This dataset represents the dam density and storage volumes within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Anthropogenic Barrier Dataset (NABD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. The main objective of this project was to develop a dataset of large, anthropogenic barriers that are spatially linked to the National Hydrography Dataset Plus Version 1 (NHDPlusV1) for the conterminous U.S. to facilitate GIS analyses based on the NHDPlusV1/NHD and NID datasets. To meet this objective, Michigan State University conducted a spatial linkage of the point dataset of the 2009 National Inventory of Dams (NID) created by the U.S. Army Corps of Engineers (USACE) to the NHDPlusV1/NHD. The pool of dam data included were modified based on 1) dam removals that occurred after development of the 2009 NID and 2) the identification of duplicate dam records along state boundaries (cases where more than one state reported the same dam). The US Geological Survey (USGS) Aquatic GAP Program supported this work. The (dams/catchment) and (dam_storage/catchment) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a point 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 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 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 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 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.