e5453290-5f03-4417-97d4-0dc486bc0c84 eng; USA utf8 dataset EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development - Center for Public Health and Environmental Assessment (CPHEA), EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact 2025-06-30 ISO 19115 Geographic Information - Metadata ISO 19115:2003/Cor.1:2006(E) EnviroAtlas - Average Annual Sediment Yield to the Nearest Waterbody Avoided due to Vegetation (Scaled) for the Conterminous United States 2025-04-24 publication U.S. EPA Office of Research and Development (ORD) - Center for Public Health and Environmental Assessment (CPHEA) Research Triangle Park NC publisher U.S. Environmental Protection Agency (US EPA) originator This EnviroAtlas national dataset shows the average annual sediment yield in metric tons per hectare*10,000 to the nearest waterbody from each pixel avoided due to natural vegetation for the conterminous United States for 2011. It is the difference between sediment yield with existing land use / land cover and under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. 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 dataset can be used to assess threats to the ecosystem services provided by soils, such as carbon sequestration, moderating floods, and growing food. This dataset can also be used to study the effects of erosion on streams and water bodies, and to analyze the ability of vegetation to reduce erosion. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation. completed EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development - Center for Public Health and Environmental Assessment (CPHEA), EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact asNeeded Ecosystem Environment theme EPA GIS Keyword Thesaurus Ecosystem Services EnviroAtlas Environment Environmental Atlas theme EnviroAtlas Ecosystem Services EnviroAtlas Environment Environmental Atlas Land Soils Surface Water Water theme User Alabama Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas United States Utah Vermont Virginia Washington Washington DC West Virginia Wisconsin Wyoming place None https://edg.epa.gov/EPA_Data_License.html otherRestrictions Use Constraints: None. Please check sources, scale, accuracy, currency and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgment of the EPA would be appreciated. otherRestrictions Access Constraints: None PUBLIC https://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment -128.028392 -65.203364 22.734889 51.677360 2011-01-01 true Entity and Attribute Information Entity and Attribute Overview: Average annual soil loss (A) was calculated for two scenarios - one using current land cover and one in which natural vegetation has been removed - using the RUSLE equation (A = R x K x LS x C x P), where A is the average annual soil loss (Mg/ha/yr), R is the annual rainfall-runoff erosivity factor (MJ mm/ha/h/yr), K is the soil erodibility factor (Mg/ha hr ha/MJ/mm), LS is the slope length and steepness factor (dimensionless), C is the cover-management factor (dimensionless). For this analysis, P was not included. The sediment delivery ratio was also estimated for both scenarios using flow path connectivity; this was used to calculate estimated net sediment yield to streams and water bodies. These two scenarios were compared to estimate sediment retention by natural vegetative cover. Soil loss and sediment yield under the vegetation removal scenario were subtracted from soil loss and sediment yield under the current scenario to estimate the soil loss and sediment yield avoided due to natural vegetation. Please see the processing steps for a detailed description. A comprehensive list of metric creation steps is included in the Processing Steps. Citations: Borselli, L., Cassi, P., Torri, D., 2008. Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. Catena 75, 268-277. https://doi.org/10.1016/j.catena.2008.07.006. Cavalli, M., Trevisani, S., Comiti, F., Marchi, L., 2013. Geomorphometric assessment of spatial sediment connectivity in small alpine catchments. Geomorphology 188, 31-41. https://doi.org/10.1016/j.geomorph.2012.05.007. Cavalli, M., Crema, S., Marchi, L., 2014. Guidelines on the Sediment Connectivity ArcGIS Toolbox and Stand-alone Application (no Release 1.0). pp. 1-33. Conrad, O., Bechtel, B., Bock,M., Dietrich, H., Fischer, E., Gerlitz, L.,Wehberg, J.,Wichmann, V., Böhner, J., 2015. System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geosci. Model Dev. 8, 1991-2007. https://doi.org/10.5194/gmd-8-1991-2015. Daly, C., Taylor, G.H., 2002. Development of New Spatial Grids of R-Factor and 10-Yr EI30 for the Conterminous United States, Internal EPA Report, NERL-LV. Spatial Climate Analysis Service, Oregon State University, Corvallis, OR. Desmet, P.J.J., Govers, G., 1996. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J. Soil Water Conserv. 51, 427-433. Foster, G. R., D. K. McCool, K. G. Renard, and W. C. Moldenhauer, 1981. Conversion of the universal soil loss equation to SI metric units. J. Soil Water Conserv. 36, 355-359. https://www.engr.colostate.edu/~pierre/ce_old/Projects/linkfiles/USLE%20Unit%20conversions.pdf Hamel, P., Chaplin-Kramer, R., Sim, S., Mueller, C., 2015. A new approach to modeling the sediment retention service (InVEST 3.0): case study of the Cape Fear catchment, North Carolina, USA. Sci. Total Environ. 524, 166-177. https://doi.org/10.1016/j.scitotenv.2015.04.027. Hamel, P., Falinski, K., Sharp, R., Auerbach, D.A., Sánchez-Canales, M., Dennedy-Frank, P.J., 2017. Sediment delivery modeling in practice: comparing the effects of watershed characteristics and data resolution across hydroclimatic regions. Sci. Total Environ. 580, 1381-1388. https://doi.org/10.1016/j.scitotenv.2016.12.103. Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). United States Department of Agriculture. Washington, DC. Tarboton, D.G., 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour. Res. 33, 309-319. https://doi.org/10.1029/96WR03137. Van der Knijff, J.M., Jones, R.J.A., Montanarella, L., 2000. Soil Erosion Risk: Assessment in Europe. European Soil Bureau. Vigiak, O., Borselli, L., Newham, L.T.H., McInnes, J., Roberts, A.M., 2012. Comparison of conceptual landscape metrics to define hillslope-scale sediment delivery ratio. Geomorphology 138, 74-88. https://doi.org/10.1016/j.geomorph.2011.08.026. Entity and Attribute Detail Citation: https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development - Center for Public Health and Environmental Assessment (CPHEA), EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas distributor https://enviroatlas.epa.gov/download/SYLD_avoided_MgHa_1e4.zip download Download The URL providing direct access to the downloadable dataset. download https://enviroatlas.epa.gov/arcgis/rest/services/Rasters/SedimentYield_AvoidedDueToNaturalVeg/ImageServer ESRI:ArcGIS API The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). webMapService https://www.epa.gov/enviroatlas/enviroatlas-data information Documentation URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. information https://www.epa.gov/enviroatlas information Home Page Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API information dataset Horizontal Positional Accuracy Report Data were collected using methods that have unknown accuracy (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 10). For more information, please see EPA's NGDP at http://epa.gov/geospatial/policies.html Features represented have not been tested for completeness Features represented have not been tested for completeness Tests for integrity have not been performed. This lineage was generated by the FGDC CSDGM to 19139 transformation 1. Acquired mean annual Rainfall-runoff erosivity factor (R) for 1971-2000 at 4 km resolution by request from the PRISM climate Group (Daly and Taylor, 2002). 2019-04-19T00:00:00 2. Acquired the soil erodibility factor (K). The U.S. Geological Survey's Earth Resources Observation and Science (EROS) processed soil data for EnviroAtlas (https://www.epa.gov/enviroatlas) by converting the Soil Survey Geographic Database (SSURGO) erodibility factor (kffact) to a 30m raster, with gaps in SSURGO being filled with the State Soil Geographic Database (STATSGO2). All calculations done in SI, so the initial English units were converted to SI units (t ha hr / ha MJ mm), by multiplying the English units by 0.1317 (Foster et al., 1981). This was done in ArcMap when creating the RUSLE raster rather than writing a new Kfact raster with SI units. 2019-02-21T00:00:00 3. Derived the slope length and steepness factor (LS). 2019-05-28T00:00:00 3.a. Acquired the 30-meter digital elevation model (DEM) from the U.S. Geological Survey 3D Elevation Program (https://apps.nationalmap.gov/downloader/#/) 2019-05-23T00:00:00 3.b. The Desmet and Govers (1996) LS factor method was implemented using the System for Automated Geoscientific Analysis (SAGA, http://www.saga-gis.org/saga_tool_doc/2.2.0/ta_hydrology_22.html) LS factor, Field Based module (Conrad et al., 2015). The LS factor was limited to a maximum of 72.15 as specified by RUSLE (Renard et al., 1997). 2019-05-28T00:00:00 4. Cover-management factor (C) was estimated using the Normalized Difference Vegetation Index (NDVI), assuming that as greenness decreases, erosion potential increases. 2019-05-29T00:00:00 4.a. Used MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI values at 250m resolution for 16-day intervals between 2000 and 2014 (https://modis.gsfc.nasa.gov/data/dataprod/mod09.php) to calculate a mean annual NDVI. 2019-03-23T00:00:00 4.b. Downscaled the NDVI to 30m resolution using the 2011 National Land Cover Database (NLCD, https://www.mrlc.gov/data/nlcd-2011-land-cover-conus-0). 2019-03-23T00:00:00 4.c. Median NDVI was calculated for all NLCD land cover classes in each of 73 USDA crop management zones (CMZs) (https://fargo.nserl.purdue.edu/RUSLE2_ftp/Crop_Management_Templates/CMZ%20maps/CMZ%20map%20shape%20files/). 2019-03-24T00:00:00 4.d. The median NLCD-level NDVI was applied to corresponding NLCD pixels in that land cover class/CMZ combination. 2019-03-24T00:00:00 4.e. To relate NDVI to C, we used the generalized equation developed by Van der Knijff et al. (2000) for the European Union: C = γe^[−α (NDVI/β−NDVI)], where α=2, β=1, and γ =1. The α, β, and γ parameters were used to calibrate RUSLE average annual soil loss to the USDA 2015 National Resources Inventory (NRI) state-level cropland erosion by water estimates. Rather than using a single parameter set, calibration was performed spatially for the 73 CMZs. A total of 200,000 parameter sets were created using Latin hypercube sampling, with each set containing 219 parameters (a unique α, β, and γ for each CMZ). Parameter ranges were set to: α = [1, 8], β = [1,2], and γ = [0.2, 1] for cropland and α = [1, 8], β = [1, 2], and γ = [0.01, 0.7] for pasture. Spatially stratified samples of R, K, LS, and NDVI were drawn from each state's pasture/hay and cultivated crops NLCD classes, with a minimum of 1000 samples per state and class (number of samples were proportional to amount of pasture/hay and cultivated crops in the state). The RUSLE predicted average annual soil loss (Mg ha−1) calculated using each parameter set were compared to state-level NRI sheet and rill erosion by water estimates using the mean absolute error (MAE). Rather than using a single best parameter set, an ensemble average of parameter sets was used, in which the number of ensemble members was determined by minimizing the MAE. This process was completed separately for cultivated crops and pasture/hay NLCD classes, resulting in unique α, β, and γ for each class in each CMZ. 2019-05-29T00:00:00 4.f. Using NDVI to calibrate C factor resulted in unrealistically high values for forest, grassland, wetlands, and developed areas. Therefore, C factors for non-agricultural NLCD classes were derived from literature. 2019-05-29T00:00:00 4.g. A second raster was created to represent C-factors under a scenario in which current natural vegetative cover was removed. Replaced the C factors for forest (NLCD 41, 42, 43), shrub (NLCD 52), grassland/herbaceous (NLCD 71), and wetland (NLCD 90, 95) classes with the maximum C factor from barren land, applied uniquely for each CMZ. This was used to estimate sediment yield and sediment delivery ratio using the same steps described above. 2019-05-30T00:00:00 5. The support practice factor (P) is not explicitly used here given the lack of a national database However, we expect that the impact of some support practices (e.g., winter cover crops, no-till, etc.) will be reflected in the calibrated C-factor using NDVI on cultivated cropland and pasture. 2019-05-29T00:00:00 6. Calculated average annual soil loss (A) for both scenarios using the RUSLE equation (A = R x K x LS x C). P, the support practice factor, was not included. 2019-05-30T00:00:00 7. Estimated Sediment Delivery Ratio (SDR) for both scenarios. RUSLE only predicts soil loss and does not account for off-site sediment yield. Therefore, the SDR was applied to the soil loss estimates from RUSLE to calculate estimated net sediment yield to downstream waterways. The SDR was calculated for each pixel in the CONUS using flow path connectivity. The methods used here are adapted from previous work by Hamel et al. (2015, 2017). 2019-06-12T00:00:00 7.a. The Index of Connectivity (IC), defined here as a given pixel's connectivity to the nearest waterbody, was calculated using the ArcGIS Connectivity Index Toolbox (Cavalli et al., 2013, 2014, https://github.com/HydrogeomorphologyTools/Connectivity-Index-ArcGIS-toolbox/tree/master/ArcGIS10_3_Toolbox) based on the methods of Borselli et al. (2008). 2019-06-05T00:00:00 7.a.i. Used TauDEM (Tarboton, 1997) to calculate upslope contributing area. This was used as an input in calculating IC. The downslope flow path was calculated using D8 flow direction along the steepest downward slope. 2019-06-05T00:00:00 7.a.ii. The C factor was adjusted based on the disparity of low sediment generation by impervious surfaces versus the greater potential for connectivity due to those impervious surfaces. Therefore, the NLCD Urban Imperviousness 2011 dataset (https://www.mrlc.gov/data/nlcd-2011-percent-developed-imperviousness-conus-0) was used to represent developed land cover C factors. Imperviousness in the original dataset ranges from 0 to 1; these values were used directly for the developed land cover C factor, allowing them to vary from pixel to pixel. The mean C factor values increased with increasing density: 0.05 (developed, open space), 0.3 (developed, low intensity), 0.61 (developed, medium intensity), 0.88 (developed, high intensity). In the initial C-factor datasets, if NLCD 2011 land cover pixel = class 21, 22, 23, 24, we replaced the C-factor value for that pixel with the NLCD 2011 percent impervious value. In some cases, the class is 21-24, yet the imperviousness is 0%. The minimum W-factor value is 0.001. Therefore, C-factor 0 values that were not water (NLCD 11) are replaced with 0.001 in the final W-factor rasters. 2019-06-05T00:00:00 7.a.iii. Sinks (delivery endpoints) were defined using the National Hydrography Dataset Plus Version 2 (NHDPlusV2, http://www.horizon-systems.com/nhdplus/NHDplusV2_home.php) streams and waterbodies, converted to a binary raster where sinks = 1 and everything else is nodata. 2019-06-05T00:00:00 7.b. The SDR for pixel i is then calculated using the equation SDRi = SDRmax,i / {1 + exp[(IC0-ICi)/kb]}. SDRmax,i is the maximum possible delivery ratio for the ith pixel, defined as the fraction of topsoil particles finer than coarse sand (less than 1 mm in diameter) (Vigiak et al., 2012), derived from SSURGO and STATSGO2. In rare cases where data were missing, we set SDRmax,i = 0.8. IC0 was set to 0.5 and kb was set equal to 2 (Vigiak et al., 2012, Borselli et al., 2008). Since the connectivity index tool does not calculate values for pixels that border sinks, these pixels were filled with SDRmax. 2019-06-12T00:00:00 7.c. Sediment yield from each pixel was then calculated as the product of RUSLE's average annual sheet and rill erosion (A) and the SDR. 2019-06-12T00:00:00 8. Determined how much sediment is retained by natural land cover by comparing the scenario with existing vegetative cover to a scenario where current natural vegetative cover was removed. The difference shows how much erosion is prevented by natural vegetative cover. 2019-06-12T00:00:00 9. Subtracted the sediment yield base scenario raster from the sediment yield with vegetation removed scenario raster to produce a raster showing sediment yield avoided due to natural vegetation. In some places, this resulted in a negative number. Converted any negative pixels to 0. 2024-12-26T00:00:00 unknown 2029-06-24 This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2024-02-09T11:11:00 version of the FGDC CSDGM to ISO 19139 transform. EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development - Center for Public Health and Environmental Assessment (CPHEA), EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact