This EnviroAtlas dataset contains annual (2002) simulated estimations of edge-of-field agricultural nitrogen (N) and phosphorus (P) lost in surface runoff, subsurface flow (tile and non-tile) and percolate, N and P attached to eroding soil (sediment loss) and associated surface, subsurface and vertical water flow and surface soil erosion. The dataset was generated using Weather Research Forecast (WRF) modeled weather, Community Multi-Scale Air Quality (CMAQ) model deposition and the Environmental Policy Integrated Climate (EPIC) model as implemented under the Fertilizer Emission Scenario Tool for CMAQ (FEST-C), all run for 12-km rectangular grids across the continental US. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains data on Agricultural Land Coverage on Hydric Soils for each Watershed Boundary Dataset (WBD) 12-Digit Hydrologic Unit Code (HUC-12) of the conterminous United States that is considered to be both hydric and agricultural based on the December 30, 2009 Natural Resources Conservation Service Soil Survey Geographic Database (NRCS SSURGO), the 2006 National Land Cover Database (NLCD), and the USDA's 2010 Cropland Data Layer (CDL). Because these data are based on SSURGO map units' percent hydric there are four thresholds used to classify if a particular map unit would be hydric. These thresholds are 20%, 40%, 60% and 80%, where a map unit area that had a value of 42% for the SSURGO hydric measurement (based on the raster dataset named "sum_comppct_r2_hydric") would be considered non-hydric under the 60% threshold but hydric under both the 20% and 40% thresholds. For this dataset, the threshold of 80% is used. This dataset was produced by Tetra Tech, Inc. to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas national dataset shows the average annual soil loss and sediment yield to waterbodies by 12-digit HUC subwatershed for the conterminous United States for 2011 with existing land use / land cover and under a scenario in which natural vegetation is removed. It also includes the soil loss and sediment yield prevented by natural vegetation, calculated as the difference between soil loss or sediment yield with existing land cover and under the vegetation removal scenario. This dataset is based on a collection of six rasters showing runoff, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas national dataset shows the average annual soil loss and sediment yield to waterbodies by 12-digit HUC subwatershed for the conterminous United States for 2011 with existing land use / land cover and under a scenario in which natural vegetation is removed. It also includes the soil loss and sediment yield prevented by natural vegetation, calculated as the difference between soil loss or sediment yield with existing land cover and under the vegetation removal scenario. This dataset is based on a collection of six rasters showing runoff, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas national map displays the mean phosphorus (P) balance between inorganic fertilizer and confined manure inputs and P crop removal on croplands in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on International Plant Nutrition Institute compilations of county-level fertilizer sales data, confined manure production, and major crop harvest and P content of these crops, as well as the cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas national map displays the mean phosphorus (P) balance between inorganic fertilizer and confined manure inputs and P crop removal on croplands in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on International Plant Nutrition Institute compilations of county-level fertilizer sales data, confined manure production, and major crop harvest and P content of these crops, as well as the cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Percentage Potential Wetland Area by HUC-12 on Cultivated Cropland (Pct_PWAC) layer shows areas where conditions may be suitable for wetland restoration or creation on cropland at a 10-m resolution. Since the 1600's, an estimated 53% of wetlands in the Conterminous United States have been lost, with many areas being converted for agricultural or urban use. The ecosystems services provided by wetlands are extremely valuable, providing flood attenuation, water filtration, nutrient sequestration, vital habitat, and many others. Wetland restoration or creation can help restore these benefits for the surrounding community. There are several government and community projects that can utilize these data to assist in site selection for wetland restoration projects. This layer was created using the Random Forest (RF) machine learning algorithm in Google Earth Engine (GEE). The RF model utilized 17 data inputs to identify areas where attributes on the landscape are similar to the attributes found in existing wetlands. The input data for this layer fall into three categories: topographic variables, soils, and satellite imagery. Topographic - DEM's sourced from USGS 3D Elevation Program (10-m) -Elevation -Aspect -Slope -Compound Topographic Index (CTI) -Vertical Overland Flow Distance (VOFD) -Horizontal Overland Flow Distance (HOFD) -Pythagoras Overland Flow Distance (POFD) -Soils - Natural Resource Conservation Service's gNATSGO and gSSURGO products · Potential Wetland Soils (PWS) -European Space Agency's Sentinel-1 Synthetic Aperture Radar (10-m) Using these variables, the Random Forest model was run for each 2 digit Hydrologic Unit Code (HUC) in Google Earth Engine. The model used wetlands from the National Wetland Inventory (NWI) to create training data, masking out deep water areas such as the centers of lakes and rivers, and excluding estuarine and marine wetlands. For each HUC an equal number of wetland and non-wetland training points proportional to the size of the HUC were generated, with 30% of those points being reserved for accuracy assessment. The model results were then summarized to calculate the areal coverage of PWA within each HUC-12 watershed in the United States. This dataset shows the percentage of each 12-digit HUC that is both potential wetland area and cropland. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Percentage Potential Wetland Area by HUC-12 on Cultivated Cropland (Pct_PWAC) layer shows areas where conditions may be suitable for wetland restoration or creation on cropland at a 10-m resolution. Since the 1600's, an estimated 53% of wetlands in the Conterminous United States have been lost, with many areas being converted for agricultural or urban use. The ecosystems services provided by wetlands are extremely valuable, providing flood attenuation, water filtration, nutrient sequestration, vital habitat, and many others. Wetland restoration or creation can help restore these benefits for the surrounding community. There are several government and community projects that can utilize these data to assist in site selection for wetland restoration projects. This layer was created using the Random Forest (RF) machine learning algorithm in Google Earth Engine (GEE). The RF model utilized 17 data inputs to identify areas where attributes on the landscape are similar to the attributes found in existing wetlands. The input data for this layer fall into three categories: topographic variables, soils, and satellite imagery. Topographic - DEM's sourced from USGS 3D Elevation Program (10-m) -Elevation -Aspect -Slope -Compound Topographic Index (CTI) -Vertical Overland Flow Distance (VOFD) -Horizontal Overland Flow Distance (HOFD) -Pythagoras Overland Flow Distance (POFD) -Soils - Natural Resource Conservation Service's gNATSGO and gSSURGO products · Potential Wetland Soils (PWS) -European Space Agency's Sentinel-1 Synthetic Aperture Radar (10-m) Using these variables, the Random Forest model was run for each 2 digit Hydrologic Unit Code (HUC) in Google Earth Engine. The model used wetlands from the National Wetland Inventory (NWI) to create training data, masking out deep water areas such as the centers of lakes and rivers, and excluding estuarine and marine wetlands. For each HUC an equal number of wetland and non-wetland training points proportional to the size of the HUC were generated, with 30% of those points being reserved for accuracy assessment. The model results were then summarized to calculate the areal coverage of PWA within each HUC-12 watershed in the United States. This dataset shows the percentage of each 12-digit HUC that is both potential wetland area and cropland. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).