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Recharge, hydraulic-conductivity, \nspecific-storage, and effective-porosity distributions were estimated using parameter \nestimation (PEST) by minimizing a weighted composite, sum-of-squares objective function. \nSimulated water-level altitudes in 10 wells, vertical water-level differences between 5 well \npairs, aquifer-test transmissivity estimates in 9 wells, tritium concentrations in 7 wells, \nand drawdowns in wells PM-3-1 and PM-3-2 from groundwater withdrawals in borehole \nER-20-12 were compared to measured equivalents during parameter estimation and \nformally defined the goodness-of-fit or improvement of calibration. 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The SEAWAT code was used to couple \nMODFLOW and MT3DMS models, where the coupled model simulated groundwater-flow \nand tritium migration from the HANDLEY test for 50 years, from the date of detonation \nof the HANDLEY test (March 26, 1970) to March 26, 2020. Recharge, hydraulic-conductivity, \nspecific-storage, and effective-porosity distributions were estimated using parameter \nestimation (PEST) by minimizing a weighted composite, sum-of-squares objective function. \nSimulated water-level altitudes in 10 wells, vertical water-level differences between 5 well \npairs, aquifer-test transmissivity estimates in 9 wells, tritium concentrations in 7 wells, \nand drawdowns in wells PM-3-1 and PM-3-2 from groundwater withdrawals in borehole \nER-20-12 were compared to measured equivalents during parameter estimation and \nformally defined the goodness-of-fit or improvement of calibration. 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The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), \nconsisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the \nU.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service \n(USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management \n(BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to \ngenerate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United \nStates at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, \nrefer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp).\n\t\t\nThe NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see https://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States \nbased on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat \nmosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones \ncan be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 \nor mrlc@usgs.gov.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/156f42fd-4c3e-46f5-8d96-09d1ceeb7c80","harvest_record_raw":"https://catalog.data.gov/harvest_record/156f42fd-4c3e-46f5-8d96-09d1ceeb7c80/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_77113ab7-dba8-445d-bb7a-c3a7d273b652","keyword":["NAWQA","NLCD","National Land Cover Data Set","National Water-Quality Assessment","USGS:77113ab7-dba8-445d-bb7a-c3a7d273b652","environment","geoscientificInformation","inland waters","inlandWaters","land cover","land use"],"last_harvested_date":"2026-08-30T19:06:10.413096","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"national-land-cover-database-2001-nlcd01-tile-3-southwest-united-states-nlcd01_3","spatial_centroid":{"lat":29.59153,"lon":-113.1107698},"spatial_shape":{"coordinates":[[[-123.305923,22.736542],[-123.305923,39.874012],[-97.81804,39.874012],[-97.81804,22.736542],[-123.305923,22.736542]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Land Cover Database 2001 (NLCD01) Tile 3, Southwest United States: NLCD01_3","type":"dataset"},{"_score":9.297205,"_sort":[1788116761864,9.297205,0,"51e1c49f-c719-4da0-9447-b337cbab3fcd"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b4142eb66b018f981b82ef.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b4142eb66b018f981b82ef","keyword":["US","USGS:69b4142eb66b018f981b82ef","United States","WI","Wisconsin","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.27022, 44.28172, -88.27022, 44.28172","theme":["geospatial"],"title":"Imagery Station 218 [Fox River Above Kaukauna Lock]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1a1ab637-5340-436e-b706-ab25db1a9088","harvest_record_raw":"https://catalog.data.gov/harvest_record/1a1ab637-5340-436e-b706-ab25db1a9088/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b4142eb66b018f981b82ef","keyword":["US","USGS:69b4142eb66b018f981b82ef","United States","WI","Wisconsin","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:06:01.864201","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-218-fox-river-above-kaukauna-lock","spatial_centroid":{"lat":44.28172,"lon":-88.27022},"spatial_shape":{"coordinates":[-88.27022,44.28172],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 218 [Fox River Above Kaukauna Lock]","type":"dataset"},{"_score":4.3651896,"_sort":[1788116753725,4.3651896,1,"92f5ad9e-713a-4752-a4b0-855a24434033"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Claudia Faunt","hasEmail":"mailto:ccfaunt@usgs.gov"},"description":"This digital dataset defines the traces of geologic and hydrogeologic cross sections that were used in the \nconstruction of a digital three-dimensional (3D) hydrogeologic framework model (HFM) of the Death Valley \nregional ground-water flow system (DVRFS). The HFM represents the geometry and extent of hydrogeologic \nunits and major structures in an approximately 45,000 square-kilometer region of southern Nevada and \nCalifornia. The HFM was constructed from digital elevation models, geologic maps, borehole information, \ngeologic and hydrogeologic cross sections, and other 3D models to represent the geometry of the \nhydrogeologic units. Cross sections from five sources were used as input to the HFM: (1) 28 sections \nof the DVRFS region, (2) six sections of southern Nevada and eastern California, (3) 22 sections from a \nhydrogeologic framework model of the Nevada Test Site region, (4) four sections of the Yucca Mountain \narea, and (5) three sections of the southern part of Yucca Mountain and the northern part of Amargosa \nDesert (Faunt and others, 2004, Chapter E, pages 176-179). The HFM represents principal aquifers and \nconfining units and is an integral component of the DVRFS transient ground-water flow model. The DVRFS \nHFM and transient ground-water flow models are the most recent in a number of regional-scale models \ndeveloped by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support \ninvestigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see \"Larger Work Citation\", \nChapter A, page 8).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?pp1711_geology_xsecs","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6f7f10e1-c14d-4060-a89b-cb48524c113f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f7f10e1-c14d-4060-a89b-cb48524c113f","keyword":["Amargosa Desert","Ash Meadows","California","California Valley","Chicago Valley","China Ranch","Clark County","Clayton Valley","Coal Valley","Death Valley","Death Valley regional ground-water flow system","Esmeralda County","Eureka Valley","Franklin Lake","Franklin Well","Garden Valley","Inyo County","Kern County","Las Vegas Valley","Lincoln County","Mesquite Valley","Mineral County","Mono County","Nevada","Nevada Test Site","Nye County","Oasis Valley","Owlshead Mountains","Pahranagat Range","Pahrump Valley","Panamint Range","Penoyer Valley","Railroad Valley","Resting Spring","Saline Valley","San Bernadino County","Sarcobatus Flat","Sheep Range","Shoshone","Silurian Valley","Spring Mountains","Stewart Valley","Stone Cabin Valley","Tecopa","USGS:6f7f10e1-c14d-4060-a89b-cb48524c113f","Yucca Mountain","cross section","environment","flow model","geologic cross section","geoscientificInformation","ground water","hydrogeologic cross section","hydrogeologic framework model","hydrogeology","hydrology","inlandWaters","southern Nevada","transient ground-water model"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-118.006374, 35.486264, -114.514715, 38.119023","theme":["geospatial"],"title":"Traces of geologic and hydrogeologic cross sections used to develop the hydrogeologic framework model of the Death Valley regional ground-water flow system, Nevada and California"},"description":"This digital dataset defines the traces of geologic and hydrogeologic cross sections that were used in the \nconstruction of a digital three-dimensional (3D) hydrogeologic framework model (HFM) of the Death Valley \nregional ground-water flow system (DVRFS). The HFM represents the geometry and extent of hydrogeologic \nunits and major structures in an approximately 45,000 square-kilometer region of southern Nevada and \nCalifornia. The HFM was constructed from digital elevation models, geologic maps, borehole information, \ngeologic and hydrogeologic cross sections, and other 3D models to represent the geometry of the \nhydrogeologic units. Cross sections from five sources were used as input to the HFM: (1) 28 sections \nof the DVRFS region, (2) six sections of southern Nevada and eastern California, (3) 22 sections from a \nhydrogeologic framework model of the Nevada Test Site region, (4) four sections of the Yucca Mountain \narea, and (5) three sections of the southern part of Yucca Mountain and the northern part of Amargosa \nDesert (Faunt and others, 2004, Chapter E, pages 176-179). The HFM represents principal aquifers and \nconfining units and is an integral component of the DVRFS transient ground-water flow model. The DVRFS \nHFM and transient ground-water flow models are the most recent in a number of regional-scale models \ndeveloped by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support \ninvestigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see \"Larger Work Citation\", \nChapter A, page 8).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1d0591c4-0297-465e-a82e-31748c5b2891","harvest_record_raw":"https://catalog.data.gov/harvest_record/1d0591c4-0297-465e-a82e-31748c5b2891/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f7f10e1-c14d-4060-a89b-cb48524c113f","keyword":["Amargosa Desert","Ash Meadows","California","California Valley","Chicago Valley","China Ranch","Clark County","Clayton Valley","Coal Valley","Death Valley","Death Valley regional ground-water flow system","Esmeralda County","Eureka Valley","Franklin Lake","Franklin Well","Garden Valley","Inyo County","Kern County","Las Vegas Valley","Lincoln County","Mesquite Valley","Mineral County","Mono County","Nevada","Nevada Test Site","Nye County","Oasis Valley","Owlshead Mountains","Pahranagat Range","Pahrump Valley","Panamint Range","Penoyer Valley","Railroad Valley","Resting Spring","Saline Valley","San Bernadino County","Sarcobatus Flat","Sheep Range","Shoshone","Silurian Valley","Spring Mountains","Stewart Valley","Stone Cabin Valley","Tecopa","USGS:6f7f10e1-c14d-4060-a89b-cb48524c113f","Yucca Mountain","cross section","environment","flow model","geologic cross section","geoscientificInformation","ground water","hydrogeologic cross section","hydrogeologic framework model","hydrogeology","hydrology","inlandWaters","southern Nevada","transient ground-water model"],"last_harvested_date":"2026-08-30T19:05:53.725875","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"traces-of-geologic-and-hydrogeologic-cross-sections-used-to-develop-the-hydrogeologic-fram-f7d9c","spatial_centroid":{"lat":36.5393676,"lon":-116.6097104},"spatial_shape":{"coordinates":[[[-118.006374,35.486264],[-118.006374,38.119023],[-114.514715,38.119023],[-114.514715,35.486264],[-118.006374,35.486264]]],"type":"Polygon"},"theme":["geospatial"],"title":"Traces of geologic and hydrogeologic cross sections used to develop the hydrogeologic framework model of the Death Valley regional ground-water flow system, Nevada and California","type":"dataset"},{"_score":9.836294,"_sort":[1788116751829,9.836294,1,"1b2124bc-49bf-4145-8142-d0919655fb7e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject \nto the agricultural conservation practice (CPIT01), Gravity Irrigation Source (GI) on agricultural land \nby county.  Gravity Irrigation Source is described as irrigation \"delivered to the farm and/or field by \ncanals or pipelines open to the atmosphere; and, water is distributed by the force of gravity down \nthe field by: 1) A surface irrigation system (border, basin, furrow, corrugation, wild flooding, etc.) \nor, 2) Sub- surface irrigation pipelines or ditches.\" (U.S. Department of Agriculture, 1995)  This \ndata set was created with geographic information systems (GIS) and database management tools. \nThe acres on which GI's are applied were totaled at the county level in the tabular NRI database \nand then apportioned to a raster coverage of agricultural land within the county based on the \nEnhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids \n(Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not \nrecord information on those lands.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nri_it01","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.709bfbc6-269d-450c-bb1c-4967fd334978.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_709bfbc6-269d-450c-bb1c-4967fd334978","keyword":["Agricultural Practices","Gravity Irrigation","National Resources Inventory","USGS:709bfbc6-269d-450c-bb1c-4967fd334978","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.887748, 22.860749, -65.346810, 51.608770","theme":["geospatial"],"title":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIT01), Gravity Irrigation Source (GI) on agricultural land by county (nri_it01)"},"description":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject \nto the agricultural conservation practice (CPIT01), Gravity Irrigation Source (GI) on agricultural land \nby county.  Gravity Irrigation Source is described as irrigation \"delivered to the farm and/or field by \ncanals or pipelines open to the atmosphere; and, water is distributed by the force of gravity down \nthe field by: 1) A surface irrigation system (border, basin, furrow, corrugation, wild flooding, etc.) \nor, 2) Sub- surface irrigation pipelines or ditches.\" (U.S. Department of Agriculture, 1995)  This \ndata set was created with geographic information systems (GIS) and database management tools. \nThe acres on which GI's are applied were totaled at the county level in the tabular NRI database \nand then apportioned to a raster coverage of agricultural land within the county based on the \nEnhanced National Land Cover Dataset (NLCDe) 1-kilometer resolution land cover grids \n(Nakagaki, 2003). Federal land is not considered in this analysis because NRI does not \nrecord information on those lands.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e0be3349-6146-425a-beca-bdefe216f529","harvest_record_raw":"https://catalog.data.gov/harvest_record/e0be3349-6146-425a-beca-bdefe216f529/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_709bfbc6-269d-450c-bb1c-4967fd334978","keyword":["Agricultural Practices","Gravity Irrigation","National Resources Inventory","USGS:709bfbc6-269d-450c-bb1c-4967fd334978","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T19:05:51.829523","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"gravity-irrigation-sources-on-agricultural-land-in-the-conterminous-united-states-1992-nat","spatial_centroid":{"lat":34.3599574,"lon":-102.87137279999999},"spatial_shape":{"coordinates":[[[-127.887748,22.860749],[-127.887748,51.60877],[-65.34681,51.60877],[-65.34681,22.860749],[-127.887748,22.860749]]],"type":"Polygon"},"theme":["geospatial"],"title":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIT01), Gravity Irrigation Source (GI) on agricultural land by county (nri_it01)","type":"dataset"},{"_score":7.8517737,"_sort":[1788116742069,7.8517737,2,"db62c9d8-a380-44e4-baa9-6e9840190a03"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joann Dixon","hasEmail":"mailto:jdixon@usgs.gov"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological \nSurvey Groundwater Resources Program. This feature class contains lines that show the \nboundary of permeable strata in the Intermediate aquifer system (IAS) or Intermediate \nconfining unit (ICU).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ds926_fig21_IAS_ICU_permeable_zone_extent","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.04916e82-a9cc-485e-9477-e277349e9ed1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_04916e82-a9cc-485e-9477-e277349e9ed1","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:04916e82-a9cc-485e-9477-e277349e9ed1","United States Geological Survey","environment","extent","geoscientificInformation","inlandWaters","intermediate aquifer system","regions","thickness","upper confining unit"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-82.638827, 25.659840, -80.488121, 32.022248","theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Extent lines for permeable zones of the intermediate aquifer system and Brunswick aquifer system"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological \nSurvey Groundwater Resources Program. This feature class contains lines that show the \nboundary of permeable strata in the Intermediate aquifer system (IAS) or Intermediate \nconfining unit (ICU).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/514f72c8-b793-4d3b-8353-64eb4eb3e03f","harvest_record_raw":"https://catalog.data.gov/harvest_record/514f72c8-b793-4d3b-8353-64eb4eb3e03f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_04916e82-a9cc-485e-9477-e277349e9ed1","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:04916e82-a9cc-485e-9477-e277349e9ed1","United States Geological Survey","environment","extent","geoscientificInformation","inlandWaters","intermediate aquifer system","regions","thickness","upper confining unit"],"last_harvested_date":"2026-08-30T19:05:42.069939","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"ds926-digital-surfaces-and-thicknesses-of-selected-hydrogeologic-units-of-the-floridan-aqu-9911f","spatial_centroid":{"lat":28.204803199999997,"lon":-81.7785446},"spatial_shape":{"coordinates":[[[-82.638827,25.65984],[-82.638827,32.022248],[-80.488121,32.022248],[-80.488121,25.65984],[-82.638827,25.65984]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Extent lines for permeable zones of the intermediate aquifer system and Brunswick aquifer system","type":"dataset"},{"_score":9.297205,"_sort":[1788116740880,9.297205,0,"d3c91f1d-82f0-48dc-995b-048e84a2c7fc"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b40fb5b66b018f981b823b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b40fb5b66b018f981b823b","keyword":["OK","Oklahoma","US","USGS:69b40fb5b66b018f981b823b","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.913025, 35.985847, -96.913025, 35.985847","theme":["geospatial"],"title":"Imagery Station 144 [Cimarron River near Ripley]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5bf846f5-7fba-4937-94a5-b980c0ff0922","harvest_record_raw":"https://catalog.data.gov/harvest_record/5bf846f5-7fba-4937-94a5-b980c0ff0922/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b40fb5b66b018f981b823b","keyword":["OK","Oklahoma","US","USGS:69b40fb5b66b018f981b823b","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:05:40.880404","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-144-cimarron-river-near-ripley","spatial_centroid":{"lat":35.985847,"lon":-96.913025},"spatial_shape":{"coordinates":[-96.913025,35.985847],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 144 [Cimarron River near Ripley]","type":"dataset"},{"_score":5.519351,"_sort":[1788116735002,5.519351,3,"f1260e18-df4c-42cf-8db5-3aa1443b159e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Donna L. Runkle","hasEmail":"mailto:dlrunkle@usgs.gov"},"description":"This data set consists of digital water-level elevation contours\nfor the alluvial and terrace deposits along the North Canadian\nRiver from Canton Lake to Lake Overholser in central Oklahoma.\nGround water in approximately 400 square miles of Quaternary-age\nalluvial and terrace aquifer is an important source of water for\nirrigation, industrial, municipal, stock, and domestic supplies.\nThe aquifer consists of clay, silt, sand, and gravel. Sand-sized\nsediments dominate the poorly sorted, fine to coarse,\nunconsolidated quartz grains in the aquifer. The hydraulically\nconnected alluvial and terrace deposits unconformably overlie\nPermian-age formations. The aquifer is overlain by a layer of\nwind-blown sand in parts of the area.\n\nWater-level elevation contours, based on water-levels measured\nin 1980, were digitized from a folded paper map in a\nground-water flow modeling report for the aquifer. The source\nmap was published at a scale of 1:250,000. Water-level\nelevations in the alluvial and terrace deposits ranged from\n1,600 feet above sea level in the northwest to 1,220 feet above\nsea level in the southeast.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr96-447_wlelev","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.43abdb8d-0190-4c6f-b4e4-1cd68d29163a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_43abdb8d-0190-4c6f-b4e4-1cd68d29163a","keyword":["North Canadian River alluvial and terrace aquifer","North Canadian alluvial and terrace aquifer","USGS:43abdb8d-0190-4c6f-b4e4-1cd68d29163a","alluvial and terrace aquifer","alluvial aquifer","alluvium","aquifers","environment","geoscientificInformation","ground water","ground-water level elevation","ground-water level elevation contours","ground-water levels","ground-water vulnerability","groundwater","groundwater level elevation","groundwater level elevation contours","groundwater levels","groundwater vulnerability","inlandWaters","terrace","terrace aquifer","water level contours","water level elevation","water level elevation contours","water levels","water-level contours","water-level elevation","water-level elevation contours"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-98.5673, 35.4955, -97.6716, 36.1223","theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the alluvial and terrace deposits along the North Canadian River from Canton Lake to Lake Overholser in central Oklahoma"},"description":"This data set consists of digital water-level elevation contours\nfor the alluvial and terrace deposits along the North Canadian\nRiver from Canton Lake to Lake Overholser in central Oklahoma.\nGround water in approximately 400 square miles of Quaternary-age\nalluvial and terrace aquifer is an important source of water for\nirrigation, industrial, municipal, stock, and domestic supplies.\nThe aquifer consists of clay, silt, sand, and gravel. Sand-sized\nsediments dominate the poorly sorted, fine to coarse,\nunconsolidated quartz grains in the aquifer. The hydraulically\nconnected alluvial and terrace deposits unconformably overlie\nPermian-age formations. The aquifer is overlain by a layer of\nwind-blown sand in parts of the area.\n\nWater-level elevation contours, based on water-levels measured\nin 1980, were digitized from a folded paper map in a\nground-water flow modeling report for the aquifer. The source\nmap was published at a scale of 1:250,000. Water-level\nelevations in the alluvial and terrace deposits ranged from\n1,600 feet above sea level in the northwest to 1,220 feet above\nsea level in the southeast.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3490dfe6-c766-428f-bdfe-94be3a9f0497","harvest_record_raw":"https://catalog.data.gov/harvest_record/3490dfe6-c766-428f-bdfe-94be3a9f0497/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_43abdb8d-0190-4c6f-b4e4-1cd68d29163a","keyword":["North Canadian River alluvial and terrace aquifer","North Canadian alluvial and terrace aquifer","USGS:43abdb8d-0190-4c6f-b4e4-1cd68d29163a","alluvial and terrace aquifer","alluvial aquifer","alluvium","aquifers","environment","geoscientificInformation","ground water","ground-water level elevation","ground-water level elevation contours","ground-water levels","ground-water vulnerability","groundwater","groundwater level elevation","groundwater level elevation contours","groundwater levels","groundwater vulnerability","inlandWaters","terrace","terrace aquifer","water level contours","water level elevation","water level elevation contours","water levels","water-level contours","water-level elevation","water-level elevation contours"],"last_harvested_date":"2026-08-30T19:05:35.002249","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"digital-data-sets-that-describe-aquifer-characteristics-of-the-alluvial-and-terrace-deposi-6ade8","spatial_centroid":{"lat":35.74622,"lon":-98.20902},"spatial_shape":{"coordinates":[[[-98.5673,35.4955],[-98.5673,36.1223],[-97.6716,36.1223],[-97.6716,35.4955],[-98.5673,35.4955]]],"type":"Polygon"},"theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the alluvial and terrace deposits along the North Canadian River from Canton Lake to Lake Overholser in central Oklahoma","type":"dataset"},{"_score":9.008351,"_sort":[1788116732128,9.008351,4,"c5068c75-c9b7-4ff4-a809-f6ea35fe138d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Naomi Nakagaki","hasEmail":"mailto:nakagaki@usgs.gov"},"description":"This spatial dataset consists of 199 1-kilometer (km) resolution\ngrids depicting estimated agricultural use of 199 pesticides in 1992\nfor the conterminous United States. Each grid cell value in the\nnational grids of this dataset is the estimated total kilograms (kg) of\na pesticide applied to row crops, small grain crops and fallow land,\npasture and hay crops, and orchard and vineyard crops within the\n1- by 1-km area. Nonagricultural uses of pesticides are not included\nin this dataset. Of the 199 pesticides represented in the grids, 92\nare herbicides, 58 are insecticides, and 32 are fungicides. The\nremaining 17 grids are composed of the category \"other pesticides\",\nwhich consists of fumigants, growth regulators, and defoliants.\nAlthough this data set is referenced to 1992, it generally represents\na composite of estimated pesticide use during the early 1990s.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/GIS/dsdl/agpest92grd/index92.html","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.1483ef8c-b96e-4c6d-ac11-b0b6192aad37.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1483ef8c-b96e-4c6d-ac11-b0b6192aad37","keyword":["National Water Quality Assessment (NAWQA) Program","USGS:1483ef8c-b96e-4c6d-ac11-b0b6192aad37","agricultural","defoliant","environment","fumigant","fungicide","geoscientificInformation","growth regulator","herbicide","inlandWaters","insecticide","pesticide"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.307859, 22.736598, -65.143387, 51.857984","theme":["geospatial"],"title":"Grids of Agricultural Pesticide Use in the Conterminous United States, 1992"},"description":"This spatial dataset consists of 199 1-kilometer (km) resolution\ngrids depicting estimated agricultural use of 199 pesticides in 1992\nfor the conterminous United States. Each grid cell value in the\nnational grids of this dataset is the estimated total kilograms (kg) of\na pesticide applied to row crops, small grain crops and fallow land,\npasture and hay crops, and orchard and vineyard crops within the\n1- by 1-km area. Nonagricultural uses of pesticides are not included\nin this dataset. Of the 199 pesticides represented in the grids, 92\nare herbicides, 58 are insecticides, and 32 are fungicides. The\nremaining 17 grids are composed of the category \"other pesticides\",\nwhich consists of fumigants, growth regulators, and defoliants.\nAlthough this data set is referenced to 1992, it generally represents\na composite of estimated pesticide use during the early 1990s.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c1fd0b94-2d9c-4c87-9e7a-aa2f1b76d67a","harvest_record_raw":"https://catalog.data.gov/harvest_record/c1fd0b94-2d9c-4c87-9e7a-aa2f1b76d67a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1483ef8c-b96e-4c6d-ac11-b0b6192aad37","keyword":["National Water Quality Assessment (NAWQA) Program","USGS:1483ef8c-b96e-4c6d-ac11-b0b6192aad37","agricultural","defoliant","environment","fumigant","fungicide","geoscientificInformation","growth regulator","herbicide","inlandWaters","insecticide","pesticide"],"last_harvested_date":"2026-08-30T19:05:32.128137","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":4,"publisher":"U.S. Geological Survey","slug":"grids-of-agricultural-pesticide-use-in-the-conterminous-united-states-1992","spatial_centroid":{"lat":34.3851524,"lon":-103.04207020000001},"spatial_shape":{"coordinates":[[[-128.307859,22.736598],[-128.307859,51.857984],[-65.143387,51.857984],[-65.143387,22.736598],[-128.307859,22.736598]]],"type":"Polygon"},"theme":["geospatial"],"title":"Grids of Agricultural Pesticide Use in the Conterminous United States, 1992","type":"dataset"},{"_score":8.473431,"_sort":[1788116720406,8.473431,3,"f2faa4de-e951-4ced-9980-5b7c97cc87ed"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Oklahoma-Texas Water Science Center Chief","hasEmail":"mailto:gs-w-tx_webmaster@usgs.gov"},"description":"A previously developed groundwater flow model (https://doi.org/10.5066/P9051RUT) \nwas slightly modified to estimate the risk-based discrete relation between groundwater \nextraction and surface-water/groundwater exchange.  Previously, the concept of a \n''capture map'' has been put forward as a means to effectively summarize this relation \nfor decision-making consumption.  While capture maps have enjoyed success in the \nenvironmental simulation industry, they are deterministic, ignoring uncertainty in the \nunderlying model. Furthermore, capture maps are not typically calculated in a manner \nthat facilitates analysis of varying combinations of extraction locations and/or reaches.  \nThat is, they are typically constructed with focus on a single reach or group of reaches. \nThe former of these limitations is important for conveying risk to decision makers, while \nthe latter is important for decision-making support related to surface-water management, \nwhere future foci may include reaches that were not the focus of the original capture \nanalysis.  \n\t\t\nHerein, we use a MODFLOW-NWT groundwater/surface-water model of the lower San \nAntonio River, Texas, USA to demonstrate a technique to estimate risk-based and \nspatially discrete streamflow depletion potential. This USGS data release contains \nall of the input and output files for the simulations described in the associated journal \narticle (https://doi.org/10.1111/gwat.13080)","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9AB2MIL","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.c9d2e5dc-df28-42e8-80e3-e7ebee2fb94e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_c9d2e5dc-df28-42e8-80e3-e7ebee2fb94e","keyword":["FloPy","Goliad County","Groundwater","Groundwater Model","InlandWaters","Karnes County","Lower San Antonio River watershed","MODFLOW-NWT","PEST++","Texas","USGS:c9d2e5dc-df28-42e8-80e3-e7ebee2fb94e","Wilson County","environment","geoscientificInformation","inlandWaters","pyEMU","usgsgroundwatermodel"],"modified":"2021-06-22T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-99.024988, 27.142820, -95.982679, 30.097665 ","theme":["geospatial"],"title":"MODFLOW-NWT model used to demonstrate extending the capture map concept to estimate discrete and risk-based streamflow depletion potential"},"description":"A previously developed groundwater flow model (https://doi.org/10.5066/P9051RUT) \nwas slightly modified to estimate the risk-based discrete relation between groundwater \nextraction and surface-water/groundwater exchange.  Previously, the concept of a \n''capture map'' has been put forward as a means to effectively summarize this relation \nfor decision-making consumption.  While capture maps have enjoyed success in the \nenvironmental simulation industry, they are deterministic, ignoring uncertainty in the \nunderlying model. Furthermore, capture maps are not typically calculated in a manner \nthat facilitates analysis of varying combinations of extraction locations and/or reaches.  \nThat is, they are typically constructed with focus on a single reach or group of reaches. \nThe former of these limitations is important for conveying risk to decision makers, while \nthe latter is important for decision-making support related to surface-water management, \nwhere future foci may include reaches that were not the focus of the original capture \nanalysis.  \n\t\t\nHerein, we use a MODFLOW-NWT groundwater/surface-water model of the lower San \nAntonio River, Texas, USA to demonstrate a technique to estimate risk-based and \nspatially discrete streamflow depletion potential. This USGS data release contains \nall of the input and output files for the simulations described in the associated journal \narticle (https://doi.org/10.1111/gwat.13080)","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/10cc9f6a-02d4-44e4-ab7b-66290119d442","harvest_record_raw":"https://catalog.data.gov/harvest_record/10cc9f6a-02d4-44e4-ab7b-66290119d442/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_c9d2e5dc-df28-42e8-80e3-e7ebee2fb94e","keyword":["FloPy","Goliad County","Groundwater","Groundwater Model","InlandWaters","Karnes County","Lower San Antonio River watershed","MODFLOW-NWT","PEST++","Texas","USGS:c9d2e5dc-df28-42e8-80e3-e7ebee2fb94e","Wilson 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streamflow depletion potential","type":"dataset"},{"_score":9.724464,"_sort":[1788116708899,9.724464,3,"c956f41a-9dcc-4450-8fb2-d3366b737cb5"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Janet Carter","hasEmail":"mailto:jmcarter@usgs.gov"},"description":"This dataset provides lines of equal average annual precipitation for water years 1950-98 \nin the Black Hills area of South Dakota and Wyoming.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?sd_pr5098","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.0dcb49a2-a321-42c3-bcaa-3f3345fee9b3.xml","format":"XML","mediaType":"text/xml","title":"Original 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Silk Creek from River Mile 1.7 \nto the confluence with the Coast Fork. The basis for these features is the Willamette Flood Insurance Study \u2013 \nPhase One (2013). The hydraulics and hydrology for the FIS were reused in the production of these polygons; \nthe reports and information associated with the FIS are applicable to this product. The Digital Elevation Model \n(DEM) utilized for the Willamette FIS submittal was produced by combining multiple overlapping topographic \nsurveys for the Middle Fork and Coast Fork of the Willamette River. This DEM was created from four sources: \nLiDAR of the Springfield area that was flown in 2008, LiDAR of Silk Creek that was flown in 2011, LiDAR of Fall \nCreek that was flown in 2012, and photogrammetry of the Middle Fork and Coast Fork of the Willamette River \nthat was flown in 2004. In areas where no high-resolution elevation data were available, USGS National Elevation \nDataset (NED) data were used to supplement the DEM. The shapefiles Hi_Res_Extents.shp and \nLow_Res_Extents.shp define the limits of these areas. The horizontal datum of the DEM is NAD 1983 State \nPlane-Oregon South HARN with units of International Feet (NAD83). The vertical datum of the elevation model \nis NAVD 1988 with units of international feet (NAVD-88). In addition, some areas show surveyed bathymetry \nwithin the channel. These can be noted by the sharp increase in apparent depth, creating a stripe across the \ndepth grid when compared to the LiDAR data, which represents the water surface elevation at the time of the \naerial data collection. Bridge decks are generally removed from DEMs as standard practice. Therefore, these \nfeatures may be shown as inundated when they are not. An effort to clip flood extents on bridge decks was \nmade, but judgement should be used when estimating the usefulness of a bridge during flood flow. Comparing \nthe bridge to the surrounding ground can be more informative in this respect than simply looking at the bridge \nitself. The features and depth grids stop as the Coast Fork approaches the Middle Fork on the northern end of \nthe reach. See cfwgoshOR_breach.shp for information regarding this file. This represents the depth grid for the \n27,900 cfs profile.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?sir2016-5029_cfwgoshor_4b","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.90c4f661-d9b0-48f5-96ad-136caf43524b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_90c4f661-d9b0-48f5-96ad-136caf43524b","keyword":["Coast Fork Willamette River","Creswell","Goshen","Oregon","USGS:90c4f661-d9b0-48f5-96ad-136caf43524b","Willamette Valley","elevation","environment","flood","flood-inundation maps","flooded area","geoscientificInformation","geospatial analysis","high-water marks","inlandWaters","river/stream"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-123.048840045, 43.909466547, -122.949900456, 44.011530659","theme":["geospatial"],"title":"SIR2016-5029_cfwgoshor_4b: Flood Inundation Depth for a Flow of 27,900 cfs at the Gage Coast Fork Willamette River at Goshen, Oregon (Area of Uncertainty)"},"description":"The domain of the model is as follows: Row River from Dorena dam to the confluence with the Coast Fork; \nCoast Fork from Cottage Grove dam to the confluence with the Middle Fork; Silk Creek from River Mile 1.7 \nto the confluence with the Coast Fork. The basis for these features is the Willamette Flood Insurance Study \u2013 \nPhase One (2013). The hydraulics and hydrology for the FIS were reused in the production of these polygons; \nthe reports and information associated with the FIS are applicable to this product. The Digital Elevation Model \n(DEM) utilized for the Willamette FIS submittal was produced by combining multiple overlapping topographic \nsurveys for the Middle Fork and Coast Fork of the Willamette River. This DEM was created from four sources: \nLiDAR of the Springfield area that was flown in 2008, LiDAR of Silk Creek that was flown in 2011, LiDAR of Fall \nCreek that was flown in 2012, and photogrammetry of the Middle Fork and Coast Fork of the Willamette River \nthat was flown in 2004. In areas where no high-resolution elevation data were available, USGS National Elevation \nDataset (NED) data were used to supplement the DEM. The shapefiles Hi_Res_Extents.shp and \nLow_Res_Extents.shp define the limits of these areas. The horizontal datum of the DEM is NAD 1983 State \nPlane-Oregon South HARN with units of International Feet (NAD83). The vertical datum of the elevation model \nis NAVD 1988 with units of international feet (NAVD-88). In addition, some areas show surveyed bathymetry \nwithin the channel. These can be noted by the sharp increase in apparent depth, creating a stripe across the \ndepth grid when compared to the LiDAR data, which represents the water surface elevation at the time of the \naerial data collection. Bridge decks are generally removed from DEMs as standard practice. Therefore, these \nfeatures may be shown as inundated when they are not. An effort to clip flood extents on bridge decks was \nmade, but judgement should be used when estimating the usefulness of a bridge during flood flow. Comparing \nthe bridge to the surrounding ground can be more informative in this respect than simply looking at the bridge \nitself. The features and depth grids stop as the Coast Fork approaches the Middle Fork on the northern end of \nthe reach. See cfwgoshOR_breach.shp for information regarding this file. This represents the depth grid for the \n27,900 cfs profile.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/08b5ec50-f08b-41c4-a8e6-646def86840d","harvest_record_raw":"https://catalog.data.gov/harvest_record/08b5ec50-f08b-41c4-a8e6-646def86840d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_90c4f661-d9b0-48f5-96ad-136caf43524b","keyword":["Coast Fork Willamette River","Creswell","Goshen","Oregon","USGS:90c4f661-d9b0-48f5-96ad-136caf43524b","Willamette Valley","elevation","environment","flood","flood-inundation maps","flooded area","geoscientificInformation","geospatial analysis","high-water marks","inlandWaters","river/stream"],"last_harvested_date":"2026-08-30T19:04:45.311776","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"sir2016-5029_cfwgoshor_4b-flood-inundation-depth-for-a-flow-of-27900-cfs-at-the-gage-coast","spatial_centroid":{"lat":43.9502921918,"lon":-123.0092642094},"spatial_shape":{"coordinates":[[[-123.048840045,43.909466547],[-123.048840045,44.011530659],[-122.949900456,44.011530659],[-122.949900456,43.909466547],[-123.048840045,43.909466547]]],"type":"Polygon"},"theme":["geospatial"],"title":"SIR2016-5029_cfwgoshor_4b: Flood Inundation Depth for a Flow of 27,900 cfs at the Gage Coast Fork Willamette River at Goshen, Oregon (Area of Uncertainty)","type":"dataset"},{"_score":8.81318,"_sort":[1788116665631,8.81318,2,"70d66442-bb2b-4e2e-8020-409af13c895d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Roy Sando","hasEmail":"mailto:tsando@usgs.gov"},"description":"These data represent the altitude, in feet above North American Vertical Datum of 1988 (NAVD88), \nof the Upper Hell Creek hydrogeologic unit in the Williston structural basin. The data are presented \nas ASCII text files that can be converted to continuous raster format.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?sir2014-5047_altitude_of_top_of_Upper_Hell_Creek_hydrogeologic_unit_in_Williston_basin","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.80311104-958e-4439-b602-ecf3836ea68a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_80311104-958e-4439-b602-ecf3836ea68a","keyword":["Canada","Groundwater","Groundwater availability","Powder River","USGS:80311104-958e-4439-b602-ecf3836ea68a","United States","Upper Hell Creek","Williston River","Williston basin","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.658717203, 82.184032462, -80.640438346, 86.467134019","theme":["geospatial"],"title":"Altitude of the top of the Upper Hell Creek hydrogeologic unit in the Williston structural basin"},"description":"These data represent the altitude, in feet above North American Vertical Datum of 1988 (NAVD88), \nof the Upper Hell Creek hydrogeologic unit in the Williston structural basin. The data are presented \nas ASCII text files that can be converted to continuous raster format.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2c917fe3-931b-4f0a-8b54-a6e96208c9d1","harvest_record_raw":"https://catalog.data.gov/harvest_record/2c917fe3-931b-4f0a-8b54-a6e96208c9d1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_80311104-958e-4439-b602-ecf3836ea68a","keyword":["Canada","Groundwater","Groundwater availability","Powder River","USGS:80311104-958e-4439-b602-ecf3836ea68a","United States","Upper Hell Creek","Williston River","Williston basin","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T19:04:25.631278","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"altitude-of-the-top-of-the-upper-hell-creek-hydrogeologic-unit-in-the-williston-structural","spatial_centroid":{"lat":83.8972730848,"lon":-107.6514056602},"spatial_shape":{"coordinates":[[[-125.658717203,82.184032462],[-125.658717203,86.467134019],[-80.640438346,86.467134019],[-80.640438346,82.184032462],[-125.658717203,82.184032462]]],"type":"Polygon"},"theme":["geospatial"],"title":"Altitude of the top of the Upper Hell Creek hydrogeologic unit in the Williston structural basin","type":"dataset"},{"_score":5.985482,"_sort":[1788116660685,5.985482,1,"620d2f7b-4de6-49c0-9afa-fc9e1338594d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael E. Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This data set represents the average monthly maximum temperature in Celsius multiplied by \n100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. \nThe source data were the Near-Real-Time High-Resolution Monthly Average Maximum/Minimum \nTemperature for the Conterminous United States for 2002 raster dataset produced by the Spatial \nClimate Analysis Service at Oregon State University.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that \nincorporates many of the best features of the National Hydrography Dataset (NHD) and the \nNational Elevation Dataset (NED). The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency \n(USEPA), and contractors, found that this method produces the best quality NHD catchments \nusing an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 \nProduction Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins \n(MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River \nbasins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf \nand Tennessee River basins, contains NHDPlus Production Units 3 and 6.  MRB3, covering \nthe Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains \nNHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering the Missouri River basins, contains \nNHDPlus Production Units 10-lower and 10-upper.  MRB5, covering the Lower Mississippi, \nArkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, \n11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains \nNHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the Pacific Northwest River \nbasins, contains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains \nNHDPlus Production Unit 18.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nhd_tmax02","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.35475ddd-e876-426c-aebf-a7a41154d452.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_35475ddd-e876-426c-aebf-a7a41154d452","keyword":["Average monthly maximum temperature","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:35475ddd-e876-426c-aebf-a7a41154d452","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.910792, 23.243486, -65.327751, 51.657387","theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Annual Daily Maximum Temperature, 2002"},"description":"This data set represents the average monthly maximum temperature in Celsius multiplied by \n100 for 2002 compiled for every catchment of NHDPlus for the conterminous United States. \nThe source data were the Near-Real-Time High-Resolution Monthly Average Maximum/Minimum \nTemperature for the Conterminous United States for 2002 raster dataset produced by the Spatial \nClimate Analysis Service at Oregon State University.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that \nincorporates many of the best features of the National Hydrography Dataset (NHD) and the \nNational Elevation Dataset (NED). The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency \n(USEPA), and contractors, found that this method produces the best quality NHD catchments \nusing an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 \nProduction Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins \n(MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River \nbasins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf \nand Tennessee River basins, contains NHDPlus Production Units 3 and 6.  MRB3, covering \nthe Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains \nNHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering the Missouri River basins, contains \nNHDPlus Production Units 10-lower and 10-upper.  MRB5, covering the Lower Mississippi, \nArkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, \n11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains \nNHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the Pacific Northwest River \nbasins, contains NHDPlus Production Unit 17.  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Becker","hasEmail":"mailto:mfbecker@usgs.gov"},"description":"This digital data set consists of contours of predevelopment to\n1980 water-level elevation changes for the High Plains aquifer\nin the central United States.  The High Plains aquifer extends\nfrom south of 32 degrees to almost 44 degrees north latitude and\nfrom 96 degrees 30 minutes to 106 degrees west longitude.  The\noutcrop area covers 174,000 square miles and is present in\nColorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming.\n\nThis digital data set was created by digitizing the contours for\npredevelopment to 1980 water-level elevation change from a\n1:1,000,000-scale base map created by the U.S. Geological Survey\nHigh Plains Regional Aquifer-System Analysis (RASA) project\n(Gutentag, E.D., Heimes, F.J., Krothe, N.C., Luckey, R.R., and\nWeeks, J.B., 1984, Geohydrology of the High Plains aquifer in\nparts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming: U.S. Geological Survey Professional\nPaper 1400-B, 63 p.)  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The High Plains aquifer extends\nfrom south of 32 degrees to almost 44 degrees north latitude and\nfrom 96 degrees 30 minutes to 106 degrees west longitude.  The\noutcrop area covers 174,000 square miles and is present in\nColorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming.\n\nThis digital data set was created by digitizing the contours for\npredevelopment to 1980 water-level elevation change from a\n1:1,000,000-scale base map created by the U.S. Geological Survey\nHigh Plains Regional Aquifer-System Analysis (RASA) project\n(Gutentag, E.D., Heimes, F.J., Krothe, N.C., Luckey, R.R., and\nWeeks, J.B., 1984, Geohydrology of the High Plains aquifer in\nparts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming: U.S. Geological Survey Professional\nPaper 1400-B, 63 p.)  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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/45c6c2a9-50d8-4a3d-86ba-28f0e0247265","harvest_record_raw":"https://catalog.data.gov/harvest_record/45c6c2a9-50d8-4a3d-86ba-28f0e0247265/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41d06b66b018f981b8478","keyword":["NY","New York","US","USGS:69b41d06b66b018f981b8478","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:04:09.780036","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-95-01362342_lanesville","spatial_centroid":{"lat":42.14222,"lon":-74.265},"spatial_shape":{"coordinates":[-74.265,42.14222],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 95 [01362342_Lanesville]","type":"dataset"},{"_score":9.147108,"_sort":[1788116635860,9.147108,0,"c7141c24-2160-492b-a90c-225950e43bca"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41ca7b66b018f981b8467.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41ca7b66b018f981b8467","keyword":["NY","New York","US","USGS:69b41ca7b66b018f981b8467","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-74.27694, 42.185, -74.27694, 42.185","theme":["geospatial"],"title":"Imagery Station 89 [01349711_West Kill]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b8f26d13-3f2d-47ea-8687-c61c64cf5c2a","harvest_record_raw":"https://catalog.data.gov/harvest_record/b8f26d13-3f2d-47ea-8687-c61c64cf5c2a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41ca7b66b018f981b8467","keyword":["NY","New York","US","USGS:69b41ca7b66b018f981b8467","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:03:55.860128","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-89-01349711_west-kill","spatial_centroid":{"lat":42.185,"lon":-74.27694},"spatial_shape":{"coordinates":[-74.27694,42.185],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 89 [01349711_West Kill]","type":"dataset"},{"_score":9.297205,"_sort":[1788116630542,9.297205,0,"9502807f-354d-4071-8154-bd3441169bb4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b40fc7b66b018f981b8240.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b40fc7b66b018f981b8240","keyword":["MA","Massachusetts","US","USGS:69b40fc7b66b018f981b8240","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-72.676, 42.437496, -72.676, 42.437496","theme":["geospatial"],"title":"Imagery Station 145 [West Brook Upper_New23_01171030]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). 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Both QWDATA and GWSI are subsystems of\nNWIS (National Water Inventory System)of the USGS (United States\nGeologic Survey).\n\t\t\nThis map is for Weber County, Utah.\n\t\t\nThe scope and purpose of NWIS is defined on the web site:\n\t\t\nhttp://water.usgs.gov/public/pubs/FS/FS-027-98/","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f0e229c5-6e37-4cbb-bb57-afee8b7bad68","harvest_record_raw":"https://catalog.data.gov/harvest_record/f0e229c5-6e37-4cbb-bb57-afee8b7bad68/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_b901375a-f667-41f5-8b23-6f1be9d9a401","keyword":["Alkalinity","Ammonia","Ammonia unionize","Dissolved solids","Flow Rate","Hardness","Hardness total","Nitrogen","Nitrogen nitrate","Quality","Specific conductance","State of Utah","USGS:b901375a-f667-41f5-8b23-6f1be9d9a401","Utah","Water","Water Level","Water Quality","Water Quality Site","Water temperature","Weber","Weber County","environment","geoscientificInformation","inlandWaters","pH"],"last_harvested_date":"2026-08-30T19:02:50.122553","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"specific-water-quality-sites-for-weber-county-utah","spatial_centroid":{"lat":41.265220644,"lon":-111.97733306999999},"spatial_shape":{"coordinates":[[[-112.18000031,41.18222046],[-112.18000031,41.38972092],[-111.67333221,41.38972092],[-111.67333221,41.18222046],[-112.18000031,41.18222046]]],"type":"Polygon"},"theme":["geospatial"],"title":"Specific Water Quality Sites for Weber County, Utah","type":"dataset"},{"_score":9.248421,"_sort":[1788116551345,9.248421,0,"7c91654b-0e95-4815-91cf-922de0b443b9"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b416ccb66b018f981b835b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b416ccb66b018f981b835b","keyword":["ME","Maine","US","USGS:69b416ccb66b018f981b835b","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-68.63415, 45.44343, -68.63415, 45.44343","theme":["geospatial"],"title":"Imagery Station 287 [PIN-JAB]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d85b08e3-9013-4517-88ae-f0768977d760","harvest_record_raw":"https://catalog.data.gov/harvest_record/d85b08e3-9013-4517-88ae-f0768977d760/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b416ccb66b018f981b835b","keyword":["ME","Maine","US","USGS:69b416ccb66b018f981b835b","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:02:31.345543","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-287-pin-jab","spatial_centroid":{"lat":45.44343,"lon":-68.63415},"spatial_shape":{"coordinates":[-68.63415,45.44343],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 287 [PIN-JAB]","type":"dataset"},{"_score":7.8732553,"_sort":[1788116549963,7.8732553,0,"867adf8c-0605-4909-9887-677505381d77"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joann Dixon","hasEmail":"mailto:jdixon@usgs.gov"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological \nSurvey Groundwater Resources Program. This feature class contains polygon regions of units \nthat overlie the Lower Floridan aquifer. The regions are defined by geographic and hydraulic \nproperties.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ds926_fig44_top_LF_MCU_regions","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.ea252e82-639b-4728-b8b0-e82a65a0013a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ea252e82-639b-4728-b8b0-e82a65a0013a","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","LF","Lower Floridan","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:ea252e82-639b-4728-b8b0-e82a65a0013a","United States Geological Survey","environment","geoscientificInformation","inlandWaters","middle confining unit","regional","regions"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.582583, 24.759444, -79.259418, 33.751807","theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Polygon regions depicting the low-permeability units that overlie the Lower Floridan aquifer"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological \nSurvey Groundwater Resources Program. This feature class contains polygon regions of units \nthat overlie the Lower Floridan aquifer. The regions are defined by geographic and hydraulic \nproperties.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0cbe57cc-5d90-49ed-acb8-c2c6666f6618","harvest_record_raw":"https://catalog.data.gov/harvest_record/0cbe57cc-5d90-49ed-acb8-c2c6666f6618/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ea252e82-639b-4728-b8b0-e82a65a0013a","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","LF","Lower Floridan","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:ea252e82-639b-4728-b8b0-e82a65a0013a","United States Geological Survey","environment","geoscientificInformation","inlandWaters","middle confining unit","regional","regions"],"last_harvested_date":"2026-08-30T19:02:29.963268","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"ds926-digital-surfaces-and-thicknesses-of-selected-hydrogeologic-units-of-the-floridan-aqu-8337f","spatial_centroid":{"lat":28.356389200000002,"lon":-84.85331699999999},"spatial_shape":{"coordinates":[[[-88.582583,24.759444],[-88.582583,33.751807],[-79.259418,33.751807],[-79.259418,24.759444],[-88.582583,24.759444]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Polygon regions depicting the low-permeability units that overlie the Lower Floridan aquifer","type":"dataset"},{"_score":8.460795,"_sort":[1788116525601,8.460795,2,"9868252b-1bed-4de2-830c-6727b631f2af"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Daniel J Goode","hasEmail":"mailto:djgoode@usgs.gov"},"description":"Simulations of radial diffusion, adsorption, and reactions of volatile organic compounds (VOCs) - trichloroethene (TCE), \ncis-1,2-dichloroethene (cDCE), vinyl chloride (VC), trichlorofluoroethene (TCFE) - and bromide (Br) in a porous media \nare conducted using rock properties identified from a mudstone aquifer in the Newark Basin, near West Trenton, New \nJersey. The simulations are conducted using a finite-difference algorithm that was prepared for this investigation to \nsolve the equation for radial diffusion, linear equilibrium adsorption, and zero- and first-order biodegradation. The \nsimulations are conducted for a section of a rock matrix and the georeferencing is based on the locations of the well \nfrom which concentration data were collected and analyzed as part of this investigation. The model simulates \nconcentrations in the porous media and in a borehole, which provides the boundary condition for the porous media \ndomain. Rock properties controlling the diffusion, adsorption, and reaction of VOCs in the rock matrix are assumed to \nbe spatially uniform. This program simulates radial diffusion, adsorption, and reaction of TCE, cDCE, VC, and TCFE in \na borehole diffusion test. Adsorption is modeled by a linear sorption isotherm. Two version of the software are provided \nthat differ only in the assumed model for biodegradation: \n\t\t\nRDAR_0 - Biodegradation of TCE and TCFE in the borehole is assumed to be zero-order. Biodegradation of cDCE and \nVC in the borehole is assumed to be first-order. Abiotic degradation of TCE, cDCE, VC, and TCFE in the rock matrix \n(for both dissolved and sorbed phases) is assumbed to be first-order. \n\t\t\nRDAR_1 - All degradation is assumed to be first-order. As described in the body of the paper, two sets of TCFE data \nwere used for parameter estimation: \n(1) Adjusted TCFE, and \n(2) Original (Unadjusted) TCFE.\n\t\t\nSimulations are conducted for five cases, with cases I-IV using entire history of VOCs in the borehole for the pre-test \nsimulation phase. These cases are: \n(I) Adjusted TCFE, simultaneous fit to Br and VOC; \n(II) Adjusted TCFE, separate fits to Br and VOC; \n(III) Unadjusted TCFE, simultaneous fit to Br and VOC; \n(IV) Unadjusted TCFE, separate fits to Br and VOC; \n(V) Unadjusted TCFE, simultaneous fits to Br and VOC, borehole history simulation limited to only 2015-2017. \n\t\t\nThis USGS data release contains all of the software, input, and output files for the simulations described in the \nassociated journal article (https://doi.org/10.5066/P99I50JE).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P99I50JE","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.43328dd5-7803-46c9-9adc-ff7e3a3218d3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_43328dd5-7803-46c9-9adc-ff7e3a3218d3","keyword":["Lockatong Formation","Mercer County","Mudstone","New Jersey","Newark Basin","USGS:43328dd5-7803-46c9-9adc-ff7e3a3218d3","West Trenton","adsorption","contaminant transport","environment","geoscientificInformation","inlandWaters","mathematical simulation","sedimentary rocks","usgsgroundwatermodel"],"modified":"2022-01-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-74.813488329, 40.268733486, -74.813376178, 40.26880436","theme":["geospatial"],"title":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"},"description":"Simulations of radial diffusion, adsorption, and reactions of volatile organic compounds (VOCs) - trichloroethene (TCE), \ncis-1,2-dichloroethene (cDCE), vinyl chloride (VC), trichlorofluoroethene (TCFE) - and bromide (Br) in a porous media \nare conducted using rock properties identified from a mudstone aquifer in the Newark Basin, near West Trenton, New \nJersey. The simulations are conducted using a finite-difference algorithm that was prepared for this investigation to \nsolve the equation for radial diffusion, linear equilibrium adsorption, and zero- and first-order biodegradation. The \nsimulations are conducted for a section of a rock matrix and the georeferencing is based on the locations of the well \nfrom which concentration data were collected and analyzed as part of this investigation. The model simulates \nconcentrations in the porous media and in a borehole, which provides the boundary condition for the porous media \ndomain. Rock properties controlling the diffusion, adsorption, and reaction of VOCs in the rock matrix are assumed to \nbe spatially uniform. This program simulates radial diffusion, adsorption, and reaction of TCE, cDCE, VC, and TCFE in \na borehole diffusion test. Adsorption is modeled by a linear sorption isotherm. Two version of the software are provided \nthat differ only in the assumed model for biodegradation: \n\t\t\nRDAR_0 - Biodegradation of TCE and TCFE in the borehole is assumed to be zero-order. Biodegradation of cDCE and \nVC in the borehole is assumed to be first-order. Abiotic degradation of TCE, cDCE, VC, and TCFE in the rock matrix \n(for both dissolved and sorbed phases) is assumbed to be first-order. \n\t\t\nRDAR_1 - All degradation is assumed to be first-order. As described in the body of the paper, two sets of TCFE data \nwere used for parameter estimation: \n(1) Adjusted TCFE, and \n(2) Original (Unadjusted) TCFE.\n\t\t\nSimulations are conducted for five cases, with cases I-IV using entire history of VOCs in the borehole for the pre-test \nsimulation phase. 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For selected streams,\npoints of zero flow upstream of the first zero flow site also were\nchecked.\n\nStreamflow gain or loss for each stream reach was calculated by\nsubtracting the streamflow values measured at the upstream end of\nthe reach and values for contributing tributaries from the\ndownstream value.  The data obtained reflected base-flow conditions\nsuitable for estimating streamflow gains and losses for\nstream reaches between sites.\n\nThis digital data set was created by manually splitting the lines\nfrom a 1:250,000 hydrography data set (Soenksen and others, 1999) at\nevery streamflow-measurement site.  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This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41489b66b018f981b82fe.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41489b66b018f981b82fe","keyword":["MT","Montana","US","USGS:69b41489b66b018f981b82fe","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-113.680466, 48.795498, -113.680466, 48.795498","theme":["geospatial"],"title":"Imagery Station 226 [Swiftcurrent Creek]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/498922dd-1357-4a98-b0ba-61caefca3022","harvest_record_raw":"https://catalog.data.gov/harvest_record/498922dd-1357-4a98-b0ba-61caefca3022/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41489b66b018f981b82fe","keyword":["MT","Montana","US","USGS:69b41489b66b018f981b82fe","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:01:30.024949","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-226-swiftcurrent-creek","spatial_centroid":{"lat":48.795498,"lon":-113.680466},"spatial_shape":{"coordinates":[-113.680466,48.795498],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 226 [Swiftcurrent Creek]","type":"dataset"},{"_score":7.4422693,"_sort":[1788116478323,7.4422693,1,"ba18a285-98f8-430d-b333-1f60f5fbe928"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jennifer S. 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The \nmethodologies used to compute the averages and volumes for the data in this geodatabase are slightly \ndifferent for different components and models.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ds777_High_Plains_water_budget_components-idw_precip_avein_4049","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.e9d1330a-40fb-4f85-b435-458f2a97c1b7.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_e9d1330a-40fb-4f85-b435-458f2a97c1b7","keyword":["Colorado","Great Plains","High Plains","High Plains aquifer","Kansas","Nebraska","New Mexico","Ogallala aquifer","Oklahoma","Precipitation","South Dakota","Texas","USGS:e9d1330a-40fb-4f85-b435-458f2a97c1b7","Weather Station data","Wyoming","aquifers","central High Plains","environment","geoscientificInformation","ground water","groundwater","inlandWaters","northern High Plains","southern High Plains"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.019428, 31.594384, -96.222657, 43.807131","theme":["geospatial"],"title":"DS-777 Average Annual Precipitation Data, 1940 to 1949, in inches estimated from an Inverse-Distance-Weighted (IDW) interpolation for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming"},"description":"The water-budget-components geodatabase contains selected data from maps in the, \n\"Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 \nthrough 1949 and 2000 through 2009\" report (Stanton and others, 2011).  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The numerical ground-water flow model simulates \nprepumping conditions before 1913 and transient-flow conditions from 1913 to 1998 after pumping of ground \nwater began. The DVRFS transient ground-water flow model is the most recent in a number of regional-scale \nmodels developed by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to \nsupport investigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see \"Larger Work \nCitation\", Chapter A, page 8, for details).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?pp1711_hd16_t0_tr","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.3651953a-70e7-44d7-a963-1980d44473b1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3651953a-70e7-44d7-a963-1980d44473b1","keyword":["Amargosa Desert","Ash Meadows","California","California Valley","Chicago Valley","China Ranch","Clark County","Clayton Valley","Coal Valley","Death Valley","Death Valley regional ground-water flow system","Esmeralda County","Eureka Valley","Franklin Lake","Franklin Well","Garden Valley","Inyo County","Kern County","Las Vegas Valley","Lincoln County","MODFLOW-2000","Mesquite Valley","Mineral County","Mono County","Nevada","Nevada Test Site","Nye County","Oasis Valley","Owlshead Mountains","Pahranagat Range","Pahrump Valley","Panamint Range","Penoyer Valley","Railroad Valley","Resting Spring","Saline Valley","San Bernadino County","Sarcobatus Flat","Sheep Range","Shoshone","Silurian Valley","Spring Mountains","Stewart Valley","Stone Cabin Valley","Tecopa","USGS:3651953a-70e7-44d7-a963-1980d44473b1","Yucca Mountain","environment","flow model","geoscientificInformation","ground water","hydraulic head","hydraulic-head observation","hydrogeology","hydrology","inlandWaters","southern Nevada","steady state ground-water model","transient ground-water model"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.691126, 35.531306, -115.029481, 37.918095","theme":["geospatial"],"title":"Simulated potentiometric surface contours of prepumping conditions in layer 16 of the transient ground-water flow model of the Death Valley regional ground-water flow system, Nevada and California"},"description":"These simulated potentiometric surface contours represent prepumping (or steady-state) conditions for model \nlayer 16 of the Death Valley regional ground-water flow system (DVRFS), an approximately 45,000 square-\nkilometer region of southern Nevada and California. 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Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This tabular dataset represents the estimated area of artificial drainage for the year 1992 \nand irrigation types for the year 1997 compiled for every catchment of NHDPlus for the \nconterminous United States. The source datasets were derived from tabular National \nResource Inventory (NRI) datasets created by the National Resources Conservation \nService (NRCS, U.S. Department of Agriculture, 1995, 1997).  Artificial drainage is \ndefined as subsurface drains and ditches.  Irrigation types are defined as gravity and \npressure.  Subsurface drains are described as conduits, such as corrugated plastic \ntubing, tile, or pipe, installed beneath the ground surface to collect and/or convey \ndrainage. Surface drainage field ditches are described as graded ditches for collecting \nexcess water.  Gravity irrigation source is described as irrigation delivered to the farm \nand/or field by canals or pipelines open to the atmosphere; and water is distributed by \nthe force of gravity down the field by:\n\t\t\t (1) A surface irrigation system (border, basin, furrow, corrugation, wild flooding, etc.) or\n\t\t\t (2) Sub-surface irrigation pipelines or ditches. Pressure irrigation source is described \nas irrigation delivered to the farm and/or field in pump or elevation-induced pressure pipelines, \nand water is distributed across the field by:\n\t\t\t  (1) Sprinkle irrigation (center pivot, linear move, traveling gun, side roll, hand move, \nbig gun, or fixed set sprinklers), or\n\t\t\t  (2) Micro irrigation (drip emitters, continuous tube bubblers, micro spray or micro sprinklers). \nNRI data do not include Federal lands and are thus excluded from this dataset.  The tabular data for \ndrainage were spatially apportioned to the National Land Cover Dataset (NLCD, Kerie Hitt, \nwritten commun., 2005) and the tabular data for irrigation were spatially apportioned to an enhanced \nversion of the National Land Cover Dataset (NLCDe, Nakagaki and others 2007)\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that \nincorporates many of the best features of the National Hydrography Dataset (NHD) and the \nNational Elevation Dataset (NED). The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection \nAgency (USEPA), and contractors, found that this method produces the best quality NHD \ncatchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized \nby 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geological Survey's  Major River \nBasins (MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic \nRiver basins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South \nAtlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6.  \nMRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River \nbasins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering the Missouri \nRiver basins, contains NHDPlus Production Units 10-lower and 10-upper.  MRB5, covering \nthe Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus \nProduction Units 8, 11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin \nRiver basins, contains NHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the \nPacific Northwest River basins, contains NHDPlus Production Unit 17.  MRB8, covering \nCalifornia River basins, contains NHDPlus Production Unit 18.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nhd_adrain","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6f608721-c54a-4413-b1ae-04fc09527bf9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f608721-c54a-4413-b1ae-04fc09527bf9","keyword":["Artificial Drainage","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","Irrigation","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","National Resource Inventory","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","Subsurface drains Drainage","Tile drains","USGS:6f608721-c54a-4413-b1ae-04fc09527bf9","ditches","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.910792, 23.243486, -65.327751, 51.657387","theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) in the Conterminous United States: Artificial Drainage (1992) and Irrigation Types (1997)"},"description":"This tabular dataset represents the estimated area of artificial drainage for the year 1992 \nand irrigation types for the year 1997 compiled for every catchment of NHDPlus for the \nconterminous United States. The source datasets were derived from tabular National \nResource Inventory (NRI) datasets created by the National Resources Conservation \nService (NRCS, U.S. Department of Agriculture, 1995, 1997).  Artificial drainage is \ndefined as subsurface drains and ditches.  Irrigation types are defined as gravity and \npressure.  Subsurface drains are described as conduits, such as corrugated plastic \ntubing, tile, or pipe, installed beneath the ground surface to collect and/or convey \ndrainage. Surface drainage field ditches are described as graded ditches for collecting \nexcess water.  Gravity irrigation source is described as irrigation delivered to the farm \nand/or field by canals or pipelines open to the atmosphere; and water is distributed by \nthe force of gravity down the field by:\n\t\t\t (1) A surface irrigation system (border, basin, furrow, corrugation, wild flooding, etc.) or\n\t\t\t (2) Sub-surface irrigation pipelines or ditches. Pressure irrigation source is described \nas irrigation delivered to the farm and/or field in pump or elevation-induced pressure pipelines, \nand water is distributed across the field by:\n\t\t\t  (1) Sprinkle irrigation (center pivot, linear move, traveling gun, side roll, hand move, \nbig gun, or fixed set sprinklers), or\n\t\t\t  (2) Micro irrigation (drip emitters, continuous tube bubblers, micro spray or micro sprinklers). \nNRI data do not include Federal lands and are thus excluded from this dataset.  The tabular data for \ndrainage were spatially apportioned to the National Land Cover Dataset (NLCD, Kerie Hitt, \nwritten commun., 2005) and the tabular data for irrigation were spatially apportioned to an enhanced \nversion of the National Land Cover Dataset (NLCDe, Nakagaki and others 2007)\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that \nincorporates many of the best features of the National Hydrography Dataset (NHD) and the \nNational Elevation Dataset (NED). The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection \nAgency (USEPA), and contractors, found that this method produces the best quality NHD \ncatchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized \nby 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geological Survey's  Major River \nBasins (MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic \nRiver basins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South \nAtlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6.  \nMRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River \nbasins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering the Missouri \nRiver basins, contains NHDPlus Production Units 10-lower and 10-upper.  MRB5, covering \nthe Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus \nProduction Units 8, 11 and 12.  MRB6, covering the Rio Grande, Colorado and Great Basin \nRiver basins, contains NHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the \nPacific Northwest River basins, contains NHDPlus Production Unit 17.  MRB8, covering \nCalifornia River basins, contains NHDPlus Production Unit 18.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/10e855ba-2c45-430e-a5ab-fda494ab0101","harvest_record_raw":"https://catalog.data.gov/harvest_record/10e855ba-2c45-430e-a5ab-fda494ab0101/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f608721-c54a-4413-b1ae-04fc09527bf9","keyword":["Artificial Drainage","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","Irrigation","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","National Resource Inventory","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","Subsurface drains Drainage","Tile drains","USGS:6f608721-c54a-4413-b1ae-04fc09527bf9","ditches","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T19:00:48.105262","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"attributes-for-nhdplus-catchments-version-1-1-in-the-conterminous-united-states-artif-1997","spatial_centroid":{"lat":34.6090464,"lon":-102.8775756},"spatial_shape":{"coordinates":[[[-127.910792,23.243486],[-127.910792,51.657387],[-65.327751,51.657387],[-65.327751,23.243486],[-127.910792,23.243486]]],"type":"Polygon"},"theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) in the Conterminous United States: Artificial Drainage (1992) and Irrigation Types (1997)","type":"dataset"},{"_score":10.18741,"_sort":[1788116437736,10.18741,2,"64798eed-40bf-4e88-ba6f-5948fb124b6c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Curtis V. 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The data set also includes\npolygon features for unconsolidated watercourses taken from\n1:7,500,000-scale U.S. Geological Survey map of productive\naquifers.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr99-77_gwreguw","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.369b4164-a40d-46cf-a29e-5db721070c37.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_369b4164-a40d-46cf-a29e-5db721070c37","keyword":["USGS:369b4164-a40d-46cf-a29e-5db721070c37","aquifer","environment","geoscientificInformation","ground water","inlandWaters","region"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.84205584, 23.25354341, -65.40873623, 48.1924194","theme":["geospatial"],"title":"Digital data set describing ground-water regions with unconsolidated watercourses in the conterminous US"},"description":"This data set describes ground-water regions in the United\nStates defined by the U.S. Geological Survey.  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Regional ground-water\nflow is generally southeast to southwest towards the Cimarron\nRiver, except where the flow direction is affected by perennial\ntributaries. The water-level elevation contours were digitized\nfrom a mylar map at a scale of 1:250,000. The maps were\npublished at a scale of 1:900,000.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr96-445_wlelev","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.17235e98-036b-48fd-9ee9-df37712e7f7b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_17235e98-036b-48fd-9ee9-df37712e7f7b","keyword":["Cimarron River alluvial and terrace aquifer","Cimarron River alluvial aquifer","Cimarron River aquifer","Cimarron River terrace aquifer","Cimarron alluvial and terrace aquifer","Cimarron alluvial aquifer","Cimarron aquifer","USGS:17235e98-036b-48fd-9ee9-df37712e7f7b","alluvial aquifer","aquifers","environment","geoscientificInformation","ground water","ground-water level elevation","ground-water level elevation contours","ground-water levels","ground-water vulnerability","groundwater","groundwater level elevation","groundwater level elevation contours","groundwater levels","groundwater vulnerability","inlandWaters","terrace aquifer","water level contours","water level elevation","water level elevation contours","water levels","water-level contours","water-level elevation","water-level elevation contours"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-99.0721, 35.8204, -97.5609, 36.8137","theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the alluvial and terrace deposits along the Cimarron River from Freedom to Guthrie in northwestern Oklahoma"},"description":"This data set consists of digital water-level elevation contours\nfor the alluvial and terrace deposits along the Cimarron River\nin northwestern Oklahoma during 1985-86. Ground water in 1,305\nsquare miles of Quaternary-age alluvial and terrace deposits\nalong the the Cimarron River from Freedom to Guthrie is an\nimportant source of water for irrigation, industrial, municipal,\nstock, and domestic supplies. Alluvial and terrace deposits are\ncomposed of interfingering lenses of clay, sandy clay, and\ncross-bedded poorly sorted sand and gravel. The aquifer is\ncomposed of hydraulically connected alluvial and terrace\ndeposits that unconformably overlie the Permian-age Formations.\n\t\t\nWater-level elevations measured in 1985 and 1986 ranged from\n1,650 feet to 950 feet above sea level. Regional ground-water\nflow is generally southeast to southwest towards the Cimarron\nRiver, except where the flow direction is affected by perennial\ntributaries. The water-level elevation contours were digitized\nfrom a mylar map at a scale of 1:250,000. 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Both QWDATA and GWSI are subsystems of\nNWIS (National Water Inventory System)of the USGS (United States\nGeologic Survey).\n\t\t\nThis map is for Uintah County, Utah.\n\t\t\nThe scope and purpose of NWIS is defined on the web site:\n\t\t\nhttp://water.usgs.gov/public/pubs/FS/FS-027-98/","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ut_uintah_qw","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.064d5718-6328-4e46-9448-bf5a016fb903.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_064d5718-6328-4e46-9448-bf5a016fb903","keyword":["Alkalinity","Ammonia","Ammonia unionize","Dissolved solids","Flow Rate","Hardness","Hardness total","Nitrogen","Nitrogen nitrate","Quality","Specific conductance","State of Utah","USGS:064d5718-6328-4e46-9448-bf5a016fb903","Uintah","Uintah County","Utah","Water","Water Level","Water Quality","Water Quality Site","Water temperature","environment","geoscientificInformation","inlandWaters","pH"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.96083069, 39.52583313, -109.07389069, 40.74666595","theme":["geospatial"],"title":"Specific Water Quality Sites for Uintah County, Utah"},"description":"This map shows specific water-quality items and hydrologic data site\ninformation which come from QWDATA (Water Quality) and GWSI (Ground\nWater Information System). Both QWDATA and GWSI are subsystems of\nNWIS (National Water Inventory System)of the USGS (United States\nGeologic Survey).\n\t\t\nThis map is for Uintah County, Utah.\n\t\t\nThe scope and purpose of NWIS is defined on the web site:\n\t\t\nhttp://water.usgs.gov/public/pubs/FS/FS-027-98/","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/69e9d939-5686-436a-84d6-49f7b51084bf","harvest_record_raw":"https://catalog.data.gov/harvest_record/69e9d939-5686-436a-84d6-49f7b51084bf/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_064d5718-6328-4e46-9448-bf5a016fb903","keyword":["Alkalinity","Ammonia","Ammonia unionize","Dissolved solids","Flow Rate","Hardness","Hardness total","Nitrogen","Nitrogen nitrate","Quality","Specific conductance","State of Utah","USGS:064d5718-6328-4e46-9448-bf5a016fb903","Uintah","Uintah County","Utah","Water","Water Level","Water Quality","Water Quality Site","Water temperature","environment","geoscientificInformation","inlandWaters","pH"],"last_harvested_date":"2026-08-30T19:00:26.179255","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"specific-water-quality-sites-for-uintah-county-utah","spatial_centroid":{"lat":40.014166258,"lon":-109.60605469},"spatial_shape":{"coordinates":[[[-109.96083069,39.52583313],[-109.96083069,40.74666595],[-109.07389069,40.74666595],[-109.07389069,39.52583313],[-109.96083069,39.52583313]]],"type":"Polygon"},"theme":["geospatial"],"title":"Specific Water Quality Sites for Uintah County, Utah","type":"dataset"},{"_score":9.508575,"_sort":[1788116422823,9.508575,0,"7896f51c-e5e9-4242-8244-ad569a656695"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b4154cb66b018f981b831a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b4154cb66b018f981b831a","keyword":["US","USGS:69b4154cb66b018f981b831a","United States","VT","Vermont","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-73.09783, 43.26558, -73.09783, 43.26558","theme":["geospatial"],"title":"Imagery Station 246 [Kirby Hollow Brook]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a9351fc2-2b7a-4fd9-a9b8-bc68cec91c33","harvest_record_raw":"https://catalog.data.gov/harvest_record/a9351fc2-2b7a-4fd9-a9b8-bc68cec91c33/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b4154cb66b018f981b831a","keyword":["US","USGS:69b4154cb66b018f981b831a","United States","VT","Vermont","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T19:00:22.823882","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-246-kirby-hollow-brook","spatial_centroid":{"lat":43.26558,"lon":-73.09783},"spatial_shape":{"coordinates":[-73.09783,43.26558],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 246 [Kirby Hollow Brook]","type":"dataset"},{"_score":9.56629,"_sort":[1788116387353,9.56629,1,"780f97b1-91b0-4a5b-996e-4567a8b09e03"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or \nsubject to the agricultural conservation practice (CPIS02), Pond, Lake or Reservoir as an \nIrrigation Source (PLRIS) on agricultural land by county.  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This feature class contains contour lines generated \nfrom the Lower Floridan below LISAPCU raster.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ds926_fig45_top_LF_below_LISAPCU_contour","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.3dee0593-3299-4181-83a0-fc07ce7f7323.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3dee0593-3299-4181-83a0-fc07ce7f7323","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","LISAPCU","Lisbon-Avon Park","Lower Floridan","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:3dee0593-3299-4181-83a0-fc07ce7f7323","United States Geological Survey","altitude","below","confining unit","contour","environment","first","geoscientificInformation","inlandWaters","permeable zone","top"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.512051, 28.044973, -79.729769, 33.598960","theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Contours for the top of the first permeable zone below the LISAPCU"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological \nSurvey Groundwater Resources Program. This feature class contains contour lines generated \nfrom the Lower Floridan below LISAPCU raster.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e31f819a-3493-44cd-821a-06d7ecb8b260","harvest_record_raw":"https://catalog.data.gov/harvest_record/e31f819a-3493-44cd-821a-06d7ecb8b260/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3dee0593-3299-4181-83a0-fc07ce7f7323","keyword":["Alabama","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","LISAPCU","Lisbon-Avon Park","Lower Floridan","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:3dee0593-3299-4181-83a0-fc07ce7f7323","United States Geological Survey","altitude","below","confining unit","contour","environment","first","geoscientificInformation","inlandWaters","permeable zone","top"],"last_harvested_date":"2026-08-30T18:59:42.241898","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":5,"publisher":"U.S. Geological Survey","slug":"ds926-digital-surfaces-and-thicknesses-of-selected-hydrogeologic-units-of-the-floridan-aqu-83524","spatial_centroid":{"lat":30.266567799999997,"lon":-84.9991382},"spatial_shape":{"coordinates":[[[-88.512051,28.044973],[-88.512051,33.59896],[-79.729769,33.59896],[-79.729769,28.044973],[-88.512051,28.044973]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Contours for the top of the first permeable zone below the LISAPCU","type":"dataset"},{"_score":9.237335,"_sort":[1788116372374,9.237335,0,"715e6e28-06a1-4242-bd7d-0f0cc53f9c3d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41110b66b018f981b8275.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41110b66b018f981b8275","keyword":["PA","Pennsylvania","US","USGS:69b41110b66b018f981b8275","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.60083, 40.531113, -75.60083, 40.531113","theme":["geospatial"],"title":"Imagery Station 168 [Little Lehigh Creek, 01451380]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/60928c5c-8078-4bac-8483-1db3e2610bfb","harvest_record_raw":"https://catalog.data.gov/harvest_record/60928c5c-8078-4bac-8483-1db3e2610bfb/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41110b66b018f981b8275","keyword":["PA","Pennsylvania","US","USGS:69b41110b66b018f981b8275","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T18:59:32.374076","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-168-little-lehigh-creek-01451380","spatial_centroid":{"lat":40.531113,"lon":-75.60083},"spatial_shape":{"coordinates":[-75.60083,40.531113],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 168 [Little Lehigh Creek, 01451380]","type":"dataset"},{"_score":8.672108,"_sort":[1788116363725,8.672108,1,"df45bdfd-4357-4e09-a2e2-ba793dd5f86f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andrew LaMotte","hasEmail":"mailto:alamotte@usgs.gov"},"description":"This 30-meter resolution data set represents the imperviousness layer for the conterminous United States \nfor the 2001 time period. The data have been arranged into four tiles to facilitate timely display and \nmanipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National \nLand Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution \nLand Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies \n(www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric \nAdministration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of \nAgriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish \nand Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources \nConservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, \nseamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium \nspatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to \nHomer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning \nthe United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg \nwere delineated within the conterminous United States based on ecoregion and geographical characteristics, \nedge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole \nor parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land \nCover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?impv01_2","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.f605ea51-3c0a-42fa-816e-6668a8f142fb.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f605ea51-3c0a-42fa-816e-6668a8f142fb","keyword":["Imperviousness","Inlandwaters","NAWQA","NLCD","National Land Cover Data Set","National Water-Quality Assessment","USGS:f605ea51-3c0a-42fa-816e-6668a8f142fb","environment","geoscientificInformation","inlandWaters","land cover","land use"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"98.612036, 37.105324, -65.143599, 51.857938","theme":["geospatial"],"title":"National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 2, Northeast United States: IMPV01_2"},"description":"This 30-meter resolution data set represents the imperviousness layer for the conterminous United States \nfor the 2001 time period. The data have been arranged into four tiles to facilitate timely display and \nmanipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National \nLand Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution \nLand Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies \n(www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric \nAdministration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of \nAgriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish \nand Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources \nConservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, \nseamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium \nspatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to \nHomer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning \nthe United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg \nwere delineated within the conterminous United States based on ecoregion and geographical characteristics, \nedge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole \nor parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land \nCover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6674d20f-bc5d-49de-8807-5133cb8d267f","harvest_record_raw":"https://catalog.data.gov/harvest_record/6674d20f-bc5d-49de-8807-5133cb8d267f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f605ea51-3c0a-42fa-816e-6668a8f142fb","keyword":["Imperviousness","Inlandwaters","NAWQA","NLCD","National Land Cover Data Set","National Water-Quality Assessment","USGS:f605ea51-3c0a-42fa-816e-6668a8f142fb","environment","geoscientificInformation","inlandWaters","land cover","land use"],"last_harvested_date":"2026-08-30T18:59:23.725307","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"national-land-cover-database-2001-nlcd01-imperviousness-layer-tile-2-northeast-united-stat","spatial_centroid":{"lat":43.0063696,"lon":33.10978200000001},"spatial_shape":{"coordinates":[[[98.612036,37.105324],[98.612036,51.857938],[-65.143599,51.857938],[-65.143599,37.105324],[98.612036,37.105324]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 2, Northeast United States: IMPV01_2","type":"dataset"},{"_score":9.196194,"_sort":[1788116358275,9.196194,2,"4a4de452-c2e3-43fd-b889-7bdb9c119fd4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Charles Cannon","hasEmail":"mailto:ccannon@usgs.gov"},"description":"The Umpqua River drains 12,103 square kilometers (4,673 square miles) in southwest Oregon before flowing \ninto the Pacific Ocean at Winchester Bay near the city of Reedsport. In cooperation with the Portland District \nof the U.S. Army Corps of Engineers (USACE), the USGS evaluated sediment transport and gravel storage \nalong the downstream alluvial reaches of the North and South Umpqua Rivers and the entire mainstem \nUmpqua River. This includes the lower 46.8 kilometers (29.1 miles) of the North Umpqua River and the \nlower 122.6 kilometers (76.2 miles) of the South Umpqua River. \n\t\t\nThe Umpqua River gravel transport study involved multiple analyses, including tracking patterns of historical \nchannel change and estimation of a sediment budget. To support these analyses, digital channel maps were \nproduced to depict channel and floodplain conditions along the Umpqua River system from different time periods.\n\t\t\nGIS layers defining the active channel of the Umpqua River system were developed for three time periods: \n1939, 1967, and 2005. For the South Umpqua River and the 19 kilometers (12 miles) of the mainstem \nUmpqua River downstream from the confluence of the North and South Umpqua Rivers, GIS layers were \nalso developed for the time periods 1994, 2000, and 2009.\n\t\t\nFor this project, the active channel was defined as area typically inundated during annual high flows, and \nincludes the low-flow channel as well as side channels, islands, and channel-flanking gravel bars. The active \nchannel datasets were developed by digitizing from aerial photographs. Aerial photographs from 1939 and \n1967 were scanned, rectified, and mosaiced for this project. Digital orthophotographs from 1994, 2000, 2005, \nand 2009 are publicly available (See metadata for each photograph set for more information on the rectification \nprocess and resolution of each dataset). Although our study area encompasses the Umpqua River and lower \nreaches of the North and South Umpqua Rivers, the extent of each dataset depended upon the underlying \naerial photographs; for example, the 1967 photographs extend only as far downstream as floodplain kilometer \n7, whereas the 1939 and 2005 datasets extend to the mouth of the Umpqua River at the Pacific Ocean.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?umpqua_River_Oregon_Photo_Data_1939","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5fca0390-a65f-4af9-b809-d844d29579a3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fca0390-a65f-4af9-b809-d844d29579a3","keyword":["Douglas County","Oregon","USGS:5fca0390-a65f-4af9-b809-d844d29579a3","Umpqua River","active channel","environment","fluvial geomorphology","geoscientificInformation","inlandWaters","sediment transport"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.211726, 42.920153, -122.934460, 43.756118","theme":["geospatial"],"title":"Umpqua River Oregon Aerial Photograph Data for 1939"},"description":"The Umpqua River drains 12,103 square kilometers (4,673 square miles) in southwest Oregon before flowing \ninto the Pacific Ocean at Winchester Bay near the city of Reedsport. In cooperation with the Portland District \nof the U.S. Army Corps of Engineers (USACE), the USGS evaluated sediment transport and gravel storage \nalong the downstream alluvial reaches of the North and South Umpqua Rivers and the entire mainstem \nUmpqua River. This includes the lower 46.8 kilometers (29.1 miles) of the North Umpqua River and the \nlower 122.6 kilometers (76.2 miles) of the South Umpqua River. \n\t\t\nThe Umpqua River gravel transport study involved multiple analyses, including tracking patterns of historical \nchannel change and estimation of a sediment budget. To support these analyses, digital channel maps were \nproduced to depict channel and floodplain conditions along the Umpqua River system from different time periods.\n\t\t\nGIS layers defining the active channel of the Umpqua River system were developed for three time periods: \n1939, 1967, and 2005. For the South Umpqua River and the 19 kilometers (12 miles) of the mainstem \nUmpqua River downstream from the confluence of the North and South Umpqua Rivers, GIS layers were \nalso developed for the time periods 1994, 2000, and 2009.\n\t\t\nFor this project, the active channel was defined as area typically inundated during annual high flows, and \nincludes the low-flow channel as well as side channels, islands, and channel-flanking gravel bars. The active \nchannel datasets were developed by digitizing from aerial photographs. Aerial photographs from 1939 and \n1967 were scanned, rectified, and mosaiced for this project. Digital orthophotographs from 1994, 2000, 2005, \nand 2009 are publicly available (See metadata for each photograph set for more information on the rectification \nprocess and resolution of each dataset). 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River and Williston structural basins.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?sir2014-5047_extent_of_upper_Fort_Union_aquifer_in_Powder_River_and_Williston_basins","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.f7dcee8b-8027-43b0-880e-45216f5419bc.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f7dcee8b-8027-43b0-880e-45216f5419bc","keyword":["Bedrock geology","Canada","Groundwater","Groundwater availability","Powder River","Powder River basin","USGS:f7dcee8b-8027-43b0-880e-45216f5419bc","United States","Williston River","Williston 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Johnson","hasEmail":"mailto:mrjohns@usgs.gov"},"description":"This arc and point data set contains streamflow-measurement sites and\nreaches indicating streamflow gain or loss under base-flow conditions\nalong the Republican River and tributaries in Nebraska during October\n25 to 28, 1998 (Boohar and others, 2000).  The streamflow measurements were\nmade to obtain data on ground-water/surface-water interaction.\nFlow was observed visually to be zero, was measured, or was estimated\nat 374 sites.  The measurements were made on the main stem of the\nRepublican River and all flowing tributaries that enter the Republican\nRiver between Swanson and Harlan County Reservoirs in the Nebraska part\nof the Republican River Basin. Tributaries were followed upstream until\nthe first road crossing where zero flow was encountered. Measurements also\nincluded estimates of average wastewater discharge during the period when\nthe flow measurements were made, based upon data obtained from towns\nhaving measureable wastewater discharge.  For selected streams, points of\nzero flow upstream of the first zero flow site also were checked.\n\t\t\nStreamflow gain or loss for each stream reach was calculated by\nsubtracting the streamflow values measured at the upstream end of\nthe reach and values for contributing tributaries from the\ndownstream value.  The data obtained reflected base-flow conditions\nsuitable for estimating streamflow gains and losses for\nstream reaches between sites.  However, due to fluctuations in streamflow\non the main stem of the Republican River above Swanson Reservoir during the\nmeasurement period, flow measurements on the main stem could not be used\nfor calculating reach gains and losses. Flows measured on tributaries to\nthe Republican River above Swanson Reservoir were of suitable quality\nto use for streamflow gain/loss calculations. Flows on Sappa Creek did\nnot reflect base-flow conditions and thus were not suitable for\ninvestigating ground-water/surface-water interaction.\n\t\t\nThis digital data set was created by manually splitting the lines\nfrom a 1:250,000 hydrography data set (Soenksen and others, 1999) at\nevery streamflow-measurement site.  Each set of stream segments between\nmeasurement sites was assigned a unique reach number.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr0288_oct98","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.06153087-42b1-4cc3-867a-83e937ae2ff5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_06153087-42b1-4cc3-867a-83e937ae2ff5","keyword":["Nebraska","Republican River","Republican River Basin","USGS:06153087-42b1-4cc3-867a-83e937ae2ff5","base flow","environment","gain/loss","geoscientificInformation","inlandWaters","low-flow investigations","seepage run","streamflow"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.11396913, 39.93417935, -99.43321047, 40.92298837","theme":["geospatial"],"title":"Streamflow gain/loss in the Republican River Basin, Nebraska, October 1998"},"description":"This arc and point data set contains streamflow-measurement sites and\nreaches indicating streamflow gain or loss under base-flow conditions\nalong the Republican River and tributaries in Nebraska during October\n25 to 28, 1998 (Boohar and others, 2000).  The streamflow measurements were\nmade to obtain data on ground-water/surface-water interaction.\nFlow was observed visually to be zero, was measured, or was estimated\nat 374 sites.  The measurements were made on the main stem of the\nRepublican River and all flowing tributaries that enter the Republican\nRiver between Swanson and Harlan County Reservoirs in the Nebraska part\nof the Republican River Basin. Tributaries were followed upstream until\nthe first road crossing where zero flow was encountered. Measurements also\nincluded estimates of average wastewater discharge during the period when\nthe flow measurements were made, based upon data obtained from towns\nhaving measureable wastewater discharge.  For selected streams, points of\nzero flow upstream of the first zero flow site also were checked.\n\t\t\nStreamflow gain or loss for each stream reach was calculated by\nsubtracting the streamflow values measured at the upstream end of\nthe reach and values for contributing tributaries from the\ndownstream value.  The data obtained reflected base-flow conditions\nsuitable for estimating streamflow gains and losses for\nstream reaches between sites.  However, due to fluctuations in streamflow\non the main stem of the Republican River above Swanson Reservoir during the\nmeasurement period, flow measurements on the main stem could not be used\nfor calculating reach gains and losses. Flows measured on tributaries to\nthe Republican River above Swanson Reservoir were of suitable quality\nto use for streamflow gain/loss calculations. Flows on Sappa Creek did\nnot reflect base-flow conditions and thus were not suitable for\ninvestigating ground-water/surface-water interaction.\n\t\t\nThis digital data set was created by manually splitting the lines\nfrom a 1:250,000 hydrography data set (Soenksen and others, 1999) at\nevery streamflow-measurement site.  Each set of stream segments between\nmeasurement sites was assigned a unique reach number.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/918de25f-174a-4e09-9503-540090419715","harvest_record_raw":"https://catalog.data.gov/harvest_record/918de25f-174a-4e09-9503-540090419715/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_06153087-42b1-4cc3-867a-83e937ae2ff5","keyword":["Nebraska","Republican River","Republican River Basin","USGS:06153087-42b1-4cc3-867a-83e937ae2ff5","base flow","environment","gain/loss","geoscientificInformation","inlandWaters","low-flow investigations","seepage run","streamflow"],"last_harvested_date":"2026-08-30T18:58:35.387865","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"streamflow-gain-loss-in-the-republican-river-basin-nebraska-october-1998","spatial_centroid":{"lat":40.329702958,"lon":-101.041665666},"spatial_shape":{"coordinates":[[[-102.11396913,39.93417935],[-102.11396913,40.92298837],[-99.43321047,40.92298837],[-99.43321047,39.93417935],[-102.11396913,39.93417935]]],"type":"Polygon"},"theme":["geospatial"],"title":"Streamflow gain/loss in the Republican River Basin, Nebraska, October 1998","type":"dataset"},{"_score":9.248421,"_sort":[1788116309192,9.248421,0,"e7567cfe-f45d-4f48-9cf9-53b0ac2a4db3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41799b66b018f981b8378.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41799b66b018f981b8378","keyword":["OK","Oklahoma","US","USGS:69b41799b66b018f981b8378","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-97.47662, 35.28937, -97.47662, 35.28937","theme":["geospatial"],"title":"Imagery Station 325 [Little River (TG-1) ]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). 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The \nobjective of the surveys was to map the aquifers and bedrock topography of selected areas to help \nimprove the understanding of groundwater-surface-water relationships to be used in water management \ndecisions. Frequency-domain (2008 and 2009) and time-domain (2010) helicopter electromagnetic \nsurveys were completed, using a unique survey flight line design, to collect resistivity data that can \nbe related to lithologic information for refinement of groundwater model inputs.  To make the geophysical \ndata useful for multidimensional groundwater models, numerical inversion is necessary to convert the \nmeasured data into a depth-dependent subsurface resistivity model.  This inversion model, in conjunction\n with sensitivity analysis, geological ground truth (boreholes), and geological interpretation, is used to \ncharacterize hydrogeologic features.  The two- and three- dimensional interpretation provides the \ngroundwater modeler with a high-resolution hydrogeologic framework and a quantitative estimate of \nframework uncertainty.  This method of creating hydrogeologic frameworks improved the understanding \nof the actual flow path orientation by redefining the location of the paleochannels and associated bedrock \nhighs. The improved models represent the hydrogeology at a level of accuracy not achievable using \nprevious data sets.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?sim3310_AEM_SPNRD_boundary_2014","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.0ede1aeb-cee7-4b27-b5e1-979b1989650d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_0ede1aeb-cee7-4b27-b5e1-979b1989650d","keyword":["Nebraska","Ogallala","Scottsbluff","Sidney","USGS:0ede1aeb-cee7-4b27-b5e1-979b1989650d","environment","geoscientificInformation","inlandWaters","principal aquifer boundary"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-104.2, 41.0, -102.5, 41.44","theme":["geospatial"],"title":"Base of principal aquifer boundary for portions of the North Platte, South Platte, and Twin Platte Natural Resources Districts, western Nebraska"},"description":"The U.S. Geological Survey and its partners have collaborated to complete airborne geophysical surveys \nfor areas of the North and South Platte River valleys and Lodgepole Creek in western Nebraska. 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The two- and three- dimensional interpretation provides the \ngroundwater modeler with a high-resolution hydrogeologic framework and a quantitative estimate of \nframework uncertainty.  This method of creating hydrogeologic frameworks improved the understanding \nof the actual flow path orientation by redefining the location of the paleochannels and associated bedrock \nhighs. The improved models represent the hydrogeology at a level of accuracy not achievable using \nprevious data sets.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/551803c2-83aa-406c-a561-81315e59efc3","harvest_record_raw":"https://catalog.data.gov/harvest_record/551803c2-83aa-406c-a561-81315e59efc3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_0ede1aeb-cee7-4b27-b5e1-979b1989650d","keyword":["Nebraska","Ogallala","Scottsbluff","Sidney","USGS:0ede1aeb-cee7-4b27-b5e1-979b1989650d","environment","geoscientificInformation","inlandWaters","principal aquifer boundary"],"last_harvested_date":"2026-08-30T18:58:26.519897","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"base-of-principal-aquifer-boundary-for-portions-of-the-north-platte-south-platte-and-twin-","spatial_centroid":{"lat":41.176,"lon":-103.52000000000001},"spatial_shape":{"coordinates":[[[-104.2,41.0],[-104.2,41.44],[-102.5,41.44],[-102.5,41.0],[-104.2,41.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Base of principal aquifer boundary for portions of the North Platte, South Platte, and Twin Platte Natural Resources Districts, western Nebraska","type":"dataset"},{"_score":5.858383,"_sort":[1788116306175,5.858383,1,"bdf3ffa2-701f-4312-a83c-ea756dc9a8f4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael E. 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The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection \nAgency (USEPA), and contractors, found that this method produces the best quality NHD \ncatchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized \nby 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins \n(MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River \nbasins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf \nand Tennessee River basins, contains NHDPlus Production Units 3 and 6.  MRB3, covering the \nGreat Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus \nProduction Units 4, 5, 7 and 9.  MRB4, covering the Missouri River basins, contains NHDPlus \nProduction Units 10-lower and 10-upper.  MRB5, covering the Lower Mississippi, Arkansas-White-Red, \nand Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12.  MRB6, covering the \nRio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 \nand 16.  MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17.  \nMRB8, covering California River basins, contains NHDPlus Production Unit 18.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nhd_ppt02","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.07a308c4-7329-4354-a287-1d7c696b7220.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_07a308c4-7329-4354-a287-1d7c696b7220","keyword":["Average monthly precipitation","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:07a308c4-7329-4354-a287-1d7c696b7220","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.910792, 23.243486, -65.327751, 51.657387","theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Monthly Precipitation, 2002"},"description":"This data set represents the average monthly precipitation in millimeters multiplied by 100 for \n2002 compiled for every catchment of NHDPlus for the conterminous United States. 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The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection \nAgency (USEPA), and contractors, found that this method produces the best quality NHD \ncatchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized \nby 18 Production Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins \n(MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River \nbasins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf \nand Tennessee River basins, contains NHDPlus Production Units 3 and 6.  MRB3, covering the \nGreat Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus \nProduction Units 4, 5, 7 and 9.  MRB4, covering the Missouri River basins, contains NHDPlus \nProduction Units 10-lower and 10-upper.  MRB5, covering the Lower Mississippi, Arkansas-White-Red, \nand Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12.  MRB6, covering the \nRio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 \nand 16.  MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17.  \nMRB8, covering California River basins, contains NHDPlus Production Unit 18.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a5a4c696-c36f-4217-9b6e-2e7001f5dd42","harvest_record_raw":"https://catalog.data.gov/harvest_record/a5a4c696-c36f-4217-9b6e-2e7001f5dd42/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_07a308c4-7329-4354-a287-1d7c696b7220","keyword":["Average monthly precipitation","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:07a308c4-7329-4354-a287-1d7c696b7220","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T18:58:26.175337","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"attributes-for-nhdplus-catchments-version-1-1-for-the-conterminous-united-states-aver-2002-a0fde","spatial_centroid":{"lat":34.6090464,"lon":-102.8775756},"spatial_shape":{"coordinates":[[[-127.910792,23.243486],[-127.910792,51.657387],[-65.327751,51.657387],[-65.327751,23.243486],[-127.910792,23.243486]]],"type":"Polygon"},"theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: Average Monthly Precipitation, 2002","type":"dataset"},{"_score":9.335264,"_sort":[1788116289781,9.335264,0,"87be26b1-12bd-474b-b4f0-9862a7246e89"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41168b66b018f981b828d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41168b66b018f981b828d","keyword":["IL","Illinois","US","USGS:69b41168b66b018f981b828d","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.610306, 40.557335, -88.610306, 40.557335","theme":["geospatial"],"title":"Imagery Station 179 [Colfax]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). 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The NHDPlus includes a stream network (based on the \n1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). \nNHDPlus also includes elevation-derived catchments (drainage areas) produced using a \ndrainage enforcement technique first widely used in New England, and thus referred to as \n\"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale NHD \nand when available building \"walls\" using the National Watershed Boundary Dataset (WBD). \nThe resulting modified digital elevation model (HydroDEM) is used to produce hydrologic \nderivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary \nteam from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency \n(USEPA), and contractors, found that this method produces the best quality NHD catchments \nusing an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 \nProduction Units that cover the conterminous United States.\n\t\t\nThe NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's  Major River Basins \n(MRBs, Crawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River \nbasins, contains NHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf \nand Tennessee River basins, contains NHDPlus Production Units 3 and 6.  MRB3, covering the \nGreat Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus \nProduction Units 4, 5, 7 and 9.  MRB4, covering the Missouri River basins, contains NHDPlus \nProduction Units 10-lower and 10-upper.  MRB5, covering the Lower Mississippi, \nArkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 \nand 12.  MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains \nNHDPlus Production Units 13, 14, 15 and 16.  MRB7, covering the Pacific Northwest River \nbasins, contains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains \nNHDPlus Production Unit 18.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f42ded50-1b71-4cbe-8625-4ebc8722fd4d","harvest_record_raw":"https://catalog.data.gov/harvest_record/f42ded50-1b71-4cbe-8625-4ebc8722fd4d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1d97020e-c2a7-4d52-9153-a00d73feb1b4","keyword":["30-year average annual minimum temperature","CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Inlandwaters","LMTG","Lower Mississippi, Arkansas-White-Red, and Texas-Gulf","MORI","MRB","MRB1","MRB2","MRB3","MRB4","MRB5","MRB6","MRB7","MRB8","Major River Basin","Missouri","NAWQA","NEMA","NHDPlus","New England and Mid-Atlantic","PANW","Pacific Northwest","Rio Grande, Colorado, and Great Basin","SAGT","SPARROW","South Atlantic-Gulf and Tennessee","USGS:1d97020e-c2a7-4d52-9153-a00d73feb1b4","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T18:56:12.488546","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"attributes-for-nhdplus-catchments-version-1-1-for-the-conterminous-united-states-1971-2000-b252c","spatial_centroid":{"lat":34.6090464,"lon":-102.8775756},"spatial_shape":{"coordinates":[[[-127.910792,23.243486],[-127.910792,51.657387],[-65.327751,51.657387],[-65.327751,23.243486],[-127.910792,23.243486]]],"type":"Polygon"},"theme":["geospatial"],"title":"Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: 30-Year Average Annual Minimum Temperature, 1971-2000","type":"dataset"},{"_score":8.851817,"_sort":[1788116163264,8.851817,4,"0aec3a78-32a0-4bf9-a958-6d0c31e2fd79"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Marvin M. Abbott","hasEmail":"mailto:mmabbott@usgs.gov"},"description":"This data set consists of digitized aquifer boundaries of the\nAntlers aquifer in southeastern Oklahoma. The Early Cretaceous-age\nAntlers Sandstone is an important source of water in an area that\nunderlies about 4,400-square miles of all or part of Atoka,\nBryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and\nPushmataha Counties. The Antlers aquifer consists of sand, clay,\nconglomerate, and limestone in the outcrop area. The upper part\nof the Antlers aquifer consists of beds of sand, poorly cemented\nsandstone, sandy shale, silt, and clay. The Antlers aquifer is\nunconfined where it outcrops in about an 1,800-square-mile area.\n\t\t\nThe data set includes the outcrop area of the Antlers Sandstone\nin Oklahoma and areas where the Antlers is overlain by alluvial\nand terrace deposits and a few small thin outcrops of the\nGoodland Limestone. Most of the aquifer boundary lines were\nextracted from published digital geology data sets. Some of the\nlines were interpolated in areas where the Antlers aquifer is\noverlain by alluvial and terrace deposits near streams and\nrivers. The interpolated lines are very similar to the aquifer\nboundaries published in a ground-water modeling report for the\nAntlers aquifer. The maps from which this data set was derived\nwere scanned or digitized from maps published at a scale of\n1:250,000.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr96-443_aqbound","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.1dc9dbb5-993b-4073-9d37-1d4e8672228d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1dc9dbb5-993b-4073-9d37-1d4e8672228d","keyword":["Antlers Sandstone","Antlers aquifer","USGS:1dc9dbb5-993b-4073-9d37-1d4e8672228d","aquifer boundary","aquifers","environment","geoscientificInformation","ground water","ground-water vulnerability","groundwater","groundwater vulnerability","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-97.4976, 33.7288, -94.4684, 34.3644","theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the Antlers aquifer in southeastern Oklahoma"},"description":"This data set consists of digitized aquifer boundaries of the\nAntlers aquifer in southeastern Oklahoma. The Early Cretaceous-age\nAntlers Sandstone is an important source of water in an area that\nunderlies about 4,400-square miles of all or part of Atoka,\nBryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and\nPushmataha Counties. The Antlers aquifer consists of sand, clay,\nconglomerate, and limestone in the outcrop area. The upper part\nof the Antlers aquifer consists of beds of sand, poorly cemented\nsandstone, sandy shale, silt, and clay. The Antlers aquifer is\nunconfined where it outcrops in about an 1,800-square-mile area.\n\t\t\nThe data set includes the outcrop area of the Antlers Sandstone\nin Oklahoma and areas where the Antlers is overlain by alluvial\nand terrace deposits and a few small thin outcrops of the\nGoodland Limestone. Most of the aquifer boundary lines were\nextracted from published digital geology data sets. Some of the\nlines were interpolated in areas where the Antlers aquifer is\noverlain by alluvial and terrace deposits near streams and\nrivers. The interpolated lines are very similar to the aquifer\nboundaries published in a ground-water modeling report for the\nAntlers aquifer. The maps from which this data set was derived\nwere scanned or digitized from maps published at a scale of\n1:250,000.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/73ddb6e0-c7a8-4cb0-a9af-f4de3b4a08d2","harvest_record_raw":"https://catalog.data.gov/harvest_record/73ddb6e0-c7a8-4cb0-a9af-f4de3b4a08d2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1dc9dbb5-993b-4073-9d37-1d4e8672228d","keyword":["Antlers Sandstone","Antlers aquifer","USGS:1dc9dbb5-993b-4073-9d37-1d4e8672228d","aquifer boundary","aquifers","environment","geoscientificInformation","ground water","ground-water vulnerability","groundwater","groundwater vulnerability","inlandWaters"],"last_harvested_date":"2026-08-30T18:56:03.264829","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":4,"publisher":"U.S. Geological Survey","slug":"digital-data-sets-that-describe-aquifer-characteristics-of-the-antlers-aquifer-in-southeas-9269c","spatial_centroid":{"lat":33.98304,"lon":-96.28592},"spatial_shape":{"coordinates":[[[-97.4976,33.7288],[-97.4976,34.3644],[-94.4684,34.3644],[-94.4684,33.7288],[-97.4976,33.7288]]],"type":"Polygon"},"theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the Antlers aquifer in southeastern Oklahoma","type":"dataset"},{"_score":8.950998,"_sort":[1788116151883,8.950998,4,"e2714ff8-adf8-4a22-a8eb-601f30c5f482"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Martin A. Briggs","hasEmail":"mailto:mbriggs@usgs.gov"},"description":"1D transient numerical simulations with a modified version of the SUTRA model \n(preliminary code) that accounts for variably-saturated freeze-thaw dynamics \n(e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature \ndynamics using coupled fluid and heat transport physics. The model simulations \nwere run with a modified version of SUTRA_ICE (unreleased) that accomadates \na time-variable sinusiodal upper temperature boundary. This data release also \nincludes the source code and Argus One GUI files used to build the models, \nthough this proprietary software is not needed to run the models as described \nin the upper-level \"readme\" file.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7F47M8Q","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.8ec2db4f-7c68-42da-8beb-0c4a7cb2d060.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_8ec2db4f-7c68-42da-8beb-0c4a7cb2d060","keyword":["Groundwater","InlandWaters","SUTRA","SUTRA-ice","Shenandoah National Park","Surface Water","Thermal","USGS:8ec2db4f-7c68-42da-8beb-0c4a7cb2d060","Virginia","environment","geoscientificInformation","inlandWaters","refugia"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-78.378933, 38.53969, -78.347222, 38.58403","theme":["geospatial"],"title":"Modeled temperature data developed for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release"},"description":"1D transient numerical simulations with a modified version of the SUTRA model \n(preliminary code) that accounts for variably-saturated freeze-thaw dynamics \n(e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature \ndynamics using coupled fluid and heat transport physics. The model simulations \nwere run with a modified version of SUTRA_ICE (unreleased) that accomadates \na time-variable sinusiodal upper temperature boundary. This data release also \nincludes the source code and Argus One GUI files used to build the models, \nthough this proprietary software is not needed to run the models as described \nin the upper-level \"readme\" file.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/efdf683f-d1e3-4522-aa30-9af3de200a37","harvest_record_raw":"https://catalog.data.gov/harvest_record/efdf683f-d1e3-4522-aa30-9af3de200a37/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_8ec2db4f-7c68-42da-8beb-0c4a7cb2d060","keyword":["Groundwater","InlandWaters","SUTRA","SUTRA-ice","Shenandoah National Park","Surface 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seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release","type":"dataset"},{"_score":9.196194,"_sort":[1788116142376,9.196194,1,"009ba329-ef2d-4d82-afd1-51aea4f2ad13"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Water Webserver Team","hasEmail":"mailto:h2oteam@usgs.gov"},"description":"This data set represents the extent of the Upper Cretaceous aquifers in the states \nof North Dakota, South Dakota, Wyoming, and Montana.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?upper_cretaceous_aquifers","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.afdbe14d-24b9-4163-bb4f-a2656e5990d9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_afdbe14d-24b9-4163-bb4f-a2656e5990d9","keyword":["Montana","North Dakota","South Dakota","USGS:afdbe14d-24b9-4163-bb4f-a2656e5990d9","United States","Wyoming","aquifer","aquifer extent","environment","geoscientificInformation","groundwater","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-108.634047, 41.017515, -99.557350, 49.365460","theme":["geospatial"],"title":"Upper Cretaceous aquifers"},"description":"This data set represents the extent of the Upper Cretaceous aquifers in the states \nof North Dakota, South Dakota, Wyoming, and Montana.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/65884251-60f7-4297-ad75-d0c0a41fb354","harvest_record_raw":"https://catalog.data.gov/harvest_record/65884251-60f7-4297-ad75-d0c0a41fb354/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_afdbe14d-24b9-4163-bb4f-a2656e5990d9","keyword":["Montana","North Dakota","South Dakota","USGS:afdbe14d-24b9-4163-bb4f-a2656e5990d9","United States","Wyoming","aquifer","aquifer extent","environment","geoscientificInformation","groundwater","inlandWaters"],"last_harvested_date":"2026-08-30T18:55:42.376202","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"upper-cretaceous-aquifers","spatial_centroid":{"lat":44.356693,"lon":-105.0033682},"spatial_shape":{"coordinates":[[[-108.634047,41.017515],[-108.634047,49.36546],[-99.55735,49.36546],[-99.55735,41.017515],[-108.634047,41.017515]]],"type":"Polygon"},"theme":["geospatial"],"title":"Upper Cretaceous aquifers","type":"dataset"},{"_score":7.6226435,"_sort":[1788116134420,7.6226435,2,"5f590336-2131-4235-bb41-e807ba26c81f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Carol J. Becker","hasEmail":"mailto:cjbecker@usgs.gov"},"description":"This data set consists of digital polygons of constant recharge\nrates for the High Plains aquifer in Oklahoma. This area\nencompasses the panhandle counties of Cimarron, Texas, and\nBeaver, and the western counties of Harper, Ellis, Woodward,\nDewey, and Roger Mills. The High Plains aquifer underlies\napproximately 7,000 square miles of Oklahoma and is used\nextensively for irrigation. The High Plains aquifer is a\nwater-table aquifer and consists predominately of the\nTertiary-age Ogallala Formation and overlying Quaternary-age\nalluvial and terrace deposits. In some areas the aquifer is\nabsent and the underlying Triassic, Jurassic, or Cretaceous-age\nrocks are exposed at the surface. These rocks are hydraulically\nconnected with the aquifer in some areas.\n\nThe High Plains aquifer is composed of interbedded sand,\nsiltstone, clay, gravel, thin limestones, and caliche. The\nproportion of various lithological materials changes rapidly\nfrom place to place, but poorly sorted sand and gravel\npredominate. The rocks are poorly to moderately well cemented by\ncalcium carbonate.\n\nThe High Plains aquifer was divided into an east and west half\nwith each half having an assigned recharge that was used as\ninput to a ground-water flow model on the High Plains aquifer,\nduring the calibration of the steady-state model. The east half was\nassigned a constant recharge value of 0.45 inches per year and the\nwest half 0.225 inches per year.\n\nThe polygon boundaries and constant recharge rates were\nconstructed by extracting lines from digital surficial geology\ndata sets based on a scale of 1:125,000 for the panhandle\ncounties and 1:250,000 for the western counties. Some of the\nlines were digitized from maps in a published water-level\nelevation map for 1980.\n\nGround-water flow models are numerical representations that\nsimplify and aggregate natural systems. Models are not unique;\ndifferent combinations of aquifer characteristics may produce\nsimilar results. Therefore, values of recharge used in the\nmodel and presented in this data set are not precise, but are\nwithin a reasonable range when compared to independently\ncollected data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr96-451_recharg","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.ae78c471-8f07-4b29-a170-633a5e02011e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ae78c471-8f07-4b29-a170-633a5e02011e","keyword":["High Plains aquifer","Ogallala Formation","Ogallala aquifer","USGS:ae78c471-8f07-4b29-a170-633a5e02011e","aquifers","environment","geoscientificInformation","ground water","ground-water recharge","ground-water vulnerability","groundwater","groundwater vulnerability","inlandWaters","northwestern Oklahoma","panhandle of Oklahoma","recharge","recharge rate","western Oklahoma","western counties in Oklahoma"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.8856, 35.2500, -99.2367, 37.1297","theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the High Plains aquifer in western Oklahoma"},"description":"This data set consists of digital polygons of constant recharge\nrates for the High Plains aquifer in Oklahoma. This area\nencompasses the panhandle counties of Cimarron, Texas, and\nBeaver, and the western counties of Harper, Ellis, Woodward,\nDewey, and Roger Mills. The High Plains aquifer underlies\napproximately 7,000 square miles of Oklahoma and is used\nextensively for irrigation. The High Plains aquifer is a\nwater-table aquifer and consists predominately of the\nTertiary-age Ogallala Formation and overlying Quaternary-age\nalluvial and terrace deposits. In some areas the aquifer is\nabsent and the underlying Triassic, Jurassic, or Cretaceous-age\nrocks are exposed at the surface. These rocks are hydraulically\nconnected with the aquifer in some areas.\n\nThe High Plains aquifer is composed of interbedded sand,\nsiltstone, clay, gravel, thin limestones, and caliche. The\nproportion of various lithological materials changes rapidly\nfrom place to place, but poorly sorted sand and gravel\npredominate. The rocks are poorly to moderately well cemented by\ncalcium carbonate.\n\nThe High Plains aquifer was divided into an east and west half\nwith each half having an assigned recharge that was used as\ninput to a ground-water flow model on the High Plains aquifer,\nduring the calibration of the steady-state model. The east half was\nassigned a constant recharge value of 0.45 inches per year and the\nwest half 0.225 inches per year.\n\nThe polygon boundaries and constant recharge rates were\nconstructed by extracting lines from digital surficial geology\ndata sets based on a scale of 1:125,000 for the panhandle\ncounties and 1:250,000 for the western counties. Some of the\nlines were digitized from maps in a published water-level\nelevation map for 1980.\n\nGround-water flow models are numerical representations that\nsimplify and aggregate natural systems. Models are not unique;\ndifferent combinations of aquifer characteristics may produce\nsimilar results. Therefore, values of recharge used in the\nmodel and presented in this data set are not precise, but are\nwithin a reasonable range when compared to independently\ncollected data.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ef5781b0-8d1c-40df-9a6f-e98c43f4e78a","harvest_record_raw":"https://catalog.data.gov/harvest_record/ef5781b0-8d1c-40df-9a6f-e98c43f4e78a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ae78c471-8f07-4b29-a170-633a5e02011e","keyword":["High Plains aquifer","Ogallala Formation","Ogallala aquifer","USGS:ae78c471-8f07-4b29-a170-633a5e02011e","aquifers","environment","geoscientificInformation","ground water","ground-water recharge","ground-water vulnerability","groundwater","groundwater vulnerability","inlandWaters","northwestern Oklahoma","panhandle of Oklahoma","recharge","recharge rate","western Oklahoma","western counties in Oklahoma"],"last_harvested_date":"2026-08-30T18:55:34.420945","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"digital-data-sets-that-describe-aquifer-characteristics-of-the-high-plains-aquifer-in-west-a4986","spatial_centroid":{"lat":36.00188,"lon":-101.42604},"spatial_shape":{"coordinates":[[[-102.8856,35.25],[-102.8856,37.1297],[-99.2367,37.1297],[-99.2367,35.25],[-102.8856,35.25]]],"type":"Polygon"},"theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the High Plains aquifer in western Oklahoma","type":"dataset"},{"_score":8.732707,"_sort":[1788116124117,8.732707,2,"b9359be1-07d8-4a06-ae94-194031c8f9b2"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kerie J. 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The coverage is intended only for drawing\nILLUSTRATIONS, NOT for spatial analysis.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nawqagencyc2","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6f2fcdc5-9b68-49cd-9c9a-839cc124ce87.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f2fcdc5-9b68-49cd-9c9a-839cc124ce87","keyword":["Aquifer system","Ground water","NAWQA","National Water-Quality Assessment","River basin","Study Unit boundary","Surface water","USGS:6f2fcdc5-9b68-49cd-9c9a-839cc124ce87","Water quality","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.849844, 23.254474, -65.379042, 51.553462","theme":["geospatial"],"title":"Generalized boundaries of the National Water-Quality Assessment (NAWQA) Study-Unit Investigations in the conterminous United States 2001-2012"},"description":"This is a GENERALIZED version of the boundaries and codes used\nfor the U.S. Geological Survey's National Water-Quality\nAssessment (NAWQA) Program Study-Unit investigations in the\nconterminous United States, excluding the High Plains Regional\nGround-Water Study.  The data set represents the areas to be studied\nduring the second decade of the NAWQA Program, from 2001-2012\n(\"cycle 2\").  The coverage is intended only for drawing\nILLUSTRATIONS, NOT for spatial analysis.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/61050407-05d7-42fb-9ead-0300a4e4be17","harvest_record_raw":"https://catalog.data.gov/harvest_record/61050407-05d7-42fb-9ead-0300a4e4be17/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6f2fcdc5-9b68-49cd-9c9a-839cc124ce87","keyword":["Aquifer system","Ground water","NAWQA","National Water-Quality Assessment","River basin","Study Unit boundary","Surface water","USGS:6f2fcdc5-9b68-49cd-9c9a-839cc124ce87","Water quality","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T18:55:24.117125","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"generalized-boundaries-of-the-national-water-quality-assessment-nawqa-study-unit-2001-2012","spatial_centroid":{"lat":34.574069200000004,"lon":-102.86152320000001},"spatial_shape":{"coordinates":[[[-127.849844,23.254474],[-127.849844,51.553462],[-65.379042,51.553462],[-65.379042,23.254474],[-127.849844,23.254474]]],"type":"Polygon"},"theme":["geospatial"],"title":"Generalized boundaries of the National Water-Quality Assessment (NAWQA) Study-Unit Investigations in the conterminous United States 2001-2012","type":"dataset"},{"_score":8.415976,"_sort":[1788116118744,8.415976,2,"6a66e03b-0f7f-440e-9d90-481b16dcdf29"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Mark F. Becker","hasEmail":"mailto:mfbecker@usgs.gov"},"description":"This digital data set consists of boundaries for areas of little\nor no saturated thickness within the High Plains aquifer in the\ncentral United States.  The High Plains aquifer extends from\nsouth of 32 degrees to almost 44 degrees north latitude and from\n96 degrees 30 minutes to 106 degrees west longitude.  The\noutcrop area covers 174,000 square miles and is present in\nColorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming.\n\nThis digital data set was created by digitizing the areas of\nlittle or no saturated thickness from a 1:1,000,000-scale base\nmap created by the U.S. Geological Survey High Plains Regional\nAquifer-Systems Analysis (RASA) project (Gutentag, E.D., Heimes,\nF.J., Krothe, N.C., Luckey, R.R., and Weeks, J.B., 1984,\nGeohydrology of the High Plains aquifer in parts of Colorado,\nKansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and\nWyoming: U.S. Geological Survey Professional Paper 1400-B, 63\np.)  The data are not intended for use at scales larger than\n1:1,000,000.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr99-266","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.1d3bd8a2-211a-4468-8719-561fd62555ea.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1d3bd8a2-211a-4468-8719-561fd62555ea","keyword":["Great Plains region","High Plains","High Plains aquifer","Ogallala Formation","Ogallala aquifer","USGS:1d3bd8a2-211a-4468-8719-561fd62555ea","aquifer boundary","aquifers","environment","geoscientificInformation","ground water","groundwater","inlandWaters","western U.S."],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-104.17715031, 31.98681217, -97.74069276, 40.05624302","theme":["geospatial"],"title":"Digital map of areas of little or no saturated thickness for the High Plains aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming"},"description":"This digital data set consists of boundaries for areas of little\nor no saturated thickness within the High Plains aquifer in the\ncentral United States.  The High Plains aquifer extends from\nsouth of 32 degrees to almost 44 degrees north latitude and from\n96 degrees 30 minutes to 106 degrees west longitude.  The\noutcrop area covers 174,000 square miles and is present in\nColorado, Kansas, Nebraska, New Mexico, Oklahoma, South\nDakota, Texas, and Wyoming.\n\nThis digital data set was created by digitizing the areas of\nlittle or no saturated thickness from a 1:1,000,000-scale base\nmap created by the U.S. Geological Survey High Plains Regional\nAquifer-Systems Analysis (RASA) project (Gutentag, E.D., Heimes,\nF.J., Krothe, N.C., Luckey, R.R., and Weeks, J.B., 1984,\nGeohydrology of the High Plains aquifer in parts of Colorado,\nKansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and\nWyoming: U.S. Geological Survey Professional Paper 1400-B, 63\np.)  The data are not intended for use at scales larger than\n1:1,000,000.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/740da665-4c33-412b-bd6a-fb7169288435","harvest_record_raw":"https://catalog.data.gov/harvest_record/740da665-4c33-412b-bd6a-fb7169288435/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1d3bd8a2-211a-4468-8719-561fd62555ea","keyword":["Great Plains region","High Plains","High Plains aquifer","Ogallala Formation","Ogallala aquifer","USGS:1d3bd8a2-211a-4468-8719-561fd62555ea","aquifer boundary","aquifers","environment","geoscientificInformation","ground water","groundwater","inlandWaters","western 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Wyoming","type":"dataset"},{"_score":9.352005,"_sort":[1788116106591,9.352005,2,"84fb83b5-96e3-4349-a0e7-61bf50d54d83"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Janet Carter","hasEmail":"mailto:jmcarter@usgs.gov"},"description":"This data set describes areas where the Madison Limestone is\ndirectly overlain by surficial deposits, as well as those\nareas where the Madison Limestone is absent in the Black\nHills area, South Dakota.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr00471_mdsnsurf","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.145148ae-26ae-4e9b-a499-9b682a53abd0.xml","format":"XML","mediaType":"text/xml","title":"Original 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The model was not calibrated;\n however, various water budget components from the model output compared reasonably well with other estimates.\n The model was used to estimate recharge to the groundwater flow system as part of a preliminary water budget\n exercise described in the associated report (http://doi.org/10.3133/sir20185030).","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7CJ8BMS","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.4538ef93-ff1a-45fa-b9b0-b1746b5bb5c9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4538ef93-ff1a-45fa-b9b0-b1746b5bb5c9","keyword":["Alabama","Florida","Georgia","Groundwater","InlandWaters","Recharge","SWB","South Carolina","USGS:4538ef93-ff1a-45fa-b9b0-b1746b5bb5c9","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.358324, 25.046620, -79.004810, 36.192502","theme":["geospatial"],"title":"Soil-Water Balance model datasets used to estimate groundwater recharge in Florida and parts of Georgia, Alabama, and South Carolina, 1995-2010"},"description":"A soil-water balance model (SWB) was developed to estimate recharge to the groundwater flow system in Florida\n and parts of Georgia, Alabama, and South Carolina for the period 1995 through 2010. The model was not calibrated;\n however, various water budget components from the model output compared reasonably well with other estimates.\n The model was used to estimate recharge to the groundwater flow system as part of a preliminary water budget\n exercise described in the associated report (http://doi.org/10.3133/sir20185030).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/392cc994-6bcb-4b2a-be1b-cc3331fb3abd","harvest_record_raw":"https://catalog.data.gov/harvest_record/392cc994-6bcb-4b2a-be1b-cc3331fb3abd/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4538ef93-ff1a-45fa-b9b0-b1746b5bb5c9","keyword":["Alabama","Florida","Georgia","Groundwater","InlandWaters","Recharge","SWB","South 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Alabama, and South Carolina, 1995-2010","type":"dataset"},{"_score":9.400461,"_sort":[1788116094571,9.400461,0,"a62daef9-4a2d-418c-90ff-8c811c67ca49"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Water Webserver Team","hasEmail":"mailto:h2oteam@usgs.gov"},"description":"This data set represents the extent of the Ozark Plateaus aquifer system in the states of Missouri, \nKansas, Oklahoma, Arkansas, and Illinois.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ozark_plateaus_aquifer_system","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.1707bcb6-24c7-4546-a6c1-47c6215282ec.xml","format":"XML","mediaType":"text/xml","title":"Original 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models to predict\ncontamination of ground water by nonpoint sources of\nnitrate. The nonlinear approach to national-scale Ground-WAter\nVulnerability Assessment (GWAVA) uses components representing\nnitrogen (N) sources, transport, and attenuation.\n\t\t\nOne model (GWAVA-S) predicts nitrate contamination of shallow\n(typically less than 5 meters deep), recently recharged ground\nwater, which may or may not be used for drinking.  The other\n(GWAVA-DW) predicts ambient nitrate concentration in deeper\nsupplies used for drinking.\n\t\t\nThis data set is one of 14 data sets (1 output data set and 13\ninput data sets) associated with the GWAVA-DW model. Full details\nof the model development are in Nolan and Hitt (2006).\n\t\t\nFor inputs to the model, spatial attributes representing 13\nnitrogen loading and transport and attenuation factors were\ncompiled as raster data sets (1-km by 1-km grid cell size) for\nthe conterminous United States (see table 1).\n\t\t\n>Table 1.-- Parameters of nonlinear regression model for\n>           nitrate in ground water used for drinking (GWAVA-DW)\n>           and corresponding input spatial data sets.\n>           [kg, kilograms; km2, square kilometers.]\n>\n>Nitrogen Source Factors                  Data Set Name\n>   1 farm fertilizer (kg/hectare)        gwava-dw_ffer\n>   2 confined manure (kg/hectare)        gwava-dw_conf\n>   3 orchards/vineyards (percent)        gwava-dw_orvi\n>   4 population density  (people/km2)    gwava-dw_popd\n>\n>Transport to Aquifer Factors\n>   5 water input (km2/cm)                gwava-dw_wtin\n>   6 glacial till (yes/no)               gwava-dw_gtil\n>   7 semiconsolidated sand aquifers      gwava-dw_semc\n>     (yes/no)\n>   8 sandstone and carbonate rocks       gwava-dw_sscb\n>     (yes/no)\n>   9 drainage ditch (km2)                gwava-dw_ddit\n>  10 Hortonian overland flow             gwava-dw_hor\n>     (percent of streamflow)\n>\n>Attenuation Factors\n>  11 fresh surface water withdrawal      gwava-dw_swus\n>     for irrigation (megaliters/day)\n>  12 irrigation tailwater recovery (km2) gwava-dw_twre\n>  13 Dunne overland flow                 gwava-dw_dun\n>     (percent of streamflow)\n>  14 well depth (meters)                 -\n\t\t\n\"Farm fertilizer\" is the average annual nitrogen input from\ncommercial fertilizer applied to agricultural lands, 1992-2001, in\nkilograms per hectare.\n\t\t\n\"Confined manure\" is the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per\nhectare.\n\t\t\n\"Orchards/vineyards\" is the percent of orchards/vineyards land\ncover classification.\n\t\t\n\"Population density\" is 1990 block group population density, in\npeople per square kilometer.\n\t\t\n\"Water input\" is the ratio of the total area of irrigated land\nto precipitation, in square kilometers per centimeter.\n\t\t\n\"Glacial till\" is the presence or absence of poorly sorted\nglacial till east of the Rocky Mountains.\n\t\t\n\"Semiconsolidated sand aquifers\" is the presence or absence of\nsemiconsolidated sand aquifers.\n\t\t\n\"Sandstone and carbonate rocks\" is the presence or absence of\nsandstone and carbonate rock aquifers.\n\t\t\n\"Drainage ditch\" is the area of National Resources Inventory surface\ndrainage, field ditch conservation practice, in square kilometers.\n\t\t\n\"Hortonian overland flow\" is infiltration excess overland flow\nestimated by TOPMODEL, in percent of streamflow.\n\t\t\n\"Fresh surface water withdrawal for irrigation\" is the amount of\nfresh surface water withdrawal for irrigation, in megaliters per day.\n\t\t\n\"Irrigation tailwater recovery\" is the area of National\nResources Inventory irrigation system, tailwater recovery\nconservation practice, in square kilometers.\n\t\t\n\"Dunne overland flow\" is saturation overland flow estimated by\nTOPMODEL, in percent of streamflow.\n\t\t\n\"Well depth\" is the depth of the well, in meters.  Well depth\nwas not compiled as a spatial data set.  Well depth equals 50\nmeters for the model simulation being presented.\n\t\t\nReference cited:\n\t\t\nNolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow\nground water and drinking-water wells to nitrate in the United\nStates: Environmental Science and Technology, vol. 40, no. 24,\npages 7834-7840.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/GIS/dsdl/gwava-dw/index.html","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.b85957b8-08eb-44f5-9823-0e5d6604c7b6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_b85957b8-08eb-44f5-9823-0e5d6604c7b6","keyword":["Drinking water","Farm fertilizer","Ground water","Ground water contamination","Ground water pollution","Ground water susceptibility","NAWQA","National Land Cover Data","National Water-Quality Assessment Program","Nitrate","Nitrate concentration","Nonlinear model","Nutrients","USGS:b85957b8-08eb-44f5-9823-0e5d6604c7b6","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.30785909, 22.73659812, -65.14338696, 51.857984","theme":["geospatial"],"title":"Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Model of predicted nitrate concentration in U.S. ground water used for drinking (simulation depth 50 meters) -- Input data set for farmfertilizer (gwava-dw_ffer)"},"description":"This data set represents the average annual nitrogen input from\ncommercial fertilizer applied to agricultural lands, 1992-2001,\nin kilograms per hectare, in the conterminous United States.\n\t\t\nThe data set was used as an input data layer for a national\nmodel to predict nitrate concentration in ground water used for\ndrinking.\n\t\t\nNolan and Hitt (2006) developed two national models to predict\ncontamination of ground water by nonpoint sources of\nnitrate. The nonlinear approach to national-scale Ground-WAter\nVulnerability Assessment (GWAVA) uses components representing\nnitrogen (N) sources, transport, and attenuation.\n\t\t\nOne model (GWAVA-S) predicts nitrate contamination of shallow\n(typically less than 5 meters deep), recently recharged ground\nwater, which may or may not be used for drinking.  The other\n(GWAVA-DW) predicts ambient nitrate concentration in deeper\nsupplies used for drinking.\n\t\t\nThis data set is one of 14 data sets (1 output data set and 13\ninput data sets) associated with the GWAVA-DW model. Full details\nof the model development are in Nolan and Hitt (2006).\n\t\t\nFor inputs to the model, spatial attributes representing 13\nnitrogen loading and transport and attenuation factors were\ncompiled as raster data sets (1-km by 1-km grid cell size) for\nthe conterminous United States (see table 1).\n\t\t\n>Table 1.-- Parameters of nonlinear regression model for\n>           nitrate in ground water used for drinking (GWAVA-DW)\n>           and corresponding input spatial data sets.\n>           [kg, kilograms; km2, square kilometers.]\n>\n>Nitrogen Source Factors                  Data Set Name\n>   1 farm fertilizer (kg/hectare)        gwava-dw_ffer\n>   2 confined manure (kg/hectare)        gwava-dw_conf\n>   3 orchards/vineyards (percent)        gwava-dw_orvi\n>   4 population density  (people/km2)    gwava-dw_popd\n>\n>Transport to Aquifer Factors\n>   5 water input (km2/cm)                gwava-dw_wtin\n>   6 glacial till (yes/no)               gwava-dw_gtil\n>   7 semiconsolidated sand aquifers      gwava-dw_semc\n>     (yes/no)\n>   8 sandstone and carbonate rocks       gwava-dw_sscb\n>     (yes/no)\n>   9 drainage ditch (km2)                gwava-dw_ddit\n>  10 Hortonian overland flow             gwava-dw_hor\n>     (percent of streamflow)\n>\n>Attenuation Factors\n>  11 fresh surface water withdrawal      gwava-dw_swus\n>     for irrigation (megaliters/day)\n>  12 irrigation tailwater recovery (km2) gwava-dw_twre\n>  13 Dunne overland flow                 gwava-dw_dun\n>     (percent of streamflow)\n>  14 well depth (meters)                 -\n\t\t\n\"Farm fertilizer\" is the average annual nitrogen input from\ncommercial fertilizer applied to agricultural lands, 1992-2001, in\nkilograms per hectare.\n\t\t\n\"Confined manure\" is the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per\nhectare.\n\t\t\n\"Orchards/vineyards\" is the percent of orchards/vineyards land\ncover classification.\n\t\t\n\"Population density\" is 1990 block group population density, in\npeople per square kilometer.\n\t\t\n\"Water input\" is the ratio of the total area of irrigated land\nto precipitation, in square kilometers per centimeter.\n\t\t\n\"Glacial till\" is the presence or absence of poorly sorted\nglacial till east of the Rocky Mountains.\n\t\t\n\"Semiconsolidated sand aquifers\" is the presence or absence of\nsemiconsolidated sand aquifers.\n\t\t\n\"Sandstone and carbonate rocks\" is the presence or absence of\nsandstone and carbonate rock aquifers.\n\t\t\n\"Drainage ditch\" is the area of National Resources Inventory surface\ndrainage, field ditch conservation practice, in square kilometers.\n\t\t\n\"Hortonian overland flow\" is infiltration excess overland flow\nestimated by TOPMODEL, in percent of streamflow.\n\t\t\n\"Fresh surface water withdrawal for irrigation\" is the amount of\nfresh surface water withdrawal for irrigation, in megaliters per day.\n\t\t\n\"Irrigation tailwater recovery\" is the area of National\nResources Inventory irrigation system, tailwater recovery\nconservation practice, in square kilometers.\n\t\t\n\"Dunne overland flow\" is saturation overland flow estimated by\nTOPMODEL, in percent of streamflow.\n\t\t\n\"Well depth\" is the depth of the well, in meters.  Well depth\nwas not compiled as a spatial data set.  Well depth equals 50\nmeters for the model simulation being presented.\n\t\t\nReference cited:\n\t\t\nNolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow\nground water and drinking-water wells to nitrate in the United\nStates: Environmental Science and Technology, vol. 40, no. 24,\npages 7834-7840.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8b651023-0475-4dd6-ad7d-d9013f6d7ab7","harvest_record_raw":"https://catalog.data.gov/harvest_record/8b651023-0475-4dd6-ad7d-d9013f6d7ab7/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_b85957b8-08eb-44f5-9823-0e5d6604c7b6","keyword":["Drinking water","Farm fertilizer","Ground water","Ground water contamination","Ground water pollution","Ground water susceptibility","NAWQA","National Land Cover Data","National Water-Quality Assessment Program","Nitrate","Nitrate concentration","Nonlinear model","Nutrients","USGS:b85957b8-08eb-44f5-9823-0e5d6604c7b6","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T18:54:32.545547","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":5,"publisher":"U.S. Geological Survey","slug":"vulnerability-of-shallow-ground-water-and-drinking-water-wells-to-nitrate-in-the-united-st-935da","spatial_centroid":{"lat":34.385152472,"lon":-103.042070238},"spatial_shape":{"coordinates":[[[-128.30785909,22.73659812],[-128.30785909,51.857984],[-65.14338696,51.857984],[-65.14338696,22.73659812],[-128.30785909,22.73659812]]],"type":"Polygon"},"theme":["geospatial"],"title":"Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Model of predicted nitrate concentration in U.S. ground water used for drinking (simulation depth 50 meters) -- Input data set for farmfertilizer (gwava-dw_ffer)","type":"dataset"},{"_score":8.21446,"_sort":[1788116064401,8.21446,2,"c5d0f01b-fb81-4499-848f-0b38c29da7cd"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joann Dixon","hasEmail":"mailto:jdixon@usgs.gov"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological Survey \nGroundwater Resources Program. This feature class contains a line showing the approximate updip \nlimit of the permeable Upper Floridan aquifer. North of this line very few Upper Floridan wells are \nknown to exist.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ds926_fas_extent_poly","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.caec75ca-925e-417d-bd7e-d154f3d00535.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_caec75ca-925e-417d-bd7e-d154f3d00535","keyword":["Alabama","FAS","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:caec75ca-925e-417d-bd7e-d154f3d00535","United States Geological Survey","contour","environment","geoscientificInformation","inlandWaters","thickness"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.449191, 30.384972, -79.976218, 33.101663","theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Line showing the approximate updip limit of permeable rocks forming the Upper Floridan aquifer"},"description":"Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system \nwere developed to define an updated hydrogeologic framework as part of the U.S. Geological Survey \nGroundwater Resources Program. This feature class contains a line showing the approximate updip \nlimit of the permeable Upper Floridan aquifer. North of this line very few Upper Floridan wells are \nknown to exist.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6b1266d4-739f-411c-8622-a09dd4428c3a","harvest_record_raw":"https://catalog.data.gov/harvest_record/6b1266d4-739f-411c-8622-a09dd4428c3a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_caec75ca-925e-417d-bd7e-d154f3d00535","keyword":["Alabama","FAS","Florida","Floridan aquifer system","Geology","Georgia","Hydrogeology","Regional Groundwater Availability Study","South Carolina","Stratigraphy","USGS","USGS:caec75ca-925e-417d-bd7e-d154f3d00535","United States Geological Survey","contour","environment","geoscientificInformation","inlandWaters","thickness"],"last_harvested_date":"2026-08-30T18:54:24.401335","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"ds926-digital-surfaces-and-thicknesses-of-selected-hydrogeologic-units-of-the-floridan-aqu-cf0d7","spatial_centroid":{"lat":31.471648400000003,"lon":-85.6600018},"spatial_shape":{"coordinates":[[[-89.449191,30.384972],[-89.449191,33.101663],[-79.976218,33.101663],[-79.976218,30.384972],[-89.449191,30.384972]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS926 Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system in Florida and parts of Georgia, Alabama, and South Carolina -- Line showing the approximate updip limit of permeable rocks forming the Upper Floridan aquifer","type":"dataset"},{"_score":9.442859,"_sort":[1788116049812,9.442859,1,"bacc1639-f083-4bc7-8547-be71f034d5ed"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or \nsubject to the agricultural conservation practice (CPIS03), Stream, Ditch or Canal as an \nIrrigation Source (SDCIS) on agricultural land by county.  Stream, Ditch or Canal as an I\nrrigation Source is described as: \"Stream : A flow of water in a channel or bed, as a brook, \nrivulet, or small river. Ditch : A long, narrow trench or furrow dug in the ground, as for irrigation. \nCanal : An artificial waterway used for irrigation.\" (U.S. Department of Agriculture, 1995)  \nThis data set was created with geographic information systems (GIS) and database \nmanagement tools. The acres on which SDCIS's are applied were totaled at the county \nlevel in the tabular NRI database and then apportioned to a raster coverage of agricultural \nland within the county based on the Enhanced National Land Cover Dataset (NLCDe) \n1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered \nin this analysis because NRI does not record information on those lands.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?nri_is03","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.9d4f207b-b8a9-4887-b49a-d856c196b418.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9d4f207b-b8a9-4887-b49a-d856c196b418","keyword":["Agricultural Practices","National Resources Inventory","Stream, Ditch, Canal","USGS:9d4f207b-b8a9-4887-b49a-d856c196b418","environment","geoscientificInformation","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.887748, 22.860749, -65.346810, 51.608770","theme":["geospatial"],"title":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS03), Stream, Ditch or Canal as an Irrigation Source (SDCIS) on agricultural land by county (nri_is03)"},"description":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or \nsubject to the agricultural conservation practice (CPIS03), Stream, Ditch or Canal as an \nIrrigation Source (SDCIS) on agricultural land by county.  Stream, Ditch or Canal as an I\nrrigation Source is described as: \"Stream : A flow of water in a channel or bed, as a brook, \nrivulet, or small river. Ditch : A long, narrow trench or furrow dug in the ground, as for irrigation. \nCanal : An artificial waterway used for irrigation.\" (U.S. Department of Agriculture, 1995)  \nThis data set was created with geographic information systems (GIS) and database \nmanagement tools. The acres on which SDCIS's are applied were totaled at the county \nlevel in the tabular NRI database and then apportioned to a raster coverage of agricultural \nland within the county based on the Enhanced National Land Cover Dataset (NLCDe) \n1-kilometer resolution land cover grids (Nakagaki, 2003). Federal land is not considered \nin this analysis because NRI does not record information on those lands.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/db87351b-9683-4915-b8de-0801a6c83ffc","harvest_record_raw":"https://catalog.data.gov/harvest_record/db87351b-9683-4915-b8de-0801a6c83ffc/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9d4f207b-b8a9-4887-b49a-d856c196b418","keyword":["Agricultural Practices","National Resources Inventory","Stream, Ditch, Canal","USGS:9d4f207b-b8a9-4887-b49a-d856c196b418","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-08-30T18:54:09.812325","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"stream-ditch-or-canal-as-an-irrigation-source-on-agricultural-land-in-the-conterminous-uni","spatial_centroid":{"lat":34.3599574,"lon":-102.87137279999999},"spatial_shape":{"coordinates":[[[-127.887748,22.860749],[-127.887748,51.60877],[-65.34681,51.60877],[-65.34681,22.860749],[-127.887748,22.860749]]],"type":"Polygon"},"theme":["geospatial"],"title":"This data set represents the estimated percentage of the 1-km grid cell that is covered by or subject to the agricultural conservation practice (CPIS03), Stream, Ditch or Canal as an Irrigation Source (SDCIS) on agricultural land by county (nri_is03)","type":"dataset"},{"_score":6.907155,"_sort":[1788116048729,6.907155,5,"6ea33984-9ed2-4cbc-840b-f8dfcbbf5a58"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Carol J. Becker","hasEmail":"mailto:cjbecker@usgs.gov"},"description":"This data set consists of digital polygons of constant hydraulic\nconductivity values for the Tillman terrace and alluvial aquifer\nin southwestern Oklahoma. The Tillman terrace and alluvial\naquifer encompasses the unconsolidated terrace deposits and\nalluvium associated with the North Fork of the Red River and the\nRed River in the western half of Tillman County. These sediments\nconsist of discontinuous layers of clay, sandy clay, sand, and\ngravel. The aquifer extends over an area of 285 square miles and\nis used for irrigation and domestic purposes. Granite and the\nHennessey Formation outcrop in northern parts of the aquifer\nwhere alluvial deposits are absent. These outcrops were included\nas part of the aquifer in a thesis in which the ground-water\nflow in the aquifer was modeled.\n\nAn average hydraulic conductivity value of 92.5 feet per day was\nused for both the terrace and alluvial deposits in this data set\nand was reported in the thesis. The hydraulic conductivity\npolygons were derived from two sources. The outer polygon\nrepresenting the outer shell of a model grid for a ground-water\nflow model of the Tillman terrace and alluvial aquifer was\ndigitized from a paper map in a thesis at a scale of 1:249,695.\nPolygons and lines representing geologic contacts were extracted\nfrom a published digital surficial geology data set based on a\nscale of 1:250,000. Small polygons along the eastern boundary of\nthe aquifer were created when the outer polygon of the model\ngrid and the geology polygons and lines were combined. These\nsmall polygons represent geologic units other than the Tillman\nterrace and alluvial aquifer within the model grid. Three small\npolygons representing outcrops of granite and the Hennessey\nFormation in the northern parts of the aquifer also were\nextracted from the digital surficial geology data set.\n\nGround-water flow models are numerical representations that\nsimplify and aggregate natural systems. Models are not unique;\ndifferent combinations of aquifer characteristics may produce\nsimilar results. Therefore, values of hydraulic conductivity\nused in the model and presented in this data set are not\nprecise, but are within a reasonable range when compared to\nindependently collected data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?ofr96-452_cond","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.771b04b4-5d88-43f0-ac01-0c076cf6dc30.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_771b04b4-5d88-43f0-ac01-0c076cf6dc30","keyword":["Tillman alluvial and terrace aquifer","Tillman alluvial aquifer","Tillman aquifer","Tillman terrace and alluvial aquifer","Tillman terrace aquifer","USGS:771b04b4-5d88-43f0-ac01-0c076cf6dc30","alluvial aquifer","alluvial deposit","alluvium","aquifers","coefficent of permeability","environment","geoscientificInformation","ground water","ground-water vulnerability","groundwater","groundwater vulnerability","hydraulic conductivity","inlandWaters","permeability","permeability coefficent","terrace aquifer","terrace deposit"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-99.2286, 34.1912, -98.9482, 34.6411","theme":["geospatial"],"title":"Digital data sets that describe aquifer characteristics of the Tillman terrace and alluvial aquifer in southwestern Oklahoma"},"description":"This data set consists of digital polygons of constant hydraulic\nconductivity values for the Tillman terrace and alluvial aquifer\nin southwestern Oklahoma. The Tillman terrace and alluvial\naquifer encompasses the unconsolidated terrace deposits and\nalluvium associated with the North Fork of the Red River and the\nRed River in the western half of Tillman County. These sediments\nconsist of discontinuous layers of clay, sandy clay, sand, and\ngravel. The aquifer extends over an area of 285 square miles and\nis used for irrigation and domestic purposes. Granite and the\nHennessey Formation outcrop in northern parts of the aquifer\nwhere alluvial deposits are absent. These outcrops were included\nas part of the aquifer in a thesis in which the ground-water\nflow in the aquifer was modeled.\n\nAn average hydraulic conductivity value of 92.5 feet per day was\nused for both the terrace and alluvial deposits in this data set\nand was reported in the thesis. The hydraulic conductivity\npolygons were derived from two sources. The outer polygon\nrepresenting the outer shell of a model grid for a ground-water\nflow model of the Tillman terrace and alluvial aquifer was\ndigitized from a paper map in a thesis at a scale of 1:249,695.\nPolygons and lines representing geologic contacts were extracted\nfrom a published digital surficial geology data set based on a\nscale of 1:250,000. Small polygons along the eastern boundary of\nthe aquifer were created when the outer polygon of the model\ngrid and the geology polygons and lines were combined. These\nsmall polygons represent geologic units other than the Tillman\nterrace and alluvial aquifer within the model grid. Three small\npolygons representing outcrops of granite and the Hennessey\nFormation in the northern parts of the aquifer also were\nextracted from the digital surficial geology data set.\n\nGround-water flow models are numerical representations that\nsimplify and aggregate natural systems. Models are not unique;\ndifferent combinations of aquifer characteristics may produce\nsimilar results. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b41182b66b018f981b8290.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41182b66b018f981b8290","keyword":["MA","Massachusetts","US","USGS:69b41182b66b018f981b8290","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-72.67154, 42.434288, -72.67154, 42.434288","theme":["geospatial"],"title":"Imagery Station 18 [Obear Brook Lower_01171070]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b35f5f11-eb71-47b4-9f4f-6070e9ed4bed","harvest_record_raw":"https://catalog.data.gov/harvest_record/b35f5f11-eb71-47b4-9f4f-6070e9ed4bed/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b41182b66b018f981b8290","keyword":["MA","Massachusetts","US","USGS:69b41182b66b018f981b8290","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T18:53:42.245120","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-18-obear-brook-lower_01171070","spatial_centroid":{"lat":42.434288,"lon":-72.67154},"spatial_shape":{"coordinates":[-72.67154,42.434288],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 18 [Obear Brook Lower_01171070]","type":"dataset"},{"_score":8.944458,"_sort":[1788116019275,8.944458,3,"03563d79-708f-4d5b-a09d-df1db312cb9b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Water Webserver Team","hasEmail":"mailto:h2oteam@usgs.gov"},"description":"This data set represents the extent of the Seymour aquifer \nin Texas.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/lookup/getspatial?seymour_aquifer","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.75c55af2-8cbf-411b-952d-631ed33de6f2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_75c55af2-8cbf-411b-952d-631ed33de6f2","keyword":["Baylor County","Haskell County","Knox County","North Central Texas","Texas","USGS:75c55af2-8cbf-411b-952d-631ed33de6f2","aquifer","aquifer extent","environment","geoscientificInformation","groundwater","inlandWaters"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-101.029339, 32.452657, -99.026392, 35.061648","theme":["geospatial"],"title":"Seymour aquifer"},"description":"This data set represents the extent of the Seymour aquifer \nin Texas.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/85d2a766-b890-4d42-a6ff-d7d9e5871f19","harvest_record_raw":"https://catalog.data.gov/harvest_record/85d2a766-b890-4d42-a6ff-d7d9e5871f19/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_75c55af2-8cbf-411b-952d-631ed33de6f2","keyword":["Baylor County","Haskell County","Knox County","North Central Texas","Texas","USGS:75c55af2-8cbf-411b-952d-631ed33de6f2","aquifer","aquifer extent","environment","geoscientificInformation","groundwater","inlandWaters"],"last_harvested_date":"2026-08-30T18:53:39.275627","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":3,"publisher":"U.S. Geological Survey","slug":"seymour-aquifer","spatial_centroid":{"lat":33.4962534,"lon":-100.2281602},"spatial_shape":{"coordinates":[[[-101.029339,32.452657],[-101.029339,35.061648],[-99.026392,35.061648],[-99.026392,32.452657],[-101.029339,32.452657]]],"type":"Polygon"},"theme":["geospatial"],"title":"Seymour aquifer","type":"dataset"},{"_score":9.237335,"_sort":[1788116006751,9.237335,0,"9ff2ee1b-dd4b-4d19-90ec-818feb94852a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Phillip Goodling","hasEmail":"mailto:pgoodling@usgs.gov"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b416b7b66b018f981b8356.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b416b7b66b018f981b8356","keyword":["ME","Maine","US","USGS:69b416b7b66b018f981b8356","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-68.62863, 45.468662, -68.62863, 45.468662","theme":["geospatial"],"title":"Imagery Station 286 [PIN-SA]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. This site and others like it can be viewed at https://www.usgs.gov/apps/ecosheds/fpe/. These images collected at the site are provided, along with data annotations, to a deep learning ranking model which transforms each image into a relative quanitative estimate. This release contains one or more zip folders containing 1) the deep learning model object, 2) annotations used to train the model object, and  3) the predictions from the model object. Each zip folder is named for the variable modelled and for the date it was created. A summary of all the data and models available at the site are contained in a comma separated value (csv) file in this data release. The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7929d110-fd54-4513-87c9-64f60622c0d0","harvest_record_raw":"https://catalog.data.gov/harvest_record/7929d110-fd54-4513-87c9-64f60622c0d0/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b416b7b66b018f981b8356","keyword":["ME","Maine","US","USGS:69b416b7b66b018f981b8356","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T18:53:26.751897","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-286-pin-sa","spatial_centroid":{"lat":45.468662,"lon":-68.62863},"spatial_shape":{"coordinates":[-68.62863,45.468662],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 286 [PIN-SA]","type":"dataset"},{"_score":8.732707,"_sort":[1788116006317,8.732707,1,"7ebccbdd-bbb2-426d-b7fb-33d767a91b37"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Daniel T. Snyder","hasEmail":"mailto:dtsnyder@usgs.gov"},"description":"This image is a mosaic of Landsat-7 images of the upper Klamath Basin. The original images \nwere obtained from the U.S. Geological Survey Earth Resources Observation and Science \nCenter (EROS). EROS is responsible for archive management and distribution of Landsat \ndata products. The Landsat-7 satellite is part of an ongoing mission to provide quality remote \nsensing data in support of research and applications activities. The launch of Landsat-7 on \nApril 15, 1999 marks the addition of the latest satellite to the Landsat series. The Landsat-7 \nsatellite carries the Enhanced Thematic Mapper Plus (ETM+) sensor.  A mechanical failure \nof the ETM+ Scan Line Corrector (SLC) occurred on May 31, 2003, with the result that all \nLandsat 7 scenes acquired from July 14, 2003 to present have been collected in 'SLC-off' \nmode. 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The original images \nwere obtained from the U.S. Geological Survey Earth Resources Observation and Science \nCenter (EROS). EROS is responsible for archive management and distribution of Landsat \ndata products. The Landsat-7 satellite is part of an ongoing mission to provide quality remote \nsensing data in support of research and applications activities. The launch of Landsat-7 on \nApril 15, 1999 marks the addition of the latest satellite to the Landsat series. The Landsat-7 \nsatellite carries the Enhanced Thematic Mapper Plus (ETM+) sensor.  A mechanical failure \nof the ETM+ Scan Line Corrector (SLC) occurred on May 31, 2003, with the result that all \nLandsat 7 scenes acquired from July 14, 2003 to present have been collected in 'SLC-off' \nmode. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1DYVVFC","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.69b40e3ab66b018f981b8204.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b40e3ab66b018f981b8204","keyword":["KY","Kentucky","US","USGS:69b40e3ab66b018f981b8204","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-83.153206, 37.47358, -83.153206, 37.47358","theme":["geospatial"],"title":"Imagery Station 114 [Little Millseat Branch, Robinson Forest, KY]"},"description":"This data release contains relative timeseries predictions for a single site at which a low-cost trail camera collects images at a fixed frequency. 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The release is versioned over time as additional models or data are added for the site. \nFor more information about the modeling methodology, view these publications:\n[1] Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M., and Letcher, B.H., 2025, Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model: Hydrology and Earth System Sciences, v. 29, no. 22, p. 6445-6460, at https://doi.org/10.5194/hess-29-6445-2025.\n[2] Gupta, A., Chang, T., Walker, J., and B. Letcher (2022). Towards Continuous Streamflow Monitoring with Time-Lapse Cameras and Deep Learning. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (COMPASS '22). Association for Computing Machinery, New York, NY, USA, 353-363. https://doi.org/10.1145/3530190.3534805\nAnd for modeling code that can use the model objects in this data release see this software release: \n[3] Walker, J.D., Gupta, A., Fair, J.B., Goodling, P.J., and Letcher, B.A., 2025, Streamflow Rank Estimation (SRE) Model: U.S. Geological Survey software release, https://doi.org/10.5066/P1YSANVM.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c754004e-f1f9-45c8-9819-d0da7917e38b","harvest_record_raw":"https://catalog.data.gov/harvest_record/c754004e-f1f9-45c8-9819-d0da7917e38b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b40e3ab66b018f981b8204","keyword":["KY","Kentucky","US","USGS:69b40e3ab66b018f981b8204","United States","environment","field monitoring stations","hydrology","image analysis","inlandWaters","monitoring networks","water resources"],"last_harvested_date":"2026-08-30T18:53:25.580181","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"imagery-station-114-little-millseat-branch-robinson-forest-ky","spatial_centroid":{"lat":37.47358,"lon":-83.153206},"spatial_shape":{"coordinates":[-83.153206,37.47358],"type":"Point"},"theme":["geospatial"],"title":"Imagery Station 114 [Little Millseat Branch, Robinson Forest, KY]","type":"dataset"},{"_score":9.196194,"_sort":[1788115999617,9.196194,0,"19ff698a-4da2-4dce-a377-7cd7b52e1aba"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"R/P1W","contactPoint":{"@type":"vcard:Contact","fn":"U.S. Environmental Protection Agency, Office of Land and Emergency Management","hasEmail":"mailto:whalen.kelsey@epa.gov"},"describedByType":"application/octet-stream","description":"This GIS dataset contains polygons depicting U.S. EPA Superfund features. 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This dataset is reserved for important Superfund site features that are best captured as polygon features in geospatial datasets. Superfund features are managed by regional teams of geospatial professionals and remedial program managers (RPMs), and SEGS harvests regional data on a weekly basis to refresh the national dataset and feature services.","distribution_titles":["EPA GeoPlatform Hosted Feature Service"],"harvest_record":"https://catalog.data.gov/harvest_record/a79d706c-5359-4b54-abad-89f8b28d24ee","harvest_record_raw":"https://catalog.data.gov/harvest_record/a79d706c-5359-4b54-abad-89f8b28d24ee/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/a79d706c-5359-4b54-abad-89f8b28d24ee/transformed","has_download":true,"has_spatial":true,"identifier":"https://edg.epa.gov/WAFer_harvest/ISO/olem-harvest_Superfund_NPL_SITE_FEATURE_POLYS_SF.xml","keyword":["United States","Cleanup","Contaminant","Environment","Facilities","Health","Human","Impact","Management","Monitoring","Regulatory","Remediation","Sites","Toxics","020:108","FAC","Facilities","SEGS"],"last_harvested_date":"2026-08-30T18:53:19.617665","organization":{"aliases":["EPA"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"82b85475-f85d-404a-b95b-89d1a42e9f6b","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/epa.png","name":"U.S. Environmental Protection Agency","organization_type":"Federal Government","slug":"epa"},"parent_identifier":null,"popularity":0,"publisher":"U.S. EPA Office of Environmental Information (OEI)","slug":"npl-superfund-site-feature-areas-epa-ec447","spatial_centroid":{"lat":42.058006999999996,"lon":-109.30235520000001},"spatial_shape":{"coordinates":[[[-105.19895,37.785345],[-105.19895,48.467],[-115.457463,48.467],[-115.457463,37.785345],[-105.19895,37.785345]]],"type":"Polygon"},"theme":["geospatial"],"title":"NPL Superfund Site Feature Areas (EPA)","type":"dataset"},{"_score":7.6405196,"_sort":[1788115998127,7.6405196,1,"be1ced3d-b80c-435e-ab30-945f757b85e1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kerri Hitt.","hasEmail":"mailto:khitt@usgs.gov"},"description":"This data set represents the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per hectare,\nin the conterminous United States.\n\nThe data set was used as an input data layer for a national\nmodel to predict nitrate concentration in shallow ground water.\n\nNolan and Hitt (2006) developed two national models to predict\ncontamination of ground water by nonpoint sources of\nnitrate. The nonlinear approach to national-scale Ground-WAter\nVulnerability Assessment (GWAVA) uses components representing\nnitrogen (N) sources, transport, and attenuation.\n\nOne model (GWAVA-S) predicts nitrate contamination of shallow\n(typically less than 5 meters deep), recently recharged ground\nwater, which may or may not be used for drinking.  The other\n(GWAVA-DW) predicts ambient nitrate concentration in deeper\nsupplies used for drinking.\n\nThis data set is one of 17 data sets (1 output data set and 16\ninput data sets) associated with the GWAVA-S model. Full details\nof the model development are in Nolan and Hitt (2006).\n\nFor inputs to the model, spatial attributes representing 16\nnitrogen loading and transport and attenuation factors were\ncompiled as raster data sets (1-km by 1-km grid cell size) for\nthe conterminous United States (see table 1).\n\n>Table 1.-- Parameters of nonlinear regression model for nitrate in shallow\n>           ground water (GWAVA-S) and corresponding input spatial data sets.\n>           [kg, kilograms; km2, square kilometers.]\n>\n>Nitrogen Source Factors                  Data Set Name\n>   1 farm fertilizer (kg/hectare)        gwava-s_ffer\n>   2 confined manure (kg/hectare)        gwava-s_conf\n>   3 orchards/vineyards (percent)        gwava-s_orvi\n>   4 population density  (people/km2)    gwava-s_popd\n>   5 cropland/pasture/fallow (percent)   gwava-s_crpa\n>\n>Transport to Aquifer Factors\n>   6 water input (km2/cm)                gwava-s_wtin\n>   7 carbonate rocks (yes/no)            gwava-s_crox\n>   8 basalt and volcanic rocks (yes/no)  gwava-s_vrox\n>   9 drainage ditch (km2)                gwava-s_ddit\n>  10 slope (percent x 1000)              gwava-s_slop\n>  11 glacial till (yes/no)               gwava-s_gtil\n>  12 clay sediment (percent x 1000)      gwava-s_clay\n>\n>Attenuation Factors\n>  13 fresh surface water withdrawal      gwava-s_swus\n>     for irrigation (megaliters/day)\n>  14 irrigation tailwater recovery (km2) gwava-s_twre\n>  15 histosol soil type (percent)        gwava-s_hist\n>  16 wetlands (percent)                  gwava-s_wetl\n\n\"Farm fertilizer\" is the average annual nitrogen input from\ncommercial fertilizer applied to agricultural lands, 1992-2001, in\nkilograms per hectare.\n\n\"Confined manure\" is the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per\nhectare.\n\n\"Orchards/vineyards\" is the percent of orchards/vineyards land\ncover classification.\n\n\"Population density\" is 1990 block group population density, in\npeople per square kilometer.\n\n\"Cropland/pasture/fallow\" is the percent of\ncropland/pasture/fallow land cover classifications.\n\n\"Water input\" is the ratio of the total area of irrigated land\nto precipitation, in square kilometers per centimeter.\n\n\"Carbonate rocks\" is the presence or absence of Valley and Ridge\ncarbonate rocks.\n\n\"Basalt and volcanic rocks\" is the presence or absence of basalt\nand volcanic rocks.\n\n\"Drainage ditch\" is the area of National Resources Inventory surface\ndrainage, field ditch conservation practice, in square kilometers.\n\n\"Slope\" is the soil surface slope, in percent times 1000.\n\n\"Glacial till\" is the presence or absence of poorly sorted\nglacial till east of the Rocky Mountains.\n\n\"Clay sediment\" is the amount of clay sediment in the soil, in\npercent times 1000.\n\n\"Fresh surface water withdrawal for irrigation\" is the amount of\nfresh surface water withdrawal for irrigation, in megaliters per day.\n\n\"Irrigation tailwater recovery\" is the area of National\nResources Inventory irrigation system, tailwater recovery\nconservation practice, in square kilometers.\n\n\"Histosol soil type\" is the amount of histosols soil taxonomic\norder, in percent.\n\n\"Wetlands\" is the percent of woody wetlands and emergent\nherbaceous wetlands land cover classifications.\n\nReference cited:\n\nNolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow\nground water and drinking-water wells to nitrate in the United\nStates: Environmental Science and Technology, vol. 40, no. 24,\npages 7834-7840.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://water.usgs.gov/GIS/dsdl/gwava-s/index.html","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original 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shallow, recently recharged ground water -- Input data set for confined manure (gwava-s_conf)"},"description":"This data set represents the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per hectare,\nin the conterminous United States.\n\nThe data set was used as an input data layer for a national\nmodel to predict nitrate concentration in shallow ground water.\n\nNolan and Hitt (2006) developed two national models to predict\ncontamination of ground water by nonpoint sources of\nnitrate. The nonlinear approach to national-scale Ground-WAter\nVulnerability Assessment (GWAVA) uses components representing\nnitrogen (N) sources, transport, and attenuation.\n\nOne model (GWAVA-S) predicts nitrate contamination of shallow\n(typically less than 5 meters deep), recently recharged ground\nwater, which may or may not be used for drinking.  The other\n(GWAVA-DW) predicts ambient nitrate concentration in deeper\nsupplies used for drinking.\n\nThis data set is one of 17 data sets (1 output data set and 16\ninput data sets) associated with the GWAVA-S model. Full details\nof the model development are in Nolan and Hitt (2006).\n\nFor inputs to the model, spatial attributes representing 16\nnitrogen loading and transport and attenuation factors were\ncompiled as raster data sets (1-km by 1-km grid cell size) for\nthe conterminous United States (see table 1).\n\n>Table 1.-- Parameters of nonlinear regression model for nitrate in shallow\n>           ground water (GWAVA-S) and corresponding input spatial data sets.\n>           [kg, kilograms; km2, square kilometers.]\n>\n>Nitrogen Source Factors                  Data Set Name\n>   1 farm fertilizer (kg/hectare)        gwava-s_ffer\n>   2 confined manure (kg/hectare)        gwava-s_conf\n>   3 orchards/vineyards (percent)        gwava-s_orvi\n>   4 population density  (people/km2)    gwava-s_popd\n>   5 cropland/pasture/fallow (percent)   gwava-s_crpa\n>\n>Transport to Aquifer Factors\n>   6 water input (km2/cm)                gwava-s_wtin\n>   7 carbonate rocks (yes/no)            gwava-s_crox\n>   8 basalt and volcanic rocks (yes/no)  gwava-s_vrox\n>   9 drainage ditch (km2)                gwava-s_ddit\n>  10 slope (percent x 1000)              gwava-s_slop\n>  11 glacial till (yes/no)               gwava-s_gtil\n>  12 clay sediment (percent x 1000)      gwava-s_clay\n>\n>Attenuation Factors\n>  13 fresh surface water withdrawal      gwava-s_swus\n>     for irrigation (megaliters/day)\n>  14 irrigation tailwater recovery (km2) gwava-s_twre\n>  15 histosol soil type (percent)        gwava-s_hist\n>  16 wetlands (percent)                  gwava-s_wetl\n\n\"Farm fertilizer\" is the average annual nitrogen input from\ncommercial fertilizer applied to agricultural lands, 1992-2001, in\nkilograms per hectare.\n\n\"Confined manure\" is the average annual nitrogen input from\nconfined animal manure, 1992 and 1997, in kilograms per\nhectare.\n\n\"Orchards/vineyards\" is the percent of orchards/vineyards land\ncover classification.\n\n\"Population density\" is 1990 block group population density, in\npeople per square kilometer.\n\n\"Cropland/pasture/fallow\" is the percent of\ncropland/pasture/fallow land cover classifications.\n\n\"Water input\" is the ratio of the total area of irrigated land\nto precipitation, in square kilometers per centimeter.\n\n\"Carbonate rocks\" is the presence or absence of Valley and Ridge\ncarbonate rocks.\n\n\"Basalt and volcanic rocks\" is the presence or absence of basalt\nand volcanic rocks.\n\n\"Drainage ditch\" is the area of National Resources Inventory surface\ndrainage, field ditch conservation practice, in square kilometers.\n\n\"Slope\" is the soil surface slope, in percent times 1000.\n\n\"Glacial till\" is the presence or absence of poorly sorted\nglacial till east of the Rocky Mountains.\n\n\"Clay sediment\" is the amount of clay sediment in the soil, in\npercent times 1000.\n\n\"Fresh surface water withdrawal for irrigation\" is the amount of\nfresh surface water withdrawal for irrigation, in megaliters per 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