This dataset represents estimated surface water TN load within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as kg Nitrogen.

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 density of 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 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 the mine density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on mine plants and operations monitored by the USGS National Minerals Information Center. The National Minerals Information Center canvasses the nonfuel mining and mineral-processing industry in the United States for data on mineral production, consumption, recycling, stocks, and shipments. Mine plants and operations for commodities are expressed as points in a shapefile that was downloaded from the USGS directly. The (mines / catchment) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a point data type.

StreamCat currently contains over 600 metrics that include local catchment (Cat), watershed (Ws), and special metrics. The special metrics were derived through modeling or by combining other StreamCat metrics. These variables include predicted water temperature, predicted biological condition, and the indexes of catchment and watershed integrity. See Geospatial Framework and Terms below for definitions of catchment and watershed as used with the StreamCat Dataset. These metrics are available for ~2.65 million stream segments and their associated catchments across the conterminous US. StreamCat metrics represent both natural (e.g., soils and geology) and anthropogenic (e.g, urban areas and agriculture) landscape information. No Agricultural Drainage 다운로드

description: 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 (see Data Structure and Attribute Information for a description of each metric).; abstract: 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 (see Data Structure and Attribute Information for a description of each metric).

This dataset represents mean percent are burned from wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018. The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions

description: 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 (see Data Structure and Attribute Information for a description).; abstract: 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 (see Data Structure and Attribute Information for a description).

This dataset represents the historical fire perimeters within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the GeoMAC (Geospatial Multi-Agency Coordination) mapping tool. Fire perimeters contain data as they are submitted by field offices to GeoMAC (Geospatial Multi-Agency Coordination) in a polygon format. Fire perimeter data is based on input from incident intelligence sources, GPS data, infrared (IR) imagery from fixed wing and satellite platforms. Polygons are selected by year and then converted into a binary raster format where values of 1 represent fire perimeters of the given year and 0 describes the remaining areas across the CONUS, leaving No Data to be anything outside the CONUS border. The wildland fire characteristics (% forest loss to fire) were summarized by year to produce local catchment-level and watershed-level metrics as a continuous data type.

StreamCat currently contains over 600 metrics that include local catchment (Cat), watershed (Ws), and special metrics. The special metrics were derived through modeling or by combining other StreamCat metrics. These variables include predicted water temperature, predicted biological condition, and the indexes of catchment and watershed integrity. See Geospatial Framework and Terms below for definitions of catchment and watershed as used with the StreamCat Dataset. These metrics are available for ~2.65 million stream segments and their associated catchments across the conterminous US. StreamCat metrics represent both natural (e.g., soils and geology) and anthropogenic (e.g, urban areas and agriculture) landscape information. Nitrogen Surplus Density 다운로드 This dataset represents the density of nitrogen surplus as kg N / yr, excluding biological N Fixation, 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 riparian buffers to provide watershed-level metrics for classes within the NLCD. This data set is derived from the NLCD raster composed of 16 land cover classes (categorical data type) for the conterminous USA. Four classes of the NLCD were excluded as they were specific to Alaska land covers. This raster was produced based on a decision-tree classification of circa 2011 Landsat satellite data (see Data Structure and Attribute Information for a description of each metric).

This dataset represents the road density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian buffers. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. (See Supplementary Info for Glossary of Terms) This data set is derived from TIGER/Line Files of roads in the conterminous United States. Road density describes how many kilometers of road exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. (see Data Sources for links to NHDPlusV2 data and Census Data) The (kilometer of road/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type (see Data Structure and Attribute Information for a description).

This dataset represents deposition estimates of nutrients within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Atmospheric Deposition Program. The National Trends Network provides long-term records of precipitation chemistry across the United States. Individual rasters describe ammonium, nitrate, inorganic nitrogen, and average sulfur/nitrogen deposition per year. See Source Info for links to NADP. The nitrogen and sulfur characteristics (kg N/ha/yr) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents water input, measured as km2/cm: Ratio of the total area of irrigated land to precipitation within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

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 file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

This dataset represents percent area consisting of carbonate-rock aquifers, igneous and metamorphic-rock, sandstone, sandstone and carbonate-rock, semiconsolidated sand, and unconsolidated sand and gravel aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

View Experience Builder (Item Details).Rapid, qualitative biological and habitat surveys for wadeable streams and rivers are conducted using the Great Lakes Watersheds Assessment, Restoration, and Management (GLWARM) section Procedure 51. Procedure 51 consists of separate qualitative evaluations of the macroinvertebrate community, fish community, and habitat quality. These protocols can be used to assess the existing condition of Michigan's wadeable streams and rivers as well as detect spatial and temporal trends.This data was developed to classify sites for the Water Resources Division Procedure 51 scoring process. The classifications were partly derived from EPA's StreamCat Dataset catchments and Level 3 Ecoregions, where ecoregion, catchment slope, and wetland composition, are components of the site class determination. Learn more about the data by visiting the EGLE Maps and Open Data Portal, where it can also be viewed in a table format or downloaded directly in a variety of formats.Learn more about EGLE Biological AssessmentsQuestions about the data and P51 site classifications: Sarah Holden at HoldenS1@Michigan.govMap feedback: VandenbergC@Michigan.gov

This dataset represents Nitrogen from rock weathering (kg/ km2) within AOI