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, representing loss detected primarily in the year 2000-2013, 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 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, 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, 201313, representing loss detected primarily in the year 2001, 2013, 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 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, 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, 201313, representing loss detected primarily in the year 2001, 2013, 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 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 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 the elevation values within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Elevation Dataset (see Data Sources for links to NHDPlusV2 data and NED data). NHDPlusV2 records NED snapshot dates as follows: August 2010 - VPU04 February 2011 - VPUs 05, 06 June 2011 - VPU 17 August 2011 - VPUs 07, 10L, 10U, 11, 18 December 2011 - VPUs 01, 02, 03N, 03S, 03W, 08, 09, 12, 13, 14, 15, 16. The elevation characteristics were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the elevation values within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Elevation Dataset (see Data Sources for links to NHDPlusV2 data and NED data). NHDPlusV2 records NED snapshot dates as follows: August 2010 - VPU04 February 2011 - VPUs 05, 06 June 2011 - VPU 17 August 2011 - VPUs 07, 10L, 10U, 11, 18 December 2011 - VPUs 01, 02, 03N, 03S, 03W, 08, 09, 12, 13, 14, 15, 16. The elevation characteristics were summarized 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 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).