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Location Folder Show Tree ROOT ScienceBase Catalog Users Filters Date Range Past year (0) Past month (0) Past week (0) Past day (0) Custom range... To   Search Extensions NetCDF OPeNDAP Service (14) Shapefile (2) Types NetCDF OPeNDAP Service (14) Downloadable (2) Map Service (2) OGC WFS Layer (2) OGC WMS Layer (2) Shapefile (2) OGC WMS Service (1) Contacts Farshid (Contractor) Felfelani U.S. Geological Survey U.S. Geological Survey - ScienceBase Samantha K. Oliver U.S. Geological Survey More... Tag Types Place (7) Theme (7) theme (14) ISO Topic Category (14) dataset (14) service (14) Tag Schemes U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of the United States, and associated areas (Federal Information Processing Standard 5-2): Washington, D. C., NIST (7) none (7) scopecode (14) Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions, Federal Information Processing Standard (FIPS) 10-4, Washington, D.C., National Institute of Standards and Technology (7) ISO 19115 Topic Category (7) URL of ISO Codelist? (14) USGS Thesaurus (3) View Results as: JSON ATOM CSV Browse Folder

Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Red River, climatologyMeteorologyAtmosphere, dataset, downscaling, service JMA Non-Hydrostatic Model (NHM): Puerto Rico and US Virgin Islands Dynamical Downscaled Climate Change Projections The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) is nested inside the Regional Spectral Model (RSM) at 10 km grid resolution which in turn is forced at the lateral boundaries to dynamically downscale two general circulation models (GCMs) that participated in the Coupled Model Intercomparison Project (CMIP5). The downscaled regional climate change projections were developed for two twenty-year timeslices for the high Greenhouse Gas Emission Scenario, RCP8.5. These climate change projections were developed to provide information about climate change for various climate change applications within Puerto Rico and the US Virgin Islands. In particular, the model output parameters were saved in response to... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climatologyMeteorologyAtmosphere, dataset, service, time Data to support water quality modeling efforts in the Delaware River Basin: 2) River and Reservoir Observations This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section contains observations related to the amount and quality of water in the Delaware River Basin. Data from a subset of reservoirs in the basin include observed daily depth-resolved water temperature, water levels, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Observations were compiled from a variety of sources, including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream... Categories: Data; Tags: DE, Delaware, MD, Maryland, NJ, All tags... NY, New Jersey, New York, PA, Pennsylvania, US, United States, environment, inlandWaters, modeling, reservoirs, streams, temperature, water, water quality, water resources, Fewer tags South Central Climate Projections Evaluation Project (C-PrEP) Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climate, climate change, climate projections, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Dynamical Downscaling for the Midwest and Great Lakes Basin Six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) were dynamically downscaled to 25-km grid spacing according to the representative concentration pathway 8.5 (RCP8.5) scenario using the International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (RegCM4), interactively coupled to a 1D lake model to represent the Great Lakes. These GCMs include the Centre National de Recherches Meteorologiques Coupled Global Climate Model Version Five (CNRM-CM5), the Model for Interdisciplinary Research on Climate Version Five (MIROC5), the Institut Pierre Simon Laplace Coupled Model Version Five-Medium Resolution (IPSL-CM5-MR), the Meteorological Research... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Great Lakes, climatologyMeteorologyAtmosphere, dataset, downscaling, service Very fine resolution dynamically downscaled climate data for Hawaii Clouds often come in contact with vegetation (often named fogs) within a certain elevation range on Hawai‘i’s mountains. Propelled by strong winds, cloud droplets are driven onto the stems and leaves of plants where they are deposited. Some of the water that accumulates on the plants in this way drips to the ground, adding additional water over and above the water supplied by rainfall. Prior observations show that the amount of cloud water intercepted by vegetation is substantial, but also quite variable from place to place. It is, therefore, important to create a map for the complex spatial patterns of cloud water interception (CWI) in Hawai‘i. In this project, we propose to create the CWI map at 0.8-km resolution... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Hawaii, Maui, climate, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Data to support water quality modeling efforts in the Delaware River Basin: 1) Spatial data for rivers, reservoirs, and monitoring locations This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites... Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service, Shapefile; Tags: DE, Delaware, MD, Maryland, NJ, All tags... NY, New Jersey, New York, PA, Pennsylvania, US, United States, environment, inlandWaters, modeling, reservoirs, streams, temperature, water, water quality, water resources, Fewer tags 2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30. Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Red River, climatologyMeteorologyAtmosphere, dataset, downscaling, service Dynamical Downscaling for the Midwest and Great Lakes Basin Six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) were dynamically downscaled to 25-km grid spacing according to the representative concentration pathway 8.5 (RCP8.5) scenario using the International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (RegCM4), interactively coupled to a 1D lake model to represent the Great Lakes. These GCMs include the Centre National de Recherches Meteorologiques Coupled Global Climate Model Version Five (CNRM-CM5), the Model for Interdisciplinary Research on Climate Version Five (MIROC5), the Institut Pierre Simon Laplace Coupled Model Version Five-Medium Resolution (IPSL-CM5-MR), the Meteorological Research... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Great Lakes, climatologyMeteorologyAtmosphere, dataset, downscaling, service Data to support water quality modeling efforts in the Delaware River Basin: 3) Model Driver Data This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section includes model drivers such as gridded weather data (NOAA GEFS and GridMET), and the stream network distance matrix for the Delaware River Basin. Additionally, inputs and outputs from an uncalibrated process-based stream temperature model (PRMS-SNTemp) are included. Categories: Data; Tags: DE, Delaware, MD, Maryland, NJ, All tags... NY, New Jersey, New York, PA, Pennsylvania, US, United States, environment, inlandWaters, modeling, reservoirs, streams, temperature, water, water quality, water resources, Fewer tags High-Resolution Precipitation Projections for the South Central U.S. Precipitation-related indicators for seasonal precipiation, extreme precipiation, and drought have been generated for 8701 weather stations covering the entire United States, and for a 198x337 grid (on a resolution of 1/16 th degree) covering the South Central region from the future downscaled projections using the Asynchronous Regional Regression Model. The data covers the period from 1950 to 2100. The high-resolution future projections are statistically downscaled from simulations by 12 global climate models from the Coupled Model Intercomparison Project phase 5 (ACCESS1-0, ACCESS1-3, CCSM4, CMCC-CM, CNRM-CM5, CSIRO-Mk3.6.0, MPI-ESM-LR, HadGEM2-CC, INMCM4, IPSL-CM5A-LR, MIROC5 and MRI-CGCM under the lower Representative... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climatologyMeteorologyAtmosphere, dataset, drought, extreme precipitation, high-resolution precipitation projections, All tags... service, Fewer tags JMA Non-Hydrostatic Model (NHM): Puerto Rico and US Virgin Islands Dynamical Downscaled Climate Change Projections The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) is nested inside the Regional Spectral Model (RSM) at 10 km grid resolution which in turn is forced at the lateral boundaries to dynamically downscale two general circulation models (GCMs) that participated in the Coupled Model Intercomparison Project (CMIP5). The downscaled regional climate change projections were developed for two twenty-year timeslices for the high Greenhouse Gas Emission Scenario, RCP8.5. These climate change projections were developed to provide information about climate change for various climate change applications within Puerto Rico and the US Virgin Islands. In particular, the model output parameters were saved in response to... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climatologyMeteorologyAtmosphere, dataset, service, time Gridded 20km Daily Reference Evapotranspiration for the State of Alaska from 1979 to 2017 Reference evapotranspiration (ET0), like potential evapotranspiration, is a measure of atmospheric evaporative demand. It was used in the context of this study to evaluate drought conditions that can lead to wildfire activity in Alaska using the Evaporative Demand Drought Index (EDDI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The ET0 data are on a 20km grid with daily temporal resolution and were computed using the meteorological inputs from the dynamically downscaled ERA-Interim reanalysis and two global climate model projections (CCSM4 and GFDL-CM3). The model projections are from CMIP5 and use the RCP8.5 scenario. The dynamically downscaled data are available at https://registry.opendata.aws/wrf-alaska-snap/.... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Alaska, climatologyMeteorologyAtmosphere, dataset, drought, reference evapotranspiration, All tags... service, wildfire, Fewer tags High-Resolution Precipitation Projections for the South Central U.S. Precipitation-related indicators for seasonal precipiation, extreme precipiation, and drought have been generated for 8701 weather stations covering the entire United States, and for a 198x337 grid (on a resolution of 1/16 th degree) covering the South Central region from the future downscaled projections using the Asynchronous Regional Regression Model. The data covers the period from 1950 to 2100. The high-resolution future projections are statistically downscaled from simulations by 12 global climate models from the Coupled Model Intercomparison Project phase 5 (ACCESS1-0, ACCESS1-3, CCSM4, CMCC-CM, CNRM-CM5, CSIRO-Mk3.6.0, MPI-ESM-LR, HadGEM2-CC, INMCM4, IPSL-CM5A-LR, MIROC5 and MRI-CGCM under the lower Representative... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climatologyMeteorologyAtmosphere, dataset, drought, extreme precipitation, high-resolution precipitation projections, All tags... service, Fewer tags South Central Climate Projections Evaluation Project (C-PrEP) Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: climate, climate change, climate projections, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags Gridded 20km Daily Reference Evapotranspiration for the State of Alaska from 1979 to 2017 Reference evapotranspiration (ET0), like potential evapotranspiration, is a measure of atmospheric evaporative demand. It was used in the context of this study to evaluate drought conditions that can lead to wildfire activity in Alaska using the Evaporative Demand Drought Index (EDDI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The ET0 data are on a 20km grid with daily temporal resolution and were computed using the meteorological inputs from the dynamically downscaled ERA-Interim reanalysis and two global climate model projections (CCSM4 and GFDL-CM3). The model projections are from CMIP5 and use the RCP8.5 scenario. The dynamically downscaled data are available at https://registry.opendata.aws/wrf-alaska-snap/.... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Alaska, climatologyMeteorologyAtmosphere, dataset, drought, reference evapotranspiration, All tags... service, wildfire, Fewer tags Very fine resolution dynamically downscaled climate data for Hawaii Clouds often come in contact with vegetation (often named fogs) within a certain elevation range on Hawai‘i’s mountains. Propelled by strong winds, cloud droplets are driven onto the stems and leaves of plants where they are deposited. Some of the water that accumulates on the plants in this way drips to the ground, adding additional water over and above the water supplied by rainfall. Prior observations show that the amount of cloud water intercepted by vegetation is substantial, but also quite variable from place to place. It is, therefore, important to create a map for the complex spatial patterns of cloud water interception (CWI) in Hawai‘i. In this project, we propose to create the CWI map at 0.8-km resolution... Categories: Data; Types: NetCDF OPeNDAP Service; Tags: Hawaii, Maui, climate, climatologyMeteorologyAtmosphere, dataset, All tags... downscaling, service, Fewer tags 1 Site Information: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. A table of basin attributes is also supplied. Attributes, observations, and weather forcing data for these basins were used to train and test the stream temperature prediction models of Rahmani et al. (2021b).<\p> Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 3 Model Forcings: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge. Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 1 2 »