This data release contains a crosswalk between subwatersheds (12-digit hydrologic unit codes; hereafter, HUC12s) and hydrologic regions (sometimes called "Van Metre regions"). This crosswalk allows for data at the HUC12 scale to be summarized regionally. Hydrologic regions are boundaries of hydrologically distinct areas modified from hydrologic subregions (4-digit Hydrologic units; HUC4s) defined by Qi and Mason (2023; https://doi.org/10.5066/P98194QR) for use in Van Meter et al. (2020; https://doi.org/10.1007/s10661-020-08403-1). These hydrologic regions should not be confused with 2-digit hydrologic unit codes (HUC2 or HU2), also referred to as "hydroregions" or "HydroRegions." Although they are similar in number and size, they represent different concepts: HUC2s denote drainage basins of major rivers, while the hydrologic regions defined by Van Metre et al. (2020) are areas with similar hydrology and water availability concerns that were originally developed to help inform selection of basins for more in-depth sampling, analysis, and modeling. For comparative purposes, we further grouped the hydrologic regions into four CONUS aggregated hydrologic regions based on location and shared water-availability characteristics and challenges (Northeast through Midwest, Southeast, High Plains, and Western). The HUC12 boundaries used are those made available in the Mainstems data release (https://doi.org/10.5066/P92U7ZUT), which are modified from the stable NHDPlusV2 snapshot of the Watershed Boundary Dataset.

This subset of the USGS Water Boundary Dataset contains the polygons of the 50 8-digit Hydrologic Units that comprise the greater Central Valley study site. The Watershed Boundary Dataset is a comprehensive set of digital spatial data that represents the surface drainages areas of the United States. The information included with the features includes a feature date, a unique common identifier, name, the feature length or area, and other characteristics. Names and their identifiers are assigned from the Geographic Names Information System. The data also contains relations that encode metadata. The names and definitions of all these feature attributes are in the Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD). The document is available online at https://pubs.usgs.gov/tm/11/a3/

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

This dataset is the digital hydrologic unit boundary layer for the 8-digit subwatershed boundaries for the conterminous United States. This dataset is intended to be used with the following two tabular dBase files: https://water.usgs.gov/lookup/getspatial?ds573_tillage_lu92e and https://water.usgs.gov/lookup/getspatial?ds573_tillage_lu01. The two tabular datasets contain the Tillage Practices in the Conterminous United States, 1989-2004---Datasets Aggregated by Watershed. This dataset and the two tabular datasets can be linked using the common attribute HUC8_N. Information about how the tabular data and geospatial data can be related are given in the data series report: https://pubs.usgs.gov/ds/ds573/ .The original dataset is the 12-digit Subwatershed boundaries (WBD_archive_17nov2009_9.2_file). The 12-digit boundaries were dissolved to 8-digit boundaries to be used with the two tabular .dbase data files containing the tillage practice data for the United States. ORIGINAL METADATA: This data set is a complete digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the entire United States. This data set consists of geo-referenced digital data and associated attributes created in accordance with the "Federal Guidelines, Requirements, and Procedures for the National Watershed Boundary Dataset; Chapter 3 of Section A, Federal Standards, Book 11, Collection and Delineation of Spatial Data; Techniques and Methods 11-A3" (04/01/2009). http://www.ncgc.nrcs.usda.gov/products/datasets/watershed/index.html . Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downstream hydrologic unit, type of watershed, non-contributing areas and flow modification.

This dataset provides numerical and categorical descriptions of 48 basin characteristics for 9,314 ungaged basins coinciding with 12-digit hydrologic unit code (HUC12) pour points that drain to the Gulf of Mexico. Characteristics are indexed by National Hydrography Dataset (NHD) version 2 COMID (integer that uniquely identifies each feature in the NHD) and HUC12 identifying number. The variables represent mutable and immutable basin characteristics and are organized by characteristic type: physical (5), hydrologic (6), categorical (12), climate (6), landscape alteration (7), and land cover (12). Mutable characteristics such as climate, land cover, and landscape alteration variables are reported in decadal increments (for example, average percent forest for the decade 1950-1959, 1960-1969, etc). The majority of basin characteristics in this dataset were calculated using divergence-routing methods and are often referred to as “network-accumulated”. This method uses a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the values derived from the reach catchment scale (Schwarz, G.E., and Wieczorek, M.E., 2018, Database of modified routing for NHDPlus version 2.1 flowlines: ENHDPlusV2_us: U.S. Geological Survey data release, https://doi.org/10.5066/P9PA63SM ). In four instances, values are also provided for the entire catchment above a site and area designated using the “CAT_” prefix.

This file contains hydrologic unit boundaries and codes for the conterminous United States along with Alaska, Hawaii, Puerto Rico and the U.S. Virgin Islands. It was revised for inclusion in the National Atlas of the United States of America, and updated to match the streams file created by the USGS National Mapping Division (NMD) for the National Atlas of the United States of America. For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

Two methods of calculating hydrologic alteration were applied to modeled daily streamflow data for 9,201 12-digit hydrologic unit code (HUC12) pour points draining to the Gulf of Mexico (Robinson and others, 2020). The first method is a new modified method of calculating ecosurplus and ecodeficit called hydro change. For this project, ecosurplus and ecodeficit have been combined to assess overall hydrologic regime change. The second method is the confidence interval hypothesis test (Kroll and others, 2015). The first method is a means of quantifying hydrologic alteration while the second is a hypothesis test to simply determine if statistically significant alteration has occurred. Both methods are employed to determine which is best at analyzing alteration of the hydrologic regime in the Gulf Coast Ecosystem Restoration Council (RESTORE) study area. Statistical analysis was done in RStudio (2020). The data release includes four attached files: (1) metadata .xml file, (2) csv with the p-values for each HUC12, (3) csv with results from the hydrologic change analysis, and (4) the shapefile of the pour point locations for the HUC12s used in the analyses.

Two methods of calculating hydrologic alteration were applied to modeled daily streamflow data for 9,201 12-digit hydrologic unit code (HUC12) pour points draining to the Gulf of Mexico (Robinson and others, 2020). The first method is a new modified method of calculating ecosurplus and ecodeficit called hydro change. For this project, ecosurplus and ecodeficit have been combined to assess overall hydrologic regime change. The second method is the confidence interval hypothesis test (Kroll and others, 2015). The first method is a means of quantifying hydrologic alteration while the second is a hypothesis test to simply determine if statistically significant alteration has occurred. Both methods are employed to determine which is best at analyzing alteration of the hydrologic regime in the Gulf Coast Ecosystem Restoration Council (RESTORE) study area. Statistical analysis was done in RStudio (2020). The data release includes four attached files: (1) metadata .xml file, (2) csv with the p-values for each HUC12, (3) csv with results from the hydrologic change analysis, and (4) the shapefile of the pour point locations for the HUC12s used in the analyses.

This page contains 15 estimated quantiles for 9,203 level-12 Hydrologic Unit Code in the Southeastern United States for the decades 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009. A multi-output neural network was used to generate the estimated quantiles (Worland and others, 2019). The R scripts that generated the predictions are also included along with a README file. The 15 quantiles are associated with the following 15 non-exceedance probabilities (NEPs): 0.0003, 0.0050, 0.0500, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000, 0.9500, 0.9950, and 0.9997. The quantiles were calculated using the Weibull plotting position (more details can be found in the accompanying manuscript). In addition to the median estimate of the quantiles, 68th, 95th, and 99.7th percentile intervals are also included in .csv file. The percentile intervals were estimated using Monte-Carlo dropout for 500 forward passes of the neural network. The intervals are represented in the .csv file as p0.0015, p0.0250, p0.1600, p0.5000, p0.8400, p0.975, and p0.9985 which indicates the 68th, 95th, and 99.7th percentile intervals. The median (p0.5000) and the mean estimate should be used if only a single realization of the estimated quantiles is needed. The neural network was trained using streamflow data at sites with records that contained only non-zero streamflow values. However, the model was used to make predictions for every HUC12 pour point. Some of these predictions are likely for sites that have streamflow values equal to zero. Worland, S. C., Steinschneider, S., Asquith, W., Knight, R. and Wieczorek, M., 2019, Prediction and inference of flow-duration curves using multi-output neural networks, Water Resources Research , submitted.

This page contains 15 estimated quantiles for 9,203 level-12 Hydrologic Unit Code in the Southeastern United States for the decades 1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009. A multi-output neural network was used to generate the estimated quantiles (Worland and others, 2019). The R scripts that generated the predictions are also included along with a README file. The 15 quantiles are associated with the following 15 non-exceedance probabilities (NEPs): 0.0003, 0.0050, 0.0500, 0.1000, 0.2000, 0.3000, 0.4000, 0.5000, 0.6000, 0.7000, 0.8000, 0.9000, 0.9500, 0.9950, and 0.9997. The quantiles were calculated using the Weibull plotting position (more details can be found in the accompanying manuscript). In addition to the median estimate of the quantiles, 68th, 95th, and 99.7th percentile intervals are also included in .csv file. The percentile intervals were estimated using Monte-Carlo dropout for 500 forward passes of the neural network. The intervals are represented in the .csv file as p0.0015, p0.0250, p0.1600, p0.5000, p0.8400, p0.975, and p0.9985 which indicates the 68th, 95th, and 99.7th percentile intervals. The median (p0.5000) and the mean estimate should be used if only a single realization of the estimated quantiles is needed. The neural network was trained using streamflow data at sites with records that contained only non-zero streamflow values. However, the model was used to make predictions for every HUC12 pour point. Some of these predictions are likely for sites that have streamflow values equal to zero. Worland, S. C., Steinschneider, S., Asquith, W., Knight, R. and Wieczorek, M., 2019, Prediction and inference of flow-duration curves using multi-output neural networks, Water Resources Research , submitted.