). The inclusion of commodity price effects tends to yield a larger gain in skill in livestockdominant regions (coastal, high-rainfall zones and inland, pastoral zones) given the greater relative exposure of these farms to price risk. Correlation maps (Fig. 4) show that the skill of the profit indicators tends to be higher in eastern Australia, with lower correlation observed in parts of South Australia (SA) and Western Australia (WA) along with far north Queensland. Appendix B presents higher-resolution correlation maps using interpolated observational data (Fig. B2).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Local Government Areas (LGA), Australia","kind":"region","role":"administrative units for socio-economic data and indicator validation","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"These observational data are compared with historical AADI percentile values (for the period 1990– 1991 to 2021–2022) that are aggregated to match the same regional scales. In addition to the four indicators in the current AADI UI (“climate only” farm profit, pasture growth, winter crop yield and summer crop yield), we also consider the “with prices” farm profit indicator, the AussieGRASS pasture biomass (total standing dry matter) indicator and rainfall percentiles. In Appendix B, we also present correlation maps at the grid scale, comparing AADI data against interpolated AAGIS variables. Agricultural production data are obtained from annual ABS and ABARES surveys, including a range of regional data from AAGIS over the period 1990–1991 to 2022–2023 (Table 1). Nat. Hazards Earth Syst. Sci., 25, 3461–3482, 2025 https://doi.org/10.5194/nhess-25-3461-2025","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Interpolation errors in gridded climate and agricultural data due to sparse weather stations and site observations.","Some validation data (e.g., pasture biomass proxies) are limited in availability and spatial coverage.","Predictive skill varies spatially with areas of weaker performance identified, notably in parts of South Australia, Western Australia and northern Queensland.","Observational validation datasets vary in spatial and temporal resolution and completeness.","Gridded observational data are partly interpolated from limited survey samples, which may introduce uncertainty."],"river_ids":["43640269","43643828","43645307"],"site_ids":["doc-6f7ec41aafac52d7ab597d6f-site-2"],"anchor":[147.425295,-37.033997],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-7b7ccd7a15b2ef5d5acf614d","title":"Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia","display_title":"Assessing BARRA and ERA5 on Australian Precipitation","doi":"https://doi.org/10.5194/hess-29-3527-2025","theme":"Climate","color":"#98d4f1","year":2025,"publication_date":"2025-08-05","authors":["Kevin K. W. Cheung","Fei Ji","Nidhi Nishant","Jin Teng","James Bennett","De Li Liu"],"summary":"This study evaluates the performance of two atmospheric reanalysis datasets, BARRA and ERA5, in simulating daily mean and extreme precipitation over Australia during 1990–2018. Using observational data from the Australian Gridded Climate Dataset (AGCD) as a benchmark, the study assesses climatology, temporal correlation, coefficient of variation, and trends across seven precipitation extreme indices. Both datasets successfully replicate spatial precipitation patterns and distributions, with ERA5 showing generally stronger temporal correlations and smaller biases, especially in northern Australia. However, both datasets underestimate increasing precipitation trends in Northern Australia, and biases become more pronounced for precipitation extremes and their variability.","finding":"ERA5 demonstrates higher accuracy than BARRA in capturing inter-annual precipitation variability and extreme precipitation indices over Australia, particularly in northern regions, although both underestimate increasing precipitation trends in Northern Australia.","finding_evidence":{"passage_id":"p007-002","page":7,"quote":"Both BARRA and ERA5 reproduce the major trend pattern; however clear biases can be observed over Northern Australia where both BARRA and ERA5 underestimate trend more than 100 % (i.e. trend of 0.08 mm d−1 per year with bias of similar magnitude). BARRA overestimated decreasing trend over Northern Queensland but ERA5 underestimate it (even increasing trend instead). In summary, evaluation of annual mean precipitation indicates both BARRA and ERA5 possess small biases (∼ 20 %) in the spatial precipitation patterns. ERA5 shows stronger temporal correlations than BARRA, particularly in northern Australia. Overall, ERA5 demonstrates higher accuracy in capturing inter-annual precipitation variability. Both BARRA and ERA5 captured spatial distribution of coefficient of variation reasonably well but with large biases (∼ 50 %). BARRA shows much larger biases than ERA5 especially for Northern Australia.","verified_in_page":true},"topics":["reanalysis datasets","precipitation extremes","climatology","temporal correlation","coefficient of variation","trend analysis","Australia","regional climate"],"study_period":"1990-2018","locations":[{"name":"Australia","kind":"region","role":"study region for precipitation extremes and mean climate evaluation","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-002","page":1,"quote":"Reanalysis datasets are critical in climate research and weather analysis, offering consistent historical weather and climate data crucial for understanding atmospheric phenomena and validating climate models. However, biases exist in reanalysis datasets that would affect their applications under circumstances. This study evaluates BARRA, which is a high-resolution reanalysis for the Australian region, and ERA5 in simulating mean precipitation and six selected precipitation extremes for their climatology, temporal correlation, coefficient of variation, and trend. Both datasets reproduce daily timescale probability density distributions and spatial patterns of mean precipitation well with minor biases. ERA5 shows stronger temporal correlations, superior interannual precipitation accuracy, and lower biases in coefficient of variation compared to BARRA, especially in Northern Australia.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Australia","kind":"region","role":"key subregion with noted biases and trends in precipitation and extremes","river_name":null,"geocode_query":"Northern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Both BARRA and ERA5 reproduce the major trend pattern; however clear biases can be observed over Northern Australia where both BARRA and ERA5 underestimate trend more than 100 % (i.e. trend of 0.08 mm d−1 per year with bias of similar magnitude). BARRA overestimated decreasing trend over Northern Queensland but ERA5 underestimate it (even increasing trend instead). In summary, evaluation of annual mean precipitation indicates both BARRA and ERA5 possess small biases (∼ 20 %) in the spatial precipitation patterns. ERA5 shows stronger temporal correlations than BARRA, particularly in northern Australia. Overall, ERA5 demonstrates higher accuracy in capturing inter-annual precipitation variability. Both BARRA and ERA5 captured spatial distribution of coefficient of variation reasonably well but with large biases (∼ 50 %). BARRA shows much larger biases than ERA5 especially for Northern Australia.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Northern Queensland","kind":"region","role":"subregion with contrasting bias trends between BARRA and ERA5","river_name":null,"geocode_query":"Northern Queensland, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Both BARRA and ERA5 reproduce the major trend pattern; however clear biases can be observed over Northern Australia where both BARRA and ERA5 underestimate trend more than 100 % (i.e. trend of 0.08 mm d−1 per year with bias of similar magnitude). BARRA overestimated decreasing trend over Northern Queensland but ERA5 underestimate it (even increasing trend instead). In summary, evaluation of annual mean precipitation indicates both BARRA and ERA5 possess small biases (∼ 20 %) in the spatial precipitation patterns. ERA5 shows stronger temporal correlations than BARRA, particularly in northern Australia. Overall, ERA5 demonstrates higher accuracy in capturing inter-annual precipitation variability. Both BARRA and ERA5 captured spatial distribution of coefficient of variation reasonably well but with large biases (∼ 50 %). BARRA shows much larger biases than ERA5 especially for Northern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.724157,-19.249674],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652762,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Queensland, QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-7b7ccd7a15b2ef5d5acf614d-site-2","true_coordinates":[146.7615238,-19.2982152],"spread_km":6.7},{"name":"Southeastern Australia","kind":"region","role":"region where BARRA shows better capture of local precipitation influenced by complex terrain","river_name":null,"geocode_query":"Southeastern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-001","page":5,"quote":"In contrast, BARRA does not show a clear spatial pattern, with more variability across different locations. Overall, ERA5 outperforms BARRA, particularly in inland areas, likely due to its global data assimilation approach. However, BARRA shows better agreement in the southeastern coastal regions, indicating its advantage in capturing local precipitation patterns influenced by complex terrain and coastal effects. 4.1.2 Bias and temporal correlation We evaluate precipitation simulated by BARRA and ERA5 against observations (AGCD). The mean annual precipitation from the three datasets and biases in BARRA and ERA5 compared to AGCD are shown in Fig. 2 (and Fig. S2 on the observation grid). Results show that both BARRA and ERA5 simulate the spatial patterns of mean annual precipitation very well with high rainfall in northern Australian, eastern Australia coast, and western Tasmania and low rainfall inland, albeit with clear biases.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Western Tasmania","kind":"region","role":"region noted for precipitation bias in both datasets","river_name":null,"geocode_query":"Western Tasmania, Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Compared to AGCD, both BARRA and ERA5 underestimate precipitation up to 20 % for Eastern Australian coast, southwest western Australia, and western Tasmania, but overestimate annual precipitation up to 30 % inland (Fig. S3). Some clear differences in biases between BARRA and ERA5 can be observed in central western Australia and northern Queensland where BARRA overestimate precipitation but ERA5 underestimate it. Domain averaged absolute bias in annual precipitation is about 0.17 mm d−1 (∼ 12.7 % relative bias with respect to domain average) for BARRA and 0.15 mm d−1 (∼ 10.5 % relative bias) for ERA5 (Table 2). The skill of simulated precipitation from BARRA and ERA5 is further demonstrated in the temporal correlations between BARRA/ERA5 and AGCD shown in Fig. 3 (and Fig. S4 on the observation grid). Temporal correlation of annual precipitation is larger in southeast Australia and northern Tasmania for both BARRA and ERA5, which is above 0.85.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Central Western Australia","kind":"region","role":"region showing divergence in precipitation bias between BARRA and ERA5","river_name":null,"geocode_query":"Central Western Australia, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-002","page":5,"quote":"Compared to AGCD, both BARRA and ERA5 underestimate precipitation up to 20 % for Eastern Australian coast, southwest western Australia, and western Tasmania, but overestimate annual precipitation up to 30 % inland (Fig. S3). Some clear differences in biases between BARRA and ERA5 can be observed in central western Australia and northern Queensland where BARRA overestimate precipitation but ERA5 underestimate it. Domain averaged absolute bias in annual precipitation is about 0.17 mm d−1 (∼ 12.7 % relative bias with respect to domain average) for BARRA and 0.15 mm d−1 (∼ 10.5 % relative bias) for ERA5 (Table 2). The skill of simulated precipitation from BARRA and ERA5 is further demonstrated in the temporal correlations between BARRA/ERA5 and AGCD shown in Fig. 3 (and Fig. S4 on the observation grid). Temporal correlation of annual precipitation is larger in southeast Australia and northern Tasmania for both BARRA and ERA5, which is above 0.85.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tasmania","kind":"region","role":"region assessed for precipitation PDF skills and biases","river_name":null,"geocode_query":"Tasmania, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"K. K. W. Cheung et al.: Comparison of BARRA and ERA5 in replicating mean and extreme precipitation 3531 Figure 1. Perkins skill score (PSS) of daily precipitation PDF between (a) BARRA and AGCD, and (b) ERA5 and AGCD. The regions with low density of station observations in AGCD has been masked and was not considered in all subsequent evaluation. 4.1.1 Daily precipitation PDF We first compare the Perkins Score for daily precipitation between BARRA, ERA5, and AGCD (Fig. 1). Both reanalyses generally capture the daily precipitation PDF well, with Perkins Scores exceeding 0.9 across most of Australia, indicating a strong agreement with observations. However, there are regional variations in performance. Scores are relatively lower in northern Australia and Tasmania, suggesting greater discrepancies in these areas. ERA5 exhibits noticeably higher scores inland compared to coastal regions, reflecting its improved representation of precipitation in interior regions.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Southeast Australia coast","kind":"region","role":"region with stronger temporal correlations and precipitation skill by reanalysis datasets","river_name":null,"geocode_query":"Southeast Australia coast, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-003","page":5,"quote":"This indicates inter-annual variability of precipitation is well captured by BARRA and ERA5. In contrast, temporal correlation is weaker for western inland and northern Australia. ERA5 generally has larger temporal correlation when compared with BARRA, especially for northern Australia, where temporal correlation for BARRA is below 0.5. On average, temporal correlation for ERA5 is 0.85, which is larger than 0.77 for BARRA (Table 2). 4.1.3 CV (coefficient of variation) and trend CV of annual precipitation for AGCD and biases between BARRA/ERA5 and AGCD are presented in Fig. 4 (and https://doi.org/10.5194/hess-29-3527-2025 Hydrol. Earth Syst. Sci., 29, 3527–3543, 2025","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Northwestern Australia","kind":"region","role":"region with noted precipitation PDF lower skill and bias variability","river_name":null,"geocode_query":"Northwestern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p005-000","page":5,"quote":"K. K. W. Cheung et al.: Comparison of BARRA and ERA5 in replicating mean and extreme precipitation 3531 Figure 1. Perkins skill score (PSS) of daily precipitation PDF between (a) BARRA and AGCD, and (b) ERA5 and AGCD. The regions with low density of station observations in AGCD has been masked and was not considered in all subsequent evaluation. 4.1.1 Daily precipitation PDF We first compare the Perkins Score for daily precipitation between BARRA, ERA5, and AGCD (Fig. 1). Both reanalyses generally capture the daily precipitation PDF well, with Perkins Scores exceeding 0.9 across most of Australia, indicating a strong agreement with observations. However, there are regional variations in performance. Scores are relatively lower in northern Australia and Tasmania, suggesting greater discrepancies in these areas. ERA5 exhibits noticeably higher scores inland compared to coastal regions, reflecting its improved representation of precipitation in interior regions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Australian Great Dividing Range","kind":"region","role":"region with precipitation biases and trend assessments","river_name":null,"geocode_query":"Great Dividing Range, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Both BARRA and ERA5 reproduce the major trend pattern; however clear biases can be observed over Northern Australia where both BARRA and ERA5 underestimate trend more than 100 % (i.e. trend of 0.08 mm d−1 per year with bias of similar magnitude). BARRA overestimated decreasing trend over Northern Queensland but ERA5 underestimate it (even increasing trend instead). In summary, evaluation of annual mean precipitation indicates both BARRA and ERA5 possess small biases (∼ 20 %) in the spatial precipitation patterns. ERA5 shows stronger temporal correlations than BARRA, particularly in northern Australia. Overall, ERA5 demonstrates higher accuracy in capturing inter-annual precipitation variability. Both BARRA and ERA5 captured spatial distribution of coefficient of variation reasonably well but with large biases (∼ 50 %). BARRA shows much larger biases than ERA5 especially for Northern Australia.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.942667,-24.97],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12124900,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Great Dividing Range, Caldervale QLD 4478, Australia","basin_id":5040069590,"main_basin_id":5040069590,"basin_region":"Australasia","basin_area_km2":140911.7,"hydrology_source":"geofabric","site_id":"doc-7b7ccd7a15b2ef5d5acf614d-site-9","true_coordinates":[147,-25],"spread_km":6.7}],"limitations":["Observational AGCD dataset has sparse station coverage in arid interior and high elevation regions in Australia leading to increased uncertainty in these areas.","Interpolation uncertainties affect gridded observation reliability, especially for extreme events and sparsely monitored regions like the Gibson Desert and Great Dividing Range.","The study period is limited to 1990–2018, constrained by BARRA dataset availability.","Coordinate data for specific sites is not provided, so latitude and longitude are set to null."],"river_ids":[],"site_ids":["doc-7b7ccd7a15b2ef5d5acf614d-site-2","doc-7b7ccd7a15b2ef5d5acf614d-site-9"],"anchor":[146.942667,-24.97],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040067860,5040069590],"main_basin_ids":[5040067860,5040069590]},{"id":"doc-7e8108d481bb74b97487c993","title":"Groundwater δ2H/δ18O isoscapes for New South Wales (Australia)","display_title":"Groundwater Isoscapes for New South Wales","doi":"https://doi.org/10.1016/j.ejrh.2025.102732","theme":"Groundwater","color":"#a8b9ff","year":2025,"publication_date":"2025-09-04","authors":["Diοni I. Cendón","Stuart Hankin","Jagoda Crawford","Liza K. McDonough","Catherine E. Hughes","Karina Meredith","Bryce F. J. Kelly","Stephen J. Harris","Matthias Raiber","Sue Gray","Richard T. Green","Fabienne Boudoux d’Hautefeuille"],"summary":"This study presents the first detailed multi-layer groundwater stable isotope maps (isoscapes) for New South Wales (NSW), Australia, based on over 4200 isotope data points from a range of aquifers. Using ordinary kriging across four depth intervals (1–30 m, 30–50 m, 50–300 m, 300–1000 m), the research reveals spatial patterns of groundwater recharge sources, including flood and snowmelt recharge impacts, evaporation effects increasing toward the arid west, and differences in isotopic signatures between shallow and deeper groundwater reflecting historical recharge. The isoscapes provide valuable insight into groundwater dynamics relevant for sustainable water management, agriculture, and ecosystem support, with applicability at both regional and catchment scales including detailed 5 × 5 km resolution mapping in the Namoi catchment.","finding":"The isoscapes reveal a clear east-west isotopic gradient across NSW with lower δ18O values in the east reflecting flood and snowmelt recharge and progressively higher δ18O values and lower deuterium excess toward the arid west due to evaporative enrichment, with isotopic signatures also varying vertically showing different recharge processes and water ages at depth.","finding_evidence":{"passage_id":"p014-003","page":14,"quote":"For example, coastal areas near Taree where high δ18O values are linked to local mixing with seawater via tidal rivers. The areal extent and depth could be easily refined by a combination of additional sampling, improved aquifer definition and refinement of the interpolation/extrapolation algorithm applied. 6. Conclusions This study represents the first isoscape (25x25 km resolution) of groundwater stable isotopes (δ2 H/δ18O) in NSW (Australia). It provides a view of processes currently impacting groundwater sources and recharge at different depths intervals (1–30 m; 30–50 m; 50–300 m; and 300–1000 m). At a wide scale, groundwater isoscapes 1–300 m in depth show an E-W gradient across NSW, corresponding with the transition from coastal to arid inland climates. This results in increased δ-values (δ18O and δ2 H) inland and decreased D-excess, highlighting the prevalence of water evaporation prior to groundwater recharge west of the Great Dividing Range.","verified_in_page":true},"topics":["stable isotopes","groundwater recharge","hydrogeology","isoscapes","aquifer dynamics","groundwater flow","evaporation","water management"],"study_period":null,"locations":[{"name":"New South Wales","kind":"region","role":"Primary study area where groundwater isotope data were collected and isoscapes generated","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Groundwater δ2 H/δ18O isoscapes for New South Wales (Australia) Dioni I. Cendon ́ a,b,* , Stuart I. Hankin a , Jagoda Crawford a , Liza K. McDonough a,b , Catherine E. Hughes a,b , Karina T. Meredith a,b , Bryce F.J. Kelly b , Stephen J. Harris b , Matthias Raiber c , Sharon S. Gray d,e , Richard T. Green d , Fabienne B. d’Hautefeuille d a Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW 2234, Australia b School of Biological, Earth and Environmental Sciences, UNSW Sydney, NSW 2052, Australia c CSIRO Environment, Brisbane, Queensland, Australia d Department of Climate Change, Energy, the Environment and Water, NSW 2150, Australia e ANU Research School of Earth Sciences, Australia ARTICLE INFO Keywords: New South Wales Water stable isotopes Isoscape Groundwater Recharge Namoi catchment ABSTRACT Study region: New South Wales (NSW), Australia.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Namoi catchment","kind":"basin","role":"Detailed catchment-scale site with high-resolution isoscape (5x5 km) to assess local groundwater recharge processes and anthropogenic influences","river_name":"Namoi River","geocode_query":"Namoi catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"5.1.3. Darling River The Darling River region extends west from the Western Plains, following the Darling (Baaka) River valley from Bourke to its confluence with the Murray River. The region has groundwater isotopic patterns that indicate complex mixing between surface water and groundwater and infiltration of evaporated waters. The highest δ18O values and lowest D-excess follow the Darling River valley with a reversal towards the edge of the basin around Broken Hill, which has slightly higher elevations. Shallow groundwater with low D-excess values near Bourke could be linked to recharge driven by leakage of evaporated water from natural or large surface water storage dams and flood plain river capture in the region (5587 ha in the Barwon-Darling Region, Pena-Arancibia ̃ et al., 2024). Lower δ18O and increasing D-excess with depth (50–300 m) in the Darling River valley suggest past recharge under wetter climates than that of today.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.504774,-30.495113],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-1","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7},{"name":"Great Dividing Range","kind":"region","role":"Mountain range contributing snowmelt and precipitation affecting groundwater recharge isotopic signatures in eastern NSW","river_name":null,"geocode_query":"Great Dividing Range, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Conditions become neutral or gaining near the Darling and Murray rivers due to basement highs and reduced aquifer transmissivity, forcing groundwater levels closer to the surface (Parsons et al., 2008). Surface water may travel significant distances from its precipitation source before it is recharged to groundwater. Flood induced recharge in the drier climate zones towards western NSW may inherit the stable isotope signature of precipitation that fell in wetter climatic zones along the Great Dividing Range (Meredith et al., 2015; Iverach et al., 2017). In these cases, the stable isotope signature of the recharge water may be different from that of local precipitation. East of the Great Dividing Range, the Snowy Mountains also contributes snowmelt to groundwater and rivers located in the Snowy River Catchment in south-east NSW (Cassidy and Weatherill, 2017).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Murrumbidgee River valley","kind":"basin","role":"Major alluvial valley where groundwater recharge incorporates flood and snowmelt sources with isotopic evidence at multiple depths","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River valley, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Groundwater between 1 and 30 m in depth Fig. 5-A presents the δ18O isoscape layer for groundwater between 1 m and 30 m depth. Only δ18O isoscapes are shown here; δ2 H isoscapes are similar, and are available in Supplementary Figures 3. The lower δ18O values (blue-green colours in Fig. 5-A) are generally found east of the Great Dividing Range and following the inland alluvial systems (Fig. 1-D). In general, west of a line connecting the towns of Griffith, Dubbo and Narrabri (Fig. 1), δ18O values increase in response to increasing inland aridity (yellow in Fig. 5-A). To the south-west lower δ18O values (green colours) extend further west following the lower reaches of the Murray and Murrumbidgee rivers, with some fluctuations between lower and higher δ-values. It is not possible to differentiate whether these variations correspond to a natural phenomenon or could be influenced by irrigation use of deeper groundwater with lower δ18O values.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.128191,-35.252873],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43665563,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-3","true_coordinates":[146.2016652340651,-35.2528729004432],"spread_km":6.7},{"name":"Murray River valley","kind":"basin","role":"Southern NSW major valley showing isotopic groundwater recharge patterns influenced by floods and river recharge","river_name":"Murray River","geocode_query":"Murray River valley, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-002","page":7,"quote":"Groundwater between 1 and 30 m in depth Fig. 5-A presents the δ18O isoscape layer for groundwater between 1 m and 30 m depth. Only δ18O isoscapes are shown here; δ2 H isoscapes are similar, and are available in Supplementary Figures 3. The lower δ18O values (blue-green colours in Fig. 5-A) are generally found east of the Great Dividing Range and following the inland alluvial systems (Fig. 1-D). In general, west of a line connecting the towns of Griffith, Dubbo and Narrabri (Fig. 1), δ18O values increase in response to increasing inland aridity (yellow in Fig. 5-A). To the south-west lower δ18O values (green colours) extend further west following the lower reaches of the Murray and Murrumbidgee rivers, with some fluctuations between lower and higher δ-values. It is not possible to differentiate whether these variations correspond to a natural phenomenon or could be influenced by irrigation use of deeper groundwater with lower δ18O values.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.834525,-35.419679],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-4","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Darling River valley","kind":"basin","role":"Western NSW river valley demonstrating isotopic enrichment due to evaporation and complex surface-groundwater mixing processes","river_name":"Darling River","geocode_query":"Darling River valley, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"5.1.3. Darling River The Darling River region extends west from the Western Plains, following the Darling (Baaka) River valley from Bourke to its confluence with the Murray River. The region has groundwater isotopic patterns that indicate complex mixing between surface water and groundwater and infiltration of evaporated waters. The highest δ18O values and lowest D-excess follow the Darling River valley with a reversal towards the edge of the basin around Broken Hill, which has slightly higher elevations. Shallow groundwater with low D-excess values near Bourke could be linked to recharge driven by leakage of evaporated water from natural or large surface water storage dams and flood plain river capture in the region (5587 ha in the Barwon-Darling Region, Pena-Arancibia ̃ et al., 2024). Lower δ18O and increasing D-excess with depth (50–300 m) in the Darling River valley suggest past recharge under wetter climates than that of today.","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.083787,-31.940557],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43662686,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-5","true_coordinates":[144.08378715163,-32.00055714361058],"spread_km":6.7},{"name":"Sydney and Taree regions","kind":"settlement","role":"Coastal urban and semi-rural sites showing evidence of evaporative isotopic enrichment in shallow groundwater and seawater mixing effects","river_name":null,"geocode_query":"Sydney, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-006","page":10,"quote":"Similarly, in coastal regions, seawater trapped in recent geological times (Lee et al., 2016) or mobilised due to pumping can drastically change water stable isotope values. To compare isoscape layers across the state we differentiate and discuss three broad regions: the eastern coastal regions; the western slopes of the Great Dividing Range; and the Darling River. 5.1.1. Coastal regions The coastal region of NSW extends east of the Great Diving Range (Fig. 1). The coastal river catchments in NSW are relatively small (<23,000 km2 ) but provide drinking water to most of the state’s population (WaterNSW, 2024). Water demand from these catchments is expected to increase with projected future population growth along the coastal fringe (Merkens et al., 2016). Groundwater plays a crucial role in sustaining baseflow to coastal rivers and can be locally important for irrigation, mining, and town water supply (Pells and Pells, 2012; Cendon ́ et al., 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.138964,-33.880791],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054910,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sydney NSW, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-6","true_coordinates":[151.207684,-33.8622503],"spread_km":6.7},{"name":"Alstonville Plateau","kind":"landscape","role":"Northern coastal horticultural region with high δ18O attributed to orographic rainfall, rapid recharge, and fractured rock system","river_name":null,"geocode_query":"Alstonville Plateau, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-007","page":10,"quote":"In the coastal areas to the north and south of the Sydney region, groundwater isotope data density is low. However, available data align closely with rainfall stable isotopes, which show a latitudinal gradient in coastal isotope values across NSW (Hollins et al., 2018). Variability in depth reflects a general trend towards lower δ18O observed across the state. In urban and semi-rural areas to the west of Sydney, shallow groundwater shows mildly evaporated signatures with slightly higher δ18O values (Fig. 5-A, point 6). These areas are dominated by outcropping of clay-rich sediments from the Wianamatta Shale (Taylor, 2016; Russell, 2001). Groundwater D-excess values in these regions (~10 ‰) are observed to be lower than those in precipitation, D.I. Cendon ́ et al. Journal of Hydrology: Regional Studies 61 (2025) 102732 10","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.4454712,-28.8411202],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26971052,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"10 Deegan Dr, Alstonville NSW 2477, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-7"},{"name":"Boggabri area","kind":"settlement","role":"Area in Namoi catchment where elevated δ18O values at depth suggest influence of coal mining activities altering groundwater recharge signatures","river_name":null,"geocode_query":"Boggabri, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-005","page":13,"quote":"Lower δ-values are found between Narrabri and Wee Waa, close to the river and further away from the river to the north. These areas are receiving GAB discharge into the alluvium, typically containing lower δ18O values (Lamontagne et al., 2015; Iverach et al., 2017). The GAB contribution is well constrained in the 5 × 5 km resolution isoscape, while at the 25x25 km state-scale it is difficult to observe. In general, δ18O isotope values decrease with depth, relative to shallower layers, with only < 2.8 % of sampled wells potentially screened in GAB. An exception to decreasing δ18O with depth is an area east north-east of the town of Boggabri where higher δ18O values, not observed in the shallower (30–50 m) layer, have been identified (Fig. 9, point 5). This may be due to activities at the Boggabri Coal Mine that could be a conduit for evaporated surface water to penetrate deeper, with original shallower groundwater absent, as the area has been mined.","verified_in_page":true},"resolution_status":"resolved","coordinates":[150.0425655,-30.7047163],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43674354,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Boggabri NSW 2382, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-8"},{"name":"Barrington Tops area","kind":"landscape","role":"High altitude area in Hunter River catchment with lower δ18O groundwater compared to adjacent coastal regions showing elevation influence","river_name":"Hunter River","geocode_query":"Barrington Tops, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"The Hunter River catchment, with the relatively high altitude of the Barrington Tops (Fig. 7-A, point 2), is differentiated by lower δ18O values compared to coastal samples immediately to the south. The highest δ18O values are observed in the Lower Darling River north of Mildura and extending up near Menindee Lakes. Similarly, high δ18O values are observed near Bourke and south towards Cobar (Fig. 7-A, point 3), consistent with those observed in the 30–50 m layer above (Fig. 6-A). In the interval between 50 and 300 m (Fig. 7-B), the westward extent of higher D-excess water increases along main rivers. However, lower D-excess values associated with evaporation are well defined in all major rivers flowing west with lower D-excess closer to the coastline to the north of the state.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.5172457,-31.8534217],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26966945,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barrington Tops NSW 2422, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-9"},{"name":"Enngonia area near Queensland border","kind":"settlement","role":"Northern NSW deep groundwater sampling site in the Warrego River catchment with elevated δ18O possibly linked to shallow groundwater or local recharge conditions","river_name":null,"geocode_query":"Enngonia, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"This sample corresponds to a water supply well, constructed in 2004 and screened between 470–551 m. There is no reason to suspect a mixed sample and additionally other hydrochemical data such as 36Cl do not show values different to other in the region. 5. Discussion Groundwater isoscapes provide a visual tool to interpret large-scale changes in groundwater recharge sources and processes, and a predictive tool to enable applications at locations where data are not available. The predictive power of the isoscapes can be assessed Fig. 7. (A and B). δ18O and D-excess isoscapes for the interval between 50 and 300 m below ground level. Cells depicted in white indicate no information available in the current layer. Orange arrows indicate areas highlighted in the text. (1 and 5) Border Rivers; (2) Barrington Tops; (3) Cobar region; (4 and 9) Coleambally region; (6) Upper Namoi River, Mooki River; (7) East of Dubbo, Macquarie River; (8) Lachlan River; and (10) Lower Murray River.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.8465909,-29.3201587],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43664974,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Enngonia NSW 2840, Australia","basin_id":5040533380,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":53422.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-10"},{"name":"Lower Namoi Groundwater Source","kind":"basin","role":"Important region within Namoi catchment showing evaporative enrichment and groundwater sourced from Great Artesian Basin discharge","river_name":"Namoi River","geocode_query":"Lower Namoi Groundwater Source, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"Relatively high δ18O values are seen following the main river in the shallow 0–30 m isoscape. Interpretations are well-constrained downstream from Narrabri, with lower sample numbers along the edges of the upper catchment (Tamworth, Gunnedah). The shallow groundwater in the Lower Namoi Groundwater Source, particularly downstream from Narrabri, has high, and more evaporated δ18O signatures based on D-excess values (Fig. 9, point 1). This is the combined effect of water storage/evaporation at a large weir (Mollee Weir) and more broadly, the Namoi River being a losing stream throughout this area (Crosbie et al., 2023). This means that water lost to groundwater above the weir and downstream retains an evaporated signature in shallow groundwater close to the river. Another area of high δ18O values was identified near Wee Waa to the west. This area has many water storage dams where leakage from these dams may be transferring an evaporated signature to shallow groundwater.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.518222,-30.545873],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43673027,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-7e8108d481bb74b97487c993-site-11","true_coordinates":[149.5726700150579,-30.50846384954584],"spread_km":6.7}],"limitations":["Some geographic coordinates are unavailable and therefore latitudes and longitudes are set to null.","Spatial resolution is limited by data availability with some areas, particularly coastal and northern regions, underrepresented.","Deep aquifer data is limited mainly to Great Artesian Basin and coal/gas exploration areas, restricting deep isoscape resolution.","Interpolation methods (kriging) may extrapolate in data-sparse regions, potentially overextending isotopic features beyond actual extent."],"river_ids":[],"site_ids":["doc-7e8108d481bb74b97487c993-site-1","doc-7e8108d481bb74b97487c993-site-3","doc-7e8108d481bb74b97487c993-site-4","doc-7e8108d481bb74b97487c993-site-5","doc-7e8108d481bb74b97487c993-site-6","doc-7e8108d481bb74b97487c993-site-7","doc-7e8108d481bb74b97487c993-site-8","doc-7e8108d481bb74b97487c993-site-9","doc-7e8108d481bb74b97487c993-site-10","doc-7e8108d481bb74b97487c993-site-11"],"anchor":[150.0425655,-30.7047163],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040069970,5040070710,5040524870,5040533380,5040591010,5040591050],"main_basin_ids":[5040069970,5040070710,5040073410]},{"id":"doc-8a0d094d790053ab248ce409","title":"Land use change and soil salinization in the Sundarbans: a machine-learning based analysis of long-term transformation and future projections","display_title":"Land use, erosion and salinity in Sundarbans","doi":"https://doi.org/10.1007/s10661-025-14829-2","theme":"Land & ecosystems","color":"#c8df98","year":2025,"publication_date":"2025-11-28","authors":["Uttam Kumar Mandal","Amit Ghosh","Fazlul Karim","Sonali Mallick","Dibyendu Bikas Nayak","Rinchen Nopu Bhutia","Ajay Kumar Bhardwaj","T.D. Lama","D. Burman","Priyanka Choudhury","Kshirendra Kumar Mahanta","Shishir Raut","Subhasis Mandal","Mohammed Mainuddin"],"summary":"This study analyzed long-term land use and land cover (LULC) changes, erosion, accretion, and soil salinity dynamics in the Indian Sundarbans from 1973 to 2022 using Landsat satellite data, incorporating machine learning (CA-ANN) to project future scenarios to 2049. It quantified land loss and gain due to erosion and accretion, documented shifts from single-cropped agriculture to aquaculture and settlements, and found stable mangrove coverage due to conservation. Soil salinity increased in arable lands, linked to climate factors and land use change, with projections indicating further salinization and LULC shifts by 2049. The study provides insights for sustainable land and resource management under climate and socio-economic pressures in the Sundarbans delta.","finding":"Between 1973 and 2022, approximately 478 km² of land was lost to erosion, whereas 408 km² were gained through accretion, with erosion primarily in the southern Sundarbans while mangrove area remained stable due to protection efforts. Single-cropped agricultural land decreased by about 13.35%, while aquaculture and settlement areas expanded significantly. Soil salinity in arable land increased from 2246.2 km² in 1989 to 2669 km² in 2019, and machine learning projections indicate soil salinity will increase by about 5% by 2049, alongside continued expansion of settlements and aquaculture and decreases in vegetation and cropland areas.","finding_evidence":{"passage_id":"p001-001","page":1,"quote":"Over this period, approximately 478 km2 of land was lost to erosion, while 408 km2 were gained through accretion. Although single cropping remains the predominant land use system, the areas dedicated to settlements and aquaculture grew 7 times and 2.4 times, respectively. The doublecropped area expanded by 75.8%, while the mangrove area remained unchanged. Among all land use categories, single-cropped land was found to be the most vulnerable to loss. The soil salinity area of the arable land, calculated using the canopy response salinity index (CRSI) from satellite imagery, rose from 2246.2 km2 to 2669 km2 in the 30 years since 1989. Using a machine learning algorithm (celluSupplementary information The online version lar automata-artificial neural network (CA-ANN)), contains supplementary material available at https://doi. org/10.1007/s10661-025-14829-2. U. K. Mandal · A. Ghosh · S. Mallick (*) · D. B. Nayak · R. N. Bhutia (*) · T. D. Lama · D. Burman · P. Choudhury · K. K.","verified_in_page":true},"topics":["land use change","soil salinity","coastal erosion","sedimentation","remote sensing","machine learning","climate change impacts","land cover classification"],"study_period":"1973-2022 with projections to 2049","locations":[{"name":"Indian Sundarbans","kind":"region","role":"primary study region where LULC, erosion/accretion, and soil salinity were analyzed","river_name":null,"geocode_query":"Indian Sundarbans, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The information from this study will be helpful for local planners, administrative departments (such as agriculture and fishery), policymakers, researchers, and other stakeholders involved in the region’s development and assist in identifying the vulnerable areas so that ameliorative measures can be taken in advance before permanent degradation. Materials and methods Study area: Indian Sundarbans Our study site included the Indian part of the Sundarbans, a tropical region spanning latitudes 21°31′46′′N to 22°39′31′′N and longitudes 88°02′14′′E to 89°06′58′′E. It covers 19 administrative blocks—13 in South 24 Parganas district and 6 in North 24 Parganas district of West Bengal, India (Fig. 1). Since 1970, the Sundarbans mangrove has been safeguarded under various legal provisions mainly to protect the endangered royal Bengal tiger and to conserve the mangrove forest.","verified_in_page":true},"resolution_status":"resolved","coordinates":[89.118641,21.949727],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sundarbans","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-0","true_coordinates":[89.1833304,21.9497274],"spread_km":6.7},{"name":"South 24 Parganas district, West Bengal, India","kind":"region","role":"administrative region comprising part of the study area","river_name":null,"geocode_query":"South 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The information from this study will be helpful for local planners, administrative departments (such as agriculture and fishery), policymakers, researchers, and other stakeholders involved in the region’s development and assist in identifying the vulnerable areas so that ameliorative measures can be taken in advance before permanent degradation. Materials and methods Study area: Indian Sundarbans Our study site included the Indian part of the Sundarbans, a tropical region spanning latitudes 21°31′46′′N to 22°39′31′′N and longitudes 88°02′14′′E to 89°06′58′′E. It covers 19 administrative blocks—13 in South 24 Parganas district and 6 in North 24 Parganas district of West Bengal, India (Fig. 1). Since 1970, the Sundarbans mangrove has been safeguarded under various legal provisions mainly to protect the endangered royal Bengal tiger and to conserve the mangrove forest.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.556531,22.196673],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South 24 Parganas, West Bengal, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-1","true_coordinates":[88.55653099999999,22.1366735],"spread_km":6.7},{"name":"North 24 Parganas district, West Bengal, India","kind":"region","role":"administrative region comprising part of the study area","river_name":null,"geocode_query":"North 24 Parganas district, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The information from this study will be helpful for local planners, administrative departments (such as agriculture and fishery), policymakers, researchers, and other stakeholders involved in the region’s development and assist in identifying the vulnerable areas so that ameliorative measures can be taken in advance before permanent degradation. Materials and methods Study area: Indian Sundarbans Our study site included the Indian part of the Sundarbans, a tropical region spanning latitudes 21°31′46′′N to 22°39′31′′N and longitudes 88°02′14′′E to 89°06′58′′E. It covers 19 administrative blocks—13 in South 24 Parganas district and 6 in North 24 Parganas district of West Bengal, India (Fig. 1). Since 1970, the Sundarbans mangrove has been safeguarded under various legal provisions mainly to protect the endangered royal Bengal tiger and to conserve the mangrove forest.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.710896,22.770047],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North 24 Parganas, West Bengal, India","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-2","true_coordinates":[88.7108964,22.710047],"spread_km":6.7},{"name":"Bedford Island, Indian Sundarbans, West Bengal, India","kind":"landscape","role":"island studied for erosion/accretion and land loss (disappeared after 1989)","river_name":null,"geocode_query":"Bedford Island, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Environ Monit Assess (2025) 197:1380 Page 13 of 35 1380 Vol.: (0123456789) Trend of land surface area of Sundarbans islands We studied the land surface dynamics of the islands in the Indian Sundarbans during 1973–2022. Out of 47 islands, the land surface area of 11 islands is highly dynamic in the Indian Sundarbans (Table 4). Bedford Island had already disappeared after 1989 from its original location. More than half the area of the Jambudwip and Ghoramara islands vanished within 49 years of our study. The populous Sagar Island showed 1% growth in the area from 1973 to 2022, whereas 2.3% of the area disappeared from 1989 to 2022. Around 7.9 km2 area of the island experienced sedimentation between 1973 and 1989. Though most of the islands’ areas are eroding, Nayachar Island in the river Hooghly has grown more than 52% between 1973 and 2022. Some of the islands are densely populated, and displacement is a great challenge in the Sundarbans region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.0851909,21.8636818],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bedford Island, West Bengal, India","hydrology_source":"none","site_id":"doc-8a0d094d790053ab248ce409-site-3"},{"name":"Ghoramara Island, Indian Sundarbans, West Bengal, India","kind":"landscape","role":"island with significant area loss (~56%) over 49 years","river_name":null,"geocode_query":"Ghoramara Island, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Environ Monit Assess (2025) 197:1380 Page 13 of 35 1380 Vol.: (0123456789) Trend of land surface area of Sundarbans islands We studied the land surface dynamics of the islands in the Indian Sundarbans during 1973–2022. Out of 47 islands, the land surface area of 11 islands is highly dynamic in the Indian Sundarbans (Table 4). Bedford Island had already disappeared after 1989 from its original location. More than half the area of the Jambudwip and Ghoramara islands vanished within 49 years of our study. The populous Sagar Island showed 1% growth in the area from 1973 to 2022, whereas 2.3% of the area disappeared from 1989 to 2022. Around 7.9 km2 area of the island experienced sedimentation between 1973 and 1989. Though most of the islands’ areas are eroding, Nayachar Island in the river Hooghly has grown more than 52% between 1973 and 2022. Some of the islands are densely populated, and displacement is a great challenge in the Sundarbans region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.12972219999999,21.9133333],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ghoramara Island, Ghoramara, West Bengal 743347","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-4"},{"name":"Jambudwip, Indian Sundarbans, West Bengal, India","kind":"landscape","role":"island showing significant area loss (~52%) and no habitation","river_name":null,"geocode_query":"Jambudwip, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Environ Monit Assess (2025) 197:1380 Page 13 of 35 1380 Vol.: (0123456789) Trend of land surface area of Sundarbans islands We studied the land surface dynamics of the islands in the Indian Sundarbans during 1973–2022. Out of 47 islands, the land surface area of 11 islands is highly dynamic in the Indian Sundarbans (Table 4). Bedford Island had already disappeared after 1989 from its original location. More than half the area of the Jambudwip and Ghoramara islands vanished within 49 years of our study. The populous Sagar Island showed 1% growth in the area from 1973 to 2022, whereas 2.3% of the area disappeared from 1989 to 2022. Around 7.9 km2 area of the island experienced sedimentation between 1973 and 1989. Though most of the islands’ areas are eroding, Nayachar Island in the river Hooghly has grown more than 52% between 1973 and 2022. Some of the islands are densely populated, and displacement is a great challenge in the Sundarbans region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.182778,21.581111],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jambudwip, Jammu Island, West Bengal 743357","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-5"},{"name":"Nayachar, Indian Sundarbans, West Bengal, India","kind":"landscape","role":"island with notable area gain (~52%), showing estuarine accretion","river_name":null,"geocode_query":"Nayachar, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Environ Monit Assess (2025) 197:1380 Page 13 of 35 1380 Vol.: (0123456789) Trend of land surface area of Sundarbans islands We studied the land surface dynamics of the islands in the Indian Sundarbans during 1973–2022. Out of 47 islands, the land surface area of 11 islands is highly dynamic in the Indian Sundarbans (Table 4). Bedford Island had already disappeared after 1989 from its original location. More than half the area of the Jambudwip and Ghoramara islands vanished within 49 years of our study. The populous Sagar Island showed 1% growth in the area from 1973 to 2022, whereas 2.3% of the area disappeared from 1989 to 2022. Around 7.9 km2 area of the island experienced sedimentation between 1973 and 1989. Though most of the islands’ areas are eroding, Nayachar Island in the river Hooghly has grown more than 52% between 1973 and 2022. Some of the islands are densely populated, and displacement is a great challenge in the Sundarbans region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.08030509999999,21.9768549],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nayachar, West Bengal","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-6"},{"name":"Sagar Island, Indian Sundarbans, West Bengal, India","kind":"landscape","role":"populous island showing small net area increase over study period","river_name":null,"geocode_query":"Sagar Island, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-000","page":13,"quote":"Environ Monit Assess (2025) 197:1380 Page 13 of 35 1380 Vol.: (0123456789) Trend of land surface area of Sundarbans islands We studied the land surface dynamics of the islands in the Indian Sundarbans during 1973–2022. Out of 47 islands, the land surface area of 11 islands is highly dynamic in the Indian Sundarbans (Table 4). Bedford Island had already disappeared after 1989 from its original location. More than half the area of the Jambudwip and Ghoramara islands vanished within 49 years of our study. The populous Sagar Island showed 1% growth in the area from 1973 to 2022, whereas 2.3% of the area disappeared from 1989 to 2022. Around 7.9 km2 area of the island experienced sedimentation between 1973 and 1989. Though most of the islands’ areas are eroding, Nayachar Island in the river Hooghly has grown more than 52% between 1973 and 2022. Some of the islands are densely populated, and displacement is a great challenge in the Sundarbans region.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.1096118,21.7268896],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sagar Island, West Bengal 743373","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-7"},{"name":"River Hooghly, West Bengal, India","kind":"river","role":"freshwater source affecting salinity levels and accretion influencing Nayachar growth","river_name":"Hooghly","geocode_query":"River Hooghly, West Bengal, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The delta has evolved by natural deposition of upstream sediments, the millennia, from the GBM river systems accompanied by intertidal segregation. Many tidal saline water rivers crisscross the region with the only freshwater flow being within river Hooghly on the west, affecting the salinity levels in the soil and water. While embankments have been constructed along the majority of the areas of Sundarbans to prevent inundation, the mangrove coastline of the region remains un-embanked to protect its unique ecosystem. The Sundarbans has a moist sub-humid climate, characterized by a moderate amount of rainfall throughout the year. The region receives an average annual rainfall of about 1800 mm with monsoon months (June–September) accounting for 75% of it. The soils are poorly drained, heavy-textured, deep, and free from gravel and hardpan, and salinity appears as the dry season progresses.","verified_in_page":true},"resolution_status":"resolved","coordinates":[88.14709253366303,23.07182967186557],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Hooghly River, West Bengal","basin_id":4040025460,"main_basin_id":4040025460,"basin_region":"Central and South-East Asia","basin_area_km2":205062.4,"hydrology_source":"hydrobasins","site_id":"doc-8a0d094d790053ab248ce409-site-8"},{"name":"Ganga-Brahmaputra-Meghna (GBM) river system, India and Bangladesh","kind":"other","role":"major river system contributing sediments to Sundarbans delta and influencing hydrology","river_name":null,"geocode_query":"Ganga-Brahmaputra-Meghna river system, India and Bangladesh","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"These transformations have led to widespread land degradation and the loss of vital ecosystem services (Lal, 2022). Various factors contribute to the changing dynamics of land in coastal regions, including coastal compaction and subsidence, sea level rise (SLR), saltwater intrusion, changes in rainfall patterns, frequent tropical cyclones, and fluctuations in freshwater flow into the sea (Day et al., 2008; Lespinas et al., 2010; Paul et al., 2023). These environmental alterations have had profound effects on natural hydrological systems, geomorphology, and ecological conditions. Human activities have exacerbated the imbalance in sediment transport equilibrium, leading to accretion and erosion processes along the coastline. The Sundarbans, a pristine eco-region, represents one of the world’s most extensively damaged coastal landscapes on earth. It is located at the confluence of the Ganga-Brahmaputra-Meghna (GBM) river systems.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Key environmental factors such as seawater intrusion, groundwater salinity, and sea level rise were not included in analysis.","Land use classifications aggregated diverse landforms and species into broad classes, potentially overlooking finer ecological details.","Socioeconomic factors such as policy changes and market dynamics were not fully incorporated.","CA-ANN model may produce inconsistent results due to lack of transparency and parameter weighting challenges.","Reliance on moderate resolution Landsat satellite data limits ability to detect fine-scale salinity and land use changes."],"river_ids":[],"site_ids":["doc-8a0d094d790053ab248ce409-site-0","doc-8a0d094d790053ab248ce409-site-1","doc-8a0d094d790053ab248ce409-site-2","doc-8a0d094d790053ab248ce409-site-3","doc-8a0d094d790053ab248ce409-site-4","doc-8a0d094d790053ab248ce409-site-5","doc-8a0d094d790053ab248ce409-site-6","doc-8a0d094d790053ab248ce409-site-7","doc-8a0d094d790053ab248ce409-site-8"],"anchor":[88.556531,22.196673],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-1","basin_ids":[4040025460],"main_basin_ids":[4040025460]},{"id":"doc-8f98c87ddb71c2b72ffb683e","title":"The influence of lateral flow on land surface fluxes in southeast Australia varies with model resolution","display_title":"Lateral Flow Effects on Southeast Australia Fluxes","doi":"https://doi.org/10.5194/hess-29-4491-2025","theme":"Land & ecosystems","color":"#c8df98","year":2025,"publication_date":"2025-09-19","authors":["Anjana Devanand","Jason P. Evans","Andy J. Pitman","Sujan Pal","David Gochis","Kevin Sampson"],"summary":"This study quantifies the influence of lateral flow on land surface fluxes, particularly evapotranspiration, in southeast Australia using standalone WRF-Hydro simulations at multiple spatial resolutions (1, 4, 10 km). The authors found that lateral flow substantially alters evapotranspiration patterns, especially near major river channels and during the warm season after wet winters, with effects dependent on model resolution.","finding":"In southeast Australia, including lateral flow in land surface models at 4 and 1 km resolutions increases evapotranspiration near major river channels by up to 50%, with widespread drying of upstream ridges at 1 km resolution; by contrast, at 10 km resolution, these increases are confined to mountainous regions, indicating the necessity to include lateral flow at resolutions higher than 10 km for improved drought and water availability simulations.","finding_evidence":{"passage_id":"p011-000","page":11,"quote":"A. Devanand et al.: Lateral flow in southeast Australia 4501 Figure 5. Similar to Fig. 4e–p but for simulations with 100 m routing. The mean seasonal changes in ET with inclusion of lateral flow (in mm d−1 ) (a–d) LAT1-100 minus CTL1 (e–h) LAT4-100 minus CTL4 (i–l) LAT10-100 minus CTL10. The mean and absolute mean changes averaged over all the grids in the domain in mm d−1 and percentage are indicated in the panel title. ing dry down periods when compared to flux tower observations (Ukkola et al., 2016). Our results show that lateral flow could be one of the processes that contribute to dry down biases in LSMs, especially in regions that are downstream of topography. The increase in ET due to lateral flow results in cooler maximum air temperatures in Summer.","verified_in_page":true},"topics":["lateral flow","evapotranspiration","land surface modeling","model resolution effects","hydrology","river basins","drought modeling","surface flux partitioning"],"study_period":"2013-2017","locations":[{"name":"Southeast Australia","kind":"region","role":"study area","river_name":null,"geocode_query":"Southeast Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"A. Devanand et al.: Lateral flow in southeast Australia 4493 This study aims to understand the influence of lateral flow on land surface states in southeast Australia, and the impact of model resolution on the results. We use standalone LSM simulations at varying spatial resolutions to examine the changes in ET and surface water partitioning due to lateral flow. In standard LSM simulations, surface and subsurface runoff are removed from the system at the end of each model time step. In LSM simulations with lateral flow representation, runoff remains in the system and continues to affect other water cycle components such as ET and soil moisture. 2 Methods 2.1 Experiment Design We perform standalone simulations using WRF-Hydro version 5.2 (Gochis et al., 2020) with the Noah-MP LSM to study the influence of lateral water flow on surface fluxes in a domain that covers an area in southeast Australia (Fig. 1).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Upper Murray basin","kind":"basin","role":"study basin within domain for detailed analysis","river_name":"Murray River","geocode_query":"Upper Murray basin, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"There is substantial topographic variation in the domain with low lying areas in the northwest and mountainous regions with elevations above 1000 m in the southeast. The domain includes upper reaches of the Murray River system that flows west across the region (Appendix Fig. A1a). Figure A1a shows the surface water catchments and major rivers in the region – the Murrumbidgee, Upper Murray, Ovens and Goulburn rivers. In the results Sect. 3.2.3, we analyse the modelling results in more detail in the connected basins outlined in blue in Fig. 1 – Upper basins (the combined Upper Murray and Kiewa basins), Ovens, and Murray Riverina. The WRF-Hydro modelling system consists of a column LSM (here we use Noah-MP) that simulates the vertical exchange of water and energy at the surface at resolutions of 1 to 10 km, coupled to routing modules that simulate lateral flow at finer resolutions (100s of meters).","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.029985,-36.2483832],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43668739,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-1"},{"name":"Kiewa basin","kind":"basin","role":"study basin within domain for detailed analysis","river_name":null,"geocode_query":"Kiewa basin, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"There is substantial topographic variation in the domain with low lying areas in the northwest and mountainous regions with elevations above 1000 m in the southeast. The domain includes upper reaches of the Murray River system that flows west across the region (Appendix Fig. A1a). Figure A1a shows the surface water catchments and major rivers in the region – the Murrumbidgee, Upper Murray, Ovens and Goulburn rivers. In the results Sect. 3.2.3, we analyse the modelling results in more detail in the connected basins outlined in blue in Fig. 1 – Upper basins (the combined Upper Murray and Kiewa basins), Ovens, and Murray Riverina. The WRF-Hydro modelling system consists of a column LSM (here we use Noah-MP) that simulates the vertical exchange of water and energy at the surface at resolutions of 1 to 10 km, coupled to routing modules that simulate lateral flow at finer resolutions (100s of meters).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.0065428,-36.2578467],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43666848,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-2"},{"name":"Ovens basin","kind":"basin","role":"study basin within domain for detailed analysis","river_name":null,"geocode_query":"Ovens basin, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"There is substantial topographic variation in the domain with low lying areas in the northwest and mountainous regions with elevations above 1000 m in the southeast. The domain includes upper reaches of the Murray River system that flows west across the region (Appendix Fig. A1a). Figure A1a shows the surface water catchments and major rivers in the region – the Murrumbidgee, Upper Murray, Ovens and Goulburn rivers. In the results Sect. 3.2.3, we analyse the modelling results in more detail in the connected basins outlined in blue in Fig. 1 – Upper basins (the combined Upper Murray and Kiewa basins), Ovens, and Murray Riverina. The WRF-Hydro modelling system consists of a column LSM (here we use Noah-MP) that simulates the vertical exchange of water and energy at the surface at resolutions of 1 to 10 km, coupled to routing modules that simulate lateral flow at finer resolutions (100s of meters).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.723065,-36.576702],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43666411,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-3","true_coordinates":[146.7877948,-36.6067022],"spread_km":6.7},{"name":"Murray Riverina basin","kind":"basin","role":"study basin within domain for detailed analysis and influence of lateral flow","river_name":"Murray River","geocode_query":"Murray Riverina, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"There is substantial topographic variation in the domain with low lying areas in the northwest and mountainous regions with elevations above 1000 m in the southeast. The domain includes upper reaches of the Murray River system that flows west across the region (Appendix Fig. A1a). Figure A1a shows the surface water catchments and major rivers in the region – the Murrumbidgee, Upper Murray, Ovens and Goulburn rivers. In the results Sect. 3.2.3, we analyse the modelling results in more detail in the connected basins outlined in blue in Fig. 1 – Upper basins (the combined Upper Murray and Kiewa basins), Ovens, and Murray Riverina. The WRF-Hydro modelling system consists of a column LSM (here we use Noah-MP) that simulates the vertical exchange of water and energy at the surface at resolutions of 1 to 10 km, coupled to routing modules that simulate lateral flow at finer resolutions (100s of meters).","verified_in_page":true},"resolution_status":"resolved","coordinates":[144.6094954,-35.74483900000001],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43663241,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-4"},{"name":"Murrumbidgee River","kind":"river","role":"major river channel affecting evapotranspiration patterns in the study domain","river_name":"Murrumbidgee River","geocode_query":"Murrumbidgee River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"In spring, the domain average ET increases by about 0.1 to 0.2 mm d−1 (4 % to 9 %). The domain average changes in summer are slightly smaller in magnitude. The spatial pattern of changes in ET varies with the resolution of the LSM. At a resolution of 1 km (Fig. 4h), there is a reduction in ET in upstream areas that do not exist in the coarser resolution runs (Fig. 4l, p). In summer, the reduction in ET in upstream ridges are visible at all resolutions, but more widespread and prominent in the 1 km LSM simulation (Fig. 4e). At LSM resolutions of 1 and 4 km, the patterns of major rivers in the domain, the Murrumbidgee, Ovens, and Goulburn rivers (river network is shown in Fig. A1) are visible in the changes in ET. At 10 km, the influence of drainage networks is not visible in the ET changes. The Murray Riverina region located downstream of the Ovens and Goulburn rivers experience the largest changes in ET in the 4 and 1 km runs.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.304517,-35.069972],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677696,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-5","true_coordinates":[147.36802785600003,-35.09997214699996],"spread_km":6.7},{"name":"Goulburn River","kind":"river","role":"major river channel affecting evapotranspiration patterns in the study domain","river_name":"Goulburn River","geocode_query":"Goulburn River, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-003","page":8,"quote":"In spring, the domain average ET increases by about 0.1 to 0.2 mm d−1 (4 % to 9 %). The domain average changes in summer are slightly smaller in magnitude. The spatial pattern of changes in ET varies with the resolution of the LSM. At a resolution of 1 km (Fig. 4h), there is a reduction in ET in upstream areas that do not exist in the coarser resolution runs (Fig. 4l, p). In summer, the reduction in ET in upstream ridges are visible at all resolutions, but more widespread and prominent in the 1 km LSM simulation (Fig. 4e). At LSM resolutions of 1 and 4 km, the patterns of major rivers in the domain, the Murrumbidgee, Ovens, and Goulburn rivers (river network is shown in Fig. A1) are visible in the changes in ET. At 10 km, the influence of drainage networks is not visible in the ET changes. The Murray Riverina region located downstream of the Ovens and Goulburn rivers experience the largest changes in ET in the 4 and 1 km runs.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.874167,-37.195556],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43664960,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8f98c87ddb71c2b72ffb683e-site-6","true_coordinates":[145.87416676200007,-37.25555552599997],"spread_km":6.7}],"limitations":["Soil data uncertainties and parameter sensitivity may affect simulation accuracy.","Baseflow and channel leakages are not represented in the current simulations.","Simulations use a fixed soil depth of 2m; deeper soil layers are not modeled.","Model calibration was performed at one resolution (4 km) and applied to others without recalibration.","Radiative feedbacks and fine-scale processes such as seepage from channels to soil are not currently modeled.","Implications may be region-specific and require testing in other areas."],"river_ids":["43639555","43639562","43639604","43639619","43639979","43640015","43640246","43640255","43640367","43640372","43640441","43640606","43640741","43640767","43641225","43641428","43641738","43642972","43643044","43643136","43643327","43644769","43644968","43645529","43645552","43645940","43646164","43646210","43646273","43646295","43646309","43646330","43646395","43646400","43646427","43646432","43646472","43646490","43646493","43647165","43647196","43647220","43647236","43647673","43648171","43648218","43648764","43648765","43648769","43648786","43648801","43648826","43648874","43648875","43649124","43649144","43649164","43649185","43649194","43649202","43649205","43649211","43649308","43649310","43649369","43649374","43649375","43649408","43649486","43649556","43649599","43649678","43649679","43649681","43649682","43649683","43649743","43649749","43649836","43650153","43650348","43650373","43650375","43650376","43650389","43650390","43650391","43650414","43650420","43650423","43650429","43650433","43650588","43650591","43650625","43650792","43650807","43650872","43650880","43650881","43650886","43650952","43650954","43651042","43652896","43653302","43657744","43657795","43659349","43659355","43659361","43659366","43659371","43659376","43659381","43659386","43659391","43659396","43659400","43659403","43659404","43659405","43659406","43659407","43659412","43659423","43659426","43659427","43659428","43659429","43659430","43660286","43660287","43660291","43660295","43660299","43660301","43660303","43660305","43660306","43660307","43660309"],"site_ids":["doc-8f98c87ddb71c2b72ffb683e-site-1","doc-8f98c87ddb71c2b72ffb683e-site-2","doc-8f98c87ddb71c2b72ffb683e-site-3","doc-8f98c87ddb71c2b72ffb683e-site-4","doc-8f98c87ddb71c2b72ffb683e-site-5","doc-8f98c87ddb71c2b72ffb683e-site-6"],"anchor":[145.874167,-37.195556],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040591050],"main_basin_ids":[5040073410]},{"id":"doc-a413bc58f1c24d8be6f3f32d","title":"Subaqueous speleothems as archives of groundwater recharge on Australia's southern arid margin","display_title":"Mairs Cave Pendulites Record Groundwater Recharge","doi":"https://doi.org/10.5194/cp-21-857-2025","theme":"Groundwater","color":"#a8b9ff","year":2025,"publication_date":"2025-05-07","authors":["Calla N. Gould-Whaley","Russell N. Drysdale","Pauline C. Treble","Jan-Hendrik May","Stacey C. Priestley","John C. Hellstrom","Christopher R. Vardanega","Clare C. Buswell"],"summary":"This study investigates Mairs Cave, located in the southern Ikara-Flinders Ranges on the arid margin of Australia, where subaqueous speleothems called pendulites form during cave flooding related to groundwater recharge. Geochemical evidence and uranium-thorium dating reveal multiple growth phases during the Last Glacial Period, indicating sustained periods of higher water tables. Groundwater sampling supports local recharge with minimal mixing with older waters, confirming the cave's response to hydroclimatic variability. The pendulites provide a well-dated archive for past hydroclimate, with implications for understanding arid zone climate in southern Australia.","finding":"The subaqueous growth phases of pendulites in Mairs Cave correspond to intervals of higher local groundwater levels due to enhanced groundwater recharge, which occurred mainly during the Last Glacial Period, whereas Holocene cave flooding was episodic and brief, indicating shifts in regional hydroclimate over millennial timescales.","finding_evidence":{"passage_id":"p014-002","page":14,"quote":"The presence of tritium (0.13 TU) in the groundwater sample indicates that the bore and cave are within an active recharge region, and the 14C age (589 yrBP) indicates negligible mixing with a deeper and older regional aquifer. Therefore, geochemical signals contained in the subaqueous speleothems are unlikely to exhibit smoothing or a temporal offset ≥ 102 yr. Dating of the subaqueous pendulite material returned ages in stratigraphic order, so it appears unlikely that thorium scavenging occurred. We therefore conclude that the subaqueous pendulite growth may be ideal for further palaeoclimate investigations, as it is a record of past water table highstands, which are a function of groundwater recharge and hydroclimate variability. Historical observations and the lack of pendulite growth indicate that strong water deficits under warm Holocene interglacial conditions give rise to episodic, rather than persistent, cave flooding.","verified_in_page":true},"topics":["groundwater recharge","paleoclimate reconstruction","speleothems","isotope geochemistry","karst hydrology","last glacial period","climate variability","arid zone hydroclimate"],"study_period":"68.5 to 16.4 thousand years ago, with recent Holocene events detected","locations":[{"name":"Mairs Cave","kind":"field_site","role":"Main study site of speleothem sampling and groundwater analysis for paleoclimate reconstruction","river_name":null,"geocode_query":"Mairs Cave, Ikara-Flinders Ranges, South Australia, Australia","latitude":-32.18,"longitude":138.87,"evidence":{"passage_id":"p003-000","page":3,"quote":"C. N. Gould-Whaley et al.: Subaqueous speleothems as archives of groundwater recharge 859 Figure 1. Location of Mairs Cave (study site) relative to Adelaide (the capital city of the state of South Australia). Location of Kati Thanda / Lake Eyre and the extent of the Kati Thanda / Lake Eyre basin. Regions designated arid are shown in dark orange (0.05 < AI < 0.2), and regions designated semi-arid are shown in light orange (0.2 < AI < 0.5; Trabucco and Zomer et al., 2022). Also depicted are the primary systems that influence climate at the study site. Colours were assigned according to recommendations of Crameri et al. (2020). ing in subaqueous deposition of calcite on the existing stalactite surface (Field et al., 2002; Gillieson, 1996; Grimes, 2006; Jennings, 1979; Martini and Grimes, 2012). Hiatuses in subaqueous growth occur when water levels drop below the pendulite or when the water is no longer supersaturated with respect to calcium carbonate.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.57523,-31.5606299],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":111004876,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ikara, Flinders Ranges SA 5434, Australia","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-0"},{"name":"Ikara-Flinders Ranges","kind":"region","role":"Regional setting encompassing study cave and influencing hydrology and climate systems","river_name":null,"geocode_query":"Ikara-Flinders Ranges, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-000","page":3,"quote":"C. N. Gould-Whaley et al.: Subaqueous speleothems as archives of groundwater recharge 859 Figure 1. Location of Mairs Cave (study site) relative to Adelaide (the capital city of the state of South Australia). Location of Kati Thanda / Lake Eyre and the extent of the Kati Thanda / Lake Eyre basin. Regions designated arid are shown in dark orange (0.05 < AI < 0.2), and regions designated semi-arid are shown in light orange (0.2 < AI < 0.5; Trabucco and Zomer et al., 2022). Also depicted are the primary systems that influence climate at the study site. Colours were assigned according to recommendations of Crameri et al. (2020). ing in subaqueous deposition of calcite on the existing stalactite surface (Field et al., 2002; Gillieson, 1996; Grimes, 2006; Jennings, 1979; Martini and Grimes, 2012). Hiatuses in subaqueous growth occur when water levels drop below the pendulite or when the water is no longer supersaturated with respect to calcium carbonate.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.6628474,-31.3735989],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289585,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Ikara-Flinders Ranges National Park, Blinman SA 5730, Australia","basin_id":5040087760,"main_basin_id":5040087760,"basin_region":"Australasia","basin_area_km2":146408.6,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-1"},{"name":"Buckalowie Creek","kind":"river","role":"Ephemeral waterway near Mairs Cave used to evaluate hydrological connectivity and flooding mechanism","river_name":"Buckalowie Creek","geocode_query":"Buckalowie Creek, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"Although there are many cave-bearing carbonate rock formations scattered across the Ikara-Flinders Ranges, the largest and most decorated caves, including Mairs, tend to occur in the Etina Formation (∼ 655Ma), which is a sandy, oolitic, and stromatolitic limestone inter-bedded with siltstones (Fromhold and Wallace, 2012; Lawrence, 2009). Mairs Cave is situated in a strike ridge of Etina limestone that rises 16 m above the floodplain of Buckalowie Creek (Fig. 2a), an ephemeral waterway that runs parallel to the Etina outcrop in an NNE direction (Fig. 2b). Mairs Cave appears to have formed along the boundary between two distinct facies within the limestone, one easily weathered (the eastern wall) and the other less soluble (the western wall). The cave is accessed via a 17 m vertical shaft formed within the more weatherable facies. The base of the entrance shaft opens out to the main chamber of the cave (Fig. 2c), which is ∼ 100m long and 12 m wide.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Kati Thanda / Lake Eyre","kind":"basin","role":"Large basin defining southern margin of arid zone; regional moisture source and comparative paleoenvironmental context","river_name":null,"geocode_query":"Kati Thanda Lake Eyre, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"However, these findings cannot be extrapolated to the arid interior. Kati Thanda / Lake Eyre, the ephemerally filled endorheic salt lake at the heart of the continent, has thus far been a focus of much palaeoclimate research in arid Australia (Cohen et al., 2022; DeVogel et al., 2004; Fu et al., 2017; Magee et al., 1995, 2004; May et al., 2022; Nanson et al., 1998). Beach ridges and other lacustrine landforms and sediments provide indisputable evidence of lakefilling events. However, the 1 200 000 km2 catchment responsible for filling the lake sits largely to the north and receives rainfall from the Indo-Australian Summer Monsoon (IASM; Pook et al., 2014). Therefore, records of lake filling reflect hydroclimatic changes in the northern part of the continent rather than the climate of the interior itself. Munda / Lake Frome is another endorheic salt lake 400 km SE of Kati Thanda.","verified_in_page":true},"resolution_status":"resolved","coordinates":[137.227656,-28.193551],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":119370154,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491380,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":19208.6,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-3","true_coordinates":[137.2923893,-28.1750101],"spread_km":6.7},{"name":"Munda / Lake Frome","kind":"basin","role":"Endorheic basin southeast of Lake Eyre receiving runoff relevant to regional paleohydrology","river_name":null,"geocode_query":"Lake Frome, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"However, these findings cannot be extrapolated to the arid interior. Kati Thanda / Lake Eyre, the ephemerally filled endorheic salt lake at the heart of the continent, has thus far been a focus of much palaeoclimate research in arid Australia (Cohen et al., 2022; DeVogel et al., 2004; Fu et al., 2017; Magee et al., 1995, 2004; May et al., 2022; Nanson et al., 1998). Beach ridges and other lacustrine landforms and sediments provide indisputable evidence of lakefilling events. However, the 1 200 000 km2 catchment responsible for filling the lake sits largely to the north and receives rainfall from the Indo-Australian Summer Monsoon (IASM; Pook et al., 2014). Therefore, records of lake filling reflect hydroclimatic changes in the northern part of the continent rather than the climate of the interior itself. Munda / Lake Frome is another endorheic salt lake 400 km SE of Kati Thanda.","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.8137871,-30.6965975],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111005109,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-4"},{"name":"Malakanha / Lake Callabonna","kind":"basin","role":"Smaller lake in string of lakes near Munda and Lake Eyre, part of mega-lake system during pluvial periods","river_name":null,"geocode_query":"Lake Callabonna, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Inflow to Munda is primarily received as runoff from the Ikara-Flinders Ranges to the west, but lake filling can also occur if monsoonal rainfall in the northern reaches of the continent is especially high (May et al., 2015). Between Munda and Kati Thanda, there is a string of smaller lakes: Malakanha / Lake Callabonna, Lake Blanche, and Lake Gregory. During periods of especially high recharge, Munda, Malakanha, Blanche, and Gregory can overflow into one another and become a hydrologically connected mega-lake (mega-Munda; Cohen et al., 2012, 2015; May et al., 2015). There is also evidence to suggest that, during the most extreme pluvial periods of the LGP, mega-Munda and Kati Thanda became connected via overflow through the Warrawoocara Channel (Cohen et al., 2011, 2012; Leon and Cohen, 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.0803953,-29.6153691],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111005442,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-5"},{"name":"Lake Blanche","kind":"basin","role":"Lake in chain between Munda and Lake Eyre involved in paleolake connectivity","river_name":null,"geocode_query":"Lake Blanche, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Inflow to Munda is primarily received as runoff from the Ikara-Flinders Ranges to the west, but lake filling can also occur if monsoonal rainfall in the northern reaches of the continent is especially high (May et al., 2015). Between Munda and Kati Thanda, there is a string of smaller lakes: Malakanha / Lake Callabonna, Lake Blanche, and Lake Gregory. During periods of especially high recharge, Munda, Malakanha, Blanche, and Gregory can overflow into one another and become a hydrologically connected mega-lake (mega-Munda; Cohen et al., 2012, 2015; May et al., 2015). There is also evidence to suggest that, during the most extreme pluvial periods of the LGP, mega-Munda and Kati Thanda became connected via overflow through the Warrawoocara Channel (Cohen et al., 2011, 2012; Leon and Cohen, 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.6503106,-29.2468908],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111005275,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-6"},{"name":"Lake Gregory","kind":"basin","role":"Lake in regional chain contributing to mega-Munda paleolake during high recharge intervals","river_name":null,"geocode_query":"Lake Gregory, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Inflow to Munda is primarily received as runoff from the Ikara-Flinders Ranges to the west, but lake filling can also occur if monsoonal rainfall in the northern reaches of the continent is especially high (May et al., 2015). Between Munda and Kati Thanda, there is a string of smaller lakes: Malakanha / Lake Callabonna, Lake Blanche, and Lake Gregory. During periods of especially high recharge, Munda, Malakanha, Blanche, and Gregory can overflow into one another and become a hydrologically connected mega-lake (mega-Munda; Cohen et al., 2012, 2015; May et al., 2015). There is also evidence to suggest that, during the most extreme pluvial periods of the LGP, mega-Munda and Kati Thanda became connected via overflow through the Warrawoocara Channel (Cohen et al., 2011, 2012; Leon and Cohen, 2012).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.0211225,-28.8820764],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":111004990,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040491510,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":416738.9,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-7"},{"name":"Naracoorte Cave Complex","kind":"field_site","role":"Comparative site with speleothem growth linked to glacial climate and tropically sourced moisture","river_name":null,"geocode_query":"Naracoorte Cave Complex, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"The two extended phases of subaqueous calcite deposition on MC19 reveal that, during the LGP, cave flooding and thus enhanced groundwater recharge occurred not just as sporadic and ephemeral bursts but were sustained over millennia. This is indicative of a much more positive water balance than in the present day. These findings support an emerging paradigm shift which suggests that, in an Australian context, moisture availability is elevated during glacials relative to interglacials (Weij et al., 2024). Flooding of Mairs Cave sustained over multi-millennial periods during the LGP could reflect more frequent southward incursions and/or increased intensity of the IASM, in conjunction with a reduced recharge threshold due to lower evapotranspiration. This interpretation is consistent with findings from the Naracoorte Cave Complex (600 km SSE of Mairs Cave), where speleothem growth under glacial climates has been linked to tropically sourced warm-season moisture (Weij et al., 2024).","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.7968,-36.97463],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":21562702,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Naracoorte Caves, Joanna SA 5271, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-8","true_coordinates":[140.7968,-37.0346299],"spread_km":6.7},{"name":"Paruku / Gregory Lakes","kind":"basin","role":"Regional lake site providing evidence for high lake levels during late glacial period, supporting moisture delivery interpretations","river_name":null,"geocode_query":"Paruku Gregory Lakes, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"We propose that this period of pendulite growth can also be attributed to the IASM, since (1) there is SH summer insolation maximum at 46 ka (Fig. 7b; Laskar et al., 2004); (2) the most recent period of interconnectivity between Kati Thanda and Munda occurred between 49.4 (±2.4) and 46.8 (±2.3) ka (Cohen et al., 2015); (3) high lake levels were sustained between ∼ 50 and 37 ka at Paruku / Gregory Lakes (20° S; Bowler et al., 2001; Fitzsimmons et al., 2012; Veth et al., 2009); (4) a highstand at Lake Woods (18° S) between 53 and 30 ka was proposed by Bowler et al. (1998); (5) neodymium isotope data from MD03-2607 (Fig. 7e) suggest an increase in outflow from the Darling River between 48 and 46 ka (Bayon et al., 2017); and, (6) from 60–40 ka, low percentages of subpolar and transitional foraminifera species in ocean core MD03-2611 (Fig. 7f; 36° S) would suggest that the subtropical and subpolar fronts were positioned further south relative to the present (De Deckker et al., 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[127.7880149,-20.105132],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":90880602,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087640,"main_basin_id":5040087640,"basin_region":"Australasia","basin_area_km2":80788.8,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-9"},{"name":"Lake Woods","kind":"basin","role":"Regional lake site with highstand during late glacial, relevant for paleoclimate context","river_name":null,"geocode_query":"Lake Woods, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"We propose that this period of pendulite growth can also be attributed to the IASM, since (1) there is SH summer insolation maximum at 46 ka (Fig. 7b; Laskar et al., 2004); (2) the most recent period of interconnectivity between Kati Thanda and Munda occurred between 49.4 (±2.4) and 46.8 (±2.3) ka (Cohen et al., 2015); (3) high lake levels were sustained between ∼ 50 and 37 ka at Paruku / Gregory Lakes (20° S; Bowler et al., 2001; Fitzsimmons et al., 2012; Veth et al., 2009); (4) a highstand at Lake Woods (18° S) between 53 and 30 ka was proposed by Bowler et al. (1998); (5) neodymium isotope data from MD03-2607 (Fig. 7e) suggest an increase in outflow from the Darling River between 48 and 46 ka (Bayon et al., 2017); and, (6) from 60–40 ka, low percentages of subpolar and transitional foraminifera species in ocean core MD03-2611 (Fig. 7f; 36° S) would suggest that the subtropical and subpolar fronts were positioned further south relative to the present (De Deckker et al., 2020).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.5222313,-17.8181887],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":102215734,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040087780,"main_basin_id":5040087780,"basin_region":"Australasia","basin_area_km2":64813.4,"hydrology_source":"geofabric","site_id":"doc-a413bc58f1c24d8be6f3f32d-site-10"},{"name":"MD03-2607","kind":"station","role":"Marine sediment core site providing neodymium isotope data indicating river outflow changes linked to monsoon strength","river_name":null,"geocode_query":"MD03-2607 marine core site, Indian Ocean near Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"We propose that this period of pendulite growth can also be attributed to the IASM, since (1) there is SH summer insolation maximum at 46 ka (Fig. 7b; Laskar et al., 2004); (2) the most recent period of interconnectivity between Kati Thanda and Munda occurred between 49.4 (±2.4) and 46.8 (±2.3) ka (Cohen et al., 2015); (3) high lake levels were sustained between ∼ 50 and 37 ka at Paruku / Gregory Lakes (20° S; Bowler et al., 2001; Fitzsimmons et al., 2012; Veth et al., 2009); (4) a highstand at Lake Woods (18° S) between 53 and 30 ka was proposed by Bowler et al. (1998); (5) neodymium isotope data from MD03-2607 (Fig. 7e) suggest an increase in outflow from the Darling River between 48 and 46 ka (Bayon et al., 2017); and, (6) from 60–40 ka, low percentages of subpolar and transitional foraminifera species in ocean core MD03-2611 (Fig. 7f; 36° S) would suggest that the subtropical and subpolar fronts were positioned further south relative to the present (De Deckker et al., 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"MD03-2611","kind":"station","role":"Marine sediment core site with foraminifera data indicating shifts in subpolar and subtropical fronts affecting southern Australian climate","river_name":null,"geocode_query":"MD03-2611 marine core site, Murray Canyon, Indian Ocean near Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-003","page":13,"quote":"We propose that this period of pendulite growth can also be attributed to the IASM, since (1) there is SH summer insolation maximum at 46 ka (Fig. 7b; Laskar et al., 2004); (2) the most recent period of interconnectivity between Kati Thanda and Munda occurred between 49.4 (±2.4) and 46.8 (±2.3) ka (Cohen et al., 2015); (3) high lake levels were sustained between ∼ 50 and 37 ka at Paruku / Gregory Lakes (20° S; Bowler et al., 2001; Fitzsimmons et al., 2012; Veth et al., 2009); (4) a highstand at Lake Woods (18° S) between 53 and 30 ka was proposed by Bowler et al. (1998); (5) neodymium isotope data from MD03-2607 (Fig. 7e) suggest an increase in outflow from the Darling River between 48 and 46 ka (Bayon et al., 2017); and, (6) from 60–40 ka, low percentages of subpolar and transitional foraminifera species in ocean core MD03-2611 (Fig. 7f; 36° S) would suggest that the subtropical and subpolar fronts were positioned further south relative to the present (De Deckker et al., 2020).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Exact coordinates not given for most regional lakes and marine cores, limiting precise spatial resolution","Interpretation of some paleoclimate phases is complicated by contradictory regional records","Temporal resolution of some calcite rafts is limited by dating uncertainties","Hydrogeological data for Buckalowie Valley aquifer are limited, precluding detailed flow modeling"],"river_ids":[],"site_ids":["doc-a413bc58f1c24d8be6f3f32d-site-0","doc-a413bc58f1c24d8be6f3f32d-site-1","doc-a413bc58f1c24d8be6f3f32d-site-3","doc-a413bc58f1c24d8be6f3f32d-site-4","doc-a413bc58f1c24d8be6f3f32d-site-5","doc-a413bc58f1c24d8be6f3f32d-site-6","doc-a413bc58f1c24d8be6f3f32d-site-7","doc-a413bc58f1c24d8be6f3f32d-site-8","doc-a413bc58f1c24d8be6f3f32d-site-9","doc-a413bc58f1c24d8be6f3f32d-site-10"],"anchor":[138.57523,-31.5606299],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040070710,5040087640,5040087760,5040087780,5040491380,5040491510],"main_basin_ids":[5040070710,5040087590,5040087640,5040087760,5040087780]},{"id":"doc-a41efdff6390af613206a638","title":"Extreme summer storm elicits shifts in biogeochemistry, primary productivity, and plankton community structure in a large‐scale lake enclosure experiment","display_title":"Storm Effects on Lake Ecosystem Dynamics","doi":"https://doi.org/10.1002/lol2.70058","theme":"Climate","color":"#98d4f1","year":2025,"publication_date":"2025-10-21","authors":["Hans‐Peter Grossart","Thomas Hornick","Stella A. Berger","Jens C. Nejstgaard","Michael T. Monaghan","Takeshi Miki","Darren P. Giling","Géza B. Selmeczy","Judit Padisák","Jörg Sareyka","Peter Kasprzak","Christian Wurzbacher","Georgiy Kirillin","Christof Engelhardt","Mark O. Gessner"],"summary":"This study reports results from a large-scale, replicated lake enclosure experiment conducted in Lake Stechlin, Germany, simulating an extreme summer storm causing deep mixing of the water column. The experiment isolated effects of physical mixing independent of external nutrient inputs, revealing that storm-induced deep mixing disrupts thermal stratification, redistributes nutrients and phytoplankton from deep layers to surface waters, triggering significant ecological and biogeochemical changes that persist for weeks. Notably, primary production and algal biomass surged, with shifts in phytoplankton community composition favoring motile cryptophytes initially and followed by filamentous cyanobacteria blooms which may have implications for eutrophication risk and carbon sequestration. These findings advance understanding of how extreme weather events impact lake ecosystems and have implications for lake management under climate change.","finding":"Extreme summer storms causing deep mixing in a stratified lake deliver nutrients and phytoplankton from deep to surface waters, leading to increased primary production, shifts in phytoplankton community composition including cyanobacteria blooms, and enhanced carbon sedimentation, with effects lasting weeks post-storm.","finding_evidence":{"passage_id":"p004-005","page":4,"quote":"Results and discussion Mixing-induced changes in plankton succession Our experimental storm effectively disrupted the established thermal stratification and dispersed the DCM (Fig. 1b,c), resulting in both immediate and protracted changes to biological and chemical variables (Fig. 2a–h). Mixing instantly introduced plankton into the surface water, and significantly changed the concentrations and stoichiometry of epilimnetic nutrients and other ions (Tables S1, S2) for at least 4 weeks (Fig. 2f–h). After a 2-d delay, the new conditions boosted primary production, which remained elevated throughout the experiment (Fig. 2a) at levels expected based on a striking linear relationship with a composite proxy of nutrient supply (Fig. 3a). The stimulation of primary production increased Chl-a from Days 4–27 after deep mixing (Fig. 2b) and the ratio of net autotrophy to heterotrophy (Fig. S1b).","verified_in_page":true},"topics":["lake physical mixing","nutrient cycling","phytoplankton community dynamics","primary production","cyanobacteria blooms","carbon sequestration","climate change impacts","lake ecosystem processes"],"study_period":null,"locations":[{"name":"Lake Stechlin","kind":"basin","role":"experimental study site where large-scale lake enclosure facility was deployed for storm-mixing experiment","river_name":null,"geocode_query":"Lake Stechlin, Brandenburg, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Methods We tested this hypothesis in a large lake enclosure facility (Giling et al. 2017b) installed in Lake Stechlin, a temperate deep, formerly oligotrophic lake (Padis\u0001ak et al. 1997), enabling full replication in realistic field conditions (Fig. 1a; Supporting Information Methods). We used 8 enclosures as experimental units, each 9 m in diameter and ca. 18–20 m deep, enclosing water volumes of > 1200 m3 each. After water exchange and the establishment of a DCM in all enclosures by using highcapacity impeller pumps deployed at precisely defined depths (Supporting Information Methods). All enclosures were monitored for 3 d to test for similar starting conditions and confirm the successful establishment of the DCM (Fig. 1b,c).","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.0233035,53.1520282],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-0"},{"name":"Berlin","kind":"settlement","role":"home of the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) coordinating institute and related marine ecology departments involved in the study","river_name":null,"geocode_query":"Berlin, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.404954,52.580007],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Berlin, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-1","true_coordinates":[13.404954,52.52000659999999],"spread_km":6.7},{"name":"Potsdam","kind":"settlement","role":"location of Potsdam University associated with study team","river_name":null,"geocode_query":"Potsdam, Brandenburg, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.064473,52.330569],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Potsdam, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-2","true_coordinates":[13.0644729,52.3905689],"spread_km":6.7},{"name":"Leipzig","kind":"settlement","role":"location of UFZ Helmholtz-Centre for Environmental Research and German Centre for Integrative Biodiversity Research related to authors","river_name":null,"geocode_query":"Leipzig, Saxony, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[12.373075,51.279695],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Leipzig, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-3","true_coordinates":[12.3730747,51.3396955],"spread_km":6.7},{"name":"Veszprém","kind":"settlement","role":"location of Department of Limnology, University of Pannonia, associated with phytoplankton analysis in this research","river_name":null,"geocode_query":"Veszprém, Hungary","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[17.9093019,47.1028087],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Veszprém, Hungary","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-4"},{"name":"Garching","kind":"settlement","role":"location of Technical University of Munich department conducting metabarcoding and sequence analyses","river_name":null,"geocode_query":"Garching, Bavaria, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[11.6532477,48.2488721],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"85748 Garching, Germany","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-a41efdff6390af613206a638-site-5"},{"name":"Albury","kind":"settlement","role":"location of CSIRO Environment and Charles Sturt University involved in related research collaborations","river_name":null,"geocode_query":"Albury, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Gessner 1,3,14* 1 Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Stechlin, Germany; 2 Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany; 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany; 4 Department of Physiological Diversity, UFZ— Helmholtz-Centre for Environmental Research, Leipzig, Germany; 5 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; 6 Department of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 7 Institut für Biologie, Freie Universität Berlin, Berlin, Germany; 8 Faculty of Advanced Science and Technology, Ryukoku University, Seta, Shiga, Japan; 9 CSIRO Environment, Albury, New South Wales, Australia; 10Gulbali Institute, Charles Sturt University, Albury, New South Wales, Australia; 11Department of Limnology, University of Pannonia, Veszprém, Hungary; 12Urban Water Systems Engineering, Technical University of Munich, Garching, Germany; 13Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany; 14Institute of Ecology, Berlin Institute of Technology (TU Berlin), Berlin, Germany Scientific Significance Statement Coping with risks of extreme weather, including storms, requires insight into how such rare events drive ecosystem change.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.909485,-36.015119],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666674,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Albury NSW 2640, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-a41efdff6390af613206a638-site-6","true_coordinates":[146.9094852,-36.0751193],"spread_km":6.7}],"limitations":["Exact geographic coordinates for study sites are not provided in the text.","Some region-level locations mentioned are broad (e.g., cities) without precise in situ sampling points described.","The large enclosure facility is deployed within Lake Stechlin but exact position in lake not specified."],"river_ids":[],"site_ids":["doc-a41efdff6390af613206a638-site-0","doc-a41efdff6390af613206a638-site-1","doc-a41efdff6390af613206a638-site-2","doc-a41efdff6390af613206a638-site-3","doc-a41efdff6390af613206a638-site-4","doc-a41efdff6390af613206a638-site-5","doc-a41efdff6390af613206a638-site-6"],"anchor":[17.9093019,47.1028087],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[2040024170,2040539930,5040591050],"main_basin_ids":[2040008490,2040024170,5040073410]},{"id":"doc-bacd94ba5a94d8443df98c34","title":"Vegetation Effects on Total Atmospheric Chloride Deposition and Its Variability in Small Islands: Insights From South Pacific's Norfolk Island","display_title":"Vegetation Enhances Chloride Deposition on Norfolk Island","doi":"https://doi.org/10.1002/hyp.70336","theme":"Land & ecosystems","color":"#c8df98","year":2025,"publication_date":"2025-11-27","authors":["Paula Campos Teixeira","Carlos M. Ordens","Neil McIntyre","Liliana Pagliero","Russell S. Crosbie"],"summary":"This study investigates the influence of different vegetation types on the spatial and temporal variability of chloride deposition in throughfall on Norfolk Island, a small volcanic island in the South Pacific. Using 17 throughfall collectors across native and invasive vegetation types over approximately two years, the study quantifies how vegetation canopy intercepts marine aerosols, significantly increasing chloride deposition compared to open areas. Key findings include the highest chloride deposition rates beneath isolated Norfolk pine trees, white oak, and hardwood forests, with enhancement values up to 3742% relative to nearby open sites. Vegetation structural factors such as leaf area index and exposure to wind-driven marine aerosols strongly influence chloride deposition. Temporal analysis shows rainfall depth and wind gust speed as major drivers of deposition variability. These findings advance understanding critical for accurate chloride mass balance groundwater recharge estimations and have broader implications for hydrological and ecological assessments in coastal and island environments.","finding":"Vegetation, especially Norfolk pine, white oak, and hardwood forest, significantly enhances chloride deposition on Norfolk Island, with throughfall chloride deposition up to 3742% higher than nearby open sites, driven by vegetation type, leaf area index, and exposure to marine aerosols.","finding_evidence":{"passage_id":"p001-002","page":1,"quote":"Therefore, understanding the spatial and temporal variability of chloride deposition in throughfall is crucial, particularly in coastal areas where chloride deposition exhibits significant variation. This study aims to estimate chloride throughfall deposition and analyze its distribution among different vegetation types on a small island, while concurrently comparing deposition rates in vegetated sites and open areas. The monitoring network consisted of 17 throughfall collectors positioned beneath the primary vegetation types. The findings demonstrate that vegetation plays a substantial role in enhancing chloride deposition by effectively intercepting marine aerosols, resulting in total deposition values among throughfall sites ranging from 8% to 3742% higher than nearby open sites over a ~ 2-year period. Norfolk pines, white oak, and hardwood forest exhibit significantly elevated chloride deposition in throughfall compared to a shrub stand (Hawaiian holly) and a mixed palm forest.","verified_in_page":true},"topics":["vegetation-canopy-interception","chloride-deposition","throughfall","leaf-area-index","marine-aerosols","groundwater-recharge","spatial-variability","temporal-variability"],"study_period":"2020-09 to 2022-11","locations":[{"name":"Norfolk Island","kind":"landscape","role":"study site and landscape where chloride deposition under vegetation was measured across multiple vegetation types","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"4 of 21 Hydrological Processes, 2025 highly vulnerable to climate change impacts on its water resources (Karnauskas et al. 2018; Thomas et al. 2020). 2 | Geographical Context Norfolk Island is a volcanic island with an area of 35.7 km2 located in the South Pacific Ocean between New Zealand and Australia (Figure 1). Norfolk Island's predominant land uses are rural agriculture, residential and conservation, with a population of approximately 2188 residents (Australian Bureau of Statistics 2021), and it attracts around 28 000 tourists annually (Maurin et al. 2021). Concerns about the island's future water security have led to four recent projects (Hughes et al. 2022; Maurin et al. 2021; McJannet et al. 2023; Petheram et al. 2020) that aimed at improving the understanding of Norfolk Island's water balance and proposing potential water security solutions.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Norfolk pine stand, Norfolk Island","kind":"other","role":"field site representing coniferous vegetation sampling chloride throughfall and deposition","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Site description Woody vegetation names/species Leaf area index (LAI) Average tree height (m) Canopy closure (%) Distance from coast (m) Elevation (m) Number of collectors Collector codes Number of sampling periods Days sampling TF (mm) TF (%) Volume- weighted Cl (mgL−1) DTF (mgm−2 d−1) E (%) Overflow (n) Hawaiian holly stand (middle of stand) Hawaiian holly (Schinus terebinthifolia) 5.4 4.5 91 480 62 1 3HE 25 778 1703.6 61 29.7 65.1 71 5 Hawaiian holly stand (edge of stand) 5.4 7 95 495 63 1 3HM 25 778 1775.5 64 33.6 76.6 101 6 Oak tree (isolated tree) White oak (Lagunaria patersonia) 5.5 20 90 475 62 1 3O 25 778 2254.1 81 154.8 448.4 1076 8 Norfolk pine tree (isolated tree- distance to nearest tree ~10m) Norfolk pine (Araucaria heterophylla) 5.3 25 83 570 94 1 3P 24 719 2449.6 94 434.6 1480.7 3742 10 Norfolk pine tree (isolated tree— distance to nearest tall tree ~24m) 10.5 27 77 1826 119 3 5PA, 5PB and 5PC 6 221 1567.8 136 170.1 1206.4 3575 2 Norfolk pine stand (edge of stand) 6.2 30 96 2000 110 1 6PSE 23 720 1008.4 37 303 424.3 1499 2 Norfolk pine stand (middle of stand) 6.2 87 2010 112 1 6PSM 22 694 1263.9 47 162 294.9 1015 6 Moist palm Valley Forest (edge of stand) Mix forest; key species: Norfolk Island palm (Rhopalostylis baueri); Ironwood (Nestegis apetala); Maple (Elaeodendron curtipendula); Smooth tree fern (Cyathea brownii); Rough tree fern (Cyathea australis norfolkensis), Pennantia (Pennantia endlicheri); Ti (Cordyline obtecta). 4.8 — 93 1485 250 2 8PEA and 8PEB 25 778 1313.6 54 17.4 29.4 24 5 Moist palm Valley Forest (middle of stand) 4.8 96 1500 250 2 8PMA and 8PMB 25 778 860.3 35 23.2 25.6 8 4 (Continues) 10991085, 2025, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hyp.70336, Wiley Online Library on [28/11/2025].","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Isolated Norfolk pine tree, Norfolk Island","kind":"other","role":"field site with isolated Norfolk pine to measure chloride enhancement near single trees","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Site description Woody vegetation names/species Leaf area index (LAI) Average tree height (m) Canopy closure (%) Distance from coast (m) Elevation (m) Number of collectors Collector codes Number of sampling periods Days sampling TF (mm) TF (%) Volume- weighted Cl (mgL−1) DTF (mgm−2 d−1) E (%) Overflow (n) Hawaiian holly stand (middle of stand) Hawaiian holly (Schinus terebinthifolia) 5.4 4.5 91 480 62 1 3HE 25 778 1703.6 61 29.7 65.1 71 5 Hawaiian holly stand (edge of stand) 5.4 7 95 495 63 1 3HM 25 778 1775.5 64 33.6 76.6 101 6 Oak tree (isolated tree) White oak (Lagunaria patersonia) 5.5 20 90 475 62 1 3O 25 778 2254.1 81 154.8 448.4 1076 8 Norfolk pine tree (isolated tree- distance to nearest tree ~10m) Norfolk pine (Araucaria heterophylla) 5.3 25 83 570 94 1 3P 24 719 2449.6 94 434.6 1480.7 3742 10 Norfolk pine tree (isolated tree— distance to nearest tall tree ~24m) 10.5 27 77 1826 119 3 5PA, 5PB and 5PC 6 221 1567.8 136 170.1 1206.4 3575 2 Norfolk pine stand (edge of stand) 6.2 30 96 2000 110 1 6PSE 23 720 1008.4 37 303 424.3 1499 2 Norfolk pine stand (middle of stand) 6.2 87 2010 112 1 6PSM 22 694 1263.9 47 162 294.9 1015 6 Moist palm Valley Forest (edge of stand) Mix forest; key species: Norfolk Island palm (Rhopalostylis baueri); Ironwood (Nestegis apetala); Maple (Elaeodendron curtipendula); Smooth tree fern (Cyathea brownii); Rough tree fern (Cyathea australis norfolkensis), Pennantia (Pennantia endlicheri); Ti (Cordyline obtecta). 4.8 — 93 1485 250 2 8PEA and 8PEB 25 778 1313.6 54 17.4 29.4 24 5 Moist palm Valley Forest (middle of stand) 4.8 96 1500 250 2 8PMA and 8PMB 25 778 860.3 35 23.2 25.6 8 4 (Continues) 10991085, 2025, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hyp.70336, Wiley Online Library on [28/11/2025].","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"White oak tree, Norfolk Island","kind":"other","role":"isolated tree site sampling chloride deposition in throughfall","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Site description Woody vegetation names/species Leaf area index (LAI) Average tree height (m) Canopy closure (%) Distance from coast (m) Elevation (m) Number of collectors Collector codes Number of sampling periods Days sampling TF (mm) TF (%) Volume- weighted Cl (mgL−1) DTF (mgm−2 d−1) E (%) Overflow (n) Hawaiian holly stand (middle of stand) Hawaiian holly (Schinus terebinthifolia) 5.4 4.5 91 480 62 1 3HE 25 778 1703.6 61 29.7 65.1 71 5 Hawaiian holly stand (edge of stand) 5.4 7 95 495 63 1 3HM 25 778 1775.5 64 33.6 76.6 101 6 Oak tree (isolated tree) White oak (Lagunaria patersonia) 5.5 20 90 475 62 1 3O 25 778 2254.1 81 154.8 448.4 1076 8 Norfolk pine tree (isolated tree- distance to nearest tree ~10m) Norfolk pine (Araucaria heterophylla) 5.3 25 83 570 94 1 3P 24 719 2449.6 94 434.6 1480.7 3742 10 Norfolk pine tree (isolated tree— distance to nearest tall tree ~24m) 10.5 27 77 1826 119 3 5PA, 5PB and 5PC 6 221 1567.8 136 170.1 1206.4 3575 2 Norfolk pine stand (edge of stand) 6.2 30 96 2000 110 1 6PSE 23 720 1008.4 37 303 424.3 1499 2 Norfolk pine stand (middle of stand) 6.2 87 2010 112 1 6PSM 22 694 1263.9 47 162 294.9 1015 6 Moist palm Valley Forest (edge of stand) Mix forest; key species: Norfolk Island palm (Rhopalostylis baueri); Ironwood (Nestegis apetala); Maple (Elaeodendron curtipendula); Smooth tree fern (Cyathea brownii); Rough tree fern (Cyathea australis norfolkensis), Pennantia (Pennantia endlicheri); Ti (Cordyline obtecta). 4.8 — 93 1485 250 2 8PEA and 8PEB 25 778 1313.6 54 17.4 29.4 24 5 Moist palm Valley Forest (middle of stand) 4.8 96 1500 250 2 8PMA and 8PMB 25 778 860.3 35 23.2 25.6 8 4 (Continues) 10991085, 2025, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hyp.70336, Wiley Online Library on [28/11/2025].","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Hawaiian holly shrub stand, Norfolk Island","kind":"other","role":"invasive shrub stand site sampled for chloride throughfall and deposition","river_name":null,"geocode_query":"Norfolk Island, South Pacific Ocean, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Site description Woody vegetation names/species Leaf area index (LAI) Average tree height (m) Canopy closure (%) Distance from coast (m) Elevation (m) Number of collectors Collector codes Number of sampling periods Days sampling TF (mm) TF (%) Volume- weighted Cl (mgL−1) DTF (mgm−2 d−1) E (%) Overflow (n) Hawaiian holly stand (middle of stand) Hawaiian holly (Schinus terebinthifolia) 5.4 4.5 91 480 62 1 3HE 25 778 1703.6 61 29.7 65.1 71 5 Hawaiian holly stand (edge of stand) 5.4 7 95 495 63 1 3HM 25 778 1775.5 64 33.6 76.6 101 6 Oak tree (isolated tree) White oak (Lagunaria patersonia) 5.5 20 90 475 62 1 3O 25 778 2254.1 81 154.8 448.4 1076 8 Norfolk pine tree (isolated tree- distance to nearest tree ~10m) Norfolk pine (Araucaria heterophylla) 5.3 25 83 570 94 1 3P 24 719 2449.6 94 434.6 1480.7 3742 10 Norfolk pine tree (isolated tree— distance to nearest tall tree ~24m) 10.5 27 77 1826 119 3 5PA, 5PB and 5PC 6 221 1567.8 136 170.1 1206.4 3575 2 Norfolk pine stand (edge of stand) 6.2 30 96 2000 110 1 6PSE 23 720 1008.4 37 303 424.3 1499 2 Norfolk pine stand (middle of stand) 6.2 87 2010 112 1 6PSM 22 694 1263.9 47 162 294.9 1015 6 Moist palm Valley Forest (edge of stand) Mix forest; key species: Norfolk Island palm (Rhopalostylis baueri); Ironwood (Nestegis apetala); Maple (Elaeodendron curtipendula); Smooth tree fern (Cyathea brownii); Rough tree fern (Cyathea australis norfolkensis), Pennantia (Pennantia endlicheri); Ti (Cordyline obtecta). 4.8 — 93 1485 250 2 8PEA and 8PEB 25 778 1313.6 54 17.4 29.4 24 5 Moist palm Valley Forest (middle of stand) 4.8 96 1500 250 2 8PMA and 8PMB 25 778 860.3 35 23.2 25.6 8 4 (Continues) 10991085, 2025, 12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/hyp.70336, Wiley Online Library on [28/11/2025].","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Norfolk Island National Park","kind":"facility","role":"partner supporting site selection and providing access to vegetation monitoring sites","river_name":null,"geocode_query":"Norfolk Island National Park, Norfolk Island, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p019-000","page":19,"quote":"Hydrological Processes, 2025 19 of 21 By expanding the limited database on chloride deposition in small islands, this research lays the groundwork for future investigations and supports effective environmental management and conservation strategies in coastal regions. Acknowledgements This research was supported by the Australian Government Research Training Program (RTP) Scholarship. The authors gratefully acknowledge the technical and logistical support provided by the Norfolk Island Regional Council, which provided laboratory facilities and materials, fieldwork materials, office space and fieldwork support. The authors are also thankful to the National Park and all private landowners who contributed to the data collection of this study by agreeing and volunteering to have rainwater collectors installed on their land/properties. Support from the Norfolk Island National park team in site selection is gratefully acknowledged.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Coordinates of specific sampling sites on Norfolk Island are not provided and thus latitude and longitude are null.","Some sampling sites are described broadly (e.g., vegetation stands) without precise geographic coordinates.","The study is limited in spatial extent to Norfolk Island and its vegetation types and does not provide detail for applicability to other small islands without further study.","Stemflow chloride deposition was not measured during the main two-year period and was only evaluated in a later associated study phase."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[],"main_basin_ids":[]},{"id":"doc-d63c54602e24ae43696d85f6","title":"Floodplain inundation in the Murray–Darling Basin under current and future climate conditions","display_title":"Floodplain Inundation Trends in Murray–Darling Basin","doi":"https://doi.org/10.1038/s41598-025-93670-6","theme":"Floodplains","color":"#e9bd77","year":2025,"publication_date":"2025-03-14","authors":["Jin Teng","Francis H. S. Chiew","Hongxing Zheng","Ang Yang","Dave Penton","Catherine Ticehurst","Steve Marvanek","Jai Vaze","Fathaha Khanam","David Post","Carmel Pollino"],"summary":"This study develops a framework combining satellite remote sensing and hydrological modeling to analyze floodplain inundation variability, trends, and frequency over 35 years (1988–2022) and project future changes under climate scenarios in the Murray–Darling Basin (MDB), Australia's largest river system. It identifies the maximum 30-day runoff as the dominant hydrological driver of inundation and uses it to compare recent floodplain inundation with a 123-year baseline and future projections. Results show increased floodplain inundation frequency in recent decades, especially in the southern MDB, despite projected declines in water availability. Future changes vary regionally due to local hydroclimate, human intervention, and interplay of more intense rainfall and drier catchments, implying the need for regionally tailored water management strategies.","finding":"Annual maximum 30-day runoff strongly correlates with floodplain inundation extent and serves as a reliable proxy; floodplain inundation frequency has increased in recent decades compared to the long-term baseline, particularly in the southern MDB, and despite projected decreases in mean annual runoff under climate change, floodplain inundation is less affected, with some northern MDB events even projected to increase due to intensifying rainfall extremes.","finding_evidence":{"passage_id":"p005-000","page":5,"quote":"Overall, the three ecological sites demonstrate greater sensitivity to hydroclimate conditions than the irrigation sites. This is likely due to changes in the relationship between inundation and hydroclimate conditions resulting from development and practice over time at irrigation sites, particularly at the CIA. Floodplain inundation under a changing climate Further investigation reveals shifts in the magnitude and frequency of the annual maximum inundation extent under climate change using the maximum 30-day runoff as a proxy. The annual exceedance probabilities (AEPs) of maximum 30-day runoff in the recent period (1988–2022) are shown to be greater than in the long-term baseline (1900–2022) (Fig. 4). As indicated by the proxy, the higher inundation frequency in the last 35 years is more prominent in the southern MDB (Fig. 4c). This is particularly evident for more extreme floodplain inundation events (AEP<0.1 or ARI>10 years), consistent with the results of previous studies15,20.","verified_in_page":true},"topics":["hydrology","remote sensing","climate change","flood frequency","hydrological modeling","environmental water","land use","rainfall extremes"],"study_period":"1900–2022 (baseline), 1988–2022 (observation), 2046–2075 (future projection)","locations":[{"name":"Murray–Darling Basin","kind":"basin","role":"Primary study area encompassing diverse floodplain environments, hydrology, and water management","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Floodplain inundation in the Murray–Darling Basin under current and future climate conditions JinTeng 1, Francis H. S. Chiew 1, Hongxing Zheng 1, AngYang1, David J. Penton 1, CatherineTicehurst 1, Steve Marvanek 1, JaiVaze1, Fathaha Khanam2, David A. Post1 & Carmel Pollino1 Predicting floodplain inundation under a changing climate is essential for adaptive management of water resources and ecosystems worldwide. This study presents a framework combining satellite observations and hydrological modeling to explore changes in floodplain inundation. We examine variability, trends, and frequency of inundation across the Murray–Darling Basin (MDB), Australia’s largest river system, over the past 35 years (1988–2022). Our analysis shows that annual maximum 30-day runoff is a primary hydrological factor influencing floodplain inundation.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.466512,-31.16076],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Northern Murray–Darling Basin","kind":"region","role":"Subregion of MDB with higher hydroclimate variability, more arid conditions, and ephemeral rivers studied for spatial flood patterns","river_name":null,"geocode_query":"Northern Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"The northern MDB experiences higher seasonal and interannual hydroclimate variability, with more erratic rainfall patterns and drier conditions compared to the southern MDB. Most of the runoff originates from the high-elevation areas in the southeast of the basin, where most large reservoirs are located. In contrast, the northwest and west are arid, with long ephemeral and intermittent rivers flowing through relatively flatter landscapes28,29. We also analyzed the results for three regulated areas—Coleambally Irrigation Area (CIA), Lower Balonne System (LBS), and Menindee Lakes (Menindee)—where river operators manage water storage and releases to meet surrounding and downstream irrigation demands (referred to as irrigation sites hereafter).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.473175,-31.170638],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-1","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Southern Murray–Darling Basin","kind":"region","role":"Subregion of MDB characterized by more perennial systems and higher runoff, focal for comparative inundation frequency and trend analysis","river_name":null,"geocode_query":"Southern Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"The northern MDB experiences higher seasonal and interannual hydroclimate variability, with more erratic rainfall patterns and drier conditions compared to the southern MDB. Most of the runoff originates from the high-elevation areas in the southeast of the basin, where most large reservoirs are located. In contrast, the northwest and west are arid, with long ephemeral and intermittent rivers flowing through relatively flatter landscapes28,29. We also analyzed the results for three regulated areas—Coleambally Irrigation Area (CIA), Lower Balonne System (LBS), and Menindee Lakes (Menindee)—where river operators manage water storage and releases to meet surrounding and downstream irrigation demands (referred to as irrigation sites hereafter).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.481902,-31.179259],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-2","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Coleambally Irrigation Area (CIA)","kind":"region","role":"Key irrigation district within MDB with managed water storage impacting floodplain inundation dynamics","river_name":null,"geocode_query":"Coleambally Irrigation Area, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.891321,-34.829081],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43663510,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Coleambally Irrigation Area, NSW, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-3","true_coordinates":[145.8913207,-34.8890807],"spread_km":6.7},{"name":"Lower Balonne System (LBS)","kind":"region","role":"Irrigation area with managed river systems facing climate change pressures; studied for managed floodplain inundation","river_name":null,"geocode_query":"Lower Balonne System, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.1892921,-28.4646073],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677360,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Balonne, QLD, Australia","basin_id":5040511570,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":95584.8,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-4"},{"name":"Menindee Lakes (Menindee)","kind":"region","role":"Naturally occurring and man-made lakes used for irrigation and environmental flows, with observed flood declines and ecological impacts","river_name":null,"geocode_query":"Menindee Lakes, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.3347222,-32.3316667],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43661317,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Menindee Lake, Menindee NSW 2879, Australia","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-5"},{"name":"Barmah Forest","kind":"region","role":"Ecologically significant floodplain forest reliant on periodic flooding to maintain ecosystem health within southern MDB","river_name":null,"geocode_query":"Barmah Forest, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.0129857,-36.0087359],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43676990,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Barmah VIC 3639, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-6"},{"name":"Chowilla Riverland Floodplain (Chowilla)","kind":"region","role":"Critical floodplain-wetland complex supporting biodiversity and water quality in the lower Murray region","river_name":null,"geocode_query":"Chowilla Riverland Floodplain, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[140.9497647,-33.9271874],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677709,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Chowilla SA 5341, Australia","basin_id":5040073410,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":98950.4,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-7"},{"name":"Macquarie Marshes","kind":"region","role":"Large inland wetlands in northern MDB highly dependent on natural flooding for diverse aquatic and bird habitats","river_name":null,"geocode_query":"Macquarie Marshes, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Additionally, we examined three ecologically significant areas—Barmah Forest (Barmah), Chowilla Riverland Floodplain (Chowilla), and Macquarie Marshes (Macquarie)—which sustain diverse ecosystems, provide critical habitats, and support high levels of ecological productivity (referred to as ecological sites; see Fig. 1 for locations). Each of these sites holds unique significance in terms of ecological value, human influence, and environmental challenges. The Coleambally Irrigation Area is one of the most productive irrigation districts in the MDB, where water management plays a crucial role in sustaining agricultural output. Similarly, the Lower Balonne System Fig. 1. Floodplain inundation recurrence maps of the MDB. The frequency of surface water observed in the past 35 years. The flood extents for the various return periods are shown in separate panels (b–g) on the right. The main map (a) also shows the locations of the three irrigation sites and the three ecological sites.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.632165,-30.645984],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677087,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Macquarie Marshes NSW 2831, Australia","basin_id":5040524870,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":243193.7,"hydrology_source":"geofabric","site_id":"doc-d63c54602e24ae43696d85f6-site-8","true_coordinates":[147.6321653,-30.705984],"spread_km":6.7}],"limitations":["Landsat-derived water extent can be obscured by cloud cover and vegetation, leading to underestimation of inundation especially under forest canopy.","Temporal revisit frequency of satellites results in missed short-duration flood peaks, potentially underestimating flood extent and depth.","Hydrological modeling assumptions and parameter regionalizations introduce uncertainty, especially when extrapolating from historical to future conditions.","Human interventions such as dam operations, irrigation, and land use changes were not explicitly modeled, limiting ability to isolate their impact from hydroclimate drivers.","Study focuses on flood intensity and frequency, not addressing potential changes in flood duration that affect ecological and socio-economic outcomes.","Climate projections carry inherent uncertainty due to model and scenario 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-9","basin_ids":[5040073410,5040511570,5040524870,5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-da03aab13889b1eb4211c6ed","title":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water","display_title":"Correcting Cosmic-Ray Neutron Soil Moisture Data","doi":"https://doi.org/10.1029/2025wr040527","theme":"Other science","color":"#d1b5e7","year":2025,"publication_date":"2025-08-28","authors":["Lasse Hertle","Steffen Zacharias","Nicholas Larsen","Daniel Rasche","David McJannet","Martin Schrön"],"summary":"This study evaluates methods to correct cosmic-ray neutron sensing (CRNS) measurements of environmental water for variations in incoming cosmic-ray flux caused by solar and geomagnetic activity. Using long-term data from a global network of neutron monitor (NM) stations and artificial CRNS data, the authors assess existing scaling methods and propose improved time-dependent corrections as well as a globally robust model-based approach using the PARMA atmospheric radiation model. They demonstrate that choice of correction method can cause up to 30% deviations in soil moisture estimates, highlighting the importance of site-appropriate corrections for hydrological applications.","finding":"Incorporating temporal geomagnetic variability and using multiple neutron monitor stations through the PARMA model improves correction of CRNS measurements, yielding either lower errors at individual sites or robust global performance. The choice of correction method can result in up to 30% differences in soil moisture products derived from CRNS data.","finding_evidence":{"passage_id":"p014-004","page":14,"quote":"The impact of utilizing different correction methods was then assessed using artificial CRNS data, where a deviation between the methods of up to approximately 30% of the soil moisture level could be observed. This proves it vital to select a suitable correction method for CRNS data processing. Based on the analysis, two main recommendations can be provided for data correction. If there are sufficient resources available to find an optimal (nearby, gap‐free, and robust) NM for correcting the data of an individual CRNS site and period, then either the methods McJannet and Desilets (2023) or the method derived in this work would likely provide the best performance. Figures B1 and B2 can be helpful to decide between the two methods for each site and NM. If large data sets of CRNS stations across the globe have to be corrected in an automated and efficient manner, then PARMA is the advisable correction method.","verified_in_page":true},"topics":["cosmic-ray neutron sensing","soil moisture","geomagnetic field","heliospheric modulation","neutron monitors","radiation modeling","data correction","solar activity"],"study_period":null,"locations":[{"name":"Leipzig","kind":"settlement","role":"lead research centre","river_name":null,"geocode_query":"Leipzig, Saxony, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[12.373075,51.399695],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Leipzig, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-0","true_coordinates":[12.3730747,51.3396955],"spread_km":6.7},{"name":"Heidelberg University","kind":"facility","role":"academic institution","river_name":null,"geocode_query":"Heidelberg, Baden-Württemberg, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[8.6724335,49.3987524],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Heidelberg, Germany","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-1"},{"name":"Sodankylä Geophysical Observatory","kind":"facility","role":"neutron monitor data source and analysis","river_name":null,"geocode_query":"Sodankylä, Lapland, Finland","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[26.5889727,67.415999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"99600 Sodankylä, Finland","basin_id":2040030060,"main_basin_id":2040030060,"basin_region":"Europe and Middle East","basin_area_km2":53537.6,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-2"},{"name":"University of Nagoya","kind":"facility","role":"academic institution contributing to study","river_name":null,"geocode_query":"Nagoya, Aichi, Japan","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[136.9054486,35.1820124],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Nagoya, Aichi, Japan","basin_id":4040039450,"main_basin_id":4040039450,"basin_region":"Central and South-East Asia","basin_area_km2":295817.7,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-3"},{"name":"GFZ Helmholtz Centre for Geosciences","kind":"facility","role":"research institution","river_name":null,"geocode_query":"Potsdam, Brandenburg, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.064473,52.450569],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Potsdam, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-4","true_coordinates":[13.0644729,52.3905689],"spread_km":6.7},{"name":"CSIRO Environment, Dutton Park","kind":"facility","role":"collaborating research entity","river_name":null,"geocode_query":"Dutton Park, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"Neutron Monitor Based Incoming Flux Correction for Cosmic‐Ray Neutron Sensing of Environmental Water Lasse Hertle1,2 , Steffen Zacharias1 , Nicholas Larsen3,4 , Daniel Rasche5 , David McJannet6 , and Martin Schrön1 1 Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, 2 Physikalisches Institut, Heidelberg University, Heidelberg, Germany, 3 Sodankylä Geophysical Observatory, University of Oulu, Oulu, Finland, 4 Institute for Space‐Earth Environmental Research, University of Nagoya, Nagoya, Japan, 5 GFZ Helmholtz Centre for Geosciences, Potsdam, Germany, 6 CSIRO Environment, Dutton Park, QLD, Australia Abstract Monitoring of near‐surface water content with cosmic‐ray neutrons is strongly dependent on heliospheric and geomagnetic conditions. Neutron monitor (NM) stations are commonly used as a reference for the incoming cosmic radiation to correct the hydrological neutron signal.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.025192,-27.4954044],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130334,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dutton Park QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-da03aab13889b1eb4211c6ed-site-5"},{"name":"Neutron monitor network","kind":"other","role":"global reference system for cosmic-ray flux data","river_name":null,"geocode_query":"Global","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"In order to correct for the long term variation in CR flux, it is necessary to quantify the changes in heliospheric conditions independently from the local hydrogen abundance. High‐energy particles originating in CR showers, such as neutrons or muons, are usually considered insensitive to these local effects. These cascade neutrons are regularly monitored by the global Neutron Monitor (NM) network with stations on each continent (Väisänen et al., 2021). The global neutron monitor database (NMDB) provides a service to freely access a plethora of real‐ time data. There exists also a global muon network, but it contains far fewer sites (Rockenbach et al., 2014). Correction methods based on NMs are more commonly used in CRNS, being utilized by networks such as COSMOS UK (Evans et al., 2016), COSMOS‐Europe (Bogena et al., 2022), or CosmOz Australia (Hawdon et al., 2014).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Jungfraujoch","kind":"station","role":"high-elevation neutron monitor site with snow influence","river_name":null,"geocode_query":"Jungfraujoch, Bernese Alps, Switzerland","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"Both corrections are only possible during quiet geomagnetic conditions, as during disturbed conditions phase and amplitude might be drastically different to the background variation, therefore the fitted sinusoid will not be applicable to those time periods. Furthermore, both methods are directly applied to hourly values of the NM data. For this, the amplitude and phases needed to be known. They can either be calculated directly from the available data (for details see Thomas et al., 2017), or estimated using Equation 11. In either case, only the amplitudes of the NMs, ANM, needs to be known, which were calculated for the 25 NM stations used in this study and are provided in Table A1. 3.4. The Influence of Local Effects The NM signal is not a pure observation of incoming CR fluxes, it can also be influenced by local environmental effects (Kobelev et al., 2022; Mangeard et al., 2018). A well‐known example for this can be found in the Jungfraujoch NM data set, shown in Figure 5.","verified_in_page":true},"resolution_status":"resolved","coordinates":[7.983150999999999,46.548308],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Jungfraujoch, 3801 Lauterbrunnen, Switzerland","basin_id":2040023010,"main_basin_id":2040023010,"basin_region":"Europe and Middle East","basin_area_km2":163122.5,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-7"},{"name":"Kiel","kind":"station","role":"mid-latitude neutron monitor site for correction evaluation","river_name":null,"geocode_query":"Kiel, Schleswig-Holstein, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Since this conversion function is highly non‐linear, we showcase three different scenarios and corresponding N0 parameters that would lead to constant gravimetric soil moisture of 5%, 20% and 50%. Based on this setup, we have investigated the deviations from these three baselines for all five correction methods. Figure 7 shows the resulting soil moisture time series, where panels a–c showcase three different site pairings: (a) CRNS in Kiel corrected with an NM at Jungfraujoch, (b) CRNS in Jungfraujoch corrected with an NM in Oulu, and (c) CRNS in Thule corrected with an NM in Mexico City. It is important to note that the figure only shows the impact of different correction methods in comparison to the conventionally used correction method by Zreda et al. (2012). There can be no inference made about the validity of each correction method at this point.","verified_in_page":true},"resolution_status":"resolved","coordinates":[10.1227652,54.3232927],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kiel, Germany","basin_id":2040024230,"main_basin_id":2040024230,"basin_region":"Europe and Middle East","basin_area_km2":76316.8,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-8"},{"name":"Oulu","kind":"station","role":"neutron monitor site used in correction method evaluation","river_name":null,"geocode_query":"Oulu, Finland","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Since this conversion function is highly non‐linear, we showcase three different scenarios and corresponding N0 parameters that would lead to constant gravimetric soil moisture of 5%, 20% and 50%. Based on this setup, we have investigated the deviations from these three baselines for all five correction methods. Figure 7 shows the resulting soil moisture time series, where panels a–c showcase three different site pairings: (a) CRNS in Kiel corrected with an NM at Jungfraujoch, (b) CRNS in Jungfraujoch corrected with an NM in Oulu, and (c) CRNS in Thule corrected with an NM in Mexico City. It is important to note that the figure only shows the impact of different correction methods in comparison to the conventionally used correction method by Zreda et al. (2012). There can be no inference made about the validity of each correction method at this point.","verified_in_page":true},"resolution_status":"resolved","coordinates":[25.4650773,65.0120888],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Oulu, Finland","basin_id":2040029950,"main_basin_id":2040029950,"basin_region":"Europe and Middle East","basin_area_km2":21435.1,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-9"},{"name":"Thule","kind":"station","role":"neutron monitor site at high latitude used for method testing","river_name":null,"geocode_query":"Thule Air Base, Greenland","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Since this conversion function is highly non‐linear, we showcase three different scenarios and corresponding N0 parameters that would lead to constant gravimetric soil moisture of 5%, 20% and 50%. Based on this setup, we have investigated the deviations from these three baselines for all five correction methods. Figure 7 shows the resulting soil moisture time series, where panels a–c showcase three different site pairings: (a) CRNS in Kiel corrected with an NM at Jungfraujoch, (b) CRNS in Jungfraujoch corrected with an NM in Oulu, and (c) CRNS in Thule corrected with an NM in Mexico City. It is important to note that the figure only shows the impact of different correction methods in comparison to the conventionally used correction method by Zreda et al. (2012). There can be no inference made about the validity of each correction method at this point.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Mexico City","kind":"station","role":"neutron monitor site at low latitude used for method evaluation","river_name":null,"geocode_query":"Mexico City, Mexico","latitude":null,"longitude":null,"evidence":{"passage_id":"p013-001","page":13,"quote":"Since this conversion function is highly non‐linear, we showcase three different scenarios and corresponding N0 parameters that would lead to constant gravimetric soil moisture of 5%, 20% and 50%. Based on this setup, we have investigated the deviations from these three baselines for all five correction methods. Figure 7 shows the resulting soil moisture time series, where panels a–c showcase three different site pairings: (a) CRNS in Kiel corrected with an NM at Jungfraujoch, (b) CRNS in Jungfraujoch corrected with an NM in Oulu, and (c) CRNS in Thule corrected with an NM in Mexico City. It is important to note that the figure only shows the impact of different correction methods in comparison to the conventionally used correction method by Zreda et al. (2012). There can be no inference made about the validity of each correction method at this point.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-99.133208,19.492608],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mexico City, CDMX, Mexico","basin_id":7040049990,"main_basin_id":7040049990,"basin_region":"North America and Caribbean","basin_area_km2":153217.8,"hydrology_source":"hydrobasins","site_id":"doc-da03aab13889b1eb4211c6ed-site-11","true_coordinates":[-99.133208,19.4326077],"spread_km":6.7},{"name":"Lake Ross","kind":"field_site","role":"overwater CRNS measurement site for variability studies","river_name":null,"geocode_query":"Lake Ross, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-003","page":7,"quote":"The data of all CRNS shown has been corrected for pressure and humidity following commonly used methods (Bogena et al., 2022; Power et al., 2021) laid out by Dunai (2000) and Desilets and Zreda (2001) for pressure and Rosolem et al. (2013) for humidity. The amplitude observed between different CRNS stations varies to a higher degree than in NMs, with generally higher amplitudes being observed. The site of Lake Ross, Australia, is an overwater observation, which is described in detail in McJannet et al. (2025). Overwater observations using epithermal neutrons seek to isolate the neutron signal from hydrological influences by placing the sensor in a hydrogen‐saturated environment (Schrön et al., 2024). The OakValley, Australia,site issituated over soil at a distance of approximately 6 km to the overwater observation on Lake Ross.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.7692933,-19.4393538],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652759,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lake Ross, Queensland, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-da03aab13889b1eb4211c6ed-site-12"},{"name":"OakValley","kind":"field_site","role":"over soil CRNS measurement for comparison with overwater site","river_name":null,"geocode_query":"Oak Valley, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-003","page":7,"quote":"The data of all CRNS shown has been corrected for pressure and humidity following commonly used methods (Bogena et al., 2022; Power et al., 2021) laid out by Dunai (2000) and Desilets and Zreda (2001) for pressure and Rosolem et al. (2013) for humidity. The amplitude observed between different CRNS stations varies to a higher degree than in NMs, with generally higher amplitudes being observed. The site of Lake Ross, Australia, is an overwater observation, which is described in detail in McJannet et al. (2025). Overwater observations using epithermal neutrons seek to isolate the neutron signal from hydrological influences by placing the sensor in a hydrogen‐saturated environment (Schrön et al., 2024). The OakValley, Australia,site issituated over soil at a distance of approximately 6 km to the overwater observation on Lake Ross.","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.8247331,-19.416043],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652781,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Oak Valley QLD 4811, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-da03aab13889b1eb4211c6ed-site-13"}],"limitations":["Some locations have no precise latitude/longitude due to lack of explicit coordinates in the source.","Study locations are mostly neutron monitor stations or research facilities; natural river or wetland features are not primary focus.","Some referenced sites represent general regions or institutions rather than precise field sites.","Use of artificial data for soil moisture impact analysis limits direct empirical soil moisture validation."],"river_ids":["ne-1327"],"site_ids":["doc-da03aab13889b1eb4211c6ed-site-0","doc-da03aab13889b1eb4211c6ed-site-1","doc-da03aab13889b1eb4211c6ed-site-2","doc-da03aab13889b1eb4211c6ed-site-3","doc-da03aab13889b1eb4211c6ed-site-4","doc-da03aab13889b1eb4211c6ed-site-5","doc-da03aab13889b1eb4211c6ed-site-7","doc-da03aab13889b1eb4211c6ed-site-8","doc-da03aab13889b1eb4211c6ed-site-9","doc-da03aab13889b1eb4211c6ed-site-11","doc-da03aab13889b1eb4211c6ed-site-12","doc-da03aab13889b1eb4211c6ed-site-13"],"anchor":[25.4650773,65.0120888],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[2040023010,2040024170,2040024230,2040029950,2040030060,4040039450,5040067860,5040069970,7040049990],"main_basin_ids":[2040023010,2040024170,2040024230,2040029950,2040030060,4040039450,5040067860,5040069970,7040049990]},{"id":"doc-e09863ff94e3ddd829f0ce71","title":"Species Richness of Freshwater Fish Trophic Guilds Increases With Tropical River Discharge and Decreases With Variability","display_title":"Developing Northern Australia: White Paper","doi":"https://doi.org/10.1002/ece3.72343","theme":"Other science","color":"#d1b5e7","year":2025,"publication_date":"2025-10-01","authors":["Colton Perna","David Sternberg","Mark J. Kennard","Osmar J. Luiz","Dylan J. Irvine","Danial Stratford","R. Keller Kopf"],"summary":"The White Paper outlines a comprehensive policy and investment framework to unlock Northern Australia's economic potential by addressing land and water resource management, infrastructure, workforce, governance, and trade opportunities. It emphasizes partnerships with Indigenous Australians, securing land and water rights, improving transport and communication infrastructure, fostering diverse industries, and enhancing governance for sustainable development over 20 years.","finding":"Complex land tenure in Northern Australia, including extensive native title and pastoral leases, has constrained investment and economic development; hence, simplifying land arrangements and providing secure, tradeable land rights to Indigenous and pastoral landholders will unlock economic opportunities and attract private investment.","finding_evidence":{"passage_id":"p012-001","page":12,"quote":"REDUCED BARRIERS TO BETTER USE OF LAND AND WATER RESOURCES The north will never reach its potential without secure, tradeable titles to land and water. Complex land tenure systems across the north are not easily understood by potential investors or financial institutions. Much of the north is Crown land held under state and territory pastoral leases. Pastoral leaseholders are generally unable to use their land for activities other than grazing, such as horticulture or tourism. Pastoralists trying to broaden economic activity often need approvals from various government bodies. Further, lessees do not have the same security as those with freehold tenure. The Commonwealth Government supports the efforts of northern jurisdictions to accelerate pastoral lease reforms, making it easier for pastoral owners to diversify their businesses.","verified_in_page":true},"topics":["land-tenure","indigenous-economics","water-resource-assessment","infrastructure-planning","foreign-investment","workforce-development","tropical-health","governance"],"study_period":null,"locations":[{"name":"Northern Australia","kind":"region","role":"primary study region","river_name":null,"geocode_query":"Northern Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p139-001","page":139,"quote":"A VAST, DIVERSE AND VIBRANT REGION Northern Australia, covering those parts of Western Australia and Queensland above the Figure 20: Northern Australia ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (( ( ( ( ( ( ( ( ( ( ( Weipa Derby Bowen Broome Onslow Bamaga Darwin Ingham Newman Winton Cairns Mackay Dampier Clermont Karratha Atherton Kununurra Mount Isa Normanton Katherine Tom Price Gladstone Townsville Palmerston Pine Creek Borroloola Rockhampton Tennant Creek Alice Springs Fitzroy Crossing Port Hedland Adelaide River Papua New Guinea Indonesia Timor Leste Indonesia 0 500 1,000 ́ Scale (km) Timor Sea Indian Ocean Gulf of Carpentaria Coral Sea Tropic of Capricorn Source: Department of Infrastructure and Regional Development, 2015b tropic of Capricorn as well as all of the Northern Territory, is a huge and diverse region of over 3 million km2 , with a population of over one million people.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Mitchell River catchment","kind":"basin","role":"priority water resource assessment site","river_name":"Mitchell River","geocode_query":"Mitchell River, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p049-000","page":49,"quote":"42 combination of in-stream and on-farm dams (CSIRO, 2015b). That is about four times the size of the current Ord scheme in northern Western Australia, and would increase the total area under irrigation in the north by almost 40 per cent. Before this assessment, such irrigation potential went largely unrecognised. The Queensland Government is proposing to release 755 GL of previously unallocated water in these catchments as a direct result of this assessment. THE WAY FORWARD The Commonwealth Government will work with northern jurisdictions to undertake more water resource assessments in priority catchments. These will be rolled out over the next four years. Assessments will target areas likely to deliver the most valuable water for economic development and provide the information needed to establish water management plans.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.560318,-15.803806],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":123317410,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-1","true_coordinates":[142.5603181507045,-15.86380594713086],"spread_km":6.7},{"name":"Darwin region","kind":"region","role":"priority water resource assessment site and location for single point of entry office","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p049-001","page":49,"quote":"To inform this White Paper, CSIRO was asked to undertake a rapid appraisal of river catchments across northern Australia to identify new opportunities for water storage and irrigated agriculture (CSIRO, 2015b). This appraisal has provided important information to guide decisions on the most promising regions for further investigation. Figure 7: River basins in northern Australia The figure identifies the larger river basins in northern Australia (as defined by the Australian Water Resources Council). The inset shows the major drainage divisions and key population centres in northern Australia. Source: Petheram, et al., 2015.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.788549,-12.488658],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-2","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Northern Territory","kind":"region","role":"key jurisdiction partner","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p123-003","page":123,"quote":"GNARTN is also exploring the unique opportunities for development and innovation flowing from the north’s proximity to Asia and the Tropics. • The National Critical Care and Trauma Response Centre, based in Darwin, which coordinates Australia’s emergency medical response to major incidents (see Box 40).","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Queensland","kind":"region","role":"key jurisdiction partner","river_name":null,"geocode_query":"Northern Queensland, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p123-003","page":123,"quote":"GNARTN is also exploring the unique opportunities for development and innovation flowing from the north’s proximity to Asia and the Tropics. • The National Critical Care and Trauma Response Centre, based in Darwin, which coordinates Australia’s emergency medical response to major incidents (see Box 40).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.701063,-19.316756],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652762,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"North Queensland, QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-4","true_coordinates":[146.7615238,-19.2982152],"spread_km":6.7},{"name":"Northern Western Australia","kind":"region","role":"key jurisdiction partner","river_name":null,"geocode_query":"Northern Western Australia, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p123-003","page":123,"quote":"GNARTN is also exploring the unique opportunities for development and innovation flowing from the north’s proximity to Asia and the Tropics. • The National Critical Care and Trauma Response Centre, based in Darwin, which coordinates Australia’s emergency medical response to major incidents (see Box 40).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Pilbara region","kind":"region","role":"mineral resource and labour market centre","river_name":null,"geocode_query":"Pilbara, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p146-001","page":146,"quote":"Northern Australia is also a hub for our defence cooperation with regional partners and the primary staging ground for the US force posture initiatives over the next decade. This will include rotational US Marine Corps deployments and enhanced aircraft cooperation. Northern Australia hosts a range of combined exercises and activities with international defence partners, including: • Exercise Talisman Sabre — the major biennial Australia-US exercise • Exercise Kakadu — the Royal Australian Navy’s largest multinational maritime warfare exercise held biennially • Exercise Pitch Black — the Royal Australian Air Force’s largest and most complex biennial air exercise • Exercise Kowari — held for the first time in October 2014, and is the first land based trilateral military exercise between Australia, China and the United States of America.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.5236826,-21.5921433],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":90876689,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pilbara, WA, Australia","basin_id":5040087730,"main_basin_id":5040087730,"basin_region":"Australasia","basin_area_km2":154843.5,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-6"},{"name":"Cairns","kind":"settlement","role":"major population and tourist centre in the north","river_name":null,"geocode_query":"Cairns, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p158-001","page":158,"quote":"Northern Australia is popular with international backpackers because of the diverse work and recreation opportunities, and almost one in five young tourists who come to Australia visit the Northern Territory (Tourism NT, 2014). The region also appeals to older Australians who divide their time between the northern dry season and the southern summer. In 2013-14, northern Australia was the destination of choice for 15.9 per cent of international overnight visitors compared with only 6.7 per cent of domestic overnight visitors (Tourism Research Australia, 2014b). Of visitors to northern Australia, approximately 36.9 per cent went to Tropical North Queensland, making it the most popular destination for overnight visitors in 2013-14 (Tourism Research Australia, 2015). Following a period of declining tourist numbers in the Northern Territory, visitors in 2014 stayed longer and spent the most money they had in five years ($1.85 billion) (Conlan, M., 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.7709529,-16.9203476],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16651731,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cairns QLD, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-7"},{"name":"Townsville","kind":"settlement","role":"major population and trade centre","river_name":null,"geocode_query":"Townsville, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p145-000","page":145,"quote":"138 THE PUBLIC SECTOR AND DEFENCE AN IMPORTANT PLAYER IN NORTHERN AUSTRALIA The public sector also plays a major role in the northern economy with nearly one in ten workers employed in public administration (ABS, 2011a). The Australian Public Service (APS) presence in the north totals over 5,400 across 38 agencies including the Departments of the Prime Minister and Cabinet, Defence, Human Services, Agriculture, and Immigration and Border Protection (Australian Public Service Commission, 2014). Locations of APS staff vary from major centres such as Broome, Darwin and Townsville, to remote locations including Fitzroy Crossing, Nhulunbuy and Weipa (see Figure 25). Figure 25: APS employee locations in northern Australia Figure compiled using geocoding of Australian Public Service Employment Database postcode data (Australian Public Service Commission, 2014).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.753213,-19.278419],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":16652811,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Townsville City QLD 4810, Australia","basin_id":5040067860,"main_basin_id":5040067860,"basin_region":"Australasia","basin_area_km2":26957.6,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-8","true_coordinates":[146.8136596,-19.2598781],"spread_km":6.7},{"name":"Broome","kind":"settlement","role":"centre of indigenous business and regional lobby","river_name":null,"geocode_query":"Broome, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p123-002","page":123,"quote":"These include: • The newly established Northern Australia Alliance: a grass roots initiative that unites businesses across the north through Broome Futures, the Northern Territory Chamber of Commerce and Advance Cairns. • The Empowered Communities project, where the Government, jurisdictions and Indigenous leaders collaboratively designed a new and flexible Indigenous governance model for regions. • The Northern Regional Development Australia (RDA) Alliance: a collaboration of the eight RDA committees above the Tropic of Capricorn, which tightens the relationship between the Commonwealth Government and the jurisdictions, and identifies shared priority policies and investment opportunities. • The Greater Northern Australia Regional Training Network (GNARTN), which drives collaboration across northern Australia’s clinical health sector. It aims to improve health care (particularly in Indigenous communities) and build the capacity of the local health workforce.","verified_in_page":true},"resolution_status":"resolved","coordinates":[122.173924,-17.96182],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102208974,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Broome WA 6725, Australia","basin_id":5040059020,"main_basin_id":5040059020,"basin_region":"Australasia","basin_area_km2":23538.1,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-9","true_coordinates":[122.236998,-17.96182],"spread_km":6.7},{"name":"Alice Springs","kind":"settlement","role":"regional centre and potential international airport expansion","river_name":null,"geocode_query":"Alice Springs, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p169-000","page":169,"quote":"162 QUEENSLAND GOVERNMENT Mining and Energy • Bowen and Galilee Basins — there is in excess of 50 proposed mines or mine expansions under consideration in these two basins. The Queensland Government is supportive of the opening up of the Galilee Basin on a commercially viable and environmentally sustainable basis. It is working with proponents to assist them navigate through the necessary approval processes and to facilitate the provision, where possible, of common use infrastructure. The Bowen Basin is also a coal seam gas producing region, with gas going to the Australian domestic gas market or to the Curtis Island LNG export facility at Gladstone. • North West Queensland Mineral Province — Queensland is Australia’s and one of the world’s largest producers of zinc, lead and silver. It ranks second in Australia as a copper and bauxite producer and is also an important producer of gold, mineral sands, phosphate fertilisers and magnesite.","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.838628,-23.744005],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":115715194,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Alice Springs NT 0870, Australia","basin_id":5040491370,"main_basin_id":5040087590,"basin_region":"Australasia","basin_area_km2":255334.9,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-10","true_coordinates":[133.8807471,-23.698042],"spread_km":6.7},{"name":"Darwin","kind":"settlement","role":"regional capital, centre of Defence and governance","river_name":null,"geocode_query":"Darwin, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p123-002","page":123,"quote":"These include: • The newly established Northern Australia Alliance: a grass roots initiative that unites businesses across the north through Broome Futures, the Northern Territory Chamber of Commerce and Advance Cairns. • The Empowered Communities project, where the Government, jurisdictions and Indigenous leaders collaboratively designed a new and flexible Indigenous governance model for regions. • The Northern Regional Development Australia (RDA) Alliance: a collaboration of the eight RDA committees above the Tropic of Capricorn, which tightens the relationship between the Commonwealth Government and the jurisdictions, and identifies shared priority policies and investment opportunities. • The Greater Northern Australia Regional Training Network (GNARTN), which drives collaboration across northern Australia’s clinical health sector. It aims to improve health care (particularly in Indigenous communities) and build the capacity of the local health workforce.","verified_in_page":true},"resolution_status":"resolved","coordinates":[130.811223,-12.514209],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213238,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Darwin NT, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-11","true_coordinates":[130.8444446,-12.4637333],"spread_km":6.7},{"name":"Abbot Point","kind":"facility","role":"port for coal exports and new projects","river_name":null,"geocode_query":"Abbot Point, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p169-001","page":169,"quote":"The North West Queensland Mineral Province in the Mount Isa region dominates Queensland base metal production. North Queensland is also a leading mineral province and an important producer of bauxite, silica sand, base metals, gold and, more recently, tungsten. The Queensland Government provides support for the mining and exploration industry through initiatives lead by the Geological Survey of Queensland. • South of the Embley bauxite project, Weipa — Queensland is the largest bauxite producer in Australia, and Australia is the largest bauxite producer in the world with 30 per cent of the world’s production in 2012. The Cape York bauxite deposits are some of the richest and largest in the world, second only to Guinea. The South of Embley bauxite project represents a new mine with up to 40 years of production at 25 to 50 million tonnes per year. The Weipa mine has been in operation since 1963.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"},{"name":"Kakadu National Park","kind":"field_site","role":"joint Indigenous/Commonwealth environmental management model","river_name":null,"geocode_query":"Kakadu National Park, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p041-000","page":41,"quote":"34 Box 6. Joint management of national parks Kakadu and Uluṟu-Kata Tjuṯa National Parks are managed in a cooperative arrangement between the Commonwealth Government and Aboriginal traditional owners. This arrangement (known as Joint Management) is seen as a leading model within national parks in Australia and overseas. To further strengthen this partnership the Parks Boards of Management — which comprises a majority Indigenous representation — and the Director of National Parks, have initiated the Joint Management Futures Project which will improve access to the economic opportunities parks provide. This includes significantly improving Indigenous participation in related job opportunities and building the capacity of local Indigenous business and contractors to deliver park services. MORE ECONOMIC ACTIVITY ON PASTORAL LAND The north is made up of large tracts of land held under pastoral leases — much more than in the south, where freehold dominates (see Figure 6 and Box 7).","verified_in_page":true},"resolution_status":"resolved","coordinates":[132.393766,-13.032293],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102214982,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kakadu National Park, Kakadu Hwy, Jabiru NT 0886, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-13","true_coordinates":[132.3937658,-13.0922931],"spread_km":6.7},{"name":"Uluru-Kata Tjuta National Park","kind":"field_site","role":"Indigenous heritage and tourism icon, joint management","river_name":null,"geocode_query":"Uluru-Kata Tjuta National Park, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p041-000","page":41,"quote":"34 Box 6. Joint management of national parks Kakadu and Uluṟu-Kata Tjuṯa National Parks are managed in a cooperative arrangement between the Commonwealth Government and Aboriginal traditional owners. This arrangement (known as Joint Management) is seen as a leading model within national parks in Australia and overseas. To further strengthen this partnership the Parks Boards of Management — which comprises a majority Indigenous representation — and the Director of National Parks, have initiated the Joint Management Futures Project which will improve access to the economic opportunities parks provide. This includes significantly improving Indigenous participation in related job opportunities and building the capacity of local Indigenous business and contractors to deliver park services. MORE ECONOMIC ACTIVITY ON PASTORAL LAND The north is made up of large tracts of land held under pastoral leases — much more than in the south, where freehold dominates (see Figure 6 and Box 7).","verified_in_page":true},"resolution_status":"resolved","coordinates":[131.0346514,-25.3437797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102213433,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Uluṟu-Kata Tjuṯa National Park, Lasseter Hwy, Uluru NT 0872, Australia","basin_id":5040087840,"main_basin_id":5040087840,"basin_region":"Australasia","basin_area_km2":89261.1,"hydrology_source":"geofabric","site_id":"doc-e09863ff94e3ddd829f0ce71-site-14"}],"limitations":["Broad regional geographies without coordinates","Some passages contain multiple overlapping initiatives making precise role assignment challenging","No explicit study period stated in text"],"river_ids":[],"site_ids":["doc-e09863ff94e3ddd829f0ce71-site-1","doc-e09863ff94e3ddd829f0ce71-site-2","doc-e09863ff94e3ddd829f0ce71-site-4","doc-e09863ff94e3ddd829f0ce71-site-6","doc-e09863ff94e3ddd829f0ce71-site-7","doc-e09863ff94e3ddd829f0ce71-site-8","doc-e09863ff94e3ddd829f0ce71-site-9","doc-e09863ff94e3ddd829f0ce71-site-10","doc-e09863ff94e3ddd829f0ce71-site-11","doc-e09863ff94e3ddd829f0ce71-site-13","doc-e09863ff94e3ddd829f0ce71-site-14"],"anchor":[132.393766,-13.032293],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[5040049720,5040059020,5040061670,5040067860,5040087730,5040087840,5040491370],"main_basin_ids":[5040049720,5040059020,5040061670,5040067860,5040087590,5040087730,5040087840]},{"id":"doc-e99b69744dbda7ec371e70dc","title":"Technical note: Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe)","display_title":"Fast Analytical Solution for Hydrological Reservoir Equation","doi":"https://doi.org/10.5194/hess-29-2003-2025","theme":"Water management","color":"#eeabac","year":2025,"publication_date":"2025-04-22","authors":["Julien Lerat"],"summary":"This study introduces the QuaSoARe method, a piecewise quadratic analytical solution to the scalar reservoir equation common in hydrological models. The method approximates flux functions by quadratic interpolation, enabling explicit and mass-conservative solutions. QuaSoARe was applied to four reservoir model types and tested on catchments in eastern Australia that experienced extreme flooding events, demonstrating high accuracy and significant runtime reductions compared to standard numerical solvers.","finding":"QuaSoARe achieves accuracy comparable to high-order implicit Runge–Kutta methods while reducing runtime by factors of 10 to 50 across diverse and complex reservoir models under extreme climate conditions.","finding_evidence":{"passage_id":"p011-001","page":11,"quote":"Overall, 60 simulations are produced for each model and ODE solver combination. The Radau method is considered to be the reference against which the error of other methods is computed. The numbers shown in each plot of Fig. 6 report the median value of the metrics over the 60 corresponding simulations, while the blue boxes show their minimum– maximum range. All the plots in Fig. 6 reveal that QuaSoARe simulations are extremely close to the Radau outputs if QuaSoARe is configured with a high number of nodes (500). The median absolute simulation error of QuaSoARe (E m) varies between 4.4 × 10−6 mm d−1 for the GR model (Fig. 6g) to https://doi.org/10.5194/hess-29-2003-2025 Hydrol. Earth Syst. Sci., 29, 2003–2021, 2025","verified_in_page":true},"topics":["numerical methods","hydrological modeling","reservoir equations","quadratic interpolation","runoff routing","flood hydrology","differential equations","model calibration"],"study_period":"2010-2022","locations":[{"name":"Richmond River catchment","kind":"basin","role":"case study area for method evaluation encompassing six subcatchments subject to extreme flooding","river_name":null,"geocode_query":"Richmond River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-003","page":9,"quote":"This metric provided similar results to R m but is more challenging to interpret because QuaSoARe and the two discrete methods do not evaluate the same functions (analytical solutions for QuaSoARe and original flux functions for RK45 and Radau). Consequently, the simple runtime metric R m is preferred here. 5.3 Case study area QuaSoARe performance is evaluated for six sites in the Richmond River catchment in eastern Australia, close to the city of Lismore. The catchments are presented in Table 1, and their location is shown in Fig. 3. This area was chosen because it experienced a devastating flood in February 2022, which prompted an in-depth analysis of the event (Lerat et al., 2022). In addition, the maximum rainfall totals observed during this flood exceeded 700 mm in 24 h. Such extreme values constitute a challenging test for ODE solvers, as pointed out by La Follette et al. (2021).","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.144792,-28.781033],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26971431,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e99b69744dbda7ec371e70dc-site-0","true_coordinates":[153.1447918188741,-28.72103323072004],"spread_km":6.7},{"name":"Coopers Creek at Ewing Bridge catchment","kind":"basin","role":"specific catchment used for example application of QuaSoARe with GR reservoir model","river_name":"Coopers Creek","geocode_query":"Coopers Creek at Ewing Bridge catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"The daily catchment average climate data from 1 January 2010 to 31 December 2022 are used to run the two hydrological models (GR and GRM). As highlighted in Sect. 5.2, the aim of this paper is not to simulate hydrological processes accurately in these catchments but only to obtain realistic forcing data for the numerical experiments presented here. 6 Results 6.1 Simulation of the GR4J production store As an example of the application of QuaSoARe, this section details the results obtained with the GR4J production store (GR) presented in Table 1 for the Coopers Creek at Ewing Bridge catchment. The GR simulation is run with a storage scaling factor of 500 mm, a typical value for this parameter in Australia. Figure 4 shows the interpolation performed by QuaSoARe to approximate the three fluxes of the GR model using 10 nodes. The plots show the storage level S divided by the scaling factor θ on the x axis and the instantaneous flux on the y axis.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.3741541490074,-28.65686687140094],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26970967,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e99b69744dbda7ec371e70dc-site-1"}],"limitations":["Applies only to scalar (single reservoir) ODEs, not coupled reservoir systems","Accuracy depends on quality of quadratic interpolation and number of nodes","Cannot handle flux functions lacking Lipschitz continuity, limiting some common non-smooth hydrological fluxes","Possible interpolation-induced oscillations and erroneous steady-state roots if interpolation is not carefully constrained","Extension to vector or coupled systems is non-trivial and not provided"],"river_ids":[],"site_ids":["doc-e99b69744dbda7ec371e70dc-site-0","doc-e99b69744dbda7ec371e70dc-site-1"],"anchor":[153.3741541490074,-28.65686687140094],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-2","basin_ids":[5040069970],"main_basin_ids":[5040069970]},{"id":"doc-e9f26d5f1648e695a3c32d23","title":"How February 2022 redefines extreme floods in Australia","display_title":"February 2022 Extreme Floods in Eastern Australia","doi":"https://doi.org/10.1038/s43247-025-02307-z","theme":"Floodplains","color":"#e9bd77","year":2025,"publication_date":"2025-05-15","authors":["Julien Lerat","Jai Vaze"],"summary":"This study analyses the extreme flood event in February 2022 in eastern Australia, particularly across northeast New South Wales and southeast Queensland. The event featured record-breaking rainfall and soil saturation, resulting in unprecedented flood volumes and peak flows. The flood surpassed previous historical records, including those from 1954 and the 2010 Queensland flood. It caused significant human, social, and economic impacts, prompting comprehensive changes in flood risk management and infrastructure. The authors use a detailed dataset from 1094 Australian gauging stations spanning 1950 to 2022 to characterize this event's uniqueness and its implications for flood design standards.","finding":"The February 2022 floods caused the largest observed increase in 1% Annual Exceedance Probability (AEP) flood volumes in the affected areas, with design flood volumes expected to increase by 16% on average across the region, profoundly altering flood hazard levels and risk assessments.","finding_evidence":{"passage_id":"p004-002","page":4,"quote":"White dots represent the mean index value for each regional event. Figure 5 reveals that the 2022 flood has the highest mean surprise index for ten-day runoff totals, reaching a value of 0.16. In other words, the 1% AEP design flood hydrographs estimated after the 2022 event are likely to see an increase in flood volumes (estimated here as 10 days total runoff) by 16% on average across the affected region, thus increasing flood hazard levels substantially compared to the pre-2022 situation. Note that the surprise index exceeds 30% for certain sites during the 2022 event (see distribution of red points in Fig. 5) leading to a similar increase in 1% AEP values, and hence a pronounced impact on protection levels offered by flood defence infrastructure. This result also confirms the extreme nature of flood volumes during the 2022 flood, as seen in Fig. 3.","verified_in_page":true},"topics":["extreme-precipitation","flood-frequency-analysis","hydroclimate-data","flood-risk-management","river-hydrology","flood-volume-characterization","soil-moisture-saturation","flood-propagation-dynamics"],"study_period":"1950–2024","locations":[{"name":"Lismore","kind":"settlement","role":"Site of record peak flood water level (14.4 m) on Wilsons River during 2022 flood, and key flood impact city","river_name":"Wilsons River","geocode_query":"Lismore, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The paper concludes with a summary of the main factors governing the 2022 flood, which can serve as a reference for future floods in coastal catchments and humid sub-tropical climates, especially around the Pacific. Results and discussion The impossible flood Australia experienced a series of floods between 2020 and the beginning of 2024 that culminated in February andMarch 2022 with a regional event that affected the north-east part of the state of New South Wales (NSW) and the south-east part of the state of Queensland (QLD) as shown in Fig. 1a with further location details in Fig. 2. This flood resulted in 24 deaths25,26 and is considered the costliest flood event in Australia, with a total insurance claim estimated at over AUD$6 billion27 and combined infrastructure damage and loss of economic opportunity reaching AUD$7.7 billion in QLD alone28.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.287891,-28.80943079999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26970860,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lismore NSW 2480, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-0"},{"name":"Wilsons River reach between Eltham and Woodlawn stations","kind":"reach","role":"River reach analyzed for flood peak timing and attenuation during 2022 flood event","river_name":"Wilsons River","geocode_query":"Wilsons River near Lismore, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-003","page":2,"quote":"Upstream of Lismore (see Fig. 1e, f, with precise location shown in Fig. 2), the hydrograph shows two distinct peaks: the first on the 25 February and the second on the 28. In addition to reaching water levels never observed before in Lismore, this particular sequence exacerbated the impact of the flood by creating a false sense of security among local communities following the fall of the first peak25. Figure 2 provides more information about the propagation of the flood along a reach of the Wilsons River between the stations of Eltham (upstream) andWoodlawn (downstream), located 22 and 5 km upstream of Lismore, respectively (see inset in Fig. 2).","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.3741787171316,-28.78712381389069],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26970932,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wilsons River, New South Wales, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-1"},{"name":"Bellbird Creek gauging station","kind":"station","role":"Streamflow gauging station recording rapid peak flow rises during 2022 flood","river_name":null,"geocode_query":"Bellbird Creek, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Compared to 24-hour design rainfall in the region, daily totals at several stations in the area were above the 0.05% AEP threshold34,40. Remarkably, the areas with the most intense rainfall in Fig. 1b coincides with the highest soil saturation before the flood, as seen in Fig. 1a. In these areas, soil water profiles close to their maximum historical values led to negligible rainfall infiltration and increased overland flow, directly contributing to river flows. Following record rainfall intensities, river flows across the area reacted quickly, as shown by the hydrographs presented in Fig. 1c–f where streamflow rose by several hundreds of metre cube per second in less than three hours, including a flow rise of 1083 m3 s −1 in one hour in Bellbird Creek station. The peak flow measured at the Durrumbul station (Fig. 1d) reached 792 m3 s −1 for a catchment area of 34 km2 , noting that high-flow measurements at this location are known to be underestimated41.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.1945617060581,-28.19541691145296],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130670,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Bell Bird Creek, Queensland 4211, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-2"},{"name":"Durrumbul gauging station","kind":"station","role":"Measurement site of peak flow (792 m3/s) during 2022 flood in a smaller catchment area","river_name":null,"geocode_query":"Durrumbul, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-002","page":2,"quote":"Compared to 24-hour design rainfall in the region, daily totals at several stations in the area were above the 0.05% AEP threshold34,40. Remarkably, the areas with the most intense rainfall in Fig. 1b coincides with the highest soil saturation before the flood, as seen in Fig. 1a. In these areas, soil water profiles close to their maximum historical values led to negligible rainfall infiltration and increased overland flow, directly contributing to river flows. Following record rainfall intensities, river flows across the area reacted quickly, as shown by the hydrographs presented in Fig. 1c–f where streamflow rose by several hundreds of metre cube per second in less than three hours, including a flow rise of 1083 m3 s −1 in one hour in Bellbird Creek station. The peak flow measured at the Durrumbul station (Fig. 1d) reached 792 m3 s −1 for a catchment area of 34 km2 , noting that high-flow measurements at this location are known to be underestimated41.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Dunoon gauging station","kind":"station","role":"Location recording extreme 775 mm rainfall in 24 h during 2022 flood, near Lismore","river_name":null,"geocode_query":"Dunoon, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"It is worth noting that this wet phase followed an extreme dry period known as the Tinderbox Drought37 during which the region suffered from intense wildfires. A first weather system reached the region on the 24 February. The system was generated by a Rossby wave break-up combined with a blocking high in the Tasman Sea and warm and moist air inputs from the Coral Sea25. A second system developed on the 25, affecting southeast QLD, which moved to NSW on the 27th and generated extreme rainfall totals38. Figure 1b shows the maximum five-day total rainfall during February and March 2022. In several areas, the totals exceeded 1000 mm, representing more than half of the mean annual total in the region. A value of 775 mm was recorded in 24 h at the gauging station at Dunoon, located upstream of Lismore36. This value is just below the highest maximum daily rainfall recorded in NSW (809.2 mm)39.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.3127685,-28.6916531],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26970872,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Dunoon NSW 2480, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-4"},{"name":"Gympie","kind":"settlement","role":"Town near area of high soil moisture saturation and intense rainfall during 2022 flood","river_name":null,"geocode_query":"Gympie, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"Lismore (see Fig. 1a), breaking the previous record of 12.3 m observed in 1954. The observations at this station started in 191734. Figure 1 summarises several important characteristics of the 2022 event. Figure 1a shows the saturation level of the topsoil layer simulated by the Australian Water Resources Assessment landscape model35 on the 23 February, just before the main rainfall event started. The plot highlights two areas – one between the cities of Gympie and Sunshine Coast and one between the cities of Gold Coast and Lismore – where root zone soil saturation was above the 90th percentile of its historical distribution (1911–2024). Such initial conditions that were consistently wetter than normal resulted from higherthan-average rainfall since the end of 2019 in the context of an active La Niña phase of the tropical Pacific Ocean36.","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.6623743,-26.1836445],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12129678,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gympie QLD 4570, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-5"},{"name":"Sunshine Coast","kind":"region","role":"Area near one of the highest soil root zone saturation zones before 2022 flood","river_name":null,"geocode_query":"Sunshine Coast, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"Lismore (see Fig. 1a), breaking the previous record of 12.3 m observed in 1954. The observations at this station started in 191734. Figure 1 summarises several important characteristics of the 2022 event. Figure 1a shows the saturation level of the topsoil layer simulated by the Australian Water Resources Assessment landscape model35 on the 23 February, just before the main rainfall event started. The plot highlights two areas – one between the cities of Gympie and Sunshine Coast and one between the cities of Gold Coast and Lismore – where root zone soil saturation was above the 90th percentile of its historical distribution (1911–2024). Such initial conditions that were consistently wetter than normal resulted from higherthan-average rainfall since the end of 2019 in the context of an active La Niña phase of the tropical Pacific Ocean36.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.0895855,-26.6527751],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130545,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Sunshine Coast QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-6"},{"name":"Gold Coast","kind":"settlement","role":"Area near high topsoil saturation before the flood event in February 2022","river_name":null,"geocode_query":"Gold Coast, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-000","page":2,"quote":"Lismore (see Fig. 1a), breaking the previous record of 12.3 m observed in 1954. The observations at this station started in 191734. Figure 1 summarises several important characteristics of the 2022 event. Figure 1a shows the saturation level of the topsoil layer simulated by the Australian Water Resources Assessment landscape model35 on the 23 February, just before the main rainfall event started. The plot highlights two areas – one between the cities of Gympie and Sunshine Coast and one between the cities of Gold Coast and Lismore – where root zone soil saturation was above the 90th percentile of its historical distribution (1911–2024). Such initial conditions that were consistently wetter than normal resulted from higherthan-average rainfall since the end of 2019 in the context of an active La Niña phase of the tropical Pacific Ocean36.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.380936,-27.9769248],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":12130823,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gold Coast QLD, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-7"},{"name":"Richmond River catchment","kind":"basin","role":"Catchment encompassing the 2022 flood impact area, involved in flood risk modeling and storm surge analysis","river_name":"Richmond River","geocode_query":"Richmond River catchment, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"Similar impacts of recordbreaking events on flood frequency fitting have been reported46, but they have not been systematically measured as is done in Fig. 5. It is noteworthy that the 2022 event ranks third in terms of the mean index for peak flow with a surprise index mean of 0.07 (Fig. 5.a). Two other events, the Victoria floods in January 2011 (mean of 0.11) and a flood that affected the Kimberley region inWestern Australia in March 2011 (mean of 0.10), reached higher index values. This result highlights the importance of considering multiple metrics beyond peak values in analysing flood events. It could have been worse, so what’s next? Two elements could have exacerbated the 2022 event. First, the spatial extent of the weather system was centred on the eastern parts of the Richmond catchment and upstream parts of the Mary catchment, as shown in Fig. 1.b, which means that the rainfall event covered an area of less than 40,000 km2 .","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.144792,-28.661033],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":26971431,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-8","true_coordinates":[153.1447918188741,-28.72103323072004],"spread_km":6.7},{"name":"Queensland (QLD)","kind":"region","role":"State impacted heavily by the 2022 flood event with substantial infrastructure damage and economic loss","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The paper concludes with a summary of the main factors governing the 2022 flood, which can serve as a reference for future floods in coastal catchments and humid sub-tropical climates, especially around the Pacific. Results and discussion The impossible flood Australia experienced a series of floods between 2020 and the beginning of 2024 that culminated in February andMarch 2022 with a regional event that affected the north-east part of the state of New South Wales (NSW) and the south-east part of the state of Queensland (QLD) as shown in Fig. 1a with further location details in Fig. 2. This flood resulted in 24 deaths25,26 and is considered the costliest flood event in Australia, with a total insurance claim estimated at over AUD$6 billion27 and combined infrastructure damage and loss of economic opportunity reaching AUD$7.7 billion in QLD alone28.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"New South Wales (NSW)","kind":"region","role":"State where flood event caused deaths and prompted state-level inquiry, includes Lismore and Wilsons River","river_name":null,"geocode_query":"New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The paper concludes with a summary of the main factors governing the 2022 flood, which can serve as a reference for future floods in coastal catchments and humid sub-tropical climates, especially around the Pacific. Results and discussion The impossible flood Australia experienced a series of floods between 2020 and the beginning of 2024 that culminated in February andMarch 2022 with a regional event that affected the north-east part of the state of New South Wales (NSW) and the south-east part of the state of Queensland (QLD) as shown in Fig. 1a with further location details in Fig. 2. This flood resulted in 24 deaths25,26 and is considered the costliest flood event in Australia, with a total insurance claim estimated at over AUD$6 billion27 and combined infrastructure damage and loss of economic opportunity reaching AUD$7.7 billion in QLD alone28.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Tasman Sea","kind":"other","role":"Location of a blocking high pressure system influencing weather patterns causing 2022 flood","river_name":null,"geocode_query":"Tasman Sea, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"It is worth noting that this wet phase followed an extreme dry period known as the Tinderbox Drought37 during which the region suffered from intense wildfires. A first weather system reached the region on the 24 February. The system was generated by a Rossby wave break-up combined with a blocking high in the Tasman Sea and warm and moist air inputs from the Coral Sea25. A second system developed on the 25, affecting southeast QLD, which moved to NSW on the 27th and generated extreme rainfall totals38. Figure 1b shows the maximum five-day total rainfall during February and March 2022. In several areas, the totals exceeded 1000 mm, representing more than half of the mean annual total in the region. A value of 775 mm was recorded in 24 h at the gauging station at Dunoon, located upstream of Lismore36. This value is just below the highest maximum daily rainfall recorded in NSW (809.2 mm)39.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Coral Sea","kind":"other","role":"Source of warm and moist air feeding the extreme rainfall events in February 2022","river_name":null,"geocode_query":"Coral Sea, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"It is worth noting that this wet phase followed an extreme dry period known as the Tinderbox Drought37 during which the region suffered from intense wildfires. A first weather system reached the region on the 24 February. The system was generated by a Rossby wave break-up combined with a blocking high in the Tasman Sea and warm and moist air inputs from the Coral Sea25. A second system developed on the 25, affecting southeast QLD, which moved to NSW on the 27th and generated extreme rainfall totals38. Figure 1b shows the maximum five-day total rainfall during February and March 2022. In several areas, the totals exceeded 1000 mm, representing more than half of the mean annual total in the region. A value of 775 mm was recorded in 24 h at the gauging station at Dunoon, located upstream of Lismore36. This value is just below the highest maximum daily rainfall recorded in NSW (809.2 mm)39.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Australia","kind":"region","role":"Country of study exhibiting largest recorded flood volumes during 2022 event","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-001","page":3,"quote":"Figure 2 also reveals that the 2022 peak attenuation (right axis) was the lowest, with a value close to 3 m, compared to earlier floods where attenuation was above 5 m. The combination of negative peak time difference with reduced attenuation suggests that forecasting the 2022 flood dynamic based on data from the Eltham upstream station was particularly challenging, partly explaining the difficulty local communities and emergency response teams faced during this event25. One of the most surprising floods in recorded Australian history The facts highlighted in the previous section set the 2022 flood apart. To assess the significance of this flood at the national levelinAustralia, observed flood data were collected at 1094 streamflow gauging stations across the country with records starting from 1950 (see data and code availability for details). These stations were selected based on a range of quality metrics described in Supplementary Note 1.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Northern Rivers Region, NSW","kind":"region","role":"Primary area of study and impact for the February 2022 floods","river_name":null,"geocode_query":"Northern Rivers Region, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The paper concludes with a summary of the main factors governing the 2022 flood, which can serve as a reference for future floods in coastal catchments and humid sub-tropical climates, especially around the Pacific. Results and discussion The impossible flood Australia experienced a series of floods between 2020 and the beginning of 2024 that culminated in February andMarch 2022 with a regional event that affected the north-east part of the state of New South Wales (NSW) and the south-east part of the state of Queensland (QLD) as shown in Fig. 1a with further location details in Fig. 2. This flood resulted in 24 deaths25,26 and is considered the costliest flood event in Australia, with a total insurance claim estimated at over AUD$6 billion27 and combined infrastructure damage and loss of economic opportunity reaching AUD$7.7 billion in QLD alone28.","verified_in_page":true},"resolution_status":"resolved","coordinates":[153.0147578,-29.2758152],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26970628,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Northern Rivers, NSW, Australia","basin_id":5040069970,"main_basin_id":5040069970,"basin_region":"Australasia","basin_area_km2":48498.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-14"},{"name":"Queensland 2010 flood event area","kind":"region","role":"Comparison regional flood event used to contextualize extremity of 2022 flood","river_name":null,"geocode_query":"Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"To compare the 2022 flood with past regional events, a grouping of site events was undertaken based on 52 regional floods identified by the Australian Institute for Disaster Resilience44, using additional information from the Historical Normalised Catastrophe List from the Insurance Council of Australia45. The result of this grouping is shown in Fig. 4 for the 2010 flood in QLD, which is considered one of the most notable recent floods in Australia30,31, and for the 2022 flood. Although it is based on a published flood list, it is acknowledged that this selection process remains subjective. In addition, the number of site events selected varies depending on the extent and duration of the regional event, as can be seen in Fig. 4 where the 2010 flood includes 103 site events against 61for the 2022flood. Further details on regional events, including their most frequent months of occurrence, are provided in Section “Methods” of the paper and in Supplementary Note 2.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Kimberley region, Western Australia","kind":"region","role":"Region of a major flood event used for comparison (March 2011) in surprise index analysis","river_name":null,"geocode_query":"Kimberley region, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"White dots represent the mean index value for each regional event. Figure 5 reveals that the 2022 flood has the highest mean surprise index for ten-day runoff totals, reaching a value of 0.16. In other words, the 1% AEP design flood hydrographs estimated after the 2022 event are likely to see an increase in flood volumes (estimated here as 10 days total runoff) by 16% on average across the affected region, thus increasing flood hazard levels substantially compared to the pre-2022 situation. Note that the surprise index exceeds 30% for certain sites during the 2022 event (see distribution of red points in Fig. 5) leading to a similar increase in 1% AEP values, and hence a pronounced impact on protection levels offered by flood defence infrastructure. This result also confirms the extreme nature of flood volumes during the 2022 flood, as seen in Fig. 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[125.9152074,-17.3491799],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102209735,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kimberley, WA, Australia","basin_id":5040059440,"main_basin_id":5040059440,"basin_region":"Australasia","basin_area_km2":95695.6,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-16"},{"name":"Victoria, Australia","kind":"region","role":"Region of major January 2011 flood used for surprise index comparison","river_name":null,"geocode_query":"Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"White dots represent the mean index value for each regional event. Figure 5 reveals that the 2022 flood has the highest mean surprise index for ten-day runoff totals, reaching a value of 0.16. In other words, the 1% AEP design flood hydrographs estimated after the 2022 event are likely to see an increase in flood volumes (estimated here as 10 days total runoff) by 16% on average across the affected region, thus increasing flood hazard levels substantially compared to the pre-2022 situation. Note that the surprise index exceeds 30% for certain sites during the 2022 event (see distribution of red points in Fig. 5) leading to a similar increase in 1% AEP values, and hence a pronounced impact on protection levels offered by flood defence infrastructure. This result also confirms the extreme nature of flood volumes during the 2022 flood, as seen in Fig. 3.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.3846,-37.074704],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-e9f26d5f1648e695a3c32d23-site-17","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7}],"limitations":["Lack of precise latitude and longitude coordinates for gauging stations and towns mentioned","Rainfall data used are daily interpolated gridded products, insufficient for sub-daily flood dynamic analysis","Limited streamflow measurement stations in northern and north-west Australia, challenging full spatial analysis","Uncertainty in extreme streamflow measurements can exceed 50%, impacting accuracy of peak flow data","Some flood design and risk analysis rely on probabilistic modeling assumptions that introduce uncertainty","The grouping of site events into regional floods involves subjective decisions","Spatial extent analysis is limited by available measurement networks and data resolution"],"river_ids":["43640269","43643828","43645307"],"site_ids":["doc-e9f26d5f1648e695a3c32d23-site-0","doc-e9f26d5f1648e695a3c32d23-site-1","doc-e9f26d5f1648e695a3c32d23-site-2","doc-e9f26d5f1648e695a3c32d23-site-4","doc-e9f26d5f1648e695a3c32d23-site-5","doc-e9f26d5f1648e695a3c32d23-site-6","doc-e9f26d5f1648e695a3c32d23-site-7","doc-e9f26d5f1648e695a3c32d23-site-8","doc-e9f26d5f1648e695a3c32d23-site-14","doc-e9f26d5f1648e695a3c32d23-site-16","doc-e9f26d5f1648e695a3c32d23-site-17"],"anchor":[152.6623743,-26.1836445],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-9","basin_ids":[5040059440,5040069970,5040591050],"main_basin_ids":[5040059440,5040069970,5040073410]},{"id":"doc-f1cddad5ae32020d7d9a9a3d","title":"Season and Flow Drive Productivity of a Regulated River","display_title":"Season and Flow Drive River Productivity","doi":"https://doi.org/10.1007/s10021-024-00945-6","theme":"Flow & ecology","color":"#80edc2","year":2025,"publication_date":"2025-01-06","authors":["Darren P. Giling","Ben Broadhurst","Fiona Dyer","Michael Grace","Klaus Jöhnk","Paul McInerney","Carmel Pollino","Gavin N. Rees","Ashmita Sengupta","Alica Tschierschke","Ross M. Thompson"],"summary":"This study investigates the drivers of ecosystem metabolism—gross primary productivity (GPP) and ecosystem respiration (ER)—in the regulated Lachlan River, a distributary river within the Murray–Darling Basin in central New South Wales, Australia. Using high-frequency dissolved oxygen monitoring at eight sites, and spatio-temporal stream network modeling, it identifies hydrological and seasonal factors influencing carbon cycling in the river and assesses the predictive ability of metabolic rates at unmonitored locations.","finding":"Gross primary productivity (GPP) was higher at sites with lower mean annual discharge and was strongly influenced by seasonal variation in solar exposure, while ecosystem respiration (ER) was higher at sites with lower mean annual discharge and lower flow variability, and increased slightly with higher daily flows and air temperature. Flow pulses can modulate these metabolic rates, suggesting environmental water deliveries could be optimized to manage ecosystem productivity in the regulated river.","finding_evidence":{"passage_id":"p008-002","page":8,"quote":"We assessed the predictive accuracy for GPP and ER under both validation scenarios using the root mean square prediction error (RMSPE). The effect of distance downstream from the dam and discharge percentile on nutrient, DOC, and chlorophyll-a concentration was assessed with linear mixed models including a random effect of site. RESULTS Drivers of Spatio-Temporal Variability in Ecosystem Metabolism Across the eight sites, mean daily GPP ranged from 2.7 to 8.3 g O2 m-2 day-1 and mean daily ER ranged from 3.8 to 11.5 g O2 m-2 day-1 . Seven of the eight sites were heterotrophic on 80 to 100% of days (mean NEP ranged - 8.4 to - 1.1 g O2 m2 day-1 for these seven sites), but one site (CC) was autotrophic on nearly all days for which there were adequate metabolism model fits (mean NEP = 4.4 g O2 m-2 day-1 ; Figure 2). The spatio-temporal network model showed that spatial variability in both GPP and ER was negatively associated with the mean annual flow (Table S2; Figure 3; Figure S4 & S5).","verified_in_page":true},"topics":["ecosystem metabolism","river regulation","spatial stream network modeling","flow regime","primary productivity","ecosystem respiration","carbon cycling","environmental flow management"],"study_period":"July 2018 to July 2019","locations":[{"name":"Lachlan River","kind":"river","role":"study catchment and main river system analyzed","river_name":null,"geocode_query":"Lachlan River, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"We focus on the approximately 1300 km of the Lachlan River between a major impoundment and water storage (Wyangala Dam) and the Great Cumbung Swamp (Figure 1). A series of smaller off-channel storages and weirs between the dam and the terminal wetland are used to manage water resources. The riparian vegetation typically consists of a narrow strip of remnant river red gum (Eucalyptus camaldulensis), blackbox (Eucalyptus largiflorens), and river cooba (Acacia stenophylla) woodland. The magnitude and seasonality of flow in the catchment have been highly modified for consumptive uses (Higgisson and others 2020) and cold water releases from the dam reduce water temperatures for approximately 400 km downstream (Lugg and Copeland 2014). Environmental watering actions in the Lachlan catchment often include the delivery of flow pulses with the aim of maintaining water quality, providing cues and habitat for fish and bird breeding, and providing aquatic productivity pulses (Dyer and others 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.496189,-33.202023],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677700,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-0","true_coordinates":[147.49618934400007,-33.26202250799997],"spread_km":6.7},{"name":"Wyangala Dam","kind":"facility","role":"upstream major dam and impoundment influencing flow regime","river_name":"Lachlan River","geocode_query":"Wyangala Dam, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"We focus on the approximately 1300 km of the Lachlan River between a major impoundment and water storage (Wyangala Dam) and the Great Cumbung Swamp (Figure 1). A series of smaller off-channel storages and weirs between the dam and the terminal wetland are used to manage water resources. The riparian vegetation typically consists of a narrow strip of remnant river red gum (Eucalyptus camaldulensis), blackbox (Eucalyptus largiflorens), and river cooba (Acacia stenophylla) woodland. The magnitude and seasonality of flow in the catchment have been highly modified for consumptive uses (Higgisson and others 2020) and cold water releases from the dam reduce water temperatures for approximately 400 km downstream (Lugg and Copeland 2014). Environmental watering actions in the Lachlan catchment often include the delivery of flow pulses with the aim of maintaining water quality, providing cues and habitat for fish and bird breeding, and providing aquatic productivity pulses (Dyer and others 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[148.9474092,-33.9804069],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677256,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wyangala Dam, Wyangala NSW 2808, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-1"},{"name":"Great Cumbung Swamp","kind":"wetland","role":"terminal wetland of Lachlan River distributary system","river_name":null,"geocode_query":"Great Cumbung Swamp, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-004","page":3,"quote":"We focus on the approximately 1300 km of the Lachlan River between a major impoundment and water storage (Wyangala Dam) and the Great Cumbung Swamp (Figure 1). A series of smaller off-channel storages and weirs between the dam and the terminal wetland are used to manage water resources. The riparian vegetation typically consists of a narrow strip of remnant river red gum (Eucalyptus camaldulensis), blackbox (Eucalyptus largiflorens), and river cooba (Acacia stenophylla) woodland. The magnitude and seasonality of flow in the catchment have been highly modified for consumptive uses (Higgisson and others 2020) and cold water releases from the dam reduce water temperatures for approximately 400 km downstream (Lugg and Copeland 2014). Environmental watering actions in the Lachlan catchment often include the delivery of flow pulses with the aim of maintaining water quality, providing cues and habitat for fish and bird breeding, and providing aquatic productivity pulses (Dyer and others 2019).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.9528862,-34.3742939],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662345,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cumbung Swamp, Maude NSW 2711, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-2"},{"name":"Kirkup Park (KP)","kind":"station","role":"upstream sampling site for metabolism monitoring","river_name":"Lachlan River","geocode_query":"Kirkup Park, New South Wales, Australia","latitude":-33.32,"longitude":147.62,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.6339716,-33.3234626],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667807,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kirkup Park, 3943 Bedgerabong Rd, Bedgerabong NSW 2871, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-3"},{"name":"Mulgutherie (MG)","kind":"station","role":"sampling site within an anabranch with lowest mean discharge","river_name":"Lachlan River","geocode_query":"Mulgutherie, New South Wales, Australia","latitude":-33.23,"longitude":147.44,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.417313,-33.18925110000001],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43667583,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mulguthrie NSW 2877, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-4"},{"name":"Techam (TH)","kind":"station","role":"mid-river metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Techam, New South Wales, Australia","latitude":-33.08,"longitude":146.93,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.93,-33.08],"precision":"Extracted coordinate · not found as a literal decimal in the cited passage","within_mdb":true,"catchment_id":43666559,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-5"},{"name":"Euabalong (EU)","kind":"station","role":"mid-river metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Euabalong, New South Wales, Australia","latitude":-33.16,"longitude":146.47,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.4728717,-33.1102795],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677286,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Euabalong NSW 2877, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-6"},{"name":"Wallanthery (WAL)","kind":"station","role":"downriver metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Wallanthery, New South Wales, Australia","latitude":-33.34,"longitude":145.84,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.8364509,-33.3449057],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43664930,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Wallanthery NSW 2675, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-7"},{"name":"Lane’s Bridge (LB)","kind":"station","role":"downriver metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Lane’s Bridge, New South Wales, Australia","latitude":-33.39,"longitude":145.58,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[152.8561648,-30.6408579],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":26970179,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lanes bridge, 2 Belmore St, Bowraville NSW 2449, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-8"},{"name":"Cowl Cowl (CC)","kind":"station","role":"downriver metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Cowl Cowl, New South Wales, Australia","latitude":-33.54,"longitude":145.44,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.2886959,-33.618031],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43677665,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Cowl Cowl, Hillston NSW 2675, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-9"},{"name":"Whealbah (WB)","kind":"station","role":"furthest downstream metabolism monitoring site","river_name":"Lachlan River","geocode_query":"Whealbah, New South Wales, Australia","latitude":-33.65,"longitude":145.25,"evidence":{"passage_id":"p004-003","page":4,"quote":"The dark blue rivers are part of the landscape network object, while light blue rivers are not included in the spatial model. Grey labels show the location of Wyangala Dam, the Great Cumbung Swamp, and towns. Inset: outline of Australia with the Murray–Darling Basin shaded grey, and the Lachlan River catchment outlined in the thick black line. 5 Page 4 of 19 D. P. Giling and others","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.0327677,-33.3525729],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43663239,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Whealbah Rd, New South Wales, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-10"},{"name":"Griffith, New South Wales, Australia","kind":"settlement","role":"location of nearest Bureau of Meteorology station providing climatic data","river_name":null,"geocode_query":"Griffith, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Data Collection Daily rates of ecosystem metabolism at each of the eight sites were estimated from diel (sub-daily) variation in dissolved oxygen concentration, water temperature, and photosynthetic active radiation (PAR) (CEWH 2023). Dissolved oxygen and water temperature were monitored continuously at 10- min intervals using miniDOT loggers (PME, California, USA). Photosynthetic active radiation (PAR) was measured at 10-min intervals in a nearby unshaded location using PAR loggers (Odyssey, Christchurch, New Zealand). Half-hourly barometric pressure data were obtained from the nearest available Bureau of Meteorology station (either Griffith or Condobolin) and converted from sea level to absolute pressure. There was a total of 2172 days with complete data after removing days with missing data or poor data quality (between 195 and 347 days per site over the 12-month period).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.0323982,-34.2886639],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43665177,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Griffith NSW 2680, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-11"},{"name":"Condobolin, New South Wales, Australia","kind":"settlement","role":"location of Bureau of Meteorology station for barometric pressure and temperature data","river_name":null,"geocode_query":"Condobolin, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-001","page":4,"quote":"Data Collection Daily rates of ecosystem metabolism at each of the eight sites were estimated from diel (sub-daily) variation in dissolved oxygen concentration, water temperature, and photosynthetic active radiation (PAR) (CEWH 2023). Dissolved oxygen and water temperature were monitored continuously at 10- min intervals using miniDOT loggers (PME, California, USA). Photosynthetic active radiation (PAR) was measured at 10-min intervals in a nearby unshaded location using PAR loggers (Odyssey, Christchurch, New Zealand). Half-hourly barometric pressure data were obtained from the nearest available Bureau of Meteorology station (either Griffith or Condobolin) and converted from sea level to absolute pressure. There was a total of 2172 days with complete data after removing days with missing data or poor data quality (between 195 and 347 days per site over the 12-month period).","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.1471605,-33.0885253],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43666561,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Condobolin NSW 2877, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-f1cddad5ae32020d7d9a9a3d-site-12"}],"limitations":["Spatial stream network model required simplification excluding river anabranches and distributaries.","Lack of direct nutrient and GPP data at unmonitored times or sites limited modeling of nutrient effects.","Some prediction locations had mean annual discharges outside the range observed at monitoring sites, possibly reducing prediction accuracy.","Metabolism rates estimates depend on modeling assumptions regarding reaeration coefficients and may vary from other published datasets."],"river_ids":["43639764","43639794","43639860","43639866","43639906","43639930","43639980","43639983","43640318","43640425","43640469","43642764","43643793","43644873","43645493","43646060","43646185","43646328","43647021","43647032","43647061","43647078","43647085","43647421","43647509","43647719","43647753","43647770","43647813","43647837","43647890","43648031","43648048","43648082","43648104","43648389","43648393","43648458","43648550","43648584","43648623","43648634","43648646","43648684","43648689","43648699","43648700","43648711","43648802","43648804","43648805","43648828","43649485","43649490","43649492","43649493","43649496","43649499","43649531","43649562","43649591","43649592","43649593","43649805","43649807","43649809","43649810","43649813","43649814","43649842","43649848","43649914","43649924","43649925","43650117","43650123","43650138","43650245","43650251","43650252","43650254","43650258","43650274","43650276","43650277","43650632","43650633","43650757","43650766","43650769","43659433","43659436","43659439","43659442","43659445","43659451","43659454","43659455","43659457","43659458","43659462","43659464","43659466","43659467","43659468","43659476","43659481","43659495","43659497","43659499","43659502","43659503"],"site_ids":["doc-f1cddad5ae32020d7d9a9a3d-site-0","doc-f1cddad5ae32020d7d9a9a3d-site-1","doc-f1cddad5ae32020d7d9a9a3d-site-2","doc-f1cddad5ae32020d7d9a9a3d-site-3","doc-f1cddad5ae32020d7d9a9a3d-site-4","doc-f1cddad5ae32020d7d9a9a3d-site-5","doc-f1cddad5ae32020d7d9a9a3d-site-6","doc-f1cddad5ae32020d7d9a9a3d-site-7","doc-f1cddad5ae32020d7d9a9a3d-site-8","doc-f1cddad5ae32020d7d9a9a3d-site-9","doc-f1cddad5ae32020d7d9a9a3d-site-10","doc-f1cddad5ae32020d7d9a9a3d-site-11","doc-f1cddad5ae32020d7d9a9a3d-site-12"],"anchor":[146.93,-33.08],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040070710,5040591050],"main_basin_ids":[5040070710,5040073410]},{"id":"doc-485ec376c1e9e19209da9f05","title":"Evaluating the ecological outcomes of environmental watering: the role of the counterfactual","display_title":"Evaluating Ecological Outcomes of CEW in MDB","doi":"https://doi.org/10.36334/modsim2025.f04.cuddy","theme":"Flow & ecology","color":"#80edc2","year":2026,"publication_date":"2026-05-01","authors":["Susan Cuddy"],"summary":"The Australian government's Flow-MER program evaluates the ecological outcomes of Commonwealth environmental water (CEW) in the Murray–Darling Basin using counterfactual models. These models isolate CEW's contribution to river flow regimes and vegetation assemblages over a monitoring period, demonstrating CEW's critical role in maintaining river connectivity, ecosystem inundation, and vegetation diversity. The program addresses challenges such as data limitations to improve future assessments.","finding":"CEW contributions helped raise flows beyond ecologically important thresholds, enabling the inundation of critical ecosystems such as the Barmah-Millewa Forest Ramsar site in the 2023–24 water year, which would not have occurred without CEW.","finding_evidence":{"passage_id":"p001-004","page":1,"quote":"The CEW contributions in spring were targeted for the inundation of the Barmah-Millewa Forest, an iconic Ramsar site. This was achieved with the contribution of CEW and would not have been achieved otherwise. Figure 1 Hydrograph of (modelled) flow at Murray River d/s Yarrawonga Weir for 2023–24 water year (from Sengupta et al. 2025) © MSSANZ and The Authors, CC-BY 0503","verified_in_page":true},"topics":["environmental flows","ecological monitoring","eco-hydrological modelling","river connectivity","vegetation assemblages","flow regimes","inundation modelling","environmental water management"],"study_period":"2023–24 water year","locations":[{"name":"Murray–Darling Basin","kind":"basin","role":"study area for ecological outcomes evaluation of CEW","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"INTRODUCTION The Australian government’s Flow-MER program (Cuddy et al. 2024) is a long-term monitoring, evaluation and research program, designed to provide the Commonwealth with the evidence it needs to report (annually) on its obligations under the Water Act and the 2012 (Murray–Darling) Basin Plan to protect, maintain and restore the aquatic ecosystems of the Murray–Darling Basin. The Commonwealth is a major holder of environmental water, which it purchases through licence buy-backs and/or on the open water market. Its Commonwealth Environmental Water Holder (CEWH) division manages this water, and prepares annual watering event plans designed to meet one or more ecological objectives as set out in the Basin Plan.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.492377,-31.18631],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-485ec376c1e9e19209da9f05-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Murray River downstream of Yarrawonga Weir","kind":"reach","role":"hydrological monitoring site to evaluate CEW flow contributions","river_name":"Murray River","geocode_query":"Yarrawonga Weir, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"CEW is known from knowledge of the watering events (start and end dates, volume, flow component) and is removed from the observed streamflow time series(O’Sullivan & Cuddy 2025). To demonstrate its use, Error! Reference source not found. shows the flow regime at the Murray River d/s of Yarrawonga Weir gauge. In this figure, the dashed line is the (modelled) mean of the natural flow and the purple shading is the percentiles around the mean. CEW is shown in green. The top horizontal line (indicated by top arrow at bottom left of plot) is the highfresh threshold at this gauge. As you can see, CEW contributions helped to push flows about this threshold between September and December 2023, and to move flows towards and above the low-fresh threshold (indicated by bottom arrow at bottom left of plot) for much of the remainder of the water year. It also helped to move flows closer to the natural flow regime range (an ecological objective).","verified_in_page":true},"resolution_status":"resolved","coordinates":[146.9466667700001,-36.11333329399997],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677195,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-485ec376c1e9e19209da9f05-site-1"},{"name":"Barmah-Millewa Forest","kind":"wetland","role":"key Ramsar site targeted for inundation via CEW releases","river_name":null,"geocode_query":"Barmah–Millewa Forest, Victoria/New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-004","page":1,"quote":"The CEW contributions in spring were targeted for the inundation of the Barmah-Millewa Forest, an iconic Ramsar site. This was achieved with the contribution of CEW and would not have been achieved otherwise. Figure 1 Hydrograph of (modelled) flow at Murray River d/s Yarrawonga Weir for 2023–24 water year (from Sengupta et al. 2025) © MSSANZ and The Authors, CC-BY 0503","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"}],"limitations":["Lack of observed data reduces precision in environmental water accounting and impact evaluation","Broad regional scope without detailed coordinates limits geographic 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-4d639d0a0b9e5b9af2a60d70","title":"Partitioning uncertainty in climate projections for the Murray Darling Basin using large ensembles","display_title":"Partitioning Uncertainty in MDB Climate Projections","doi":"https://doi.org/10.1088/2515-7620/ae4a77","theme":"Climate","color":"#98d4f1","year":2026,"publication_date":"2026-02-25","authors":["Anjana Devanand","Francis H. S. Chiew","David A. Post","David Robertson","Michael Grose","Hongxing Zheng","Zaved Khan"],"summary":"This study assesses uncertainties in climate projections for the Murray Darling Basin (MDB), a crucial agricultural and ecologically sensitive region of southeast Australia. Using multi-model CMIP6 ensembles and large initial condition ensembles (SMILEs), the study partitions sources of uncertainty in projections of temperature, precipitation, and potential evapotranspiration (PET). It compares traditional time slice methods with pattern scaling to isolate climate change signals from natural variability. The findings show winter rainfall decline with warming, summer rainfall changes with more uncertainty, increasing temperatures and PET, and variations in internal climate variability’s contribution to uncertainty. These results are imperative for water resources planning up to 2050 under climate change.","finding":"Pattern scaling estimates provide a climate change signal with lower internal variability than traditional time slice methods, making them more effective for visualizing forced climate change and natural variability in the MDB.","finding_evidence":{"passage_id":"p010-002","page":10,"quote":"Herewe demonstrate that pattern scaling estimates of future change have lowerinternal variability than time slice-based estimates, more correctly reflecting the climate change signal and model uncertainty in the projections.Impact assessments often use change signalsfrom climate modelsin conjunctionwith stochastic methodsto represent variability (Kiem et al 2021, Potteret al 2023, Leonard et al 2024).In such applications, the range of future projectionsfrom climate models are used to represent the climate change signal,while stochastic methods are used to incorporate the uncertainty from natural variability. Future projectionsfrom time slice-based methodsif used forsuch assessments(e.g. Leonard et al 2024, Lesteret al 2026) already incorporate the fullrange of variability, including internal variability,which can widen the range of future climates considered in the assessment.","verified_in_page":true},"topics":["climate change","climate model uncertainty","internal climate variability","pattern scaling","hydroclimate projections","precipitation projections","temperature projections","potential evapotranspiration"],"study_period":"1976-2005 baseline, projections through to ~2050","locations":[{"name":"Murray Darling Basin","kind":"region","role":"study area for climate projections and uncertainty analysis in Australia","river_name":null,"geocode_query":"Murray Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-001","page":2,"quote":"E-mail: anjana.devanand@csiro.au Keywords: Murray Darling Basin, climate projections, uncertainty partitioning, internal variability Supplementary material forthis article is available online Abstract The Murray Darling Basin (MDB)in southeastAustralia is a highly managed system, aiming to deliverresilient and sustainable outcomesfor communities, industries, and the environment that share thewaterin the basin.Climate projectionsfrom theCoupled ModelIntercomparison Project Phase 6 (CMIP6) are informing the upcoming reviewof the Basin Plan. However, internal variability in future climate projectionsis often underappreciated in regional impact assessments,such asthose undertaken in the MDB.Applying methodsthat partition uncertainty can be useful to quantify the climate change ‘signal’ to considerin planning.In thisstudy two methods are used to estimate changesin climate in the MDB (1)future time slice approach, and (2) pattern scaling.","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.504222,-31.191537],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-4d639d0a0b9e5b9af2a60d70-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"Northern Basin Division","kind":"region","role":"subregion of MDB with summer-dominant rainfall and higher temperatures, used in regional projections","river_name":null,"geocode_query":"Northern Murray Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The basin average future changes are calculated as the difference between spatial averaged future projections and the spatial averaged historical baseline. We presentresults aggregated overthe Northern and Southern basin divisions of the MDB (figure 1). The Northern basin is characterised by summer dominated rainfall and highertemperatures, in contrast to the Southern basin. This north-south contrast is particularly relevant forwater availability assessments as most of the Southern basin streamflowis generated duringwinter and spring, making it more vulnerable to the observed and projected coolseason rainfall decline in Southern Australia (McKay et al 2023,CSIRO and BOM 2024, Zheng et al 2024). Figure 1. The Northern and Southern basin division of the Murray Darling Basin.Insetshowsthe location of the MDB in Australia. 3 Environ. Res. Commun. 8 (2026) 031003","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.825781,-35.37926],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-4d639d0a0b9e5b9af2a60d70-site-1","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Southern Basin Division","kind":"region","role":"subregion of MDB with winter/spring rainfall and higher vulnerability to cool season rainfall decline, used in regional projections","river_name":null,"geocode_query":"Southern Murray Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-002","page":4,"quote":"The basin average future changes are calculated as the difference between spatial averaged future projections and the spatial averaged historical baseline. We presentresults aggregated overthe Northern and Southern basin divisions of the MDB (figure 1). The Northern basin is characterised by summer dominated rainfall and highertemperatures, in contrast to the Southern basin. This north-south contrast is particularly relevant forwater availability assessments as most of the Southern basin streamflowis generated duringwinter and spring, making it more vulnerable to the observed and projected coolseason rainfall decline in Southern Australia (McKay et al 2023,CSIRO and BOM 2024, Zheng et al 2024). Figure 1. The Northern and Southern basin division of the Murray Darling Basin.Insetshowsthe location of the MDB in Australia. 3 Environ. Res. Commun. 8 (2026) 031003","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.787205,-35.352879],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-4d639d0a0b9e5b9af2a60d70-site-2","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Southeast Australia","kind":"region","role":"regional context for observed and projected cool season rainfall declines affecting MDB","river_name":null,"geocode_query":"Southeast Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-001","page":6,"quote":"The median changesindicate a decrease in australwinter (JJA)rainfall in both the basins, and a slight increase in australsummer(DJF)rainfall in the Northern Basin. Estimatesfrom the time slice by global temperature and pattern scaling methodsshowsimilar median results (listed in panels(a),(d),(g),(j) of figure 2). Thewinter projections are consistentwith observed coolseason rainfall declines(McKay et al 2023) and expected changesin global and regional circulation in awarmerworld driving precipitation in southern Australia (Hu et al 2018, Nguyen et al 2015). The medianwinterrainfall changes amount to −3 to −4% per degree increase in global temperature. The direction of the summerrainfall changesin southeast Australia isless clearin literature due to contrasting influences of circulation and different weathersystems(Hendon et al 2007, Pepleret al 2021, McKay et al 2023).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"National Computational Infrastructure (NCI)","kind":"facility","role":"computational resource supporting the climate projection analyses","river_name":null,"geocode_query":"National Computational Infrastructure, Canberra, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p011-001","page":11,"quote":"Evaluation is particularly challenging asinternal variability varies between models, and observations are only one realisation depicting howthe historical climate might have evolved. Lastly, herewe study the uncertainty partitioning in climate variablesin the MDB.It is necessary to quantify howsuch uncertainties propagate through to specific hydrological and socioenvironmental impact metricsto aid decision making (Schwarzwald and Lenssen 2022, Faghih and Brissette 2023). This aspect is difficult to and rarely explored. Acknowledgments Thisresearch is part of the Sustainable Yields project(funded by theAustralian Department ofClimateChange, Energy, the Environment and Water) developing hydroclimate projectionsto assessimpact and adaptation optionsin the Murray Darling Basin. The research is also supported by theVictorian Water andClimate Initiative.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Lack of explicit coordinate data limits precise geographic tagging.","Study focuses on broad basin scale; finer-scale geographic variability not detailed.","Pattern scaling assumes linear response which may not hold beyond studied temperature ranges.","Internal variability magnitude and model variability evaluation remain challenging due to single realization observational 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-2","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-52546dc5511ddac0b0e197ac","title":"A common ontology for stable isotope data","display_title":"Common Ontology for Stable Isotope Data","doi":"https://doi.org/10.1016/j.apgeochem.2026.106682","theme":"Other science","color":"#d1b5e7","year":2026,"publication_date":"2026-01-06","authors":["Nina Welti","Steph Hawkins","Kathryn Waltenberg","Cath Jackson Ca Hughes","Jagoda Crawford","Yanfeng Shu","Regina Campbell","Alex Leslie","Athina Puccini","Christoph Gerber","Axel Suckow","Geoff Fraser","Lian Flick"],"summary":"This paper presents the development of a common ontology for stable isotope data and associated metadata. The ontology harmonizes disparate datasets across scientific domains by standardizing metadata attributes describing sample site, sample, method, measurement, agent, and traceability. It is designed to improve findability, accessibility, interoperability, and reuse (FAIR) of stable isotope data by facilitating mapping between existing data models and workflows without disruption. Developed collaboratively by Australian scientific organizations, the ontology accommodates various stable isotopes and is extensible to additional domains and complex data types. It supports flexible outputs for data sharing ranging from simple spreadsheets to web services and aims for broad community adoption with ongoing governance and updates.","finding":"The developed stable isotope ontology successfully harmonizes stable isotope data from multiple Australian scientific organizations by defining a core set of common metadata attributes and establishing mapping across existing datasets, enabling interoperable, reusable, and discoverable stable isotope datasets across scientific domains.","finding_evidence":{"passage_id":"p002-006","page":2,"quote":"Existing stable isotope datasets (Geoscience Australia, 2021) as well as associated data standards and vocabularies (Hawkins, 2023; Hofstra et al., 2021; Raymond et al., 2016) were then used to further refine the common attributes and ensure that this ontology can translate across existing data models. To reduce complexity in the initial ontology development, only datasets from Australian continental coverage were used, with any oceanic or near-shore samples out of scope. The common attributes described the sample matrix, location of field sampling, and analytical methods. Standard, human-readable terms were chosen for these attributes and data was mapped between the organisations. The selected attributes were reviewed throughout the ontology development to ensure that they were compatible.","verified_in_page":true},"topics":["data interoperability","environmental isotope data","data standards","scientific metadata","data harmonization","data stewardship","stable isotopes","FAIR principles"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"Study region for ontology development and application across continental stable isotope datasets","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Existing stable isotope datasets (Geoscience Australia, 2021) as well as associated data standards and vocabularies (Hawkins, 2023; Hofstra et al., 2021; Raymond et al., 2016) were then used to further refine the common attributes and ensure that this ontology can translate across existing data models. To reduce complexity in the initial ontology development, only datasets from Australian continental coverage were used, with any oceanic or near-shore samples out of scope. The common attributes described the sample matrix, location of field sampling, and analytical methods. Standard, human-readable terms were chosen for these attributes and data was mapped between the organisations. The selected attributes were reviewed throughout the ontology development to ensure that they were compatible.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Canberra, ACT, Australia","kind":"settlement","role":"Location of Geoscience Australia head office, one of the contributing agencies and key data custodians involved in ontology development","river_name":null,"geocode_query":"Canberra, ACT, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-003","page":1,"quote":"There is a rapid uptick in tools that use big data, such as the Internet of Things, large languages models (LLM), and machine learning (ML) tools, which rely on the availability of machine-readable data in both structured and unstructured formats (Klocking ̈ et al., 2023). Data volumes are growing at an exponential rate, with Geoscience Australia, Australia's lead agency for geoscience, responsible for providing authoritative information on Australia's geology, geography, and natural resources, seeing a 350 % increase in their minerals, energy and groundwater data holdings from July 2016 to July 2024. This highlights the need to increase the discoverability, accessibility and use of existing data, especially in the public sector (Overton et al., 2022). Here we propose a common ontology for stable isotope data and associated metadata providing a methodology to harmonise stable isotope data across scientific domains and applications. * Corresponding author.","verified_in_page":true},"resolution_status":"resolved","coordinates":[149.057533,-35.280185],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43671612,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Canberra ACT, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-52546dc5511ddac0b0e197ac-site-1","true_coordinates":[149.1310324,-35.2801846],"spread_km":6.7},{"name":"Urrbrae, South Australia, Australia","kind":"settlement","role":"Location of CSIRO, primary research organization involved in ontology development and authorship","river_name":null,"geocode_query":"Urrbrae, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"A common ontology for stable isotope data Nina Welti a,*,1 , Steph Hawkins b,1 , Kathryn Waltenberg b , Cath Hughes c , Jagoda Crawford c , Yanfeng Shu a , Regina Campbell a , Alex Leslie c , Athina Puccini a , Christoph Gerber a , Axel Suckow a , Geoff Fraser b , Lian Flick a a CSIRO, 40 Waite Rd, Urrbrae, SA, 5064, Australia b Geoscience Australia, ACT, Canberra, 2601, Australia c ANSTO, Locked Bag 2001 Kirrawee, DC, NSW, 2232, Australia ARTICLE INFO Editorial handling by: Dr. Tao Wen Keywords: Stable isotope data FAIR data Data standards ABSTRACT Stable isotope data has broad applicability across multiple science domains. As more datasets are published and stable isotope data systems are built, the findability and accessibility of stable isotope data increases. However, due to a lack of a common reporting framework, the interoperability of these data systems is limited.","verified_in_page":true},"resolution_status":"resolved","coordinates":[138.6356821,-34.9667205],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":50289741,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Urrbrae SA 5064, Australia","basin_id":5040073420,"main_basin_id":5040073420,"basin_region":"Australasia","basin_area_km2":27853.3,"hydrology_source":"geofabric","site_id":"doc-52546dc5511ddac0b0e197ac-site-2"},{"name":"Kirrawee, New South Wales, Australia","kind":"settlement","role":"Location of ANSTO, contributing organization for stable isotope data ontology development","river_name":null,"geocode_query":"Kirrawee, New South Wales, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"A common ontology for stable isotope data Nina Welti a,*,1 , Steph Hawkins b,1 , Kathryn Waltenberg b , Cath Hughes c , Jagoda Crawford c , Yanfeng Shu a , Regina Campbell a , Alex Leslie c , Athina Puccini a , Christoph Gerber a , Axel Suckow a , Geoff Fraser b , Lian Flick a a CSIRO, 40 Waite Rd, Urrbrae, SA, 5064, Australia b Geoscience Australia, ACT, Canberra, 2601, Australia c ANSTO, Locked Bag 2001 Kirrawee, DC, NSW, 2232, Australia ARTICLE INFO Editorial handling by: Dr. Tao Wen Keywords: Stable isotope data FAIR data Data standards ABSTRACT Stable isotope data has broad applicability across multiple science domains. As more datasets are published and stable isotope data systems are built, the findability and accessibility of stable isotope data increases. However, due to a lack of a common reporting framework, the interoperability of these data systems is limited.","verified_in_page":true},"resolution_status":"resolved","coordinates":[151.071563,-34.0320366],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":29054829,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Kirrawee NSW 2232, Australia","basin_id":5040070710,"main_basin_id":5040070710,"basin_region":"Australasia","basin_area_km2":238223.7,"hydrology_source":"geofabric","site_id":"doc-52546dc5511ddac0b0e197ac-site-3"}],"limitations":["Study restricted to Australian continental stable isotope datasets for initial ontology development, excluding oceanic or near-shore samples, potentially limiting initial scope","Coordinates for specific sample sites not provided in the text—regional scales provided only","Ontology governance and user group primarily Australian-based, with expansion potential but initially geographically limited"],"river_ids":[],"site_ids":["doc-52546dc5511ddac0b0e197ac-site-1","doc-52546dc5511ddac0b0e197ac-site-2","doc-52546dc5511ddac0b0e197ac-site-3"],"anchor":[149.057533,-35.280185],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-3","basin_ids":[5040070710,5040073420,5040591050],"main_basin_ids":[5040070710,5040073410,5040073420]},{"id":"doc-5a9efcc377fb1346ec8ebcdb","title":"Streamflow elasticity as a function of aridity","display_title":"Streamflow Elasticity's Dependence on Aridity","doi":"https://doi.org/10.5194/hess-30-1865-2026","theme":"Flow & ecology","color":"#80edc2","year":2026,"publication_date":"2026-04-09","authors":["Vazken Andréassian","Guilherme M. Guimarães","Julien Lerat","Alban de Lavenne"],"summary":"This study analyzes the relationship between streamflow elasticity and aridity using a large dataset of 4122 catchments spanning Europe, Australia, North America, and South America. It verifies empirically that streamflow elasticity depends on catchment aridity and proposes a regionalized model wherein elasticity coefficients are constrained by \"Budyko-type\" water balance formulas, particularly the Oldekop formula. This parameterization captures aridity-dependent elasticity patterns with physical realism and improves prediction of streamflow anomalies with fewer parameters compared to local calibrations.","finding":"Streamflow elasticity coefficients vary systematically with catchment aridity, with precipitation elasticity decreasing and potential evaporation elasticity increasing as aridity increases. By parameterizing elasticities as functions of aridity based on the Oldekop water balance derivatives, the study produces physically realistic, spatially regionalized elasticity estimates that outperform uniform global elasticity models.","finding_evidence":{"passage_id":"p006-003","page":6,"quote":"For the potential evaporation elasticity, a satisfying trend is visible but many physically unrealistic elasticities show that additional constraints are required for this term. Finally, the 3-elasticity of streamflow (i.e. the streamflow elasticity towards the synchronous amounts of precipitation and streamflow), shows no clear dependency on the aridity index (but we did not expect any relationship). 3.2 Results by aridity class We also calibrated the three elasticity coefficients to obtain a single triplet of values for each of the aridity classes as defined in Sects. 2.4 and 3.2. The resulting class-calibrated values are presented in Table 3. As reference, the performance of the local (catchment-specific) estimation is also provided (by construction, it represents the upper limit of performance).","verified_in_page":true},"topics":["streamflow elasticity","aridity index","hydrological modeling","water balance formulas","Budyko framework","catchment hydrology","hydrological sensitivity","climate impact on hydrology"],"study_period":null,"locations":[{"name":"Europe","kind":"region","role":"study region containing multiple catchments analyzed","river_name":null,"geocode_query":"Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Australia","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Brazil","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Brazil","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Denmark","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Denmark","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"France","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"France","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Germany","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Sweden","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Sweden","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Switzerland","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"Switzerland","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United Kingdom","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"United Kingdom","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"United States of America","kind":"region","role":"study region containing catchments analyzed using hydrological data","river_name":null,"geocode_query":"United States of America","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"It includes catchments from Australia (Fowler et al., 2025), Brazil (Almagro et al., 2021), Denmark (Liu et al., 2025), France (Delaigue et al., 2025), Germany (Loritz et al., 2024), Sweden (de Lavenne et al., 2022), Switzerland (Höge et al., 2023), the United Kingdom (Coxon et al., 2020) and the USA (Addor et al., 2017). Table 2. Aridity classes used in this study (we only kept the classes counting more than 100 catchments). Aridity class Average aridity Number of Name of the class catchments [0.25,0.50[ 0.39 484 Very humid [0.50,0.75[ 0.64 1461 Humid [0.75,1.00[ 0.85 1238 Fairly humid [1.00,1.25[ 1.09 434 Fairly arid [1.25,1.50[ 1.37 186 Arid [1.50,1.75[ 1.61 109 Very arid Because this dataset is exactly the same as the one used by Andréassian et al. (2025), we refer the reader to this paper for the details of the selection of the catchments from the original datasets.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Elasticity relationships were developed for catchments without long interannual memory, excluding those with significant multi-year hydrological storage effects.","Potential evaporation was computed using the Oudin et al. (2005) formula; use of other formulas may require recalibration of parameters.","Aridity is treated as the primary driver of elasticity globally, but other factors also influence elasticity, so the model is a first-order approximation."],"river_ids":[],"site_ids":[],"anchor":null,"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[],"main_basin_ids":[]},{"id":"doc-5adb1a7afb367b52a3120fea","title":"Freshwater biodiversity impacts of global hydropower dams","display_title":"Global Freshwater Biodiversity Risks from Hydropower Dams","doi":"https://doi.org/10.1038/s43247-026-03263-y","theme":"Flow & ecology","color":"#80edc2","year":2026,"publication_date":"2026-02-09","authors":["Na Ding","Zunyi Xie","Xiaoming Wang","Zongming Wang","Miao Li","Beiming Cai","Qinghe Zhao","Eve McDonald-Madden","Jianguo Li","Hongkai Gao","Sicong Gao","Mingming Jia","Kaishan Song","Guanpeng Dong","Changhong Miao","Christopher O’Bryan"],"summary":"This global-scale study combines remote sensing and IUCN threatened species data to assess the impacts of existing and proposed hydropower dams on freshwater biodiversity. It quantifies threat levels and spatial distributions of endangered freshwater taxa across dam-influenced areas and evaluates correlations between river fragmentation due to dams and extinction risk, highlighting that future dam development in Global South biodiversity hotspots may exacerbate threats.","finding":"Threatened freshwater species are more concentrated near hydropower dams globally, with over 85% of species with changed risk category between 1996 and 2022 shifting towards higher threat levels due mainly to dam-induced habitat loss and reduced river connectivity.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Among species whose conservation status did change, more than 85% shifted to higher threat categories, whereas fewer than 15% showed improvements (Fig. 4a). Among these species with altered risk levels, amphibians exhibited the most considerable change, with approximately 4 6 species affected, followed by fish with 21 species (Table S 3). According to our high - versus low -risk classification, larger -bodied freshwater mammals (e.g., the Irrawaddy dolphin and Platanista gangetica) exhibited the highest proportion of high -risk outcomes (47%), followed by freshwater molluscs (39.3%) (Fig. 4b). Overlap of dams with changes in riverine environments The connectivity of total 70,000 global rivers at various lengths with constructed dams was assessed, ranging from less than 10 km to over 1,000 km. Our analysis revealed that North America and Asia bear the greatest number of non -free -flowing rivers (>10 km) in the world (Table S 4).","verified_in_page":true},"topics":["freshwater biodiversity","river connectivity","species extinction risk","hydropower dams","habitat fragmentation","global biodiversity hotspots","freshwater mammals","threatened species"],"study_period":"1996-2022","locations":[{"name":"Amazon River Basin, South America","kind":"region","role":"Biodiversity hotspot with dams showing high proportion of threatened freshwater mammals and amphibians","river_name":"Amazon River","geocode_query":"Amazon River Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Both of the two regions own many important long rivers such as the Amazon and the Mekong rivers, where numerous dams have been built and more are coming. Freshwater mollusks in dam areas were predominantly found in North America and Europe, of which the high threats were located in the river systems of southeastern North America, like the Mississippi and in Western Europe’s Danube, ranging from 10% to 30% (Fig. 2c). We found fish were the most broadly affected freshwater species by the dam construction, which experienced threats in all dam areas across the","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.702388,-1.686555],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Amazon River","basin_id":6040280400,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":714738.5,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-0","true_coordinates":[-61.65994439265526,-1.644128149168597],"spread_km":6.7},{"name":"Mekong River Basin, Southeast Asia","kind":"region","role":"Biodiversity hotspot with extensive damming and high threatened freshwater mammal and fish species richness","river_name":"Mekong River","geocode_query":"Mekong River Basin, Southeast Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p006-002","page":6,"quote":"Both of the two regions own many important long rivers such as the Amazon and the Mekong rivers, where numerous dams have been built and more are coming. Freshwater mollusks in dam areas were predominantly found in North America and Europe, of which the high threats were located in the river systems of southeastern North America, like the Mississippi and in Western Europe’s Danube, ranging from 10% to 30% (Fig. 2c). We found fish were the most broadly affected freshwater species by the dam construction, which experienced threats in all dam areas across the","verified_in_page":true},"resolution_status":"resolved","coordinates":[103.4492837629503,15.93358942004928],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mekong River","basin_id":4041108580,"main_basin_id":4040017020,"basin_region":"Central and South-East Asia","basin_area_km2":119866.0,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-1"},{"name":"Yangtze River Basin, China","kind":"region","role":"Location with high amphibian threat proportions and documented species extinctions linked to dams","river_name":"Yangtze River","geocode_query":"Yangtze River Basin, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-000","page":7,"quote":"ARTICLE IN PRESS ARTICLE IN PRESS globe (Fig. 2d). For instance, the southern Nile basin in East Africa had over 18 threatened fish species, bearing the highest number among all global basins. In addition, rivers in western North America and western Asia that are densely dammed showed a high proportion of threatened species, ranging from 25% to 50%. Notably, amphibians, which require specific freshwater and forest habitats, exhibited the highest threat proportion near the Amazon River in western South America, exceeding 50%. This was followed by the Yangtze River basin in China, with a threat ratio of around 25% (Fig. 2e). Compared to other groups, Odonata (dragonflies and damselflies) appeared less affected by dams, exhibiting low threat ratios that were primarily distributed in southeastern Africa and northwestern Europe (Fig. 2f).","verified_in_page":true},"resolution_status":"resolved","coordinates":[112.581053,29.720415],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Yangtze River, Shishou, Jingzhou, China, 434404","basin_id":4040783120,"main_basin_id":4040009880,"basin_region":"Central and South-East Asia","basin_area_km2":1025899.4,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-2","true_coordinates":[112.650141,29.720415],"spread_km":6.7},{"name":"Eastern Mississippi River Basin, North America","kind":"region","role":"Hotspot of threatened freshwater species in fragmented (non-free-flowing) rivers caused by dense dam network","river_name":"Mississippi River","geocode_query":"Mississippi River Basin, United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"The analysis revealed that the eastern Mississippi River Basin in North America, the Mekong River Basin in Southeast Asia, the Zambezi River in southern Africa, and the middle and lower reaches of the Yangtze River Basin in China host a high concentration of threatened freshwater species. In our comparative analysis within each of the 56 bio -realms containing dams worldwide, we found that the proportion of non -free -flowing rivers is generally positively associated with the degree of freshwater biodiversity threat (Fig. 6a). Specifically, 31 bio -realms exhibited a positive correlation, with about one -third of these results being statistically significant changes (p < 0.05), and the relationship was particularly robust in regions with larger sample sizes, underscoring the pervasive negative impacts of dams on freshwater biodiversity.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-95.20832050000001,47.2393876],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Mississippi River System, Itasca Township, MN 56676, USA","basin_id":7040569650,"main_basin_id":7040047060,"basin_region":"North America and Caribbean","basin_area_km2":446352.2,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-3"},{"name":"Zambezi River Basin, Southern Africa","kind":"region","role":"Biodiversity hotspot with heavy fragmentation from dams and concentrated threatened freshwater species","river_name":"Zambezi River","geocode_query":"Zambezi River Basin, Southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-001","page":9,"quote":"The analysis revealed that the eastern Mississippi River Basin in North America, the Mekong River Basin in Southeast Asia, the Zambezi River in southern Africa, and the middle and lower reaches of the Yangtze River Basin in China host a high concentration of threatened freshwater species. In our comparative analysis within each of the 56 bio -realms containing dams worldwide, we found that the proportion of non -free -flowing rivers is generally positively associated with the degree of freshwater biodiversity threat (Fig. 6a). Specifically, 31 bio -realms exhibited a positive correlation, with about one -third of these results being statistically significant changes (p < 0.05), and the relationship was particularly robust in regions with larger sample sizes, underscoring the pervasive negative impacts of dams on freshwater biodiversity.","verified_in_page":true},"resolution_status":"resolved","coordinates":[29.523737,-14.921883],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Zambezi River","basin_id":1041431480,"main_basin_id":1040011660,"basin_region":"Africa","basin_area_km2":149538.4,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-4","true_coordinates":[29.52373671029859,-14.98188331479953],"spread_km":6.7},{"name":"Orinoco Basin, South America","kind":"region","role":"Region with many free-flowing rivers and relatively less dam fragmentation","river_name":"Orinoco River","geocode_query":"Orinoco River Basin, South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"This pattern reflects the cumulative impacts of widespread dam construction across all size classes . Notably, according to the International Commission on Large Dams (ICOLD), these continents were also the epicentres of intensive dam construction in the last century. In contrast, regions such as the Amazon Basin and the Orinoco Basin","verified_in_page":true},"resolution_status":"resolved","coordinates":[-65.01373140261339,5.459619310406225],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Orinoco","basin_id":6040075390,"main_basin_id":6040004470,"basin_region":"South America","basin_area_km2":145132.6,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-5"},{"name":"Congo Basin, Africa","kind":"region","role":"Region with many free-flowing rivers, currently less impacted by dams","river_name":"Congo River","geocode_query":"Congo River Basin, Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"This pattern reflects the cumulative impacts of widespread dam construction across all size classes . Notably, according to the International Commission on Large Dams (ICOLD), these continents were also the epicentres of intensive dam construction in the last century. In contrast, regions such as the Amazon Basin and the Orinoco Basin","verified_in_page":true},"resolution_status":"resolved","coordinates":[18.722412,-1.965431],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Congo River","basin_id":1041213640,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":894488.8,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-6","true_coordinates":[18.77950841330257,-1.946890029824763],"spread_km":6.7},{"name":"Western Ghats, India","kind":"region","role":"Bio-realm with dam impacts showing possible positive effects due to protected areas shielding impacted reservoirs","river_name":null,"geocode_query":"Western Ghats, Maharashtra, India","latitude":null,"longitude":null,"evidence":{"passage_id":"p009-002","page":9,"quote":"By contrast, only 9 bio-realms showed negative correlations, most of which were not statistically significant, a pattern that may reflect limited sample sizes, data uncertainties, or the establishment of protected areas . For instance, in the Western Ghats of India, although dams initially had negative ecological effects, some reservoirs were later incorporated into protected areas, which in current assessments appear to show “positive effects”. Further analyses revealed a similar pattern between the proportion of non - free -flowing rivers and species population declines (Fig. 6b), with 25 bio -realms showing positive correlations, 15 showing negative correlations, and the remainder displaying no clear trend. Notably, more than 90% of statistically significant cases were positive, and the association was","verified_in_page":true},"resolution_status":"resolved","coordinates":[74.0001559,16.0005978],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"2222+63, Verle, Maharashtra 416510, India","basin_id":4040029530,"main_basin_id":4040029530,"basin_region":"Central and South-East Asia","basin_area_km2":316845.9,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-7"},{"name":"Asia","kind":"region","role":"Region with the largest area of critical and natural habitats impacted by dams, and majority of future dams planned here","river_name":null,"geocode_query":"Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"Such changes may lead to the loss of biodiversity and the degradation of wetland and riverine ecosystems. The future dams proposed in ecologically important habitats Among the 2,989 dams scheduled for construction, 1,049 (38%) are to be located within natural habitats, and 827 (32%) within critical habitats (Fig. S3 a). Compared to current dams, a larger proportion of future dams are proposed within habitats that hold high ecological values. Based on the minimum buffer zone, the area of critical habitats impacted by dams in Asia is the largest, totalling 1,035.5 km2, followed by South","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"South America","kind":"region","role":"Region with large area of critical and natural habitats impacted by dams and many future dams proposed in critical habitats","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p010-002","page":10,"quote":"Such changes may lead to the loss of biodiversity and the degradation of wetland and riverine ecosystems. The future dams proposed in ecologically important habitats Among the 2,989 dams scheduled for construction, 1,049 (38%) are to be located within natural habitats, and 827 (32%) within critical habitats (Fig. S3 a). Compared to current dams, a larger proportion of future dams are proposed within habitats that hold high ecological values. Based on the minimum buffer zone, the area of critical habitats impacted by dams in Asia is the largest, totalling 1,035.5 km2, followed by South","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"North America","kind":"region","role":"Continent with significant river fragmentation from dams and highest number of non-free-flowing rivers","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p008-002","page":8,"quote":"This pattern reflects the cumulative impacts of widespread dam construction across all size classes . Notably, according to the International Commission on Large Dams (ICOLD), these continents were also the epicentres of intensive dam construction in the last century. In contrast, regions such as the Amazon Basin and the Orinoco Basin","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.344656,54.495961],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-5adb1a7afb367b52a3120fea-site-10","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"United States","kind":"region","role":"Example high-income country with many existing dams but very few planned future dams","river_name":null,"geocode_query":"United States","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-002","page":12,"quote":"We found that over 90% of river connectivity loss was driven by Degree of Fragmentation (DOF) . A cascade of adverse consequences would emerge from the decline in river connectivity. It obstructs the migration of freshwater species, preventing them from locating breeding grounds, foraging areas, and habitats, subsequently leading to a decrease in their populations47 . One study has identified that if all planned dams are constructed in future, over 260,000 km of rivers, including major arteries such as the Amazon, Congo, Irrawaddy, and Salween, will lose their free -flowing status, causing great potential damage to the ecosystems in these rivers48 .","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Some freshwater taxa such as mollusks have incomplete IUCN coverage potentially biasing threat estimates.","Lack of temporal update frequency and data gaps in IUCN threat categories may affect accuracy of extinction risk trends.","IUCN range maps are static and represent potential distributions, thus may not fully reflect current species presence.","Data deficiency species were excluded which may cause underestimation or overestimation of threat proportions.","Some positive correlations with dam presence and decreased threat in protected or managed sites may mask otherwise negative impacts."],"river_ids":["ne-233","ne-685"],"site_ids":["doc-5adb1a7afb367b52a3120fea-site-0","doc-5adb1a7afb367b52a3120fea-site-1","doc-5adb1a7afb367b52a3120fea-site-2","doc-5adb1a7afb367b52a3120fea-site-3","doc-5adb1a7afb367b52a3120fea-site-4","doc-5adb1a7afb367b52a3120fea-site-5","doc-5adb1a7afb367b52a3120fea-site-6","doc-5adb1a7afb367b52a3120fea-site-7","doc-5adb1a7afb367b52a3120fea-site-10"],"anchor":[18.722412,-1.965431],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[1041213640,1041431480,4040029530,4040783120,4041108580,6040075390,6040280400,7040021740,7040569650],"main_basin_ids":[1040011660,1040020040,4040009880,4040017020,4040029530,6040004470,6040007000,7040021740,7040047060]},{"id":"doc-637a2ee83c11e91587846b6c","title":"Diurnal asymmetry in heat stress intensification across Bangladesh, 1985–2024: Accelerated nighttime warming and emerging urban risk","display_title":"Heat Stress Impact on Global Labour Productivity","doi":"https://doi.org/10.1371/journal.pclm.0000848","theme":"Climate","color":"#98d4f1","year":2026,"publication_date":"2026-08-05","authors":["Mohammad Kamruzzaman","Abu Yousuf Md Abdullah","A. K. M. Saiful Islam","Afifa Talukder","Shahriar Wahid","Shamsuddin Shahid"],"summary":"This report examines the effect of heat stress on labour productivity and decent work worldwide. It projects that by 2030, due to rising global temperatures, over 2% of working hours globally may be lost from heat stress, which disproportionately affects outdoor workers in agriculture and construction sectors, especially in tropical and subtropical regions. The study highlights significant geographic and sectoral disparities in vulnerability to heat stress, driven by climate, employment composition, and socio-economic factors such as informality and social protection coverage. It underscores the need for a mix of adaptation and mitigation policies, involving international labour standards, social dialogue, government regulations, employer measures, and worker actions, to address occupational heat stress and protect vulnerable workers.","finding":"By 2030, the equivalent of more than 2% of total working hours worldwide will be lost annually due to heat stress, equating to 80 million full-time jobs; this loss is more severe in tropical regions with high agricultural employment and low adaptive capacity, such as Southern Asia and Western Africa, where losses may reach 5%.","finding_evidence":{"passage_id":"p011-003","page":11,"quote":"Heat stress is projected to reduce total working hours worldwide by 2.2 per cent and global GDP by US$2,400 billion in 2030 Projections based on a global temperature rise of 1.5°C by the end of the twenty-first century, and also on labour force trends, suggest that, in 2030, 2.2 per cent of total working hours worldwide will be lost to high temperatures – a productivity loss equivalent to 80 million full-time jobs. This is, however, a conservative estimate because, apart from postulating that the long-term increase in global mean temperature will not exceed 1.5°C, it rests on the assumption that agricultural and construction work is carried out in the shade. This assumption is based partly on the fact that in tropical countries about 40 per cent of days are cloudy, not sunny, and partly on the fact that some tasks, especially in subsistence agriculture, can often be moved to times of the day when it is less hot.","verified_in_page":true},"topics":["heat stress","labour productivity","climate change","occupational health","labour markets","migration","social protection","employment sectors"],"study_period":"1995-2030 projections","locations":[{"name":"Southern Asia","kind":"region","role":"high heat stress impact subregion with large labour productivity loss","river_name":null,"geocode_query":"Southern Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"A general pattern for most countries is that the greater the number of working hours expected to be lost as a result of heat stress, the lower the coverage of their social protection systems. The impact of heat stress is unevenly distributed geographically, with the expected reduction in working hours in 2030 amounting to around 5 per cent in both Southern Asia and Western Africa Some subregions are at a higher risk of suffering the adverse consequences of global warming. Southern Asia and Western Africa are expected to be the worst affected. Under a scenario of 1.5°C global warming by the end of the century, heat stress in these two subregions would lead to the loss of 5.3 per cent and 4.8 per cent of working hours in 2030, corresponding to around 43 million and 9 million full‐time jobs, respectively. The European subregions are expected to experience a smaller impact, with their productivity losses projected to be less than 0.1 per cent in all cases.","verified_in_page":true},"resolution_status":"resolved","coordinates":[76.513659,25.06764],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Asia","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-0","true_coordinates":[76.4563087,25.03764],"spread_km":6.7},{"name":"Western Africa","kind":"region","role":"high heat stress impact subregion with large labour productivity loss","river_name":null,"geocode_query":"Western Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-001","page":12,"quote":"A general pattern for most countries is that the greater the number of working hours expected to be lost as a result of heat stress, the lower the coverage of their social protection systems. The impact of heat stress is unevenly distributed geographically, with the expected reduction in working hours in 2030 amounting to around 5 per cent in both Southern Asia and Western Africa Some subregions are at a higher risk of suffering the adverse consequences of global warming. Southern Asia and Western Africa are expected to be the worst affected. Under a scenario of 1.5°C global warming by the end of the century, heat stress in these two subregions would lead to the loss of 5.3 per cent and 4.8 per cent of working hours in 2030, corresponding to around 43 million and 9 million full‐time jobs, respectively. The European subregions are expected to experience a smaller impact, with their productivity losses projected to be less than 0.1 per cent in all cases.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.460415,13.471665],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"West Africa","basin_id":1040873650,"main_basin_id":1040023300,"basin_region":"Africa","basin_area_km2":111517.9,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-1","true_coordinates":[-2.4604145,13.531665],"spread_km":6.7},{"name":"Africa","kind":"region","role":"second largest continent, major study area for heat stress impact on labour productivity","river_name":null,"geocode_query":"Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p030-000","page":30,"quote":"33 3.1 Current and projected heat levels With a population of around 1.2 billion and a land area of approximately 30.1 million km2 , Africa1 is the world’s second most populated region after Asia and the Pacific. It is also one of the most vulnerable to heat stress because of its high heat exposure and low adaptive capacity.2 According to the IPCC’s Fifth Assessment Report, during the last 50 to 100 years, near-surface temperatures have increased by at least 0.5°C across most parts of Africa, with minimum temperatures rising more rapidly than maximum temperatures. The report also concludes that temperatures in Africa are projected to rise faster than the global average during the twenty-first century. Indeed, projections indicate that the mean annual temperature rise is likely to exceed 2°C by the end of the present century.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Central Africa","kind":"region","role":"subregion with significant heat stress impact and agriculture-dominated employment","river_name":null,"geocode_query":"Central Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p033-000","page":33,"quote":"36 Working on a warmer planet: The impact of heat stress on labour productivity and decent work Countries in Central Africa (table 3.2) are affected by heat stress to a greater extent than those in Northern Africa. Indeed, more than half of the countries in Central Africa experienced a loss of at least 1 per cent of working hours in 1995. Moreover, only three countries in the subregion are projected to experience a loss of less than 1 per cent in 2030. The greatest impact is felt by Chad, with 3.9 per cent of working hours lost in 1995 and 7.1 per cent projected to be lost in 2030. This can be attributed to Chad’s exposure to extreme heat, and also to the vulnerability of its agricultural workers, who constitute the majority of the labour force. Because of its large population, the Democratic Republic of the Congo is expected to lose the equivalent of more than 1.2 million jobs in 2030 as a result of rising temperatures.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.5687103,2.3184621],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Africa","basin_id":1041156950,"main_basin_id":1040020040,"basin_region":"Africa","basin_area_km2":648276.2,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-3"},{"name":"Eastern Africa","kind":"region","role":"subregion with moderate heat stress impact and high agricultural employment","river_name":null,"geocode_query":"Eastern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p033-000","page":33,"quote":"36 Working on a warmer planet: The impact of heat stress on labour productivity and decent work Countries in Central Africa (table 3.2) are affected by heat stress to a greater extent than those in Northern Africa. Indeed, more than half of the countries in Central Africa experienced a loss of at least 1 per cent of working hours in 1995. Moreover, only three countries in the subregion are projected to experience a loss of less than 1 per cent in 2030. The greatest impact is felt by Chad, with 3.9 per cent of working hours lost in 1995 and 7.1 per cent projected to be lost in 2030. This can be attributed to Chad’s exposure to extreme heat, and also to the vulnerability of its agricultural workers, who constitute the majority of the labour force. Because of its large population, the Democratic Republic of the Congo is expected to lose the equivalent of more than 1.2 million jobs in 2030 as a result of rising temperatures.","verified_in_page":true},"resolution_status":"resolved","coordinates":[37.245213,1.987709],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Africa","basin_id":1041145090,"main_basin_id":1040008100,"basin_region":"Africa","basin_area_km2":230636.1,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-4","true_coordinates":[37.2972044,1.957709],"spread_km":6.7},{"name":"Southern Africa","kind":"region","role":"subregion least affected by heat stress in Africa due to higher altitudes and lower agricultural employment","river_name":null,"geocode_query":"Southern Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p033-003","page":33,"quote":"This can be explained on the one hand by these countries’ distance from the equator, their high altitudes and more temperate climates, and on the other hand by the smaller share of agricultural employment in the subregion, where it accounts for just 19 per cent of total employment. The country most affected is Eswatini, with 0.3 per cent of working hours lost to heat stress in 1995 and 0.5 per cent projected to be lost in 2030. By contrast, the impact of heat stress on Lesotho’s labour productivity is practically zero.","verified_in_page":true},"resolution_status":"resolved","coordinates":[19.511672,-24.387077],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Africa","basin_id":1041624310,"main_basin_id":1040015850,"basin_region":"Africa","basin_area_km2":361380.7,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-5","true_coordinates":[19.5687103,-24.3570767],"spread_km":6.7},{"name":"Western Africa","kind":"region","role":"subregion with highest heat stress impacts and economic losses in Africa","river_name":null,"geocode_query":"Western Africa","latitude":null,"longitude":null,"evidence":{"passage_id":"p035-002","page":35,"quote":"Table 3.5 Working hours lost to heat stress, by sector and country, Western Africa, 1995 and 2030 (projections) Country 1995 2030 Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Benin 7.21 3.08 7.21 0.37 3.88 49 12.43 6.33 12.43 1.20 6.18 246 Burkina Faso 4.62 2.06 4.62 0.32 4.08 175 8.50 4.49 8.50 1.00 7.08 894 Côte d’Ivoire 6.24 2.44 6.24 0.24 4.09 204 10.61 5.01 10.61 0.75 6.01 763 The Gambia 4.21 1.56 4.21 0.13 2.34 8 7.08 3.19 7.08 0.40 2.88 28 Ghana 6.54 2.49 6.54 0.24 4.41 298 11.69 5.53 11.69 0.79 5.54 1038 Guinea 2.17 0.67 2.17 0.06 1.70 43 4.44 1.65 4.44 0.19 3.20 244 Guinea-Bissau 3.17 1.01 3.17 0.08 2.15 9 6.20 2.49 6.20 0.24 3.72 39 Liberia 4.29 1.48 4.29 0.13 2.79 18 7.79 3.20 7.79 0.39 3.88 85 Mali 4.24 1.91 4.24 0.32 2.40 57 7.45 3.90 7.45 0.88 5.01 448 Mauritania 4.09 1.99 4.09 0.37 2.40 11 7.26 4.15 7.26 1.12 3.65 45 Niger 5.02 2.45 5.02 0.48 3.56 86 9.22 5.40 9.22 1.55 6.83 651 Nigeria 5.40 2.27 5.40 0.33 3.18 932 9.79 4.84 9.79 0.96 3.89 3639 Senegal 3.69 1.46 3.69 0.16 2.23 62 6.55 3.11 6.55 0.50 3.88 234 Sierra Leone 5.23 1.93 5.23 0.17 3.76 54 9.31 4.07 9.31 0.53 6.63 189 Togo 5.84 2.29 5.84 0.24 4.12 82 10.61 5.10 10.61 0.84 7.18 425 Western Africa 5.23 2.20 5.23 0.29 3.37 2088 9.17 4.71 9.17 0.90 4.77 8968 Note: The table shows the percentage of working hours lost to heat stress (and the associated health, well-being and productivity effects) in each sector and in the economy as a whole.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-2.460415,13.591665],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"West Africa","basin_id":1040873650,"main_basin_id":1040023300,"basin_region":"Africa","basin_area_km2":111517.9,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-6","true_coordinates":[-2.4604145,13.531665],"spread_km":6.7},{"name":"North America","kind":"region","role":"region with low heat stress impact overall but affected in southern states","river_name":null,"geocode_query":"North America","latitude":null,"longitude":null,"evidence":{"passage_id":"p040-003","page":40,"quote":"This can be explained partly by the large proportion of the subregion located close to the Arctic and therefore experiencing cold or temperate climates, and partly also by the relatively small share of agricultural employment, which accounts for less than 3 per cent of total employment in the subregion. Since the share of employment in construction is relatively high, at around 7 per cent, over one third of the working hours lost to heat stress were concentrated in the construction sector in 1995. Whereas the impact of heat stress on labour productivity in Canada is practically zero, the United States lost 0.11 per cent of total working hours as a result of heat stress in 1995 and is projected to lose 0.21 per cent in 2030. The expected productivity loss in 2030 is equivalent to 389,000 full-time jobs. This effect is concentrated in the southern states of the country and concerns mostly outdoor workers, such as construction workers and farm workers in California (see box 4.1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[-105.306813,54.474],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Americas","basin_id":7040021740,"main_basin_id":7040021740,"basin_region":"North America and Caribbean","basin_area_km2":302496.3,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-7","true_coordinates":[-105.2551187,54.5259614],"spread_km":6.7},{"name":"Latin America","kind":"region","role":"region with moderate heat stress impact and high informal employment levels","river_name":null,"geocode_query":"Latin America","latitude":null,"longitude":null,"evidence":{"passage_id":"p040-001","page":40,"quote":"The Americas feature several areas that are at high risk of heat exposure; these areas are mainly in Central America, South America and the Caribbean. The impact of heat stress on labour productivity can already be observed, but it is low compared with other regions of the world because agricultural employment is relatively less widespread. In 1995, around 0.3 per cent of the total number of working hours in the region were lost to heat stress, the equivalent of more than 948,000 full-time jobs. Our analysis shows that 55 per cent of this loss was concentrated in the agricultural sector. The impact of heat stress is expected to intensify in the future. In particular, projections suggest that 0.6 per cent of total working hours will be lost to heat stress in 2030 – the equivalent of around 2.9 million full-time jobs.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-61.32685350000001,-4.4420385],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Latin America","basin_id":6040285990,"main_basin_id":6040007000,"basin_region":"South America","basin_area_km2":1368849.7,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-8"},{"name":"Central America","kind":"region","role":"subregion most affected by heat stress in Americas with tropical heat and agriculture","river_name":null,"geocode_query":"Central America","latitude":null,"longitude":null,"evidence":{"passage_id":"p041-004","page":41,"quote":"For example, the Division of Occupational Safety and Health of the State of California requires farmers to provide their workers with training on heat illness prevention and also to offer them regular breaks so that they can cool down and rehydrate in the shade (Cal/OSHA, 2006). The regulations in force in California also include requirements relating to heat illness prevention plans, training, acclimatization assessment and emergency response procedures. In Canada, the “Sun Safety at Work Canada” programme aims to enhance sun safety in the country’s workplaces, thereby protecting workers from skin cancer, heat stress and eye damage. The programme publishes fact sheets and helps with the development of preventive measures and regulatory frameworks to deal with sun exposure at work. of 0.61 per cent of total working hours (the equivalent of 272,000 full-time jobs) as a result of heat stress in 1995.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-85.655643,12.799013],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central America","basin_id":7040050930,"main_basin_id":7040050930,"basin_region":"North America and Caribbean","basin_area_km2":382831.5,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-9","true_coordinates":[-85.60236429999999,12.7690126],"spread_km":6.7},{"name":"South America","kind":"region","role":"region affected by heat stress with losses primarily in agricultural and construction sectors","river_name":null,"geocode_query":"South America","latitude":null,"longitude":null,"evidence":{"passage_id":"p044-000","page":44,"quote":"4. Americas 47 Labour productivity in South America is also impaired by heat stress (table 4.3). Thus, rising temperatures reduced working hours by 0.37 per cent in 1995 (equivalent to 481,000 full-time jobs), and this productivity loss is expected to reach 0.76 per cent in 2030 (equivalent to 1.6 million full-time jobs). However, the impact varies considerably within the subregion. In 1995, the countries with the highest losses included Guyana (1.56 per cent), Suriname (0.64 per cent) and Colombia (0.55 per cent); other countries, such as Uruguay, Argentina and Peru, exhibited much lower rates. Although the estimated share of working hours lost in Brazil was 0.44 per cent in 1995, its sizeable population means that this productivity loss translated into an equivalent of 314,000 full-time jobs, accounting for more than half of the loss incurred by the subregion.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Caribbean","kind":"region","role":"subregion with increasing productivity loss due to heat stress","river_name":null,"geocode_query":"Caribbean","latitude":null,"longitude":null,"evidence":{"passage_id":"p043-003","page":43,"quote":"Table 4.4 Working hours lost to heat stress, by sector and country/territory, Caribbean, 1995 and 2030 (projections) Country 1995 2030 Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Bahamas 0.72 0.01 0.72 0 0.09 0.1 2.70 0.38 2.70 0 0.35 0.7 Barbados 0.41 0 0.41 0 0.06 0.1 3.10 0.26 3.10 0 0.40 0.5 Cuba 2.05 0.50 2.05 0.01 0.70 30.3 4.26 1.52 4.26 0.09 0.76 34.1 Dominican Republic 0.98 0.19 0.98 0 0.30 8.1 2.63 0.81 2.63 0.04 0.56 26.3 Haiti 0.27 0.03 0.27 0 0.14 3.9 1.09 0.20 1.09 0 0.56 29.2 Jamaica 0 0 0 0 0 0.0 0.15 0 0.15 0 0.04 0.4 Puerto Rico (USA) 0.02 0 0.02 0 0 0.0 0.31 0.01 0.31 0 0.02 0.2 Saint Lucia 0.12 0 0.12 0 0.05 0.0 0.80 0.07 0.80 0 0.20 0.2 Saint Vincent and the Grenadines 3.00 0.36 3.00 0 0.78 0.3 7.71 2.40 7.71 0.03 2.10 0.8 Trinidad and Tobago 1.66 0.26 1.66 0.01 0.42 1.9 5.05 1.17 5.05 0.03 1.22 7.1 Virgin Islands (USA) 0.03 0 0.03 0 0.01 0.0 1.17 0.02 1.17 0 0.23 0.1 Caribbean 0.97 0.25 0.97 0 0.35 44.8 1.76 0.67 1.76 0.04 0.56 99.7 Note: The table shows the percentage of working hours lost to heat stress (and the associated health, well-being and productivity effects) in each sector and in the economy as a whole.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-78.71273,21.499114],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Caribbean","hydrology_source":"none","site_id":"doc-637a2ee83c11e91587846b6c-site-11","true_coordinates":[-78.6568942,21.4691137],"spread_km":6.7},{"name":"Arab States","kind":"region","role":"region studied for heat stress impacts with high construction sector and migrant workers' vulnerability","river_name":null,"geocode_query":"Arab States","latitude":null,"longitude":null,"evidence":{"passage_id":"p046-000","page":46,"quote":"49 5.1 Current and projected heat levels With a population of approximately 161 million and a land area of around 3.8 million km2 , the Arab States1 are the smallest and least populated region in the world. Although the region as a whole is vulnerable to heat exposure, some countries face greater risks of heat stress than others. In particular, the wealthier countries that are members of the Gulf Cooperation Council (GCC) have greater adaptive capacity than non-GCC countries. The Arab region also has widely contrasting topography and distinctive landforms. Thus, it is characterized by large mountainous zones (e.g. the Hijaz and Asir Mountains in Saudi Arabia and the highlands of Hadhramaut in Yemen) and vast deserts that cover most of the area. These desert expanses are interspersed with oases that create microclimates which make agriculture possible to a limited extent. The Arab States mostly experience a hot desert climate with less than 100 mm of rainfall per year.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Gulf Cooperation Council countries","kind":"region","role":"subregion with high occupational heat risk and emerging migrant worker protection measures","river_name":null,"geocode_query":"Gulf Cooperation Council countries","latitude":null,"longitude":null,"evidence":{"passage_id":"p050-000","page":50,"quote":"5. Arab States 53 Box 5.1 Heat-related risks and occupational safety and health measures in the GCC countries Outdoor workers are highly exposed to heat-related risks in the countries of the GCC, because the regional climate is characterized by dry and subtropical desert conditions. During the summer, temperature and humidity levels are high and rainfall is scarce. Between April and September, temperatures can reach 55°C even in the shade, with humidity exceeding 80 per cent. In the GCC countries, the construction sector accounted for 23 per cent of employment in 2017 (ILOSTAT), putting a large number of workers at risk of heat stress. In the United Arab Emirates, heat-related illnesses and heightened risk of accidents are, together with long working hours, the main health issues faced by construction workers (Sönmez et al., 2011).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Asia and the Pacific","kind":"region","role":"most populated region with great heat stress impact mostly in Southern and Southeast Asia","river_name":null,"geocode_query":"Asia and the Pacific","latitude":null,"longitude":null,"evidence":{"passage_id":"p052-000","page":52,"quote":"55 6. Asia and the Pacific 6.1 Current and projected heat levels With a population of approximately 4.2 billion and a land area of around 32 million km2 , Asia and the Pacific1 is the world’s most populated region. It is particularly vulnerable to heat stress because of its high exposure in some subregions. The region also has varying degrees of adaptive capacity. Comprising some of the world’s most diverse subregions in terms of geography and climate, Asia is bounded to the north by the Arctic Ocean, to the east by the Pacific Ocean, and to the south by the Indian Ocean. Moreover, it features 11 climate zones, which range from a tropical monsoon climate in the far south, through humid, cool and temperate climates in the north, to a desert climate in the west and north-west. In the rest of its populated areas, a humid, temperate climate prevails.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"},{"name":"Eastern Asia","kind":"region","role":"subregion with moderate heat stress impact and large population","river_name":null,"geocode_query":"Eastern Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p055-000","page":55,"quote":"58 Working on a warmer planet: The impact of heat stress on labour productivity and decent work 6.3 Subregional and national estimates Table 6.1 shows the productivity loss due to heat stress for countries and territories in Eastern Asia. The impact across the subregion is relatively small. For instance, in 1995, the highest productivity loss in terms of percentage of working hours was 0.55 per cent (observed in China); the highest productivity loss projected for 2030 is expected to be just 1.13 per cent (in Macau, China). Nonetheless, given the vast population of Eastern Asia, subregional averages of 0.49 per cent and 0.70 per cent of working hours lost in 1995 and projected to be lost in 2030 respectively translate into 3.9 million and 5.7 million full-time jobs. The impact of heat stress is expected to increase in all countries in the subregion between 1995 and 2030.","verified_in_page":true},"resolution_status":"resolved","coordinates":[106.457855,38.794595],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"East Asia","basin_id":4040548370,"main_basin_id":4040007850,"basin_region":"Central and South-East Asia","basin_area_km2":248039.1,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-15","true_coordinates":[106.5348379,38.7945952],"spread_km":6.7},{"name":"Southern Asia","kind":"region","role":"subregion most affected by heat stress with major losses in agricultural employment","river_name":null,"geocode_query":"Southern Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p055-001","page":55,"quote":"In China, the significant climatic differences within its national territory mean that its southern provinces face much greater risks than the country as a whole. Countries in Southern Asia are the most affected by heat stress in the Asia and the Pacific region (see table 6.2). Indeed, this subregion lost an average of 4 per cent of total working hours in 1995 (the equivalent of 19 million full-time jobs), with more than half of the countries there suffering losses of at least 1 per cent. A third of the countries in Southern Asia actually incurred losses greater than 4 per cent. In 2030, the impact of heat stress on labour productivity is expected to be even more pronounced. In particular, up to 5.3 per cent of total working hours (the equivalent of 43 million full-time jobs) are projected to be lost, with two-thirds of Southern Asian countries facing losses of at least 2 per cent. However, there is considerable variation within the subregion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[76.398958,25.06764],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South Asia","basin_id":4040960480,"main_basin_id":4040025450,"basin_region":"Central and South-East Asia","basin_area_km2":954206.0,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-16","true_coordinates":[76.4563087,25.03764],"spread_km":6.7},{"name":"South-East Asia","kind":"region","role":"subregion affected by heat stress with vulnerable low-income outdoor workers","river_name":null,"geocode_query":"South-East Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p058-000","page":58,"quote":"6. Asia and the Pacifi 61 Labour productivity in South-East Asia is also severely affected by heat stress (see table 6.3). Thus, rising temperatures caused the loss of 3.1 per cent of working hours in this subregion in 2015 (equivalent to 6.9 million full-time jobs) and are projected to lead to the loss of 3.7 per cent of working hours in 2030 (equivalent to 13 million full-time jobs). However, the impact varies considerably within the subregion. In 1995, some countries suffered high losses, such as Cambodia (7.5 per cent), Thailand (5.3 per cent) and Viet Nam (4.4 per cent). Projected temperature increases will put extra pressure on the most vulnerable workers in these countries (see boxes 6.4 and 6.5). Other countries experienced much smaller losses, including Timor-Leste (0.1 per cent) and Malaysia (1.1 per cent).","verified_in_page":true},"resolution_status":"resolved","coordinates":[115.61083,-2.24797],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"South East Asia","basin_id":5040015660,"main_basin_id":5040015660,"basin_region":"Australasia","basin_area_km2":19957.2,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-17","true_coordinates":[115.66283,-2.2179704],"spread_km":6.7},{"name":"Pacific Islands","kind":"region","role":"subregion least affected by heat stress in Asia Pacific due to low population and diverse climates","river_name":null,"geocode_query":"Pacific Islands","latitude":null,"longitude":null,"evidence":{"passage_id":"p060-000","page":60,"quote":"6. Asia and the Pacifi 63 working hours in 1995 (equivalent to 30,000 full-time jobs) and is expected to lose 3.1 per cent in 2030 (equivalent to 147,000 full-time jobs). Although the impact of heat stress in Australia in terms of the percentage of working hours lost is low, with 0.04 per cent in 1995 and 0.07 per cent projected for 2030, the equivalent in terms of the number of full-time jobs lost is in fact the second highest in the subregion because of the size of the country’s population. With regard to GDP losses due to the impact of heat stress on labour productivity, Asia and the Pacific is the most affected region in the world, with an estimated reduction in regional GDP of 1.4 per cent in 1995 and a projected reduction of 2.3 per cent in 2030 (figure 6.3). Since heat stress exposure and adaptive capacity vary widely within the region, countries in South-East Asia and Southern Asia are affected to a greater extent than those in Eastern Asia and the Pacific Islands subregion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[-124.538879,-8.835157],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Pacific Ocean","hydrology_source":"none","site_id":"doc-637a2ee83c11e91587846b6c-site-18","true_coordinates":[-124.508523,-8.783195],"spread_km":6.7},{"name":"Europe and Central Asia","kind":"region","role":"region least affected by heat stress overall but vulnerable to heatwaves, especially Western and Central Asia","river_name":null,"geocode_query":"Europe and Central Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p062-000","page":62,"quote":"65 7.1 Current and projected heat levels The Europe and Central Asia1 region has a population of approximately 925 million and a land area of 27 million km2 . Although small in comparison with other regions such as Asia and the Pacific and Africa, three of its subregions – Northern, Southern and Western Europe – taken together have the second-highest population density in the world. This amplifies the effect of local weather conditions in terms of the number of people affected. Nevertheless, thanks to its low exposure and high adaptive capacity, the region is relatively less vulnerable to heat stress. Although a significant proportion of Europe lies in the northern latitudes, the relatively warm seas that border the continent give most of Central and Western Europe a temperate climate, with mild winters and summers. The winds from the west bring precipitation throughout most of the year.","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.781628,45.499229],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Asia","basin_id":4040050300,"main_basin_id":4040050300,"basin_region":"Central and South-East Asia","basin_area_km2":241047.3,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-19","true_coordinates":[68.8319005,45.4506875],"spread_km":6.7},{"name":"Northern Europe","kind":"region","role":"subregion with negligible heat stress impact due to northern latitude and low agricultural employment","river_name":null,"geocode_query":"Northern Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p064-003","page":64,"quote":"By 2030, the impact of heat stress on labour productivity will have increased, but the loss of working hours is still expected to remain under 0.01 per cent. In Eastern Europe also, labour productivity is not affected significantly by heat stress (table 7.1). Its estimated productivity loss measured as the percentage of working hours lost was under 0.01 per cent in 1995 and is expected to remain at just 0.01 per cent in 2030 – the equivalent of 8,700 full‐time jobs. The impact of heat stress on labour productivity is relatively small in Southern Europe, too, though it is higher than in the other European subregions (see table 7.2). In 1995, an estimated 0.01 per cent of working hours were lost owing to heat stress (the equivalent of 6,300 full-time jobs); it is projected that 0.02 per cent will be lost in 2030 (the equivalent of 14,400 full-time jobs). However, there is considerable heterogeneity within the subregion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[12.340171,62.338648],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Northern Europe","basin_id":2040033320,"main_basin_id":2040033320,"basin_region":"Europe and Middle East","basin_area_km2":49858.8,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-20","true_coordinates":[12.3401708,62.27864750000001],"spread_km":6.7},{"name":"Western Europe","kind":"region","role":"subregion with very low heat stress impact due to temperate climate and employment structure","river_name":null,"geocode_query":"Western Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p064-003","page":64,"quote":"By 2030, the impact of heat stress on labour productivity will have increased, but the loss of working hours is still expected to remain under 0.01 per cent. In Eastern Europe also, labour productivity is not affected significantly by heat stress (table 7.1). Its estimated productivity loss measured as the percentage of working hours lost was under 0.01 per cent in 1995 and is expected to remain at just 0.01 per cent in 2030 – the equivalent of 8,700 full‐time jobs. The impact of heat stress on labour productivity is relatively small in Southern Europe, too, though it is higher than in the other European subregions (see table 7.2). In 1995, an estimated 0.01 per cent of working hours were lost owing to heat stress (the equivalent of 6,300 full-time jobs); it is projected that 0.02 per cent will be lost in 2030 (the equivalent of 14,400 full-time jobs). However, there is considerable heterogeneity within the subregion.","verified_in_page":true},"resolution_status":"resolved","coordinates":[1.2643875,46.2021848],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Western Europe","basin_id":2040021030,"main_basin_id":2040021030,"basin_region":"Europe and Middle East","basin_area_km2":116722.3,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-21"},{"name":"Eastern Europe","kind":"region","role":"subregion with very low heat stress impact but some vulnerable informal employment","river_name":null,"geocode_query":"Eastern Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p065-000","page":65,"quote":"68 Working on a warmer planet: The impact of heat stress on labour productivity and decent work Table 7.1 Working hours lost to heat stress, by sector and country, Eastern Europe, 1995 and 2030 (projections) Country 1995 2030 Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Agriculture (in shade) (%) Industry (%) Construction (in shade) (%) Services (%) Total (%) Total (thousand full-time jobs) Belarus 0 0 0 0 0 0.0 0 0 0 0 0 0.0 Bulgaria 0.03 0.01 0.03 0 0.01 0.3 0.09 0.03 0.09 0 0.02 0.4 Czech Republic 0 0 0 0 0 0.0 0.01 0 0.01 0 0 0.1 Hungary 0.02 0.01 0.02 0 0 0.2 0.05 0.01 0.05 0 0.01 0.3 Moldova, Republic of 0.01 0 0.01 0 0.01 0.1 0.05 0.01 0.05 0 0.02 0.2 Poland 0 0 0 0 0 0.0 0.01 0 0.01 0 0 0.2 Romania 0.03 0.01 0.03 0 0.01 1.6 0.07 0.02 0.07 0 0.03 2.1 Russian Federation 0.01 0 0.01 0 0 1.6 0.03 0.01 0.03 0 0.01 4.3 Slovakia 0.01 0 0.01 0 0 0.0 0.01 0 0.01 0 0 0.1 Ukraine 0.01 0 0.01 0 0 0.5 0.02 0 0.02 0 0.01 1.0 Eastern Europe 0.01 0 0.01 0 0 4.3 0.03 0.01 0.03 0 0.01 8.7 Note: The table shows the percentage of working hours lost to heat stress (and the associated health, well-being and productivity effects) in each sector and in the economy as a whole.","verified_in_page":true},"resolution_status":"resolved","coordinates":[37.95874939999999,52.0055148],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Eastern Europe","basin_id":2040439160,"main_basin_id":2040006590,"basin_region":"Europe and Middle East","basin_area_km2":107148.0,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-22"},{"name":"Southern Europe","kind":"region","role":"subregion with small but higher heat stress impact, with heatwaves significant","river_name":null,"geocode_query":"Southern Europe","latitude":null,"longitude":null,"evidence":{"passage_id":"p064-004","page":64,"quote":"The effect is most pronounced in Albania, where 0.07 per cent of working hours are projected to be lost in 2030 as a result of heat stress. Source: ILO estimates based on data from the ILOSTAT database. Figure 7.2 Breakdown of total employment by sector, Europe and Central Asia subregions, 1995 and 2030 (projections) Agriculture Construction Industry Services 1995 2030 1995 2030 1995 2030 1995 2030 1995 2030 Western Europe Northern Europe 1995 2030 Eastern Europe Southern Europe Western Asia Central Asia 150 0 Number of workers (millions) 50 100","verified_in_page":true},"resolution_status":"resolved","coordinates":[-1.2121322,41.2745004],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Southern Europe","basin_id":2040016230,"main_basin_id":2040016230,"basin_region":"Europe and Middle East","basin_area_km2":310600.1,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-23"},{"name":"Central Asia","kind":"region","role":"subregion with increasing heat stress impact and high agricultural employment","river_name":null,"geocode_query":"Central Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p066-000","page":66,"quote":"7. Europe and Central Asia 69 Table 7.3 presents data on the impact of heat stress on labour productivity in Central Asian countries. Although the overall effect is relatively low, the impact of heat stress in Central Asia is expected to increase significantly by 2030. Thus, 0.03 per cent of working hours were lost to heat stress in 1995 (the equivalent of 5,400 full-time jobs) and 0.1 per cent of working hours are projected to be lost in 2030 (the equivalent of 32,300 full-time jobs).","verified_in_page":true},"resolution_status":"resolved","coordinates":[68.750558,45.432146],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Central Asia","basin_id":4040050300,"main_basin_id":4040050300,"basin_region":"Central and South-East Asia","basin_area_km2":241047.3,"hydrology_source":"hydrobasins","site_id":"doc-637a2ee83c11e91587846b6c-site-24","true_coordinates":[68.8319005,45.4506875],"spread_km":6.7},{"name":"Western Asia","kind":"region","role":"subregion most affected by heat stress in Europe and Central Asia, high construction employment and productivity loss","river_name":null,"geocode_query":"Western Asia","latitude":null,"longitude":null,"evidence":{"passage_id":"p067-000","page":67,"quote":"70 Working on a warmer planet: The impact of heat stress on labour productivity and decent work employment, and in 2030 it is still expected to account for 24 per cent), and partly by the expansion of the construction sector, whose share of total employment is expected to increase from 6 to 11 per cent over the same period. The country most affected is Turkmenistan, with 0.2 per cent of working hours projected to be lost owing to heat stress in 2030 (the equivalent of 4,900 full-time jobs). Western Asia includes the countries most affected by heat stress in the Europe and Central Asia region (table 7.4). Not only are these countries the worst affected in terms of working hours lost, but their absolute loss in terms of full-time jobs is also the highest in the whole region.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a continent/establishment/natural_feature centroid"}],"limitations":["Native PDF text is incomplete and extracted passages are partial.","Exact coordinates for regions and broad geographic areas are unavailable and not specified.","The study period extends mainly up to 2030 with projections; precise contemporaneous study periods are not defined in all regional analyses."],"river_ids":[],"site_ids":["doc-637a2ee83c11e91587846b6c-site-0","doc-637a2ee83c11e91587846b6c-site-1","doc-637a2ee83c11e91587846b6c-site-3","doc-637a2ee83c11e91587846b6c-site-4","doc-637a2ee83c11e91587846b6c-site-5","doc-637a2ee83c11e91587846b6c-site-6","doc-637a2ee83c11e91587846b6c-site-7","doc-637a2ee83c11e91587846b6c-site-8","doc-637a2ee83c11e91587846b6c-site-9","doc-637a2ee83c11e91587846b6c-site-11","doc-637a2ee83c11e91587846b6c-site-15","doc-637a2ee83c11e91587846b6c-site-16","doc-637a2ee83c11e91587846b6c-site-17","doc-637a2ee83c11e91587846b6c-site-18","doc-637a2ee83c11e91587846b6c-site-19","doc-637a2ee83c11e91587846b6c-site-20","doc-637a2ee83c11e91587846b6c-site-21","doc-637a2ee83c11e91587846b6c-site-22","doc-637a2ee83c11e91587846b6c-site-23","doc-637a2ee83c11e91587846b6c-site-24"],"anchor":[-2.460415,13.591665],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-6","basin_ids":[1040873650,1041145090,1041156950,1041624310,2040016230,2040021030,2040033320,2040439160,4040050300,4040548370,4040960480,5040015660,6040285990,7040021740,7040050930],"main_basin_ids":[1040008100,1040015850,1040020040,1040023300,2040006590,2040016230,2040021030,2040033320,4040007850,4040025450,4040050300,5040015660,6040007000,7040021740,7040050930]},{"id":"doc-8076a0055779011ea0baf081","title":"Multi-species approaches are necessary to optimize conservation: integrating a genetic algorithm with stochastic models in the Murray–Darling Basin (Australia)","display_title":"Optimizing Multi-Species Conservation in Murray–Darling Basin","doi":"https://doi.org/10.1017/s0376892926100496","theme":"Water management","color":"#eeabac","year":2026,"publication_date":"2026-07-08","authors":["Rupert Mathwin","Aaron Zecchin","Skye; id_orcid 0000-0001-8886-8426 Wassens","Matthew S. Gibbs","Kate Mason","Corey J.A. Bradshaw"],"summary":"This study developed and applied a novel optimization framework combining a genetic algorithm with a stochastic metapopulation ecological model to optimize environmental water allocation for conservation in a 70-km reach of the highly regulated Murray–Darling Basin, Australia. The approach balanced competing ecological needs of three priority species — Duma florulenta (a shrub), Eucalyptus camaldulensis (river red gum tree), and the southern bell frog (Ranoidea raniformis) — over a 25-year forecast window. Optimization using 16 wetland attributes improved ecological outcomes, doubling surviving frog populations without requiring additional water resources. The study demonstrates the utility of multi-species, multi-objective optimization as a decision-support tool for sustainable environmental water management in regulated river systems.","finding":"Multi-species optimization of environmental water allocation in the Murray–Darling Basin can double the surviving subpopulations of the southern bell frog and improve outcomes for key wetland plants using existing water resources, whereas focusing solely on the frog benefits that species but harms the plants, highlighting the risks of single-species allocation approaches and the value of multi-species models to balance competing conservation needs.","finding_evidence":{"passage_id":"p001-003","page":1,"quote":"To optimize water allocation for conservation we developed a novel optimization framework that integrated a genetic algorithm with an integrated systems model to refine near-optimal allocations that maximize outcomes for a shrub (Duma florulenta), tree (Eucalyptus camaldulensis) and tree frog (Ranoidea raniformis) in one of the world’s most regulated catchments: the Murray–Darling Basin (Australia). Our algorithm weighted 16 wetland attributes and optimized those weights to prioritize allocation over a 25-year forecast window. The best-performing solutions required no additional resources but improved outcomes for the two priority plants and doubled the surviving frog populations. Modifying the multi-user model to only consider R. raniformis improved outcomes for that species at the expense of the two plant species, highlighting the risks of allocating a shared resource through a single-species lens.","verified_in_page":true},"topics":["environmental water allocation","genetic algorithm optimization","stochastic metapopulation model","wetland ecology","multi-species conservation","environmental flow management","threatened species management","optimization in conservation"],"study_period":null,"locations":[{"name":"Murray–Darling Basin","kind":"basin","role":"study area representing one of the world’s most regulated catchments where the multi-species environmental water allocation optimization was applied","river_name":null,"geocode_query":"Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-004","page":2,"quote":"Several metaheuristic optimization approaches have been developed for this catchment, but they have typically focused on logistics and abiotic processes, such as scheduling (Szemis at al. 2014) and salinity management (Rana et al. 2008). The novelty of our approach was the optimization of 16 decision weights that corresponded to wetland attributes and prioritized annual environmental water allocations. Our objective function sought solutions that met the existing management plans for two wetland plant species (river red gum Eucalyptus camaldulensis and tangled lignum Duma florulenta), controlled the presence of aquatic predators (fish and crustaceans) and maximized the population response of a Vulnerable national priority species, the southern bell frog (Ranoidea raniformis). Materials and methods Our core ecosystem model simulated a 70-km reach in Australia’s second largest catchment: the Murray–Darling Basin (Fig. 1).","verified_in_page":true},"resolution_status":"resolved","coordinates":[145.517009,-31.194751],"precision":"Basin-wide study · representative map anchor","within_mdb":true,"catchment_id":43664394,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591010,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":78799.2,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-0","true_coordinates":[145.53027500000002,-31.135835],"spread_km":6.7},{"name":"70-km river reach between Locks 3 and 2, Murray–Darling Basin, Australia","kind":"reach","role":"precise spatial study site for applying and testing the integrated ecological and optimization model","river_name":"Murray River","geocode_query":"Murray River between Locks 3 and 2, Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-002","page":3,"quote":"Each solution was evaluated for all 100 hydrological scenarios to calculate the objective function (solution fitness): the mean surviving R. raniformis subpopulations (extant nodes) at the 25th modelled year. Although we stochastically resampled all model parameters, the 100 hydrological scenarios were each allocated a seed to ensure consistent evaluation. Our initial solutions were generated using Latin-hypercube sampling to ensure broad initial coverage of the solution space (a 16-dimensional hypervolume). Figure 1. The modelled reach lies within the Murray–Darling Basin (top plot, shaded blue). It is c. 70 km of river channel between two main channel-damming structures (Locks 3 and 2). There are 23 wetlands in the reach with historical records of Ranoidea raniformis, numbered incrementally from upstream to downstream.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.496433,-35.361041],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43676891,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-1","true_coordinates":[143.5663889650001,-35.34249995499994],"spread_km":6.7},{"name":"Wetlands 1 to 23 within the 70-km study reach, Murray–Darling Basin, Australia","kind":"wetland","role":"nodes in the ecosystem model representing individual off-channel wetlands used for environmental water allocation and habitat management","river_name":null,"geocode_query":"Wetlands 1 to 23, Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Our ecosystem model follows Mathwin et al. (2024) and can be conceptualized as three integrated components: wetlands, biota and hydrology. For wetlands, we created a node for each of the 23 off-channel wetlands in the reach, incorporating sill height (lowest river height that fills the wetland), surface area when full (expressed as a size category: medium, n = 3; large, n = 4; or very large, n = 17), volume when full (calculated in QGIS 3.36.3 from highresolution digital elevation models), flow conditions for the wetland to remain full for the year and a matrix of dispersal distances between each node (incorporating movement rules and landscape resistance). For biota, we populated the ecosystem model with two vegetation management priorities (Fig. 1). Wetlands 5 and 7 have management plans for D. florulenta that fill wetlands 3 years in every 10. Additional inundation is acceptable but can impact D. florulenta and is generally avoided.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.736849,-35.352879],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-2","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Wetlands 5 and 7, Murray–Darling Basin, Australia","kind":"wetland","role":"priority wetlands with management plans for Duma florulenta requiring water delivery 3 years every 10","river_name":null,"geocode_query":"Wetlands 5 and 7, Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Our ecosystem model follows Mathwin et al. (2024) and can be conceptualized as three integrated components: wetlands, biota and hydrology. For wetlands, we created a node for each of the 23 off-channel wetlands in the reach, incorporating sill height (lowest river height that fills the wetland), surface area when full (expressed as a size category: medium, n = 3; large, n = 4; or very large, n = 17), volume when full (calculated in QGIS 3.36.3 from highresolution digital elevation models), flow conditions for the wetland to remain full for the year and a matrix of dispersal distances between each node (incorporating movement rules and landscape resistance). For biota, we populated the ecosystem model with two vegetation management priorities (Fig. 1). Wetlands 5 and 7 have management plans for D. florulenta that fill wetlands 3 years in every 10. Additional inundation is acceptable but can impact D. florulenta and is generally avoided.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.698273,-35.37926],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-3","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Wetlands 4, 8, 9, 11, 13, 14 and 15, Murray–Darling Basin, Australia","kind":"wetland","role":"priority wetlands for Eucalyptus camaldulensis with prescribed delivery after 2 dry years","river_name":null,"geocode_query":"Wetlands 4, 8, 9, 11, 13, 14 and 15, Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-006","page":2,"quote":"Our ecosystem model follows Mathwin et al. (2024) and can be conceptualized as three integrated components: wetlands, biota and hydrology. For wetlands, we created a node for each of the 23 off-channel wetlands in the reach, incorporating sill height (lowest river height that fills the wetland), surface area when full (expressed as a size category: medium, n = 3; large, n = 4; or very large, n = 17), volume when full (calculated in QGIS 3.36.3 from highresolution digital elevation models), flow conditions for the wetland to remain full for the year and a matrix of dispersal distances between each node (incorporating movement rules and landscape resistance). For biota, we populated the ecosystem model with two vegetation management priorities (Fig. 1). Wetlands 5 and 7 have management plans for D. florulenta that fill wetlands 3 years in every 10. Additional inundation is acceptable but can impact D. florulenta and is generally avoided.","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.689529,-35.419679],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-4","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Wetlands 18, 21, 22 and 23, Murray–Darling Basin, Australia","kind":"wetland","role":"downstream wetlands in the study reach which typically did not receive environmental water in the optimized solutions due to model prioritization","river_name":null,"geocode_query":"Wetlands 18, 21, 22 and 23, Murray–Darling Basin, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"This resulted in an average of four R. raniformis subpopulations in the 25th modelled year (fitness). The simulated fitness of the evaluated solutions ranged from 3.63 to 7.62 (Fig. 2), increased the average count of annual water deliveries by 1.17–1.97 (58.5–98.5%) above the control scenario and increased the annual volume delivered by 56–172 ML per annum (34.1–104.8%; Fig. 2). Both total volume and the number of deliveries showed a positive linear relationship with solution fitness (Fig. 2); however, fitness varied broadly at each volume (and delivery) increment (Fig. 2). The 500 best-performing solutions had a mean fitness of 7.6 without exceeding the maximum prescriptions for D. florulenta or E. camaldulensis (Fig. 3). These solutions prioritized delivery to wetlands 3, 4, 5, 7, 8 and 9, whereas the 500 worst-performing solutions spread the available water more broadly across wetlands 1–15 (Fig. 3).","verified_in_page":true},"resolution_status":"resolved","coordinates":[143.714707,-35.455223],"precision":"Named-place geocode · approximate map anchor","within_mdb":true,"catchment_id":43662032,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Murray River, Australia","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-8076a0055779011ea0baf081-site-5","true_coordinates":[143.7620269497911,-35.40926030372019],"spread_km":6.7},{"name":"Flinders University High-Performance Computing Array \"DeepThought\", Australia","kind":"facility","role":"computing infrastructure used to run the genetic algorithm optimization computing simulations","river_name":null,"geocode_query":"Flinders University, Adelaide, South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"In each subsequent generation, we allowed the 100 fittest solutions an equal probability of breeding (roulette-wheel selection with replacement) using one-point crossover (selecting a single point along the 16-dimensional vector, after which all subsequent weights were exchanged) to generate two novel solutions (offspring; Goldberg 1989). Each element in the offspring vectors had an independent probability of 1/16 to ‘mutate’ (modify the attribute weight; Eiben & Smith 2015). Mutation extent was resampled from a symmetric triangular distribution, initially ranging from −2.6 to 2.6 and narrowing to −0.6 to 0.6 over 250 generations. This prioritized early exploration of the solution space (reducing the probability of converging on local maxima) and later generations prioritized exploitation, convergence and polishing. Mutations were deflected from the extremities to maintain the interval [−10, 10] (see Appendix S1 Fig. S1 for an integrated conceptual diagram).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder reported only a partial name match"}],"limitations":["Latitude and longitude coordinates were not provided for specific wetlands or study reach; geographic locations are identified by name and region only.","Study focuses on a 70-km reach within the Murray–Darling Basin without precise geospatial coordinates for individual wetlands, limiting exact spatial referencing.","Information on hydrological conditions is modeled and stochastic rather than observed, limiting temporal validation.","Details on delivery cost, soil types, and 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BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":true,"cluster":"cluster-0","basin_ids":[5040591010,5040591050],"main_basin_ids":[5040073410]},{"id":"doc-d381f8d1c910144ae6313a08","title":"The functional effect of gape size on intraspecific body condition is variable across trophic groups and resource seasonality","display_title":"Gape Size and Body Condition in Northern Australia Fish","doi":"https://doi.org/10.1007/s10641-026-01803-1","theme":"Flow & ecology","color":"#80edc2","year":2026,"publication_date":"2026-02-05","authors":["Osmar J. Luiz","R. Keller Kopf","Julian D. Olden","David A. Crook","Mark J. Kennard","Thor Saunders","Michael M. Douglas","Dion Wedd","Alison J. King"],"summary":"This study investigates how relative gape size affects body condition (Kn) across 15 tropical freshwater fish species from northern Australia, spanning four trophic groups (predators, micro-carnivores, omnivores, herbivores/detritivores). Using linear mixed-effects models on data collected from 14 sites in four river basins near Darwin, Northern Territory, the research reveals that larger gape sizes are linked to better body condition primarily in predators and micro-carnivores, especially during the mid- and late-dry seasons when food resources are scarce. In contrast, omnivores and herbivores/detritivores show weaker or no consistent relationships, reflecting their feeding strategies and diets. The study highlights the context-dependent functional role of gape size shaped by trophic position and hydrological seasonality in tropical river ecosystems.","finding":"Larger gape sizes significantly enhance fish body condition in predators and micro-carnivores during resource-limited dry seasons, but this functional advantage diminishes in omnivores and herbivores/detritivores.","finding_evidence":{"passage_id":"p006-001","page":6,"quote":"Results A total of 4254 fish across the 15 taxa was collected for analysis. Our analysis revealed that the strength of the relationship between gape size and condition factor (Kn) varied significantly across trophic groups, with predators exhibiting particularly strong positive trends, followed closely by micro-carnivores, which also showed positive trends but of slightly lesser magnitude. Omnivores and herbivores/detritivores exhibited progressively weaker relationships, respectively. These findings indicate that the influence of gape size on body condition decreases along a gradient from diets rich in high-calorie prey (carnivores) to diets composed primarily of lower-calorie food sources (herbivores/detritivores) (Fig. 2, Tables 1, 2). When analysed at the species level, predators and micro-carnivores consistently showed positive trends and the most pronounced increases in condition factor with increasing gape size (Fig. S1a, b).","verified_in_page":true},"topics":["functional morphology","body condition","trophic ecology","seasonal hydrology","trait-fitness relationships","resource availability","tropical freshwater fish","river ecology"],"study_period":"2016-2017","locations":[{"name":"Four river basins in north-western Northern Territory, near Darwin","kind":"basin","role":"study area for fish sampling across hydrological conditions","river_name":null,"geocode_query":"Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-000","page":4,"quote":"41 Page 4 of 14 Environ Biol Fish (2026) 109:41 Vol:. (1234567890) were selected to represent a range of hydrological conditions with varying flood durations and frequencies of high-water events during the wet season (King et al. 2025). Field sampling occurred on four occasions: the late-dry season in 2016 (October–November 2016), early-dry season (May–June 2017), middry season (August 2017), and the late-dry season in 2017 (late September–November 2017). To capture a substantial portion of the region’s fish diversity, we targeted 15 common species from 11 families and 5 orders, chosen for their varied life-history strategies relative to different environmental regimes (King et al. 2013; Sternberg and Kennard 2014). Fish were collected using standardised boatmounted (Engineering Technical Services Model MBS-2DHP-SRC; www.etselectrofishing.com) or backpack (Smith Root Model LR20B; www.smithroot.com) electrofishing unit.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Adelaide River fish sampling site, Northern Territory, Australia","kind":"station","role":"location of hydrological discharge data used in study","river_name":"Adelaide River","geocode_query":"Adelaide River, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p004-003","page":4,"quote":"The timing of fish sampling periods is indicated with black bars. b Location of fish sampling sites (green circles) in the four study rivers. Inset map shows the location of the study region in northern Australia. Discharge data shown in a sourced from stream gauge located at the Adelaide River fish sampling site indicated in b with crossed green circle","verified_in_page":true},"resolution_status":"resolved","coordinates":[131.1064307,-13.2394862],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":102217152,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Adelaide River NT 0846, Australia","basin_id":5040061670,"main_basin_id":5040061670,"basin_region":"Australasia","basin_area_km2":205417.1,"hydrology_source":"geofabric","site_id":"doc-d381f8d1c910144ae6313a08-site-1"}],"limitations":["No specific coordinates provided for sampling sites beyond regional descriptions","Broad regional scale location limits fine spatial resolution"],"river_ids":[],"site_ids":["doc-d381f8d1c910144ae6313a08-site-1"],"anchor":[131.1064307,-13.2394862],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040061670],"main_basin_ids":[5040061670]},{"id":"doc-dac723a63ac36bed1ddfa9fc","title":"Fluorinated pesticides are a growing source of PFAS contamination in the environment: An Australian perspective on use, fate and ecotoxicity","display_title":"Fluorinated Pesticides as Emerging PFAS Sources","doi":"https://doi.org/10.1016/j.ecoenv.2026.120038","theme":"Other science","color":"#d1b5e7","year":2026,"publication_date":"2026-03-23","authors":["Md Khairul Alam","Bin Qian","Greg B. Davis","Mike Donn","Anand Kumar","Adrian Trinchi","Ilias Louis Kyratzis","Jason K. Kirby","Minbo Lan","Hongli Zhao","Gavin E. Collis","Zongli Xie","Ahmad E. Kandjani","John L. Rayner"],"summary":"This paper reviews the use, environmental fate, and ecotoxicology of fluorinated pesticides in Australia, emphasizing their role as emerging persistent and bioaccumulative contaminants akin to PFAS compounds. It synthesizes data on 91 approved fluorinated pesticides, their degradation into persistent metabolites like trifluoroacetic acid (TFA), environmental transport, bioaccumulation in plants, ecotoxicological impacts, and regulatory challenges. The study highlights knowledge gaps and calls for targeted research on environmental behavior, toxicological thresholds, and sustainable management in Australian contexts.","finding":"Fluorinated pesticides approved for use in Australia transform into persistent degradation products such as trifluoroacetic acid (TFA), which accumulate in surface and groundwater and can be taken up by crops, posing long-term ecological and human health risks.","finding_evidence":{"passage_id":"p003-008","page":3,"quote":"Although this pesticide was banned in 2017 under Australia’s “National Phase-Out of PFOS,” its persistence highlights broader environmental concerns linked to fluorinated pesticides in both urban and agricultural settings. Research by Gorji et_al. also reports the presence of ultrashort-chain PFAS, such as perfluoropropionic acid (PFPrA), perfluorobutanesulfonate (PFBS) and trifluoroacetic acid (TFA), in Australian soils (Gorji et al., 2024) and groundwater. Joerss et al. (2024) attribute much of this contamination to the widespread use of PPPs. However, gaps remain in understanding the extent of PFAS use and persistence, complicating mitigation efforts (Bolan et al., 2021). The lack of comprehensive data on fluorinated degradation products from pesticides highlights the need for improved monitoring and regulatory transparency (Donley et al., 2024).","verified_in_page":true},"topics":["fluorinated pesticides","ecotoxicology","environmental fate","plant uptake","persistence","perfluoroalkyl substances","degradation products","water contamination"],"study_period":null,"locations":[{"name":"Australia","kind":"region","role":"study region and regulatory context for fluorinated pesticide use and PFAS contamination","river_name":null,"geocode_query":"Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p002-005","page":2,"quote":"Australian and regulatory context In Australia, the use of fluorinated pesticides in agriculture has contributed to the contamination of agricultural land with PFAS, including groundwater, surface water and biological samples (Gorji et al., 2024; Goosey and Harrad, 2011), leading to contamination of drinking water supplies (Hale et al., 2017; Wallis et al., 2022) and bioaccumulation in food chains (Mei et al., 2021; Qian et al., 2024; Evangelou and Robinson, 2022). Globally, until February 2024, 202 out of 1934 entries in the Pesticide Properties Database (Lewis et al., 2016; PPDB, 2025), contain a -CF3 group (Joerss et al., 2024), making them potential precursors of TFA. Of these substances, 39 are approved for use in the European Union, 48 in the United States and 68 in China.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"South Australia","kind":"region","role":"site of soil toxicity study for fluorinated pesticide fipronil impacting nitrogen cycle","river_name":null,"geocode_query":"South Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"However, their prolonged environmental persistence poses risks, as these chemicals resist natural degradation processes, leading to accumulation in the environment (Libenson et al., 2024; Nascimento et al., 2018; Whitehead and Peaslee, 2023). Table 1 and S1 (supplementary materials) outline the 91 fluorinated pesticides approved for use in Australia along with key physicochemical properties. Evidence of fluorinated pesticide toxicity was examined through the impact of Fipronil on Australian soils (Sim et al., 2022). They reported adverse effects on nitrogen cycle related microbial functions, including reduced NAG (N-acetyl-beta-D-glucosaminidase) enzymatic activity and potential nitrification in Hart soil (South Australia), though these effects were not observed in other soils (Sim et al., 2022). This variability suggests that fluorinated pesticide toxicity is soil specific.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Western Australia","kind":"region","role":"location of Commonwealth Scientific and Industrial Research Organisation (CSIRO) Environment Research Unit contributing to study","river_name":null,"geocode_query":"Waterford, Western Australia, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Rayner b a Commonwealth Scientific and Industrial Research Organisation (CSIRO), Environment Research Unit (ERU), 7 Conlon St, Waterford, Western Australia 6152, Australia b CSIRO, ERU, 7 Conlon St, Waterford, Western Australia 6152, Australia c Paul Scherrer Institut, PSI Schweiz, Forschungsstrasse 111, Villigen 5232, Switzerland d CSIRO, Manufacturing Research Unit (MRU), Research Way, Clayton, Melbourne, Victoria 3168, Australia e CSIRO, MRU, Research Way, Clayton, Melbourne, Victoria 3168, Australia f CSIRO, ERU, Urrbrae, South Australia 5064, Australia g Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, People's Republic of China ARTICLE INFO Edited by Dr Hyo-Bang Moon Keywords: Fluorinated Pesticides Lipophilicity Metabolites Degradation Remediation TFA ABSTRACT Fluorinated pesticides, often referred to as “forever pesticides,” are contaminants of emerging concern due to their persistent and bio-accumulative behaviour in the environment.","verified_in_page":true},"resolution_status":"unmapped","coordinates":null,"precision":"Kept as evidence only · too broad, affiliated, or not a mappable place","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint."},{"name":"Victoria","kind":"region","role":"location of CSIRO Manufacturing Research Unit involved in research","river_name":null,"geocode_query":"Clayton, Victoria, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Rayner b a Commonwealth Scientific and Industrial Research Organisation (CSIRO), Environment Research Unit (ERU), 7 Conlon St, Waterford, Western Australia 6152, Australia b CSIRO, ERU, 7 Conlon St, Waterford, Western Australia 6152, Australia c Paul Scherrer Institut, PSI Schweiz, Forschungsstrasse 111, Villigen 5232, Switzerland d CSIRO, Manufacturing Research Unit (MRU), Research Way, Clayton, Melbourne, Victoria 3168, Australia e CSIRO, MRU, Research Way, Clayton, Melbourne, Victoria 3168, Australia f CSIRO, ERU, Urrbrae, South Australia 5064, Australia g Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, People's Republic of China ARTICLE INFO Edited by Dr Hyo-Bang Moon Keywords: Fluorinated Pesticides Lipophilicity Metabolites Degradation Remediation TFA ABSTRACT Fluorinated pesticides, often referred to as “forever pesticides,” are contaminants of emerging concern due to their persistent and bio-accumulative behaviour in the environment.","verified_in_page":true},"resolution_status":"resolved","coordinates":[147.399124,-37.125465],"precision":"Named river context · approximate map anchor","within_mdb":true,"catchment_id":43677509,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040591050,"main_basin_id":5040073410,"basin_region":"Australasia","basin_area_km2":347168.9,"hydrology_source":"geofabric","site_id":"doc-dac723a63ac36bed1ddfa9fc-site-3","true_coordinates":[147.45792930100004,-37.08805552399997],"spread_km":6.7},{"name":"Shanghai","kind":"region","role":"location of Shanghai Key Laboratory of Functional Materials Chemistry contributing research","river_name":null,"geocode_query":"Shanghai, China","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-001","page":1,"quote":"Rayner b a Commonwealth Scientific and Industrial Research Organisation (CSIRO), Environment Research Unit (ERU), 7 Conlon St, Waterford, Western Australia 6152, Australia b CSIRO, ERU, 7 Conlon St, Waterford, Western Australia 6152, Australia c Paul Scherrer Institut, PSI Schweiz, Forschungsstrasse 111, Villigen 5232, Switzerland d CSIRO, Manufacturing Research Unit (MRU), Research Way, Clayton, Melbourne, Victoria 3168, Australia e CSIRO, MRU, Research Way, Clayton, Melbourne, Victoria 3168, Australia f CSIRO, ERU, Urrbrae, South Australia 5064, Australia g Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, People's Republic of China ARTICLE INFO Edited by Dr Hyo-Bang Moon Keywords: Fluorinated Pesticides Lipophilicity Metabolites Degradation Remediation TFA ABSTRACT Fluorinated pesticides, often referred to as “forever pesticides,” are contaminants of emerging concern due to their persistent and bio-accumulative behaviour in the environment.","verified_in_page":true},"resolution_status":"resolved","coordinates":[121.473701,31.230416],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Shanghai, China","basin_id":4040009890,"main_basin_id":4040009890,"basin_region":"Central and South-East Asia","basin_area_km2":212335.4,"hydrology_source":"hydrobasins","site_id":"doc-dac723a63ac36bed1ddfa9fc-site-4"}],"limitations":["Coordinates for specific field sites are not provided or unavailable.","Study areas are described at regional/national scale rather than detailed localities.","Environmental fate and toxicity data rely on synthesized literature with some knowledge gaps and incomplete toxicological benchmarks."],"river_ids":["43640269","43643828","43645307"],"site_ids":["doc-dac723a63ac36bed1ddfa9fc-site-3","doc-dac723a63ac36bed1ddfa9fc-site-4"],"anchor":[121.473701,31.230416],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-4","basin_ids":[4040009890,5040591050],"main_basin_ids":[4040009890,5040073410]},{"id":"doc-e7a3ea1cefeba5d3d19ca18b","title":"Environmental Flows Should Be Climate-Adapted and Can Reduce Downstream Impacts of River Flow Modification","display_title":"Impacts of River Flow Changes on Roper River Estuary Biota","doi":"https://doi.org/10.1007/s12237-026-01746-x","theme":"Flow & ecology","color":"#80edc2","year":2026,"publication_date":"2026-05-28","authors":["R.A. Kenyon","E.P. Plaganyi","Laura K. Blamey","Roy Aijun Deng","Justin D. Hughes"],"summary":"The study used an integrated ecological model (MICE) to quantify the impacts of various water resource development scenarios and future climate changes on key estuarine and riverine species in the Roper River catchment, Northern Territory, Australia. The research examined effects of dams, water extraction, groundwater harvest, and climate change scenarios on species such as prawns, barramundi, mud crabs, largetooth sawfish, mangroves, and seagrass. It highlighted nonlinear ecological responses to flow modifications, with timing and volume of water extraction affecting sensitive species differently. The study evaluated environmental flow mitigation strategies to reduce negative impacts and emphasized the importance of accounting for climate resilience and species-specific flood flow needs in water resource management.","finding":"Water extraction from the Roper River catchment has a larger negative impact on downstream estuarine biota biomass and fishery catch than dams or groundwater harvest, with species-specific sensitivities such as sawfish and mangroves showing significant declines, especially under combined water resource development and dry future climate scenarios.","finding_evidence":{"passage_id":"p008-001","page":8,"quote":"Downstream Impacts by Type of Flow Alteration We explored the impact of altered river flows on downstream estuarine ecosystems due to water resource development (WRD), future climate and future climate combined with WRD. Overall, largetooth sawfish and mangroves were most sensitive to changes in altered river flows, followed by common banana prawns, mud crabs, barramundi and seagrass (Fig. 3a). Of the three WRD scenario types, the greatest impact on the biomass (and abundance) of biota was generally found to be due to water extraction from rivers, followed by impoundments (dams) and then groundwater harvest (Fig. 3a, Fig. S7.1;Table S7.1) (see impact rationale and summary in Table 3). For mangroves, mud crab, prawns and sawfish, impacts from water extraction were at least double compared to dams and groundwater harvest (Fig. 3a).","verified_in_page":true},"topics":["river flow alteration","environmental flows","ecosystem modelling","fisheries ecology","climate change impacts","tropical estuaries","species-specific sensitivity","water resource management"],"study_period":null,"locations":[{"name":"Roper River catchment","kind":"basin","role":"study area and main river catchment subject to water resource development and flow modification scenarios","river_name":"Roper River","geocode_query":"Roper River catchment, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"We use this model to show that (1) these impacts could be exacerbated under future climate regimes and hence water planning needs to be climate-adapted and (2) application of environmental flows as mitigation measures,such as a guaranteed season-first flow volume, raising threshold levels of river flow prior to pump initiation, and environmental flows from dams, can reduce the downstream impacts of river flow modification. Methods Study Area The Roper River is a large perennial river in the Northern Territory, Australia, draining an 82,000 km2 catchment into the western Gulf of Carpentaria (Fig. 2a). Located in the wet–dry tropics, the region experiences prolonged high temperatures (e.g. 30 °C for many months of the year) and highly seasonal rainfall. The wet season (November to April) accounts for ~96% of the 792 mm average annual rainfall and 97% of annual runoff, with 80% of runoff occurring between January-March (Hughes et al., 2017, 2023; Petheram et al., 2018).","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.237538,-14.886286],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":120769459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-e7a3ea1cefeba5d3d19ca18b-site-0","true_coordinates":[133.2375381085312,-14.94628617529888],"spread_km":6.7},{"name":"Roper River estuary and coastal waters","kind":"region","role":"downstream estuarine and marine ecosystem influenced by river flows and habitat for key species modeled","river_name":"Roper River","geocode_query":"Roper River estuary, Northern Territory, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-005","page":3,"quote":"Modelled historical (baseline) river flows were developed by (Hughes et al., 2023) for the Roper River as part of a significant Australian initiative, the ‘Water Resource Assessment for the Roper Catchment’ (Watson et al., 2023) and made available to our study. Altered river flow scenarios were also developed including flow modification due to water resource development (dams, water extraction, or groundwater harvest), future climate and a combination of water resource development and future climate. The baseline daily end-of-system flow outputs were linked as a key driver of recruitment and/or movement to species in our model, along with other secondary environmental drivers (Fig. 2b). For fishery biota (prawns, barramundi and mud crab) we linked flow through fitting to catch data and estimating parameters in flow-biota relationships. For non-fishery biota we developed mechanistic flow relationships based on literature and created","verified_in_page":true},"resolution_status":"resolved","coordinates":[133.175437,-14.946286],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120769459,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Roper River, Elsey NT 0852, Australia","basin_id":5040064130,"main_basin_id":5040064130,"basin_region":"Australasia","basin_area_km2":77474.5,"hydrology_source":"geofabric","site_id":"doc-e7a3ea1cefeba5d3d19ca18b-site-1","true_coordinates":[133.2375381085312,-14.94628617529888],"spread_km":6.7},{"name":"Western Gulf of Carpentaria","kind":"region","role":"marine region receiving outflow from Roper River, containing coastal habitats modeled for flow impacts","river_name":null,"geocode_query":"Western Gulf of Carpentaria, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p003-003","page":3,"quote":"We use this model to show that (1) these impacts could be exacerbated under future climate regimes and hence water planning needs to be climate-adapted and (2) application of environmental flows as mitigation measures,such as a guaranteed season-first flow volume, raising threshold levels of river flow prior to pump initiation, and environmental flows from dams, can reduce the downstream impacts of river flow modification. Methods Study Area The Roper River is a large perennial river in the Northern Territory, Australia, draining an 82,000 km2 catchment into the western Gulf of Carpentaria (Fig. 2a). Located in the wet–dry tropics, the region experiences prolonged high temperatures (e.g. 30 °C for many months of the year) and highly seasonal rainfall. The wet season (November to April) accounts for ~96% of the 792 mm average annual rainfall and 97% of annual runoff, with 80% of runoff occurring between January-March (Hughes et al., 2017, 2023; Petheram et al., 2018).","verified_in_page":true},"resolution_status":"resolved","coordinates":[139.560782,-16.419356],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":120771125,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gulf Of Carpentaria, Wellesley Islands QLD, Australia","basin_id":5040064140,"main_basin_id":5040064140,"basin_region":"Australasia","basin_area_km2":178643.3,"hydrology_source":"geofabric","site_id":"doc-e7a3ea1cefeba5d3d19ca18b-site-2","true_coordinates":[139.5607819825435,-16.47935646596641],"spread_km":6.7},{"name":"Mitchell River","kind":"river","role":"comparison river catchment with perennial flow used to support mangrove extent patterns","river_name":"Mitchell River","geocode_query":"Mitchell River, Gulf of Carpentaria region, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"Moreover, the largest impacts were observed during multiple wet years (e.g., 2000s), when mangroves were in a growth phase, suggesting that water extraction during critical times in such periods may constrain recovery potential, hence estuarine habitat resilience. Model predictions were consistent with observed differencesin mangrove extent between Gulf of Carpentaria rivers with perennial and ephemeral flow regimes. For example, the Mitchell River (mean annual flow: 15,570 GL) supports 56.9 km2 of estuarine mangroves, while the ephemeral Gilbert River (mean annual flow: 3,706 GL) supports only 7.2 km2 (Burford & Faggotter, 2021). These estimates support the conclusion that anthropogenic flow modification reduces estuarine mangrove biomass, particularly in systems with naturally lower flows. All extraction scenarios (100–660 GL y−1) reduced predicted biomass and catch of banana prawns, barramundi, and mud crabs.","verified_in_page":true},"resolution_status":"resolved","coordinates":[142.302826,-15.919366],"precision":"Named river context · approximate map anchor","within_mdb":false,"catchment_id":123317213,"provider":"WaterAI Geofabric API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-e7a3ea1cefeba5d3d19ca18b-site-3"},{"name":"Gilbert River","kind":"river","role":"comparison river catchment with ephemeral flow regime cited for mangrove extent contrast","river_name":"Gilbert River","geocode_query":"Gilbert River, Gulf of Carpentaria region, Queensland, Australia","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-001","page":15,"quote":"Moreover, the largest impacts were observed during multiple wet years (e.g., 2000s), when mangroves were in a growth phase, suggesting that water extraction during critical times in such periods may constrain recovery potential, hence estuarine habitat resilience. Model predictions were consistent with observed differencesin mangrove extent between Gulf of Carpentaria rivers with perennial and ephemeral flow regimes. For example, the Mitchell River (mean annual flow: 15,570 GL) supports 56.9 km2 of estuarine mangroves, while the ephemeral Gilbert River (mean annual flow: 3,706 GL) supports only 7.2 km2 (Burford & Faggotter, 2021). These estimates support the conclusion that anthropogenic flow modification reduces estuarine mangrove biomass, particularly in systems with naturally lower flows. All extraction scenarios (100–660 GL y−1) reduced predicted biomass and catch of banana prawns, barramundi, and mud crabs.","verified_in_page":true},"resolution_status":"resolved","coordinates":[141.399656052937,-16.77887713722385],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":122859581,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Gilbert River, Queensland, Australia","basin_id":5040049720,"main_basin_id":5040049720,"basin_region":"Australasia","basin_area_km2":305832.2,"hydrology_source":"geofabric","site_id":"doc-e7a3ea1cefeba5d3d19ca18b-site-4"}],"limitations":["Model parameter uncertainty higher for largetooth sawfish due to limited biological data.","Lack of representation of secondary impacts such as sediment, water quality, and connectivity barriers in model.","Regional model domain spans broad catchment without fine-scale spatial resolution for flow and habitat variation.","Climate projections bear considerable uncertainty due to variability in global models for northern Australia rainfall patterns."],"river_ids":["ne-1081","ne-471"],"site_ids":["doc-e7a3ea1cefeba5d3d19ca18b-site-0","doc-e7a3ea1cefeba5d3d19ca18b-site-1","doc-e7a3ea1cefeba5d3d19ca18b-site-2","doc-e7a3ea1cefeba5d3d19ca18b-site-3","doc-e7a3ea1cefeba5d3d19ca18b-site-4"],"anchor":[139.560782,-16.419356],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-0","basin_ids":[5040049720,5040064130,5040064140],"main_basin_ids":[5040049720,5040064130,5040064140]},{"id":"doc-ecf9efc62505ee033aab3851","title":"Quantifying the response of water and carbon balances to land cover and climate extremes across Germany","display_title":"Water & Carbon Responses to Land Cover in Germany","doi":"https://doi.org/10.5194/hess-30-4157-2026","theme":"Land & ecosystems","color":"#c8df98","year":2026,"publication_date":"2026-07-02","authors":["Karim Pyarali","Lulu Zhang","Ning Liu","Abdulhakeem Al-Qubati","Ge Sun"],"summary":"This study applies the WaSSI ecosystem model to quantify water discharge and carbon sequestration across Germany from 2001 to 2019, linking land cover and climate extremes to ecosystem services. Germany averages 84.86 billion m3 annual discharge and 106.03 Tg carbon sequestration. Eastern states are drier due to higher evapotranspiration losses. Cropland, urban areas, and evergreen needle forests supply most water, while deciduous broadleaf, mixed, and evergreen needle forests sequester most carbon. Minor land covers like wetlands disproportionately contribute to carbon sequestration. Ecosystem services show strong sensitivity to droughts and floods, with previous-year water buffers mitigating drought impact. The validated WaSSI model enables large-scale assessment useful for informing land management and climate adaptation strategies in Germany.","finding":"The study finds that eastern German states are comparatively drier than the rest of the country because most of their precipitation is lost as evapotranspiration, limiting available water supply in the region.","finding_evidence":{"passage_id":"p012-000","page":12,"quote":"12 239 had higher ET, as shown in Fig. 4a. On an annual scale, Bavaria and Lower Saxony experienced significant ET losses. The absolute losses were 39.5 billion m3 yr-1 in Bavaria and 25.7 billion m3 yr-1 240 in Lower Saxony. Bavaria had a 241 smaller fraction of its precipitation lost as ET (0.3 to 0.9) compared to Lower Saxony (0.5 to 0.9). Across Germany 242 the eastern states lost the largest share of their precipitation as ET (0.8 – 1.0), leading to a very limited available water 243 supply in the region, shown in Fig. 4b. Furthermore, to understand whether ET is limited by energy or water 244 availability, we estimated ET:PET ratio across Germany. The actual ET of watersheds near the Alps exceeds the PET. 245 These watersheds receive more precipitation compared to the rest and thus energy limits the ET values, while the 246 water availability limits ET for the rest parts of Germany (Fig. 4c).","verified_in_page":true},"topics":["carbon sequestration","evapotranspiration","water yield","land cover","climate extremes","ecosystem services","model validation","drought impact"],"study_period":"2001-2019","locations":[{"name":"Germany","kind":"region","role":"study area, national scale modelling and analysis","river_name":null,"geocode_query":"Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p001-000","page":1,"quote":"1 1 Quantifying the response of water and carbon balances to land 2 cover and climate extremes across Germany. Karim Pyarali1,2* , Lulu Zhang2* , Ning Liu4 , Abdulhakeem Al-Qubati1,2 and Ge Sun3* 3 1 4 Technische Universität Dresden, Helmholtzstr. 10, 01069, Dresden, Germany. 5 2United Nations University, Institute for Integrated Management of Material Fluxes and of Resources, Ammonstrasse 6 74, 01067, Dresden, Germany. 3 7 Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, Research 8 Triangle Park, NC 27709, USA. 4 9 CSIRO Environment, Canberra ACT 2601. 10 *Corresponding authors: Karim Pyarali (karim.pyarali@tu-dresden.de); Lulu Zhang (lzhang@unu.edu); Ge Sun 11 (Ge.Sun@usda.gov) 12 https://doi.org/10.5194/egusphere-2025-1629 Preprint. Discussion started: 4 August 2025 c Author(s) 2025. CC BY 4.0 License.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Eastern states of Germany (Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Saxony, Thuringia)","kind":"region","role":"drier part of study area with high evapotranspiration and water scarcity limiting water supply","river_name":null,"geocode_query":"Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Saxony, Thuringia, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"12 239 had higher ET, as shown in Fig. 4a. On an annual scale, Bavaria and Lower Saxony experienced significant ET losses. The absolute losses were 39.5 billion m3 yr-1 in Bavaria and 25.7 billion m3 yr-1 240 in Lower Saxony. Bavaria had a 241 smaller fraction of its precipitation lost as ET (0.3 to 0.9) compared to Lower Saxony (0.5 to 0.9). Across Germany 242 the eastern states lost the largest share of their precipitation as ET (0.8 – 1.0), leading to a very limited available water 243 supply in the region, shown in Fig. 4b. Furthermore, to understand whether ET is limited by energy or water 244 availability, we estimated ET:PET ratio across Germany. The actual ET of watersheds near the Alps exceeds the PET. 245 These watersheds receive more precipitation compared to the rest and thus energy limits the ET values, while the 246 water availability limits ET for the rest parts of Germany (Fig. 4c).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Bavaria, Germany","kind":"region","role":"southern German state showing significant evapotranspiration losses and carbon uptake","river_name":null,"geocode_query":"Bavaria, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"12 239 had higher ET, as shown in Fig. 4a. On an annual scale, Bavaria and Lower Saxony experienced significant ET losses. The absolute losses were 39.5 billion m3 yr-1 in Bavaria and 25.7 billion m3 yr-1 240 in Lower Saxony. Bavaria had a 241 smaller fraction of its precipitation lost as ET (0.3 to 0.9) compared to Lower Saxony (0.5 to 0.9). Across Germany 242 the eastern states lost the largest share of their precipitation as ET (0.8 – 1.0), leading to a very limited available water 243 supply in the region, shown in Fig. 4b. Furthermore, to understand whether ET is limited by energy or water 244 availability, we estimated ET:PET ratio across Germany. The actual ET of watersheds near the Alps exceeds the PET. 245 These watersheds receive more precipitation compared to the rest and thus energy limits the ET values, while the 246 water availability limits ET for the rest parts of Germany (Fig. 4c).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Lower Saxony, Germany","kind":"region","role":"northwestern German state with significant evapotranspiration losses and water yield","river_name":null,"geocode_query":"Lower Saxony, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p012-000","page":12,"quote":"12 239 had higher ET, as shown in Fig. 4a. On an annual scale, Bavaria and Lower Saxony experienced significant ET losses. The absolute losses were 39.5 billion m3 yr-1 in Bavaria and 25.7 billion m3 yr-1 240 in Lower Saxony. Bavaria had a 241 smaller fraction of its precipitation lost as ET (0.3 to 0.9) compared to Lower Saxony (0.5 to 0.9). Across Germany 242 the eastern states lost the largest share of their precipitation as ET (0.8 – 1.0), leading to a very limited available water 243 supply in the region, shown in Fig. 4b. Furthermore, to understand whether ET is limited by energy or water 244 availability, we estimated ET:PET ratio across Germany. The actual ET of watersheds near the Alps exceeds the PET. 245 These watersheds receive more precipitation compared to the rest and thus energy limits the ET values, while the 246 water availability limits ET for the rest parts of Germany (Fig. 4c).","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a administrative_area_level_1/political centroid"},{"name":"Elbe River basin","kind":"basin","role":"region experiencing major impact from the 2013 floods on ecosystem services","river_name":"Elbe","geocode_query":"Elbe River basin, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p018-003","page":18,"quote":"Similarly, the average carbon sequestration ranges from 0 – 666 g C m-2 yr-1 and annually sequesters 106Tg C yr-1 404 . Our simulation results 405 showed that croplands supply the largest percentage of available water, while DBF sequester the major share of carbon. 406 The analysis also emphasized the critical role of minor land covers (e.g. wetlands, open shrubland, closed shrubland, 407 and grasslands) in providing ecosystem services for carbon sequestration. The extreme events in 2003 and 2018 had 408 a significant impact on ecosystem services at the national level. Moreover, the severe flood of 2013 also played a 409 major role on a regional scale in the Elbe and Danube River basins. This rigorously verified model provides confidence 410 that the model can be used to strategic applications for developing Nature-based Solutions (NbS), which will be helpful 411 for Germany to meet its net-zero carbon emissions by 2050.","verified_in_page":true},"resolution_status":"resolved","coordinates":[12.32782288599955,52.01207223313964],"precision":"Basin-wide study · representative map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-ecf9efc62505ee033aab3851-site-4"},{"name":"Danube River basin","kind":"basin","role":"region experiencing major impact from the 2013 floods on ecosystem services","river_name":"Danube","geocode_query":"Danube River basin, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p018-003","page":18,"quote":"Similarly, the average carbon sequestration ranges from 0 – 666 g C m-2 yr-1 and annually sequesters 106Tg C yr-1 404 . Our simulation results 405 showed that croplands supply the largest percentage of available water, while DBF sequester the major share of carbon. 406 The analysis also emphasized the critical role of minor land covers (e.g. wetlands, open shrubland, closed shrubland, 407 and grasslands) in providing ecosystem services for carbon sequestration. The extreme events in 2003 and 2018 had 408 a significant impact on ecosystem services at the national level. Moreover, the severe flood of 2013 also played a 409 major role on a regional scale in the Elbe and Danube River basins. This rigorously verified model provides confidence 410 that the model can be used to strategic applications for developing Nature-based Solutions (NbS), which will be helpful 411 for Germany to meet its net-zero carbon emissions by 2050.","verified_in_page":true},"resolution_status":"context_only","coordinates":null,"precision":"River context highlighted · no reliable point","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"},{"name":"Wasserthaleben Station watershed, Germany","kind":"station","role":"gauge station used for model discharge validation with noted weak performance","river_name":null,"geocode_query":"Wasserthaleben, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p007-001","page":7,"quote":"Except for the Wasserthaleben station, where the model performance was weak with bias equal to 131.8 % and annual R2 199 of 0.18. https://doi.org/10.5194/egusphere-2025-1629 Preprint. Discussion started: 4 August 2025 c Author(s) 2025. CC BY 4.0 License.","verified_in_page":true},"resolution_status":"resolved","coordinates":[10.8942296,51.25859089999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"99718 Wasserthaleben, Germany","basin_id":2040024170,"main_basin_id":2040024170,"basin_region":"Europe and Middle East","basin_area_km2":138382.8,"hydrology_source":"hydrobasins","site_id":"doc-ecf9efc62505ee033aab3851-site-6"},{"name":"Lackenberg Forest, Germany","kind":"field_site","role":"eddy flux tower site used for GPP and ET validation with noted discrepancies","river_name":null,"geocode_query":"Lackenberg Forest, Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p016-001","page":16,"quote":"The results also highlight the importance of sparse 317 landcovers (e.g. wetlands, open shrubland, closed shrubland, and grasslands) in regulating carbon sequestration. 318 Furthermore, the study shows that ecosystem services are quite sensitive to droughts and floods, but buffers developed 319 from previous year can play a significant role in mitigating this effect. 320 The model validation results successfully demonstrate that the model can be applied across central Europe (e.g. 321 Germany). The simulated discharge had small model bias percentage and high regression values. Furthermore, the spatial and temporal variability of the discharge was modelled reasonably well with high NSE, KGE, R2, 322 and low P323 bias for most watersheds (Table S3 - S4). Except for station Wasserthaleben, which had very high flow values leading to a P-bias equal to 131.8%, KGE of -1.48, and annual R2 324 of 0.18.","verified_in_page":true},"resolution_status":"resolved","coordinates":[13.3166667,49.09999999999999],"precision":"Named-place geocode · approximate map anchor","within_mdb":false,"catchment_id":null,"provider":"Google Geocoding API","scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_label":"Lackenberg, 94227 Lindberg, Germany","basin_id":2040539930,"main_basin_id":2040008490,"basin_region":"Europe and Middle East","basin_area_km2":211260.4,"hydrology_source":"hydrobasins","site_id":"doc-ecf9efc62505ee033aab3851-site-7"},{"name":"Western states of Germany","kind":"region","role":"area with relatively close to average precipitation during drought events providing buffers","river_name":null,"geocode_query":"Western Germany","latitude":null,"longitude":null,"evidence":{"passage_id":"p015-000","page":15,"quote":"15 288 3.2.4. Spatial variability of ecosystem services during extreme weather events 289 To understand the impact of droughts and floods on ecosystem services, we examined the droughts for the year 2003 290 & 2018 and the floods for the year 2002 and 2013. During 2003, precipitation was 22.7% less than its average and 291 only western states had close to average precipitation. The total water yield was 29.6% less than average. The carbon 292 sequestration was 18.5% lower than average. While western states had close to average carbon sequestration the rest 293 experienced significantly reduced levels. Compared to 2003, the pattern and intensity of the 2018 drought was more 294 severe. During this event, Germany cumulatively received 25.5% less precipitation, had a 26.8% lower water yield, and had 24.7% less carbon sequestration. The total water yield was 62.13 billion m3 295 , total carbon uptake was 389.77 296 TgC, and total carbon sequestration was 79.82 TgC.","verified_in_page":true},"resolution_status":"unresolved","coordinates":null,"precision":"Mentioned in the paper · not yet placed on the map","within_mdb":null,"catchment_id":null,"provider":null,"scope_note":"Highlighted lines show river-system context, not a surveyed study footprint.","geocode_rejected":"Geocoder returned a country/political centroid"}],"limitations":["Model monthly temporal resolution limits accurate peak flow estimation","Limited eddy flux tower data coverage affects water use efficiency parameter accuracy","Transboundary river flow not accounted for, excluding some watersheds near borders","Crop rotation omitted, limiting model ecological realism","Discrepancies between model estimations and remote sensing data due to input, algorithm, and classification uncertainties"],"river_ids":["ne-389"],"site_ids":["doc-ecf9efc62505ee033aab3851-site-4","doc-ecf9efc62505ee033aab3851-site-6","doc-ecf9efc62505ee033aab3851-site-7"],"anchor":[13.3166667,49.09999999999999],"license":"CC BY (acquired by PubGather exact CC-BY policy)","is_mdb_study":false,"cluster":"cluster-7","basin_ids":[2040024170,2040539930],"main_basin_ids":[2040008490,2040024170]}],"connections":[{"id":"edge-0","source":"doc-e503e7fc1b483dea291758a3","target":"doc-a98ddca1bbe257f09384eaa6","kind":"shared-basin","label":"Shared drainage basin","description":"Both papers study the same drainage basin; 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