{"after":"WzE3ODkwODA1MzY0MTAsMTQuMDU2OTYsNiwiNzU4NmIyMTUtNDBmOS00MjIzLTk0NjAtYjJiNzViNDdjY2Q2Il0=","results":[{"_score":54.278313,"_sort":[1789430590137,54.278313,0,"4fe85d12-260d-4c3e-a4ac-315cf659a378"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andrew J. Hansen","hasEmail":"mailto:hansen@montana.edu"},"description":"Historical and projected suitable habitat of 33 tree and shrub species a   under  CCSM4 GCMs from 1980 to 2099 was predicted  to assess projected climate change impacts in forest communities of North Central U.S. We obtained presence/absence record of each species from Forest Inventory and Analysis (FIA) data. required ata. Historical tme period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099.\nRandom Forest was used to project historical and future suitable habitat of all species across north central U.S. using the Biomod2 software programmed in R environment.\nWe adopted  a climate change scenarios generated from the experiments conducted under fifth assessment of Coupled Model Intercomparison Project (CMIP5) for the Intergovernmental Panel on Climate Change. Selected climate change scenarios include high representative concentrative pathway (RCP8.5).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P134WA8G","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.5b199bd4e4b092d9652387ab.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b199bd4e4b092d9652387ab","keyword":["FIA","MACA","Speices Distribution Modeing","USGS:5b199bd4e4b092d9652387ab","climate change","environment","external research support","geospatial datasets"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.2461, 31.5036, -87.9958, 49.0002","theme":["geospatial"],"title":"Historical and projected habitat suitability of dominant tree and shrub species across North Central U.S. (1980-2099) under climate change"},"description":"Historical and projected suitable habitat of 33 tree and shrub species a   under  CCSM4 GCMs from 1980 to 2099 was predicted  to assess projected climate change impacts in forest communities of North Central U.S. We obtained presence/absence record of each species from Forest Inventory and Analysis (FIA) data. required ata. Historical tme period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099.\nRandom Forest was used to project historical and future suitable habitat of all species across north central U.S. using the Biomod2 software programmed in R environment.\nWe adopted  a climate change scenarios generated from the experiments conducted under fifth assessment of Coupled Model Intercomparison Project (CMIP5) for the Intergovernmental Panel on Climate Change. Selected climate change scenarios include high representative concentrative pathway (RCP8.5).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4dab31a4-6ef4-4985-adcb-6a860a7b9838","harvest_record_raw":"https://catalog.data.gov/harvest_record/4dab31a4-6ef4-4985-adcb-6a860a7b9838/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b199bd4e4b092d9652387ab","keyword":["FIA","MACA","Speices Distribution Modeing","USGS:5b199bd4e4b092d9652387ab","climate change","environment","external research support","geospatial datasets"],"last_harvested_date":"2026-09-15T00:03:10.137538","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":"historical-and-projected-habitat-suitability-of-dominant-tree-and-shrub-species-across-nor","spatial_centroid":{"lat":38.50224,"lon":-105.54598000000001},"spatial_shape":{"coordinates":[[[-117.2461,31.5036],[-117.2461,49.0002],[-87.9958,49.0002],[-87.9958,31.5036],[-117.2461,31.5036]]],"type":"Polygon"},"theme":["geospatial"],"title":"Historical and projected habitat suitability of dominant tree and shrub species across North Central U.S. (1980-2099) under climate change","type":"dataset"},{"_score":8.515263,"_sort":[1789430528472,8.515263,0,"60048b4a-2528-47d9-b4de-e60533620c12"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"This data set contains environmental DNA (eDNA) data enumerating the presence of intertidal marine vertebrates, invertebrates, and macro algae collected across 5 islands with 10 sites in the Boston Harbor in 2023. This data was collected using water samples collected in association with other intertidal biodiversity monitoring protocols developed specifically for the mixed coarse substrate habitat found on the inner islands of Boston Harbor, including Gallops, Georges, Lovell (alternatively known as Lovells Island), Peddocks, and Thompson Island. Species were identified using an eDNA metabarcoding approach targeting the 12s (vertebrate) and 18s (invertebrate and macro algae) gene regions. Total read count scores for species detected are reported, with scores greater than 1 signaling a positive detection, and scores of 0 signaling no detection. Data included in this release was used to determine community composition and diversity metrics across high biodiversity and erosional sites for the 5 islands.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1TNRR6Z","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.66cf2a9bd34e98e8a924b8a5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66cf2a9bd34e98e8a924b8a5","keyword":["Boston Harbor","Boston Harbor Islands National and State Park","Environmental DNA","Macro algae","Marine invertebrates","Metabarcoding","Mixed coarse substrate","USGS:66cf2a9bd34e98e8a924b8a5","Vertebrates","biodiversity","biota","climate change","eDNA","oceans"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-71.0100, 42.2500, -70.8600, 42.3600","theme":["geospatial"],"title":"2023 Environmental DNA (eDNA) Baseline to Monitor Intertidal Biodiversity in Waters Adjacent to Mixed Coarse Substrate Habitats Across the Boston Harbor Islands"},"description":"This data set contains environmental DNA (eDNA) data enumerating the presence of intertidal marine vertebrates, invertebrates, and macro algae collected across 5 islands with 10 sites in the Boston Harbor in 2023. This data was collected using water samples collected in association with other intertidal biodiversity monitoring protocols developed specifically for the mixed coarse substrate habitat found on the inner islands of Boston Harbor, including Gallops, Georges, Lovell (alternatively known as Lovells Island), Peddocks, and Thompson Island. Species were identified using an eDNA metabarcoding approach targeting the 12s (vertebrate) and 18s (invertebrate and macro algae) gene regions. Total read count scores for species detected are reported, with scores greater than 1 signaling a positive detection, and scores of 0 signaling no detection. Data included in this release was used to determine community composition and diversity metrics across high biodiversity and erosional sites for the 5 islands.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/116257d5-92a5-45b1-8a06-719440df7ec2","harvest_record_raw":"https://catalog.data.gov/harvest_record/116257d5-92a5-45b1-8a06-719440df7ec2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_66cf2a9bd34e98e8a924b8a5","keyword":["Boston Harbor","Boston Harbor Islands National and State Park","Environmental DNA","Macro algae","Marine invertebrates","Metabarcoding","Mixed coarse substrate","USGS:66cf2a9bd34e98e8a924b8a5","Vertebrates","biodiversity","biota","climate change","eDNA","oceans"],"last_harvested_date":"2026-09-15T00:02:08.472753","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":"2023-environmental-dna-edna-baseline-to-monitor-intertidal-biodiversity-in-waters-adjacent","spatial_centroid":{"lat":42.294,"lon":-70.95},"spatial_shape":{"coordinates":[[[-71.01,42.25],[-71.01,42.36],[-70.86,42.36],[-70.86,42.25],[-71.01,42.25]]],"type":"Polygon"},"theme":["geospatial"],"title":"2023 Environmental DNA (eDNA) Baseline to Monitor Intertidal Biodiversity in Waters Adjacent to Mixed Coarse Substrate Habitats Across the Boston Harbor Islands","type":"dataset"},{"_score":56.129543,"_sort":[1789430276279,56.129543,0,"1f39bedb-6aeb-46aa-8c2f-94286c85da89"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andrew J. Hansen","hasEmail":"mailto:hansen@montana.edu"},"description":"Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. For further details please see summary sheet. \nThis template includes data for Penman MI as an example.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13MFXRS","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.5aa969bae4b0b1c392f16bf5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5aa969bae4b0b1c392f16bf5","keyword":["MACA","Moisture Index","Potential Evapotranspiration","USGS:5aa969bae4b0b1c392f16bf5","climate change","environment","geospatial datasets","precipiation","temperature"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.2461, 31.5036, -87.9958, 49.0002","theme":["geospatial"],"title":"Water balance across regional climate gradients: A comparison of two potential evapotranspiration metrics (1980-2099)"},"description":"Historical and projected climate data and water balance data under three GCMs (CNRM-CM5, CCSM4, and IPSL-CM5A-MR) from 1980 to 2099 was used to assess projected climate change impacts in North Central U.S. We obtained required data from MACA data (https://climate.northwestknowledge.net/MACA/). Historical time period ranges from 1980 to 2005, and projected time period ranges from 2071 to 2099. The climate data includes temperature and precipitation whereas water balance data includes Potential Evapotranspiration (PET) and Moisture Index (MI) estimated using Penman-Monteith and Thornthwaite methods defining as Penman PET, Penman MI, Thornthwaite PET and Thornthwaite MI.  Both types of MI was estimated as a ratio of Precipitation and Evapotranspiration. The MACA data includes Penman PET which was estimated using Penman-Monteith methods. However, Thornthwaite PET was estimated using Thornthwaite methods for this project. For further details please see summary sheet. \nThis template includes data for Penman MI as an example.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bb04f4fa-1357-4acb-a209-f102471e221b","harvest_record_raw":"https://catalog.data.gov/harvest_record/bb04f4fa-1357-4acb-a209-f102471e221b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5aa969bae4b0b1c392f16bf5","keyword":["MACA","Moisture Index","Potential Evapotranspiration","USGS:5aa969bae4b0b1c392f16bf5","climate change","environment","geospatial datasets","precipiation","temperature"],"last_harvested_date":"2026-09-14T23:57:56.279487","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":"water-balance-across-regional-climate-gradients-a-comparison-of-two-potential-ev-1980-2099","spatial_centroid":{"lat":38.50224,"lon":-105.54598000000001},"spatial_shape":{"coordinates":[[[-117.2461,31.5036],[-117.2461,49.0002],[-87.9958,49.0002],[-87.9958,31.5036],[-117.2461,31.5036]]],"type":"Polygon"},"theme":["geospatial"],"title":"Water balance across regional climate gradients: A comparison of two potential evapotranspiration metrics (1980-2099)","type":"dataset"},{"_score":57.636177,"_sort":[1789430206604,57.636177,0,"fd7100e9-588f-4ece-bd4d-5a9eebd7bd31"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Yinphan Tsang","hasEmail":"mailto:tsangy@hawaii.edu"},"description":"Results described in the paper, \"Conserving Stream Fishes with Changing Climate: Assessing Fish Responses to Changes in Habitat Over a Large Region\": https://doi.org/10.1016/j.scitotenv.2020.142503. These data describe the climate driven ecological classification for all National Hydrography Dataset Plus Version 1 (NHDPlusV1) stream reaches in the Temperate Plains ecoregion. Multivariate Regression Tree methods were used to classify stream reaches into 10 stream classes (A-J) using five climatic measures (i.e., standard deviation of daily precipitation in winter, average minimum temperature in summer, annual median daily precipitation, total precipitation in winter, maximum daily precipitation in winter) and one natural variable (i.e., Watershed Area).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9P8IAAT","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.5f69292b82ce38aaa2425580.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f69292b82ce38aaa2425580","keyword":["USGS:5f69292b82ce38aaa2425580","climate","flow regime","freshwater fish","stream","thermal regime"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-103.776016, 35.318440, -82.133297, 49.003849","theme":["geospatial"],"title":"Climate Driven Ecological Classification for NHDPlusV1 Streams in the Temperate Plains Ecoregion (Provisional Release)"},"description":"Results described in the paper, \"Conserving Stream Fishes with Changing Climate: Assessing Fish Responses to Changes in Habitat Over a Large Region\": https://doi.org/10.1016/j.scitotenv.2020.142503. These data describe the climate driven ecological classification for all National Hydrography Dataset Plus Version 1 (NHDPlusV1) stream reaches in the Temperate Plains ecoregion. Multivariate Regression Tree methods were used to classify stream reaches into 10 stream classes (A-J) using five climatic measures (i.e., standard deviation of daily precipitation in winter, average minimum temperature in summer, annual median daily precipitation, total precipitation in winter, maximum daily precipitation in winter) and one natural variable (i.e., Watershed Area).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dd5652c8-8674-4964-bd26-6162cd564aaf","harvest_record_raw":"https://catalog.data.gov/harvest_record/dd5652c8-8674-4964-bd26-6162cd564aaf/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f69292b82ce38aaa2425580","keyword":["USGS:5f69292b82ce38aaa2425580","climate","flow regime","freshwater fish","stream","thermal regime"],"last_harvested_date":"2026-09-14T23:56:46.604539","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":"climate-driven-ecological-classification-for-nhdplusv1-streams-in-the-temperate-plains-eco","spatial_centroid":{"lat":40.7926036,"lon":-95.1189284},"spatial_shape":{"coordinates":[[[-103.776016,35.31844],[-103.776016,49.003849],[-82.133297,49.003849],[-82.133297,35.31844],[-103.776016,35.31844]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate Driven Ecological Classification for NHDPlusV1 Streams in the Temperate Plains Ecoregion (Provisional Release)","type":"dataset"},{"_score":54.906685,"_sort":[1789429232691,54.906685,1,"4596b13c-eb06-4ea7-af68-cc8d85b82060"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguer\u00eda & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010).  Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1SV9SPJ","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.672bdd7bd34e16b32e739aff.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_672bdd7bd34e16b32e739aff","keyword":["SPEI","USGS:672bdd7bd34e16b32e739aff","aridification","biota","climate change","climate projections","droughts","geospatial datasets","potential evapotranspiration","precipitation (atmospheric)"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.7388, 25.0631, -66.9287, 49.3960","theme":["geospatial"],"title":"Standardized Precipitation Evapotranspiration Index (SPEI) Projections for the Contiguous United States Based on the CMIP5 MACAv2-METDATA Downscaled Climate Dataset"},"description":"The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguer\u00eda & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010).  Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1d1b7df1-091c-4154-89a2-ca2da01c6f56","harvest_record_raw":"https://catalog.data.gov/harvest_record/1d1b7df1-091c-4154-89a2-ca2da01c6f56/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_672bdd7bd34e16b32e739aff","keyword":["SPEI","USGS:672bdd7bd34e16b32e739aff","aridification","biota","climate change","climate projections","droughts","geospatial datasets","potential evapotranspiration","precipitation (atmospheric)"],"last_harvested_date":"2026-09-14T23:40:32.691133","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":"standardized-precipitation-evapotranspiration-index-spei-projections-for-the-contiguous-un","spatial_centroid":{"lat":34.796260000000004,"lon":-101.61476},"spatial_shape":{"coordinates":[[[-124.7388,25.0631],[-124.7388,49.396],[-66.9287,49.396],[-66.9287,25.0631],[-124.7388,25.0631]]],"type":"Polygon"},"theme":["geospatial"],"title":"Standardized Precipitation Evapotranspiration Index (SPEI) Projections for the Contiguous United States Based on the CMIP5 MACAv2-METDATA Downscaled Climate Dataset","type":"dataset"},{"_score":55.476128,"_sort":[1789428810763,55.476128,3,"d2aca380-a42e-4d93-9171-d20399b61875"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Graziella V. DiRenzo","hasEmail":"mailto:gdirenzo@umass.edu"},"description":"These data comprise the results of an analysis to 1. develop freshwater fish and mussel models using survey occurrence data, land use data, stream flow data, and stream temperature data within the Northeast United states, 2. predict the impacts of climate change on vulnerable groups of freshwater fish and mussel species, and 3. assess the potential for different management interventions to mitigate impacts of climate change. Freshwater fish and mussels survey records were used to fit species specific models that predict the probability of occurrence. The list of covariates is largely the same as the covariates released in the cross-referenced data set from Rogers et al., (2025); here, the covariate file shows the covariates used in this analysis that were not also used in the prior analysis. The species projection file has the results of the predicted probabilities of occurrence for each species of freshwater fish and mussel, at the HUC12 scale, for each of the seven scenarios: 1. baseline, 2. climate change, 3. climate change and dam removal, 4. climate change and natural riparian restoration, 5. climate change and riparian impervious removal, 6. climate change and watershed forest, and 7. climate change and a combination of numbers 3 through 6. The full methods for the fish models are described  in the cited manuscript, Rogers et al., (2025); briefly, zero-inflated beta models (one for each fish species) were used to simultaneously model the probability of species occurrence and the predicted relative abundance of each fish species at the NHD Version 2 stream reach scale. The predicted probability of occurrence (from the binomial portion of the model) of each fish species at the stream reach level was averaged over the HUC12 scale to get the probability of occurrence of each of the 53 fish species by HUC12. The full methods for the mussel models are also described in Rogers et al., (In Prep); briefly we fit 12 logistic regression models (one for each mussel species) using the glm function in the stats package and the family of models set to \u2018binomial\u2019. The mussel models were fit using presence and absence data at the HUC12 scale and covariate data that was averaged across the HUC12. All models, analyses, and data visualizations were developed using R version 4.2.2 (2022-10-31 ucrt).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1WOVG7B","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.685955b4d4be024dfd7caa6f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_685955b4d4be024dfd7caa6f","keyword":["Freshwater fish","Freshwater mussel","USGS:685955b4d4be024dfd7caa6f","biota","ecosystem management","effects of climate change","environment","freshwater ecosystems"],"modified":"2026-09-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-73.9160, 40.9467, -66.7090, 47.5617","theme":["geospatial"],"title":"Freshwater fish and mussel projections in the Northeastern United States at the HUC12 scale under different climate and land use scenarios"},"description":"These data comprise the results of an analysis to 1. develop freshwater fish and mussel models using survey occurrence data, land use data, stream flow data, and stream temperature data within the Northeast United states, 2. predict the impacts of climate change on vulnerable groups of freshwater fish and mussel species, and 3. assess the potential for different management interventions to mitigate impacts of climate change. Freshwater fish and mussels survey records were used to fit species specific models that predict the probability of occurrence. The list of covariates is largely the same as the covariates released in the cross-referenced data set from Rogers et al., (2025); here, the covariate file shows the covariates used in this analysis that were not also used in the prior analysis. The species projection file has the results of the predicted probabilities of occurrence for each species of freshwater fish and mussel, at the HUC12 scale, for each of the seven scenarios: 1. baseline, 2. climate change, 3. climate change and dam removal, 4. climate change and natural riparian restoration, 5. climate change and riparian impervious removal, 6. climate change and watershed forest, and 7. climate change and a combination of numbers 3 through 6. The full methods for the fish models are described  in the cited manuscript, Rogers et al., (2025); briefly, zero-inflated beta models (one for each fish species) were used to simultaneously model the probability of species occurrence and the predicted relative abundance of each fish species at the NHD Version 2 stream reach scale. The predicted probability of occurrence (from the binomial portion of the model) of each fish species at the stream reach level was averaged over the HUC12 scale to get the probability of occurrence of each of the 53 fish species by HUC12. The full methods for the mussel models are also described in Rogers et al., (In Prep); briefly we fit 12 logistic regression models (one for each mussel species) using the glm function in the stats package and the family of models set to \u2018binomial\u2019. The mussel models were fit using presence and absence data at the HUC12 scale and covariate data that was averaged across the HUC12. 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In the T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2\u00b0C to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P19SX4T8","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.6a0e8faf-6aa4-4e1b-b4c7-e9044a10ac3f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0e8faf-6aa4-4e1b-b4c7-e9044a10ac3f","keyword":["McKenzie River Basin","Oregon","SWE","USGS:6a0e8faf-6aa4-4e1b-b4c7-e9044a10ac3f","climate change","climatologyMeteorologyAtmosphere","effects of climate change","environment","external research support","geospatial datasets","modeling","precipitation (atmospheric)","snow water equivalent"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-123.1290, 43.8333, -121.7185, 44.5237","theme":["geospatial"],"title":"Historical and Climate\u2011Scenario Snow\u2011Water Equivalent Conditions and Change Metrics under T2 and T2p10 Scenarios for the McKenzie River Basin, Oregon"},"description":"We used the observed historical meteorology and mean modeled snow-water-equivalent for the reference period (1989-2009) and mean modeled snow-water-equivalent under two climate change scenarios, T2 and T2P10.\nIn the T2 scenario the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record in the reference period meteorology, and this data is then used as input to the model. In the T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2\u00b0C to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4e838c0a-2422-4d25-b80b-9da27064c317","harvest_record_raw":"https://catalog.data.gov/harvest_record/4e838c0a-2422-4d25-b80b-9da27064c317/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0e8faf-6aa4-4e1b-b4c7-e9044a10ac3f","keyword":["McKenzie River Basin","Oregon","SWE","USGS:6a0e8faf-6aa4-4e1b-b4c7-e9044a10ac3f","climate change","climatologyMeteorologyAtmosphere","effects of climate change","environment","external research support","geospatial datasets","modeling","precipitation (atmospheric)","snow water equivalent"],"last_harvested_date":"2026-09-13T00:28:27.799211","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":"historical-and-climatescenario-snowwater-equivalent-conditions-and-change-metrics-under-t2","spatial_centroid":{"lat":44.10946,"lon":-122.56480000000002},"spatial_shape":{"coordinates":[[[-123.129,43.8333],[-123.129,44.5237],[-121.7185,44.5237],[-121.7185,43.8333],[-123.129,43.8333]]],"type":"Polygon"},"theme":["geospatial"],"title":"Historical and Climate\u2011Scenario Snow\u2011Water Equivalent Conditions and Change Metrics under T2 and T2p10 Scenarios for the McKenzie River Basin, Oregon","type":"dataset"},{"_score":66.45479,"_sort":[1789259107358,66.45479,1,"a4a18d5d-62d0-4c78-9b88-1e255ff42617"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael O'Donnell","hasEmail":"mailto:odonnellm@usgs.gov"},"description":"We provide a collection of data reflecting estimates of soil-climate properties (moisture, temperature, and regimes) based on climate normals (1981-2010). Specifically, we provide estimates for soil moisture (monthly, seasonal, and annual), trends of spring and growing season soil moisture (Theil-Sen estimates), soil temperature and moisture regimes (STMRs; discrete classes defined by United States Department of Agriculture [USDA] Natural Resources Conservation Service [NRCS]), seasonal Thornthwaite moisture index (TMI; precipitation minus PET), and seasonality of TMI and soil moisture (30-meter rasters). Moisture values were estimated using our spatial implementation of the Newhall simulation model that relies on the Thornthwaite-Matter-Sellers potential evapotranspiration (PET) index. Among many enhancements, our application is the first known soil-climate model to include the effects of snow (for example, sublimation, snowmelt, attenuated evaporation, and insulation from air temperatures). Notably, we developed procedures that facilitate data substitution using spatial_nsm, supporting many use cases and flexibility, such as assessing projected climate scenarios. Our results provide evidence of the utility of spatially explicit soil-climate products, which could support subsequent use for modeling and managing ecosystem, habitat, and species distributions. For example, we demonstrated soil-climate properties had significant correlations with vegetation patterns: soil moisture variables predicted sagebrush (R^2 = 0.51), annual herbaceous plant cover (R^2 = 0.687), exposed soil (R^2 = 0.656), and fire occurrence (R^2 = 0.343). These statistical results suggested the data captured distributions of soil moisture and STMRs that can explain landscape and vegetation patterns. \nRefer to the Cross Reference section for all citations referenced in metadata supporting methods. This section also references our software used for developing these data products (nsm_spatial).\nRefer to the Larger Citation describing this project in full.\nNormal (1981 \u2013 2010): Describes climate conditions averaged (temperature) or summed (precipitation) across 30-year climate period.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ULGC03","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.62e97eacd34e749ac04cc15e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62e97eacd34e749ac04cc15e","keyword":["California","Colorado","Idaho","Kansas","Montana","Nebraska","Nevada","Newhall simulation model","North Dakota","Oregon","South Dakota","Theil-Sen estimator of seasonal soil moisture","Theil-Sen estimator of spring soil moisture","Thornthwaite moisture index","USGS:62e97eacd34e749ac04cc15e","Utah","Washington","Wyoming","annual soil moisture","environment","geospatial datasets","monthly soil moisture","sagebrush biome","seasonal soil moisture","soil climate","soil moisture","soil moisture variability","soil temperature","soil temperature and moisture regimes","western United States"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.0995, 34.3797, -100.1504, 51.3391","theme":["geospatial"],"title":"Soil-climate estimates in the western United States: climate averages (1981-2010)"},"description":"We provide a collection of data reflecting estimates of soil-climate properties (moisture, temperature, and regimes) based on climate normals (1981-2010). Specifically, we provide estimates for soil moisture (monthly, seasonal, and annual), trends of spring and growing season soil moisture (Theil-Sen estimates), soil temperature and moisture regimes (STMRs; discrete classes defined by United States Department of Agriculture [USDA] Natural Resources Conservation Service [NRCS]), seasonal Thornthwaite moisture index (TMI; precipitation minus PET), and seasonality of TMI and soil moisture (30-meter rasters). Moisture values were estimated using our spatial implementation of the Newhall simulation model that relies on the Thornthwaite-Matter-Sellers potential evapotranspiration (PET) index. Among many enhancements, our application is the first known soil-climate model to include the effects of snow (for example, sublimation, snowmelt, attenuated evaporation, and insulation from air temperatures). Notably, we developed procedures that facilitate data substitution using spatial_nsm, supporting many use cases and flexibility, such as assessing projected climate scenarios. Our results provide evidence of the utility of spatially explicit soil-climate products, which could support subsequent use for modeling and managing ecosystem, habitat, and species distributions. For example, we demonstrated soil-climate properties had significant correlations with vegetation patterns: soil moisture variables predicted sagebrush (R^2 = 0.51), annual herbaceous plant cover (R^2 = 0.687), exposed soil (R^2 = 0.656), and fire occurrence (R^2 = 0.343). These statistical results suggested the data captured distributions of soil moisture and STMRs that can explain landscape and vegetation patterns. \nRefer to the Cross Reference section for all citations referenced in metadata supporting methods. This section also references our software used for developing these data products (nsm_spatial).\nRefer to the Larger Citation describing this project in full.\nNormal (1981 \u2013 2010): Describes climate conditions averaged (temperature) or summed (precipitation) across 30-year climate period.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5537f097-46d7-4142-9150-dea285b701b6","harvest_record_raw":"https://catalog.data.gov/harvest_record/5537f097-46d7-4142-9150-dea285b701b6/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62e97eacd34e749ac04cc15e","keyword":["California","Colorado","Idaho","Kansas","Montana","Nebraska","Nevada","Newhall simulation model","North Dakota","Oregon","South Dakota","Theil-Sen estimator of seasonal soil moisture","Theil-Sen estimator of spring soil moisture","Thornthwaite moisture index","USGS:62e97eacd34e749ac04cc15e","Utah","Washington","Wyoming","annual soil moisture","environment","geospatial datasets","monthly soil moisture","sagebrush biome","seasonal soil moisture","soil climate","soil moisture","soil moisture variability","soil temperature","soil temperature and moisture regimes","western United States"],"last_harvested_date":"2026-09-13T00:25:07.358694","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":"soil-climate-estimates-in-the-western-united-states-climate-averages-1981-2010","spatial_centroid":{"lat":41.16346,"lon":-112.71986000000001},"spatial_shape":{"coordinates":[[[-121.0995,34.3797],[-121.0995,51.3391],[-100.1504,51.3391],[-100.1504,34.3797],[-121.0995,34.3797]]],"type":"Polygon"},"theme":["geospatial"],"title":"Soil-climate estimates in the western United States: climate averages (1981-2010)","type":"dataset"},{"_score":15.121621,"_sort":[1789259059539,15.121621,0,"6b00d1de-e182-4b6c-b359-0d4545b84e5d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f773de882ce20f3301008a2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773de882ce20f3301008a2","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773de882ce20f3301008a2","biota"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Woody Debris on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7f5925b5-7f9c-4182-8c5b-31a8b688da92","harvest_record_raw":"https://catalog.data.gov/harvest_record/7f5925b5-7f9c-4182-8c5b-31a8b688da92/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773de882ce20f3301008a2","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773de882ce20f3301008a2","biota"],"last_harvested_date":"2026-09-13T00:24:19.539836","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":"influence-of-stream-woody-debris-on-eight-populations-of-rio-grande-cutthroat-trout-in-nor","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influence of Stream Woody Debris on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":13.346837,"_sort":[1789258624403,13.346837,1,"6e123995-7e5a-4a61-b26b-7fb787afb57c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region.  We collected stream temperature and stream drying to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f776cd582ce20f3301009ea.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776cd582ce20f3301009ea","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776cd582ce20f3301009ea","biota"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Temperature on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. 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Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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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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The workshops were part of the Landscape conservation design project, funded separately by the USGS. The current project had no role in identifying or selected coastal managers with whom to speak; that was the responsibility of the Landscape conservation design project and occurred before the involvement of the current project team. These data are particular to the interactions between the Landscape conservation design project team and the particular coastal managers who engaged with their project. The workshops were held in: Milton, FL; Punta Gorda, FL; Biloxi, MS; Mobile, AL; and Lake Charles, LA. Participants in the workshop were generally coastal resource managers. The goal of the workshops was to provide Gulf Coast resource managers and planners with climate change and sea level rise information relevant to their management decisions. The goal of the survey was to understand the extent to which the workshops, and any subsequent follow-up with the USGS and TNC researchers, provided useful information to those decision makers and to use that information to help the USGS and TNC teams reflect on their project outcomes. The survey asked participants for feedback about the utility of the workshops and their experiences receiving additional data products from USGS and TNC hosts. The survey was sent to the 111 people who participated in one of the workshops. 25 people completed the survey. This data pertains only to these four workshops and should not be generalized to other Gulf Coast communities or other USGS or TNC research projects. 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We ran the climate models according to two greenhouse gas concentration pathways (RCP2.6 and RCP8.5).  Datasets in this file are the results for models RCP2.6 and RCP8.5 for the years 2050 and 2070.  It shows a comparison of ensembles of suitable bioclimatic conditions between present day and future day.  The dataset shows areas where ensembles agree and suitable conditions are stable (stable represented in green), future ensemble projects new suitable conditions (gain represented in yellow), present ensemble may be converted to unsuitable in the future (loss represented in red), and areas where conditions are unsuitable in the future (non represented in gray).","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7XS5SJH","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.583324a0e4b046f05f211a7d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_583324a0e4b046f05f211a7d","keyword":["Aspidoscelis dixoni","Gray-Checkered Whiptail","Pseudemys gorzugi","Rio Grande Cooter","USGS:583324a0e4b046f05f211a7d","bioclimatic-envelope","biota","climatologyMeteorologyAtmosphere","ecology","geospatial datasets","herpetofauna","modeling"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-112.586128217, 31.332172208, -106.486128241, 35.4988388585","theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for reptile species in South Central USA"},"description":"This dataset contains the result of the bioclimatic-envelope modeling of the two reptile species -- Rio Grande Cooter (Pseudemys gorzugi) and Gray-Checkered Whiptail (Aspidoscelis dixoni) -- in the South Central US using the downscaled data provided by WorldClim.  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Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.","distribution":[{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6776f3d3d34ee88ba0b15863.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f3d3d34ee88ba0b15863","keyword":["Pacific Northwest","USGS:6776f3d3d34ee88ba0b15863","Western United States","climate change","climate impacts","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling","western United States"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.6500, 38.0900, -105.9600, 52.4000","theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data"},"description":"This archive contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes for the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. 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Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6adc051b-77ee-4e95-ad5d-86e3d220e472","harvest_record_raw":"https://catalog.data.gov/harvest_record/6adc051b-77ee-4e95-ad5d-86e3d220e472/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6776f3d3d34ee88ba0b15863","keyword":["Pacific Northwest","USGS:6776f3d3d34ee88ba0b15863","Western United States","climate change","climate impacts","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling","western United States"],"last_harvested_date":"2026-09-12T00:22:30.470932","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":11,"publisher":"U.S. Geological Survey","slug":"western-us-hydroclimate-scenarios-project-dynamically-downscaled-data","spatial_centroid":{"lat":43.814,"lon":-117.174},"spatial_shape":{"coordinates":[[[-124.65,38.09],[-124.65,52.4],[-105.96,52.4],[-105.96,38.09],[-124.65,38.09]]],"type":"Polygon"},"theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data","type":"dataset"},{"_score":61.31585,"_sort":[1789172392353,61.31585,1,"f4275485-1d12-4073-9635-208abaef3e99"],"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. 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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.5867e1a1e4b0cd2dabe7c76c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e1a1e4b0cd2dabe7c76c","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867e1a1e4b0cd2dabe7c76c","United States","Wyoming","climate change","environment","natural resource management","shrubland ecosystems"],"modified":"2026-09-09T00:00:00Z","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 shrub 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. 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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.58658fb4e4b0cd2dabe7c3d1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58658fb4e4b0cd2dabe7c3d1","keyword":["Alpine","Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:58658fb4e4b0cd2dabe7c3d1","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","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 alpine 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/736e84e4-014c-4577-bd72-0f4cd2d6f5d5","harvest_record_raw":"https://catalog.data.gov/harvest_record/736e84e4-014c-4577-bd72-0f4cd2d6f5d5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58658fb4e4b0cd2dabe7c3d1","keyword":["Alpine","Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:58658fb4e4b0cd2dabe7c3d1","United States","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-11T23:31:07.470072","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":"potential-climate-change-impacts-on-alpine-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 alpine connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":41.755173,"_sort":[1789169243065,41.755173,0,"1dfda7cf-cc33-403a-8e3d-92d7d88a6a5c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Bruce Dugger","hasEmail":"mailto:bruce.dugger@oregonstate.edu"},"description":"The baseline map of the Butte Basin, the representative basin from the Central Valley, was generated first by delineating the extent of the landscape to be modeled, in agreement with the basin boundaries identified by the Central Valley Joint Venture.The Butte Basin (CV) encompasses a region approximately 44km x 64 km, and the map used contains 10,698 individual habitat patches and 179,964 acres of possible foreageable area. Patch habitat types were identified by a combination of USDA CropScape data (to identify agricultural habitat patches including rice and corn) and other local mapping data made available through collaboration with USGS. Habitat flood schedules were generated using the Water Evaluation and Planning Model (WEAP) and modified to determine two (\"good\" and \"bad\") habitat scenarios for the region.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/a81c-jj28","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.64ca9e76d34e70357a35502b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64ca9e76d34e70357a35502b","keyword":["Butte","California","Central Valley aquifer system","Colusa","Glenn","Sutter","USGS:64ca9e76d34e70357a35502b","biota","climate change","environment","habitats","water use","wetland ecosystems"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.9720, 39.1053, -121.6359, 39.7578","theme":["geospatial"],"title":"Climate Change Habitat Scenarios for the Central Valley of California"},"description":"The baseline map of the Butte Basin, the representative basin from the Central Valley, was generated first by delineating the extent of the landscape to be modeled, in agreement with the basin boundaries identified by the Central Valley Joint Venture.The Butte Basin (CV) encompasses a region approximately 44km x 64 km, and the map used contains 10,698 individual habitat patches and 179,964 acres of possible foreageable area. Patch habitat types were identified by a combination of USDA CropScape data (to identify agricultural habitat patches including rice and corn) and other local mapping data made available through collaboration with USGS. Habitat flood schedules were generated using the Water Evaluation and Planning Model (WEAP) and modified to determine two (\"good\" and \"bad\") habitat scenarios for the region.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0ad26636-4f5c-47d5-add3-f76f4de392af","harvest_record_raw":"https://catalog.data.gov/harvest_record/0ad26636-4f5c-47d5-add3-f76f4de392af/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64ca9e76d34e70357a35502b","keyword":["Butte","California","Central Valley aquifer system","Colusa","Glenn","Sutter","USGS:64ca9e76d34e70357a35502b","biota","climate change","environment","habitats","water use","wetland ecosystems"],"last_harvested_date":"2026-09-11T23:27:23.065296","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":"climate-change-habitat-scenarios-for-the-central-valley-of-california","spatial_centroid":{"lat":39.3663,"lon":-121.83756000000001},"spatial_shape":{"coordinates":[[[-121.972,39.1053],[-121.972,39.7578],[-121.6359,39.7578],[-121.6359,39.1053],[-121.972,39.1053]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate Change Habitat Scenarios for the Central Valley of California","type":"dataset"},{"_score":29.882677,"_sort":[1789169166232,29.882677,0,"e62467ce-c2d6-49c8-814f-93a13e7ee1b4"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matt Stumbaugh","hasEmail":"mailto:mrstu@uw.edu"},"description":"This archive contains two datasets. Both cover the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) in each are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references).\nThe first dataset, \"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\", contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes.  The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future.","distribution":[{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.55e4c25ce4b05561fa208552.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_55e4c25ce4b05561fa208552","keyword":["Pacific Northwest","USGS:55e4c25ce4b05561fa208552","Western United States","change impacts","climate change","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-126.5625, 33.1376, -102.6563, 48.4584","theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Datasets"},"description":"This archive contains two datasets. Both cover the northwestern United States and part of southern British Columbia (N of about 38 degrees N and W of about 105 degrees W) at 1/16th (0.0625) degree resolution. Climate and hydrologic variables (21 total) in each are as follows: precipitation, temperature (avg./max./min.), outgoing longwave radiation, incoming shortwave radiation, relative humidity, vapor pressure deficit, evapotranspiration, runoff, baseflow, soil moisture (3-layers), snow water equivalent, snow depth, and potential evapotranspiration (5 vegetation references).\nThe first dataset, \"Western US Hydroclimate Scenarios Project Dynamically Downscaled Data\", contains daily dynamically downscaled climate projections and simulated land surface water and energy fluxes.  The downscaling is based on the Weather Research and Forecasting (WRF) regional model. WRF was run using boundary conditions from the ECHAM5 global model and the SRES A1B emissions scenario, one of the models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Climate simulations were performed using an inner grid resolution of 12-km over the region and a 100-year (1970-2070) simulation.\nThe second dataset, \"Western US Hydroclimate Scenarios Project Observations and Statistically Downscaled Data\", contains daily statistically downscaled climate projections and simulated land surface water and energy fluxes for the western United States and southern British Columbia at 1/16th (0.0625) degree resolution. The downscaling used is the Modified Delta approach (see Littell et al. 2011), based on 10 models from Phase 3 of the Coupled Model Intercomparison Project (CMIP3), a critical source of data to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Note that time-stamps on these data are not in the future.","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/dd88c315-0068-4ef3-ae09-c8f503a040cf","harvest_record_raw":"https://catalog.data.gov/harvest_record/dd88c315-0068-4ef3-ae09-c8f503a040cf/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_55e4c25ce4b05561fa208552","keyword":["Pacific Northwest","USGS:55e4c25ce4b05561fa208552","Western United States","change impacts","climate change","climatologyMeteorologyAtmosphere","dynamical downscaling","extremes","geospatial datasets","hydrologic change","statistical downscaling"],"last_harvested_date":"2026-09-11T23:26:06.232324","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":"western-us-hydroclimate-scenarios-project-datasets","spatial_centroid":{"lat":39.26592,"lon":-117.00001999999999},"spatial_shape":{"coordinates":[[[-126.5625,33.1376],[-126.5625,48.4584],[-102.6563,48.4584],[-102.6563,33.1376],[-126.5625,33.1376]]],"type":"Polygon"},"theme":["geospatial"],"title":"Western US Hydroclimate Scenarios Project Datasets","type":"dataset"},{"_score":32.876823,"_sort":[1789169075257,32.876823,0,"79707b7c-f653-4661-bb5f-110e87610934"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Sarah L. Shafer","hasEmail":"mailto:sshafer@usgs.gov"},"description":"Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions.We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0\u201358.0\u00b0N latitude by 136.6\u2013103.0\u00b0W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070\u20132099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F73X84PH","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.283f3ce9-6b5d-46db-8057-1c74b96e58ee.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_283f3ce9-6b5d-46db-8057-1c74b96e58ee","keyword":["British Columbia","Climate change","Ecosystems","Forests","Grasses","Grasslands","Idaho","Montana","Paleoclimatology","Shrubs","Simulation and modeling","USGS:283f3ce9-6b5d-46db-8057-1c74b96e58ee","Washington","climate change","geospatial datasets","modeling","vegetation"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.5042, 46.4958, -113.9958, 53.0042","theme":["geospatial"],"title":"Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model"},"description":"Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions.We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0\u201358.0\u00b0N latitude by 136.6\u2013103.0\u00b0W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070\u20132099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/e07647b3-83a4-40f1-b4bb-3ab85358f744","harvest_record_raw":"https://catalog.data.gov/harvest_record/e07647b3-83a4-40f1-b4bb-3ab85358f744/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_283f3ce9-6b5d-46db-8057-1c74b96e58ee","keyword":["British Columbia","Climate change","Ecosystems","Forests","Grasses","Grasslands","Idaho","Montana","Paleoclimatology","Shrubs","Simulation and modeling","USGS:283f3ce9-6b5d-46db-8057-1c74b96e58ee","Washington","climate change","geospatial datasets","modeling","vegetation"],"last_harvested_date":"2026-09-11T23:24:35.257831","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":"projected-future-vegetation-changes-for-the-northwest-united-states-and-southwest-canada-a","spatial_centroid":{"lat":49.09916,"lon":-120.90083999999999},"spatial_shape":{"coordinates":[[[-125.5042,46.4958],[-125.5042,53.0042],[-113.9958,53.0042],[-113.9958,46.4958],[-125.5042,46.4958]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model","type":"dataset"},{"_score":62.723614,"_sort":[1789168956296,62.723614,1,"5f067244-90e6-4d9d-b903-343030acfcb3"],"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.5867e23fe4b0cd2dabe7c76e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e23fe4b0cd2dabe7c76e","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867e23fe4b0cd2dabe7c76e","United States","Wolverine","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","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 wolverine 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. 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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/1184ae9a-a2fc-4e16-a530-71266a13565a","harvest_record_raw":"https://catalog.data.gov/harvest_record/1184ae9a-a2fc-4e16-a530-71266a13565a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e23fe4b0cd2dabe7c76e","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867e23fe4b0cd2dabe7c76e","United States","Wolverine","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-11T23:22:36.296737","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":"potential-climate-change-impacts-on-wolverine-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 wolverine connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":60.319527,"_sort":[1789168557964,60.319527,2,"e661398e-fbb4-48bd-aa3b-520610eac198"],"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. 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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.5867e0d4e4b0cd2dabe7c76a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e0d4e4b0cd2dabe7c76a","keyword":["Connectivity","Environment and Conservation","Greater sage grouse","Idaho","Montana","Rocky Mountains","USGS:5867e0d4e4b0cd2dabe7c76a","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","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 Greater sage grouse 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. 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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/64d3ba54-285c-4b75-a8ef-79fccb0bf733","harvest_record_raw":"https://catalog.data.gov/harvest_record/64d3ba54-285c-4b75-a8ef-79fccb0bf733/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e0d4e4b0cd2dabe7c76a","keyword":["Connectivity","Environment and Conservation","Greater sage grouse","Idaho","Montana","Rocky Mountains","USGS:5867e0d4e4b0cd2dabe7c76a","United States","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-11T23:15:57.964617","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":"potential-climate-change-impacts-on-greater-sage-grouse-connectivity-in-the-u-s-northern-r","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 Greater sage grouse connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":59.160324,"_sort":[1789168220771,59.160324,0,"d589c767-50b0-47cb-8244-1f136554a7b8"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity.","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.5841b45de4b04fc80e518c25.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5841b45de4b04fc80e518c25","keyword":["Idaho","Montana","USGS:5841b45de4b04fc80e518c25","Wyoming","alpine","climate change","environment","geospatial datasets","management"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -90.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Informing adaptation strategies for maintaining landscape connectivity for Northern Rockies wildlife in the face of climate change"},"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.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6704b947-d62f-4cdc-a4fe-25c7c28ec252","harvest_record_raw":"https://catalog.data.gov/harvest_record/6704b947-d62f-4cdc-a4fe-25c7c28ec252/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5841b45de4b04fc80e518c25","keyword":["Idaho","Montana","USGS:5841b45de4b04fc80e518c25","Wyoming","alpine","climate change","environment","geospatial datasets","management"],"last_harvested_date":"2026-09-11T23:10:20.771986","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":"informing-adaptation-strategies-for-maintaining-landscape-connectivity-for-northern-rockie","spatial_centroid":{"lat":-18.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-90.0],[-180.0,90.0],[180.0,90.0],[180.0,-90.0],[-180.0,-90.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Informing adaptation strategies for maintaining landscape connectivity for Northern Rockies wildlife in the face of climate change","type":"dataset"},{"_score":60.70175,"_sort":[1789168168130,60.70175,2,"b5e2f0e2-1514-43aa-a1bd-2b8acdc6cc09"],"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":"http://dx.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.5866933ae4b0cd2dabe7c57f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5866933ae4b0cd2dabe7c57f","keyword":["Bighorn sheep","Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5866933ae4b0cd2dabe7c57f","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","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 bighorn sheep 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. 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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/1088d407-a934-41b5-86e1-320800b33084","harvest_record_raw":"https://catalog.data.gov/harvest_record/1088d407-a934-41b5-86e1-320800b33084/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5866933ae4b0cd2dabe7c57f","keyword":["Bighorn sheep","Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5866933ae4b0cd2dabe7c57f","United States","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-11T23:09:28.130623","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":"potential-climate-change-impacts-on-bighorn-sheep-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 bighorn sheep connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":58.52264,"_sort":[1789167405556,58.52264,0,"e3016848-09d9-49ad-811d-52717efcf149"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Alison Meadow","hasEmail":"mailto:meadow@arizona.edu"},"description":"In October 2019, as part of our collaboration with a project focused on nexus of climate and viticulture in Arizona, we helped hold two workshops focused on reviewing the 2018-2019 wine grape growing season in Arizona. Workshops were held in two of the main viticulture regions in Arizona: the Verde Valley and Cochise County.\nTwenty-four people attended the Yavapai County workshop; 9 vineyards were represented but a number of workshop participants were students not representing a vineyard. Those participants did not contribute to the climate and weather data. Six people representing 6 vineyards participated in the Cochise County workshop.\nAt each workshop, growers were asked to list various climate- and weather-related events that had affected their vineyards over the past year. Participants made notes about climate and weather events as well as crop quality on large paper timelines taped to the wall of the meeting room. Data included here are the summaries of the notes provided by growers.\nThe goal of this data collection was to provide Arizona Cooperative Extension researchers with information about how to provide climate and weather data tailored to the needs of the Arizona viticulture industry.\nThese data represent the experiences of a non-random sample of viticulturalists for one year of time. The data have been de-identified so as to refer only to the county in which a vineyard exists. These data cannot be generalized to apply to other states, years, or vineyards. They provide some insight into the kinds of climate and weather events that affect wine grape production in the state of Arizona.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/gb4t-y589","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.62e2d417d34e394b65364f3d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62e2d417d34e394b65364f3d","keyword":["USGS:62e2d417d34e394b65364f3d","agriculture","biota","botany","climate","pest management","viticulture","weather"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.82, 31.33, -109.04, 37.0","theme":["geospatial"],"title":"Climate and Weather Impacts to Wine Grapes in Arizona in 2018-2019 growing season as described by growers"},"description":"In October 2019, as part of our collaboration with a project focused on nexus of climate and viticulture in Arizona, we helped hold two workshops focused on reviewing the 2018-2019 wine grape growing season in Arizona. Workshops were held in two of the main viticulture regions in Arizona: the Verde Valley and Cochise County.\nTwenty-four people attended the Yavapai County workshop; 9 vineyards were represented but a number of workshop participants were students not representing a vineyard. Those participants did not contribute to the climate and weather data. Six people representing 6 vineyards participated in the Cochise County workshop.\nAt each workshop, growers were asked to list various climate- and weather-related events that had affected their vineyards over the past year. Participants made notes about climate and weather events as well as crop quality on large paper timelines taped to the wall of the meeting room. Data included here are the summaries of the notes provided by growers.\nThe goal of this data collection was to provide Arizona Cooperative Extension researchers with information about how to provide climate and weather data tailored to the needs of the Arizona viticulture industry.\nThese data represent the experiences of a non-random sample of viticulturalists for one year of time. The data have been de-identified so as to refer only to the county in which a vineyard exists. These data cannot be generalized to apply to other states, years, or vineyards. 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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":9.107767,"_sort":[1789080651201,9.107767,2,"44910ba5-b6c4-4494-9313-ae768358536c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ellyn T. 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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. 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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. 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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. 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Biome-BGC simulations were run under historical conditions (1984-2015) assuming both a uniform and redistributed snow layer. Biome-BGC MuSo simulations were run under historical (1996-2015) and future climate scenarios (2046-2065) and account for the redistribution of snow. Biogeochemical simulation data sets include input files used to run Biome-BGC and Biome-BGC MuSo simulations of aspen at three sites in the Reynolds Creek Experimental Watershed under historical and mid-21st conditions. 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Biome-BGC simulations were run under historical conditions (1984-2015) assuming both a uniform and redistributed snow layer. Biome-BGC MuSo simulations were run under historical (1996-2015) and future climate scenarios (2046-2065) and account for the redistribution of snow. Biogeochemical simulation data sets include input files used to run Biome-BGC and Biome-BGC MuSo simulations of aspen at three sites in the Reynolds Creek Experimental Watershed under historical and mid-21st conditions. 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Three flight transects were conducted from small aircraft over the National Park Service's Arctic Network (ARCN; Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and Noatak National Preserve) and the U.S. Fish and Wildlife Service's Selawik National Wildlife Refuge.\n      The aerial photo surveys were flown for the WildCast Project (WILDlife Potential Habitat ForeCASTing), a collaboration of the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, and U.S.D.A. Forest Service. WildCast was devised to provide models for projecting future land cover and wildlife habitat conditions in northwest Alaska under potential scenarios of climate change, and to provide an image database for future change-comparison research. More information is available at: https://www.usgs.gov/centers/alaska-science-center/science/wildlife-potential-habitat-forecasting-framework-wildcast#overview\n      Child Item 1: \"Flight Path GPS Logs and Browse Maps of Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 2: \"Nadir Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 3: \"Oblique Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 4: \"Nadir Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 5: \"Oblique Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9KFIRWQ","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.611595d4d34e3267c61166ce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_611595d4d34e3267c61166ce","keyword":["Aerial Photography","Alaska","Arctic Network","Bering Land Bridge National Preserve","Biota","Cape Krusenstern National Monument","Climate Change","Coastal Ecosystems","Ecotypes","Environment","Frozen Ground","Gates of the Arctic National Park","Gates of the Arctic National Preserve","Geography","Geomorphic Landforms/Processes","GeoscientificInformation","Image Analysis","Image Collections","ImageryBaseMapsEarthCover","InlandWaters","Kobuk Valley National Park","Land Cover","Land Surface","Land Use and Land Cover","Land Use/Land Cover","Landscape","Low Altitude Air Photo","Low Altitude Video","Noatak National Preserve","Northwest Alaska","Northwest Arctic Borough","Photogrammetry","Selawik National Wildlife Refuge","Tundra Ecosystems","USGS:611595d4d34e3267c61166ce","Vegetation","Videography"],"modified":"2024-10-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-169.22, 64.78, -149.05, 68.87","theme":["geospatial"],"title":"Low-Altitude Photographic Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013"},"description":"This data release includes 5 child items with photos and videos taken during low altitude photo survey transects in northwest Alaska, July 2013. Three flight transects were conducted from small aircraft over the National Park Service's Arctic Network (ARCN; Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and Noatak National Preserve) and the U.S. Fish and Wildlife Service's Selawik National Wildlife Refuge.\n      The aerial photo surveys were flown for the WildCast Project (WILDlife Potential Habitat ForeCASTing), a collaboration of the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, and U.S.D.A. Forest Service. WildCast was devised to provide models for projecting future land cover and wildlife habitat conditions in northwest Alaska under potential scenarios of climate change, and to provide an image database for future change-comparison research. More information is available at: https://www.usgs.gov/centers/alaska-science-center/science/wildlife-potential-habitat-forecasting-framework-wildcast#overview\n      Child Item 1: \"Flight Path GPS Logs and Browse Maps of Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 2: \"Nadir Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 3: \"Oblique Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 4: \"Nadir Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 5: \"Oblique Videos Taken During Low-Altitude Transects of the Arctic Network of National Park 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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. 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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.\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":16.709476,"_sort":[1789080622521,16.709476,0,"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). 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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":16.016613,"_sort":[1789080614707,16.016613,4,"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":8.524797,"_sort":[1789080613808,8.524797,1,"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":13.645028,"_sort":[1789080612904,13.645028,1,"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":9.188137,"_sort":[1789080611523,9.188137,2,"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":18.281525,"_sort":[1789080608908,18.281525,2,"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. 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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":14.185159,"_sort":[1789080599948,14.185159,6,"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":11.984654,"_sort":[1789080598489,11.984654,1,"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":8.188471,"_sort":[1789080592151,8.188471,3,"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":18.760128,"_sort":[1789080591019,18.760128,1,"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":9.16614,"_sort":[1789080587844,9.16614,1,"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":7.5854387,"_sort":[1789080585591,7.5854387,2,"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. Mundt National Wildlife Refuge","Lacreek National Wildlife Refuge","Lake Andes National Wildlife Refuge","National Wildlife Refuge","Sand Lake National Wildlife Refuge","South Dakota","USGS:638a1b78d34ed907bf790640","Waubay National Wildlife Refuge","effects of climate change","inlandWaters"],"modified":"2024-04-30T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-104.0600, 42.4800, -96.4300, 45.9400","theme":["geospatial"],"title":"Hydroclimate Projections for Select U.S. Fish and Wildlife Service Properties - Mountain-Prairie Region, 1951-2099"},"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_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. Mundt National Wildlife Refuge","Lacreek National Wildlife Refuge","Lake Andes National Wildlife Refuge","National Wildlife Refuge","Sand Lake National Wildlife Refuge","South Dakota","USGS:638a1b78d34ed907bf790640","Waubay National Wildlife Refuge","effects of climate change","inlandWaters"],"last_harvested_date":"2026-09-10T22:49:45.591039","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":"hydroclimate-projections-for-select-u-s-fish-and-wildlife-service-properties-mou-1951-2099-f147f","spatial_centroid":{"lat":43.864,"lon":-101.00800000000001},"spatial_shape":{"coordinates":[[[-104.06,42.48],[-104.06,45.94],[-96.43,45.94],[-96.43,42.48],[-104.06,42.48]]],"type":"Polygon"},"theme":["geospatial"],"title":"Hydroclimate Projections for Select U.S. Fish and Wildlife Service Properties - Mountain-Prairie Region, 1951-2099","type":"dataset"},{"_score":39.269302,"_sort":[1789080585132,39.269302,3,"15df8228-d00c-43f4-92a2-6b5ec894383a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael O'Donnell","hasEmail":"mailto:odonnellm@usgs.gov"},"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":[{"@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":20.368813,"_sort":[1789080580345,20.368813,2,"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. 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.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. 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/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":10.810585,"_sort":[1789080579184,10.810585,2,"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":20.197289,"_sort":[1789080578753,20.197289,3,"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. 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/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":29.763058,"_sort":[1789080570060,29.763058,1,"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). 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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). In addition to this study, FaSTMECH 2D flow models have been developed for numerous Kootenai River studies dating back to 2005. The methods used to develop, calibrate, and simulate FaSTMECH 2D flow models are described at length in multiple previous studies (Fosness and Dudunake, in press; Barton and others, 2005; Barton and others, 2007; Logan and others, 2011; McDonald and others, 2016; McDonald and Nelson, 2018; McDonald and Nelson, 2020). Model simulations were combined with white sturgeon telemetry data to explain fish positions with respect to selected depths and depth-averaged velocity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1e5ddd1c-2565-4d34-835e-8f6ee94cab2a","harvest_record_raw":"https://catalog.data.gov/harvest_record/1e5ddd1c-2565-4d34-835e-8f6ee94cab2a/raw","has_download":true,"has_spatial":true,"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"],"last_harvested_date":"2026-09-10T22:49:29.156820","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":"white-sturgeon-fine-scale-habitat-model-archive-kootenai-river-near-bonners-ferry-ida-2017","spatial_centroid":{"lat":48.6969724,"lon":-116.318336},"spatial_shape":{"coordinates":[[[-116.32976,48.693554],[-116.32976,48.7021],[-116.3012,48.7021],[-116.3012,48.693554],[-116.32976,48.693554]]],"type":"Polygon"},"theme":["geospatial"],"title":"White sturgeon fine-scale habitat model archive, Kootenai River near Bonners Ferry, Idaho, 2017","type":"dataset"},{"_score":8.401669,"_sort":[1789080566922,8.401669,1,"393e05a1-9846-4b53-9f4c-b54f4eeea4a7"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jordan S. Read","hasEmail":"mailto:jread@usgs.gov"},"description":"This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/P9AQPIVD","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.5d89d90ce4b0c4f70d0ae4df.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d89d90ce4b0c4f70d0ae4df","keyword":["007","012","MN","Minnesota","US","USGS:5d89d90ce4b0c4f70d0ae4df","United States","WI","Wisconsin","climate change","deep learning","environment","hybrid modeling","inlandWaters","machine learning","modeling","reservoirs","temperate lakes","temperature","thermal profiles","water"],"modified":"2020-08-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-94.2609062307949, 42.5692312672573, -87.9475441739278, 48.6427837911633","theme":["geospatial"],"title":"Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags)"},"description":"This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6601d68a-70d2-49ec-915e-743719b19b25","harvest_record_raw":"https://catalog.data.gov/harvest_record/6601d68a-70d2-49ec-915e-743719b19b25/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d89d90ce4b0c4f70d0ae4df","keyword":["007","012","MN","Minnesota","US","USGS:5d89d90ce4b0c4f70d0ae4df","United States","WI","Wisconsin","climate change","deep learning","environment","hybrid modeling","inlandWaters","machine learning","modeling","reservoirs","temperate lakes","temperature","thermal profiles","water"],"last_harvested_date":"2026-09-10T22:49:26.922958","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":"process-guided-deep-learning-water-temperature-predictions-3-model-inputs-meteorological-i","spatial_centroid":{"lat":44.9986522768197,"lon":-91.73556140804806},"spatial_shape":{"coordinates":[[[-94.2609062307949,42.5692312672573],[-94.2609062307949,48.6427837911633],[-87.9475441739278,48.6427837911633],[-87.9475441739278,42.5692312672573],[-94.2609062307949,42.5692312672573]]],"type":"Polygon"},"theme":["geospatial"],"title":"Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags)","type":"dataset"},{"_score":12.299829,"_sort":[1789080566683,12.299829,1,"10f4a62c-9d9f-4e72-bb44-24baeff6ffff"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael C. Duniway","hasEmail":"mailto:mduniway@usgs.gov"},"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":[{"@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":25.470984,"_sort":[1789080563219,25.470984,3,"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. 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_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/25a9b8d2-fb53-4670-bdaf-5a420b3433d0","harvest_record_raw":"https://catalog.data.gov/harvest_record/25a9b8d2-fb53-4670-bdaf-5a420b3433d0/raw","has_download":true,"has_spatial":true,"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"],"last_harvested_date":"2026-09-10T22:49:23.219129","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":"snowmodel-simulations-and-supporting-observations-for-the-north-central-colorado-rock-2015","spatial_centroid":{"lat":40.099221616,"lon":-105.76336292},"spatial_shape":{"coordinates":[[[-105.9524194,39.77492324],[-105.9524194,40.58566918],[-105.4797782,40.58566918],[-105.4797782,39.77492324],[-105.9524194,39.77492324]]],"type":"Polygon"},"theme":["geospatial"],"title":"SnowModel simulations and supporting observations for the north-central Colorado Rocky Mountains during water years 2011 through 2015","type":"dataset"},{"_score":8.624765,"_sort":[1789080562992,8.624765,1,"e8648515-02ef-48f8-a4da-258d23ae71e4"],"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 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":[{"@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":19.09304,"_sort":[1789080561631,19.09304,2,"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":9.886621,"_sort":[1789080559104,9.886621,1,"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":11.984654,"_sort":[1789080554661,11.984654,4,"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. 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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":28.532621,"_sort":[1789080543534,28.532621,2,"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. For the 13 years of record at this site, the water table was always within 2 meters of land surface.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ba56f8c9-b12c-4f04-b117-a76664cd8c82","harvest_record_raw":"https://catalog.data.gov/harvest_record/ba56f8c9-b12c-4f04-b117-a76664cd8c82/raw","has_download":true,"has_spatial":true,"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"],"last_harvested_date":"2026-09-10T22:49:02.876955","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":"evapotranspiration-at-starkey-pasture-site-30-minute-data-pasco-county-florida-januar-2016","spatial_centroid":{"lat":28.225800000000003,"lon":-82.56020000000001},"spatial_shape":{"coordinates":[[[-82.561,28.225],[-82.561,28.227],[-82.559,28.227],[-82.559,28.225],[-82.561,28.225]]],"type":"Polygon"},"theme":["geospatial"],"title":"Evapotranspiration at Starkey pasture site, 30-minute data, Pasco County, Florida, January 2010 - April 2016","type":"dataset"},{"_score":62.331078,"_sort":[1789080541895,62.331078,2,"36cabe03-a5be-40e7-8e65-33b87fdbbe7f"],"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 MIROC5 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 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). Journal of hydrometeorology, 15(6), pp.2558-2585.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9K23J25","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.656e667fd34e7ca10833fe69.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_656e667fd34e7ca10833fe69","keyword":["California","USGS:656e667fd34e7ca10833fe69","United States","atmospheric and climatic processes","climate change","climatologyMeteorologyAtmosphere","evaporation","geoscientificInformation","hydrology","inlandWaters","mathematical modeling","permeability","precipitation (atmospheric)","snow and ice cover","soil moisture","streamflow","surface water (non-marine)","transpiration","water budget","water cycle","water resources","watershed management"],"modified":"2026-06-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.9805, 32.5300, -114.1200, 43.4210","theme":["geospatial"],"title":"MIROC5 - RCP - 4.5 : Monthly climate variables"},"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 MIROC5 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 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). Journal of hydrometeorology, 15(6), pp.2558-2585.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c8910438-17b3-46b8-bfc3-d33a5f143730","harvest_record_raw":"https://catalog.data.gov/harvest_record/c8910438-17b3-46b8-bfc3-d33a5f143730/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_656e667fd34e7ca10833fe69","keyword":["California","USGS:656e667fd34e7ca10833fe69","United States","atmospheric and climatic processes","climate change","climatologyMeteorologyAtmosphere","evaporation","geoscientificInformation","hydrology","inlandWaters","mathematical modeling","permeability","precipitation (atmospheric)","snow and ice cover","soil moisture","streamflow","surface water (non-marine)","transpiration","water budget","water cycle","water resources","watershed management"],"last_harvested_date":"2026-09-10T22:49:01.895231","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":"miroc5-rcp-4-5-monthly-climate-variables","spatial_centroid":{"lat":36.8864,"lon":-120.6363},"spatial_shape":{"coordinates":[[[-124.9805,32.53],[-124.9805,43.421],[-114.12,43.421],[-114.12,32.53],[-124.9805,32.53]]],"type":"Polygon"},"theme":["geospatial"],"title":"MIROC5 - RCP - 4.5 : Monthly climate variables","type":"dataset"},{"_score":14.05696,"_sort":[1789080536410,14.05696,6,"7586b215-40f9-4223-9460-b2b75b47ccd6"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Evan H Grant","hasEmail":"mailto:ehgrant@usgs.gov"},"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":[{"@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"}],"sort":"last_harvested_date"}