State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

State DOT HPMS Section Attributes for Western States

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster(imp2006) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2006 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster(imp2011) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2011 Landsat satellite data.

This dataset represents the estimated density of georeferenced sites within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the EPA's Facility Registry Services (FRS) geodatabase. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. The FRS geodatabase is a collection of point locations of facilities or sites subject to environmental regulation. TRI, NPDES, and Superfund sites were extracted individually to summarize for each in the resulting . Csv. The (site locations / catchment) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a points data type.

This dataset represents the characterization of global forest extent and change by year from 2001 through 2013 within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the Global Forest Change 2000–2013. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. These data are based on global tree cover loss for the period from 2001 to 2013 at a spatial resolution of 30m. The analysis used to create the landscape layer is based on Landsat data. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. This landscape layer is a disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1–13, representing loss detected primarily in the year 2001–2013, respectively. The forest loss by year characteristics (%) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset (STATSGO_Set1 and STATSGO_Set2) represents the soil characteristics within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the STATSGO landscape rasters. Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. This data set is derived from the STATSGO landscape rasters for the conterminous USA. Individual rasters (Landscape Layers) of organic material (om), permeability (perm), water table depth (wtdep), depth to bedrock (rckdep), percent clay (clay), and percent sand (sand) were used to calculate soil characteristics for each NHDPlusV2 catchment. The soil characteristics were summarized to produce local catchment-level and watershed-level metrics as a continuous data type. The STATSGO data are distributed in two sets, STATSGO_Set1 and STATSGO_Set2, based on common NoData locations in each set of soil GIS layers (see ftp://newftp.epa.gov/EPADataCommons/ORD/NHDPlusLandscapeAttributes/StreamCat/Documentation/ReadMe.html).

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2001) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2001 Landsat satellite data.(see Data Structure and Attribute Information for a description of each metric).

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2006) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2006 Landsat satellite data.

This dataset represents the population and housing unit density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian buffers based on 2010 US Census data. Densities are calculated for every block group and watershed averages are calculated for every local NHDPlusV2 catchment(see Data Sources for links to NHDPlusV2 data and Census Data). This data set is derived from The TIGER/Line Files and related database (.dbf) files for the conterminous USA. It was downloaded as Block Group-Level Census 2010 SF1 Data in File Geodatabase Format (ArcGIS version 10.0). The landscape raster (LR) was produced based on the data compiled from the questions asked of all people and about every housing unit. The (block-group population / block group area) and (block-group housing units / block group area) were summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2011) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2011 Landsat satellite data.(see Data Structure and Attribute Information for a description of each metric).

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2001 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2004 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2008 Landsat satellite data.

This dataset represents the population and housing unit density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on 2010 US Census data. Densities are calculated for every block group and watershed averages are calculated for every local NHDPlusV2 catchment. This data set is derived from The TIGER/Line Files and related database (.dbf) files for the conterminous USA. It was downloaded as Block Group-Level Census 2010 SF1 Data in File Geodatabase Format (ArcGIS version 10.0). The landscape raster (LR) was produced based on the data compiled from the questions asked of all people and about every housing unit. The (block-group population / block group area) and (block-group housing units / block group area) were summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2013 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2019 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2016 Landsat satellite data.

This dataset represents the impervious surface coefficients within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Land Cover Data. AOI boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-AOI boundaries and then links them through an off-network flow table. This data set is derived from the NLCD Impervious Surfaces raster which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2019 Landsat satellite data.

This dataset represents total fresh surface-water withdrawals in agricultural land within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as L/day as described in DOI: 10.1016/j.scitotenv.2020.137661

This dataset represents density of total fresh surface-water withdrawals in agricultural land within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as L/day as described in DOI: 10.1016/j.scitotenv.2020.137661

This dataset represents density of all wastewater treatment plants within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as number/ km2.

This dataset represents net anthropogenic Nitrogen within AOI: farm fertilizer + urban fertilizer + NOx deposition + CBNF + Human and Livestock food - crop N content - livestock N content within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents mean hillslope percent within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents deposition estimates of nutrients within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Atmospheric Deposition Program. The National Trends Network provides long-term records of precipitation chemistry across the United States. Individual rasters describe ammonium, nitrate, inorganic nitrogen, and average sulfur/nitrogen deposition per year. See Source Info for links to NADP. The nitrogen and sulfur characteristics (kg N/ha/yr) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

N from rock weathering (kg/ km2) within AOI

This dataset represents nitrogen surplus as kg N / yr, excluding biological N Fixation, within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents % of land without agricultural drainage, described in DOI: 10.1016/j.scitotenv.2020.137661, within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents predicted channel widths and depths from Doyle et al. 2023. Values include: Predicted wetted width: distance of the water’s edge from left to right bank, Predicted thalweg depth: deepest point in the channel cross section from the bottom substrate to the water surface, Predicted bankfull width: distance from left to right bank at bankfull stage where the potential water height would spill outside of the channel and into the floodplain, and Predicted bankfull depth: thalweg depth plus bankfull height, which is the height from the water surface to the bankfull stage.

This dataset represents the density of road and stream crossings within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and then accumulated to provide watershed-level metrics. The landscape layer (raster) was developed by James Falcone of the USGS. US Census TIGER 2000 line files of roads and the NHDPlusV1 line files of all streams were converted to 30-meter grids where the presence of a street or stream was a 1 and everything else a 0. These were intersected and anything that was a 1 in both grids is the result. The density of road and stream crossings (crossings / square kilometer) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents percent area consisting of sandstone aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents percent area consisting of semiconsolidated sand aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents estimated surface water TN flux within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as kg N/km2.

This dataset represents mean percent are burned from wildfires within individual, local NHDPlusV2 catchments and upstream, contributing watersheds for each year for 1984-2018.

This dataset represents the characterization of global forest extent and change by year from 2001 through 2013 within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Global Forest Change 2000–2013. These data are based on global tree cover loss for the period from 2001 to 2013 at a spatial resolution of 30m. The analysis used to create the landscape layer is based on Landsat data. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. This landscape layer is a disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1–13, representing loss detected primarily in the year 2001–2013, respectively. The forest loss by year characteristics (%) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the Index of Watershed Integrity / Index of Catchment Integrity (IWI/ICI) within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on 23 other StreamCat metrics. The Index of Watershed Integrity (IWI) is based on first order approximations of relationships between stressors and six watershed functions: hydrologic regulation, regulation of water chemistry, sediment regulation, hydrologic connectivity, temperature regulation, and habitat provision. Link to paper: https://doi.org/10.1016/j.ecolind.2017.10.070 The Index of Watershed Integrity / Index of Catchment Integrity (IWI/ICI) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the wetness index within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Composite Topographic Index (See Supplementary Info for Glossary of Terms). The Composite Topographic Index (CTI) is based on contributing area, slope, and overland flow and has been developed internally at the EPA for the EnviroAtls (http://edg.epa.gov/data/Public/ORD/EnviroAtlas/National/). As defined for use in EnviroAtlas datasets and as used here, “wet areas are typically created by runoff from natural land cover when rain falls on saturated soil. Surface and rill (or small channel) runoff carries excess water to lowland depressions or wet areas. Runoff collects in wet areas until they fill and overflow downstream. In this way, stream networks can be extended into new areas that would not be hydrologically connected during drier times. Wet area expansion and watershed hydrological connectivity differ between humid temperate and semi-arid and arid climates (where drought and soil crusts limit infiltration and produce flashier runoff)” (from https://enviroatlas.epa.gov/enviroatlas/datafactsheets/pdf/ESN/PercentForestonWetAreas.pdf). The Mean Composite Topographic Index (CTI)[Wetness Index] were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.