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In addition, extreme water level recurrence for the 1-,2-,5-,10-,20-,50-, and 100-year water levels computed from annual Maxima/Generalized Extreme Value (AM/GEV) and peak-over-threshold (POT) extreme value analyses across the entire domain are presented.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/52620ee0-4ce5-4fb3-8a19-7964bdb9fd96","harvest_record_raw":"https://catalog.data.gov/harvest_record/52620ee0-4ce5-4fb3-8a19-7964bdb9fd96/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63ac9989d34e92aad3ca1445","keyword":["CMHRP","Climate Change","Coastal and Marine Hazards and Resources Program","Distributions","Extreme Weather","Hazards Planning","Ocean Winds","PCMSC","Pacific Coastal and Marine Science Center","Predictions","Puget Sound","Salish Sea","State of Washington","Storms","U.S. Geological Survey","USGS","USGS:63ac9989d34e92aad3ca1445","Wind","coastal processes","geoscientificInformation","numerical modeling","oceans","water level measurements"],"last_harvested_date":"2026-09-10T22:50:53.194603","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":"salish-sea-water-level-hindcast-simulations-1985-2015","spatial_centroid":{"lat":48.827999999999996,"lon":-126.34400000000001},"spatial_shape":{"coordinates":[[[-129.14,47.0],[-129.14,51.57],[-122.15,51.57],[-122.15,47.0],[-129.14,47.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Salish Sea water level hindcast simulations: 1985-2015","type":"dataset"},{"_score":8.778986,"_sort":[1789080651201,8.778986,2,"44910ba5-b6c4-4494-9313-ae768358536c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ellyn T. 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The files may be downloaded or\naccessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP\nframework allows users to access data from anywhere on the Internet using a variety of Web services\nincluding Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data\ncompliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://stellwagen.er.usgs.gov/thredds/catalog/TSdata/catalog.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.cd2c4288-56f3-46ec-ab12-734c435bf349.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_cd2c4288-56f3-46ec-ab12-734c435bf349","keyword":["AIR TEMPERATURE","ANEMOMETERS","ATMOSPHERIC PRESSURE MEASUREMENTS","ATTENUATION/TRANSMISSION","Adriatic Sea","Air Temperature","Atlantic Ocean","Average Burst Pressure","BAROMETERS","Barnegat Bay, NJ","Beam Attenuation","Blackwater National Wildlife Refuge, MD","Buzzard's Bay, MA","CMGP","CONDUCTIVITY","CONDUCTIVITY METERS","CONDUCTIVITY, TEMPERATURE, DEPTH","CURRENT METERS","Cape Hatteras, NC","Chandeleur Islands, LA","Chincoteague Bay, MD and VA","Coastal and Marine Geology Program","Current Direction (t)","Current Speed","DENSITY","DODS > DISTRIBUTED OCEANOGRAPHIC DATA SYSTEM","Dauphin Island, LA","Dissolved Oxygen","ESIP > EARTH SCIENCE INFORMATION PARTNERS PROGRAM","Fire Island, NY","GLOBEC > GLOBAL OCEAN ECOSYSTEM DYNAMICS, IGBP","GOMODP > GULF OF MAINE OCEAN DATA PARTNERSHIP","Georges Bank, ME","Gulf of Maine","Gulf of Mexico","Hawaiian waters","Hudson Shelf Valley, NY","IGBP > INTERNATIONAL GEOSPHERE-BIOSPHERE PROGRAMME","INCLINOMETERS","Martha's Vineyard, MA","Massachusetts Bay, MA","Meteorology","Monterrey Bay, CA","North America","OCEAN CURRENTS","OPENDAP > OPEN-SOURCE PROJECT FOR A NETWORK DATA ACCESS PROTOCOL","OXYGEN","Oxygen","PAR","POTENTIAL DENSITY","PRESSURE GAUGES","PRESSURE TRANSDUCERS","Pacific Ocean","Palos Verdes Shelf, CA","Rachel Carlson National Wildlife Refuge, ME","SALINITY","SALINITY, TEMPERATURE, DEPTH","SEA LEVEL PRESSURE","SURFACE AIR TEMPERATURE","SURFACE PRESSURE","SURFACE WINDS","Salinity","Sea Surface Temperature","Seal Beach, Pt. 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Each experiment page has documentation and maps that provide details of what\ndata were collected at each site. Links to related publications with additional information about the\nresearch are also provided. The data are stored in Network Common Data Format (netCDF) files using the\nEquatorial Pacific Information Collection (EPIC) conventions defined by the National Oceanic and\nAtmospheric Administration (NOAA) Pacific Marine Environmental Laboratory. NetCDF is a general,\nself-documenting, machine-independent, open source data format created and supported by the University\nCorporation for Atmospheric Research (UCAR). EPIC is an early set of standards designed to allow\nresearchers from different organizations to share oceanographic data. The files may be downloaded or\naccessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP\nframework allows users to access data from anywhere on the Internet using a variety of Web services\nincluding Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data\ncompliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/22897cda-10fc-4860-9e10-5c872c1b8086","harvest_record_raw":"https://catalog.data.gov/harvest_record/22897cda-10fc-4860-9e10-5c872c1b8086/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_cd2c4288-56f3-46ec-ab12-734c435bf349","keyword":["AIR TEMPERATURE","ANEMOMETERS","ATMOSPHERIC PRESSURE MEASUREMENTS","ATTENUATION/TRANSMISSION","Adriatic Sea","Air Temperature","Atlantic Ocean","Average Burst Pressure","BAROMETERS","Barnegat Bay, NJ","Beam Attenuation","Blackwater National Wildlife Refuge, MD","Buzzard's Bay, MA","CMGP","CONDUCTIVITY","CONDUCTIVITY METERS","CONDUCTIVITY, TEMPERATURE, DEPTH","CURRENT METERS","Cape Hatteras, NC","Chandeleur Islands, LA","Chincoteague Bay, MD and VA","Coastal and Marine Geology Program","Current Direction (t)","Current Speed","DENSITY","DODS > DISTRIBUTED OCEANOGRAPHIC DATA SYSTEM","Dauphin Island, LA","Dissolved Oxygen","ESIP > EARTH SCIENCE INFORMATION PARTNERS PROGRAM","Fire Island, NY","GLOBEC > GLOBAL OCEAN ECOSYSTEM DYNAMICS, IGBP","GOMODP > GULF OF MAINE OCEAN DATA PARTNERSHIP","Georges Bank, ME","Gulf of Maine","Gulf of Mexico","Hawaiian waters","Hudson Shelf Valley, NY","IGBP > INTERNATIONAL GEOSPHERE-BIOSPHERE PROGRAMME","INCLINOMETERS","Martha's Vineyard, MA","Massachusetts Bay, MA","Meteorology","Monterrey Bay, CA","North America","OCEAN CURRENTS","OPENDAP > OPEN-SOURCE PROJECT FOR A NETWORK DATA ACCESS PROTOCOL","OXYGEN","Oxygen","PAR","POTENTIAL DENSITY","PRESSURE GAUGES","PRESSURE TRANSDUCERS","Pacific Ocean","Palos Verdes Shelf, CA","Rachel Carlson National Wildlife Refuge, ME","SALINITY","SALINITY, TEMPERATURE, DEPTH","SEA LEVEL PRESSURE","SURFACE AIR TEMPERATURE","SURFACE PRESSURE","SURFACE WINDS","Salinity","Sea Surface Temperature","Seal Beach, Pt. 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This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P95BV7GE","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.606b19cad34edc0435c364a5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_606b19cad34edc0435c364a5","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:606b19cad34edc0435c364a5","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"modified":"2021-09-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.1475, 25.0631, -73.6063, 43.1045","theme":["geospatial"],"title":"Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. 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The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and transport scenarios. For this study, the ADCIRC unstructured finite element mesh domain spans to the 60th meridian west in the Atlantic Ocean and has higher spatial resolution elements (20 - 100 meters (m)) along the northern Gulf of Mexico coast from Louisiana through the Florida Panhandle. The ADCIRC model setup requires the input of topographic and bathymetric elevations at each mesh node. Model inputs in the form of topography and bathymetry and model outputs in the form of water levels and velocities at each mesh node are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Smith and others (2020).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9WGJO0S","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.570e7e83-059a-4289-87bd-6da7bff87c19.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_570e7e83-059a-4289-87bd-6da7bff87c19","keyword":["Alabama","Gulf of Mexico","Mobile Bay","North America","SPCMSC","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:570e7e83-059a-4289-87bd-6da7bff87c19","United States","bathymetry","coastal processes","digital elevation models","elevation","estuarine processes","geomorphology","geoscientificInformation","marine geology","morphologic change","oceans","tides (oceanic)","topography"],"modified":"2022-01-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-97.8570, 7.9130, -59.9440, 45.8370","theme":["geospatial"],"title":"Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_MP)"},"description":"Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and transport scenarios. For this study, the ADCIRC unstructured finite element mesh domain spans to the 60th meridian west in the Atlantic Ocean and has higher spatial resolution elements (20 - 100 meters (m)) along the northern Gulf of Mexico coast from Louisiana through the Florida Panhandle. The ADCIRC model setup requires the input of topographic and bathymetric elevations at each mesh node. Model inputs in the form of topography and bathymetry and model outputs in the form of water levels and velocities at each mesh node are provided in this data release. 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These data were used to: 1) quantify the direction and magnitude of expected changes in several measures of soil temperature and soil moisture, including the key variables used to distinguish the regimes used in the R and R categories; 2) assess how these changes will impact the geographic distribution of soil temperature and moisture regimes; and 3) explore the implications for using R and R categories for estimating future ecosystem resilience and resistance.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9PJFE82","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.5cdb058fe4b0ab16db3a83b4.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5cdb058fe4b0ab16db3a83b4","keyword":["Alberta","Arizona","British Columbia","California","Canada","Colorado","Idaho","Kansas","Manitoba","Minnesota","Montana","Nebraska","Nevada","New Mexico","North Dakota","Oklahoma","Oregon","Saskatchewan","South Dakota","Texas","USGS:5cdb058fe4b0ab16db3a83b4","United States","Utah","Washington","Wyoming","aridification","aridity","big sagebrush ecosystems","biota","cheatgrass","clay loam","climate","climate change","climate models","concentration pathways","drought resistance","droughts","ecological model","ecological transformation","ecosystem resilience","future time periods","historical conditions","resilience","sandy loam","silt loam","soil temperature","soil types","vulnerability"],"modified":"2020-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-126.00, 28.00, -95.00, 56.00","theme":["geospatial"],"title":"Historical and 21st century soil temperature and moisture data for drylands of western U.S. and Canada"},"description":"These data represent simulated soil temperature and moisture conditions for current climate, and for future climate represented by all available climate models at two time periods during the 21st century.  These data were used to: 1) quantify the direction and magnitude of expected changes in several measures of soil temperature and soil moisture, including the key variables used to distinguish the regimes used in the R and R categories; 2) assess how these changes will impact the geographic distribution of soil temperature and moisture regimes; and 3) explore the implications for using R and R categories for estimating future ecosystem resilience and resistance.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7ef81843-9d6a-4e2e-9bdb-72b0e47198d1","harvest_record_raw":"https://catalog.data.gov/harvest_record/7ef81843-9d6a-4e2e-9bdb-72b0e47198d1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5cdb058fe4b0ab16db3a83b4","keyword":["Alberta","Arizona","British Columbia","California","Canada","Colorado","Idaho","Kansas","Manitoba","Minnesota","Montana","Nebraska","Nevada","New Mexico","North Dakota","Oklahoma","Oregon","Saskatchewan","South Dakota","Texas","USGS:5cdb058fe4b0ab16db3a83b4","United States","Utah","Washington","Wyoming","aridification","aridity","big sagebrush ecosystems","biota","cheatgrass","clay loam","climate","climate change","climate models","concentration pathways","drought resistance","droughts","ecological model","ecological transformation","ecosystem resilience","future time periods","historical conditions","resilience","sandy loam","silt loam","soil temperature","soil types","vulnerability"],"last_harvested_date":"2026-09-10T22:50:34.405423","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":"historical-and-21st-century-soil-temperature-and-moisture-data-for-drylands-of-western-u-s","spatial_centroid":{"lat":39.2,"lon":-113.6},"spatial_shape":{"coordinates":[[[-126.0,28.0],[-126.0,56.0],[-95.0,56.0],[-95.0,28.0],[-126.0,28.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Historical and 21st century soil temperature and moisture data for drylands of western U.S. and Canada","type":"dataset"},{"_score":52.413063,"_sort":[1789080633051,52.413063,1,"14e8b9ec-253a-4937-9192-34458291c757"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adrienne M. Wootten","hasEmail":"mailto:amwootte@ou.edu"},"description":"Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided.\nApplied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13NMKWU","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.66bb5ff6d34e0338828136e0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66bb5ff6d34e0338828136e0","keyword":["Edwards Aquifer","San Antonio","Texas","USGS:66bb5ff6d34e0338828136e0","atmospheric and climatic processes","climate change","climatologyMeteorologyAtmosphere","downscaling"],"modified":"2024-09-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-104.8975, 27.0983, -94.1309, 34.3797","theme":["geospatial"],"title":"Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 \u2013 2100 and CMIP6 for the years 2015 \u2013 2100"},"description":"Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided.\nApplied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d48921b7-7713-4fd1-a326-3d506ed555c3","harvest_record_raw":"https://catalog.data.gov/harvest_record/d48921b7-7713-4fd1-a326-3d506ed555c3/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66bb5ff6d34e0338828136e0","keyword":["Edwards Aquifer","San Antonio","Texas","USGS:66bb5ff6d34e0338828136e0","atmospheric and climatic processes","climate change","climatologyMeteorologyAtmosphere","downscaling"],"last_harvested_date":"2026-09-10T22:50:33.051165","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":"downscaled-climate-projections-for-the-edwards-aquifer-region-ear-using-cmip5-fo-2015-2100","spatial_centroid":{"lat":30.010859999999997,"lon":-100.59085999999999},"spatial_shape":{"coordinates":[[[-104.8975,27.0983],[-104.8975,34.3797],[-94.1309,34.3797],[-94.1309,27.0983],[-104.8975,27.0983]]],"type":"Polygon"},"theme":["geospatial"],"title":"Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 \u2013 2100 and CMIP6 for the years 2015 \u2013 2100","type":"dataset"},{"_score":19.642805,"_sort":[1789080631706,19.642805,1,"4a1507fe-0d0b-4f09-906e-6705c1299f6e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adam J Terando","hasEmail":"mailto:aterando@usgs.gov"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P95BV7GE","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.609c874cd34ea221ce3ac1e0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609c874cd34ea221ce3ac1e0","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609c874cd34ea221ce3ac1e0","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"modified":"2021-09-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.1475, 25.0631, -73.6063, 43.1045","theme":["geospatial"],"title":"INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/57c6aef2-5486-4321-8320-105e3efdd78d","harvest_record_raw":"https://catalog.data.gov/harvest_record/57c6aef2-5486-4321-8320-105e3efdd78d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609c874cd34ea221ce3ac1e0","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609c874cd34ea221ce3ac1e0","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"last_harvested_date":"2026-09-10T22:50:31.706960","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":"inmcm-historical-prescribed-burn-windows-for-the-southeast-united-states-1950-1999","spatial_centroid":{"lat":32.27966,"lon":-90.73102},"spatial_shape":{"coordinates":[[[-102.1475,25.0631],[-102.1475,43.1045],[-73.6063,43.1045],[-73.6063,25.0631],[-102.1475,25.0631]]],"type":"Polygon"},"theme":["geospatial"],"title":"INMCM Historical Prescribed Burn Windows for the Southeast United States 1950-1999","type":"dataset"},{"_score":10.532139,"_sort":[1789080630380,10.532139,1,"94327df7-4187-40c9-a568-57f9fb932e1b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Melanie Gogol-Prokurat","hasEmail":"mailto:Melanie.Gogol-Prokurat@wildlife.ca.gov"},"description":"The East Tehama herd is the largest migratory population of mule deer in California (Hill and Figura, 2020). Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife\u2019s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd\u2019s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9LSKEZQ","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.63650bafd34ebe442507ce71.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63650bafd34ebe442507ce71","keyword":["California","Lassen National Forest","USGS:63650bafd34ebe442507ce71","United States","animal behavior","biota","migration (organisms)","migration route","migratory species"],"modified":"2023-10-04T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.2265, 39.7926, -120.6566, 40.7181","theme":["geospatial"],"title":"California Mule Deer East Tehama Routes"},"description":"The East Tehama herd is the largest migratory population of mule deer in California (Hill and Figura, 2020). Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife\u2019s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd\u2019s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b","harvest_record_raw":"https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63650bafd34ebe442507ce71","keyword":["California","Lassen National Forest","USGS:63650bafd34ebe442507ce71","United States","animal behavior","biota","migration (organisms)","migration route","migratory species"],"last_harvested_date":"2026-09-10T22:50:30.380896","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":"california-mule-deer-east-tehama-routes","spatial_centroid":{"lat":40.1628,"lon":-121.59854},"spatial_shape":{"coordinates":[[[-122.2265,39.7926],[-122.2265,40.7181],[-120.6566,40.7181],[-120.6566,39.7926],[-122.2265,39.7926]]],"type":"Polygon"},"theme":["geospatial"],"title":"California Mule Deer East Tehama Routes","type":"dataset"},{"_score":19.321392,"_sort":[1789080625632,19.321392,3,"cdc0088e-dc4e-4856-a6fd-3a31b3141478"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Melinda Martinez","hasEmail":"mailto:melindamartinez@usgs.gov"},"description":"Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. There various forms to calculate fluxes using cone and frustrum shapes that were compared to LiDAR scans fromi the field, therefore surface area and volume for each geometric shape is also included in the dataset.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P164M78X","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.6647ad62d34e1955f5a4418e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6647ad62d34e1955f5a4418e","keyword":["Arkansas","Illinois","Louisiana","USGS:6647ad62d34e1955f5a4418e","carbon","cypress knees","cypress swamp","environment","freshwater wetlands","methane"],"modified":"2024-06-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.6367, 29.7644, -87.9785, 37.9269","theme":["geospatial"],"title":"Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley"},"description":"Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. There various forms to calculate fluxes using cone and frustrum shapes that were compared to LiDAR scans fromi the field, therefore surface area and volume for each geometric shape is also included in the dataset.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/320271c7-54a6-4917-a088-7c5e4c622f2b","harvest_record_raw":"https://catalog.data.gov/harvest_record/320271c7-54a6-4917-a088-7c5e4c622f2b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6647ad62d34e1955f5a4418e","keyword":["Arkansas","Illinois","Louisiana","USGS:6647ad62d34e1955f5a4418e","carbon","cypress knees","cypress swamp","environment","freshwater wetlands","methane"],"last_harvested_date":"2026-09-10T22:50:25.632336","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":"methane-emissions-associated-with-bald-cypress-knees-across-the-mississippi-river-alluvial","spatial_centroid":{"lat":33.029399999999995,"lon":-90.77342},"spatial_shape":{"coordinates":[[[-92.6367,29.7644],[-92.6367,37.9269],[-87.9785,37.9269],[-87.9785,29.7644],[-92.6367,29.7644]]],"type":"Polygon"},"theme":["geospatial"],"title":"Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley","type":"dataset"},{"_score":16.619213,"_sort":[1789080625159,16.619213,4,"e835bfe1-6254-48d6-b347-dfde6b738cb4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Customer Service Representative","hasEmail":"mailto:custserv@usgs.gov"},"description":"Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the \u201cBack-in-Time\u201d (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, \u201cbase\u201d maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P90Q8BCP","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.5de7de8fe4b02caea0eb9917.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917","keyword":["Central Basin and Range","MT","Montana","NV","Nevada","Northern Basin and Range","Northwestern Great Plains","Southern Rockies","USGS:5de7de8fe4b02caea0eb9917","United States","WY","Wyoming","Wyoming Basin","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","farming","fractional components","geoscientificInformation","grass","herbaceous","litter","rangeland","rangelands","remote sensing","sagebrush","shrub","shrubland","shrubland ecosystems","shrublands","terrestrial ecosystems","time-series","validation","vegetation"],"modified":"2020-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-119.9472, 39.7848, -104.2109, 46.8470","theme":["geospatial"],"title":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands"},"description":"Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the \u201cBack-in-Time\u201d (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, \u201cbase\u201d maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad","harvest_record_raw":"https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917","keyword":["Central Basin and Range","MT","Montana","NV","Nevada","Northern Basin and Range","Northwestern Great Plains","Southern Rockies","USGS:5de7de8fe4b02caea0eb9917","United States","WY","Wyoming","Wyoming Basin","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","farming","fractional components","geoscientificInformation","grass","herbaceous","litter","rangeland","rangelands","remote sensing","sagebrush","shrub","shrubland","shrubland ecosystems","shrublands","terrestrial ecosystems","time-series","validation","vegetation"],"last_harvested_date":"2026-09-10T22:50:25.159524","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":"temporal-and-spatio-temporal-high-resolution-satellite-data-for-the-validation-of-a-landsa","spatial_centroid":{"lat":42.60968,"lon":-113.65267999999999},"spatial_shape":{"coordinates":[[[-119.9472,39.7848],[-119.9472,46.847],[-104.2109,46.847],[-104.2109,39.7848],[-119.9472,39.7848]]],"type":"Polygon"},"theme":["geospatial"],"title":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands","type":"dataset"},{"_score":26.958138,"_sort":[1789080624715,26.958138,4,"df046ac9-912d-45b8-bf74-2d4f5ec1bc76"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kathi Jo Jankowski","hasEmail":"mailto:kjankowski@usgs.gov"},"description":"This dataset includes average, and annual average (e.g., average of 2020) watershed characteristics and environmental driver data for 189 rivers across the Northern Hemisphere. Average data includes lithology (e.g., percent of watershed covered by volcanics), land use (e.g., percent of watershed covered by cropland), maximum day length, median nitrogen and phosphorus concentrations, maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Average data includes maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Land use, lithology, snow covered area, precipitation, temperature, evapotranspiration, green-up day, and net primary productivity were sourced from public, globally available spatial datasets. Nitrogen, phosphorus, and discharge data were sourced from public and/or published datasets. Watershed characteristics and environmental variables were used in a series of random forest models to assess the drivers of 1) average fluvial silicon concentration regime; 2) annual fluvial silicon concentration regime; and 3) the minimum and maximum silicon concentrations within a given regime.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14NBAYZ","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.6684087fd34e0f592272b3da.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6684087fd34e0f592272b3da","keyword":["Canada","Finland","Norway","Russia","Sweden","USGS:6684087fd34e0f592272b3da","United States","biogeochemistry","regime","river","silicon","spatial datasets","watershed characteristics"],"modified":"2024-08-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, 15.9613, 177.8906, 76.5168","theme":["geospatial"],"title":"Average and annual watershed climate, hydrology, and productivity data for rivers across the Northern Hemisphere"},"description":"This dataset includes average, and annual average (e.g., average of 2020) watershed characteristics and environmental driver data for 189 rivers across the Northern Hemisphere. Average data includes lithology (e.g., percent of watershed covered by volcanics), land use (e.g., percent of watershed covered by cropland), maximum day length, median nitrogen and phosphorus concentrations, maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Average data includes maximum watershed proportion of snow covered area, precipitation, temperature, evapotranspiration, green-up day, net primary productivity, 5th percentile discharge, 95th percentile discharge, day of minimum discharge, day of maximum discharge, and coefficient of variation of discharge. Land use, lithology, snow covered area, precipitation, temperature, evapotranspiration, green-up day, and net primary productivity were sourced from public, globally available spatial datasets. Nitrogen, phosphorus, and discharge data were sourced from public and/or published datasets. Watershed characteristics and environmental variables were used in a series of random forest models to assess the drivers of 1) average fluvial silicon concentration regime; 2) annual fluvial silicon concentration regime; and 3) the minimum and maximum silicon concentrations within a given regime.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5c3947aa-4271-484c-97fc-0bd95a5ee7d5","harvest_record_raw":"https://catalog.data.gov/harvest_record/5c3947aa-4271-484c-97fc-0bd95a5ee7d5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6684087fd34e0f592272b3da","keyword":["Canada","Finland","Norway","Russia","Sweden","USGS:6684087fd34e0f592272b3da","United States","biogeochemistry","regime","river","silicon","spatial datasets","watershed characteristics"],"last_harvested_date":"2026-09-10T22:50:24.715318","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":"average-and-annual-watershed-climate-hydrology-and-productivity-data-for-rivers-across-the","spatial_centroid":{"lat":40.1835,"lon":-36.843759999999996},"spatial_shape":{"coordinates":[[[-180.0,15.9613],[-180.0,76.5168],[177.8906,76.5168],[177.8906,15.9613],[-180.0,15.9613]]],"type":"Polygon"},"theme":["geospatial"],"title":"Average and annual watershed climate, hydrology, and productivity data for rivers across the Northern Hemisphere","type":"dataset"},{"_score":9.048181,"_sort":[1789080622725,9.048181,1,"b616e77f-77c3-43b2-8b2a-5f814d655755"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully.  Data are complete for the information presented.\n      ","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NUO62B","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.5d0412ebe4b0e3d3115807a2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0412ebe4b0e3d3115807a2","keyword":["Beaches","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Physical Habitats and Geomorphology","San Luis Obispo County","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","USGS:5d0412ebe4b0e3d3115807a2","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"modified":"2026-03-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.641953027, 34.403744888, -120.444512138, 37.819520138","theme":["geospatial"],"title":"San Luis Obispo County: CoSMoS v3.1 Central California flood hazard projections: 20-year storm"},"description":"This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully.  Data are complete for the information presented.\n      ","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/cc2d44f7-c6c6-419c-a6cd-9b660cd13345","harvest_record_raw":"https://catalog.data.gov/harvest_record/cc2d44f7-c6c6-419c-a6cd-9b660cd13345/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0412ebe4b0e3d3115807a2","keyword":["Beaches","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Physical Habitats and Geomorphology","San Luis Obispo County","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","USGS:5d0412ebe4b0e3d3115807a2","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"last_harvested_date":"2026-09-10T22:50:22.725623","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":"cosmos-coastal-storm-modeling-system-central-california-v3-1-flood-hazard-projections-20-y-08989","spatial_centroid":{"lat":35.770054988,"lon":-121.7629766714},"spatial_shape":{"coordinates":[[[-122.641953027,34.403744888],[-122.641953027,37.819520138],[-120.444512138,37.819520138],[-120.444512138,34.403744888],[-122.641953027,34.403744888]]],"type":"Polygon"},"theme":["geospatial"],"title":"San Luis Obispo County: CoSMoS v3.1 Central California flood hazard projections: 20-year storm","type":"dataset"},{"_score":16.187523,"_sort":[1789080622521,16.187523,0,"e771089e-9d6e-4c75-8d04-aabc2eb36ae5"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Debra A Willard","hasEmail":"mailto:dwillard@usgs.gov"},"description":"Pollen data from sediment core DS-77 were generated in support of research on long-term patterns of vegetation, fire, and climate in Great Dismal Swamp National Wildlife Refuge (Willard et al., in review: IP-143520). Raw counts of pollen data are provided. These data represent new counts from a core collected by Donald R. Whitehead in 1961 and previously presented in Whitehead, 1972.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21233/6NDD-4M42","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.d0757a8e-33ea-4bcd-a808-02e36937304c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_d0757a8e-33ea-4bcd-a808-02e36937304c","keyword":["USGS:d0757a8e-33ea-4bcd-a808-02e36937304c","biota","faunal and floral census (microscopic)","geochronology"],"modified":"2022-10-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-76.4101, 36.5802, -76.41, 36.58025","theme":["geospatial"],"title":"Paleoecological data from Great Dismal Swamp: reanalysis of Whitehead Site DS-77"},"description":"Pollen data from sediment core DS-77 were generated in support of research on long-term patterns of vegetation, fire, and climate in Great Dismal Swamp National Wildlife Refuge (Willard et al., in review: IP-143520). Raw counts of pollen data are provided. These data represent new counts from a core collected by Donald R. Whitehead in 1961 and previously presented in Whitehead, 1972.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/9c13d211-4456-4c02-a77f-4474934ebaf7","harvest_record_raw":"https://catalog.data.gov/harvest_record/9c13d211-4456-4c02-a77f-4474934ebaf7/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_d0757a8e-33ea-4bcd-a808-02e36937304c","keyword":["USGS:d0757a8e-33ea-4bcd-a808-02e36937304c","biota","faunal and floral census (microscopic)","geochronology"],"last_harvested_date":"2026-09-10T22:50:22.521998","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":"paleoecological-data-from-great-dismal-swamp-reanalysis-of-whitehead-site-ds-77","spatial_centroid":{"lat":36.58022,"lon":-76.41006},"spatial_shape":{"coordinates":[[[-76.4101,36.5802],[-76.4101,36.58025],[-76.41,36.58025],[-76.41,36.5802],[-76.4101,36.5802]]],"type":"Polygon"},"theme":["geospatial"],"title":"Paleoecological data from Great Dismal Swamp: reanalysis of Whitehead Site DS-77","type":"dataset"},{"_score":9.113466,"_sort":[1789080620591,9.113466,1,"aeea9d2e-21a1-4cb5-94ec-3cd840ef296f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kaj E Williams","hasEmail":"mailto:kewilliams@usgs.gov"},"description":"A meteorological station equipped with a rain gauge, atmospheric pressure sensor, temperature and relative humidity sensor, soil moisture sensor, and an anemometer (measuring wind speed, gust speed, and direction) was deployed at Grand Falls dune field, Arizona. This dataset has been collecting data every 15 minutes with the goal to provide context for ripple and dune migration at an active dune field site.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P93T2LUL","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.62fe7a6cd34e3a4442875b5b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62fe7a6cd34e3a4442875b5b","keyword":["Arizona","Grand Falls","Navajo Indian Reservation","USGS:62fe7a6cd34e3a4442875b5b","Wind","air temperature","climate data","climatologyMeteorologyAtmosphere","geoscientificInformation","meteorological data","relative humidity"],"modified":"2022-08-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-111.1681, 35.4345, -111.1680, 35.4346","theme":["geospatial"],"title":"Meteorological data at Grand Falls dune field, Arizona, collected from April 2021 to December 2021."},"description":"A meteorological station equipped with a rain gauge, atmospheric pressure sensor, temperature and relative humidity sensor, soil moisture sensor, and an anemometer (measuring wind speed, gust speed, and direction) was deployed at Grand Falls dune field, Arizona. This dataset has been collecting data every 15 minutes with the goal to provide context for ripple and dune migration at an active dune field site.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6481c4d2-10c5-4c5f-ab22-db5504c2b41c","harvest_record_raw":"https://catalog.data.gov/harvest_record/6481c4d2-10c5-4c5f-ab22-db5504c2b41c/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62fe7a6cd34e3a4442875b5b","keyword":["Arizona","Grand Falls","Navajo Indian Reservation","USGS:62fe7a6cd34e3a4442875b5b","Wind","air temperature","climate data","climatologyMeteorologyAtmosphere","geoscientificInformation","meteorological data","relative humidity"],"last_harvested_date":"2026-09-10T22:50:20.591084","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":"meteorological-data-at-grand-falls-dune-field-arizona-collected-from-april-2021-to-de-2021","spatial_centroid":{"lat":35.434540000000005,"lon":-111.16806},"spatial_shape":{"coordinates":[[[-111.1681,35.4345],[-111.1681,35.4346],[-111.168,35.4346],[-111.168,35.4345],[-111.1681,35.4345]]],"type":"Polygon"},"theme":["geospatial"],"title":"Meteorological data at Grand Falls dune field, Arizona, collected from April 2021 to December 2021.","type":"dataset"},{"_score":15.460488,"_sort":[1789080614707,15.460488,4,"53ee2280-3053-49ea-af03-6d57971204e1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle M Irizarry-Ortiz","hasEmail":"mailto:mirizarry-ortiz@usgs.gov"},"description":"The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. \nAn areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell.  Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. A Microsoft Excel workbook is provided which tabulates areal reduction factors (ARF) by ARF region, event duration, and model grid-cell area. The ARF were developed for each ARF region based on the PRISM gridded precipitation dataset for Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model (Daly and others, 2008).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9Q3LEIL","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.649f2a0fd34ef77fcb0421c9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_649f2a0fd34ef77fcb0421c9","keyword":["Florida","Florida Flood Hub for Applied Research and Innovation","USGS:649f2a0fd34ef77fcb0421c9","climatologyMeteorologyAtmosphere","depth-duration-frequency","extremes","precipitation (atmospheric)","precipitation extremes"],"modified":"2025-08-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-87.643620946, 24.416352892, -79.989351961, 31.16271922","theme":["geospatial"],"title":"Spreadsheet of areal reduction factors by region in Florida (Areal_reduction_factors.xlsx)"},"description":"The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. \nAn areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell.  Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. A Microsoft Excel workbook is provided which tabulates areal reduction factors (ARF) by ARF region, event duration, and model grid-cell area. The ARF were developed for each ARF region based on the PRISM gridded precipitation dataset for Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model (Daly and others, 2008).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e85bef6f-1420-4200-992f-49a50c4b16d7","harvest_record_raw":"https://catalog.data.gov/harvest_record/e85bef6f-1420-4200-992f-49a50c4b16d7/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_649f2a0fd34ef77fcb0421c9","keyword":["Florida","Florida Flood Hub for Applied Research and Innovation","USGS:649f2a0fd34ef77fcb0421c9","climatologyMeteorologyAtmosphere","depth-duration-frequency","extremes","precipitation (atmospheric)","precipitation extremes"],"last_harvested_date":"2026-09-10T22:50:14.707908","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":"spreadsheet-of-areal-reduction-factors-by-region-in-florida-areal_reduction_factors-xlsx-7de85","spatial_centroid":{"lat":27.1148994232,"lon":-84.58191335199999},"spatial_shape":{"coordinates":[[[-87.643620946,24.416352892],[-87.643620946,31.16271922],[-79.989351961,31.16271922],[-79.989351961,24.416352892],[-87.643620946,24.416352892]]],"type":"Polygon"},"theme":["geospatial"],"title":"Spreadsheet of areal reduction factors by region in Florida (Areal_reduction_factors.xlsx)","type":"dataset"},{"_score":8.4253845,"_sort":[1789080613808,8.4253845,1,"bcb39f06-03c8-461b-a9f9-8f066c9c3885"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. \nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully.  Data are complete for the information presented.\n      ","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NUO62B","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.5d8a6478e4b0c4f70d0ae750.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d8a6478e4b0c4f70d0ae750","keyword":["Beaches","CMHRP","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","San Francisco County","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:5d8a6478e4b0c4f70d0ae750","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"modified":"2026-03-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.641953027, 34.403744888, -120.444512138, 37.819520138","theme":["geospatial"],"title":"San Francisco County: CoSMoS v3.1 Central California ocean-currents hazards: average conditions"},"description":"This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. \nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully.  Data are complete for the information presented.\n      ","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1a6f02a5-7971-4f67-a304-95d5531c299e","harvest_record_raw":"https://catalog.data.gov/harvest_record/1a6f02a5-7971-4f67-a304-95d5531c299e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d8a6478e4b0c4f70d0ae750","keyword":["Beaches","CMHRP","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","San Francisco County","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:5d8a6478e4b0c4f70d0ae750","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"last_harvested_date":"2026-09-10T22:50:13.808024","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":"cosmos-coastal-storm-modeling-system-central-california-v3-1-ocean-currents-projections-av-bc30a","spatial_centroid":{"lat":35.770054988,"lon":-121.7629766714},"spatial_shape":{"coordinates":[[[-122.641953027,34.403744888],[-122.641953027,37.819520138],[-120.444512138,37.819520138],[-120.444512138,34.403744888],[-122.641953027,34.403744888]]],"type":"Polygon"},"theme":["geospatial"],"title":"San Francisco County: CoSMoS v3.1 Central California ocean-currents hazards: average conditions","type":"dataset"},{"_score":13.169303,"_sort":[1789080612904,13.169303,1,"a4e2758a-7365-4034-a7b8-a5de57ce3c9c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joel T Groten","hasEmail":"mailto:jgroten@usgs.gov"},"description":"Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9V0R02R","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.5fe3cd24d34ea5387deb4b41.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe3cd24d34ea5387deb4b41","keyword":["USGS:5fe3cd24d34ea5387deb4b41","geomorphology","land surveying","sedimentation","sedimentology","streamflow","vegetation"],"modified":"2021-08-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-90.55, 42.275, -90.35000000000001, 42.475","theme":["geospatial"],"title":"The Vigil Network: Galena River, Wisconsin"},"description":"Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d8d17321-5b2c-4a49-bdcd-15038beb23c4","harvest_record_raw":"https://catalog.data.gov/harvest_record/d8d17321-5b2c-4a49-bdcd-15038beb23c4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe3cd24d34ea5387deb4b41","keyword":["USGS:5fe3cd24d34ea5387deb4b41","geomorphology","land surveying","sedimentation","sedimentology","streamflow","vegetation"],"last_harvested_date":"2026-09-10T22:50:12.904597","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":"the-vigil-network-galena-river-wisconsin","spatial_centroid":{"lat":42.355000000000004,"lon":-90.47},"spatial_shape":{"coordinates":[[[-90.55,42.275],[-90.55,42.475],[-90.35000000000001,42.475],[-90.35000000000001,42.275],[-90.55,42.275]]],"type":"Polygon"},"theme":["geospatial"],"title":"The Vigil Network: Galena River, Wisconsin","type":"dataset"},{"_score":8.877926,"_sort":[1789080611523,8.877926,2,"d9a157dc-a30e-4959-a6bb-b06c91e25907"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Nancy T. DeWitt","hasEmail":"mailto:ndewitt@usgs.gov"},"description":"In August of 2013, the U.S. Geological Survey (USGS) conducted geophysical surveys offshore of Petit Bois Island, Mississippi. These efforts are a continued part of the U.S. Geological Survey Gulf of Mexico Science Coordination partnership with the U.S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project, by mapping the shallow geologic stratigraphic framework of the Mississippi Barrier Island Complex.\nThese geophysical surveys will provide the data necessary for scientists to define, interpret, and provide baseline bathymetry and seafloor habitat for this area to aid scientists in predicting future geomorphological changes to the islands with respect to climate change, storm impacts, and sea level rise. Furthermore, these data combined with the geomorphological results will provide the properties and extent of local offshore sand sediment resources available for planning and execution of the Gulf Islands National Seashore barrier island restoration.\nThe geophysical data were collected during one cruise (USGS Field Activity Numbers 13CCT04) aboard the University of Southern Mississippi Research Vessel Tommy Munro offshore along the gulf side of Petit Bois Island, Gulf Islands National Seashore, Mississippi. Data were acquired with the following equipment: a Systems Engineering and Assessment, Ltd., SWATHplus interferometric sonar (468 kilohertz (kHz)), an EdgeTech 424 (4-24 kHz), an EdgeTech 525i chirp sub-bottom profiling system, and a Klein 3900 sidescan sonar system.\nThis report serves as an archive of the processed interferometric swath bathymetry and sidescan sonar data. Geographic information system data products include an interpolated digital elevation model, an acoustic backscatter mosaic, a trackline map, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.\nNOTE: These data are scientific in nature and are not to be used for navigation. Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S. Government.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://pubs.usgs.gov/ds/0917/data/vector/13CCT04_IFB_tracklines.zip","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.2b7271ae-b5ac-472b-b3c6-1770d2b2671e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_2b7271ae-b5ac-472b-b3c6-1770d2b2671e","keyword":["13CCT04","CMGP","Coastal Change and Transport","Coastal and Marine Geology Program","Gulf Islands National Seashore","Gulf of Mexico","HYPACK","Interferometric Bathymetry","Mississippi","Mississippi Sound","Petit Bois Island","SPCMSC","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:2b7271ae-b5ac-472b-b3c6-1770d2b2671e","bathymetry","geoscientificInformation","hydrography","imageryBaseMapsEarthCover","location","marine geology","oceans","trackline","tracklines","water"],"modified":"2020-10-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.542109, 30.076200, -88.320794, 30.193342","theme":["geospatial"],"title":"13CCT04_IFB_tracklines.shp"},"description":"In August of 2013, the U.S. Geological Survey (USGS) conducted geophysical surveys offshore of Petit Bois Island, Mississippi. These efforts are a continued part of the U.S. Geological Survey Gulf of Mexico Science Coordination partnership with the U.S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project, by mapping the shallow geologic stratigraphic framework of the Mississippi Barrier Island Complex.\nThese geophysical surveys will provide the data necessary for scientists to define, interpret, and provide baseline bathymetry and seafloor habitat for this area to aid scientists in predicting future geomorphological changes to the islands with respect to climate change, storm impacts, and sea level rise. Furthermore, these data combined with the geomorphological results will provide the properties and extent of local offshore sand sediment resources available for planning and execution of the Gulf Islands National Seashore barrier island restoration.\nThe geophysical data were collected during one cruise (USGS Field Activity Numbers 13CCT04) aboard the University of Southern Mississippi Research Vessel Tommy Munro offshore along the gulf side of Petit Bois Island, Gulf Islands National Seashore, Mississippi. Data were acquired with the following equipment: a Systems Engineering and Assessment, Ltd., SWATHplus interferometric sonar (468 kilohertz (kHz)), an EdgeTech 424 (4-24 kHz), an EdgeTech 525i chirp sub-bottom profiling system, and a Klein 3900 sidescan sonar system.\nThis report serves as an archive of the processed interferometric swath bathymetry and sidescan sonar data. Geographic information system data products include an interpolated digital elevation model, an acoustic backscatter mosaic, a trackline map, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.\nNOTE: These data are scientific in nature and are not to be used for navigation. Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S. Government.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/90fe68c6-e4fa-461d-9d14-3a9cdd8fd00b","harvest_record_raw":"https://catalog.data.gov/harvest_record/90fe68c6-e4fa-461d-9d14-3a9cdd8fd00b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_2b7271ae-b5ac-472b-b3c6-1770d2b2671e","keyword":["13CCT04","CMGP","Coastal Change and Transport","Coastal and Marine Geology Program","Gulf Islands National Seashore","Gulf of Mexico","HYPACK","Interferometric Bathymetry","Mississippi","Mississippi Sound","Petit Bois Island","SPCMSC","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USGS","USGS:2b7271ae-b5ac-472b-b3c6-1770d2b2671e","bathymetry","geoscientificInformation","hydrography","imageryBaseMapsEarthCover","location","marine geology","oceans","trackline","tracklines","water"],"last_harvested_date":"2026-09-10T22:50:11.523911","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":"13cct04_ifb_tracklines-shp","spatial_centroid":{"lat":30.1230568,"lon":-88.45358300000001},"spatial_shape":{"coordinates":[[[-88.542109,30.0762],[-88.542109,30.193342],[-88.320794,30.193342],[-88.320794,30.0762],[-88.542109,30.0762]]],"type":"Polygon"},"theme":["geospatial"],"title":"13CCT04_IFB_tracklines.shp","type":"dataset"},{"_score":17.698511,"_sort":[1789080608908,17.698511,2,"71a64087-1be3-4055-a339-993103904297"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Maitreyee Bera","hasEmail":"mailto:mbera@usgs.gov"},"description":"Watershed Data Management (WDM) database file ARGN19.WDM is an update of ARGN18.WDM (Bera, 2019) with the processed data from October 1, 2018 through September 30, 2019, appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and processed following the guidelines documented in Over and others (2010). ARGN19.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2019. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as \u201cbackup\u201d. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, was used as \"backup\" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2019) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service at the station at O'Hare International Airport and used as \"backup\". Each data source flag is of the form \"xyz\", which allows the user to determine its source and the methods used to process the data (Over and others, 2010).\nTo open this file user needs to install any of the utilities described in the section \"Related External Resources\" on this page.\nReferences Cited:\nArgonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://www.atmos.anl.gov/ANLMET/.     \nBera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, \u200bhttps://doi.org/10.5066/P9H8P0F7.\nMidwestern Regional Climate Center, 2019, Meteorological data, accessed on November 6, 2019, at https://mrcc.illinois.edu/CLIMATE/.  \nMurphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020.\nOver, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.\nWater and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2019. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9X0P4HZ","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.5ea1f51682cefae35a19192a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ea1f51682cefae35a19192a","keyword":["Argonne National Laboratory, Illinois","DeKalb, Illinois","DuPage County, Illinois","O'Hare International Airport, Illinois","St. Charles, Illinois","USGS:5ea1f51682cefae35a19192a","air temperature","data-source flag","dewpoint temperature","flood forecasting","hydrology","potential evapotranspiration","runoff","solar radiation","wind speed"],"modified":"2020-08-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-87.8948, 41.6014, -88.0948, 41.8014","theme":["geospatial"],"title":"WDM file, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019"},"description":"Watershed Data Management (WDM) database file ARGN19.WDM is an update of ARGN18.WDM (Bera, 2019) with the processed data from October 1, 2018 through September 30, 2019, appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and processed following the guidelines documented in Over and others (2010). ARGN19.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2019. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as \u201cbackup\u201d. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, was used as \"backup\" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2019) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service at the station at O'Hare International Airport and used as \"backup\". Each data source flag is of the form \"xyz\", which allows the user to determine its source and the methods used to process the data (Over and others, 2010).\nTo open this file user needs to install any of the utilities described in the section \"Related External Resources\" on this page.\nReferences Cited:\nArgonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://www.atmos.anl.gov/ANLMET/.     \nBera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, \u200bhttps://doi.org/10.5066/P9H8P0F7.\nMidwestern Regional Climate Center, 2019, Meteorological data, accessed on November 6, 2019, at https://mrcc.illinois.edu/CLIMATE/.  \nMurphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020.\nOver, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.\nWater and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2019. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ce274bfc-cf0a-45ec-a8dd-b4d4b1f8f402","harvest_record_raw":"https://catalog.data.gov/harvest_record/ce274bfc-cf0a-45ec-a8dd-b4d4b1f8f402/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ea1f51682cefae35a19192a","keyword":["Argonne National Laboratory, Illinois","DeKalb, Illinois","DuPage County, Illinois","O'Hare International Airport, Illinois","St. Charles, Illinois","USGS:5ea1f51682cefae35a19192a","air temperature","data-source flag","dewpoint temperature","flood forecasting","hydrology","potential evapotranspiration","runoff","solar radiation","wind speed"],"last_harvested_date":"2026-09-10T22:50:08.908375","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":"wdm-file-meteorological-database-argonne-national-laboratory-illinois-january-1-19-30-2019","spatial_centroid":{"lat":41.6814,"lon":-87.9748},"spatial_shape":{"coordinates":[[[-87.8948,41.6014],[-87.8948,41.8014],[-88.0948,41.8014],[-88.0948,41.6014],[-87.8948,41.6014]]],"type":"Polygon"},"theme":["geospatial"],"title":"WDM file, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019","type":"dataset"},{"_score":13.730012,"_sort":[1789080599948,13.730012,6,"ab96ef88-522e-4c9c-8ce7-08bf7bf3f871"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Johanna MT Blake","hasEmail":"mailto:jmtblake@usgs.gov"},"description":"This U.S. Geological Survey (USGS) data release presents the geospatial data used to assess the hydrologic and soil resources of the Organ Mountains-Desert Peaks National Monument managed by the U.S. Bureau of Land Management (BLM) in Do\u00f1a Ana County, New Mexico. The USGS, in cooperation with the BLM, conducted a study to assess the hydrologic and soil resources and potential effects of infrastructure and grazing within the monument area. Publicly available data as well as data provided by the BLM were used to assess these resources and effects and to identify data gaps in the monument area. The input and output files for the Rangeland Hydrologic Erosion Model are also included in this data release. This model was used to assess potential impacts from different climate and rangeland scenarios on the hydrologic and soil resources in the monument.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9JVHA4Z","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.5af5f24be4b0da30c1b5f9f4.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5af5f24be4b0da30c1b5f9f4","keyword":["Do\u00f1a Ana","New Mexico","Organ Mountains-Desert Peaks National Monument","Surface water","USGS:5af5f24be4b0da30c1b5f9f4","agriculture","biota","boundaries","environment","geologic maps","groundwater","infrastructure","land use and land cover","landforms","location","modeling","natural resources","soil survey","water resource management","water resources"],"modified":"2020-08-25T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.44079589774, 31.774505662462, -106.24877929623, 32.712987220591","theme":["geospatial"],"title":"Database Associated with the Assessment of Soil and Water Resources in The Organ Mountains-Desert Peaks National Monument, New Mexico"},"description":"This U.S. Geological Survey (USGS) data release presents the geospatial data used to assess the hydrologic and soil resources of the Organ Mountains-Desert Peaks National Monument managed by the U.S. Bureau of Land Management (BLM) in Do\u00f1a Ana County, New Mexico. The USGS, in cooperation with the BLM, conducted a study to assess the hydrologic and soil resources and potential effects of infrastructure and grazing within the monument area. Publicly available data as well as data provided by the BLM were used to assess these resources and effects and to identify data gaps in the monument area. The input and output files for the Rangeland Hydrologic Erosion Model are also included in this data release. This model was used to assess potential impacts from different climate and rangeland scenarios on the hydrologic and soil resources in the monument.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/928a8b1f-b7b1-475e-aa8e-7f3517abba34","harvest_record_raw":"https://catalog.data.gov/harvest_record/928a8b1f-b7b1-475e-aa8e-7f3517abba34/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5af5f24be4b0da30c1b5f9f4","keyword":["Do\u00f1a Ana","New Mexico","Organ Mountains-Desert Peaks National Monument","Surface water","USGS:5af5f24be4b0da30c1b5f9f4","agriculture","biota","boundaries","environment","geologic maps","groundwater","infrastructure","land use and land cover","landforms","location","modeling","natural resources","soil survey","water resource management","water resources"],"last_harvested_date":"2026-09-10T22:49:59.948163","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":6,"publisher":"U.S. Geological Survey","slug":"database-associated-with-the-assessment-of-soil-and-water-resources-in-the-organ-mountains","spatial_centroid":{"lat":32.149898285713604,"lon":-106.96398925713599},"spatial_shape":{"coordinates":[[[-107.44079589774,31.774505662462],[-107.44079589774,32.712987220591],[-106.24877929623,32.712987220591],[-106.24877929623,31.774505662462],[-107.44079589774,31.774505662462]]],"type":"Polygon"},"theme":["geospatial"],"title":"Database Associated with the Assessment of Soil and Water Resources in The Organ Mountains-Desert Peaks National Monument, New Mexico","type":"dataset"},{"_score":11.639135,"_sort":[1789080598489,11.639135,1,"d7b38722-994f-4044-a651-c3fb2ee7762f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Sara L. Zeigler","hasEmail":"mailto:szeigler@usgs.gov"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9V7F6UX","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.5daa37a4e4b09fd3b0c9ceaa.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5daa37a4e4b09fd3b0c9ceaa","keyword":["Atlantic Ocean","Barrier Island","CMHRP","Coastal Hazards","Coastal and Marine Hazards and Resources Program","Cobb Island","Delmarva Peninsula","GIS","Geographic Information Systems","MHW","Mean High Water","North America","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5daa37a4e4b09fd3b0c9ceaa","United States","VA","Virginia","Virginia Coast Reserve","Woods Hole Coastal and Marine Science Center","geomorphology","geoscientificInformation","geospatial analysis","geospatial datasets","oceans","transect sampling"],"modified":"2026-02-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.78894978, 37.2941595, -75.7370413, 37.34420315","theme":["geospatial"],"title":"Cobb Island, VA, 2014: DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/63eb996c-d96e-43f5-b560-5404465a47f5","harvest_record_raw":"https://catalog.data.gov/harvest_record/63eb996c-d96e-43f5-b560-5404465a47f5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5daa37a4e4b09fd3b0c9ceaa","keyword":["Atlantic Ocean","Barrier Island","CMHRP","Coastal Hazards","Coastal and Marine Hazards and Resources Program","Cobb Island","Delmarva Peninsula","GIS","Geographic Information Systems","MHW","Mean High Water","North America","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5daa37a4e4b09fd3b0c9ceaa","United States","VA","Virginia","Virginia Coast Reserve","Woods Hole Coastal and Marine Science Center","geomorphology","geoscientificInformation","geospatial analysis","geospatial datasets","oceans","transect sampling"],"last_harvested_date":"2026-09-10T22:49:58.489368","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":"dcpts-dtpts-slpts-dune-crest-dune-toe-and-mean-high-water-shoreline-positions-cobb-is-2014","spatial_centroid":{"lat":37.31417696,"lon":-75.768186388},"spatial_shape":{"coordinates":[[[-75.78894978,37.2941595],[-75.78894978,37.34420315],[-75.7370413,37.34420315],[-75.7370413,37.2941595],[-75.78894978,37.2941595]]],"type":"Polygon"},"theme":["geospatial"],"title":"Cobb Island, VA, 2014: DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions","type":"dataset"},{"_score":7.883436,"_sort":[1789080592151,7.883436,3,"6fb3691a-55bc-462e-82ed-7db02769e05c"],"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 area of Hydrologic Landscape Regions (HLR) compiled for every catchment \nof NHDPlus for the conterminous United States. The source data set is a 100-meter version of Hydrologic \nLandscape Regions of the United States (Wolock, 2003). HLR groups watersheds on the basis of similarities \nin land-surface form, geologic texture, and climate characteristics.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates \nmany of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset \n(NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, \nnaming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments \n(drainage areas) produced using a drainage enforcement technique first widely used in New England, \nand thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale \nNHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The \nresulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree \nwith the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey \n(USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method \nproduces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus \ndataset is organized by 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 (MRBs, \nCrawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River basins, contains \nNHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf and Tennessee River basins, \ncontains NHDPlus Production Units 3 and 6.  MRB3, covering the Great Lakes, Ohio, Upper Mississippi, \nand Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering \nthe Missouri River 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 Production \nUnits 8, 11 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 basins, \ncontains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains NHDPlus \nProduction Unit 18.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9142BM0","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.93b2f75c-33bc-4cae-b48e-41b6f85d2e39.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_93b2f75c-33bc-4cae-b48e-41b6f85d2e39","keyword":["CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Hydrologic landscape regions","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:93b2f75c-33bc-4cae-b48e-41b6f85d2e39","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: Hydrologic Landscape Regions"},"description":"This data set represents the area of Hydrologic Landscape Regions (HLR) compiled for every catchment \nof NHDPlus for the conterminous United States. The source data set is a 100-meter version of Hydrologic \nLandscape Regions of the United States (Wolock, 2003). HLR groups watersheds on the basis of similarities \nin land-surface form, geologic texture, and climate characteristics.\n\t\t\nThe NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates \nmany of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset \n(NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, \nnaming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments \n(drainage areas) produced using a drainage enforcement technique first widely used in New England, \nand thus referred to as \"the New England Method.\" This technique involves \"burning in\" the 1:100,000-scale \nNHD and when available building \"walls\" using the National Watershed Boundary Dataset (WBD). The \nresulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree \nwith the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey \n(USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method \nproduces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus \ndataset is organized by 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 (MRBs, \nCrawford and others, 2006).  MRB1, covering the New England and Mid-Atlantic River basins, contains \nNHDPlus Production Units 1 and 2.  MRB2, covering the South Atlantic-Gulf and Tennessee River basins, \ncontains NHDPlus Production Units 3 and 6.  MRB3, covering the Great Lakes, Ohio, Upper Mississippi, \nand Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9.  MRB4, covering \nthe Missouri River 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 Production \nUnits 8, 11 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 basins, \ncontains NHDPlus Production Unit 17.  MRB8, covering California River basins, contains NHDPlus \nProduction Unit 18.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/02f8fbc2-a880-46ed-be94-44c4d195a819","harvest_record_raw":"https://catalog.data.gov/harvest_record/02f8fbc2-a880-46ed-be94-44c4d195a819/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_93b2f75c-33bc-4cae-b48e-41b6f85d2e39","keyword":["CALI","COGB","California","Catchment","Conterminous United States","GLMR","Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy","Hydrologic landscape regions","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:93b2f75c-33bc-4cae-b48e-41b6f85d2e39","environment","geoscientificInformation","inlandWaters"],"last_harvested_date":"2026-09-10T22:49:52.151220","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":"attributes-for-nhdplus-catchments-version-1-1-for-the-conterminous-united-states-hydrologi","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: Hydrologic Landscape Regions","type":"dataset"},{"_score":18.248257,"_sort":[1789080591019,18.248257,1,"e0edef18-2a0a-43dd-a1ee-391db04377a3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Erikson, Li","hasEmail":"mailto:lerikson@usgs.gov"},"description":"Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7T151Q4","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.5953f50de4b062508e3c7c10.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f50de4b062508e3c7c10","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f50de4b062508e3c7c10","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"modified":"2026-03-31T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.81115722553, 32.546444355161, -116.66931152258, 34.687068180405","theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood depth and duration projections: 20-year storm"},"description":"Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6a3b3eed-a849-4875-b40d-d62cd651adb2","harvest_record_raw":"https://catalog.data.gov/harvest_record/6a3b3eed-a849-4875-b40d-d62cd651adb2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f50de4b062508e3c7c10","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f50de4b062508e3c7c10","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"last_harvested_date":"2026-09-10T22:49:51.019803","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":"cosmos-coastal-storm-modeling-system-southern-california-v3-0-phase-2-flood-hazard-depth-a-3faa3","spatial_centroid":{"lat":33.4026938852586,"lon":-119.15441894435},"spatial_shape":{"coordinates":[[[-120.81115722553,32.546444355161],[-120.81115722553,34.687068180405],[-116.66931152258,34.687068180405],[-116.66931152258,32.546444355161],[-120.81115722553,32.546444355161]]],"type":"Polygon"},"theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood depth and duration projections: 20-year storm","type":"dataset"},{"_score":8.894999,"_sort":[1789080587844,8.894999,1,"3212d405-d174-4df1-bf79-26e924601520"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Janet M Ruth","hasEmail":"mailto:janet_ruth@usgs.gov"},"description":"In 2010-2013 a variety of measurements were taken from grasshopper sparrows captured as part of the study described below and referenced in the larger work citation of this metadata record.  Measurements include age, sex, wing length, weight, and exposed culmen length.\nAmmodramus savannarum ammolegus (commonly referred to as the Arizona Grasshopper Sparrow) occurs in the desert and plains grasslands of southeastern Arizona, southwestern New Mexico, and northern Sonora, Mexico.  Although a subspecies of conservation concern, this is the first intensive study of its life history and breeding ecology, providing baseline data and facilitating comparisons with other North American Grasshopper Sparrow subspecies.  Specifically, I found that ammolegus males generally weighed less than other subspecies (16.0 \u00b1 0.8 g), but with intermediate exposed culmen length (11.6 \u00b1 0.5 mm) and wing chord length similar to the other two migratory subspecies (62.7 \u00b1 1.5 mm).  Territory size for ammolegus was 0.72 \u00b1 0.37 ha, with some variation between sites and among years, possibly indicating variation in habitat quality across spatial and temporal scales.  The return rate for ammolegus males was 39.2%.  Nest initiation for ammolegus was early to mid-July after the monsoons had begun.  Domed nests were constructed on the ground, primarily under native bunch grasses, and frequently with a tunnel extending beyond the nest rim, with nest openings oriented north.  Clutch size was 3.97 \u00b1 0.68, with no evidence of Brown-headed Cowbird (Molothrus ater) nest parasitism.  Extreme climate factors in the arid Southwest may have affected the life history and morphology of ammolegus as compared to other subspecies, influencing body size and mass, culmen length, breeding phenology, and nest orientation.  Other geographic variation occurred in return rates, clutch size, and nest parasitism rates.  The baseline data for ammolegus obtained in this study will inform future taxonomic and ecological studies as well as conservation planning.  Comparisons of ammolegus morphometrics with those of other subspecies will assist field biologists in distinguishing among subspecies where they overlap, especially on wintering grounds.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7C53JCF","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.594468a5e4b062508e323344.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_594468a5e4b062508e323344","keyword":["Ammodramus savannarum ammolegus","Arizona","Arizona Grasshopper Sparrow","Audubon Appleton-Whittell Research Ranch, Bureau of Land Management (BLM)","Las Cienegas National Conservation Area","Santa Cruz","USGS:594468a5e4b062508e323344","United States","biota","birds","desert grassland","ecology","exposed culmen","grassland bird","life history","mass","wing chord"],"modified":"2020-08-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-110.5997, 31.5974, -110.5084, 31.6991","theme":["geospatial"],"title":"Ammodramus savannarum ammolegus Grasshopper Sparrow bird measurements Arizona 2010-2013"},"description":"In 2010-2013 a variety of measurements were taken from grasshopper sparrows captured as part of the study described below and referenced in the larger work citation of this metadata record.  Measurements include age, sex, wing length, weight, and exposed culmen length.\nAmmodramus savannarum ammolegus (commonly referred to as the Arizona Grasshopper Sparrow) occurs in the desert and plains grasslands of southeastern Arizona, southwestern New Mexico, and northern Sonora, Mexico.  Although a subspecies of conservation concern, this is the first intensive study of its life history and breeding ecology, providing baseline data and facilitating comparisons with other North American Grasshopper Sparrow subspecies.  Specifically, I found that ammolegus males generally weighed less than other subspecies (16.0 \u00b1 0.8 g), but with intermediate exposed culmen length (11.6 \u00b1 0.5 mm) and wing chord length similar to the other two migratory subspecies (62.7 \u00b1 1.5 mm).  Territory size for ammolegus was 0.72 \u00b1 0.37 ha, with some variation between sites and among years, possibly indicating variation in habitat quality across spatial and temporal scales.  The return rate for ammolegus males was 39.2%.  Nest initiation for ammolegus was early to mid-July after the monsoons had begun.  Domed nests were constructed on the ground, primarily under native bunch grasses, and frequently with a tunnel extending beyond the nest rim, with nest openings oriented north.  Clutch size was 3.97 \u00b1 0.68, with no evidence of Brown-headed Cowbird (Molothrus ater) nest parasitism.  Extreme climate factors in the arid Southwest may have affected the life history and morphology of ammolegus as compared to other subspecies, influencing body size and mass, culmen length, breeding phenology, and nest orientation.  Other geographic variation occurred in return rates, clutch size, and nest parasitism rates.  The baseline data for ammolegus obtained in this study will inform future taxonomic and ecological studies as well as conservation planning.  Comparisons of ammolegus morphometrics with those of other subspecies will assist field biologists in distinguishing among subspecies where they overlap, especially on wintering grounds.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5635a36d-71fa-4c12-80ce-20121ecbf5d8","harvest_record_raw":"https://catalog.data.gov/harvest_record/5635a36d-71fa-4c12-80ce-20121ecbf5d8/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_594468a5e4b062508e323344","keyword":["Ammodramus savannarum ammolegus","Arizona","Arizona Grasshopper Sparrow","Audubon Appleton-Whittell Research Ranch, Bureau of Land Management (BLM)","Las Cienegas National Conservation Area","Santa Cruz","USGS:594468a5e4b062508e323344","United States","biota","birds","desert grassland","ecology","exposed culmen","grassland bird","life history","mass","wing chord"],"last_harvested_date":"2026-09-10T22:49:47.844306","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":"ammodramus-savannarum-ammolegus-grasshopper-sparrow-bird-measurements-arizona-2010-2013","spatial_centroid":{"lat":31.638080000000002,"lon":-110.56317999999999},"spatial_shape":{"coordinates":[[[-110.5997,31.5974],[-110.5997,31.6991],[-110.5084,31.6991],[-110.5084,31.5974],[-110.5997,31.5974]]],"type":"Polygon"},"theme":["geospatial"],"title":"Ammodramus savannarum ammolegus Grasshopper Sparrow bird measurements Arizona 2010-2013","type":"dataset"},{"_score":7.5052314,"_sort":[1789080585591,7.5052314,2,"379801b5-b08b-44c0-a4f3-1bdafdddd719"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andy Bock","hasEmail":"mailto:abock@usgs.gov"},"description":"This data release contains time-series data and plots summarizing mean monthly temperature (TAVE) and total monthly precipitation (PPT), and runoff (RO) from the U.S. Geological Survey Monthly Water Balance Model at 115 National Wildlife Refuges within the U.S. Fish and Wildlife Service Mountain-Prairie Region (Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). These three variables are derived from two sets of statistically downscaled general circulation models from 1951 through 2099. Three variables (TAVE, PPT, and RO for refuge areas) were summarized for four 19-year periods: historical (1951\u201369), baseline (1981\u201399), 2050 (2041\u201359), and 2080 (2071-89). For each refuge, mean monthly plots, seasonal box plots, and annual envelope plots were produced for each of the four periods.\nThis child item contains data and plots for wildlife refuges within the state of South Dakota (SD).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9SKDDKS","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.638a1b78d34ed907bf790640.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_638a1b78d34ed907bf790640","keyword":["Bear Butte National Wildlife Refuge","Karl E. 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These three variables are derived from two sets of statistically downscaled general circulation models from 1951 through 2099. Three variables (TAVE, PPT, and RO for refuge areas) were summarized for four 19-year periods: historical (1951\u201369), baseline (1981\u201399), 2050 (2041\u201359), and 2080 (2071-89). For each refuge, mean monthly plots, seasonal box plots, and annual envelope plots were produced for each of the four periods.\nThis child item contains data and plots for wildlife refuges within the state of South Dakota (SD).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0124edd0-61df-4e7a-9b2e-f4e59cb279e4","harvest_record_raw":"https://catalog.data.gov/harvest_record/0124edd0-61df-4e7a-9b2e-f4e59cb279e4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_638a1b78d34ed907bf790640","keyword":["Bear Butte National Wildlife Refuge","Karl E. 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The authors used a consolidated dataset of all GRSG HMAs provided by the BLM from individual Records of Decision (ROD) and Approved Resource Management Plan Amendments (ARMPA) for GRSG in Oregon and Colorado. In addition, the proposed HMAs from the Greater Sage-Grouse Rangewide Planning Proposed Resource Management Plan Amendment and Final Environmental Impact Statement (EIS) for California, Idaho, Montana, Nevada, North Dakota, South Dakota, Utah, and Wyoming were incorporated (DOI-BLM-WO-2300-2022-0001-RMP-EIS).\nThe Federal Land Policy and Management Act requires that Resource Management Plans (RMPs) for managing public lands be developed and maintained, and the National Environmental Policy Act requires that an environmental impact statement (EIS) be prepared for Federal actions significantly affecting the quality of the human environment. The EIS for the Greater Sage-Grouse RMPAs identified updated HMAs, areas of highest conservation value for the species, based on new habitat use data. A consolidated version of the HMAs were provided to USGS authors. Information about designated HMAs is described in the \"Supplemental\" section of the metadata file.\nThe authors developed three new datasets that reflect revised GRSG HMA boundaries produced by BLM. The new data include a revised GRSG boundary, cluster level 2 (neighborhood clusters; NC), and cluster level 13 (climate clusters; CC). These revisions include any designations or proposed designations of HMAs falling outside previously published population unit/cluster versions (O\u2019Donnell et al. 2022; https://doi.org/10.5066/P9D1K0LX).\nBackground information on original population units/clusters of GRSG: We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for GRSG across the western United States. Polygons (clusters) within each cluster level group a population of GRSG leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1JNGEAM","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.682799bbd4be02693eeabc79.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_682799bbd4be02693eeabc79","keyword":["California","Centrocercus urophasianus","Colorado","Greater sage-grouse","Idaho","Montana","Nevada","North Dakota","Oregon","South Dakota","USGS:682799bbd4be02693eeabc79","United States","Utah","Washington","Wyoming","adaptive management","biota","dispersal (organisms)","farming","game species","graph theory","habitat connectivity","hierarchical sampling units","long-term ecological monitoring","multivariate statistical analysis","native species","population monitoring","study areas","western United States"],"modified":"2025-07-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.9400, 31.1700, -109.0000, 49.0000","theme":["geospatial"],"title":"Revised extents of neighborhood and climate population clusters for greater sage-grouse, western U.S."},"description":"The authors and the Bureau of Land Management (BLM) have expanded the greater sage-grouse (GRSG [also referenced as sage-grouse]; Centrocercus urophasianus) hierarchical population units/clusters to ensure inclusion of the proposed revisions (2025) of the BLM habitat management areas (HMAs) in future management implementation decisions. The authors used a consolidated dataset of all GRSG HMAs provided by the BLM from individual Records of Decision (ROD) and Approved Resource Management Plan Amendments (ARMPA) for GRSG in Oregon and Colorado. In addition, the proposed HMAs from the Greater Sage-Grouse Rangewide Planning Proposed Resource Management Plan Amendment and Final Environmental Impact Statement (EIS) for California, Idaho, Montana, Nevada, North Dakota, South Dakota, Utah, and Wyoming were incorporated (DOI-BLM-WO-2300-2022-0001-RMP-EIS).\nThe Federal Land Policy and Management Act requires that Resource Management Plans (RMPs) for managing public lands be developed and maintained, and the National Environmental Policy Act requires that an environmental impact statement (EIS) be prepared for Federal actions significantly affecting the quality of the human environment. The EIS for the Greater Sage-Grouse RMPAs identified updated HMAs, areas of highest conservation value for the species, based on new habitat use data. A consolidated version of the HMAs were provided to USGS authors. Information about designated HMAs is described in the \"Supplemental\" section of the metadata file.\nThe authors developed three new datasets that reflect revised GRSG HMA boundaries produced by BLM. The new data include a revised GRSG boundary, cluster level 2 (neighborhood clusters; NC), and cluster level 13 (climate clusters; CC). These revisions include any designations or proposed designations of HMAs falling outside previously published population unit/cluster versions (O\u2019Donnell et al. 2022; https://doi.org/10.5066/P9D1K0LX).\nBackground information on original population units/clusters of GRSG: We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for GRSG across the western United States. Polygons (clusters) within each cluster level group a population of GRSG leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/acb6285b-e5b9-4fc4-b85e-5b94011b5da9","harvest_record_raw":"https://catalog.data.gov/harvest_record/acb6285b-e5b9-4fc4-b85e-5b94011b5da9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_682799bbd4be02693eeabc79","keyword":["California","Centrocercus urophasianus","Colorado","Greater sage-grouse","Idaho","Montana","Nevada","North Dakota","Oregon","South Dakota","USGS:682799bbd4be02693eeabc79","United States","Utah","Washington","Wyoming","adaptive management","biota","dispersal (organisms)","farming","game species","graph theory","habitat connectivity","hierarchical sampling units","long-term ecological monitoring","multivariate statistical analysis","native species","population monitoring","study areas","western United States"],"last_harvested_date":"2026-09-10T22:49:45.132921","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":"revised-extents-of-neighborhood-and-climate-population-clusters-for-greater-sage-grouse-we","spatial_centroid":{"lat":38.302,"lon":-118.564},"spatial_shape":{"coordinates":[[[-124.94,31.17],[-124.94,49.0],[-109.0,49.0],[-109.0,31.17],[-124.94,31.17]]],"type":"Polygon"},"theme":["geospatial"],"title":"Revised extents of neighborhood and climate population clusters for greater sage-grouse, western U.S.","type":"dataset"},{"_score":19.642805,"_sort":[1789080580345,19.642805,2,"1a02b39d-5b40-448f-9c7a-9f8c77279d18"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adam J Terando","hasEmail":"mailto:aterando@usgs.gov"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. 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In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P95BV7GE","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.609e9ec2d34ea221ce3f402e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609e9ec2d34ea221ce3f402e","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609e9ec2d34ea221ce3f402e","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"modified":"2021-09-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.1475, 25.0631, -73.6063, 43.1045","theme":["geospatial"],"title":"Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 8.5"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. 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In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/46a4d4ca-58a9-41dc-81a3-17c266e93372","harvest_record_raw":"https://catalog.data.gov/harvest_record/46a4d4ca-58a9-41dc-81a3-17c266e93372/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609e9ec2d34ea221ce3f402e","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609e9ec2d34ea221ce3f402e","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"last_harvested_date":"2026-09-10T22:49:40.345423","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":"seasonal-future-prescribed-burn-windows-for-the-southeast-united-states-june-august-2010-2-abb20","spatial_centroid":{"lat":32.27966,"lon":-90.73102},"spatial_shape":{"coordinates":[[[-102.1475,25.0631],[-102.1475,43.1045],[-73.6063,43.1045],[-73.6063,25.0631],[-102.1475,25.0631]]],"type":"Polygon"},"theme":["geospatial"],"title":"Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 8.5","type":"dataset"},{"_score":10.421673,"_sort":[1789080579184,10.421673,2,"d404d1e4-0f2a-43a6-9768-4458aa57af06"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matthew J Cannister","hasEmail":"mailto:mcannister@usgs.gov"},"description":"Defining the pre-European range of vegetation communities can enhance our understanding of the role soil, hydrology, and climate had on climax plant communities within southwest Louisiana. Coastal prairie grasslands were in a perpetual state of succession due to two primary disturbances; grazing, primarily by bison and other ungulates, and fires ignited by lightning and Native Americans. Along its borders, prairie vegetation blended into adjacent plant communities forming biologically diverse ecotones that may have fluctuated between a prairie, marsh, or forest dominated community as a result of variable conditions including climate cycles, disturbance and soil characteristics. Since European settlement, this landscape has undergone dramatic change with less than 1% of intact coastal prairie remaining. Conservation entities across the Western Gulf Coastal Plain are taking a collaborative, strategic, landscape scale approach to pollinator conservation. This effort encourages communication and implementation of restoration and habitat enhancement actions within water sheds. We have produced a spatial dataset which considers landscape position and soil type, based on Soil Survey Geographic Database (SSURGO) data, to predict appropriate vegetation associations for plantings across southwest Louisiana based on expert elicitation, and historic references. Methods to produce this product begin with soil boundaries and identification information using Map Unit Keys (MUKEY) which were gathered from SSURGO data (Soil Survey Staff, NRCS 2017). Each mukey number was reviewed on the SOIL WEB to obtain information about components. Components include the proportion and general geomorphic features associated with soil series. Natural vegetation associations were examined and documented for each soil series individually using multiple references, including USDA Soil Series descriptions, expert elicitation, and historical spatial references. Professional reference maps contributed to this spatial dataset and include an 1863 work by Henry L. Abbot and numerous General Land Office surveyor maps and surveyor descriptions from the early 1800s drawn at the scale of a township. \nGeneral vegetation categories associated with Soil Types (Mukey) were derived from reviewing the vegetation associations of the dominant components, or soil series. These general categories include: anthropogenic, prairie, transition, forest, marsh, swamp, uncertain, and water. Anthropogenic categories were generally due to significant dredging, or other industrial activities. Transitional areas included savannas and areas which may have significantly changed from prairie to forest dominated communities due to rainfall and/or fire frequency and intensity. Forest and swamp includes a range of forest types from which the distinction between these two categories primarily depend upon relative elevation and hydrology. There were a few soil series in which we are uncertain of their pre-settlement vegetation. These areas are anomalies on the landscape and include salt domes and old, disjunct river meanders which are largely comprised of Pleistocene soils and were most likely marais, yet currently much of it is heavily forested as bottomlands, and we are therefore uncertain if this result is solely due to absence of fire. Attribute data include MUKEYs within the parishes which are included in the Louisiana portion of the Gulf Coastal Plain Ecoregion. Information in the table includes symbols, common names, and components which were compiled from SSURGO dataset and Soil Web online resources (Soil Survey Staff, NRCS, accessed 2/2017). For more detailed vegetation associations for individual soil series, please refer to 'VegSoilAssoc_SWLA.pdf' or 'VegSoilAssoc_SWLA.csv'.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7BC3X18","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.5925eb8de4b0b7ff9fb3cc09.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5925eb8de4b0b7ff9fb3cc09","keyword":["USGS:5925eb8de4b0b7ff9fb3cc09","grassland ecosystems"],"modified":"2024-07-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-93.9280, 29.2407, -91.0827, 31.0008","theme":["geospatial"],"title":"Soil, Geomorphology and Pre-European Settlement Vegetation Associations of Southwest Louisiana"},"description":"Defining the pre-European range of vegetation communities can enhance our understanding of the role soil, hydrology, and climate had on climax plant communities within southwest Louisiana. Coastal prairie grasslands were in a perpetual state of succession due to two primary disturbances; grazing, primarily by bison and other ungulates, and fires ignited by lightning and Native Americans. Along its borders, prairie vegetation blended into adjacent plant communities forming biologically diverse ecotones that may have fluctuated between a prairie, marsh, or forest dominated community as a result of variable conditions including climate cycles, disturbance and soil characteristics. Since European settlement, this landscape has undergone dramatic change with less than 1% of intact coastal prairie remaining. Conservation entities across the Western Gulf Coastal Plain are taking a collaborative, strategic, landscape scale approach to pollinator conservation. This effort encourages communication and implementation of restoration and habitat enhancement actions within water sheds. We have produced a spatial dataset which considers landscape position and soil type, based on Soil Survey Geographic Database (SSURGO) data, to predict appropriate vegetation associations for plantings across southwest Louisiana based on expert elicitation, and historic references. Methods to produce this product begin with soil boundaries and identification information using Map Unit Keys (MUKEY) which were gathered from SSURGO data (Soil Survey Staff, NRCS 2017). Each mukey number was reviewed on the SOIL WEB to obtain information about components. Components include the proportion and general geomorphic features associated with soil series. Natural vegetation associations were examined and documented for each soil series individually using multiple references, including USDA Soil Series descriptions, expert elicitation, and historical spatial references. Professional reference maps contributed to this spatial dataset and include an 1863 work by Henry L. Abbot and numerous General Land Office surveyor maps and surveyor descriptions from the early 1800s drawn at the scale of a township. \nGeneral vegetation categories associated with Soil Types (Mukey) were derived from reviewing the vegetation associations of the dominant components, or soil series. These general categories include: anthropogenic, prairie, transition, forest, marsh, swamp, uncertain, and water. Anthropogenic categories were generally due to significant dredging, or other industrial activities. Transitional areas included savannas and areas which may have significantly changed from prairie to forest dominated communities due to rainfall and/or fire frequency and intensity. Forest and swamp includes a range of forest types from which the distinction between these two categories primarily depend upon relative elevation and hydrology. There were a few soil series in which we are uncertain of their pre-settlement vegetation. These areas are anomalies on the landscape and include salt domes and old, disjunct river meanders which are largely comprised of Pleistocene soils and were most likely marais, yet currently much of it is heavily forested as bottomlands, and we are therefore uncertain if this result is solely due to absence of fire. Attribute data include MUKEYs within the parishes which are included in the Louisiana portion of the Gulf Coastal Plain Ecoregion. Information in the table includes symbols, common names, and components which were compiled from SSURGO dataset and Soil Web online resources (Soil Survey Staff, NRCS, accessed 2/2017). For more detailed vegetation associations for individual soil series, please refer to 'VegSoilAssoc_SWLA.pdf' or 'VegSoilAssoc_SWLA.csv'.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2af8813a-58c6-4234-b045-085646757b58","harvest_record_raw":"https://catalog.data.gov/harvest_record/2af8813a-58c6-4234-b045-085646757b58/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5925eb8de4b0b7ff9fb3cc09","keyword":["USGS:5925eb8de4b0b7ff9fb3cc09","grassland ecosystems"],"last_harvested_date":"2026-09-10T22:49:39.184072","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":"soil-geomorphology-and-pre-european-settlement-vegetation-associations-of-southwest-louisi","spatial_centroid":{"lat":29.944740000000003,"lon":-92.78988},"spatial_shape":{"coordinates":[[[-93.928,29.2407],[-93.928,31.0008],[-91.0827,31.0008],[-91.0827,29.2407],[-93.928,29.2407]]],"type":"Polygon"},"theme":["geospatial"],"title":"Soil, Geomorphology and Pre-European Settlement Vegetation Associations of Southwest Louisiana","type":"dataset"},{"_score":19.512096,"_sort":[1789080578753,19.512096,3,"ccc96c22-4592-486d-83e0-0830c3f25232"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adam J Terando","hasEmail":"mailto:aterando@usgs.gov"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P95BV7GE","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.609e9adbd34ea221ce3f3e5c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609e9adbd34ea221ce3f3e5c","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609e9adbd34ea221ce3f3e5c","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"modified":"2021-09-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-102.1475, 25.0631, -73.6063, 43.1045","theme":["geospatial"],"title":"Seasonal Future Prescribed Burn Windows for the Southeast United States - March - May 2010-2099 RCP 8.5"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. 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In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6e57778e-6e2b-498b-8c0e-1fa3b7b4f6b8","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e57778e-6e2b-498b-8c0e-1fa3b7b4f6b8/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_609e9adbd34ea221ce3f3e5c","keyword":["Alabama","Arkansas","Florida","Georgia","Kentucky","Louisiana","Mississippi","Missouri","North Carolina","Oklahoma","South Carolina","Southeast United states","Tennessee","Texas","USGS:609e9adbd34ea221ce3f3e5c","Virginia","West Virginia","farming","fires","geoscientificInformation","managed fire regimes","statistical downscaling","wildfires"],"last_harvested_date":"2026-09-10T22:49:38.753575","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":"seasonal-future-prescribed-burn-windows-for-the-southeast-united-states-march-may-2010-209-4d095","spatial_centroid":{"lat":32.27966,"lon":-90.73102},"spatial_shape":{"coordinates":[[[-102.1475,25.0631],[-102.1475,43.1045],[-73.6063,43.1045],[-73.6063,25.0631],[-102.1475,25.0631]]],"type":"Polygon"},"theme":["geospatial"],"title":"Seasonal Future Prescribed Burn Windows for the Southeast United States - March - May 2010-2099 RCP 8.5","type":"dataset"},{"_score":28.997835,"_sort":[1789080570060,28.997835,1,"daad7655-d96a-4a88-8556-dac89b09feeb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Paul C. Selmants","hasEmail":"mailto:pselmants@usgs.gov"},"description":"Tabular data output from a series of modeling simulations for forest ecoystems of the continental United States (CONUS). We linked the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to project changes in forest ecosystem carbon balance resulting from land use, land use change, climate change, and disturbance from wildfire and insect mortality. The model was run at a 1-km spatial resolution on an annual timestep for the years 2001 to 2020. We simulated four unique scenarios, consisting of a climate change only scenario, a land-use change only scenario, a combined climate and land-use change scenario, and a no change scenario. Results presented here have been aggregated from the individual cell level and summarized for either the entire CONUS or for individual States. Model input data and the R code used to generate it, as well as R code used to summarize and analyze model output data, can be found in a GitHub repository (https://github.com/bsleeter/).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9QUIRNP","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.61aaa23bd34eb622f699e082.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61aaa23bd34eb622f699e082","keyword":["CONUS","Carbon balance","Climate change","Disturbance","Land use","Simulation model","USGS:61aaa23bd34eb622f699e082"],"modified":"2022-02-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.48, 23.95, -65.43, 49.54","theme":["geospatial"],"title":"Tabular data of carbon dynamics for conterminous U.S. forests from 2001-2020"},"description":"Tabular data output from a series of modeling simulations for forest ecoystems of the continental United States (CONUS). We linked the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to project changes in forest ecosystem carbon balance resulting from land use, land use change, climate change, and disturbance from wildfire and insect mortality. The model was run at a 1-km spatial resolution on an annual timestep for the years 2001 to 2020. We simulated four unique scenarios, consisting of a climate change only scenario, a land-use change only scenario, a combined climate and land-use change scenario, and a no change scenario. Results presented here have been aggregated from the individual cell level and summarized for either the entire CONUS or for individual States. Model input data and the R code used to generate it, as well as R code used to summarize and analyze model output data, can be found in a GitHub repository (https://github.com/bsleeter/).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6fa1346b-41c9-4db1-9cf8-0222cee8e8f5","harvest_record_raw":"https://catalog.data.gov/harvest_record/6fa1346b-41c9-4db1-9cf8-0222cee8e8f5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61aaa23bd34eb622f699e082","keyword":["CONUS","Carbon balance","Climate change","Disturbance","Land use","Simulation model","USGS:61aaa23bd34eb622f699e082"],"last_harvested_date":"2026-09-10T22:49:30.060388","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":"tabular-data-of-carbon-dynamics-for-conterminous-u-s-forests-from-2001-2020-97604","spatial_centroid":{"lat":34.186,"lon":-102.66000000000001},"spatial_shape":{"coordinates":[[[-127.48,23.95],[-127.48,49.54],[-65.43,49.54],[-65.43,23.95],[-127.48,23.95]]],"type":"Polygon"},"theme":["geospatial"],"title":"Tabular data of carbon dynamics for conterminous U.S. forests from 2001-2020","type":"dataset"},{"_score":6.156557,"_sort":[1789080569156,6.156557,3,"c9acf5db-4b2c-48a7-8d5d-f08f10ddc830"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ryan L. 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Model simulations were combined with white sturgeon telemetry data to explain fish positions with respect to selected depths and depth-averaged velocity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P97TMY3D","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.5abe5415e4b081f61ac1202e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5abe5415e4b081f61ac1202e","keyword":["Bonners Ferry","Boundary","Idaho","Kootenai","Land","Lower Kootenai","North America","Pacific Northwest","USGS:5abe5415e4b081f61ac1202e","United States","acoustic doppler current profiling","aquatic biology","bathymetry","biological and physical processes","biota","climate change","climatologyMeteorologyAtmosphere","digital elevation models","ecology","ecosystem functions","elevation","endangered species","environment","food web","geoscientificInformation","geospatial analysis","hydraulic engineering","lidar","location","mathematical modeling","multibeam sonar","sediment transport","sedimentation","stream-gage measurement","study areas","surface-water level","time series datasets"],"modified":"2021-12-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-116.329760, 48.693554, -116.301200, 48.702100","theme":["geospatial"],"title":"White sturgeon fine-scale habitat model archive, Kootenai River near Bonners Ferry, Idaho, 2017"},"description":"Kootenai river hydraulic conditions were simulated using the iRIC FaSTMECH two-dimensional hydraulic flow model (Nelson, 2003). 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These data were collected by the U.S. Geological Survey, Southwest Biological Science Center - Moab, UT, Research Station staff through field visits, that included physically collecting the sediment samples and processing them in the laboratory three times per year. These data can be used to represent the horizontal mass flux of the sampled plots averaged over the seasonal time steps of sampling, for the date ranges represented.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ZQNFMZ","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.638f657dd34ed907bf7cb46f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_638f657dd34ed907bf7cb46f","keyword":["Bears Ears National Monument","Big Springs Number Eight samplers","Canyonlands National Park","Colorado Plateau","GPS measurement","Grand County","Moab","San Juan County","Southwestern United States","USGS:638f657dd34ed907bf7cb46f","Utah","atmospheric deposition (chemical & particulate)","biogegraphy","data release","disturbance","droughts","drylands","dust","energy development","environment","field sampling","geolocation measurement","geoscientificInformation","grazing","habitat alteration and disturbance","horizontal mass flux","laboratory methods","land use and land cover","livestock","oil and gas","plot sampling","seasonal data","sediment processing","sediment transport","sedimentation","sediments","spatial analysis","spatial data","spatial patterns","temporal data","temporal patterns","unconsolidated deposits","utilitiesCommunication","wind erosion"],"modified":"2023-06-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.9505942, 37.95065052, -108.9272496, 39.35163887","theme":["geospatial"],"title":"Aeolian mass flux data for the Colorado Plateau"},"description":"These data were compiled to measure airborne horizontal mass flux of sediments moved by wind across soils, climates, vegetation types, and land uses on the Colorado Plateau. Objectives of our study were to quantify spatial and temporal patterns in wind erosion and further our understanding of how soil and site setting, climate, and land uses are controlling wind erosion and horizontal mass flux. These data represent seasonal cumulative horizontal mass flux as measured using passive aspirated sediment traps, Big Spring Number Eight (BSNE) samplers. These data were collected in Grand and San Juan counties, Utah, and Mesa County, Colorado, USA between August 2017 and November 2020. These data were collected by the U.S. Geological Survey, Southwest Biological Science Center - Moab, UT, Research Station staff through field visits, that included physically collecting the sediment samples and processing them in the laboratory three times per year. These data can be used to represent the horizontal mass flux of the sampled plots averaged over the seasonal time steps of sampling, for the date ranges represented.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/be825495-e472-4d7b-823f-32f09a82f83a","harvest_record_raw":"https://catalog.data.gov/harvest_record/be825495-e472-4d7b-823f-32f09a82f83a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_638f657dd34ed907bf7cb46f","keyword":["Bears Ears National Monument","Big Springs Number Eight samplers","Canyonlands National Park","Colorado Plateau","GPS measurement","Grand County","Moab","San Juan County","Southwestern United States","USGS:638f657dd34ed907bf7cb46f","Utah","atmospheric deposition (chemical & particulate)","biogegraphy","data release","disturbance","droughts","drylands","dust","energy development","environment","field sampling","geolocation measurement","geoscientificInformation","grazing","habitat alteration and disturbance","horizontal mass flux","laboratory methods","land use and land cover","livestock","oil and gas","plot sampling","seasonal data","sediment processing","sediment transport","sedimentation","sediments","spatial analysis","spatial data","spatial patterns","temporal data","temporal patterns","unconsolidated deposits","utilitiesCommunication","wind erosion"],"last_harvested_date":"2026-09-10T22:49:26.683124","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":"aeolian-mass-flux-data-for-the-colorado-plateau","spatial_centroid":{"lat":38.51104586,"lon":-109.54125635999999},"spatial_shape":{"coordinates":[[[-109.9505942,37.95065052],[-109.9505942,39.35163887],[-108.9272496,39.35163887],[-108.9272496,37.95065052],[-109.9505942,37.95065052]]],"type":"Polygon"},"theme":["geospatial"],"title":"Aeolian mass flux data for the Colorado Plateau","type":"dataset"},{"_score":24.767918,"_sort":[1789080563219,24.767918,3,"c6b278e8-518e-46b5-be72-3a53e1c55f3a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Graham A Sexstone","hasEmail":"mailto:sexstone@usgs.gov"},"description":"This data release includes simulation output from SnowModel (Liston and Elder, 2006), a well-validated process-based snow modeling system, and supporting snow, meteorological, and streamflow observations from the water years 2011 through 2015 (October 1, 2010, through September 30, 2015) across a 3,600 square kilometer model domain in the north-central Colorado Rocky Mountains. For each water year, SnowModel simulations were completed for a (1) baseline simulation, (2) bark-beetle disturbance condition simulation, (3) 2016 - 2035 future climate condition simulation (S1), and (4) 2046 - 2065 future climate condition simulation (S2). Sexstone and others (2018) provide details and summarize findings from each of the SnowModel simulations. SnowModel simulation output is stored in NetCDF files that have spatial (100-m grid resolution) and temporal (daily) dimensions. Simulated SnowModel outputs in the attached .zip folders include: snow water equivalent (m), snow depth (m), surface sublimation (m/day), canopy sublimation (m/day), blowing sublimation (m/day), cumulative blowing snow transport (m), precipitation (m/day), air temperature (C), surface temperature (C), relative humidity (%), wind speed (m/s), wind direction (degrees from north). Supporting station observations that were collected and used to evaluate SnowModel simulations are also provided in this data release in comma separated value files. Supporting station observations in the attached .zip folders include: daily mean snow sublimation (mm/day), mean daily snow depth (m), mean hourly air temperature (C), mean hourly relative humidity (%), mean hourly wind speed (m/s), and mean daily streamflow normalized to watershed area (mm). An inventory and description of each of the .zip folders attached to the data release are provided below. The purpose of the model simulations and supporting observations provided in this data release are to improve understanding of the importance of snow sublimation to the water balance of this region (Sexstone and others, 2018). \nInventory of data release: \nModel_Runs_WYxxxx.zip (5 zipped folders): \nBaseline model simulation output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Beetle_WYxxxx.zip (5 zipped folders): \nBark-beetle disturbance condition model simulation output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Climate_WYxxxx_s1.zip (5 zipped folders): \nFuture climate condition (2016 \u2013 2035) simulation (S1) output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Climate_WYxxxx_s2.zip (5 zipped folders): \nFuture climate condition (2046 \u2013 2065) simulation (S2) output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nSupporting_observations_WY2011-WY2015.zip (1 zipped folder) \nSupporting observations of station observations (.csv) and and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. \nReferences: \nListon, G.E., and Elder, K., 2006, A distributed snow-evolution modeling system (SnowModel): Journal of Hydrometeorology, v. 7, no. 6, p. 1259-1276. \nSexstone, G.A., Clow, D.W., Fassnacht, S.R., Liston, G.E., Hiemstra, C.A., Knowles, J.F., and Penn, C.A., 2018, Snow sublimation in mountain environments and its sensitivity to forest disturbance and climate warming, Water Resources Research [URL].","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F75M64QQ","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.59f898ebe4b063d5d309efa7.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_59f898ebe4b063d5d309efa7","keyword":["Bark beetles","Climate warming","Colorado Rocky Mountains","Eddy covariance","Rocky Mountain National Park","Snow","Snowmelt","Sublimation","USGS:59f898ebe4b063d5d309efa7"],"modified":"2020-08-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-105.9524194, 39.77492324, -105.4797782, 40.58566918","theme":["geospatial"],"title":"SnowModel simulations and supporting observations for the north-central Colorado Rocky Mountains during water years 2011 through 2015"},"description":"This data release includes simulation output from SnowModel (Liston and Elder, 2006), a well-validated process-based snow modeling system, and supporting snow, meteorological, and streamflow observations from the water years 2011 through 2015 (October 1, 2010, through September 30, 2015) across a 3,600 square kilometer model domain in the north-central Colorado Rocky Mountains. For each water year, SnowModel simulations were completed for a (1) baseline simulation, (2) bark-beetle disturbance condition simulation, (3) 2016 - 2035 future climate condition simulation (S1), and (4) 2046 - 2065 future climate condition simulation (S2). Sexstone and others (2018) provide details and summarize findings from each of the SnowModel simulations. SnowModel simulation output is stored in NetCDF files that have spatial (100-m grid resolution) and temporal (daily) dimensions. Simulated SnowModel outputs in the attached .zip folders include: snow water equivalent (m), snow depth (m), surface sublimation (m/day), canopy sublimation (m/day), blowing sublimation (m/day), cumulative blowing snow transport (m), precipitation (m/day), air temperature (C), surface temperature (C), relative humidity (%), wind speed (m/s), wind direction (degrees from north). Supporting station observations that were collected and used to evaluate SnowModel simulations are also provided in this data release in comma separated value files. Supporting station observations in the attached .zip folders include: daily mean snow sublimation (mm/day), mean daily snow depth (m), mean hourly air temperature (C), mean hourly relative humidity (%), mean hourly wind speed (m/s), and mean daily streamflow normalized to watershed area (mm). An inventory and description of each of the .zip folders attached to the data release are provided below. The purpose of the model simulations and supporting observations provided in this data release are to improve understanding of the importance of snow sublimation to the water balance of this region (Sexstone and others, 2018). \nInventory of data release: \nModel_Runs_WYxxxx.zip (5 zipped folders): \nBaseline model simulation output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Beetle_WYxxxx.zip (5 zipped folders): \nBark-beetle disturbance condition model simulation output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Climate_WYxxxx_s1.zip (5 zipped folders): \nFuture climate condition (2016 \u2013 2035) simulation (S1) output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. Each of the 5 zipped folders are labeled with the given water year (WY). \nModel_Runs_Climate_WYxxxx_s2.zip (5 zipped folders): \nFuture climate condition (2046 \u2013 2065) simulation (S2) output (.nc) and associated FGDC-compliant metadata file (.xml) for water years 2011 through 2015. 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Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.\nDue to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9NUO62B","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.5feb873ed34ea5387defbaea.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5feb873ed34ea5387defbaea","keyword":["Beaches","CMHRP","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Monterey County","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:5feb873ed34ea5387defbaea","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"modified":"2026-03-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.641953027, 34.403744888, -120.444512138, 37.819520138","theme":["geospatial"],"title":"Monterey County: CoSMoS v3.1 Central California flood depth and duration projections: average conditions"},"description":"This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. \nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.\nDue to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1a6c7989-5403-442e-93de-543b31df8696","harvest_record_raw":"https://catalog.data.gov/harvest_record/1a6c7989-5403-442e-93de-543b31df8696/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5feb873ed34ea5387defbaea","keyword":["Beaches","CMHRP","Central California","Central California Coast","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Monterey County","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","State of California","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:5feb873ed34ea5387defbaea","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"last_harvested_date":"2026-09-10T22:49:22.992917","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":"cosmos-coastal-storm-modeling-system-central-california-v3-1-flood-depth-and-duration-proj-3a2a5","spatial_centroid":{"lat":35.770054988,"lon":-121.7629766714},"spatial_shape":{"coordinates":[[[-122.641953027,34.403744888],[-122.641953027,37.819520138],[-120.444512138,37.819520138],[-120.444512138,34.403744888],[-122.641953027,34.403744888]]],"type":"Polygon"},"theme":["geospatial"],"title":"Monterey County: CoSMoS v3.1 Central California flood depth and duration projections: average conditions","type":"dataset"},{"_score":18.605844,"_sort":[1789080561631,18.605844,2,"92a6e6ef-763a-4e04-944e-283ff801947d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Erikson, Li","hasEmail":"mailto:lerikson@usgs.gov"},"description":"Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7T151Q4","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.5953f60ce4b062508e3c7c37.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f60ce4b062508e3c7c37","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f60ce4b062508e3c7c37","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"modified":"2026-03-31T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.81115722553, 32.546444355161, -116.66931152258, 34.687068180405","theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood hazard projections: average conditions"},"description":"Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6a2e4342-47a2-4c27-beb4-7fdd683a8fb1","harvest_record_raw":"https://catalog.data.gov/harvest_record/6a2e4342-47a2-4c27-beb4-7fdd683a8fb1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f60ce4b062508e3c7c37","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f60ce4b062508e3c7c37","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"last_harvested_date":"2026-09-10T22:49:21.631518","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":"cosmos-coastal-storm-modeling-system-southern-california-v3-0-phase-2-flood-hazard-project-3a406","spatial_centroid":{"lat":33.4026938852586,"lon":-119.15441894435},"spatial_shape":{"coordinates":[[[-120.81115722553,32.546444355161],[-120.81115722553,34.687068180405],[-116.66931152258,34.687068180405],[-116.66931152258,32.546444355161],[-120.81115722553,32.546444355161]]],"type":"Polygon"},"theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood hazard projections: average conditions","type":"dataset"},{"_score":9.591417,"_sort":[1789080559104,9.591417,1,"315ac74b-230c-4754-9cf4-e7447a3421ab"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey","hasEmail":"mailto:whsc_data_contact@usgs.gov"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 \u201cbase\u201d map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. \nPost-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values.\nProcessing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. \nComponent products can be downloaded https://www.mrlc.gov/data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ODAZHC","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.63851892d34ed907bf779828.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63851892d34ed907bf779828","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:63851892d34ed907bf779828","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"modified":"2022-12-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.0026, 26.4827, -99.6407, 51.5777","theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Fractional Component Time-Series Across the Western U.S. 1985-2021"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 \u201cbase\u201d map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. \nPost-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values.\nProcessing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. \nComponent products can be downloaded https://www.mrlc.gov/data.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b18a6990-9806-4477-82fb-bab000c3c488","harvest_record_raw":"https://catalog.data.gov/harvest_record/b18a6990-9806-4477-82fb-bab000c3c488/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63851892d34ed907bf779828","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:63851892d34ed907bf779828","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"last_harvested_date":"2026-09-10T22:49:19.104835","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":"rangeland-condition-monitoring-assessment-and-projection-rcmap-shrub-fractional--1985-2021","spatial_centroid":{"lat":36.5207,"lon":-116.65784},"spatial_shape":{"coordinates":[[[-128.0026,26.4827],[-128.0026,51.5777],[-99.6407,51.5777],[-99.6407,26.4827],[-128.0026,26.4827]]],"type":"Polygon"},"theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Fractional Component Time-Series Across the Western U.S. 1985-2021","type":"dataset"},{"_score":11.639135,"_sort":[1789080554661,11.639135,4,"ff880a58-8039-4988-8564-63e2787afb28"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Emily J. Sturdivant","hasEmail":"mailto:esturdivant@usgs.gov"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P944FPA4","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.5d0bc91ce4b0941bde4fc5f9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0bc91ce4b0941bde4fc5f9","keyword":["Atlantic Ocean","Barrier Island","CMGP","Coastal and Marine Geology Program","Edwin B. Forsythe NWR","Long Beach Island","NJ","New Jersey,","North America","Pullen Island","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5d0bc91ce4b0941bde4fc5f9","United States","Woods Hole Coastal and Marine Science Center","coastal processes","geomorphology","geospatial analysis","geospatial datasets","oceans","study areas"],"modified":"2026-02-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-74.37258447, 39.43688298, -74.09448993, 39.76457829","theme":["geospatial"],"title":"Edwin B. Forsythe NWR, NJ, 2014: shoreline, inletLines: Shoreline polygons and tidal inlet delineations"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/18cc19d3-95c7-4c91-8cb6-bb406512589b","harvest_record_raw":"https://catalog.data.gov/harvest_record/18cc19d3-95c7-4c91-8cb6-bb406512589b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0bc91ce4b0941bde4fc5f9","keyword":["Atlantic Ocean","Barrier Island","CMGP","Coastal and Marine Geology Program","Edwin B. 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The reach catchment information characterizes data at the local scale, whereas the catchments accumulated through the river network characterize cumulative upstream conditions.  The network-accumulated values are derived using two methods: 1) divergence routing and 2) total upstream routing. Both approaches use a modified routing database (Schwarz and Wieczorek, 2017) to navigate the NHDPlusV2 reach network and to aggregate (accumulate) the metrics derived from the reach catchment scale.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7765D7V","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.57bf5c07e4b0f2f0ceb75b1b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57bf5c07e4b0f2f0ceb75b1b","keyword":["Catchment","Inlandwaters","NAWQA","NHDPlus","SPARROW","USGS:57bf5c07e4b0f2f0ceb75b1b","average annual precipitation","water balance model"],"modified":"2026-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.910792, 23.243486, -65.327751, 51.657387","theme":["geospatial"],"title":"Select Climate and Water Balance Model Attributes: Annual Average Precipitation (millimeters) from 1945-2015"},"description":"This tabular data set represents annual average precipitation values (millimeters) described in Wolock and McCabe (2017), compiled for the NHDPlus version 2 data suite (NHDPlusV2) for the conterminous United States. Linkage of the precipitation data with NHDPlusV2 is achieved through the common unique identifier COMID. The precipitation values are estimated both for: 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The reach catchment information characterizes data at the local scale, whereas the catchments accumulated through the river network characterize cumulative upstream conditions.  The network-accumulated values are derived using two methods: 1) divergence routing and 2) total upstream routing. Both approaches use a modified routing database (Schwarz and Wieczorek, 2017) to navigate the NHDPlusV2 reach network and to aggregate (accumulate) the metrics derived from the reach catchment scale.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/62b93319-93a2-4e13-8078-1deb725028da","harvest_record_raw":"https://catalog.data.gov/harvest_record/62b93319-93a2-4e13-8078-1deb725028da/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57bf5c07e4b0f2f0ceb75b1b","keyword":["Catchment","Inlandwaters","NAWQA","NHDPlus","SPARROW","USGS:57bf5c07e4b0f2f0ceb75b1b","average annual precipitation","water balance model"],"last_harvested_date":"2026-09-10T22:49:11.716582","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":"attributes-for-nhdplus-version-2-1-reach-catchments-and-modified-routed-upstream-1945-2015-e0569","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":"Select Climate and Water Balance Model Attributes: Annual Average Precipitation (millimeters) from 1945-2015","type":"dataset"},{"_score":61.23306,"_sort":[1789080546082,61.23306,4,"da2aaec4-62bc-4c37-8d6c-4b518821d658"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Center for Large Landscape Conservation","hasEmail":"mailto:tyler@largelandscapes.org"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7VM49FN","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.5867d90de4b0cd2dabe7c756.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867d90de4b0cd2dabe7c756","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867d90de4b0cd2dabe7c756","United States","Wyoming","climate change","environment","forest ecosystems","natural resource management"],"modified":"2020-08-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.049289, 41.894474, -108.528169, 49.005950","theme":["geospatial"],"title":"Potential climate change impacts on forest connectivity in the U.S. Northern Rockies"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bf43974a-77da-464c-b50e-df8552cbd6de","harvest_record_raw":"https://catalog.data.gov/harvest_record/bf43974a-77da-464c-b50e-df8552cbd6de/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867d90de4b0cd2dabe7c756","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867d90de4b0cd2dabe7c756","United States","Wyoming","climate change","environment","forest ecosystems","natural resource management"],"last_harvested_date":"2026-09-10T22:49:06.082002","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":"potential-climate-change-impacts-on-forest-connectivity-in-the-u-s-northern-rockies","spatial_centroid":{"lat":44.739064400000004,"lon":-113.640841},"spatial_shape":{"coordinates":[[[-117.049289,41.894474],[-117.049289,49.00595],[-108.528169,49.00595],[-108.528169,41.894474],[-117.049289,41.894474]]],"type":"Polygon"},"theme":["geospatial"],"title":"Potential climate change impacts on forest connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":27.7061,"_sort":[1789080543534,27.7061,2,"817323f4-e78a-4b5b-8ff1-4229bdc38ec3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle A Stern","hasEmail":"mailto:mstern@usgs.gov"},"description":"This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled CNRM-CM5 Global Climate Model (GCM) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the CNRM-CM5 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). 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The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the CNRM-CM5 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). 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Annual evapotranspiration rates corrected to a near-surface energy-budget for the 12 calendar years of record at this site (2004-2015) varied from 718 millimeters (2007) to 903 millimeters (2010). The eddy-covariance method was used, with high-frequency sensors installed above the pasture to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, rainfall, and ground-water levels. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at that timescale. Related data sets are presented at daily and monthly time intervals. The study was conducted at a nearly flat, non-irrigated site (Latitude 28 13 31\u0094 North Longitude 82 33 33 West in degrees minutes seconds, NAD 1927, Section 13, Township 26S, Range 17E) within the Anclote River Ranch property owned by the Southwest Florida Water Management District in Pasco County, Florida. The site was also within J.B. Starkey Wilderness Park. Instrumentation was installed in April 2003. The dominant (about 80 percent of surface coverage) plant cover at the study site is bahiagrass (Paspalum notatum) that varies from a lush green during the summer to a drab brown during the winter. The bahiagrass is ungrazed and grass height can reach 0.5 meter. During the study, the pasture was mowed periodically to 0.2 meters. Vegetation tables provided with the data release list when mowing occurred. Maximum grass rooting depth at the site is about 0.5 meters. Other plants at the study site, intermixed with the bahiagrass and occurring as distinct patches, include bushy broom grass (Andropogon glomeratus), rush (Juncus spp.), dog fennel (Eupatorium capillifolium), flat-topped goldenrod (Euthamia minor), and groundsel tree (Baccharis halimifolia). Forested wetlands are present on the margins of Sandy Branch, a tributary to the Anclote River southwest of the site, and a small cypress dome (40 meter diameter) is located east of the site. The effects of these forested areas are assumed to be negligible, as the pasture area extends 175 meters away from the site in all directions and satisfies upwind fetch requirements for the height of the eddy covariance sensors (more than 100 times the final height of 1.5 meters). The soils at the site are Pomona fine sands with less than 5 percent organic content. For the 13 years of record at this site, the water table was always within 2 meters of land surface.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://dx.doi.org/10.5066/F7SF2TD9","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.5893782be4b0fa1e59b736fa.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5893782be4b0fa1e59b736fa","keyword":["Florida","Pasco County","USGS:5893782be4b0fa1e59b736fa","evapotranspiration","latent heat flux","net radiation","surface energy budget","unimproved pasture"],"modified":"2020-08-12T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-82.561, 28.225, -82.559, 28.227","theme":["geospatial"],"title":"Evapotranspiration at Starkey pasture site, 30-minute data, Pasco County, Florida, January 2010 - April 2016"},"description":"The data set consists of 30-minute evapotranspiration measurements made at the USGS Starkey pasture climate station beginning January 1, 2010 and ending April 30, 2016. Annual evapotranspiration rates corrected to a near-surface energy-budget for the 12 calendar years of record at this site (2004-2015) varied from 718 millimeters (2007) to 903 millimeters (2010). The eddy-covariance method was used, with high-frequency sensors installed above the pasture to measure sensible and latent heat fluxes. Ancillary meteorological data are also included in the data set: net radiation, soil temperature and moisture, air temperature, relative humidity, wind speed and direction, rainfall, and ground-water levels. Data were collected at 30-minute resolution, with evapotranspiration corrected to the near-surface energy-budget at that timescale. Related data sets are presented at daily and monthly time intervals. The study was conducted at a nearly flat, non-irrigated site (Latitude 28 13 31\u0094 North Longitude 82 33 33 West in degrees minutes seconds, NAD 1927, Section 13, Township 26S, Range 17E) within the Anclote River Ranch property owned by the Southwest Florida Water Management District in Pasco County, Florida. The site was also within J.B. Starkey Wilderness Park. Instrumentation was installed in April 2003. The dominant (about 80 percent of surface coverage) plant cover at the study site is bahiagrass (Paspalum notatum) that varies from a lush green during the summer to a drab brown during the winter. The bahiagrass is ungrazed and grass height can reach 0.5 meter. During the study, the pasture was mowed periodically to 0.2 meters. Vegetation tables provided with the data release list when mowing occurred. Maximum grass rooting depth at the site is about 0.5 meters. Other plants at the study site, intermixed with the bahiagrass and occurring as distinct patches, include bushy broom grass (Andropogon glomeratus), rush (Juncus spp.), dog fennel (Eupatorium capillifolium), flat-topped goldenrod (Euthamia minor), and groundsel tree (Baccharis halimifolia). Forested wetlands are present on the margins of Sandy Branch, a tributary to the Anclote River southwest of the site, and a small cypress dome (40 meter diameter) is located east of the site. The effects of these forested areas are assumed to be negligible, as the pasture area extends 175 meters away from the site in all directions and satisfies upwind fetch requirements for the height of the eddy covariance sensors (more than 100 times the final height of 1.5 meters). The soils at the site are Pomona fine sands with less than 5 percent organic content. 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The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc).\nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the MIROC5 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). 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The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc).\nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the MIROC5 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). 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Field surveys of the salamander were conducted from 2007-2022 using daytime cover object surveys. In order to capture the totality of  P. shendandoah\u2019s range we sampled from low to high elevation across a broad geographic range.  We created a spatial generalized additive model with aspect, latitude, longitude, and elevation and heat load index (HLI) to predict salamander occupancy and create a new range map based on our extended surveys. All spatial covariates were extracted from a 15m digital elevation model layer of Shenandoah National Park. Temperature and precipitaiton data were extracted at sampling locations from PRISM Climate Group datasets.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13OVUIC","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.65f9b978d34e25017b28c545.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65f9b978d34e25017b28c545","keyword":["USGS:65f9b978d34e25017b28c545","amphibian","range map","salamander"],"modified":"2024-03-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-78.4335, 38.5232, -78.2977, 38.6657","theme":["geospatial"],"title":"An updated range map for Plethodon shenandoah"},"description":"The Shenanadoah Salamander (Plethodon shenandoah) is an endangered salamander found only in the mountains of Shenandoah National Park. Field surveys of the salamander were conducted from 2007-2022 using daytime cover object surveys. In order to capture the totality of  P. shendandoah\u2019s range we sampled from low to high elevation across a broad geographic range.  We created a spatial generalized additive model with aspect, latitude, longitude, and elevation and heat load index (HLI) to predict salamander occupancy and create a new range map based on our extended surveys. All spatial covariates were extracted from a 15m digital elevation model layer of Shenandoah National Park. Temperature and precipitaiton data were extracted at sampling locations from PRISM Climate Group datasets.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c6dafca4-1ebd-4118-9316-4f30f3123b8f","harvest_record_raw":"https://catalog.data.gov/harvest_record/c6dafca4-1ebd-4118-9316-4f30f3123b8f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65f9b978d34e25017b28c545","keyword":["USGS:65f9b978d34e25017b28c545","amphibian","range map","salamander"],"last_harvested_date":"2026-09-10T22:48:56.410558","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":6,"publisher":"U.S. Geological Survey","slug":"an-updated-range-map-for-plethodon-shenandoah","spatial_centroid":{"lat":38.580200000000005,"lon":-78.37917999999999},"spatial_shape":{"coordinates":[[[-78.4335,38.5232],[-78.4335,38.6657],[-78.2977,38.6657],[-78.2977,38.5232],[-78.4335,38.5232]]],"type":"Polygon"},"theme":["geospatial"],"title":"An updated range map for Plethodon shenandoah","type":"dataset"},{"_score":22.432306,"_sort":[1789080534863,22.432306,1,"29cf8c56-12a4-45ce-b9a9-a083fcc6ad11"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the American  Samoa\u2019s most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated American Samoan Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution along the coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9RIQ7S7","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.64821e9cd34eac007b580e16.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64821e9cd34eac007b580e16","keyword":["CMHRP","Climate Change","Coastal Processes","Coastal and Marine Hazards and Resources Program","Earth sciences","Effects of climate change","Environmental Equity","Environmental Justice","Flooding","Floods","Geospatial Datasets","Mathematical modeling","Ofu Island","Olosega Island","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Predictions","Sea level change","Sea-level Change","Spatial Analysis","Storms","Tau Island","Territory of American Samoa","Tutuila Island","U.S. Geological Survey","USGS","USGS:64821e9cd34eac007b580e16","Waves","environment","geoscientificInformation","oceans"],"modified":"2026-03-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-170.846546803, -14.37292361, -169.416950927, -14.153287314","theme":["geospatial"],"title":"American Samoa: Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios"},"description":"This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the American  Samoa\u2019s most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated American Samoan Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution along the coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0a2da58b-c7fc-44a3-b09b-129b7d06b91a","harvest_record_raw":"https://catalog.data.gov/harvest_record/0a2da58b-c7fc-44a3-b09b-129b7d06b91a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64821e9cd34eac007b580e16","keyword":["CMHRP","Climate Change","Coastal Processes","Coastal and Marine Hazards and Resources Program","Earth sciences","Effects of climate change","Environmental Equity","Environmental Justice","Flooding","Floods","Geospatial Datasets","Mathematical modeling","Ofu Island","Olosega Island","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Predictions","Sea level change","Sea-level Change","Spatial Analysis","Storms","Tau Island","Territory of American Samoa","Tutuila Island","U.S. Geological Survey","USGS","USGS:64821e9cd34eac007b580e16","Waves","environment","geoscientificInformation","oceans"],"last_harvested_date":"2026-09-10T22:48:54.863808","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":"projected-coastal-flooding-depths-for-1-20-and-100-year-return-interval-storms-and-0-00-0--e38fc","spatial_centroid":{"lat":-14.285069091600002,"lon":-170.2747084526},"spatial_shape":{"coordinates":[[[-170.846546803,-14.37292361],[-170.846546803,-14.153287314],[-169.416950927,-14.153287314],[-169.416950927,-14.37292361],[-170.846546803,-14.37292361]]],"type":"Polygon"},"theme":["geospatial"],"title":"American Samoa: Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios","type":"dataset"},{"_score":12.163572,"_sort":[1789080528966,12.163572,1,"2b8763e5-2986-4413-95ca-c1dd843d08a2"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Stephanie S Romanach","hasEmail":"mailto:sromanach@usgs.gov"},"description":"Geodesign Technologies conducted an initial assessment of the development likelihood and conservation priority for the Everglades Headwaters National Wildlife Refuge and Conservation Area study region in central Florida. Geodesign used two prior analyses as the basis for this assessment, both of which are at a statewide Florida scale. The University of Florida's CLIP3 (Critical Lands and Waters Identification Project 3.0; Oetting et. al 2014) was the basis for the biodiversity assessment, and their prior statewide scenario simulations (Vargas et al. 2014) were used as an indicator of likelihood of development under a suite of divergent statewide policies. References: \n\t\t1. Oetting, J., T. Hoctor, and M. Volk. 2014. Critical Lands and Waters Identification Project (CLIP): Version 3.0. Technical Report - February 2014. 110 pp. \n\n\t\t2. Vargas, J.C., Flaxman, and B. Fradkin. 2014. Landscape Conservation and Climate Change Scenarios for the State of Florida: A Decision Support System for Strategic Conservation. Summary for Decision Makers. GeoAdaptive LLC, Boston, MA and Geodesign Technologies Inc., San Francisco CA. 22 pp.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5063/F17942SC","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.76ae539d-a1d6-49b2-a72f-9120538849d5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_76ae539d-a1d6-49b2-a72f-9120538849d5","keyword":["Central Florida","Everglades","Everglades Headwaters National Wildlife Refuge","USGS:76ae539d-a1d6-49b2-a72f-9120538849d5","biota","land use change","natural resource assessment","urbanization"],"modified":"2018-05-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-81.7565, 27.1215, -80.7411, 28.3037","theme":["geospatial"],"title":"Everglades Headwaters National Wildlife Refuge and Conservation Area: Geodesign Urbanization Layer"},"description":"Geodesign Technologies conducted an initial assessment of the development likelihood and conservation priority for the Everglades Headwaters National Wildlife Refuge and Conservation Area study region in central Florida. Geodesign used two prior analyses as the basis for this assessment, both of which are at a statewide Florida scale. The University of Florida's CLIP3 (Critical Lands and Waters Identification Project 3.0; Oetting et. al 2014) was the basis for the biodiversity assessment, and their prior statewide scenario simulations (Vargas et al. 2014) were used as an indicator of likelihood of development under a suite of divergent statewide policies. References: \n\t\t1. Oetting, J., T. Hoctor, and M. Volk. 2014. Critical Lands and Waters Identification Project (CLIP): Version 3.0. Technical Report - February 2014. 110 pp. \n\n\t\t2. Vargas, J.C., Flaxman, and B. Fradkin. 2014. Landscape Conservation and Climate Change Scenarios for the State of Florida: A Decision Support System for Strategic Conservation. Summary for Decision Makers. 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The \"moderate thickness\" and \"high thickness\" versions of the synthetic model are applied to isolate the effect of variable unsaturated zone (UZ) thickness on lags and dampening. Furthermore, the model is used to investigate the thermal inertial effects imposed on the system by the infiltrating heat forcing signal are characterized both in terms of temperature (the dependent variablein MT3D-USGS) and heat flow (the combination of flow and temperature in the MT3D-USGS budget). Recharging heat flow is parsed into its conductive, dispersive and convective components, where the last term is found to dominate.  Importantly, this investigation serves as a steppingstone to a real-world application that is far more complicated by nature's heterogeneity;  an aspect of real-world simulations that is avoided in the current simulations. Temperatures simulated by MT3D-USGS were previously verified by comparing output from detailed 1-dimensional models to VS2DH output (ADD LINK). This USGS data release contains all the input and output for the scenarios described in the associated journal article. 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The \"moderate thickness\" and \"high thickness\" versions of the synthetic model are applied to isolate the effect of variable unsaturated zone (UZ) thickness on lags and dampening. Furthermore, the model is used to investigate the thermal inertial effects imposed on the system by the infiltrating heat forcing signal are characterized both in terms of temperature (the dependent variablein MT3D-USGS) and heat flow (the combination of flow and temperature in the MT3D-USGS budget). Recharging heat flow is parsed into its conductive, dispersive and convective components, where the last term is found to dominate.  Importantly, this investigation serves as a steppingstone to a real-world application that is far more complicated by nature's heterogeneity;  an aspect of real-world simulations that is avoided in the current simulations. Temperatures simulated by MT3D-USGS were previously verified by comparing output from detailed 1-dimensional models to VS2DH output (ADD LINK). This USGS data release contains all the input and output for the scenarios described in the associated journal article. 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Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This tabular data set represents 30 year average (1961-1990) of maximum monthly number of days of measurable precipitation per month compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID.  The source data for 30 year average (1961-1990) of maximum monthly number of days of measurable precipitation was originally PRISM-based data, with some further enhancements by ClimateSource.com and then provided to the USGS by the Environmental Protection Agency (Ryan Hill, EPA, written commun., 2011). Units are percent of days/month. It should also be noted that Climatesource.com no longer exists, however the data is free to distribute and the USGS assumes no liability with its use. It is included with this data set. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions.  Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. 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This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID.  The source data for 30 year average (1961-1990) of maximum monthly number of days of measurable precipitation was originally PRISM-based data, with some further enhancements by ClimateSource.com and then provided to the USGS by the Environmental Protection Agency (Ryan Hill, EPA, written commun., 2011). Units are percent of days/month. It should also be noted that Climatesource.com no longer exists, however the data is free to distribute and the USGS assumes no liability with its use. It is included with this data set. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions.  Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. 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Zeigler","hasEmail":"mailto:szeigler@usgs.gov"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P944FPA4","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.5d0bc8dce4b0941bde4fc5a2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0bc8dce4b0941bde4fc5a2","keyword":["Atlantic Ocean","CMGP","Cedar Island","Coastal Habitat","Coastal and Marine Geology Program","Delmarva Peninsula","GIS","Geographic Information Systems","MHW","Mean High Water","North America","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5d0bc8dce4b0941bde4fc5a2","United States","VA","Virginia","Virginia Coast Reserve","Woods Hole Coastal and Marine Science Center","barrier island","environment","geomorphology","geospatial analysis","geospatial datasets","oceans"],"modified":"2026-02-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.64117637, 37.69202748, -75.58500313, 37.58001334","theme":["geospatial"],"title":"Cedar Island, VA, 2012: DisOcean: Distance to the ocean"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/64cbb70b-4be0-42b9-81a5-e172661061ed","harvest_record_raw":"https://catalog.data.gov/harvest_record/64cbb70b-4be0-42b9-81a5-e172661061ed/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d0bc8dce4b0941bde4fc5a2","keyword":["Atlantic Ocean","CMGP","Cedar Island","Coastal Habitat","Coastal and Marine Geology Program","Delmarva Peninsula","GIS","Geographic Information Systems","MHW","Mean High Water","North America","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5d0bc8dce4b0941bde4fc5a2","United States","VA","Virginia","Virginia Coast Reserve","Woods Hole Coastal and Marine Science Center","barrier island","environment","geomorphology","geospatial analysis","geospatial datasets","oceans"],"last_harvested_date":"2026-09-10T22:48:37.136870","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":"disocean-distance-to-the-ocean-cedar-island-va-2012","spatial_centroid":{"lat":37.647221824,"lon":-75.618707074},"spatial_shape":{"coordinates":[[[-75.64117637,37.69202748],[-75.64117637,37.58001334],[-75.58500313,37.58001334],[-75.58500313,37.69202748],[-75.64117637,37.69202748]]],"type":"Polygon"},"theme":["geospatial"],"title":"Cedar Island, VA, 2012: DisOcean: Distance to the ocean","type":"dataset"},{"_score":9.377296,"_sort":[1789080516699,9.377296,2,"0a44fe40-0cdf-40bf-8421-fd77d84b1043"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Taylor Woods","hasEmail":"mailto:tewoods@usgs.gov"},"description":"This tabular data set represents estimated monthly precipitation (in millimeters) over the period 2006-2099 compiled for NHDPlus version 2 data suite (NHDPlusv2) catchments. This dataset can be linked to the NHDPlusv2 by the unique identifier COMID. The source data is Multivariate Adaptive Constructed Analogs (MACA) (Abatzoglou  Brown, 2011). Summaries are provided for five regions corresponding to NHDPlus vector processing units (VPUs): VPU 02, VPU 03w, VPU 04, VPU 14, and VPU 17.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14MSPDS","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.66c4e342d34e033882892578.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66c4e342d34e033882892578","keyword":["Climate","Great Lakes","MACA","Mid-Atlantic","NHDPlusV2","Pacific Northwest","Precipitation","South Atlantic West","USGS:66c4e342d34e033882892578","Upper Colorado","inlandWaters"],"modified":"2024-11-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.9108, 23.2435, -65.3278, 51.6574","theme":["geospatial"],"title":"Multivariate Adaptive Constructed Analogs (MACA) Catchment Precipitation data, 2006-2099"},"description":"This tabular data set represents estimated monthly precipitation (in millimeters) over the period 2006-2099 compiled for NHDPlus version 2 data suite (NHDPlusv2) catchments. 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The implications of shifts in the relative balance of shallow and deep groundwater discharge sources are profound in gaining streams. These different sources exert critical controls on stream temperature and water quality as influenced by legacy groundwater contaminant transport. Groundwater discharge flux rates over time were used for the inference of source groundwater characteristics to prominent riverbank groundwater discharge faces along the mainstem Farmington River, CT USA. To estimate groundwater discharge rates, we deployed sediment temperature loggers (iButton #DS1922L, Maxim Integrated, Inc., San Jose, CA, USA) in vertical profilers installed directly into mapped preferential groundwater discharge points across extensive riverbank discharge face features.Temperature data contained in this release were collected from June 24 to November 5, 2020, at 40 distinct discharge point riverbank locations, similar to those described by Barclay et al. (2022) and Briggs et al. (2022). Saturated sediment thermal conductivity and heat capacity were measured in-situ with a TEMPOS Thermal Property Analyzer (TEMPOS, Meter Group, Inc., Pullman, WA, USA) at multiple points across each riverbank discharge face to aid in estimating groundwater discharge flux rates.\nBarclay, J. R., Briggs, M. A., Moore, E. M., Starn, J. J., Hanson, A. E. H., & Helton, A. M. (2022). Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale. Advances in Water Resources, 160. https://doi.org/10.1016/j.advwatres.2021.104108\nBriggs, M. A., Jackson, K. E., Liu, F., Moore, E. M., Bisson, A., & Helton, A. M. (2022). Exploring Local Riverbank Sediment Controls on the Occurrence of Preferential Groundwater Discharge Points. 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The implications of shifts in the relative balance of shallow and deep groundwater discharge sources are profound in gaining streams. These different sources exert critical controls on stream temperature and water quality as influenced by legacy groundwater contaminant transport. Groundwater discharge flux rates over time were used for the inference of source groundwater characteristics to prominent riverbank groundwater discharge faces along the mainstem Farmington River, CT USA. To estimate groundwater discharge rates, we deployed sediment temperature loggers (iButton #DS1922L, Maxim Integrated, Inc., San Jose, CA, USA) in vertical profilers installed directly into mapped preferential groundwater discharge points across extensive riverbank discharge face features.Temperature data contained in this release were collected from June 24 to November 5, 2020, at 40 distinct discharge point riverbank locations, similar to those described by Barclay et al. (2022) and Briggs et al. (2022). Saturated sediment thermal conductivity and heat capacity were measured in-situ with a TEMPOS Thermal Property Analyzer (TEMPOS, Meter Group, Inc., Pullman, WA, USA) at multiple points across each riverbank discharge face to aid in estimating groundwater discharge flux rates.\nBarclay, J. R., Briggs, M. A., Moore, E. M., Starn, J. J., Hanson, A. E. H., & Helton, A. M. (2022). Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale. Advances in Water Resources, 160. https://doi.org/10.1016/j.advwatres.2021.104108\nBriggs, M. A., Jackson, K. E., Liu, F., Moore, E. M., Bisson, A., & Helton, A. M. (2022). Exploring Local Riverbank Sediment Controls on the Occurrence of Preferential Groundwater Discharge Points. 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Outputs include impacts from combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 m) storm conditions including 1-year, 20-year and 100-year return interval storms and a background condition (no storm - astronomic tide and average atmospheric conditions).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9W91314","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.fe48822f-f055-4106-82d1-bae59dd2c1b0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_fe48822f-f055-4106-82d1-bae59dd2c1b0","keyword":["Beaches","CMHRP","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","State of North Carolina","State of South Carolina","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:fe48822f-f055-4106-82d1-bae59dd2c1b0","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"modified":"2024-05-23T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-81.41555, 32.03543, -75.44948, 36.55215","theme":["geospatial"],"title":"Projections of coastal flood hazards and flood potential for North Carolina and South Carolina"},"description":"Projected impacts by compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for North Carolina and South Carolina. 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Outputs include impacts from combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 m) storm conditions including 1-year, 20-year and 100-year return interval storms and a background condition (no storm - astronomic tide and average atmospheric conditions).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/09464e27-2885-4e12-aee6-b3c41ff01ced","harvest_record_raw":"https://catalog.data.gov/harvest_record/09464e27-2885-4e12-aee6-b3c41ff01ced/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_fe48822f-f055-4106-82d1-bae59dd2c1b0","keyword":["Beaches","CMHRP","Climate Change","ClimatologyMeteorologyAtmosphere","Coastal and Marine Hazards and Resources Program","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","PCMSC","Pacific Coastal and Marine Science Center","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","State of North Carolina","State of South Carolina","Storm Surge","Storms","U.S. Geological Survey","USGS","USGS:fe48822f-f055-4106-82d1-bae59dd2c1b0","Water Depth","Wind","coastal erosion","earth sciences","effects of climate change","floods","mathematical modeling","sea level change","waves"],"last_harvested_date":"2026-09-10T22:48:21.824069","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":"projections-of-coastal-flood-hazards-and-flood-potential-for-north-carolina-and-south-caro","spatial_centroid":{"lat":33.842118,"lon":-79.029122},"spatial_shape":{"coordinates":[[[-81.41555,32.03543],[-81.41555,36.55215],[-75.44948,36.55215],[-75.44948,32.03543],[-81.41555,32.03543]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projections of coastal flood hazards and flood potential for North Carolina and South Carolina","type":"dataset"},{"_score":11.480655,"_sort":[1789080500048,11.480655,2,"82e309a0-602a-426b-bbd6-77441e49a634"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Sara L. 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This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9V7F6UX","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.5daa37f8e4b09fd3b0c9cf0c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5daa37f8e4b09fd3b0c9cf0c","keyword":["Atlantic Ocean","Barrier Island","CMHRP","Coastal Habitat","Coastal and Marine Hazards and Resources Program","Delmarva Peninsula","GIS","Geographic Information Systems","MHW","Mean High Water","North America","Ship Shoal Island","St. Petersburg Coastal and Marine Science Center","U.S. Geological Survey","USA","USGS","USGS:5daa37f8e4b09fd3b0c9cf0c","United States","VA","Virginia","Virginia Coast Reserve","Woods Hole Coastal and Marine Science Center","coastal processes","environment","erosion","geographic information systems","geomorphology","geospatial analysis","geospatial datasets","hazards","image analysis","oceans","scientific interpretation","sea-level change","study areas"],"modified":"2026-02-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-75.84608892, 37.20579554, -75.79744115, 37.23799795","theme":["geospatial"],"title":"Ship Shoal Island, VA, 2014: SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type"},"description":"Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. 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Climate data is within the file titled 'BisonHerbivory_Climate.csv\".","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14MMQCJ","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.66bba9a6d34e0338828159a8.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66bba9a6d34e0338828159a8","keyword":["Arizona","Grand Canyon National Park","Kaibab National Forest","USGS:66bba9a6d34e0338828159a8","biota","bison","consumers (organisms)","ecosystem functions","grassland ecosystems","grasslands","habitat alteration and disturbance","producers (organisms)"],"modified":"2024-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-112.3256, 36.2260, -112.0166, 36.4859","theme":["geospatial"],"title":"Data Describing Effects of Bison (Bison bison) Herbivory on Herbaceous Production and Nitrogen Yield throughout Grand Canyon Grasslands from 2021 to 2022."},"description":"This dataset includes three datasets collected in 2021 and 2022 to assess the potential effects of bison (Bison bison) herbivory and climate on grassland functional properties throughout semi-arid meadows in Grand Canyon National Park and Kaibab National Forest of northern Arizona. Bison herbivory offtake (utilization) and aboveground herbaceous production are demonstrated in the production offtake dataset titled 'BisonHerbivory_ANPP_Ot.csv'. This dataset includes the experimental treatment variables (Stratum and Treatment) and estimates for seasonal offtake, total annual offtake, total aboveground net primary production, and grazing intensity. Data on herbaceous nitrogen yield provides calculations of percent nitrogen and nitrogen yield for graminoid and forb samples collected from biomass clippings in areas of bison grazing (file titled 'BisonHerbivory_N_Yield.csv'). Meteorological data are presented for seasonal and total annual climate variables including precipitation (measured in mm) and temperature (measured in Growing Degree Days). Climate data is within the file titled 'BisonHerbivory_Climate.csv\".","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/42ec4984-31de-4ce6-b96d-38e2a53b8663","harvest_record_raw":"https://catalog.data.gov/harvest_record/42ec4984-31de-4ce6-b96d-38e2a53b8663/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66bba9a6d34e0338828159a8","keyword":["Arizona","Grand Canyon National Park","Kaibab National Forest","USGS:66bba9a6d34e0338828159a8","biota","bison","consumers (organisms)","ecosystem functions","grassland ecosystems","grasslands","habitat alteration and disturbance","producers (organisms)"],"last_harvested_date":"2026-09-10T22:48:19.598025","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":"data-describing-effects-of-bison-bison-bison-herbivory-on-herbaceous-production-and-n-2022","spatial_centroid":{"lat":36.32996,"lon":-112.202},"spatial_shape":{"coordinates":[[[-112.3256,36.226],[-112.3256,36.4859],[-112.0166,36.4859],[-112.0166,36.226],[-112.3256,36.226]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data Describing Effects of Bison (Bison bison) Herbivory on Herbaceous Production and Nitrogen Yield throughout Grand Canyon Grasslands from 2021 to 2022.","type":"dataset"},{"_score":3.7447681,"_sort":[1789080498696,3.7447681,6,"2a299d22-8a4f-455c-bf40-25a7c3297a43"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California\u2019s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California\u2019s State Waters with consistent methods at a consistent scale.\nThe CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. \nThis approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of \u201clands\u201d from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California\u2019s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth.\nThis data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013).\nThe California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay\u2019s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service\u2013Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey.\nThese web services for the Point Sur to Point Arguello map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html.\nEach published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these \u201cground-truth\u201d surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The \u201cseafloor character\u201d data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The \u201cpotential habitats\u201d polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles.\nThe information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues.\nWeb services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://pubs.usgs.gov/ds/781/","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.5cf03d06e4b0b51330e22bde.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5cf03d06e4b0b51330e22bde","keyword":["Avila Beach","Bathymetry","Bathymetry and Elevation","Benthic marine habitat","CMHRP","Cambria","Coastal and Marine Hazards and Resources Program","Continental/Island Shelf","Distributions","GIS","Gamboa Point","Gorda","Lafler Rock","Marine Nearshore Subtidal","Marine Offshore Subtidal","Morro Bay","Oceano Beach","Offshore geology","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Physical Habitats and Geomorphology","Piedras Blancas","Point Arguello","Point Buchon","Point Estero","Point Purisma","Point Sal","Point Sur","Ragged Point","Rock Substrate","Seafloor Topography","Seafloor mapping","Southern California Ecoregion","State of California","Substrate","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:5cf03d06e4b0b51330e22bde","Unconsolidated Mineral Substrate","environment","geographic information systems","geoscientificInformation","geoscientificinformation","imageryBaseMapsEarthCover","oceans","visualization methods"],"modified":"2023-04-07T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-122.064886, 36.532722, -121.811775, 36.692799","theme":["geospatial"],"title":"California State Waters Map Series--Point Sur to Point Arguello Web Services"},"description":"In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California\u2019s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California\u2019s State Waters with consistent methods at a consistent scale.\nThe CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. \nThis approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of \u201clands\u201d from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California\u2019s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth.\nThis data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013).\nThe California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay\u2019s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service\u2013Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey.\nThese web services for the Point Sur to Point Arguello map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html.\nEach published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these \u201cground-truth\u201d surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The \u201cseafloor character\u201d data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The \u201cpotential habitats\u201d polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles.\nThe information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues.\nWeb services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ce123857-0d0d-46f7-9c48-e127deeb148f","harvest_record_raw":"https://catalog.data.gov/harvest_record/ce123857-0d0d-46f7-9c48-e127deeb148f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5cf03d06e4b0b51330e22bde","keyword":["Avila Beach","Bathymetry","Bathymetry and Elevation","Benthic marine habitat","CMHRP","Cambria","Coastal and Marine Hazards and Resources Program","Continental/Island Shelf","Distributions","GIS","Gamboa Point","Gorda","Lafler Rock","Marine Nearshore Subtidal","Marine Offshore Subtidal","Morro Bay","Oceano Beach","Offshore geology","PCMSC","Pacific Coastal and Marine Science Center","Pacific Ocean","Physical Habitats and Geomorphology","Piedras Blancas","Point Arguello","Point Buchon","Point Estero","Point Purisma","Point Sal","Point Sur","Ragged Point","Rock Substrate","Seafloor Topography","Seafloor mapping","Southern California Ecoregion","State of California","Substrate","Transform Continental Margin","U.S. Geological Survey","USGS","USGS:5cf03d06e4b0b51330e22bde","Unconsolidated Mineral Substrate","environment","geographic information systems","geoscientificInformation","geoscientificinformation","imageryBaseMapsEarthCover","oceans","visualization methods"],"last_harvested_date":"2026-09-10T22:48:18.696402","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":6,"publisher":"U.S. Geological Survey","slug":"california-state-waters-map-series-point-sur-to-point-arguello-web-services","spatial_centroid":{"lat":36.596752800000004,"lon":-121.9636416},"spatial_shape":{"coordinates":[[[-122.064886,36.532722],[-122.064886,36.692799],[-121.811775,36.692799],[-121.811775,36.532722],[-122.064886,36.532722]]],"type":"Polygon"},"theme":["geospatial"],"title":"California State Waters Map Series--Point Sur to Point Arguello Web Services","type":"dataset"},{"_score":9.567443,"_sort":[1789080498026,9.567443,2,"67c16530-bd83-4df6-a3b2-e6b135b35fea"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey","hasEmail":"mailto:whsc_data_contact@usgs.gov"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 \u201cbase\u201d map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. \nPost-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values.\nProcessing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. \nComponent products can be downloaded https://www.mrlc.gov/data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ODAZHC","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.6385182ad34ed907bf779826.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6385182ad34ed907bf779826","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:6385182ad34ed907bf779826","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"modified":"2022-12-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.0026, 26.4827, -99.6407, 51.5777","theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Sagebrush Fractional Component Time-Series Across the Western U.S. 1985-2021"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 \u201cbase\u201d map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites. \nPost-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values.\nProcessing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. \nComponent products can be downloaded https://www.mrlc.gov/data.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a1ae1380-df9f-4db8-bbc8-0c30b66da7f1","harvest_record_raw":"https://catalog.data.gov/harvest_record/a1ae1380-df9f-4db8-bbc8-0c30b66da7f1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6385182ad34ed907bf779826","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:6385182ad34ed907bf779826","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"last_harvested_date":"2026-09-10T22:48:18.026986","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":"rangeland-condition-monitoring-assessment-and-projection-rcmap-sagebrush-fractio-1985-2021","spatial_centroid":{"lat":36.5207,"lon":-116.65784},"spatial_shape":{"coordinates":[[[-128.0026,26.4827],[-128.0026,51.5777],[-99.6407,51.5777],[-99.6407,26.4827],[-128.0026,26.4827]]],"type":"Polygon"},"theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Sagebrush Fractional Component Time-Series Across the Western U.S. 1985-2021","type":"dataset"},{"_score":12.762491,"_sort":[1789080493909,12.762491,4,"977902de-32b0-4e89-864c-9ab3b33ee82d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey","hasEmail":"mailto:whsc_data_contact@usgs.gov"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2024. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height; in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. 1) The training database was expanded by incorporating new observations from the BLM Analysis Inventory and Monitoring (AIM) dataset, the LANDFIRE public reference database, and additional Landsat scale observations collected by RCMAP. 2) New high-resolution training sites were added in the Northern Rockies, Pacific Northwest, and Northern Great Plains. 3) High-resolution sites were subjected to a new screening approach which removes areas of topographic shadows and other likely noise from the training pool. 4)The change detection methodology was removed to create more temporally dynamic predictions. 5) Additional independent variables were added to the Landsat scale models including time-since most recent fire and 3 by 3 focal variance of all predictor variables. 6) The project\u2019s study area was expanded to include the grasslands biome as defined by the U.S. Fish and Wildlife Service Central Grassland Roadmap, which expanded the study area by 1,830,783 km2. \nRCMAP data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded at https://www.mrlc.gov/data and are available in an interactive viewer: MRLC Rangeland Viewer. Additionally, data are available on Google Earth Engine, and as WMS/WCS services Data Services Page | Multi-Resolution Land Characteristics (MRLC) Consortium.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13QF8HT","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.67659d6ed34e5335adae31ee.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67659d6ed34e5335adae31ee","keyword":["AB","AZ","Alberta","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Canada","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Great Plains","Gulf Coast","Gunnison","IA","ID","Idaho","Iowa","KS","Kansas","LA","Louisiana","MN","MO","MT","Mediterranean California","Middle Rockies","Minnesota","Missouri","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Rocky Mountains","OK","OR","Oklahoma","Oregon","Plains","Prairie Provinces","Rocky Mountains","SD","SK","Sandhills","Saskatchewan","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Plains","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","USGS:67659d6ed34e5335adae31ee","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","rangeland","rangeland management","sagebrush","shrub","shrub height","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","tree","trends","vegetation","vegetation change","vegetation height"],"modified":"2025-03-12T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-128.0026, 25.8371, -90.1430, 51.5761","theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Height Fractional Component Time-Series Across Western North America from 1985-2024"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2024. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height; in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. 1) The training database was expanded by incorporating new observations from the BLM Analysis Inventory and Monitoring (AIM) dataset, the LANDFIRE public reference database, and additional Landsat scale observations collected by RCMAP. 2) New high-resolution training sites were added in the Northern Rockies, Pacific Northwest, and Northern Great Plains. 3) High-resolution sites were subjected to a new screening approach which removes areas of topographic shadows and other likely noise from the training pool. 4)The change detection methodology was removed to create more temporally dynamic predictions. 5) Additional independent variables were added to the Landsat scale models including time-since most recent fire and 3 by 3 focal variance of all predictor variables. 6) The project\u2019s study area was expanded to include the grasslands biome as defined by the U.S. Fish and Wildlife Service Central Grassland Roadmap, which expanded the study area by 1,830,783 km2. \nRCMAP data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded at https://www.mrlc.gov/data and are available in an interactive viewer: MRLC Rangeland Viewer. Additionally, data are available on Google Earth Engine, and as WMS/WCS services Data Services Page | Multi-Resolution Land Characteristics (MRLC) Consortium.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/55dc959b-6d95-403c-8457-c0b290f8f331","harvest_record_raw":"https://catalog.data.gov/harvest_record/55dc959b-6d95-403c-8457-c0b290f8f331/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_67659d6ed34e5335adae31ee","keyword":["AB","AZ","Alberta","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Canada","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Great Plains","Gulf Coast","Gunnison","IA","ID","Idaho","Iowa","KS","Kansas","LA","Louisiana","MN","MO","MT","Mediterranean California","Middle Rockies","Minnesota","Missouri","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Rocky Mountains","OK","OR","Oklahoma","Oregon","Plains","Prairie Provinces","Rocky Mountains","SD","SK","Sandhills","Saskatchewan","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Plains","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","USGS:67659d6ed34e5335adae31ee","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","rangeland","rangeland management","sagebrush","shrub","shrub height","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","tree","trends","vegetation","vegetation change","vegetation height"],"last_harvested_date":"2026-09-10T22:48:13.909176","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":"rangeland-condition-monitoring-assessment-and-projection-rcmap-shrub-height-frac-1985-2024","spatial_centroid":{"lat":36.1327,"lon":-112.85876},"spatial_shape":{"coordinates":[[[-128.0026,25.8371],[-128.0026,51.5761],[-90.143,51.5761],[-90.143,25.8371],[-128.0026,25.8371]]],"type":"Polygon"},"theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Height Fractional Component Time-Series Across Western North America from 1985-2024","type":"dataset"},{"_score":6.9916773,"_sort":[1789080489338,6.9916773,3,"f9e8a24d-eb86-4ae0-a96a-bad923ae1f77"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Patrick W. Limber","hasEmail":"mailto:plimber@usgs.gov"},"description":"This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS) v.3.0 Phase 2 in Southern California.\nDetails: Cliff-retreat position projections and associated uncertainties are for scenarios of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, and 5 meters of SLR. Projections were made at CoSMoS cross-shore transects (CST) spaced 100 m alongshore using a baseline sea-cliff edge from 2010 (included in the dataset). Within each zip file, there are two separate datasets available: one that ignores coastal armoring, such as seawalls and revetments, and allows the cliff to retreat unimpeded (\u201cDo Not Hold the Line\u201d); and another that assumes that current coastal armoring will be maintained and 100% effective at stopping future cliff erosion (\"Hold the Line\"). \nEight numerical models synthesized from literature (Trenhaile, 2000; Walkden and Hall, 2005; Trenhaile, 2009; Trenhaile, 2011; Ruggiero and others, 2011; Hackney and others, 2013) were used to make projections. All models relate breaking-wave height and period to cliff rock or unconsolidated sediment erosion. Models range in complexity from 2-D models in which the entire profile evolves, from below water to the cliff edge, to simple 1-D empirical or statistical models in which only the cliff edge evolves as a function of wave impact intensity and frequency. The projections are a robust average of all models, and the uncertainties are proportional to 1) underlying uncertainties in the model input data, such as historical cliff retreat rates, and 2) the differences between individual model forecasts at each CST so that uncertainty is larger when the models do not agree. As sea level rises, waves break closer to the sea cliff, more wave energy impacts the cliffs, cliff erosion rates accelerate. Model behavior also includes wave run-up (Stockdon and others, 2006), wave set-up that raises the water level during big-wave events, and tidal levels.\nThe more complex 2-D models were run on idealized cliff profiles extending from about 10 m water depth to 1 kilometer inland from the cliff edge. Profiles were extracted by overlaying the cross-shore transects on a high-resolution digital elevation model (DEM) covering the Southern California study area. For all models, the presence of a beach was recorded (yes or no) for all transects using aerial photography, and the cliff toe elevation (or beach/cliff junction) was digitized from the DEM profiles. Using historic cliff edge retreat rates by Hapke and Reid (2007), unknown coefficients within the cliff-profile models were calibrated using a Monte Carlo simulation (in other words, coefficients were tuned until the modeled mean retreat rate equaled the observed mean retreat rate for a given transect). \nUncertainty was tallied using a root mean squared error (RMSE) approach. The RMSE represents cumulative uncertainty from multiple sources and assumes that different sources of error will, at times, cancel each other out. It is therefore not a 'worst-case uncertainty' (in other words, a straight sum of errors) but instead an average uncertainty. Total RMSE increased with SLR rate and varied between +/- 2-3 m to a maximum of +/-  50 m for the extreme 5 m SLR scenario.  \nFor more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7T151Q4","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.57f4234de4b0bc0bec033f90.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57f4234de4b0bc0bec033f90","keyword":["Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Hazards Planning","Los Angeles County","Ocean Waves","Oceans","Orange County","Physical Habitats and Geomorphology","San Diego County","Santa Barbara County","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storms","USGS:57f4234de4b0bc0bec033f90","Ventura County","coastal erosion","sea level change","waves"],"modified":"2021-10-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.48706054569, 32.472325899539, -117.01538085833, 34.51524902452","theme":["geospatial"],"title":"CoSMoS Southern California v3.0 Phase 2 projections of coastal cliff retreat due to 21st century sea-level rise"},"description":"This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS) v.3.0 Phase 2 in Southern California.\nDetails: Cliff-retreat position projections and associated uncertainties are for scenarios of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, and 5 meters of SLR. Projections were made at CoSMoS cross-shore transects (CST) spaced 100 m alongshore using a baseline sea-cliff edge from 2010 (included in the dataset). Within each zip file, there are two separate datasets available: one that ignores coastal armoring, such as seawalls and revetments, and allows the cliff to retreat unimpeded (\u201cDo Not Hold the Line\u201d); and another that assumes that current coastal armoring will be maintained and 100% effective at stopping future cliff erosion (\"Hold the Line\"). \nEight numerical models synthesized from literature (Trenhaile, 2000; Walkden and Hall, 2005; Trenhaile, 2009; Trenhaile, 2011; Ruggiero and others, 2011; Hackney and others, 2013) were used to make projections. All models relate breaking-wave height and period to cliff rock or unconsolidated sediment erosion. Models range in complexity from 2-D models in which the entire profile evolves, from below water to the cliff edge, to simple 1-D empirical or statistical models in which only the cliff edge evolves as a function of wave impact intensity and frequency. The projections are a robust average of all models, and the uncertainties are proportional to 1) underlying uncertainties in the model input data, such as historical cliff retreat rates, and 2) the differences between individual model forecasts at each CST so that uncertainty is larger when the models do not agree. As sea level rises, waves break closer to the sea cliff, more wave energy impacts the cliffs, cliff erosion rates accelerate. Model behavior also includes wave run-up (Stockdon and others, 2006), wave set-up that raises the water level during big-wave events, and tidal levels.\nThe more complex 2-D models were run on idealized cliff profiles extending from about 10 m water depth to 1 kilometer inland from the cliff edge. Profiles were extracted by overlaying the cross-shore transects on a high-resolution digital elevation model (DEM) covering the Southern California study area. For all models, the presence of a beach was recorded (yes or no) for all transects using aerial photography, and the cliff toe elevation (or beach/cliff junction) was digitized from the DEM profiles. Using historic cliff edge retreat rates by Hapke and Reid (2007), unknown coefficients within the cliff-profile models were calibrated using a Monte Carlo simulation (in other words, coefficients were tuned until the modeled mean retreat rate equaled the observed mean retreat rate for a given transect). \nUncertainty was tallied using a root mean squared error (RMSE) approach. The RMSE represents cumulative uncertainty from multiple sources and assumes that different sources of error will, at times, cancel each other out. It is therefore not a 'worst-case uncertainty' (in other words, a straight sum of errors) but instead an average uncertainty. Total RMSE increased with SLR rate and varied between +/- 2-3 m to a maximum of +/-  50 m for the extreme 5 m SLR scenario.  \nFor more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/117637a3-3ebd-41ce-a8e9-a082a06d4592","harvest_record_raw":"https://catalog.data.gov/harvest_record/117637a3-3ebd-41ce-a8e9-a082a06d4592/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57f4234de4b0bc0bec033f90","keyword":["Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Hazards Planning","Los Angeles County","Ocean Waves","Oceans","Orange County","Physical Habitats and Geomorphology","San Diego County","Santa Barbara County","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storms","USGS:57f4234de4b0bc0bec033f90","Ventura County","coastal erosion","sea level change","waves"],"last_harvested_date":"2026-09-10T22:48:09.338029","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":"cosmos-southern-california-v3-0-phase-2-projections-of-coastal-cliff-retreat-due-to-21st-c","spatial_centroid":{"lat":33.2894951495314,"lon":-119.098388670746},"spatial_shape":{"coordinates":[[[-120.48706054569,32.472325899539],[-120.48706054569,34.51524902452],[-117.01538085833,34.51524902452],[-117.01538085833,32.472325899539],[-120.48706054569,32.472325899539]]],"type":"Polygon"},"theme":["geospatial"],"title":"CoSMoS Southern California v3.0 Phase 2 projections of coastal cliff retreat due to 21st century sea-level rise","type":"dataset"},{"_score":12.502982,"_sort":[1789080489104,12.502982,1,"250964b9-79c7-4bba-a170-8b95f973034b"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Data Manager","hasEmail":"mailto:fresc_outreach@usgs.gov"},"description":"Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Krameria erecta within its Mojave Desert range based on known occurrences.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9XQJFEL","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.61f95e11d34e622189c51094.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61f95e11d34e622189c51094","keyword":["Arizona","California","Mojave","Nevada","USGS:61f95e11d34e622189c51094","Utah","biogeography","biota","habitats","maps and atlases","native plant materials development","native species","species distribution model"],"modified":"2022-04-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-118.9291, 33.5105, -112.6985, 37.6714","theme":["geospatial"],"title":"Species distribution model (SDM) for Krameria erecta in the Mojave Desert"},"description":"Preserving native species diversity is fundamental to ecosystem conservation. Selecting appropriate native species for use in restoration is a critical component of project design and may emphasize species attributes such as life history, functional type, pollinator services, and nutritional value for wildlife. Determining which species are likely to establish and persist in a particular environment is a key consideration. Species distribution models (SDMs) characterize relationships between species occurrences and the physical environment (e.g., climate, soil, topographic relief) and provide a mechanism for assessing which species may successfully propagate at a restoration site. In conjunction with information on species attributes, SDMs facilitate holistic ecosystem restoration by enabling practitioners to identify diverse, resilient assemblages of native species. This project develops SDMs for native species of fundamental ecosystem importance in order to guide restoration of Mojave Desert landscapes. The dataset contained herein provides an SDM for Krameria erecta within its Mojave Desert range based on known occurrences.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c81555bf-3a09-4290-a4aa-f7a236e6f50b","harvest_record_raw":"https://catalog.data.gov/harvest_record/c81555bf-3a09-4290-a4aa-f7a236e6f50b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_61f95e11d34e622189c51094","keyword":["Arizona","California","Mojave","Nevada","USGS:61f95e11d34e622189c51094","Utah","biogeography","biota","habitats","maps and atlases","native plant materials development","native species","species distribution model"],"last_harvested_date":"2026-09-10T22:48:09.104013","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":"species-distribution-model-sdm-for-krameria-erecta-in-the-mojave-desert","spatial_centroid":{"lat":35.17486,"lon":-116.43686},"spatial_shape":{"coordinates":[[[-118.9291,33.5105],[-118.9291,37.6714],[-112.6985,37.6714],[-112.6985,33.5105],[-118.9291,33.5105]]],"type":"Polygon"},"theme":["geospatial"],"title":"Species distribution model (SDM) for Krameria erecta in the Mojave Desert","type":"dataset"},{"_score":13.35595,"_sort":[1789080477092,13.35595,2,"69591adb-8cee-48de-9cae-8aab4dd23491"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Joel T Groten","hasEmail":"mailto:jgroten@usgs.gov"},"description":"Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9V0R02R","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.5fe9f395d34ea5387ded6a1f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe9f395d34ea5387ded6a1f","keyword":["USGS:5fe9f395d34ea5387ded6a1f","geomorphology","land surveying","sedimentation","sedimentology","streamflow","vegetation"],"modified":"2021-08-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-89.591667, 34.425, -89.39166700000001, 34.625","theme":["geospatial"],"title":"The Vigil Network: Graham Mill Creek, Mississippi"},"description":"Long-term monitoring data of geomorphic, hydrological, and biological characteristics of landscapes. This information provides an effective means of relating observed change to possible causes of the change. Identification of changes in basin characteristics, especially in arid areas where the response to altered climate or land use is generally rapid and readily apparent, might provide the initial direct indications that factors such as global warming and cultural impacts have affected the environment. The Vigil Network provides an opportunity for earth and life scientists to participate in a systematic monitoring effort to detect landscape changes over time, and to relate such changes to possible causes. This data release includes 70 sites and basins used to monitor landscape features. This data release includes information for Vigil Network sites monitored in the United States. The data and information in this data release are historical and were obtained from original documents.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e93c588d-0dfe-4fc3-aaf8-706cf9a16aa9","harvest_record_raw":"https://catalog.data.gov/harvest_record/e93c588d-0dfe-4fc3-aaf8-706cf9a16aa9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5fe9f395d34ea5387ded6a1f","keyword":["USGS:5fe9f395d34ea5387ded6a1f","geomorphology","land surveying","sedimentation","sedimentology","streamflow","vegetation"],"last_harvested_date":"2026-09-10T22:47:57.092154","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":"the-vigil-network-graham-mill-creek-mississippi","spatial_centroid":{"lat":34.504999999999995,"lon":-89.51166700000002},"spatial_shape":{"coordinates":[[[-89.591667,34.425],[-89.591667,34.625],[-89.39166700000001,34.625],[-89.39166700000001,34.425],[-89.591667,34.425]]],"type":"Polygon"},"theme":["geospatial"],"title":"The Vigil Network: Graham Mill Creek, Mississippi","type":"dataset"},{"_score":13.809919,"_sort":[1789080474836,13.809919,4,"5e9dd286-e462-4ef6-be9b-3864d18baf3f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kristy Guy","hasEmail":"mailto:kguy@usgs.gov"},"description":"Shorelines Extracted from 1984-2015 Landsat Imagery:  Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015.  The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys.  Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup.  Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7XW4GVG","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.f787e33e-0c56-4413-8dd4-b583d648483f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f787e33e-0c56-4413-8dd4-b583d648483f","keyword":["1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2013","2014","2015","COASTAL PROCESSES > BEACHES","DOI/USGS/CMG > COASTAL AND MARINE GEOLOGY, U.S. GEOLOGICAL SURVEY, U.S. DEPARTMENT OF INTERIOR","Enhanced Thematic Mapper Plus (ETM+)","Gulf of Mexico","Horn Island","Jackson County","LAND SURFACE > LAND USE/LAND COVER > LAND COVER","Landsat 5","Landsat 7","Landsat 8","Mississippi","Mississippi Sound","OCEAN > COASTAL PROCESSES > BARRIER ISLANDS","Operational Land Imager (OLI)","Thematic Mapper (TM)","USGS:f787e33e-0c56-4413-8dd4-b583d648483f","United States","aerial and satellite photography","barrier island","barrier island migration","coastal processes","erosion","mapping","remote sensing","satellite imagery","shore","shoreline","shoreline accretion","shoreline erosion"],"modified":"2020-10-13T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.782547, 30.216607, -88.557259, 30.255192","theme":["geospatial"],"title":"Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates)"},"description":"Shorelines Extracted from 1984-2015 Landsat Imagery:  Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015.  The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys.  Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup.  Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d1160d7f-f4de-45d0-8271-36fd58f9b15a","harvest_record_raw":"https://catalog.data.gov/harvest_record/d1160d7f-f4de-45d0-8271-36fd58f9b15a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f787e33e-0c56-4413-8dd4-b583d648483f","keyword":["1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2013","2014","2015","COASTAL PROCESSES > BEACHES","DOI/USGS/CMG > COASTAL AND MARINE GEOLOGY, U.S. GEOLOGICAL SURVEY, U.S. DEPARTMENT OF INTERIOR","Enhanced Thematic Mapper Plus (ETM+)","Gulf of Mexico","Horn Island","Jackson County","LAND SURFACE > LAND USE/LAND COVER > LAND COVER","Landsat 5","Landsat 7","Landsat 8","Mississippi","Mississippi Sound","OCEAN > COASTAL PROCESSES > BARRIER ISLANDS","Operational Land Imager (OLI)","Thematic Mapper (TM)","USGS:f787e33e-0c56-4413-8dd4-b583d648483f","United States","aerial and satellite photography","barrier island","barrier island migration","coastal processes","erosion","mapping","remote sensing","satellite imagery","shore","shoreline","shoreline accretion","shoreline erosion"],"last_harvested_date":"2026-09-10T22:47:54.836343","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":"shorelines-extracted-from-1984-2015-landsat-imagery-horn-island-mississippi-polygon-combin","spatial_centroid":{"lat":30.232041,"lon":-88.6924318},"spatial_shape":{"coordinates":[[[-88.782547,30.216607],[-88.782547,30.255192],[-88.557259,30.255192],[-88.557259,30.216607],[-88.782547,30.216607]]],"type":"Polygon"},"theme":["geospatial"],"title":"Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates)","type":"dataset"},{"_score":8.14044,"_sort":[1789080472621,8.14044,1,"7ade4296-8558-4498-bd32-ef65d297a512"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle M Irizarry-Ortiz","hasEmail":"mailto:mirizarry-ortiz@usgs.gov"},"description":"The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period.  \nAn R script (create_boxplot.R) is provided which generates boxplots of change factors for a NOAA Atlas 14 station, or for all NOAA Atlas 14 stations in a Florida HUC-8 basin or county for durations of interest (1, 3, and 7 days, or combinations thereof) and return periods of interest (5, 10, 25, 50, 100, 200, and 500 years, or combinations thereof). The user also has the option of requesting that the script save the raw change factor data used to generate the boxplots, as well as the processed quantile and outlier data shown in the figure. The script allows the user to modify the percentiles used in generating the boxplots. A Microsoft Word file documenting code usage and available options is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release.\nThe script uses basins defined in the \"Florida Hydrologic Unit Code (HUC) Basins (areas)\" from the Florida Department of Environmental Protection (FDEP;  https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida HUC-8 basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP \"lorida Hydrologic Unit Code (HUC) Basins (areas).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9Q3LEIL","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.64a2e6f8d34ef77fcb056b9a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64a2e6f8d34ef77fcb056b9a","keyword":["Florida","Florida Flood Hub for Applied Research and Innovation","USGS:64a2e6f8d34ef77fcb056b9a","climatologyMeteorologyAtmosphere","depth-duration-frequency","extremes","precipitation (atmospheric)","precipitation extremes"],"modified":"2025-08-26T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-87.643620946, 24.416352892, -79.989351961, 31.16271922","theme":["geospatial"],"title":"R script to create boxplots of change factors by NOAA Atlas 14 station, or for all stations in a Florida HUC-8 basin or county (create_boxplot.R)"},"description":"The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. 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The user also has the option of requesting that the script save the raw change factor data used to generate the boxplots, as well as the processed quantile and outlier data shown in the figure. The script allows the user to modify the percentiles used in generating the boxplots. A Microsoft Word file documenting code usage and available options is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release.\nThe script uses basins defined in the \"Florida Hydrologic Unit Code (HUC) Basins (areas)\" from the Florida Department of Environmental Protection (FDEP;  https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida HUC-8 basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. 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The return levels are modified to account for changes in the future frequency of large-scale meteorological factors conducive to precipitation by means of an analog resampling approach. 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In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. 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The following mean daily soil moisture water content layers were processed: 0-10 centimeters, 10-40 centimeters, and 40-100 centimeters.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P98IG8LO","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.6447f553d34ee8d4aded3b53.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6447f553d34ee8d4aded3b53","keyword":["CONUS","Conterminous United States","Coterminous United States","Geospatial Fabric","Hydrologic Modeling","Inlandwaters","NLDAS2","USGS:6447f553d34ee8d4aded3b53","Upper Colorado River","catchments","elevation","soil layers","soil moisture","soil moisture water content","temperature (Celsius)","water content"],"modified":"2025-07-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-129.7712, 21.0821, -63.1667, 54.8088","theme":["geospatial"],"title":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portion of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Climate Metrics Derived from NLDAS2, 1980 - 2020"},"description":"These tabular data sets represent the average daily soil moisture water content (kg/m^2) for four different soil layers processed from North American Land Data Assimilation System (NLDAS-2) data (Xia and others, 2012) for the period of record 1980 through 2020 and compiled for three spatial components: 1) select United States Geological Survey stream gage basins (Staub and Wieczorek, 2023),  2) individual reach flowline catchments of the Upper Colorado (ucol) portion of the Geospatial Fabric for the National Hydrologic Model, version 1.1 (nhgfv11, Bock and others, 2020 ), and 3) the upstream watersheds of each individual nhgfv11 flowline catchments.  Flowline reach catchment information characterizes data at the local scale using the python tool set called gdptools (McDonald, 2021). Upstream watershed values for each reach catchment were computed using the published python software package Xstrm (Wieferich and others). The following mean daily soil moisture water content layers were processed: 0-10 centimeters, 10-40 centimeters, and 40-100 centimeters.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/3e541823-9e77-4103-8ad4-01315d56c155","harvest_record_raw":"https://catalog.data.gov/harvest_record/3e541823-9e77-4103-8ad4-01315d56c155/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6447f553d34ee8d4aded3b53","keyword":["CONUS","Conterminous United States","Coterminous United States","Geospatial Fabric","Hydrologic Modeling","Inlandwaters","NLDAS2","USGS:6447f553d34ee8d4aded3b53","Upper Colorado River","catchments","elevation","soil layers","soil moisture","soil moisture water content","temperature (Celsius)","water content"],"last_harvested_date":"2026-09-10T22:46:24.632487","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":"data-driven-drought-prediction-project-model-inputs-for-upper-and-lower-colorado-1980-2020-79c5e","spatial_centroid":{"lat":34.57278,"lon":-103.12939999999999},"spatial_shape":{"coordinates":[[[-129.7712,21.0821],[-129.7712,54.8088],[-63.1667,54.8088],[-63.1667,21.0821],[-129.7712,21.0821]]],"type":"Polygon"},"theme":["geospatial"],"title":"Data-Driven Drought Prediction Project Model Inputs for Upper and Lower Colorado Portion of the National Hydrologic Geo-Spatial Fabric version 1.1 and Select U.S. Geological Survey Streamgage Basins: Daily Climate Metrics Derived from NLDAS2, 1980 - 2020","type":"dataset"},{"_score":11.782827,"_sort":[1789080379679,11.782827,1,"30bc12c0-d8fb-4957-8474-21df807e629a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle M Irizarry-Ortiz","hasEmail":"mailto:mirizarry-ortiz@usgs.gov"},"description":"The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period.  \nThe SFWMD manages the water resources of various interconnected areas in south Florida, which are defined in the SFWMD ArcHydro Enhanced Database (AHED) as \u201cAHED Rain Areas\u201d. The SFWMD is interested in summarizing change factors for each individual AHED Rain Area to use in future planning efforts. Geospatial data provided in an ArcGIS shapefile named \u201cAHED_basins.shp\u201d are described herein. The shapefile contains polygons for the AHED Rain Areas defined in the South Florida Water Management District (SFWMD)'s ArcHydro Enhanced Database (AHED) including their acreages.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P935WRTG","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.6157349fd34e0df5fb9f8393.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6157349fd34e0df5fb9f8393","keyword":["AHED","Arc Hydro","ArcHydro","Everglades","Florida","Rain areas","South Florida Water Management District","Southern Florida","USGS:6157349fd34e0df5fb9f8393","basins","boundaries","hydrography","nexrad","rain","rainarea","rainareas","south Florida"],"modified":"2022-09-16T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-82.304778062, 24.516061387, -80.015782232, 28.563170392","theme":["geospatial"],"title":"Shapefile of SFWMD basins as defined in their ArcHydro Enhanced Database (AHED) (AHED_basins.shp)"},"description":"The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period.  \nThe SFWMD manages the water resources of various interconnected areas in south Florida, which are defined in the SFWMD ArcHydro Enhanced Database (AHED) as \u201cAHED Rain Areas\u201d. The SFWMD is interested in summarizing change factors for each individual AHED Rain Area to use in future planning efforts. Geospatial data provided in an ArcGIS shapefile named \u201cAHED_basins.shp\u201d are described herein. The shapefile contains polygons for the AHED Rain Areas defined in the South Florida Water Management District (SFWMD)'s ArcHydro Enhanced Database (AHED) including their acreages.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4a05f2ac-88fd-460c-b3bb-126a73b20c1f","harvest_record_raw":"https://catalog.data.gov/harvest_record/4a05f2ac-88fd-460c-b3bb-126a73b20c1f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6157349fd34e0df5fb9f8393","keyword":["AHED","Arc Hydro","ArcHydro","Everglades","Florida","Rain areas","South Florida Water Management District","Southern Florida","USGS:6157349fd34e0df5fb9f8393","basins","boundaries","hydrography","nexrad","rain","rainarea","rainareas","south Florida"],"last_harvested_date":"2026-09-10T22:46:19.679289","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":"shapefile-of-sfwmd-basins-as-defined-in-their-archydro-enhanced-database-ahed-ahed_basins-","spatial_centroid":{"lat":26.134904989,"lon":-81.38917973},"spatial_shape":{"coordinates":[[[-82.304778062,24.516061387],[-82.304778062,28.563170392],[-80.015782232,28.563170392],[-80.015782232,24.516061387],[-82.304778062,24.516061387]]],"type":"Polygon"},"theme":["geospatial"],"title":"Shapefile of SFWMD basins as defined in their ArcHydro Enhanced Database (AHED) (AHED_basins.shp)","type":"dataset"},{"_score":36.24321,"_sort":[1789080378564,36.24321,3,"1bcd90e7-a42f-433c-9247-921b152168bd"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Center","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. All survey questions were optional to complete. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust). Results of the survey are presented as summary tables in order to protect personal identifiable information of the respondents. Summary information is in the form of tables and word cloud graphics to communicate results of open ended questions.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P93WPOS5","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.63506ff6d34e47431c15c697.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63506ff6d34e47431c15c697","keyword":["North Central Region","South Central Region","USGS:63506ff6d34e47431c15c697","United States","external research support","project evaluation","science centers","social science"],"modified":"2026-09-04T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-116.1035, 25.7207, -93.5156, 48.8647","theme":["geospatial"],"title":"Summary of North Central and South Central Climate Adaptation Science Centers Project Evaluation Survey Data Collected from 2018-2019"},"description":"The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. All survey questions were optional to complete. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust). Results of the survey are presented as summary tables in order to protect personal identifiable information of the respondents. Summary information is in the form of tables and word cloud graphics to communicate results of open ended questions.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7e07e49d-ce8f-4437-8e98-11a045464d26","harvest_record_raw":"https://catalog.data.gov/harvest_record/7e07e49d-ce8f-4437-8e98-11a045464d26/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63506ff6d34e47431c15c697","keyword":["North Central Region","South Central Region","USGS:63506ff6d34e47431c15c697","United States","external research support","project evaluation","science centers","social science"],"last_harvested_date":"2026-09-10T22:46:18.564807","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":"summary-of-north-central-and-south-central-climate-adaptation-science-centers-pr-2018-2019","spatial_centroid":{"lat":34.978300000000004,"lon":-107.06833999999999},"spatial_shape":{"coordinates":[[[-116.1035,25.7207],[-116.1035,48.8647],[-93.5156,48.8647],[-93.5156,25.7207],[-116.1035,25.7207]]],"type":"Polygon"},"theme":["geospatial"],"title":"Summary of North Central and South Central Climate Adaptation Science Centers Project Evaluation Survey Data Collected from 2018-2019","type":"dataset"},{"_score":18.338379,"_sort":[1789080377621,18.338379,1,"00b80f0d-255d-4a2e-b425-512589e067c3"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Erikson, Li","hasEmail":"mailto:lerikson@usgs.gov"},"description":"Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7T151Q4","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.5953f5eae4b062508e3c7c30.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f5eae4b062508e3c7c30","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f5eae4b062508e3c7c30","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"modified":"2026-03-31T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.81115722553, 32.546444355161, -116.66931152258, 34.687068180405","theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood hazard projections: 100-year storm"},"description":"Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated.\nThe Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\nModel details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully.  Data are complete for the information presented.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/10c133c0-911a-4bf4-a5d3-3b11e54aa24a","harvest_record_raw":"https://catalog.data.gov/harvest_record/10c133c0-911a-4bf4-a5d3-3b11e54aa24a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5953f5eae4b062508e3c7c30","keyword":["Beaches","Climate Change","ClimatologyMeteorologyAtmosphere","Erosion","Extreme Weather","Floods","Hazards Planning","Ocean Waves","Ocean Winds","Oceans","Orange County","Physical Habitats and Geomorphology","Sea Level Rise","Sea-level Change","Southern California","Southern California Bight","State of California","Storm Surge","Storms","USGS:5953f5eae4b062508e3c7c30","Water Depth","Wind","coastal erosion","floods","sea level change","waves"],"last_harvested_date":"2026-09-10T22:46:17.621475","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":"cosmos-coastal-storm-modeling-system-southern-california-v3-0-phase-2-flood-hazard-project-647fe","spatial_centroid":{"lat":33.4026938852586,"lon":-119.15441894435},"spatial_shape":{"coordinates":[[[-120.81115722553,32.546444355161],[-120.81115722553,34.687068180405],[-116.66931152258,34.687068180405],[-116.66931152258,32.546444355161],[-120.81115722553,32.546444355161]]],"type":"Polygon"},"theme":["geospatial"],"title":"Orange County: CoSMoS Southern California v3.0 Phase 2 flood hazard projections: 100-year storm","type":"dataset"},{"_score":27.816984,"_sort":[1789080377178,27.816984,0,"b4387f77-211e-444c-8062-771e1cc40ff4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle A Stern","hasEmail":"mailto:mstern@usgs.gov"},"description":"This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled GFDL-CM3 Global Climate Model (GCM) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the GFDL-CM3 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). 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The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include: actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes separate pages for each RCP 4.5 and RCP 8.5 for the GFDL-CM3 GCM:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. 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The RCMAP product suite consists of eight fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub and rule-based error maps including the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. We used an updated version of the 2016 base training data, with a more aggressive forest mask and reduced shrub and sagebrush cover bias in pinyon-juniper woodlands. We pooled training data in areas and times identified as having no spectral change relative to the base year across all years in the time-series and used a series of procedures to remove the most spatially and temporally common values. We also used composite Landsat Analysis Ready Data (ARD) in seven regions instead of using individual images by path and row. An automated method to identify change in spectral conditions between each year in the Landsat archive and the circa 2016 base map, resulted in a robust and sensitivity of subtle changes. Yearly fractional component cover outputs were inserted in the changed area while the base year values were maintained in the unchanged area. Processing efficiency has been increased through use of open-source software and High-Performance Computing (HPC) resources. The mapping area included seven regions which were subsequently mosaicked for all eight components. \nThese data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded from www.mrlc.gov.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P95IQ4BT","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.60b7cd06d34e86b938873009.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_60b7cd06d34e86b938873009","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:60b7cd06d34e86b938873009","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"modified":"2021-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-130.2380, 26.2039, -99.6688, 52.7905","theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020 - Sagebrush"},"description":"The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2020. The RCMAP product suite consists of eight fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub and rule-based error maps including the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. We used an updated version of the 2016 base training data, with a more aggressive forest mask and reduced shrub and sagebrush cover bias in pinyon-juniper woodlands. We pooled training data in areas and times identified as having no spectral change relative to the base year across all years in the time-series and used a series of procedures to remove the most spatially and temporally common values. We also used composite Landsat Analysis Ready Data (ARD) in seven regions instead of using individual images by path and row. An automated method to identify change in spectral conditions between each year in the Landsat archive and the circa 2016 base map, resulted in a robust and sensitivity of subtle changes. Yearly fractional component cover outputs were inserted in the changed area while the base year values were maintained in the unchanged area. Processing efficiency has been increased through use of open-source software and High-Performance Computing (HPC) resources. The mapping area included seven regions which were subsequently mosaicked for all eight components. \nThese data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices. Component products can be downloaded from www.mrlc.gov.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/efd51032-431a-4c18-bf01-9944803b4975","harvest_record_raw":"https://catalog.data.gov/harvest_record/efd51032-431a-4c18-bf01-9944803b4975/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_60b7cd06d34e86b938873009","keyword":["AZ","Arizona","Arizona Plateau","Black Hills","Blue Mountains","CA","CO","California","Chihuahuan","Chihuahuan Desert","Colorado","Colorado Plateau","Columbia Plateau","Desert","Grand Canyon","Great Basin","Gunnison","ID","Idaho","MT","Mediterranean California","Middle Rockies","Mojave","Montana","ND","NE","NM","NV","Nebraska","Nevada","New Mexico","North Dakota","North Plains","Northern Great Plains","Northern Great Salt Lake Desert","Northern Mountainous","Northern Rocky Mountains","OR","Oregon","Plains","Plateau","Rocky Mountains","SD","Sierra Nevada","Sonoran","Sonoran Desert","South Dakota","Southern Great Salt Lake Desert","Southern Rocky Mountains","Southwest Tablelands","TX","Texas","The Rockies","Three Forks","USGS:60b7cd06d34e86b938873009","UT","United States","Utah","WA","WY","Wasatch","Washington","Western US","Wyoming","Wyoming Basin","Yellowstone","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","climate change","environment","geoscientificInformation","grass","grassland change","herbaceous","imageryBaseMapsEarthCover","litter","mts","rangeland","rangeland management","sagebrush","shrub","shrubland","shrubland change","shrubland ecosystems","shrublands","terrestrial ecosystems","time series","trends","vegetation","vegetation change"],"last_harvested_date":"2026-09-10T22:46:14.137468","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":"rangeland-condition-monitoring-assessment-and-projection-rcmap-fractional-component-time-s-48fb5","spatial_centroid":{"lat":36.83854,"lon":-118.01032000000001},"spatial_shape":{"coordinates":[[[-130.238,26.2039],[-130.238,52.7905],[-99.6688,52.7905],[-99.6688,26.2039],[-130.238,26.2039]]],"type":"Polygon"},"theme":["geospatial"],"title":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020 - Sagebrush","type":"dataset"},{"_score":14.298394,"_sort":[1789080372504,14.298394,3,"16174f71-ac6c-4bdf-a7ae-ec8f5fdb1924"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jennifer S. Stanton","hasEmail":"mailto:jstanton@usgs.gov"},"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). Data were \ncollected and synthesized from existing climate models including the Parameter-Elevation \nRegressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the \nSnow accumulation and ablation model (SNOW-17) (Anderson, 2006),and used in \nsoil-water balance models to compute various components of a water budget. The \nmethodologies used to compute the averages and volumes for the data in this \ngeodatabase are slightly different for different components and models.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9MBLQHV","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.4fea9b7c-3ed2-4837-9788-5fe50b7dd40f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4fea9b7c-3ed2-4837-9788-5fe50b7dd40f","keyword":["Actual evapotranspiration","Colorado","Evapotranspiration","Great Plains","High Plains","High Plains aquifer","Kansas","Nebraska","New Mexico","Ogallala aquifer","Oklahoma","Soil Water Balance (SWB) Model","South Dakota","Texas","USGS:4fea9b7c-3ed2-4837-9788-5fe50b7dd40f","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 Actual Evapotranspiration, 2000 to 2009, in inches estimated from the Soil Water Balance (SWB) Model 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). Data were \ncollected and synthesized from existing climate models including the Parameter-Elevation \nRegressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the \nSnow accumulation and ablation model (SNOW-17) (Anderson, 2006),and used in \nsoil-water balance models to compute various components of a water budget. The \nmethodologies used to compute the averages and volumes for the data in this \ngeodatabase are slightly different for different components and models.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/aec2da4f-e69e-4c69-bb82-b34dda70a4fb","harvest_record_raw":"https://catalog.data.gov/harvest_record/aec2da4f-e69e-4c69-bb82-b34dda70a4fb/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4fea9b7c-3ed2-4837-9788-5fe50b7dd40f","keyword":["Actual evapotranspiration","Colorado","Evapotranspiration","Great Plains","High Plains","High Plains aquifer","Kansas","Nebraska","New Mexico","Ogallala aquifer","Oklahoma","Soil Water Balance (SWB) Model","South Dakota","Texas","USGS:4fea9b7c-3ed2-4837-9788-5fe50b7dd40f","Wyoming","aquifers","central High Plains","environment","geoscientificInformation","ground water","groundwater","inlandWaters","northern High Plains","southern High Plains"],"last_harvested_date":"2026-09-10T22:46:12.504425","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":"ds-777-average-annual-actual-evapotranspiration-2000-to-2009-in-inches-estimated-from-the--c36dd","spatial_centroid":{"lat":36.4794828,"lon":-102.1007196},"spatial_shape":{"coordinates":[[[-106.019428,31.594384],[-106.019428,43.807131],[-96.222657,43.807131],[-96.222657,31.594384],[-106.019428,31.594384]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS-777 Average Annual Actual Evapotranspiration, 2000 to 2009, in inches estimated from the Soil Water Balance (SWB) Model for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming","type":"dataset"},{"_score":58.838356,"_sort":[1789080372270,58.838356,1,"1b47995f-8fb7-4561-8a50-138495dbcd3c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Center for Large Landscape Conservation","hasEmail":"mailto:tyler@largelandscapes.org"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7VM49FN","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.5867da61e4b0cd2dabe7c75a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867da61e4b0cd2dabe7c75a","keyword":["Connectivity","Environment and Conservation","Grizzly bear","Idaho","Montana","Rocky Mountains","USGS:5867da61e4b0cd2dabe7c75a","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2020-08-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.049289, 41.894474, -108.528169, 49.005950","theme":["geospatial"],"title":"Potential climate change impacts on grizzly bear connectivity in the U.S. Northern Rockies"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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