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This child item describes Python code used to estimate average yearly and monthly tourism per 1000 residents within public-supply water service areas. Increases in population due to tourism may impact amounts of water used by public-supply water systems. This data release contains model input datasets, Python code used to develop the tourism information, and output estimates of tourism. 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. Output from this code was used as an input feature in the public supply delivery and water use machine learning models. This page includes the following files: tourism_input_data.zip...
This child item describes a public supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to commercial, industrial, institutional, and irrigation (CII) users. This model predicts total, domestic, and CII per capita rates for public-supply water service areas within the conterminous United States for 2009-2020. This child item contains model input datasets, code used to build the delivery machine learning model, and national predictions. 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. This page includes the following...
This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. 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. Data retrieved by the census data collector code were used as input...
Measures used to assess trends in the 10th, 50th, and 90th quantiles of annual peak streamflow from 1916-2015 at 2,683 U.S. Geological Survey stations and within 191 4-digit HUCs in the conterminous United States. Linear quantile regression was applied to the selected quantiles of log-transformed annual peak streamflow to represent trends for a range of flood frequencies from small, common floods to large, infrequent floods. Comparative trends in pairs of quantiles were characterized as coherent, convergent, or divergent by comparing the slopes of linear quantile regression equations.
These data depict the National Hydrography Dataset Plus Version 2.1 (NHDPlusV2.1) flowline representation of the National Wild and Scenic Rivers System throughout the conterminous United States as of 2018. U.S. Forest Service geospatial data on National Wild and Scenic River segments (USFS WSR Segment) from 3/1/2016 were joined to the NHDPlusV2.1 to create the Wild and Scenic Rivers 2018 Linked to the NHDPlusV2.1 (wsr_nhdpv2.1) data. To ensure these data correctly represented the NHDPlusV2.1 flowline delineation of the National Wild and Scenic Rivers System, each wsr_nhdpv2.1 river segment was examined against the USFS WSR Segment data to check for duplication and/or omission of Wild and Scenic River segments. Spatial...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Alabama,
Arizona,
Arkansas,
California,
Colorado,
The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. This data release contains data used in a machine learning model to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features as well as model output values by 12-digit hydrologic...
This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. 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....
This dataset contains estimates of water withdrawals from 66 principal aquifers and "other" non-principal aquifers during 2015 for various categories of use in each county or county equivalent in the United States, Puerto Rico, and the U.S. Virgin Islands. The U.S. Geological Survey's (USGS) National Water Use Science Project is responsible for compiling and disseminating the Nation's water-use data. Withdrawal estimates are summarized in USGS Circular 1464, "Estimated Groundwater Withdrawals from Principal Aquifers in the United States, 2015" (Lovelace and others, 2020). This dataset contains the estimated groundwater withdrawals from principal aquifers by county and county equivalent that are summarized by lithologic...
Categories: Data;
Tags: Ada-Vamoosa aquifer,
Alabama,
Alaska,
Alluvial aquifers,
Arbuckle-Simpson aquifer,
This child item describes Python code used to retrieve gridMET climate data for a specific area and time period. Climate data were retrieved for public-supply water service areas, but the climate data collector could be used to retrieve data for other areas of interest. 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. Data retrieved by the climate data collector code were used as input feature variables in the public supply delivery and water use machine learning models. This page includes the following file: climate_data_collector.zip - a zip file containing the climate data collector Python code used to retrieve...
An inventory of facilities that bottle water or other beverages containing water (including soft drinks, beer, wine, or spirits) or that manufacture ice was compiled by combining available datasets from multiple sources. This water bottling inventory dataset includes facilities within all 50 states of the United States, one federal district (Washington, District of Columbia), and three territories (Guam, Puerto Rico, and Virgin Islands). The inventory focuses on presently active facilities in 2023. Most closed water bottling facilities are not included; however, facilities identified as being a former production site (meaning the facility is still active but the business function has changed) or as closed during...
These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA. Figure 1 shows the areal boundary (cyan) of Bachelor...
The U.S. Geological Survey (USGS) developed a spatial water-quality model called SPAtially Referenced Regressions On Watershed attributes (SPARROW) to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in the Nation’s streams. The SPARROW model relates in-stream water-quality data to spatially referenced characteristics of watersheds, including contaminant sources and factors influencing terrestrial and aquatic transport. Based on SPARROW modeling, one of the main nutrient sources to streams is point-source facilities such as municipal waste-water treatment plants that discharge directly to streams. This dataset was developed to assist with...
The GAP National Terrestrial Ecosystems - Ver 3.0 is a 2011 update of the National Gap Analysis Project Land Cover Data - Version 2.2 for the conterminous U.S. The GAP National Terrestrial Ecosystems - Version 3.0 represents a highly thematically detailed land cover map of the U.S. The map legend includes types described by NatureServe's Ecological Systems Classification (Comer et al. 2002) as well as land use classes described in the National Land Cover Dataset 2011 (Homer et al. 2015). These data cover the entire continental U.S. and are a continuous data layer. These raster data have a 30 m x 30 m cell resolution. GAP used the best information available to create the land cover data; however GAP seeks to improve...
Categories: Data;
Types: Map Service,
OGC WMS Layer;
Tags: Alabama,
Alaska,
Appalachian,
Arizona,
Arkansas,
This child item describes R code used to determine water source fractions (groundwater (GW), surface water (SW), or spring (SP)) for public-supply water service areas, counties, and 12-digit hydrologic unit codes (HUC12) using information from a proprietary dataset from the U.S. Environmental Protection Agency. Water-use volumes per source were not available from public-supply systems so water source fractions were calculated by the number of withdrawal source types (GW/SW). For example, for a public supply system with three SW intakes and one GW well, the fractions would be 0.75 SW and 0.25 GW. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic...
This data release documents seven tables that contain environmental tracer data and lumped parameter model (LPM) results that are used for assessing the distribution of groundwater age in 21 Principal Aquifers of the continental United States. Groundwater samples were collected from 1,279 sites and analyzed for environmental tracers: tritium, tritiogenic helium-3, sulfur hexafluoride, carbon-14, and radiogenic helium-4. For each sample, groundwater age distributions were estimated by fitting LPMs to the environmental tracers using the computer program TracerLPM (Jurgens and others, 2012). Calibrated lumped parameter models provide the optimal distribution of ages in a sample that explain the measured concentrations...
National watershed boundary (HUC12) dataset for the conterminous United States, retrieved 10/26/2020
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...
This child item describes a machine learning model that was developed to estimate public-supply water use by service area boundary and 12-digit hydrologic unit code (HUC12) for the conterminous United States. This model was used to develop an annual and monthly public supply reanalysis of withdrawals for the period 2000-2020. This data release contains model input feature datasets, python codes used to develop and train the water use machine learning model, and output water use predictions by HUC12. This page includes the following files: PS_HUC12_Tot_2000_2020.csv - a csv file with monthly public supply reanalysis of withdrawals from 2000-2020 by HUC12 PS_HUC12_GW_2000_2020.csv - a csv file with estimated monthly...
This data release is a compilation of six earlier reports on natural occurrences of asbestos in the conterminous United States, which were published separately; these are Van Gosen (2005, 2006, 2007, 2008, 2010) and Van Gosen and Clinkenbeard (2011). The earlier reports were compilations divided by regions of the United States; in each report the data were provided in spreadsheet format. This data release combines the regional datasets into a single compilation for the entire conterminous United States, and includes shapefiles and spreadsheet formats. This data release provides site-by-site information on 876 natural occurrences of asbestos reported within the conterminous United States. This dataset allows the...
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service;
Tags: Alabama,
Arizona,
Arkansas,
California,
Colorado,
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