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This dataset was produced by the US Geological Survey as a supporting dataset to be used for the purpose of calculating stream gage basin characteristics in preparation for the South Carolina StreamStats application. This integer raster dataset represents runoff curve numbers for the combinations of hydrological soils groupings and land cover types within the South Carolina StreamStats study area. Soils data are from the USDA, NRCS SSURGO soils database and land cover data are USGS 2019 NLCD data. The dataset will be used in peak flow regression equations that are used to predict flow in South Carolina streams. The StreamStats application provides access to spatial analytical tools that are useful for water-resources...
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To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal land cover derived from MODIS-based IGBP land cover types compiled using Google Earth Engine (GEE).
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These rasters represent output from the Boreal ALFRESCO (Alaska Frame Based Ecosystem Code) model. Boreal ALFRESCO operates on an annual time step, in a landscape composed of 1 x 1 km pixels, a scale appropriate for interfacing with mesoscale climate and carbon models. The last four digits of the file name specifies the year represented by the raster. For example a file named Age_years_historical_1990.tif represents the year 1990. Cell values represent the age of vegetation in years since last fire, with zero (0) indicating burned area in that year. Coverage of this dataset includes much of the state of Alaska (but does exclude Southeastern AK, Kodiak Island, portions of the Alaska Peninsula, and the Aleutian Islands)....
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Potential Evapotranspiration (PET): These data represent decadal mean totals of potential evapotranspiration estimates (mm). The file name specifies the decade the raster represents. For example, a file named pet_mean_mm_decadal_CCCMA_CGCM31_A1B_annual_2000-2009.tif represents the decade spanning 2000-2009. The data were generated by using the Hamon equation and output from CCCMA (also CGCM3.1), a third generation coupled global climate model created by the Canadian Centre for Climate Modeling and Analysis. Data are at 2km x 2km resolution, and all data are stored in geotiffs. Calculations were performed using R 2.12.1 and 2.12.2 for Mac OS Leopard, and data were formatted into geotiffs using the raster and rgdal...
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Potential Evapotranspiration (PET): These data represent decadal mean totals of potential evapotranspiration estimates (mm). The file name specifies the decade the raster represents. For example, a file named pet_mean_mm_decadal_MPI_ECHAM5_A1B_annual_2000-2009.tif represents the decade spanning 2000-2009. The data were generated by using the Hamon equation and output from ECHAM5, a fifth generation general circulation model created by the Max Planck Institute for Meteorology in Hamburg Germany. Data are at 2km x 2km resolution, and all data are stored in geotiffs. Calculations were performed using R 2.12.1 and 2.12.2 for Mac OS Leopard, and data were formatted into geotiffs using the raster and rgdal packages. Users...
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Potential Evapotranspiration (PET): These data represent decadal mean totals of potential evapotranspiration estimates (mm). The file name specifies the decade the raster represents. For example, a file named pet_mean_mm_decadal_CRU_Historical_annual_1930-1939.tif represents the decade spanning 1930-1939. The data were generated by using the Hamon equation and output from a statistically downscaled version of the Hadley Centre’s CRU TS3.0 observational dataset. Data are at 2km x 2km resolution, and all data are stored in geotiffs. Calculations were performed using R 2.12.1 and 2.12.2 for Mac OS Leopard, and data were formatted into geotiffs using the raster and rgdal packages. Users are reminded that the PET estimates...
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This raster, created in 2010, is output from the Geophysical Institute Permafrost Lab (GIPL) model and represents simulated active layer thickness (ALT) in meters averaged across a decade. The file name specifies the decade the raster represents. For example, a file named ALT_1980_1989.tif represents the decade spanning 1980-1989. Cell values represent simulated maximum depth (in meters) of thaw penetration (for areas with permafrost) or frost penetration (for areas without permafrost). If the value of the cell is positive, the area is underlain by permafrost and the cell value specifies the depth of the seasonally thawing layer above permafrost. If the value of the cell is negative, the ground is only seasonally...
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These raster datasets represent historical stand age. The last four digits of the file name specifies the year represented by the raster. For example a file named Age_years_historical_1990.tif represents the year 1990. Cell values represent the age of vegetation in years since last fire, with zero (0) indicating burned area in that year. Files from years 1860-2006 use a variety of historical datasets for Boreal ALFRESCO model spin up and calibration to most closely match historical wildfire dynamics.
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We mosaicked twelve LandSat-8 OLI satellite images taken during the summer of 2014, which were used in an object based image analysis (OBIA) to classify the landscape. We mapped seventeen of the most dominant geomorphic land cover classes on the ACP: (1) Coastal saline waters, (2) Large lakes, (3) Medium lakes, (4) Small lakes, (5) Ponds, (6) Rivers, (7) Meadows, (8) Coalescent low-center polygons, (9) Low-center polygons, (10) Flat-center polygons, (11) High-center polygons, (12) Drained slope, (13) Sandy barrens, (14) Sand dunes, (15) Riparian shrub, (16) Ice, and (17) Urban (i.e. towns and roads). Mapped products were validated with an array of oblique aerial/ground based photography (Jorgenson et al., 2011)...
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This raster, created in 2010, is output from the Geophysical Institute Permafrost Lab (GIPL) model and represents simulated mean annual ground temperature (MAGT) in Celsius, averaged across a decade, at the base of active layer or at the base of the seasonally frozen soil column. The file name specifies the decade the raster represents. For example, a file named MAGT_1980_1989.tif represents the decade spanning 1980-1989. Cell values represent simulated mean annual ground temperature (degree C) at the base of the active layer (for areas with permafrost) or at the base of the soil column that is seasonally frozen (for areas without permafrost). If the value of the cell is negative,the area has permafrost and the...
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This pilot project has initiated a long-term integrated modeling project that aims todevelop a dynamically linked model framework focused on climate driven changes tovegetation, disturbance, hydrology, and permafrost, and their interactions and feedbacks.This pilot phase has developed a conceptual framework for linking current state-of-thesciencemodels of ecosystem processes in Alaska – ALFRESCO, TEM, GIPL-1 – and theprimary processes of vegetation, disturbance, hydrology, and permafrost that theysimulate. A framework that dynamically links these models has been defined and primaryinput datasets required by the models have been developed.
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The Missouri Resource Assessment Partnership (MoRAP) of the University of Missouri, in conjunction with the Oklahoma Biological Survey of the University of Oklahoma, produced a vegetation and landcover GIS data layer for the eastern portions of Oklahoma. This effort was accomplished with direction and funding from the Oklahoma Department of Wildlife Conservation and state and federal partners (particularly the Gulf Coast Prairie and Great Plains Landscape Conservation Cooperatives of the U. S. Fish and Wildlife Service). The legend for the layer is based on NatureServe’s Ecological System Classification, with finer thematic units derived from land cover and abiotic modifiers of the System unit. Data for development...
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The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
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Rangelands have immense inherent spatial and temporal variability, yet assessments of land condition and trends are often assessed relative to the condition of a limited number of representative points. Ecological Potential (EP) data are spatially comprehensive, quantitative, and needed as a baseline for comparison of current rangeland vegetation conditions, trends, and management targets. We define EP as the potential fractional cover of vegetation components bare ground, herbaceous, litter, shrub, and sagebrush represented in the least disturbed and most productive portion of the western U.S. This dataset enables: 1) setting realistic expectations for restoration and management targets at 30-meter resolution,...
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Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Acadia National Park, ArcGIS Pro, Arcpy, Autoclassification, Automation, All tags...
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Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Acadia National Park, ArcGIS Pro, Arcpy, Autoclassification, Automation, All tags...
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As part of the next generation NLCD 2016 mapping process, the NLCD research team developed a suite of intermediate products that were used to generate the final NLCD Land Cover products. Some of those products also have value as independent products and are provided here. Please read the product descriptions to understand what the product represents. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.
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This layer represents historic fire perimeters within 50km of the Crown of the Continent Ecosystem (CCE) from 2014 to 2015 within only Alberta and British Columbia. This dataset was developed by the Crown Managers Partnership, as part of a transboundary collaborative management initiative for the Crown of the Continent Ecosystem, based on commonly identified management priorities that are relevant at the landscape scale. The CMP is collaborative group of land managers, scientists, and stakeholder in the CCE. For more information on the CMP and its collaborators, programs, and projects please visit: http://crownmanagers.org/. This dataset was first published in May 2016. Note: There was not any public data available...
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This layer represents historic fire perimeters within 50km of the Crown of the Continent Ecosystem (CCE) from 1931 to 2013. This dataset was developed by the Crown Managers Partnership, as part of a transboundary collaborative management initiative for the Crown of the Continent Ecosystem, based on commonly identified management priorities that are relevant at the landscape scale. The CMP is collaborative group of land managers, scientists, and stakeholder in the CCE. For more information on the CMP and its collaborators, programs, and projects please visit: http://crownmanagers.org/. This dataset was first published in May 2016.Note: There was not any publically comparable data available for the fire cause for...
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This U.S. Geological Survey (USGS) metadata release consists of 17 different spatial layers in GeoTIFF format. They are: 1) average water capacity (AWC.zip), 2) percent sand (Sand.zip), 3) percent silt (Silt.zip), 4) percent clay (Clay.zip), 5) soil texture (TEXT_PRMS.zip), 6) land use/land cover (LULC.zip), 7) snow values (Snow.zip), 8) summer rain values (SRain.zip), 9) winter rain values (WRain.zip), 10) leaf presence values (keep.zip), 11) leaf loss values (loss.zip), 12) percent tree canopy (CNPY.zip), 13) percent impervious surface (Imperv.zip), 14) snow depletion curve numbers (Snow.zip), 15) rooting depth (RootDepth.zip), 16) permeability values (Lithology_exp_Konly_Project.zip), and 17) water bodies. All...


map background search result map search result map Fire History in the Crown of the Continent Fires of the Crown of the Continent within Alberta and British Columbia (2014-2015) Oklahoma Ecological Systems Mapping - Phase 1 dataset Alaskan Arctic Coastal Plain Polygonal Geomorphology Map Integrated Ecosystem Model Reports Mean Annual Ground Temperature 2060-2069 Stand Age Projections 2080-2089 Active Layer Thickness 1990-1999 Potential Evapotranspiration 1920-1929: CRU Historical Dataset Potential Evapotranspiration 2020-2029: ECHAM5 - A1B Scenario Potential Evapotranspiration 2000-2009: CCCMA - A1B Scenario Historical Stand Age 1960-1969 National Land Cover Database (NLCD) 2016 Land Cover Science Product Data Layers for the National Hydrologic Model, version 1.1 Ecological Potential Fractional Component Cover Based on Long-Term Satellite Observations Across the Western United States National Land Cover Database (NLCD) 2019 Land Cover Science Product (ver. 2.0, June 2021) Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood Gridded South Carolina StreamStats Runoff Curve Numbers by NLCD Landcover and SSURGO Soils Class Fires of the Crown of the Continent within Alberta and British Columbia (2014-2015) Gridded South Carolina StreamStats Runoff Curve Numbers by NLCD Landcover and SSURGO Soils Class Oklahoma Ecological Systems Mapping - Phase 1 dataset Fire History in the Crown of the Continent Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood Alaskan Arctic Coastal Plain Polygonal Geomorphology Map Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards Ecological Potential Fractional Component Cover Based on Long-Term Satellite Observations Across the Western United States Integrated Ecosystem Model Reports Mean Annual Ground Temperature 2060-2069 Stand Age Projections 2080-2089 Active Layer Thickness 1990-1999 Potential Evapotranspiration 1920-1929: CRU Historical Dataset Potential Evapotranspiration 2020-2029: ECHAM5 - A1B Scenario Potential Evapotranspiration 2000-2009: CCCMA - A1B Scenario Historical Stand Age 1960-1969 National Land Cover Database (NLCD) 2016 Land Cover Science Product National Land Cover Database (NLCD) 2019 Land Cover Science Product (ver. 2.0, June 2021) Data Layers for the National Hydrologic Model, version 1.1