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DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the A2 Climate Scenario for the High Plains Aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
Estimates of area and aerial extent of land-use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use land class data represent yearly simulated future land use for the High Plains from 2009 to 2050 These data were developed using the FOREcasting SCEnarios (FORE-SCE) of future land cover model (Sohl and others, 2007; Sohl and Sayler 2008) for two (A2 and B2) of the four Intergovernmental Panel on Climate Change (IPCC) climate scenarios and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcGIS Desktop 10.0.
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DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the A2 Climate Scenario for the High Plains Aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
Estimates of area and aerial extent of land-use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use land class data represent yearly simulated future land use for the High Plains from 2009 to 2050 These data were developed using the FOREcasting SCEnarios (FORE-SCE) of future land cover model (Sohl and others, 2007; Sohl and Sayler 2008) for two (A2 and B2) of the four Intergovernmental Panel on Climate Change (IPCC) climate scenarios and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcGIS Desktop 10.0.
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
공공데이터포털
Estimates of area and aerial extent of land-use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use land class data represent yearly simulated future land use for the High Plains from 2009 to 2050 These data were developed using the FOREcasting SCEnarios (FORE-SCE) of future land cover model (Sohl and others, 2007; Sohl and Sayler 2008) for two (A2 and B2) of the four Intergovernmental Panel on Climate Change (IPCC) climate scenarios and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcGIS Desktop 10.0.
DS-777 Annual Model-Backcasted Land-Use/Land-Cover Rasters from 1949 to 2008 for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
Estimates of land use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use data represent yearly estimated land use for the High Plains from 1949 to 2008. These data were developed using the FOREcasting SCEnarios of future land cover (FORE-SCE) model (Sohl and others, 2007) and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcGIS Desktop 9.3.1.
DS-777 Annual Model-Backcasted Land-Use/Land-Cover Rasters from 1949 to 2008 for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
Estimates of land use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use data represent yearly estimated land use for the High Plains from 1949 to 2008. These data were developed using the FOREcasting SCEnarios of future land cover (FORE-SCE) model (Sohl and others, 2007) and then processed using a Geographic Information System (GIS). The GIS software used to process these data was Environmental Systems Research Institute (ESRI, Inc.) ArcGIS Desktop 9.3.1.
DS-777 Average Annual Precipitation Data, 2000 to 2009, in inches estimated from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
The water-budget components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011).Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.
DS-777 Average Annual Precipitation Data, 2000 to 2009, in inches estimated from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
The water-budget components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011).Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.
Raster dataset of mapped water-level changes in the High Plains aquifer, predevelopment (about 1950) to 2019
공공데이터포털
The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital dataset consists of a raster of water-level changes for the High Plains aquifer, predevelopment (about 1950) to 2019. It was created using water-level measurements from 2,741 wells measured in both the predevelopment period (about 1950) and in 2019, the latest available static water level measured in 2015 to 2018 from 71 wells in New Mexico and using other published information on water-level change in areas with few water-level measurements. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.
Raster dataset of mapped water-level changes in the High Plains aquifer, predevelopment (about 1950) to 2019
공공데이터포털
The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital dataset consists of a raster of water-level changes for the High Plains aquifer, predevelopment (about 1950) to 2019. It was created using water-level measurements from 2,741 wells measured in both the predevelopment period (about 1950) and in 2019, the latest available static water level measured in 2015 to 2018 from 71 wells in New Mexico and using other published information on water-level change in areas with few water-level measurements. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.
Raster dataset of mapped water-level changes in the High Plains aquifer, 2017 to 2019
공공데이터포털
The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This dataset consists of a raster of estimated water-level changes for the High Plains aquifer from pre-irrigation season 2017 to pre-irrigation season 2019. This digital dataset was created using water-level measurements from 7,195 wells measured in both 2017 and 2019. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.
DS-777 Average Annual Precipitation data, 1940 to 1949, in inches estimated from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming
공공데이터포털
The water-budget components geodatabase contains selected data from maps in the, "Selected Approaches to Estimate Water-Budget Components of the High Plains, 1940 through 1949 and 2000 through 2009" report (Stanton and others, 2011).Data were collected and synthesized from existing climate models including the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1994), and the Snow accumulation and ablation model (SNOW-17) (Anderson, 2006), and used in soil-water balance models to compute various components of a water budget. The methodologies used to compute the averages and volumes for the data in this geodatabase are slightly different for different components and models.