This vector coverage of hydrography centerlines was derived from the National Hydrography Dataset (NHD), and encompasses all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This coverage is comprised of 105 Catalog Units (CUs) obtained from http://nhd.usgs.gov/. Each CU approximately represents a single 8-digit Hydrologic Unit Code (HUC).

This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.

In cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration, the USGS developed a database of geomorphic data for a sampling of unregulated natural alluvial streams in Ohio. The vector point shapefile that these metadata describe is based on this geomorphic database and consists of 50 points and associated attributes. The database was developed as part of a study to develop methods to estimate bankfull characteristics of Ohio streams and to relate geomorphic characteristics of Ohio streams to peak streamflows of selected recurrence intervals. The results of the study are presented in a report by Sherwood and Huitger (2005). The database developed for this study consists of geomorphic, basin, and flood-frequency characteristics for 50 study sites in Ohio, of which 40 sites are near streamflow-gaging stations. Field surveys were done at each site to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. These techniques resulted in a set of regional curves, simple-regression (drainage-area only) equations, and multiple-regression equations to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge. Explanatory variables included drainage area, main-channel slope, main- channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations ranged from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations-in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope-have the lowest average standard errors of prediction. Statistical and graphical analyses were done to investigate development of methods to estimate flood-peak discharges from geomorphic characteristics based on the 40 study sites at streamflow-gaging stations. The logarithms of the annual peak discharges for each site were fit by a Pearson Type III frequency distribution to develop a flood-peak-frequency relation for each site. The peak-frequency data were related to geomorphic, basin, and climatic variables of the 40 study sites by multiple-regression analysis. The analyses resulted in a set of multiple-regression equations to estimate flood-peak discharge having recurrence intervals of 2, 5, 10, 25, 50, and 100 years from bankfull cross-sectional area, in which the average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. Sherwood, J.M. and Huitger, C.A., 2005, Bankfull Characteristics of Ohio Streams and Their Relation to Peak Streamflows: U.S. Geological Survey Scientific Investigations Report 2005-5153

In cooperation with the Ohio Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration, the USGS developed a database of geomorphic data for a sampling of unregulated natural alluvial streams in Ohio. The vector point shapefile that these metadata describe is based on this geomorphic database and consists of 50 points and associated attributes. The database was developed as part of a study to develop methods to estimate bankfull characteristics of Ohio streams and to relate geomorphic characteristics of Ohio streams to peak streamflows of selected recurrence intervals. The results of the study are presented in a report by Sherwood and Huitger (2005). The database developed for this study consists of geomorphic, basin, and flood-frequency characteristics for 50 study sites in Ohio, of which 40 sites are near streamflow-gaging stations. Field surveys were done at each site to collect the geomorphic data. Bankfull indicators were identified and evaluated, cross-section and longitudinal profiles were surveyed, and bed- and bank-material were sampled. Field data were analyzed to determine various geomorphic characteristics such as bankfull width, bankfull mean depth, bankfull cross-sectional area, bankfull discharge, streambed slope, and bed- and bank-material particle-size distribution. The various geomorphic characteristics were analyzed by means of a combination of graphical and statistical techniques. These techniques resulted in a set of regional curves, simple-regression (drainage-area only) equations, and multiple-regression equations to estimate bankfull width, bankfull mean depth, bankfull cross-sectional area, and bankfull discharge. Explanatory variables included drainage area, main-channel slope, main- channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope. Average standard errors of prediction for bankfull width equations ranged from 20.6 to 24.8 percent; for bankfull mean depth, 18.8 to 20.6 percent; for bankfull cross-sectional area, 25.4 to 30.6 percent; and for bankfull discharge, 27.0 to 78.7 percent. The simple-regression (drainage-area only) equations have the highest average standard errors of prediction. The multiple-regression equations-in which the explanatory variables included drainage area, main-channel slope, main-channel elevation index, median bed-material particle size, bankfull cross-sectional area, and local-channel slope-have the lowest average standard errors of prediction. Statistical and graphical analyses were done to investigate development of methods to estimate flood-peak discharges from geomorphic characteristics based on the 40 study sites at streamflow-gaging stations. The logarithms of the annual peak discharges for each site were fit by a Pearson Type III frequency distribution to develop a flood-peak-frequency relation for each site. The peak-frequency data were related to geomorphic, basin, and climatic variables of the 40 study sites by multiple-regression analysis. The analyses resulted in a set of multiple-regression equations to estimate flood-peak discharge having recurrence intervals of 2, 5, 10, 25, 50, and 100 years from bankfull cross-sectional area, in which the average standard errors of prediction are 31.6, 32.6, 35.9, 41.5, 46.2, and 51.2 percent, respectively. Sherwood, J.M. and Huitger, C.A., 2005, Bankfull Characteristics of Ohio Streams and Their Relation to Peak Streamflows: U.S. Geological Survey Scientific Investigations Report 2005-5153

In 2013, the first of several Regional Stream Quality Assessments (RSQA) was done in the Midwest United States. The Midwest Stream Quality Assessment (MSQA) was a collaborative study by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA), the USGS Columbia Environmental Research Center, and the U.S. Environmental Protection Agency (USEPA) National Rivers and Streams Assessment (NRSA). One of the objectives of the RSQA, and thus the MSQA, is to characterize the relationships between water-quality stressors and stream ecology and to determine the relative effects of these stressors on aquatic biota within the streams (U.S. Geological Survey, 2012a). To meet this objective, a framework of fundamental geospatial data was required to develop physical and anthropogenic characteristics of the study region, sampled sites and corresponding watersheds, and riparian zones. This dataset represents the boundaries for the 100 watersheds studied in the MSQA, and is one of the four fundamental geospatial data layers that were developed for the Midwest study.

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This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).

This child item provides a snapshot of the watershed boundary dataset which consists of a shapefile with 87,020 12-digit hydrologic unit codes (HUC12) for the conterminous United States retrieved 10/26/2020. The National Watershed Boundary Dataset (WBD) is a comprehensive set of digital spatial data that represents the surface drainages areas of the United States. Although versions of the WBD are published as part of U.S. Geological Survey National Hydrography Products, the version used to produce the water-use reanalysis was not archived and is provided here. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Public-supply water use estimates for the HUC12s included in this shapefile are provided on the data release main landing page and on the public supply water use machine learning model child item. This page includes the following file: WBD_HUC12_CONUS_pulled10262020.zip - a zip file containing a shapefile with 12-digit hydrologic units in the conterminous United States