This dataset represents the percent of non-agricultural, non-native vegetation based on LANDFIRE existing vegetation type (EVT) for a 30-m grid cell within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on field reference data and Landsat, elevation, and ancillary data. EVTs are mapped using decision tree models, field data, Landsat imagery, elevation, and biophysical gradient data. Decision tree models are developed separately for each of the three lifeforms -tree, shrub, and herbaceous and are then used to generate lifeform specific EVT layers. The LF-GAP Map Units Descriptions provide descriptions for each LF EVT including species, distribution and classification information. Vegetation map units are primarily derived from NatureServe's Ecological Systems classification, alliances of the U.S. National Vegetation Classification (USNVC), and the National Land Cover Database and LF specific types. LANDFIRE EVT groups were reclassified into introduced managed vegetation cover where EVT_GP = (701,702,703,704,705,706,707,708,709,711,731).

This dataset represents % of land with agricultural drainage, described in DOI: 10.1016/j.scitotenv.2020.137661, within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents % of land with agricultural drainage, described in DOI: 10.1016/j.scitotenv.2020.137661, 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 % 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 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 watershed to provide watershed-level metrics for classes within the NLCD. This data set is derived from the NLCD raster composed of 16 of the modified Anderson land cover classes (categorical data type) for the conterminous USA (excluding the four Alaska-specific land cover classes). Additional agriculture on slope metrics were derived using slope based on National elevation DEMs delivered with NHDplusV2 for agriculture NLCD classes. The NLCD raster was produced based on a decision-tree classification of 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 Landsat satellite data (see Data Structure and Attribute Information for a description of each metric). This dataset will include additional years as they become available.

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 watershed to provide watershed-level metrics for classes within the NLCD. This data set is derived from the NLCD raster composed of 16 of the modified Anderson land cover classes (categorical data type) for the conterminous USA (excluding the four Alaska-specific land cover classes). Additional agriculture on slope metrics were derived using slope based on National elevation DEMs delivered with NHDplusV2 for agriculture NLCD classes. The NLCD raster was produced based on a decision-tree classification of 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 Landsat satellite data (see Data Structure and Attribute Information for a description of each metric). This dataset will include additional years as they become available.

This dataset consists of predicted probabilities of good biological condition based in the US EPA 2008/2009 National Rivers and Streams Assessment (NRSA). NRSA assesses the biological condition of rivers and streams using several approaches, including a benthic invertebrate multimetric index (BMMI). The development of the NRSA BMMI is documented in the 2008/2009 NRSA Report (https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-2009-results) and by Stoddard et al. (2008) (http://www.bioone.org/doi/abs/10.1899/08-053.1). This assessment resulted in the classification of 1,380 streams as being in good or poor biological condition. These sites were paired with StreamCat data and a random forest model was developed to predict the probable condition of streams based on the binary response of condition to catchment and watershed features. This model was then applied to NHDPlusV2 stream segments that were within the NRSA sampling frame, i.e., streams that were candidates for sampling during the 2008/2009 NRSA (~1.1 million stream segments). Model development was documented in Fox et al. (2017) (https://link.springer.com/article/10.1007/s10661-017-6025-0) and Hill et al. (2017)(http://onlinelibrary.wiley.com/doi/10.1002/eap.1617/full).

This dataset consists of predicted probabilities of good biological condition based in the US EPA 2008/2009 National Rivers and Streams Assessment (NRSA). NRSA assesses the biological condition of rivers and streams using several approaches, including a benthic invertebrate multimetric index (BMMI). The development of the NRSA BMMI is documented in the 2008/2009 NRSA Report (https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-2009-results) and by Stoddard et al. (2008) (http://www.bioone.org/doi/abs/10.1899/08-053.1). This assessment resulted in the classification of 1,380 streams as being in good or poor biological condition. These sites were paired with StreamCat data and a random forest model was developed to predict the probable condition of streams based on the binary response of condition to catchment and watershed features. This model was then applied to NHDPlusV2 stream segments that were within the NRSA sampling frame, i.e., streams that were candidates for sampling during the 2008/2009 NRSA (~1.1 million stream segments). Model development was documented in Fox et al. (2017) (https://link.springer.com/article/10.1007/s10661-017-6025-0) and Hill et al. (2017)(http://onlinelibrary.wiley.com/doi/10.1002/eap.1617/full).

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 to provide watershed-level metrics for imperviousness values within the NLCD. 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 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 Landsat satellite data. This dataset will include additional years as they become available.