This dataset represents the pesticide use within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies. 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 219, 1-kilometer (km) resolution grids depicting estimated agricultural use of each pesticide in the conterminous United States. Each grid cell value in the national grids of this dataset is the estimated total kilograms (kg) of pesticides applied to row crops, small grain crops and fallow land, pasture and hay crops, and orchard and vineyard crops within the 1- by 1-km area. A single raster was produced using the Raster Calculator Tool adding all 219 grids to form the landscape layer for analysis. The (kilograms of pesticides/square kilometer) was 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 pesticide use within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies. 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 219, 1-kilometer (km) resolution grids depicting estimated agricultural use of each pesticide in the conterminous United States. Each grid cell value in the national grids of this dataset is the estimated total kilograms (kg) of pesticides applied to row crops, small grain crops and fallow land, pasture and hay crops, and orchard and vineyard crops within the 1- by 1-km area. A single raster was produced using the Raster Calculator Tool adding all 219 grids to form the landscape layer for analysis. The (kilograms of pesticides/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

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 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 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 application of nitrogen within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based data published in EPA EnviroAtlas. Attributes were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. These data are based on county-scale estimates of N in the form of fertilizer, manure, or cultivated biological nitrogen fixation. The rasters used for this analysis were prepared for EnviroAtlas published datasets on agricultural nitrogen inputs. Links to data in each raster can be found in Data Sources below. The nitrogen characteristics (kg N/ha/yr) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

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