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Twelve hyrdologic metrics were calculated: low- and high-flow magnitude (LF_MAG(cfs/square km) and HF_MAG(cfs/square km)), low- and high-flow variability (LF_VAR(dimensionless) and HF_VAR(dimensionless)), low- and high-flow frequency (LF_FRE(number of pulses) and HF_FRE(number of pulses)), low- and high-flow duration (LF_DUR(days) and HF_DUR(days)), skewness (OT_SKW(dimensionless)), number of daily rises (OT_RIS(dimensionless)), low- and high-flow seasonality (LF_SEA(dimensionless) and HF_SEA(dimensionless)). The low- and high-flow magnitudes were calculated from the 1st and 99th percentile non-exceedence streamflows divided by the drainage area, respectively. The low- and high-flow variabilities were calculated from the coefficient of variation of the annual minimum and maximum streamflows, respectively. The low- and high-flow frequencies were calculated as the mean of the annual-time series of the number of pulses below the 10th and above the 90th percentile values, respectively. The low- and high-flow durations were calculated from the length of time (in days) that the streamflow was below the 10th percentile or above the 90th percentile, respectively. The skewness values were calculated as the third moment of the daily streamflow values. The number of daily rises values were calculated by the ratio of the number of days where the daily streamflow was greater than the previous day divided by the total number of days for the period. The low- and high-flow seasonality values were calculated based on frequency of occurrence in different seasons (for more details, please see Eng, K., Wolock, D.M., and Dettinger, M.D., 2016, Sensitivity of intermittent streams to climate variations in the USA: River Research and Applications, v. 32, no. 5, p. 885-895. [Also available at https://doi.org/10.1002/rra.2939.]). The observed daily-streamflow values are from U.S. Geological Survey National Water Information System (http://dx.doi.org/10.5066/F7P55KJN), and the estimated values were calculated by random forest statistical models. For LF_SEA and HF_SEA, the observed hydrologic metric is often subtracted by the predicted hydrologic metric. For the remaining hydrologic metrics, the observed hydrologic metric is often divided by the predicted hydrologic metric  The data are in a tab-delimited text format.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9ULGVLI","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.5b8e87f1e4b0702d0e7ebc0a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b8e87f1e4b0702d0e7ebc0a","keyword":["USGS:5b8e87f1e4b0702d0e7ebc0a","streamflow"],"modified":"2026-09-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.33203124869, 24.566710428926, -66.09375000106, 49.410688528111","theme":["geospatial"],"title":"Hydrologic metric changes across the conterminous United States"},"description":"This metadata record describes the following eight attributes: (1) the U.S. Geological Survey streamgage identification number, (2) aggregated level 2 ecoregion, (3) hydrologic metric abbreviation, (4) direct-human modification classification of watershed, (5) predicted hydrologic metric values for 1950 to 2014, (6) predicted hydrologic metric values for 1980 to 2014, (7) observed hydrologic metric values for 1980 to 2014, and (8) the direction of streamflow alteration (i.e., inflated, diminished, or indeterminant). Twelve hyrdologic metrics were calculated: low- and high-flow magnitude (LF_MAG(cfs/square km) and HF_MAG(cfs/square km)), low- and high-flow variability (LF_VAR(dimensionless) and HF_VAR(dimensionless)), low- and high-flow frequency (LF_FRE(number of pulses) and HF_FRE(number of pulses)), low- and high-flow duration (LF_DUR(days) and HF_DUR(days)), skewness (OT_SKW(dimensionless)), number of daily rises (OT_RIS(dimensionless)), low- and high-flow seasonality (LF_SEA(dimensionless) and HF_SEA(dimensionless)). The low- and high-flow magnitudes were calculated from the 1st and 99th percentile non-exceedence streamflows divided by the drainage area, respectively. The low- and high-flow variabilities were calculated from the coefficient of variation of the annual minimum and maximum streamflows, respectively. The low- and high-flow frequencies were calculated as the mean of the annual-time series of the number of pulses below the 10th and above the 90th percentile values, respectively. The low- and high-flow durations were calculated from the length of time (in days) that the streamflow was below the 10th percentile or above the 90th percentile, respectively. The skewness values were calculated as the third moment of the daily streamflow values. The number of daily rises values were calculated by the ratio of the number of days where the daily streamflow was greater than the previous day divided by the total number of days for the period. The low- and high-flow seasonality values were calculated based on frequency of occurrence in different seasons (for more details, please see Eng, K., Wolock, D.M., and Dettinger, M.D., 2016, Sensitivity of intermittent streams to climate variations in the USA: River Research and Applications, v. 32, no. 5, p. 885-895. [Also available at https://doi.org/10.1002/rra.2939.]). The observed daily-streamflow values are from U.S. Geological Survey National Water Information System (http://dx.doi.org/10.5066/F7P55KJN), and the estimated values were calculated by random forest statistical models. For LF_SEA and HF_SEA, the observed hydrologic metric is often subtracted by the predicted hydrologic metric. For the remaining hydrologic metrics, the observed hydrologic metric is often divided by the predicted hydrologic metric  The data are in a tab-delimited text format.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/38754b37-56c5-4f90-a18f-0049c4a0d19b","harvest_record_raw":"https://catalog.data.gov/harvest_record/38754b37-56c5-4f90-a18f-0049c4a0d19b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b8e87f1e4b0702d0e7ebc0a","keyword":["USGS:5b8e87f1e4b0702d0e7ebc0a","streamflow"],"last_harvested_date":"2026-09-17T23:32:19.394710","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":"hydrologic-metric-changes-across-the-conterminous-united-states-3a62a","spatial_centroid":{"lat":34.5043016686,"lon":-101.63671874963799},"spatial_shape":{"coordinates":[[[-125.33203124869,24.566710428926],[-125.33203124869,49.410688528111],[-66.09375000106,49.410688528111],[-66.09375000106,24.566710428926],[-125.33203124869,24.566710428926]]],"type":"Polygon"},"theme":["geospatial"],"title":"Hydrologic metric changes across the conterminous United States","type":"dataset"},{"_score":38.495277,"_sort":[1789687665593,38.495277,1,"b46cca48-f5fe-4fc1-834d-8a9cfcd364e1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michael E. Wieczorek","hasEmail":"mailto:mewieczo@usgs.gov"},"description":"This tabular data set represents 30 year (1971 - 2000) mean annual precipitation in millimeters compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID.  The source data for 30 year (1971 - 2000) mean annual precipitation data was produced by the PRISM Group at Oregon State University (PRISM, 2008). Units are millimeters. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions.  Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).","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.573b70a7e4b0dae0d5e3ae85.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_573b70a7e4b0dae0d5e3ae85","keyword":["Catchment","Climate","Inlandwaters","NAWQA","NHDPlus","PRISM","SPARROW","USGS:573b70a7e4b0dae0d5e3ae85","mean annual precipitation"],"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 Attributes: 30 Year (1971 - 2000) Mean Annual Precipitation"},"description":"This tabular data set represents 30 year (1971 - 2000) mean annual precipitation in millimeters compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID.  The source data for 30 year (1971 - 2000) mean annual precipitation data was produced by the PRISM Group at Oregon State University (PRISM, 2008). Units are millimeters. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions.  Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/10a20f3a-fe46-44c8-91aa-375da73ebd4d","harvest_record_raw":"https://catalog.data.gov/harvest_record/10a20f3a-fe46-44c8-91aa-375da73ebd4d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_573b70a7e4b0dae0d5e3ae85","keyword":["Catchment","Climate","Inlandwaters","NAWQA","NHDPlus","PRISM","SPARROW","USGS:573b70a7e4b0dae0d5e3ae85","mean annual precipitation"],"last_harvested_date":"2026-09-17T23:27:45.593906","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"attributes-for-nhdplus-version-2-1-catchments-and-modified-routing-of-upstream-watersheds-","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 Attributes: 30 Year (1971 - 2000) Mean Annual Precipitation","type":"dataset"},{"_score":8.164497,"_sort":[1789606216655,8.164497,3,"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/86e57537-d748-4d55-be87-50f70428f7bf","harvest_record_raw":"https://catalog.data.gov/harvest_record/86e57537-d748-4d55-be87-50f70428f7bf/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":"2026-09-17T00:50:16.655685","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":19.097061,"_sort":[1789605581640,19.097061,1,"e2714ff8-adf8-4a22-a8eb-601f30c5f482"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Virginia and West Virginia Water Science Center","hasEmail":"mailto:dlmoyer@usgs.gov"},"description":"1D transient numerical simulations with a modified version of the SUTRA model \n(preliminary code) that accounts for variably-saturated freeze-thaw dynamics \n(e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature \ndynamics using coupled fluid and heat transport physics. The model simulations \nwere run with a modified version of SUTRA_ICE (unreleased) that accomadates \na time-variable sinusiodal upper temperature boundary. This data release also \nincludes the source code and Argus One GUI files used to build the models, \nthough this proprietary software is not needed to run the models as described \nin the upper-level \"readme\" file.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7F47M8Q","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.8ec2db4f-7c68-42da-8beb-0c4a7cb2d060.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_8ec2db4f-7c68-42da-8beb-0c4a7cb2d060","keyword":["Groundwater","InlandWaters","SUTRA","SUTRA-ice","Shenandoah National Park","Surface Water","Thermal","USGS:8ec2db4f-7c68-42da-8beb-0c4a7cb2d060","Virginia","environment","geoscientificInformation","inlandWaters","refugia"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-78.378933, 38.53969, -78.347222, 38.58403","theme":["geospatial"],"title":"Modeled temperature data developed for study of shallow mountain bedrock limits seepage-based headwater climate refugia, Shenandoah National Park, Virginia: U.S. Geological Survey data release"},"description":"1D transient numerical simulations with a modified version of the SUTRA model \n(preliminary code) that accounts for variably-saturated freeze-thaw dynamics \n(e.g. McKenzie and Voss, 2013) to predict annual alluvial aquifer temperature \ndynamics using coupled fluid and heat transport physics. 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Mean-annual and mean-seasonal precipitation, recharge, and \nactual evapotranspiration (ET) estimates were derived from annual and monthly \nSoil-Water-Balance (SWB) model (McCoy and others, 2015; Westenbroek \nand others, 2010) output and compiled in a geodatabase. Precipitation estimates \nfrom the Appalachian Plateaus SWB model were derived from daily Daymet \nclimate grids (Thornton and others, 2012). Estimates of recharge from the \nSWB model were calculated using a modified Thornthwaite-Mather soil-water \naccounting method (Thornthwaite and Mather, 1957; Westenbroek and others, \n2010). Estimates of ET from the SWB model were derived by adjusting a \nspatially-variable estimate of potential ET (Hargreaves and Samani, 1985) with \nestimates of precipitation and soil-moisture (Westenbrok and others, 2010). \nThe geodatabase contains polygon and point feature classes representing the \nmodel grid cells and their centers, respectively, and two tables containing \nmean-annual and mean-seasonal estimates for each cell. Mean-annual \nestimates were computed for full calendar years (January through December) \nand are presented in inches per year (in/yr) for the 1980 through 2011 period. \nMean-seasonal estimates for spring (March through May), summer (June \nthrough August) and fall (September through November) are presented in \ninches for the 1980 through 2011 period. Mean-seasonal estimates for \nwinter (December through February), also presented in inches, were \ncalculated for December 1980 through February 2011.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7X06544","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.dba98e35-d294-402c-b31e-4ee461bc6dd1.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_dba98e35-d294-402c-b31e-4ee461bc6dd1","keyword":["Alabama","Appalalachian Plateaus","Georgia","Kentucky","Maryland","Mississippi","Mississippian aquifer","New York","North Carolina","Ohio","Pennsylvania","Pennsylvanian aquifer","Permian aquifer","Soil-Water-Balance model","Tennessee","USGS:dba98e35-d294-402c-b31e-4ee461bc6dd1","Virginia","West Virginia","aquifer","environment","evapotranspiration","geoscientificInformation","groundwater","hydrologic budget","inlandWaters","precipitation","recharge"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-88.992632, 31.180164, -75.057322, 43.797973","theme":["geospatial"],"title":"Mean-annual and mean-seasonal water-budget estimates from a Soil-Water-Balance model of the Appalachian Plateaus, 1980 through 2011"},"description":"As part of the U.S. Geological Survey Groundwater Resources Program study of \nAppalachian Plateaus aquifers, mean-annual and mean-seasonal water-budget \nestimates for the period 1980 through 2011 were determined  for a 162,000 \nsquare-mile area covering parts of New York, Pennsylvania, Maryland, Ohio, \nWest Virginia, Kentucky, Virginia, Tennessee, North Carolina, Georgia, Alabama, \nand Mississippi. Mean-annual and mean-seasonal precipitation, recharge, and \nactual evapotranspiration (ET) estimates were derived from annual and monthly \nSoil-Water-Balance (SWB) model (McCoy and others, 2015; Westenbroek \nand others, 2010) output and compiled in a geodatabase. Precipitation estimates \nfrom the Appalachian Plateaus SWB model were derived from daily Daymet \nclimate grids (Thornton and others, 2012). Estimates of recharge from the \nSWB model were calculated using a modified Thornthwaite-Mather soil-water \naccounting method (Thornthwaite and Mather, 1957; Westenbroek and others, \n2010). Estimates of ET from the SWB model were derived by adjusting a \nspatially-variable estimate of potential ET (Hargreaves and Samani, 1985) with \nestimates of precipitation and soil-moisture (Westenbrok and others, 2010). \nThe geodatabase contains polygon and point feature classes representing the \nmodel grid cells and their centers, respectively, and two tables containing \nmean-annual and mean-seasonal estimates for each cell. Mean-annual \nestimates were computed for full calendar years (January through December) \nand are presented in inches per year (in/yr) for the 1980 through 2011 period. \nMean-seasonal estimates for spring (March through May), summer (June \nthrough August) and fall (September through November) are presented in \ninches for the 1980 through 2011 period. Mean-seasonal estimates for \nwinter (December through February), also presented in inches, were \ncalculated for December 1980 through February 2011.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5668cce5-c29f-44ee-8cc4-590b8c24760b","harvest_record_raw":"https://catalog.data.gov/harvest_record/5668cce5-c29f-44ee-8cc4-590b8c24760b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_dba98e35-d294-402c-b31e-4ee461bc6dd1","keyword":["Alabama","Appalalachian Plateaus","Georgia","Kentucky","Maryland","Mississippi","Mississippian aquifer","New York","North Carolina","Ohio","Pennsylvania","Pennsylvanian aquifer","Permian aquifer","Soil-Water-Balance model","Tennessee","USGS:dba98e35-d294-402c-b31e-4ee461bc6dd1","Virginia","West Virginia","aquifer","environment","evapotranspiration","geoscientificInformation","groundwater","hydrologic budget","inlandWaters","precipitation","recharge"],"last_harvested_date":"2026-09-17T00:09:00.709909","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":"mean-annual-and-mean-seasonal-water-budget-estimates-from-a-soil-water-balance-model--2011","spatial_centroid":{"lat":36.2272876,"lon":-83.418508},"spatial_shape":{"coordinates":[[[-88.992632,31.180164],[-88.992632,43.797973],[-75.057322,43.797973],[-75.057322,31.180164],[-88.992632,31.180164]]],"type":"Polygon"},"theme":["geospatial"],"title":"Mean-annual and mean-seasonal water-budget estimates from a Soil-Water-Balance model of the Appalachian Plateaus, 1980 through 2011","type":"dataset"},{"_score":14.826714,"_sort":[1789603523602,14.826714,1,"35dc6334-671e-49a3-99cf-836c81bd89bf"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"J. LaRue Smith","hasEmail":"mailto:jlsmith@usgs.gov"},"description":"Hydrologic landscape regions group areas according to their similarity in landscape and climate characteristics.  \nThese characteristics represent variables assumed to affect hydrologic processes in the environment.  Hydrologic \nlandscape regions in Nevada were delineated using geographic information system tools and statistical methods \nincluding cluster analysis. The data layers of hydrogeology, precipitation, soil permeability, land surface slope \nand aspect were used to identify the hydrologic landscape regions. Sixteen hydrologic landscape regions were \nidentified through cluster analysis. The hydrologic landscape regions are noncontiguous in nature and can range \nfrom small areas which tend to be in the mountain ranges to very large areas in the basins.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P92HOQMU","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.901db279-906b-489f-a960-85cf610dfc9e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_901db279-906b-489f-a960-85cf610dfc9e","keyword":["Great Basin","Nevada","USGS:901db279-906b-489f-a960-85cf610dfc9e","environment","geoscientificInformation","hydrogeology","hydrologic landscape regions","inlandWaters","land surface aspect","land surface slope","precipitation","soil permeability"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.180324, 34.967066, -113.694509, 42.039355","theme":["geospatial"],"title":"Hydrologic landscape regions of Nevada"},"description":"Hydrologic landscape regions group areas according to their similarity in landscape and climate characteristics.  \nThese characteristics represent variables assumed to affect hydrologic processes in the environment.  Hydrologic \nlandscape regions in Nevada were delineated using geographic information system tools and statistical methods \nincluding cluster analysis. The data layers of hydrogeology, precipitation, soil permeability, land surface slope \nand aspect were used to identify the hydrologic landscape regions. Sixteen hydrologic landscape regions were \nidentified through cluster analysis. The hydrologic landscape regions are noncontiguous in nature and can range \nfrom small areas which tend to be in the mountain ranges to very large areas in the basins.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2f48f824-e064-46e5-883f-e70604c1c8c1","harvest_record_raw":"https://catalog.data.gov/harvest_record/2f48f824-e064-46e5-883f-e70604c1c8c1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_901db279-906b-489f-a960-85cf610dfc9e","keyword":["Great Basin","Nevada","USGS:901db279-906b-489f-a960-85cf610dfc9e","environment","geoscientificInformation","hydrogeology","hydrologic landscape regions","inlandWaters","land surface aspect","land surface slope","precipitation","soil permeability"],"last_harvested_date":"2026-09-17T00:05:23.602445","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":"hydrologic-landscape-regions-of-nevada","spatial_centroid":{"lat":37.795981600000005,"lon":-117.58599799999999},"spatial_shape":{"coordinates":[[[-120.180324,34.967066],[-120.180324,42.039355],[-113.694509,42.039355],[-113.694509,34.967066],[-120.180324,34.967066]]],"type":"Polygon"},"theme":["geospatial"],"title":"Hydrologic landscape regions of Nevada","type":"dataset"},{"_score":35.190235,"_sort":[1789603440914,35.190235,1,"f9d2b5c2-2aca-4a63-95ec-c9d9e90267e6"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Oklahoma-Texas Water Science Center","hasEmail":"mailto:gs-w-txpublic-info@usgs.gov"},"description":"Estimates of area and aerial extent of land-use categories are an essential component \nfor computing the water budget of the High Plains aquifer. These raster land-use land \nclass data represent yearly simulated future land use for the High Plains from 2009 to \n2050 These data were developed using the FOREcasting SCEnarios (FORE-SCE) of \nfuture land cover model (Sohl and others, 2007; Sohl and Sayler 2008) for two (A2 and \nB2) of the four Intergovernmental Panel on Climate Change (IPCC) climate scenarios \nand then processed using a Geographic Information System (GIS). The GIS software \nused to process these data was Environmental Systems Research Institute (ESRI, Inc.) \nArcGIS Desktop 10.0.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9FEQ22K","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.ce171d76-dfc7-49ea-9375-3e0596ecc68e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ce171d76-dfc7-49ea-9375-3e0596ecc68e","keyword":["Colorado","Great Plains","High Plains","High Plains aquifer","Kansas","LULC","Nebraska","New Mexico","Ogallala aquifer","Oklahoma","South Dakota","Texas","USGS:ce171d76-dfc7-49ea-9375-3e0596ecc68e","Wyoming","environment","geoscientificInformation","inlandWaters","land cover","land use","landcover","landuse"],"modified":"2020-11-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.016217, 31.598356, -96.227959, 43.806414","theme":["geospatial"],"title":"DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the B2 Climate Scenario for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming"},"description":"Estimates of area and aerial extent of land-use categories are an essential component \nfor computing the water budget of the High Plains aquifer. 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The GIS software \nused to process these data was Environmental Systems Research Institute (ESRI, Inc.) \nArcGIS Desktop 10.0.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b34cf7e7-7570-4e68-ba53-1614d1430389","harvest_record_raw":"https://catalog.data.gov/harvest_record/b34cf7e7-7570-4e68-ba53-1614d1430389/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ce171d76-dfc7-49ea-9375-3e0596ecc68e","keyword":["Colorado","Great Plains","High Plains","High Plains aquifer","Kansas","LULC","Nebraska","New Mexico","Ogallala aquifer","Oklahoma","South Dakota","Texas","USGS:ce171d76-dfc7-49ea-9375-3e0596ecc68e","Wyoming","environment","geoscientificInformation","inlandWaters","land cover","land use","landcover","landuse"],"last_harvested_date":"2026-09-17T00:04:00.914981","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":"ds-777-annual-model-forecasted-land-use-land-cover-rasters-from-2009-to-2050-for-the-b2-cl","spatial_centroid":{"lat":36.4815792,"lon":-102.1009138},"spatial_shape":{"coordinates":[[[-106.016217,31.598356],[-106.016217,43.806414],[-96.227959,43.806414],[-96.227959,31.598356],[-106.016217,31.598356]]],"type":"Polygon"},"theme":["geospatial"],"title":"DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the B2 Climate Scenario for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming","type":"dataset"},{"_score":17.587807,"_sort":[1789603021200,17.587807,1,"b9eaecc3-857d-495c-ab5f-5a4d22188160"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Randall J. 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A flood event is defined as a series of water depth measurements greater than 10 mm; all measured flood events presented here have undergone QC by a member of the FloodNet team. \n\nSee attached data description document for more details on data collection, analysis & the publication to cite if using the data or including it in any publication or public presentation.\n\nMore information on the FloodNet project can be found here: https://www.floodnet.nyc \n\nVisualization of the data in an online data dashboard can be found here: https://dataviz.floodnet.nyc","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/aq7i-eu5q/columns.json","describedByType":"application/json","downloadURL":"https://data.cityofnewyork.us/api/v3/views/aq7i-eu5q/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/aq7i-eu5q/columns.xml","describedByType":"application/xml","downloadURL":"https://data.cityofnewyork.us/api/v3/views/aq7i-eu5q/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cityofnewyork.us/api/v3/views/aq7i-eu5q/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.cityofnewyork.us/api/views/aq7i-eu5q","issued":"2026-02-25","keyword":["climate","flood","flood depth","floodnet","water"],"landingPage":"https://data.cityofnewyork.us/d/aq7i-eu5q","modified":"2026-09-15","publisher":{"@type":"org:Organization","name":"data.cityofnewyork.us"},"theme":["Environment"],"title":"FloodNet: Street Flooding Events Measured by FloodNet Sensors"},"description":"For the  FloodNet data collection page, please follow <a href=\"https://data.cityofnewyork.us/browse?Data-Collection_Data-Collection=FloodNet+NYC\">this link</a>.\n\nData presented here were collected by the FloodNet project, which is a collaboration between researchers at academic institutions (New York University and the City University of New York) and NYC government agencies (NYC Department of Environmental Protection, Mayor's Office of Climate & Environmental Justice, Office of Technology & Innovation), working with NYC residents. Data on flood water depth were collected by FloodNet sensors in one minute intervals, then analyzed to develop the flood event summary statistics presented here. A flood event is defined as a series of water depth measurements greater than 10 mm; all measured flood events presented here have undergone QC by a member of the FloodNet team. \n\nSee attached data description document for more details on data collection, analysis & the publication to cite if using the data or including it in any publication or public presentation.\n\nMore information on the FloodNet project can be found here: https://www.floodnet.nyc \n\nVisualization of the data in an online data dashboard can be found here: https://dataviz.floodnet.nyc","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/da9111f9-69dd-4dac-aba0-deb0100cf65b","harvest_record_raw":"https://catalog.data.gov/harvest_record/da9111f9-69dd-4dac-aba0-deb0100cf65b/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cityofnewyork.us/api/views/aq7i-eu5q","keyword":["climate","flood","flood depth","floodnet","water"],"last_harvested_date":"2026-09-15T23:04:59.090468","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"1149ee63-2fff-494e-82e5-9aace9d3b3bf","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_new_york_ny.png","name":"City of New York","organization_type":"City Government","slug":"nyc-ny"},"parent_identifier":null,"popularity":32,"publisher":"data.cityofnewyork.us","slug":"floodnet-street-flooding-events-measured-by-floodnet-sensors","spatial_centroid":null,"spatial_shape":null,"theme":["Environment"],"title":"FloodNet: Street Flooding Events Measured by FloodNet Sensors","type":"dataset"},{"_score":9.166996,"_sort":[1789512000138,9.166996,0,"0363a5bd-057a-4f24-8d75-527292769b33"],"dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"fn":"Not provided - Contact data.gov","hasEmail":"mailto:datagovsupport@gsa.gov"},"describedByType":"application/octet-stream","description":"This metadata record describes the benthic cover data collected at permanent sites under the National Coral Reef Monitoring Program (NCRMP) across the U.S. Pacific Islands, including American Samoa, CNMI, Guam, Hawai'i, and Pacific Remote Island Areas since 2013, as well as pre-NCRMP benthic cover data at benthic permanent sites from 2012 for which a subset became NCRMP permanent sites. The data provided in the distribution refers to the sites as their NCRMP OCC site name when applicable.\n\nThis record replaces InPort items 25274, 36146, 36147, 36148.\n\nThe data described here result from the annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). The photoquadrat surveys were conducted at coral reef sites according to protocols established by ESD and NCRMP during ESD-led missions across the US Pacific since 2010. NCRMP began in 2013.\n\nSCUBA divers conducted benthic photoquadrat surveys at permanent sites established in coral reef habitats by ESD. A select number of these sites were chosen in hard-bottom habitat at ~15-m depths, and a subset of the permanent sites (climate stations) were established at north, south, east, and west points around each of the islands and atolls. The divers estimated and delineated each sites rectangular perimeter by temporarily placing measuring tapes with 1-m markers starting from a permanently installed reference stake on the reef. Along the nearshore 10-m side of the survey site and the downslope 5-m side, the measuring tapes marked every meter of the L-shaped 15-m transect used for photoquadrat documentation. The divers photographed the reef at 1-m intervals on both sides of the 15-m tape, generating 30 photographs per survey site.\n\nThe benthic habitat images were quantitatively analyzed using Coral Point Count with Excel extensions (CPCe; Kohler and Gill, 2006) software from 2010-2014 and the web-based annotation tool, CoralNet (Beijbom et al. 2015), from 2015 to present. Ten points were randomly overlaid on each image and human analysts identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to genus/morphology for hard corals, and to genus/functional group for algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). In general, the analysis resulted in three levels of benthic community data, including taxa group Tier 1 (e.g., coral, soft coral, macroalgae, turf algae, etc.), Tier 2 (e.g., Coral = massive hard coral, branching hard coral, foliose hard coral, encrusting hard coral, etc.; Macroalga = upright macroalgae), and Tier 3 (e.g., Coral = Astreopora sp, Favia sp, Pocillopora, etc.; Macroalgae = Caulerpa sp, Dictyosphaeria sp, Padina sp, etc.). If Tier 3 resolution is not possible, the next finest resolution is used.. These benthic data can ultimately be used to produce estimates of relative abundance (percentage of benthic cover), frequency of occurrence, benthic community taxonomic composition, and relative generic richness.\n\nPermanent sites were first selected as 'climate stations' in 2010 as 3-4 sites per island that were selected to be roughly equally spaced, along the 15 m contour ('Mid' site), on hard bottom, and at least 1 km away from a river mouth or embayment. Here we assessed multiple features of the coral reef environment including in-situ temperature (STR), seawater carbonate, net carbonate accretion (CAU), bioerosion (BMU), and cryptobiota diversity (ARMS). In 2013 we began to establish OCC permanent sites at different depths and only collected photoquadrat imagery at Mid sites (STR,CAU,BMU data are available for these sites). Starting in 2019, photoquadrats were also collected and analyzed at sites at other depths (Shallow and Deep Sites). Data includes depth source where available.\n\nThese data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0317416","describedByType":"application/octet-stream","description":"Raw data of annotations of benthic photoquadrat images to capture benthic cover data at established permanent fixed (revisited) sites across the Pacific Islands regions including the Hawaiian Archipelago, Mariana Archipelago, American Samoa, and the Pacific Islands Heritage Marine National Monument.","mediaType":"text/html","title":"NCRMP_BENTHIC_COVER_FIXED_PACIFIC_2012-2025"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2026. GCMD Keywords, Version 24. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/78600","describedByType":"application/octet-stream","description":"View the complete metadata record on InPort for more information about this dataset.","mediaType":"text/html","title":"Full Metadata Record"}],"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/78600.xml","issued":"2025-01-01T00:00:00.000+00:00","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > BACTERIA/ARCHAEA > CYANOBACTERIA (BLUE-GREEN ALGAE)","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > ANGIOSPERMS (FLOWERING PLANTS) > MONOCOTS > SEAGRASS","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MACROALGAE (SEAWEEDS)","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","CAMERAS","743","National Coral Reef Monitoring Program","Numeric Data Sets > Benthic","EARTH SCIENCE > Biosphere > Aquatic Habitat > Benthic Habitat","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Microbiota > Blue-green Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Turf Algae","EARTH SCIENCE > Biosphere > Zoology > Corals","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Baseline studies","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring > Belt Transect","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring > Point Counts","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Photographic Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Rapid Assessment Studies","EARTH SCIENCE > Biosphere > Zoology > Mollusks > Tridacna","EARTH SCIENCE > Biosphere > Zoology > Sponges","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Benthic biology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover Live percentage","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Octocoral Cover","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral Communities","EARTH SCIENCE > Oceans > Marine Biology > Marine Invertebrates","EARTH SCIENCE > Oceans > Marine Biology > Marine Invertebrates > Macroinvertebrates","EARTH SCIENCE > Oceans > Marine Biology > Marine Plants > Seagrass","REEF AND/OR BOTTOM REGIME - PERCENT COVER","laboratory analyses","HI'IALAKAI","OSCAR ELTON SETTE","RAINIER","small boat","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division; Coral Reef Ecosystem Program","COUNTRY/TERRITORY > Northern Mariana Islands > Aguihan > Aguihan Island (Aguijan) (14N145E0006)","COUNTRY/TERRITORY > Northern Mariana Islands > Asuncion Island > Asuncion Island (19N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Northern Mariana Islands > Northern Mariana Islands ( CNMI ) (18N146E0000)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > American Samoa (14S170W0000)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ofu Island (14S169W0013)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Olosega Island (14S169W0014)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Rose Atoll (14S168W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Swains Atoll (11S171W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ta'u Island (14S169W0012)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Tutuila Island (14S170W0016)","COUNTRY/TERRITORY > United States of America > American Samoa > Ofu Island > Ofu (14S169W0002)","COUNTRY/TERRITORY > United States of America > American Samoa > Olosega Island > Olosega (14S169W0016)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii (21N160W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii Island (19N155W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Molokai Island (21N157W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > French Frigate Shoals (24N166W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Kure Atoll (28N178W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Lisianski Island (25N173W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Northwestern Hawaiian Islands (28N178W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Pearl and Hermes Reef (27N176W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kalawao > Kahoolawe Island (20N156W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kauai > Niihau Island (21N160W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Maui Island (20N156W0004","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Maui Island (20N156W0004)","COUNTRY/TERRITORY > United States of America > Hawaiian Islands (21N157W0027)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Baker Island (00N176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Howland Island (00S176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Jarvis Island (00S160W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Johnston Atoll (16N169W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Kingman Reef (06N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Palmyra Atoll (05N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Wake Atoll (19N167E0001)","OCEAN BASIN > Pacific Ocean > American Samoa > American Samoa (14S170W0000)","OCEAN BASIN > Pacific Ocean > American Samoa > Rose Atoll (14S168W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Swains Atoll (11S171W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Tutuila Island (14S170W0016)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands (21N157W0027)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Kahoolawe Island > Kahoolawe Island (20N156W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Kauai Island > Kauai Island (22N159W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Lanai Island > Lanai Island (20N156W0002)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Maui Island > Maui Island (20N156W0004)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Molokai Island > Molokai Island (21N157W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Niihau Island > Niihau Island (21N160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Oahu Island > Oahu (21N157W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Howland Island > Howland Island (00S176W0001","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Johnston Atoll > Johnston Atoll (16N169W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Jarvis Island (00S160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Kingman Reef (06N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Palmyra Atoll (05N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands (28N178W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > French Frigate Shoals (24N166W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Kure Atoll (28N178W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Lisianski Island (25N173W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Pearl and Hermes Reef (27N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Wake Atoll > Wake Atoll (19N167E0001)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu (14S169W0002)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu Island (14S169W0013)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega (14S169W0016)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega Island (14S169W0014)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ta'u Island (14S169W0012)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","Coastal Waters of Hawaii","Equatorial Pacific Ocean","Marianas Trench Marine National Monument","Northwest Pacific","Pacific Remote Islands Marine National Monument","Central Pacific Ocean","North Pacific Ocean","North Pacific Ocean","Papahanaumokuakea Marine National Monument","Rose Atoll Marine National Monument","South Pacific Ocean","photograph","scale","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","PIFSC","Pacific Islands Fisheries Science Center","AMSM","American Samoa","CNMI","Commonwealth of the Northern Marianas Islands","MHI","Main Hawaiian Islands","Marianas","NWHI","Northwestern Hawaiian Islands","PMNM","PRIA","PRIMNM","Pacific Remote Island Areas","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","Pacific-wide Benthic"],"landingPage":"https://www.fisheries.noaa.gov/inport/item/78600","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2025-01-01T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"Pacific Islands Fisheries Science Center"},"rights":"otherRestrictions, unclassified","spatial":"-168.13792,-14.559317,-171.09222,-11.0457","temporal":"2015-02-15T00:00:00+00:00/2015-03-26T00:00:00+00:00","theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Benthic Cover Derived from Analysis of Annotated Benthic Images Collected During Photoquadrat Surveys at Permanent Sites across the U.S. Pacific Islands since 2012"},"description":"This metadata record describes the benthic cover data collected at permanent sites under the National Coral Reef Monitoring Program (NCRMP) across the U.S. Pacific Islands, including American Samoa, CNMI, Guam, Hawai'i, and Pacific Remote Island Areas since 2013, as well as pre-NCRMP benthic cover data at benthic permanent sites from 2012 for which a subset became NCRMP permanent sites. The data provided in the distribution refers to the sites as their NCRMP OCC site name when applicable.\n\nThis record replaces InPort items 25274, 36146, 36147, 36148.\n\nThe data described here result from the annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). The photoquadrat surveys were conducted at coral reef sites according to protocols established by ESD and NCRMP during ESD-led missions across the US Pacific since 2010. NCRMP began in 2013.\n\nSCUBA divers conducted benthic photoquadrat surveys at permanent sites established in coral reef habitats by ESD. A select number of these sites were chosen in hard-bottom habitat at ~15-m depths, and a subset of the permanent sites (climate stations) were established at north, south, east, and west points around each of the islands and atolls. The divers estimated and delineated each sites rectangular perimeter by temporarily placing measuring tapes with 1-m markers starting from a permanently installed reference stake on the reef. Along the nearshore 10-m side of the survey site and the downslope 5-m side, the measuring tapes marked every meter of the L-shaped 15-m transect used for photoquadrat documentation. The divers photographed the reef at 1-m intervals on both sides of the 15-m tape, generating 30 photographs per survey site.\n\nThe benthic habitat images were quantitatively analyzed using Coral Point Count with Excel extensions (CPCe; Kohler and Gill, 2006) software from 2010-2014 and the web-based annotation tool, CoralNet (Beijbom et al. 2015), from 2015 to present. Ten points were randomly overlaid on each image and human analysts identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to genus/morphology for hard corals, and to genus/functional group for algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). In general, the analysis resulted in three levels of benthic community data, including taxa group Tier 1 (e.g., coral, soft coral, macroalgae, turf algae, etc.), Tier 2 (e.g., Coral = massive hard coral, branching hard coral, foliose hard coral, encrusting hard coral, etc.; Macroalga = upright macroalgae), and Tier 3 (e.g., Coral = Astreopora sp, Favia sp, Pocillopora, etc.; Macroalgae = Caulerpa sp, Dictyosphaeria sp, Padina sp, etc.). If Tier 3 resolution is not possible, the next finest resolution is used.. These benthic data can ultimately be used to produce estimates of relative abundance (percentage of benthic cover), frequency of occurrence, benthic community taxonomic composition, and relative generic richness.\n\nPermanent sites were first selected as 'climate stations' in 2010 as 3-4 sites per island that were selected to be roughly equally spaced, along the 15 m contour ('Mid' site), on hard bottom, and at least 1 km away from a river mouth or embayment. Here we assessed multiple features of the coral reef environment including in-situ temperature (STR), seawater carbonate, net carbonate accretion (CAU), bioerosion (BMU), and cryptobiota diversity (ARMS). In 2013 we began to establish OCC permanent sites at different depths and only collected photoquadrat imagery at Mid sites (STR,CAU,BMU data are available for these sites). Starting in 2019, photoquadrats were also collected and analyzed at sites at other depths (Shallow and Deep Sites). Data includes depth source where available.\n\nThese data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive.","distribution_titles":["NCRMP_BENTHIC_COVER_FIXED_PACIFIC_2012-2025","GCMD Keyword Forum Page","Full Metadata Record"],"harvest_record":"https://catalog.data.gov/harvest_record/33fe613f-1789-465b-bfe3-fab676732d26","harvest_record_raw":"https://catalog.data.gov/harvest_record/33fe613f-1789-465b-bfe3-fab676732d26/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/33fe613f-1789-465b-bfe3-fab676732d26/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/78600.xml","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES","EARTH SCIENCE > BIOLOGICAL 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Corals","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Baseline studies","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Quadrat Monitoring > Photograph Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring > Belt Transect","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Benthos Analysis > Transect Monitoring > Point Counts","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Biological","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Photographic Analysis","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Rapid Assessment Studies","EARTH SCIENCE > Biosphere > Zoology > Mollusks > Tridacna","EARTH SCIENCE > Biosphere > Zoology > Sponges","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Benthic biology","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Hard Coral Cover Live percentage","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Octocoral Cover","EARTH SCIENCE > Oceans > Marine Biology > Coral","EARTH SCIENCE > Oceans > Marine Biology > Coral 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Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Rota > Rota Island ( Luta ) (14N145E0007)","COUNTRY/TERRITORY > Northern Mariana Islands > Saipan > Saipan Island (15N145E0002)","COUNTRY/TERRITORY > Northern Mariana Islands > Sarigan Island > Sarigan Island (16N145E0003)","COUNTRY/TERRITORY > Northern Mariana Islands > Tinian > Tinian Island (14N145E0005)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > American Samoa (14S170W0000)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ofu Island (14S169W0013)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Olosega Island (14S169W0014)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Rose Atoll (14S168W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Swains Atoll (11S171W0001)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Ta'u Island (14S169W0012)","COUNTRY/TERRITORY > United States of America > American Samoa > American Samoa > Tutuila Island (14S170W0016)","COUNTRY/TERRITORY > United States of America > American Samoa > Ofu Island > Ofu (14S169W0002)","COUNTRY/TERRITORY > United States of America > American Samoa > Olosega Island > Olosega (14S169W0016)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii (21N160W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Hawaii Island (19N155W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Kauai Island (22N159W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Hawaii > Molokai Island (21N157W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > French Frigate Shoals (24N166W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Kure Atoll (28N178W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Lisianski Island (25N173W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Northwestern Hawaiian Islands (28N178W0000)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Oahu (21N157W0003)","COUNTRY/TERRITORY > United States of America > Hawaii > Honolulu > Pearl and Hermes Reef (27N176W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kalawao > Kahoolawe Island (20N156W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Kauai > Niihau Island (21N160W0001)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Lanai Island (20N156W0002)","COUNTRY/TERRITORY > United States of America > Hawaii > Maui > Maui Island 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(14S170W0000)","OCEAN BASIN > Pacific Ocean > American Samoa > Rose Atoll (14S168W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Swains Atoll (11S171W0001)","OCEAN BASIN > Pacific Ocean > American Samoa > Tutuila Island (14S170W0016)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands (21N157W0027)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii > Hawaii (21N160W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands > Hawaii Island > Hawaii Island (19N155W0003)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Hawaiian Islands 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Ocean > Line Islands > Jarvis Island (00S160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Kingman Reef (06N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Palmyra Atoll (05N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands (28N178W0000)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > French Frigate Shoals (24N166W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Kure Atoll (28N178W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Lisianski Island (25N173W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Northwestern Hawaiian Islands > Pearl and Hermes Reef (27N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Wake Atoll > Wake Atoll (19N167E0001)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu (14S169W0002)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ofu Island (14S169W0013)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega (14S169W0016)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Olosega Island (14S169W0014)","OCEAN BASIN > Pacific Ocean > Manu'a Group > Ta'u Island (14S169W0012)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Asuncion Island > Asuncion Island (19N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Mariana Archipelago > Northern Mariana Islands ( CNMI ) (18N146E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Rota Island > Rota Island ( Luta ) (14N145E0007)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Saipan Island > Saipan Island (15N145E0002)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Sarigan Island > Sarigan Island (16N145E0003)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Tinian Island Reefs > Tinian Island (14N145E0005)","Coastal Waters of Hawaii","Equatorial Pacific Ocean","Marianas Trench Marine National Monument","Northwest Pacific","Pacific Remote Islands Marine National Monument","Central Pacific Ocean","North Pacific Ocean","North Pacific Ocean","Papahanaumokuakea Marine National Monument","Rose Atoll Marine National Monument","South Pacific Ocean","photograph","scale","CRED","CREP","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","PIFSC","Pacific Islands Fisheries Science Center","AMSM","American Samoa","CNMI","Commonwealth of the Northern Marianas Islands","MHI","Main Hawaiian Islands","Marianas","NWHI","Northwestern Hawaiian Islands","PMNM","PRIA","PRIMNM","Pacific Remote Island Areas","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","Pacific-wide Benthic"],"last_harvested_date":"2026-09-15T22:40:00.138054","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":0,"publisher":"Pacific Islands Fisheries Science 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coral reef oceanographic conditions and seawater carbonate chemistry. Diel suite surveys are conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD) within coral reef ecosystems across the Pacific Islands Region as part of the NOAA National Coral Reef Monitoring Program (NCRMP). The data provided in this data set are from diel suites deployed at sites in Guam, Aguijan, Maug, and Pagan, Mariana Archipelago in 2017, 2022, and 2025.\n\nDiel suites were deployed on the reef for at least 24 hours to measure in-situ salinity, temperature, pressure, pH, current direction and magnitude, dissolved oxygen, and photosynthetically active radiation (PAR). Seawater samples were also collected for laboratory analyses of dissolved inorganic carbon (DIC) and total alkalinity (TA). Components of the carbonate system--including pH, pCO2 (partial pressure of carbon dioxide), and aragonite saturation state--are calculated from DIC, TA, temperature, salinity and pressure. Each diel suite typically consisted of: 1 SBE-19plus or RBR concerto3 CTD sensor, 1 Nortek Acoustic Doppler Current Profiler (ADCP), 1 Satlantic SeaFET Ocean pH sensor (2017 and 2022) or SAMI-pH sensor (2025), 1 SBE or RBR dissolved oxygen sensor (2022 and 2025), 1 RBR photosynthetically active radiation sensor (2022 and 2025), 1 tilt current meter (2022 only), and up to 9 Programmable Underwater Collectors (PUCs, 2017) or Sub-surface Automated Samplers (SAS, 2022), each of which collected a water sample at set intervals. Instruments deployed, samples collected, and sample intervals are recorded in the summary file enclosed with the data package, and exceptions to the standard diel suite are also noted.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0186950","describedByType":"application/octet-stream","description":"Summary of the diel suite deployment in 2017 with information on the cruise and specific sites, as well as instruments and sampling intervals; different instruments could be deployed at different sites or instruments could have malfunctioned.","mediaType":"text/html","title":"ESD_NCRMP_DS_2017_CNMI_SUMMARY"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0302434","describedByType":"application/octet-stream","description":"Instrument data from CTD sensor in the diel suite deployment in the Mariana Archipelago in 2022.","mediaType":"text/html","title":"ESD_NCRMP_DS_2022_MARIAN_CTD.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0318163","describedByType":"application/octet-stream","description":"2025 diel suite dissolved oxygen data","mediaType":"text/html","title":"ESD_NCRMP_DS_2025_MA_DO.csv"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2026. GCMD Keywords, Version 24. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/54927","describedByType":"application/octet-stream","description":"View the complete metadata record on InPort for more information about this dataset.","mediaType":"text/html","title":"Full Metadata Record"}],"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/54927.xml","issued":"2019-01-01T00:00:00.000+00:00","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ALKALINITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > INORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PH","EARTH SCIENCE > OCEANS > OCEAN CIRCULATION > OCEAN CURRENTS > SPEED PROFILES","EARTH SCIENCE > OCEANS > OCEAN OPTICS > PHOTOSYNTHETICALLY ACTIVE RADIATION","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > SALINITY/DENSITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > OCEAN SALINITY","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > NORTHERN MARIANA ISLANDS","ADCP > Acoustic Doppler Current Profiler","CTD > Conductivity, Temperature, Depth","CURRENT METERS","OXYGEN METERS > OXYGEN METERS","PAR SENSORS > Photosynthetically Active Radiation Sensors","PH METERS > PH METERS","743","National Coral Reef Monitoring Program","Numeric Data Sets > Oceanography","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Chemical","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Physical","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Alkalinity","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbon Dioxide > Partial Pressure","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Chemistry Monitoring and Assessment","EARTH SCIENCE > Oceans > Ocean Chemistry > Climate Change","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolution","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolved Gases","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolved Inorganic Carbon","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","EARTH SCIENCE > Oceans > Ocean Chemistry > Saturation State","EARTH SCIENCE > Oceans > Ocean Chemistry > pH","EARTH SCIENCE > Oceans > Ocean Circulation","EARTH SCIENCE > Oceans > Ocean Circulation > Water Current Direction","EARTH SCIENCE > Oceans > Ocean Circulation > Water Velocity","EARTH SCIENCE > Oceans > Ocean Temperature > Water Temperature","EARTH SCIENCE > Oceans > Salinity/Density > Conductivity","EARTH SCIENCE > Oceans > Salinity/Density > Density","EARTH SCIENCE > Oceans > Salinity/Density > Salinity","ALKALINITY - TOTAL [total alkalinity]","ARAGONITE SATURATION STATE","CONDUCTIVITY","CURRENT DIRECTION","CURRENT SPEED","DEPTH - SENSOR","DISSOLVED INORGANIC CARBON (DIC)","PRESSURE - WATER [HYDROSTATIC PRESSURE]","SALINITY - BOTTOM WATER","SIGMA-T","WATER TEMPERATURE","pH","partial pressure of carbon dioxide - water","CTD - moored CTD","Coulometer for DIC measurement","pH sensor","titrator","continuous","current measurements","in situ","physical","time series profile","water chemistry","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","US DOC; 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Diel suite surveys are conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD) within coral reef ecosystems across the Pacific Islands Region as part of the NOAA National Coral Reef Monitoring Program (NCRMP). The data provided in this data set are from diel suites deployed at sites in Guam, Aguijan, Maug, and Pagan, Mariana Archipelago in 2017, 2022, and 2025.\n\nDiel suites were deployed on the reef for at least 24 hours to measure in-situ salinity, temperature, pressure, pH, current direction and magnitude, dissolved oxygen, and photosynthetically active radiation (PAR). Seawater samples were also collected for laboratory analyses of dissolved inorganic carbon (DIC) and total alkalinity (TA). Components of the carbonate system--including pH, pCO2 (partial pressure of carbon dioxide), and aragonite saturation state--are calculated from DIC, TA, temperature, salinity and pressure. Each diel suite typically consisted of: 1 SBE-19plus or RBR concerto3 CTD sensor, 1 Nortek Acoustic Doppler Current Profiler (ADCP), 1 Satlantic SeaFET Ocean pH sensor (2017 and 2022) or SAMI-pH sensor (2025), 1 SBE or RBR dissolved oxygen sensor (2022 and 2025), 1 RBR photosynthetically active radiation sensor (2022 and 2025), 1 tilt current meter (2022 only), and up to 9 Programmable Underwater Collectors (PUCs, 2017) or Sub-surface Automated Samplers (SAS, 2022), each of which collected a water sample at set intervals. Instruments deployed, samples collected, and sample intervals are recorded in the summary file enclosed with the data package, and exceptions to the standard diel suite are also noted.","distribution_titles":["ESD_NCRMP_DS_2017_CNMI_SUMMARY","ESD_NCRMP_DS_2022_MARIAN_CTD.csv","ESD_NCRMP_DS_2025_MA_DO.csv","GCMD Keyword Forum Page","Full Metadata Record"],"harvest_record":"https://catalog.data.gov/harvest_record/f34d83a4-fd5e-47e6-82db-1a387cb835e2","harvest_record_raw":"https://catalog.data.gov/harvest_record/f34d83a4-fd5e-47e6-82db-1a387cb835e2/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/f34d83a4-fd5e-47e6-82db-1a387cb835e2/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/54927.xml","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ALKALINITY","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > INORGANIC CARBON","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > OXYGEN","EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PH","EARTH SCIENCE > OCEANS > OCEAN CIRCULATION > OCEAN CURRENTS > SPEED PROFILES","EARTH SCIENCE > OCEANS > OCEAN OPTICS > PHOTOSYNTHETICALLY ACTIVE RADIATION","EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE","EARTH SCIENCE > OCEANS > SALINITY/DENSITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > CONDUCTIVITY","EARTH SCIENCE > OCEANS > SALINITY/DENSITY > OCEAN SALINITY","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > GUAM","OCEAN > PACIFIC OCEAN > WESTERN PACIFIC OCEAN > MICRONESIA > NORTHERN MARIANA ISLANDS","ADCP > Acoustic Doppler Current Profiler","CTD > Conductivity, Temperature, Depth","CURRENT METERS","OXYGEN METERS > OXYGEN METERS","PAR SENSORS > Photosynthetically Active Radiation Sensors","PH METERS > PH METERS","743","National Coral Reef Monitoring Program","Numeric Data Sets > Oceanography","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Chemical","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > In Situ Physical","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Alkalinity","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbon Dioxide > Partial Pressure","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Chemistry Monitoring and Assessment","EARTH SCIENCE > Oceans > Ocean Chemistry > Climate Change","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolution","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolved Gases","EARTH SCIENCE > Oceans > Ocean Chemistry > Dissolved Inorganic Carbon","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","EARTH SCIENCE > Oceans > Ocean Chemistry > Saturation State","EARTH SCIENCE > Oceans > Ocean Chemistry > pH","EARTH SCIENCE > Oceans > Ocean Circulation","EARTH SCIENCE > Oceans > Ocean Circulation > Water Current Direction","EARTH SCIENCE > Oceans > Ocean Circulation > Water Velocity","EARTH SCIENCE > Oceans > Ocean Temperature > Water Temperature","EARTH SCIENCE > Oceans > Salinity/Density > Conductivity","EARTH SCIENCE > Oceans > Salinity/Density > Density","EARTH SCIENCE > Oceans > Salinity/Density > Salinity","ALKALINITY - TOTAL [total alkalinity]","ARAGONITE SATURATION STATE","CONDUCTIVITY","CURRENT DIRECTION","CURRENT SPEED","DEPTH - SENSOR","DISSOLVED INORGANIC CARBON (DIC)","PRESSURE - WATER [HYDROSTATIC PRESSURE]","SALINITY - BOTTOM WATER","SIGMA-T","WATER TEMPERATURE","pH","partial pressure of carbon dioxide - water","CTD - moored CTD","Coulometer for DIC measurement","pH sensor","titrator","continuous","current measurements","in situ","physical","time series profile","water chemistry","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division","COUNTRY/TERRITORY > Northern Mariana Islands > Aguihan > Aguihan Island (Aguijan) (14N145E0006)","COUNTRY/TERRITORY > Northern Mariana Islands > Maug > Maug Island (20N145E0001)","COUNTRY/TERRITORY > Northern Mariana Islands > Pagan > Pagan Island (18N145E0001)","COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Aguihan Island Reefs > Aguihan Island (Aguijan) (14N145E0006)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Maug Island > Maug Island (20N145E0001)","OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Pagan Island > Pagan Island (18N145E0001)","Marianas Trench Marine National Monument","NW Pacific","HI'IALAKAI","RAINIER","SMALL BOAT","ADCP","CRED","CREP","CTD","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","National Coral Reef Monitoring Program","PAR","PIFSC","PUC","Pacific Islands Fisheries Science Center","RAMP","REA","Rapid Ecological Assessment","Reef Assessment and Monitoring Program","SAS","Subsurface Automated Samplers","TCM","diel","dissolved oxygen","photosynthetically active radiation","programmable underwater collector","tilt current meter","water samples","CNMI","Commonwealth of the Northern Mariana Islands","Mariana Archipelago","Marianas","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","Marianas Archipelago"],"last_harvested_date":"2026-09-15T22:39:58.294035","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"5f4f1195-e770-4a2a-8f75-195cd98860ce","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/noaa.png","name":"National Oceanic and Atmospheric Administration, Department of Commerce","organization_type":"Federal Government","slug":"noaa"},"parent_identifier":null,"popularity":0,"publisher":"Pacific Islands Fisheries Science Center","slug":"national-coral-reef-monitoring-program-diel-seawater-carbonate-chemistry-observations-2025","spatial_centroid":{"lat":15.953879999999998,"lon":145.33462},"spatial_shape":{"coordinates":[[[145.7547,13.2412],[145.7547,20.0229],[144.7045,20.0229],[144.7045,13.2412],[145.7547,13.2412]]],"type":"Polygon"},"theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Diel seawater carbonate chemistry observations from a suite of instrumentation deployed at coral reef sites in the Mariana Archipelago in 2017, 2022, and 2025","type":"dataset"},{"_score":9.162546,"_sort":[1789511997221,9.162546,0,"568dfc80-0faf-4183-998b-bf8dc8f7b763"],"dcat":{"@type":"dcat:Dataset","accessLevel":"non-public","contactPoint":{"@type":"vcard:Contact","fn":"Barkley, Hannah C","hasEmail":"mailto:hannah.barkley@noaa.gov"},"describedByType":"application/octet-stream","description":"The calcification rate data described here are derived from Calcification Accretion Units (CAUs) that were deployed and retrieved at long-term climate monitoring sites during NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD) led National Coral Reef Monitoring Program (NCRMP) missions across the Pacific Remote Island Regions in 2014, 2015, 2017, 2018, and 2023 as well as pre-NCRMP missions in 2010, 2011, and 2012. CAUs are PVC settlement plates that facilitate the recruitment and colonization of crustose coralline algae, hard corals, and other reef calcifiers. Laboratory experiments show that CCA and coral calcification rates are strongly correlated with seawater chemistry, and shifts in carbonate chemistry conditions due to ocean acidification could lead to reduced calcification and accretion rates and ecological phase shifts in coral reef communities.\n\nCoral reef calcium carbonate accretion rates can be estimated by measuring the change in weight of the CAUs between deployment and retrieval. Monitoring net accretion over successive deployments allows for the detection of changes in reef calcification rates over time. Five units were deployed on the seafloor at each CAU site for 3 years in 2015 and 5 years in 2018. The number of processed CAUs for a site may be less than the number deployed, either because the units were lost or damaged at sea and therefore not recovered, or in rare instances, due to errors during laboratory processing.\n\nThis study provides information about spatial and temporal patterns of reef carbonate calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher carbon dioxide (CO2) world and on the design of reef management strategies.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0137093","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, Jarvis, Johnston, Kingman, Palmyra) by the PIFSC Ecosystem Sciences Division during pre-NCRMP mission in 2012 (HA1201). These CAU units were deployed during pre-NCRMP missions in 2010 (HA1001). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2012_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0175166","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Wake) by the PIFSC Ecosystem Sciences Division during MARAMP 2017 (HA1701). These CAU units were deployed during MARAMP 2014 (HA1401). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2017_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0231439","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, and Jarvis Islands, Kingman Reef, and Palmyra atolls) by the PIFSC Ecosystem Sciences Division during ASRAMP 2018 (HA1801). These CAU units were deployed during ASRAMP 2015 (HA1501), with the exception of one CAU unit from Jarvis Island that was deployed during pre-NCRMP mission in 2012 (HA1201). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2018_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0159156","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Baker, Howland, Jarvis, Johnston, Kingman, Palmyra) by the PIFSC Ecosystem Sciences Division during ASRAMP 2015 (HA1501). These CAU units were deployed during pre-NCRMP mission in 2012 (HA1201). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2015_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/0159151","describedByType":"application/octet-stream","description":"CAU unit recoveries collected across the Pacific Remote Island Areas (Wake) by the PIFSC Ecosystem Sciences Division during MARAMP 2014 (HA1401). These CAU units were deployed during pre-NCRMP missions in 2011 (HA1101). Data include calcification rates per CAU unit.","mediaType":"text/html","title":"ESD_NCRMP_CAU_2014_PRIAs.csv"},{"@type":"dcat:Distribution","accessURL":"https://accession.nodc.noaa.gov/accession#","describedByType":"application/octet-stream","description":"Quality control report generated for recovered CAU data from sites across the Pacific Remote Island Areas (Baker, Howland) by the PIFSC Ecosystem Sciences Division during ASRAMP 2023 (RA2301).","mediaType":"text/html","title":"ESD_NCRMP_CAU_2023_PRIAs_QC.pdf"},{"@type":"dcat:Distribution","accessURL":"https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords","describedByType":"application/octet-stream","description":"Global Change Master Directory (GCMD). 2026. GCMD Keywords, Version 24. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords","mediaType":"text/html","title":"GCMD Keyword Forum Page"},{"@type":"dcat:Distribution","accessURL":"https://www.fisheries.noaa.gov/inport/item/36069","describedByType":"application/octet-stream","description":"View the complete metadata record on InPort for more information about this dataset.","mediaType":"text/html","title":"Full Metadata Record"}],"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/36069.xml","issued":"2016-01-01T00:00:00.000+00:00","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > BAKER ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HOWLAND ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JARVIS ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JOHNSTON ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > KINGMAN REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > PALMYRA ATOLL","1221","409","587","743","C204 Pacific Reef Assessment and Monitoring Program (Pacific RAMP): Biennial monitoring for the US Pacific Islands and Atolls","National Coral Reef Monitoring Program","Ocean Acidification - Quantification of Calcification and Accretion Rates of Corals and Crustose Coralline Algae across the Pacific Ocean","Pacific Reef Assessment and Monitoring Program: Monitoring coral reef ecosystems of the US Pacific Islands and Atolls","Numeric Data Sets > Calcification Rate","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Growth > Calcification Rate","EARTH SCIENCE > Biosphere > Vegetation > Algae > Calcareous Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment > Calcification Accretion Unit (CAU)","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","CALCIFICATION","in situ","laboratory analyses","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; NOAA; Office of Oceanic and Atmospheric Research; Ocean Acidification Program","US DOC; NOAA; NMFS; Pacific Islands Fisheries Science Center; Ecosystem Sciences Division","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Baker Island (00N176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Howland Island (00S176W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Jarvis Island (00S160W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Johnston Atoll (16N169W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Kingman Reef (06N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Palmyra Atoll (05N162W0001)","COUNTRY/TERRITORY > United States of America > USA Minor Outlying Islands > Wake Atoll (19N167E0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Baker Island > Baker Island (00N176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Howland Island > Howland Island (00S176W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Johnston Atoll > Johnston Atoll (16N169W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Jarvis Island (00S160W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Kingman Reef (06N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Line Islands > Palmyra Atoll (05N162W0001)","OCEAN BASIN > Pacific Ocean > Central Pacific Ocean > Wake Atoll > Wake Atoll (19N167E0001)","Central Pacific Ocean","Equatorial Pacific Ocean","Pacific Remote Islands Marine National Monument","HI'IALAKAI","RAINIER","ASRAMP","American Samoa Reef Assessment and Monitoring Program","CRED","CREP","Calcification Plate","Coral Reef Ecosystem Division","Coral Reef Ecosystem Program","ESD","Ecosystem Sciences Division","NCRMP","Ocean Acidification","PIFSC","Pacific Islands Fisheries Science Center","RAMP","Reef Assessment and Monitoring Program","Settlement Plate","calcification accretion unit","triennial","PRIA","PRIMNM","Pacific Remote Island Areas","Calcification Accretion Unit (CAU)","DOC/NOAA/NMFS/PIFSC > Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","Pacific Remote Island Areas"],"landingPage":"https://www.fisheries.noaa.gov/inport/item/36069","language":[],"license":"https://creativecommons.org/publicdomain/zero/1.0/","modified":"2026-01-01T00:00:00.000+00:00","publisher":{"@type":"org:Organization","name":"Pacific Islands Fisheries Science Center"},"rights":"otherRestrictions, unclassified","spatial":"-159.9788,-0.38235459,-176.6239,16.7633668","temporal":"2012-03-02T00:00:00+00:00/2015-04-27T00:00:00+00:00","theme":["geospatial"],"title":"National Coral Reef Monitoring Program: Calcification Rates of Crustose Coralline Algae Derived from Calcification Accretion Units (CAUs) Deployed across the Pacific Remote Island Areas from 2010 to 2023"},"description":"The calcification rate data described here are derived from Calcification Accretion Units (CAUs) that were deployed and retrieved at long-term climate monitoring sites during NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD) led National Coral Reef Monitoring Program (NCRMP) missions across the Pacific Remote Island Regions in 2014, 2015, 2017, 2018, and 2023 as well as pre-NCRMP missions in 2010, 2011, and 2012. CAUs are PVC settlement plates that facilitate the recruitment and colonization of crustose coralline algae, hard corals, and other reef calcifiers. Laboratory experiments show that CCA and coral calcification rates are strongly correlated with seawater chemistry, and shifts in carbonate chemistry conditions due to ocean acidification could lead to reduced calcification and accretion rates and ecological phase shifts in coral reef communities.\n\nCoral reef calcium carbonate accretion rates can be estimated by measuring the change in weight of the CAUs between deployment and retrieval. Monitoring net accretion over successive deployments allows for the detection of changes in reef calcification rates over time. Five units were deployed on the seafloor at each CAU site for 3 years in 2015 and 5 years in 2018. The number of processed CAUs for a site may be less than the number deployed, either because the units were lost or damaged at sea and therefore not recovered, or in rare instances, due to errors during laboratory processing.\n\nThis study provides information about spatial and temporal patterns of reef carbonate calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher carbon dioxide (CO2) world and on the design of reef management strategies.","distribution_titles":["ESD_NCRMP_CAU_2012_PRIAs.csv","ESD_NCRMP_CAU_2017_PRIAs.csv","ESD_NCRMP_CAU_2018_PRIAs.csv","ESD_NCRMP_CAU_2015_PRIAs.csv","ESD_NCRMP_CAU_2014_PRIAs.csv","ESD_NCRMP_CAU_2023_PRIAs_QC.pdf","GCMD Keyword Forum Page","Full Metadata Record"],"harvest_record":"https://catalog.data.gov/harvest_record/80e31aa3-0ac4-4a0d-aca5-2e9f93408781","harvest_record_raw":"https://catalog.data.gov/harvest_record/80e31aa3-0ac4-4a0d-aca5-2e9f93408781/raw","harvest_record_transformed":"https://catalog.data.gov/harvest_record/80e31aa3-0ac4-4a0d-aca5-2e9f93408781/transformed","has_download":false,"has_spatial":true,"identifier":"https://data.noaa.gov/waf/NOAA/nmfs/pifsc/iso/xml/36069.xml","keyword":["DOC/NOAA/NMFS/PIFSC/ESD > Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC","EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > REEF > CORAL REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > BAKER ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > HOWLAND ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JARVIS ISLAND","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > JOHNSTON ATOLL","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > KINGMAN REEF","OCEAN > PACIFIC OCEAN > CENTRAL PACIFIC OCEAN > PALMYRA ATOLL","1221","409","587","743","C204 Pacific Reef Assessment and Monitoring Program (Pacific RAMP): Biennial monitoring for the US Pacific Islands and Atolls","National Coral Reef Monitoring Program","Ocean Acidification - Quantification of Calcification and Accretion Rates of Corals and Crustose Coralline Algae across the Pacific Ocean","Pacific Reef Assessment and Monitoring Program: Monitoring coral reef ecosystems of the US Pacific Islands and Atolls","Numeric Data Sets > Calcification Rate","EARTH SCIENCE > Biosphere > Aquatic Habitat > Reef Habitat","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover","EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Growth > Calcification Rate","EARTH SCIENCE > Biosphere > Vegetation > Algae > Calcareous Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Fleshy Macroalgae","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment","EARTH SCIENCE > Biosphere > Vegetation > Algae > Reef Monitoring and Assessment > Calcification Accretion Unit (CAU)","EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment","EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs","EARTH SCIENCE > Oceans > Ocean Chemistry > Calcification","EARTH SCIENCE > Oceans > Ocean Chemistry > Carbonate Chemistry","EARTH SCIENCE > Oceans > Ocean Chemistry > Ocean Acidification","CALCIFICATION","in situ","laboratory analyses","CORAL REEF STUDIES","Coral Reef Conservation Program","National Coral Reef Monitoring Program","Pacific Reef and Assessment Monitoring Program","US DOC; 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STOFS-3D-Atl makes use of outputs from the National Water Model (NWM) to include inland hydrology and extreme precipitation effects on coastal flooding, forecast guidance from the NCEP Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) model as the surface meteorological forcing, the combined tidal and subtidal water level, and three-dimensional water temperature, salinity, and currents from the NCEP Global Real-Time Ocean Forecast System (G-RTOFS) as the open ocean boundary forcing.\n\nSTOFS-3D-Pacific is a three-dimensional (3D) model component for the Pacific basin (STOFS-3D-Pac) based on the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) core. STOFS-3D-Pacific runs daily to provide one day of nowcast and two days of water level and surface current forecast guidance. 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A 26-year reanalysis was then conducted using this tool, spanning from 2000 to 2025, with each year being simulated separately but with a one-month overlap between years (i.e. 2001 simulation starts from Dec 1, 2000). 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The BEPS is a minimum threshold of energy performance that will be no lower than the local median?ENERGY STAR?score by property type (or equivalent metric). The standards were created to drive energy performance in existing buildings to help meet the energy and climate goals of the?Sustainable DC?plan \u2014 to reduce greenhouse gas emissions and energy consumption by 50% by 2032. DOEE established the first set of Standards on January 1, 2021. Standards will then be set every 6 years, creating BEPS Periods (BEPS Period 1, BEPS Period 2, etc.). The 2021 Building Energy Performance Standards and a Guide to the 2021 BEPS are available for viewing on DOEE\u2019s website.</span></p><p><span>To improve transparency and help building owners understand how their building performs relative to the BEPS, DOEE is publishing this BEPS Disclosure that compares a building\u2019s benchmarking data with the BEPS and provides an estimate of the building\u2019s distance from the standard and estimated performance requirement.</span></p><p><span>Please note that this dataset is based on information currently available to DOEE using calendar year 2019 benchmarking data provided by the building owner. Some buildings are still being evaluated and therefore have been designated as \u201cUnder Review\u201d in this dataset. Building owners that believe their 2019 calendar year data is incorrect should contact the Benchmarking Help Center (info.benchmark@dc.gov). 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With a look at the alfalfa growth in the region coupled with climate change in an already over allocated region in the semi-arid southern region in Idaho, this research examines the potential vulnerability of Idaho's booming dairy industry to long term drought.<br /><br />A recovered version of this item is available at <a style='background-color:rgb(255, 255, 255); border-width:0px; font-family:Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings:inherit; font-kerning:inherit; font-language-override:inherit; font-size-adjust:inherit; font-size:16px; font-stretch:inherit; font-style:normal; font-variant-alternates:inherit; font-variant-caps:normal; font-variant-east-asian:inherit; font-variant-ligatures:normal; font-variant-numeric:inherit; font-variant-position:inherit; font-weight:400; letter-spacing:normal; line-height:inherit; margin:0px; padding:0px; text-align:start; text-indent:0px; text-transform:none; word-spacing:0px;' target='_blank' href='https://cdil.lib.uidaho.edu/dairy-drought/' rel='nofollow ugc noopener noreferrer'>https://cdil.lib.uidaho.edu/dairy-drought/</a></div><div>&nbsp;</div><div>DOI:&nbsp; 10.7923/yfy7-ay26</div>","distribution":[{"@type":"dcat:Distribution","accessURL":"https://geocatalog-uidaho.hub.arcgis.com/apps/uidaho::idaho-dairy-industrys-vulnerability-to-long-term-drought-","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"},{"@type":"dcat:Distribution","accessURL":"https://uidaho.maps.arcgis.com/apps/Cascade/index.html?appid=545cd13f571b4ca087a3667951f9da44","format":"ArcGIS GeoServices REST API","mediaType":"application/json","title":"ArcGIS GeoService"}],"identifier":"https://www.arcgis.com/home/item.html?id=545cd13f571b4ca087a3667951f9da44","issued":"2018-01-22T20:55:49.000Z","keyword":["Alfalfa","Cascade","Dairy","Idaho","Story Map","Water Rights"],"landingPage":"https://geocatalog-uidaho.hub.arcgis.com/apps/uidaho::idaho-dairy-industrys-vulnerability-to-long-term-drought-","license":"https://creativecommons.org/licenses/by-nc-sa/4.0","modified":"2026-09-11T18:51:37.000Z","publisher":{"name":"University of Idaho"},"spatial":"-115.7440,41.9830,-111.5690,43.3720","theme":["geospatial"],"title":"Idaho Dairy Industry's Vulnerability to Long-Term Drought\u00a0 \u00a0"},"description":"<div>This presentation is a summary of some key findings of water rights and their spatial distribution according to priority date and water use code within the Magic Valley in Idaho.&nbsp; 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Changes in climate are expected to lead to additional impacts in stream habitats and fish assemblages in multiple ways, including changing stream water temperatures.  To manage streams for current impacts and future changes, managers need region-wide information for decision-making and developing proactive management strategies.  Our project met that need by integrating results of a current condition assessment of stream habitats based on fish response to human land use, water quality impairment, and fragmentation by dams with estimates of which stream habitats may change in the future.  Results are available for all streams in the NE CSC region through a spatially-explicit, web-based viewer (FishTail).  With this tool, managers can evaluate how streams of interest are currently impacted by land uses and assess if those habitats may change with climate.  These results, available in a comparable way throughout the NE CSC, provide natural resource managers, decision-makers, and the public with a wealth of information to better protect and conserve stream fishes and their habitats. These data are integrated into a web-based decision support viewer (FishTail): 1) current condition of streams determined from disturbances limiting stream fishes, 2) future conditions resulting from changes in climate, and, 3) changes in water temperature for key locations resulting from climate changes for all streams of the NE CSC region. The report that documents these data is: Daniel, W., N. Sievert, D. Infante, J. Whittier, J. Stewart, C. Paukert, and K. Herreman.  2016.  A decision support mapper for conserving stream fish habitats of the Northeast Climate Science Center region.  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These results, available in a comparable way throughout the NE CSC, provide natural resource managers, decision-makers, and the public with a wealth of information to better protect and conserve stream fishes and their habitats. These data are integrated into a web-based decision support viewer (FishTail): 1) current condition of streams determined from disturbances limiting stream fishes, 2) future conditions resulting from changes in climate, and, 3) changes in water temperature for key locations resulting from climate changes for all streams of the NE CSC region. The report that documents these data is: Daniel, W., N. Sievert, D. Infante, J. Whittier, J. Stewart, C. Paukert, and K. Herreman.  2016.  A decision support mapper for conserving stream fish habitats of the Northeast Climate Science Center region.  Final Report to the US Geological Survey, Northeast Climate Science Center, Amherst, MA.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bc547204-2ae9-4269-8b24-731b9850d5ba","harvest_record_raw":"https://catalog.data.gov/harvest_record/bc547204-2ae9-4269-8b24-731b9850d5ba/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_590a049de4b0fc4e44916012","keyword":["Aquatic habitats","Connecticut","Delaware","Illinois","Indiana","Iowa","Kentucky","Maine","Maryland","Massachusetts","Michigan","Minnesota","Missouri","New Hampshire","New Jersey","New York","Ohio","Pennsylvania","Rhode Island","USGS:590a049de4b0fc4e44916012","Vermont","Virginia","Washington D.C.","West Virginia","Wisconsin","climate change","climatologyMeteorologyAtmosphere","drainage basins","geospatial datasets","habitat fragmentation","inlandWaters","land use and land cover","rivers","streams","water quality"],"last_harvested_date":"2026-09-15T00:24:32.622573","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":"fishtail-indices-and-supporting-data-characterizing-the-current-and-future-risk-to-fish-ha","spatial_centroid":{"lat":41.334142805199996,"lon":-85.131141028},"spatial_shape":{"coordinates":[[[-97.226778032,35.99626825],[-97.226778032,49.340954638],[-66.987685522,49.340954638],[-66.987685522,35.99626825],[-97.226778032,35.99626825]]],"type":"Polygon"},"theme":["geospatial"],"title":"FishTail, Indices and Supporting Data Characterizing the Current and Future Risk to Fish Habitat Degradation in the Northeast Climate Science Center Region","type":"dataset"},{"_score":54.305767,"_sort":[1789430590137,54.305767,0,"4fe85d12-260d-4c3e-a4ac-315cf659a378"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Andrew J. 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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. 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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. 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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/). 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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. 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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":54.946114,"_sort":[1789429232691,54.946114,1,"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 &amp; 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 &amp; 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":55.499126,"_sort":[1789428810763,55.499126,3,"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":66.48119,"_sort":[1789259107358,66.48119,1,"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":15.108765,"_sort":[1789259059539,15.108765,0,"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. 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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). 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":13.333378,"_sort":[1789258624403,13.333378,1,"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.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4cef6f36-ca0c-4031-a1ea-cde9cb143358","harvest_record_raw":"https://catalog.data.gov/harvest_record/4cef6f36-ca0c-4031-a1ea-cde9cb143358/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776cd582ce20f3301009ea","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776cd582ce20f3301009ea","biota"],"last_harvested_date":"2026-09-13T00:17:04.403081","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":"influence-of-stream-temperature-on-eight-populations-of-rio-grande-cutthroat-trout-in-nort","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 Temperature on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":13.281013,"_sort":[1789258594132,13.281013,0,"3a27e67d-a255-4954-a852-5f363262b055"],"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 stream discharge (flow) 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. Discharge (cubic meter/second) was collected throughout the eight populations across the three seasons (summer, fall, spring) for two years.","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.5f773c9182ce20f330100894.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773c9182ce20f330100894","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773c9182ce20f330100894","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 Discharge 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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Two complementary components are included. The first is a monthly drought monitoring record derived from NASA Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Early Run data, spanning January 2000 through February 2026. For each location and each month, the dataset reports observed monthly precipitation, the 25-year climatological median, rolling 1-, 3-, and 6-month cumulative precipitation, quintile-based Rainfall Index classifications (Drier, Dry, Typical, Wet, or Wetter), and Pacific ENSO (El Ni\u00f1o\u2013Southern Oscillation) Applications Climate Center (PEAC) absolute threshold categories. The second component comprises outputs from an automated 7-day precipitation forecasting pipeline driven by NOAA Global Forecast System (GFS) GRIB2 model output at 0.25-degree spatial resolution. For each operational forecast run, the dataset includes daily precipitation forecasts (mm) for all nine study locations, site-specific bar chart graphics, georeferenced animated GIF sequences, combined multi-site visualizations, and processing logs. Forecast alert thresholds of 50 mm per day and 200 mm per 7-day period are applied to identify high-precipitation events. In addition to the data outputs, the dataset includes the complete R scripts used for data acquisition, processing, analysis, and visualization for both components, enabling reproducibility and direct reuse of the operational pipeline. 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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). 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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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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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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_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c214b52f-aa11-4dbd-b868-15da1509fa6e","harvest_record_raw":"https://catalog.data.gov/harvest_record/c214b52f-aa11-4dbd-b868-15da1509fa6e/raw","has_download":true,"has_spatial":true,"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"],"last_harvested_date":"2026-09-12T00:22:43.104874","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-bioclimate-envelope-suitability-for-reptile-species-in-south-central-usa","spatial_centroid":{"lat":32.9988388682,"lon":-110.14612822659998},"spatial_shape":{"coordinates":[[[-112.586128217,31.332172208],[-112.586128217,35.4988388585],[-106.486128241,35.4988388585],[-106.486128241,31.332172208],[-112.586128217,31.332172208]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for reptile species in South Central USA","type":"dataset"},{"_score":48.120537,"_sort":[1789172557554,48.120537,0,"14f8d8a1-b577-4e36-beb9-37d6fe5a582d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Molly Cross","hasEmail":"mailto:casc-data@usgs.gov"},"description":"A workshop was conducted to gain insight into climate change impacts and climate-informed management actions of relevance to a habitat management plan in the North Central region. 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The model focused on the more recent time periods, 0-7500 BP, shows that rapid vegetation change was initiated across these landscapes again recently with reduced rainfall.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/t51y-8s44","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.624b4d80d34e21f827635cf5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_624b4d80d34e21f827635cf5","keyword":["USGS:624b4d80d34e21f827635cf5","atmospheric and climatic processes","climate change","modeling","vegetation"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.5969, 35.2875, -103.2169, 48.9706","theme":["geospatial"],"title":"Climate drivers of rapid ecological change at the landscape scale over the last 21,000 years in the Middle and Southern Rockies, U.S.A."},"description":"These model objects are the outputs of three Boosted Regression Tree models (for three different time periods) to explore the role of climate change and variability in driving ecological change and transformation. 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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. 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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. 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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). 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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_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":58.569336,"_sort":[1789167405556,58.569336,0,"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. They provide some insight into the kinds of climate and weather events that affect wine grape production in the state of Arizona.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/8b455ec2-f091-4737-9f75-bd1d0a603e63","harvest_record_raw":"https://catalog.data.gov/harvest_record/8b455ec2-f091-4737-9f75-bd1d0a603e63/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62e2d417d34e394b65364f3d","keyword":["USGS:62e2d417d34e394b65364f3d","agriculture","biota","botany","climate","pest management","viticulture","weather"],"last_harvested_date":"2026-09-11T22:56:45.556126","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-and-weather-impacts-to-wine-grapes-in-arizona-in-2018-2019-growing-season-as-descr","spatial_centroid":{"lat":33.598,"lon":-112.508},"spatial_shape":{"coordinates":[[[-114.82,31.33],[-114.82,37.0],[-109.04,37.0],[-109.04,31.33],[-114.82,31.33]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate and Weather Impacts to Wine Grapes in Arizona in 2018-2019 growing season as described by growers","type":"dataset"},{"_score":20.217487,"_sort":[1789080653194,20.217487,1,"51f71287-6f47-45c2-a294-54e061b91aac"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"PCMSC Science Data Coordinator","hasEmail":"mailto:pcmsc_data@usgs.gov"},"description":"Simulatations of water levels in the Salish Sea for a continuous hindcast of the period October 1, 1985, to September 30, 2015 were conducted to evaluate the utility and skill of a sea-level anomaly predictor and to develop extreme water level estimates accounting for decadal climate variability. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the model predicts extreme water levels at NOAA tide gage stations within 0.15 m. Model inputs and outputs of time-series water levels along the -5 m depth isobath are presented. 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":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P946SC3L","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.63ac9989d34e92aad3ca1445.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"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"],"modified":"2023-12-11T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-129.1400, 47.0000, -122.1500, 51.5700","theme":["geospatial"],"title":"Salish Sea water level hindcast simulations: 1985-2015"},"description":"Simulatations of water levels in the Salish Sea for a continuous hindcast of the period October 1, 1985, to September 30, 2015 were conducted to evaluate the utility and skill of a sea-level anomaly predictor and to develop extreme water level estimates accounting for decadal climate variability. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the model predicts extreme water levels at NOAA tide gage stations within 0.15 m. Model inputs and outputs of time-series water levels along the -5 m depth isobath are presented. 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":9.078667,"_sort":[1789080651201,9.078667,2,"44910ba5-b6c4-4494-9313-ae768358536c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Ellyn T. Montgomery","hasEmail":"mailto:emontgomery@usgs.gov"},"description":"The oceanographic time series data collected by U.S. Geological Survey scientists and collaborators are\nserved in an online database at http://stellwagen.er.usgs.gov/index.html. These data were collected as\npart of research experiments investigating circulation and sediment transport in the coastal ocean. The\nexperiments (projects, research programs) are typically one month to several years long and have been\ncarried out since 1975. New experiments will be conducted, and the data from them will be added to the\ncollection. As of 2016, all but one of the experiments were conducted in waters abutting the U.S. coast;\nthe exception was conducted in the Adriatic Sea. Measurements acquired vary by site and experiment;\nthey usually include current velocity, wave statistics, water temperature, salinity, pressure, turbidity,\nand light transmission from one or more depths over a time period. The measurements are concentrated\nnear the sea floor but may also include data from the water column.\nThe user interface provides an interactive map, a tabular summary of the experiments, and a separate\npage for each experiment. Each experiment page has documentation and maps that provide details of what\ndata were collected at each site. Links to related publications with additional information about the\nresearch are also provided. The data are stored in Network Common Data Format (netCDF) files using the\nEquatorial Pacific Information Collection (EPIC) conventions defined by the National Oceanic and\nAtmospheric Administration (NOAA) Pacific Marine Environmental Laboratory. NetCDF is a general,\nself-documenting, machine-independent, open source data format created and supported by the University\nCorporation for Atmospheric Research (UCAR). EPIC is an early set of standards designed to allow\nresearchers from different organizations to share oceanographic data. The files may be downloaded or\naccessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP\nframework allows users to access data from anywhere on the Internet using a variety of Web services\nincluding Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data\ncompliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://stellwagen.er.usgs.gov/thredds/catalog/TSdata/catalog.html","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.cd2c4288-56f3-46ec-ab12-734c435bf349.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_cd2c4288-56f3-46ec-ab12-734c435bf349","keyword":["AIR TEMPERATURE","ANEMOMETERS","ATMOSPHERIC PRESSURE MEASUREMENTS","ATTENUATION/TRANSMISSION","Adriatic Sea","Air Temperature","Atlantic Ocean","Average Burst Pressure","BAROMETERS","Barnegat Bay, NJ","Beam Attenuation","Blackwater National Wildlife Refuge, MD","Buzzard's Bay, MA","CMGP","CONDUCTIVITY","CONDUCTIVITY METERS","CONDUCTIVITY, TEMPERATURE, DEPTH","CURRENT METERS","Cape Hatteras, NC","Chandeleur Islands, LA","Chincoteague Bay, MD and VA","Coastal and Marine Geology Program","Current Direction (t)","Current Speed","DENSITY","DODS &gt; DISTRIBUTED OCEANOGRAPHIC DATA SYSTEM","Dauphin Island, LA","Dissolved Oxygen","ESIP &gt; EARTH SCIENCE INFORMATION PARTNERS PROGRAM","Fire Island, NY","GLOBEC &gt; GLOBAL OCEAN ECOSYSTEM DYNAMICS, IGBP","GOMODP &gt; GULF OF MAINE OCEAN DATA PARTNERSHIP","Georges Bank, ME","Gulf of Maine","Gulf of Mexico","Hawaiian waters","Hudson Shelf Valley, NY","IGBP &gt; INTERNATIONAL GEOSPHERE-BIOSPHERE PROGRAMME","INCLINOMETERS","Martha's Vineyard, MA","Massachusetts Bay, MA","Meteorology","Monterrey Bay, CA","North America","OCEAN CURRENTS","OPENDAP &gt; 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. Mugu, CA","Sigma Theta","South Atlantic Bight","THERMISTORS","THERMOMETERS","TRANSMISSOMETERS","Temperature (c)","Turbidity","U.S. Geological Survey","USGS","USGS:cd2c4288-56f3-46ec-ab12-734c435bf349","Vertical Velocity","WATER PRESSURE","WATER TEMPERATURE","WAVE HEIGHT","WAVE SPEED DIRECTION","WHCMSC","WIND VANES","Wave Height","Wave Period","Wind Direction","Wind Speed","Woods Hole Coastal and Marine Science Center","geoscientificInformation","marine geology","ocean sciences","oceans","pH","sedimentology"],"modified":"2021-09-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-160, 16, 13, 44","theme":["geospatial"],"title":"U.S. Geological Survey Oceanographic Time Series Data Collection"},"description":"The oceanographic time series data collected by U.S. Geological Survey scientists and collaborators are\nserved in an online database at http://stellwagen.er.usgs.gov/index.html. These data were collected as\npart of research experiments investigating circulation and sediment transport in the coastal ocean. The\nexperiments (projects, research programs) are typically one month to several years long and have been\ncarried out since 1975. New experiments will be conducted, and the data from them will be added to the\ncollection. As of 2016, all but one of the experiments were conducted in waters abutting the U.S. coast;\nthe exception was conducted in the Adriatic Sea. Measurements acquired vary by site and experiment;\nthey usually include current velocity, wave statistics, water temperature, salinity, pressure, turbidity,\nand light transmission from one or more depths over a time period. The measurements are concentrated\nnear the sea floor but may also include data from the water column.\nThe user interface provides an interactive map, a tabular summary of the experiments, and a separate\npage for each experiment. Each experiment page has documentation and maps that provide details of what\ndata were collected at each site. Links to related publications with additional information about the\nresearch are also provided. The data are stored in Network Common Data Format (netCDF) files using the\nEquatorial Pacific Information Collection (EPIC) conventions defined by the National Oceanic and\nAtmospheric Administration (NOAA) Pacific Marine Environmental Laboratory. NetCDF is a general,\nself-documenting, machine-independent, open source data format created and supported by the University\nCorporation for Atmospheric Research (UCAR). EPIC is an early set of standards designed to allow\nresearchers from different organizations to share oceanographic data. The files may be downloaded or\naccessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP\nframework allows users to access data from anywhere on the Internet using a variety of Web services\nincluding Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data\ncompliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/22897cda-10fc-4860-9e10-5c872c1b8086","harvest_record_raw":"https://catalog.data.gov/harvest_record/22897cda-10fc-4860-9e10-5c872c1b8086/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_cd2c4288-56f3-46ec-ab12-734c435bf349","keyword":["AIR TEMPERATURE","ANEMOMETERS","ATMOSPHERIC PRESSURE MEASUREMENTS","ATTENUATION/TRANSMISSION","Adriatic Sea","Air Temperature","Atlantic Ocean","Average Burst Pressure","BAROMETERS","Barnegat Bay, NJ","Beam Attenuation","Blackwater National Wildlife Refuge, MD","Buzzard's Bay, MA","CMGP","CONDUCTIVITY","CONDUCTIVITY METERS","CONDUCTIVITY, TEMPERATURE, DEPTH","CURRENT METERS","Cape Hatteras, NC","Chandeleur Islands, LA","Chincoteague Bay, MD and VA","Coastal and Marine Geology Program","Current Direction (t)","Current Speed","DENSITY","DODS &gt; DISTRIBUTED OCEANOGRAPHIC DATA SYSTEM","Dauphin Island, LA","Dissolved Oxygen","ESIP &gt; EARTH SCIENCE INFORMATION PARTNERS PROGRAM","Fire Island, NY","GLOBEC &gt; GLOBAL OCEAN ECOSYSTEM DYNAMICS, IGBP","GOMODP &gt; GULF OF MAINE OCEAN DATA PARTNERSHIP","Georges Bank, ME","Gulf of Maine","Gulf of Mexico","Hawaiian waters","Hudson Shelf Valley, NY","IGBP &gt; INTERNATIONAL GEOSPHERE-BIOSPHERE PROGRAMME","INCLINOMETERS","Martha's Vineyard, MA","Massachusetts Bay, MA","Meteorology","Monterrey Bay, CA","North America","OCEAN CURRENTS","OPENDAP &gt; 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. Mugu, CA","Sigma Theta","South Atlantic Bight","THERMISTORS","THERMOMETERS","TRANSMISSOMETERS","Temperature (c)","Turbidity","U.S. Geological Survey","USGS","USGS:cd2c4288-56f3-46ec-ab12-734c435bf349","Vertical Velocity","WATER PRESSURE","WATER TEMPERATURE","WAVE HEIGHT","WAVE SPEED DIRECTION","WHCMSC","WIND VANES","Wave Height","Wave Period","Wind Direction","Wind Speed","Woods Hole Coastal and Marine Science Center","geoscientificInformation","marine geology","ocean sciences","oceans","pH","sedimentology"],"last_harvested_date":"2026-09-10T22:50:51.201828","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":"u-s-geological-survey-oceanographic-time-series-data-collection","spatial_centroid":{"lat":27.2,"lon":-90.8},"spatial_shape":{"coordinates":[[[-160,16],[-160,44],[13,44],[13,16],[-160,16]]],"type":"Polygon"},"theme":["geospatial"],"title":"U.S. Geological Survey Oceanographic Time Series Data Collection","type":"dataset"},{"_score":20.40442,"_sort":[1789080649035,20.40442,2,"45796c70-5520-47f3-b95f-6d0411e385b3"],"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.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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This dataset 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.5d98e0dbe4b0c4f70d1186f3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d98e0dbe4b0c4f70d1186f3","keyword":["US","USGS:5d98e0dbe4b0c4f70d1186f3","United States","WI","Wisconsin","biota","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":"-89.7037723045351, 46.002272195262, -89.6957319045477, 46.0152963952417","theme":["geospatial"],"title":"Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs"},"description":"This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. 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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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Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife\u2019s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd\u2019s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. 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Population numbers peaked in the 1960s, but have declined in recent decades (Ramsey and others, 1981; California Department of Fish and Wildlife unpublished data). These mule deer migrate from a lower elevation winter range in the foothills east of the Sacramento Valley to upper elevation summer ranges in the southern Cascades and northern Sierra Nevada. Although portions of the herd winter on the California Department of Fish and Wildlife\u2019s Tehama Wildlife Area and other public lands, the winter range also comprises many private ranchlands. The herd\u2019s summer range includes significant portions of Lassen National Forest as well as Lassen Volcanic National Park and private timberlands. Primarily oak woodlands and annual grasslands characterize the winter range, while the summer range consists of conifer forests, montane meadows, and montane chaparral. Potential threats to the herd include habitat changes resulting from fire management (including fire suppression and catastrophic wildfires), forest succession, vegetation management, and climate change. A small percentage of the herd are residents, inhabiting areas along the Sacramento River and areas of irrigated agriculture.\nThese mapping layers show the location of the migration routes for mule deer (Odocoileus hemionus) in the East Tehama population in California. They were developed from 63 migration sequences collected from a sample size of 33 animals comprising GPS locations collected every 1-23 hours.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b","harvest_record_raw":"https://catalog.data.gov/harvest_record/acefb6f8-998f-4b8c-9ee0-5be91c64d43b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63650bafd34ebe442507ce71","keyword":["California","Lassen National Forest","USGS:63650bafd34ebe442507ce71","United States","animal behavior","biota","migration (organisms)","migration route","migratory species"],"last_harvested_date":"2026-09-10T22:50:30.380896","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"california-mule-deer-east-tehama-routes","spatial_centroid":{"lat":40.1628,"lon":-121.59854},"spatial_shape":{"coordinates":[[[-122.2265,39.7926],[-122.2265,40.7181],[-120.6566,40.7181],[-120.6566,39.7926],[-122.2265,39.7926]]],"type":"Polygon"},"theme":["geospatial"],"title":"California Mule Deer East Tehama Routes","type":"dataset"},{"_score":19.960413,"_sort":[1789080625632,19.960413,3,"cdc0088e-dc4e-4856-a6fd-3a31b3141478"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Melinda Martinez","hasEmail":"mailto:melindamartinez@usgs.gov"},"description":"Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. There various forms to calculate fluxes using cone and frustrum shapes that were compared to LiDAR scans fromi the field, therefore surface area and volume for each geometric shape is also included in the dataset.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P164M78X","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6647ad62d34e1955f5a4418e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6647ad62d34e1955f5a4418e","keyword":["Arkansas","Illinois","Louisiana","USGS:6647ad62d34e1955f5a4418e","carbon","cypress knees","cypress swamp","environment","freshwater wetlands","methane"],"modified":"2024-06-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.6367, 29.7644, -87.9785, 37.9269","theme":["geospatial"],"title":"Methane emissions associated with bald cypress knees across the Mississippi River Alluvial Valley"},"description":"Data shows CH4 fluxes from the upper portion of cypress knees across various climate and flooding gradients of the North American Baldcypress Swamp Network in the Mississippi River Alluvial Valley. Climate data in the form of temperature, relative humidity, barometric pressure, and precipitation 3-days leading up to sampling date are also included. 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Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, \u201cbase\u201d maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P90Q8BCP","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5de7de8fe4b02caea0eb9917.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917","keyword":["Central Basin and Range","MT","Montana","NV","Nevada","Northern Basin and Range","Northwestern Great Plains","Southern Rockies","USGS:5de7de8fe4b02caea0eb9917","United States","WY","Wyoming","Wyoming Basin","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","farming","fractional components","geoscientificInformation","grass","herbaceous","litter","rangeland","rangelands","remote sensing","sagebrush","shrub","shrubland","shrubland ecosystems","shrublands","terrestrial ecosystems","time-series","validation","vegetation"],"modified":"2020-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-119.9472, 39.7848, -104.2109, 46.8470","theme":["geospatial"],"title":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands"},"description":"Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the \u201cBack-in-Time\u201d (BIT) dataset. 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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. We compare the two datasets, HRS and BIT, in space and time.\nTwo tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad","harvest_record_raw":"https://catalog.data.gov/harvest_record/7bde2c73-e23a-43a1-a93e-16ff8e4185ad/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5de7de8fe4b02caea0eb9917","keyword":["Central Basin and Range","MT","Montana","NV","Nevada","Northern Basin and Range","Northwestern Great Plains","Southern Rockies","USGS:5de7de8fe4b02caea0eb9917","United States","WY","Wyoming","Wyoming Basin","annual herbaceous","back-in-time","bare ground","big sagebrush","biota","farming","fractional components","geoscientificInformation","grass","herbaceous","litter","rangeland","rangelands","remote sensing","sagebrush","shrub","shrubland","shrubland ecosystems","shrublands","terrestrial ecosystems","time-series","validation","vegetation"],"last_harvested_date":"2026-09-10T22:50:25.159524","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":4,"publisher":"U.S. Geological Survey","slug":"temporal-and-spatio-temporal-high-resolution-satellite-data-for-the-validation-of-a-landsa","spatial_centroid":{"lat":42.60968,"lon":-113.65267999999999},"spatial_shape":{"coordinates":[[[-119.9472,39.7848],[-119.9472,46.847],[-104.2109,46.847],[-104.2109,39.7848],[-119.9472,39.7848]]],"type":"Polygon"},"theme":["geospatial"],"title":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands","type":"dataset"}],"sort":"last_harvested_date"}
