This dataset represents the density of 18 USGS lithology classes within individual, local NHDPlusV2 catchments and upstream, contributing watersheds(see Data Sources for links to NHDPlusV2 data and USGS). Attributes were calculated for every local NHDPlusV2 catchment and then accumulated to provide watershed-level metrics for USGS lithology data. This data set is derived from the USGS raster map of 18 lithology classes (categorical data type) for the conterminous USA. The map was produced based on texture, internal structure, thickness, and environment of deposition or formation of materials. These 18 lithology classes were summarized by local catchment and by watershed to produce 18 local catchment-level and watershed-level metrics as a categorical data type.

This dataset represents slope percent along stream within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents slope percent along stream within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents the estimated surface water runoff 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.(see Data Sources for links to NHDPlusV2 data and metadata) The landscape layer (raster) was developed with a water-balance model developed by Dave Wolock of the USGS and is detailed further in the paper "Independent effects of temperature and precipitation on modeled runoff in the conterminous United States". McCabe and Wolock[2011] Runoff is defined as the flow per unit area delivered to streams and rivers in units of millimeters per month. The runoff estimates were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the estimated surface water runoff 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.(see Data Sources for links to NHDPlusV2 data and metadata) The landscape layer (raster) was developed with a water-balance model developed by Dave Wolock of the USGS and is detailed further in the paper "Independent effects of temperature and precipitation on modeled runoff in the conterminous United States". McCabe and Wolock[2011] Runoff is defined as the flow per unit area delivered to streams and rivers in units of millimeters per month. The runoff estimates were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents climate observations within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. PRISM is a set of monthly, yearly, and single-event gridded data products of mean temperature and precipitation, max/min temperatures, and dewpoints, primarily for the United States. In-situ point measurements are ingested into the PRISM (Parameter elevation Regression on Independent Slopes Model) statistical mapping system. The PRISM products use a weighted regression scheme to account for complex climate regimes associated with orography, rain shadows, temperature inversions, slope aspect, coastal proximity, and other factors. These data are summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents climate observations within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. PRISM is a set of monthly, yearly, and single-event gridded data products of mean temperature and precipitation, max/min temperatures, and dewpoints, primarily for the United States. In-situ point measurements are ingested into the PRISM (Parameter elevation Regression on Independent Slopes Model) statistical mapping system. The PRISM products use a weighted regression scheme to account for complex climate regimes associated with orography, rain shadows, temperature inversions, slope aspect, coastal proximity, and other factors. These data are summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents geochemical or geophysical attributes in surface or near surface geology within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metric. For information regarding how the Landscape layers were created see https://www.sciencebase.gov/catalog/item/53481333e4b06f6ce034aae7. Landscape Layers are partitioned into 4 tables based on the location of no-data cells within their rasters to correctly reflect the PctFull attributes within each table.

This dataset represents geochemical or geophysical attributes in surface or near surface geology within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metric. For information regarding how the Landscape layers were created see https://www.sciencebase.gov/catalog/item/53481333e4b06f6ce034aae7. Landscape Layers are partitioned into 4 tables based on the location of no-data cells within their rasters to correctly reflect the PctFull attributes within each table.

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).