This data release contains data used to develop models and maps that estimate the occurrence of lithium in groundwater used as drinking water throughout the conterminous United States. An extreme gradient boosting model was developed to estimate the most probable lithium concentration category (≤4, >4 to ≤10, >10 to ≤30 or >30 µg/L). The model uses lithium concentration data from wells located throughout the conterminous United States and predictor variables that are available as geospatial data. The model is included in this data release in the zipped folder named Model_Archive and was used to produce maps that are also included in this data release. The model input data (predictor variables) that were used to make the maps are within a zipped folder (Map_Input_Data.zip) that contains 20 tif-raster files, one for each model predictor variable. The map probability estimates that are outputs from the model are in a zipped folder (Map_Output_Data.zip) that contains 10 tif-raster files, two model estimate maps for each of the lithium concentration categories and the category with the highest probability for public supply well depths and domestic supply well depths.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

ArcGIS Online Map Service created by Esri to provide access to: (1) Latin American and Caribbean 2015 Water Extent and (2) Latin American and Caribbean Water Bodies. The first dataset reflects the accumulation of the daily MODIS Surface Water detection product 3D3OT that is provided by the NASA’s MODIS Near Real-Time Global Flood Mapping Project, implementing the water detection algorithm of Dartmouth Flood Observatory (DFO). The dataset was produced by DFO for The Latin American Bank (CAF). The second dataset, the SRTM Water Body Data, is a by-product of the data editing performed by NGA to produce the finished SRTM Digital Terrain Elevation Data Level 2 (DTED® 2). In accordance with the DTED® 2 specification, the terrain elevation data have been edited to portray water bodies that meet minimum capture criteria. Ocean, lake and river shorelines were identified and delineated. Lake elevations were set to a constant value. Ocean elevations were set to zero. Rivers were stepped down monotonically to maintain proper flow. After this processing was done, the shorelines from the one arc second (approx. 30-meter) DTED® 2 were saved as vectors in ESRI 3-D Shapefile format. The dataset was produced by the USGS EROS for CAF. The data are hosted as tile layers in ArcGIS Online to improve performance. The water bodies layer is represented in dark blue and the water extent (aka flooding) in light blue. The original data can be downloaded from https://www.geosur.info.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as ...

The 2015 water extent dataset for Latin America and the Caribbean is reflecting the accumulation of the daily MODIS Surface Water detection product 3D3OT that is provided by the NASA’s MODIS Near Real-Time Global Flood Mapping Project, implementing the water detection algorithm of Dartmouth Flood Observatory (DFO). The applied daily 3D3OT product is a moving 3-day composite of 6 MODIS images (3 by Terra and 3 by Aqua satellite). Water will be only be marked as surface water when: 1) for this moving 3day composite, 3 or more images indicate water at a certain location; 2) for two adjacent 3-day composite files (reflecting two adjacent days) only the intersecting surface water cells that consist of 3 or more adjacent grid cells (each 250m2 large). The 3D3OT product includes a first order approach by NASA to filter out terrain shadows, this is further enhanced by applying above mentioned filter. However, accumulating daily output over a year still tends to reflect incorrect surface water extents for mountainous regions. The dataset was produced by DFO for The Latin American Bank (CAF) with funding provided by the GeoSUR Program. DFO produced additional water extent datasets for Latin America and the Caribbean covering the period 2000 – 2014, and can be found on the GeoSUR Portal

This map layer shows areal and linear water features of the United States, Puerto Rico, and the U.S. Virgin Islands. The original file was produced by joining the individual State hydrography layers from the 1:2,000,000- scale Digital Line Graph (DLG) data produced by the USGS. This map layer was formerly distributed as Hydrography Features of the United States. This is a revised version of the January 2003 map layer.

The Dynamic Surface Water Extent MODIS (DSWEmod) surface water maps for the conterminous United States were used for a study conducted by the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team quantifying seasonal and annual surface water trends within Environmental Protection Agency (EPA) Level I and Level III Ecoregions (Omernik, 1987) across the U.S. from 2003 through 2019. The overarching objectives of this study were to, (i) generate the monthly DSWEmod maps for the conterminous United States, (ii) review the spatial and temporal dynamics of surface water extent across ecoregions, and (iii) compare surface water area trends to streamgage discharge trends to determine where and how well different approaches to measuring water dynamics align. The DSWEmod model classifies the landscape (i.e., each 250-meter Moderate Resolution Imaging Spectroradiometer, or MODIS, pixel) into different classes of surface water based on quantified levels of confidence, including, (i) high-confidence surface water (class 1), (ii) moderate-confidence surface water (class 2), (iii) potential wetland (class 3), and (iv) low-confidence water/wetland (class 4), as well as a not-water class (class 0) and a no-data class (class 9). The confidence level is based on thresholds within various water- and vegetation-based indices. The level of confidence is based on how many, and, which index thresholds are met. Only high-confidence surface water (class 1) was considered in this study. This data release includes a vector shapefile consisting of 85 polygons, delineating EPA Level III Ecoregions for the conterminous United States. For each Level III Ecoregion, we include attributes identifying, (i) their respective Level I Ecoregion name and identification number, (ii) quantified seasonal and overall mean water area, (iii) comparisons with U.S. Geological Survey (USGS) National Water Information System (NWIS) streamgage discharge trends, (iv) mean surface water extent statistics (mean, minimum, maximum, standard deviation, coefficient of variation, percent of ecoregion), and (v) seasonal and overall results from the Mann-Kendall statistical analysis. An associated manuscript describes the methodology, results, and conclusions from this study.

The USGS compiles online access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, American Samoa and the Commonwealth of the Northern Mariana Islands.

Here we present a geospatial dataset representing local- and regional-scale aquifer system boundaries, defined on the basis of an extensive literature review and published in GebreEgziabher et al. (2022). Nature Communications, 13, 2129, https://www.nature.com/articles/s41467-022-29678-7

The database contains 440 polygons, each representing one study area analyzed in GebreEgziabher et al. (2022). The attribute table associated with the shapefile has two fields (column headings): (1) aquifer system title (Ocala Uplift sub-area of the broader Floridan Aquifer System), and (2) broader aquifer system title (e.g., the Floridan Aquifer System).

• to explore the available data via an interactive app please visit the following URL: https://ucsb.maps.arcgis.com/apps/instant/nearby/index.html?appid=2a238e4ed2434d21b3b53c861a064d8f
• to explore the 3D nature of a dozen aquifer systems please see the following storymap: https://storymaps.arcgis.com/stories/a91f061759e64e44af38daf6cefa4259

Use this map to explore spatial data in the American River watershed. To add additional data: (1) click Modify Map; (2) then click Add; (3) then click Search for Layers; and (4) in the Sierra Nevada Conservancy search box, type American River Watershed to find extra data.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The map graphic image at https://www.sciencebase.gov/catalog/file/get/63140561d34e36012efa2b7f?name=arsenic_map.png illustrates arsenic values, in micrograms per liter, for groundwater samples from about 31,000 wells and springs in 49 states compiled by the United States Geological Survey (USGS). The map graphic illustrates an updated version of figure 1 from Ryker (2001). Cited Reference: Ryker, S.J., Nov. 2001, Mapping arsenic in groundwater-- A real need, but a hard problem: Geotimes Newsmagazine of the Earth Sciences, v. 46 no. 11, p. 34-36 at http://www.agiweb.org/geotimes/nov01/feature_Asmap.html. An excel tabular data file, a txt file, along with a GIS shape file of arsenic concentrations (20,043 samples collected by the USGS) for a subset of the sites shown on the map. Samples were collected between 1973 and 2001 and are provided for download.

This dataset contains five maps of cumulative changes in water levels at 30-minute intervals over a 150-minute period on 2016-10-16 in the Atchafalaya Basin in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Water surface elevations were measured on six flights at 30-minute intervals, with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar (SAR) flown on the NASA Gulfstream-III aircraft. The five georeferenced maps at 6 m resolution show the cumulative change of water levels (cm) every 30 minutes relative to the first sampling flight. These Level 3 maps were generated using the InSAR time series Small Baseline Subsets (SBAS) algorithm implemented in the Generic InSAR Analysis Toolbox (GIAnT) toolbox and served to evaluate and compare hydrodynamic models.

WaterWatch (http://waterwatch.usgs.gov) is a U.S. Geological Survey (USGS) World Wide Web site that displays maps, graphs, and tables describing real-time, recent, and past streamflow conditions for the United States. The real-time information generally is updated on an hourly basis. WaterWatch provides streamgage-based maps that show the location of more than 3,000 long-term (30 years or more) USGS streamgages; use colors to represent streamflow conditions compared to historical streamflow; feature a point-and-click interface allowing users to retrieve graphs of stream stage (water elevation) and flow; and highlight locations where extreme hydrologic events, such as floods and droughts, are occurring.

The streamgage-based maps show streamflow conditions for real-time, average daily, and 7-day average streamflow. The real-time streamflow maps highlight flood and high flow conditions. The 7-day average streamflow maps highlight below-normal and drought conditions.

WaterWatch also provides hydrologic unit code (HUC) maps. HUC-based maps are derived from the streamgage-based maps and illustrate streamflow conditions in hydrologic regions. These maps show average streamflow conditions for 1-, 7-, 14-, and 28-day periods, and for monthly average streamflow; highlight regions of low flow or hydrologic drought; and provide historical runoff and streamflow conditions beginning in 1901.

WaterWatch summarizes streamflow conditions in a region (state or hydrologic unit) in terms of the long-term typical condition at streamgages in the region. Summary tables are provided along with time-series plots that depict variations through time. WaterWatch also includes tables of current streamflow information and locations of flooding.

From the site: "The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight-digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits).

The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States."

This data set provides the _location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period.

The National Waterway Network is a comprehensive network database of the nation's navigable waterways. The data set covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters.

© The National Waterway Network was created on behalf of the Bureau of Transportation Statistics, the U.S. Army Corps of Engineers, the U.S. Bureau of Census, and the U.S. Coast Guard by Vanderbilt University and Oak Ridge National Laboratory. Additional agencies with input into network development include Volpe National Transportation Systems Center, Maritime Administration, Military Traffic Management Command, Tennessee Valley Authority, U.S.Environmental Protection Agency, and the Federal Railroad Administration. This layer is sourced from maps.bts.dot.gov.

The National Waterway Network (NTAD 2015) is a comprehensive network database of the nation's navigable waterways. The data set covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters.

© The National Waterway Network was created on behalf of the Bureau of Transportation Statistics, the U.S. Army Corps of Engineers, the U.S. Bureau of Census, and the U.S. Coast Guard by Vanderbilt University and Oak Ridge National Laboratory. Additional agencies with input into network development include Volpe National Transportation Systems Center, Maritime Administration, Military Traffic Management Command, Tennessee Valley Authority, U.S.Environmental Protection Agency, and the Federal Railroad Administration.

Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class 2), iii) potential wetland (class 3), and iv) low-confidence water/wetland (class 4), as well as a not-water class (class 0) and a no-data class (class 9). This data release consists of a Parent Directory and 18 Child Items. The Parent Directory includes a zipped folder housing the complete monthly DSWEmod surface water maps for the conterminous United States from 2003 through 2019 represented in 17 multiband images, equating to one image for each year from 2003 through 2019. Each annual image – available as separate Child Items (n = 17) – consists of 12 bands, where each band value from 1-12 represents sequential months from January (Band 1) to December (Band 12). Such a structure allows for a user to download either the full time-series of DSWEmod products or a user-specified set of years. The DSWEmod surface water maps were used for a study conducted by the PLACE team quantifying seasonal and annual surface water trends within Environmental Protection Agency (EPA) Level I and Level III Ecoregions (Omernik, 1987) across the U.S. from 2003 through 2019. The results from this study are also being released as a Child Item - Surface Water Trends for the Conterminous United States using monthly DSWEmod Surface Water Maps, 2003–2019. This portion of the data release includes a vector shapefile consisting of 85 polygons, delineating EPA Level III Ecoregions for the conterminous United States. For each Level III Ecoregion, we include attributes identifying, (i) their respective Level I Ecoregion name and identification number, (ii) quantified seasonal and overall mean water area, (iii) comparisons with U.S. Geological Survey (USGS) National Water Information System (NWIS) streamgage discharge trends, (iv) mean surface water extent statistics (mean, minimum, maximum, standard deviation, coefficient of variation, percent of ecoregion), and (v) seasonal and overall results from the Mann-Kendall statistical analysis. An associated manuscript describes the methodology, results, and conclusions from this study.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m HUC, GIRAS, Hydrologic Units, 1:250 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