The USGS collects data for this national program to inform public, State and local governments, public and private utilities and other Federal agencies involved with managing water resources. The NWIS Mapper provides the ability to export descriptive information and access data for most water-data sites in the National Water Information System (NWIS). These sites are or have been operated by USGS or in some cases by its cooperative partners. Includes methyl and elemental Hg in surface water. USGS Water Quality and Bottom Sediment Data, Co-located with Higgins, and Tuttle samples, and includes additional USGS gauges at Dayton, Lloyds Bridge, Weeks Bridge, Ft Churchill, BLW Lahonton Dam, and Fallon Canals. The EPA is publishing this data in support of the Carson River Mercury NPL Site in Nevada. Data was compiled and evaluated for the OU2 Remedial Investigation Report (EPA, 2017), which describes the nature and extent of contamination from the Site. The report contains the Human Health Risk Assessment and Ecological Risk Assessment. Literature and other source Hg data are summarized in the RI for surface waters, sediments, and biological tissues.

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.

Digital flood-inundation maps for a 3.4-mile reach of North Fork Salt Creek at Nashville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 03371650, North Fork Salt Creek at Nashville, Ind. Real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/ (NWS site NFSI3). Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers ...

Digital flood-inundation maps for a 7.5-mile reach of the White River at Noblesville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 03349000, White River at Noblesville, Ind. Real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS site NBLI3). Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 03349000, White River at Noblesville, Ind. and documented high-water marks from the floods of September 4, 2003 and May 6, 2017. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 10.0 ft (the NWS “action stage”) to 24.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-foot vertical accuracy and 4.9-foott horizontal resolution) to delineate the area flooded at each stage. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

This data release is part of a cooperative study to assess streamflow availability under low-flow conditions for streams on the islands of O'ahu, Moloka'i, Maui, and Hawai'i from 2018 to 2022. This data release contains 24 child items that consist of the following files: (1) a metadata xml file describing the data release files and data attributes, (2) an annotated NWIS-Mapper screen-captured image showing the seepage-run measurement sites, and (3) a comma-delimited ascii data file with the discrete discharge measurements. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This U.S. Geological Survey data release consists of a tabular dataset containing stream temperature data. Data were collected every 12-hours from 15 sites in Soldier Meadows, Black Rock Desert - High Rock Canyon Emigrant Trails National Conservation Area, Nevada. The sites were established by the Bureau of Land Management - Winnemucca District Office in 2017. The data release includes tabular data of site names, date and time of temperature measurement, and stream temperature in degrees Celsius. The site locations are included below as a shapefile, exported from the National Water Information System (NWIS) Mapper. A site list can be accessed here: https://waterdata.usgs.gov /usa/nwis/inventory?multiple_site_no=412125119132201,412119119131601,412114119131201,412109119131001,412102119130701,412102119125901,412057119125502,412054119125001,412050119124101,412048119123301,412045119122601,412044119121701,412041119121001,412036119120301,412034119115601&format=station_list&station_list_format=xml&column_name=agency_cd&column_name=site_no&column_name=station_nm&list_of_search_criteria=multiple_site_no&column_name=site_tp_cd&column_name=dec_lat_va&column_name=dec_long_va&column_name=agency_use_cd

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Digital flood-inundation maps for a 12.6-mile reach of the Withlacoochee River from Skipper Bridge Road to St. Augustine Road, Lowndes County, Georgia, were created by the U.S. Geologicay Survey (USGS) in cooperation with the city of Valdosta and Lowndes County, Georgia. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Withlacoochee River at Skipper Bridge Road, near Bemiss, Georgia (023177483). Real-time stage information from this streamgage can be obtained at the National Water Information System Web Interface (NWISWeb; https://waterdata.usgs.gov/ga/nwis/rt) and can be used with these maps to estimate near real-time areas of inundation. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS; http:/water.weather.gov/ahps/) flood warning system (http:/water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often collocated at USGS streamgages. The forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at the streamgage Withlacoochee River at Skipper Bridge Road, near Bemiss (023177483) and documented high-water marks from recent floods. The hydraulic model was then used to determine 23 water-surface profiles for flood elevations at 1.0-foot intervals referenced to the water surface elevation and ranging from NWS action stage of 131.0 feet (10.7 feet streamgage stage) to 153.0 feet (32.7 feet streamgage stage) which is near the highest recorded water level at the streamgage. The flood-peak inundation area was modeled in a GIS by combining steady-state hydraulic modeling and available lidar digital elevation model (DEM) data. Information about the study, floods, and methods used can be found in the USGS Scientific Investigations Report 2018-5011 online at https://doi.org/10.3133/sir20185011.

The ascii grids represent regional probabilities that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL) for water quality. The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 meters (m). We utilized prediction modeling methods, specifically boosted regression trees (BRT) with a Bernoulli error distribution within a statistical learning framework within R's computing framework (http://www.r-project.org/) to produce two-dimensional probability grids at selected depths throughout the modeling domain. The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2,767 wells within the alluvial boundary of the Central Valley and over 60 predictor variables from 7 sources (see metadata) and were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. The wells used in the BRT models described above were attributed to predictor variable values in ArcGIS using a 500-m buffer. The response variable data consisted of measured DO and Mn concentrations from 2,767 wells within the alluvial boundary of the Central Valley. The data were compiled from two sources: U.S. Geological Survey (USGS) National Water Information System (NWIS) database (all data are publicly available from the USGS at http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water-quality data are publicly available from the SWRCB at http://geotracker.waterboards.ca.gov/gama/). Only wells with well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 that had the required water-quality data was used. Data were available for 932 wells for the NWIS dataset and 1,835 wells for the SWRCB-DDW dataset. Models were trained on a USGS NWIS dataset of 932 wells and evaluated on an independent hold-out dataset of 1,835 wells from the SWRCB-DDW. We used cross-validation to assess the predictive performance of models of varying complexity as a basis for selecting the final models used to create the prediction grids. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa, accuracy, and the area under the receiver operator characteristic (ROC) curve. The final trained models were used for mapping predictions at discrete depths to a depth of approximately 300 m. Trained DO and Mn models had accuracies of 86–100 percent, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95 percent, and ROC values were 0.87–0.91, indicating good predictive performance. Kappa values for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97 percent, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data. See associated journal article (Rosecrans and others, 2017) for complete summary of BRT modeling methods, model fit metrics, and relative influence of predictor variables for a given DO or Mn BRT model. The modeled response variables for the DO BRT models were based on measured DO values from wells at the following thresholds: <0.5 milligrams per liter (mg/L), <1.0 mg/L, and <2.0 mg/L, and these thresholds values were considered anoxic based on literature reviews. The modeled response variables for the Mn BRT models were based on measured Mn values from wells at the following exceedance thresholds: >50 micrograms per liter (µg/L), >150 µg/L, and >300 µg/L. (The 150 µg/L manganese threshold represents one-half the USGS HBSL.) The prediction grid discretization below land surface was in 15-m intervals to a depth of 122 m, followed by intervals of 30 m to a depth of 300 m, resulting in 14 two-dimensional probability grids for each constituent (DO and Mn) and threshold. Probability grid maps were also created for the shallow aquifer and deep aquifer represented by the median domestic and public-supply well depths, respectively. A depth of 46 m was used to stratify wells from the training dataset into the shallow and deep aquifer and was derived from depth percentiles associated with domestic and public supply in previous work by Burow and others (2013). In this work, the median well depth categorized as domestic was 30 m below land surface (bls), and the median well depth categorized as public-supply wells was 100 m bls. Therefore, datasets contained in the folders named "DO BRT prediction grids.zip" and "Mn BRT prediction grids.zip" each have 42 probability grids representing specific depths for each of the selected thresholds of DO and Mn BRT threshold models described above. The dataset contained in the folder named "PublicSupply&DomesticGrids.zip" contains probability grids represented by the domestic and public-supply drinking water depths for each of the six BRT models described above (12 grids total).

Digital flood-inundation maps for an approximate 2.5-mile (mi) reach of the Clear Fork Mohican River that extends approximately from State Route 97 to the downstream corporate boundary for Bellville, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District. The flood-inundation maps show estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Clear Fork Mohican River at Bellville (station number 03131982). The maps can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/. Near-real-time stages at this streamgage can be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/oh/nwis/uv/?site_no=03131982 or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at http://water.weather.gov/ahps2/hydrograph.php?wfo=cle&gage= ...