Groundwater potentiometric-surface contours for spring 2022 (April 4 to 8, 2022) and autumn 2022 (October 30 to November 4, 2022) were created for the alluvial aquifer in Big Lost River Valley. The well numbers and station names used to create the potentiometric-surface contours and groundwater-level change maps are provided in this data release. The location, depth to water, and potentiometric-surface altitude for these wells can be accessed on USGS National Water Information System (NWIS) or Idaho Department of Water Resources (IDWR) groundwater portal. The interpreted 20-foot contours of the potentiometric-surface are also provided in this data release. The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The potentiometric-surface contours are divided into three water-bearing units - shallow, intermediate, and deep - based on well depth, potentiometric-surface altitude, and hydrogeologic unit. The intermediate and deep units were only identified in the southern portion of the valley near Arco, Idaho. The potentiometric-surface contours ranged from 4,900 to 6,660 feet above NAVD 88. The groundwater-level change at well sites from spring to autumn 2022, spring to autumn 1968, spring 1968 to spring 2022, spring 1991 to spring 2022, and spring 1968 to spring 1991 were calculated and are provided in a shapefile.

The U.S. Geological Survey (USGS) is providing a compilation of geologic well records (n=221) collected from 2014-2020 within the Binghamton East 1:24,000 quadrangle in south-central Broome County, New York. The well records were obtained from: 1) previous U.S. Geological Survey groundwater investigations, 2) the U.S. Geological Survey’s National Water Information System (NWIS), 3) the New York State Department of Environmental Conservation (NYSDEC) Water Well Contractor Program, and 4) the New York State Department of Transportation (NYSDOT). The dataset is in comma-separated values (CSV) format. A companion report, USGS Scientific Investigations Report 2021-5026 (Van Hoesen and others, 2021; https://doi.org/10.3133/sir20215026) further describes data collection and map preparation.

Groundwater in the arid Mountain Home area is vital to agricultural, municipal, industrial and other water users who are concerned about declining groundwater levels. The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources (IDWR), developed a hydrogeologic framework to provide a conceptual understanding of groundwater resources in the Mountain Home area. As part of the hydrogeologic framework, water-table contour and groundwater-level change maps were produced to describe the occurrence, movement, and change in groundwater. Water-table contours for spring 2023 (March 20 to 24, 2023) and autumn 2023 (November 1 to 7, 2023) were created for the regional aquifer and perched groundwater zone in the Mountain Home area. The well numbers and station names for sites used to create the water-table contours and groundwater-level change and groundwater storage change rasters are provided in this data release. The _location, depth to water, and groundwater altitude for these wells can be accessed on USGS National Water Information System (NWIS), IDWR groundwater portal, or an annual water level monitoring report for IDWR permit 61-12090 (HDR, 2024). The interpreted 50-foot contours of the water table are also provided in this data release. The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The water-table contours are divided into two water-bearing units - regional and perched groundwater zone - based on well depth and groundwater altitude. The water-table contours ranged from 2,350 to 3,650 feet above NAVD 88. The groundwater-level change at well sites from spring to autumn 2023 were interpolated over the study area and are provided in a raster. Groundwater-level change ranged from 22.01 ft decline to 15.44 ft rise. Groundwater-level change was multiplied by hydrogeologic unit storativity to estimate groundwater storage change from spring to autumn 2023. More information on the generation of the water-table contours, groundwater change maps, and perched groundwater delineation and limitations can be found in the companion report, SIR 2024-5124 (Hydrogeologic framework of the Mountain Home area, southern Idaho by L.M. Zinsser and S.D. Ducar).

The regional Ozark aquifer potentiometric-surface map shows the altitude at which the water level would have risen in tightly cased wells and represents conditions during the period from November 2014 through January 2015. Water levels were measured during this period to ensure that wells had adequate time to recover from previous summer pumping and prior to the start of the 2015 summer pumping season. Groundwater-level data from 178 wells cased completely in and open to the Ozark aquifer are available from the USGS National Water Information System (NWIS; data available at http:// waterdata.usgs.gov/nwis). Streams and springs in the study area represent the intersection of the groundwater table with land surface; these features were used in the construction of the potentiometric-surface map. In Arkansas and Missouri, where the Ozark aquifer crops out, altitudes of select gaining stream reaches, compiled from previous reports on gaining and losing streams (data available at http://dx.doi.org/10.5066/F7W9577Q) and select springs (data available at ftp://msdis.missouri.edu/pub/Inland_Water_Resources/MO_2010_ Springs_shp.zip), were calculated from 10-meter digital elevation data (Knierim and others, 2015; Missouri Department of Natural Resources and others, 2010). After collecting and processing the data, a potentiometric surface was generated by using the interpolation method TopotoRaster in ArcMap. This tool is specifically designed for the creation of hydrologically correct digital elevation models while imposing constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2011). Once the raster surface was created, 100-ft contours were generated by using Contour (Spatial Analyst), which is a spatial analyst tool (available through ArcGIS Spatial Analyst Toolbox) that creates a linefeature class of contours (isolines) from the raster surface (Esri, 2008). Contours were manually adjusted based on topographical influence, a comparison with the regional map of Imes and Emmett (1994), and data point water-level altitudes to more accurately represent the potentiometric surface.

These digital maps contain information on the altitude of the base, the extent, and the potentiometric surface (i.e. altitude of the water table) of the Glacial-drift aquifer in Kansas. The Glacial-drift aquifer consists of surficial sand and gravel deposits as well as sand and gravel buried beneath unconsolidated till and loess of Pleistocene age. The bedrock digital map was developed from 1:125,000-scale source information with a contour interval of 50 feet. The extent map was developed from 1:500,000-scale source information. Included as part of the digital extent map are areas where older rocks crop out within the aquifer boundary (fensters) and areas where the glacial-drift aquifers have little or no saturated thickness. The potentiometric surface digital map was developed at a scale of 1:200,000- from water-level data collected and compiled by the Kansas Geological Survey and from data available from the U.S. Geological Survey's National Water Information System (NWIS). The potentiometric surface contour interval is 50 feet. These digital maps were produced in cooperation with the Kansas Water Office as part of the State Water Plan.

All well locations from all datasets standardized on the GAMA Program's Groundwater Information System (GAMA GIS). This is a replacement of previous versions, updated quarterly. Authoritative version. WGS 84.All groundwater wells on GAMA Groundwater Information System, accessed April 24, 2023. Sources of data include (as indicated in GM_DATA_SOURCE field):Geotracker: Wells sampled under regulated activities like cleanup and remediation. These are accessible through the California State Water Resources Control Board Geotracker web site.USGS: Wells sampled and analyzed by the U.S. Geological Survey (USGS) through the Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project.GAMA: Wells sampled by California State Water Resources Control Board staff for the GAMA Program Domestic Well Project.DDW: Division of Drinking Water (DDW) wells sampled and regulated for delivered water quality under DDW oversight.DPR: Wells sampled by the Department of Pesticide Regulation (DPR) groundwater program.WDL: Wells in the Department of Water Resources (DWR) water quality sampling network in their water data library.LLNL: Wells sampled for groundwater age, isotopes, or noble gas for the GAMA Program by Lawrence Livermore National Laboratory (LLNL).NWIS: Wells sampled by the USGS and accessible via the National Water Information System (NWIS).UC Davis: Location of wells gathered from multiple local entities for use in the UC Davis Nitrate Report, under agreement with the GAMA Program.LOCALGW: Wells sampled under various local groundwater projects. As of July 30, 2019, this only includes the domestic sampling completed by the Central Coast Regional Water Quality Control Board. ‘GAMA_LOCALGW: Wells sampled under local groundwater projects, generally sampled from private wells from various private and governmental organizations. Data was submitted through the GAMA Data Connection Portal.The field, GM_DATASET_NAME can also help explain the source of the dataset.The corresponding map image layer for these well locations can be found at the following link: All Wells on the GAMA Groundwater Information System - Overview (ca.gov)Direct any questions to: GAMA@waterboards.ca.gov.

This data release provides several data files representing groundwater levels reported through driller's reports for the State of Louisiana Department of Natural Resources (Louisiana Department of Natural Resources, 2023) within or near the Mississippi Alluvial Plain (MAP) and (or) associated with the Mississippi River Valley alluvial aquifer (MRVA). First, a retrieval of data from the State of Louisiana was made and manual preparatory filtering including complete information of location, date, water level (depth below land surface) and water level altitude in feet, and general association with the MAP or MRVA. Further manual and digitally-assisted inspection was made to confirm that the data were not already within the U.S. Geological Survey (USGS) National Water Information System (NWIS) (U.S. Geological Survey, 2023). The agency code for the water levels has been assigned "LA018" (Louisiana Department of Natural Resources) in accordance with the https://help.waterdata.usgs.gov/codes-and-parameters/code/agency_cd_query?fmt=html (accessed February 28, 2023). Use of the LA018 agency code is consistent with historical and current USGS storage practices in NWIS when in collaboration with the State of Louisiana. This first data file is titled "LADNR_drillers_working.csv" (6,374 records). Second, that data file was processed through data structure conversion software (infoGW2visGWDB) (Asquith and Seanor, 2019) and in particular removal of well locations plotting outside the MAP boundary (Painter and Westerman, 2023) was made. The resultant but transient data structure of 4,855 of the original 6,374 records was given over to quality-control and assurance using statistical modeling (visGWDBmrva software) (Asquith and others, 2019, 2020). The statistical analyses result in formation of a regional statistical time series models using generalized additive models (GAMs) and support vector machines (SVMs). Some 18 records by horizontal position having a missing altitude of the bottom of the MRVA and zero records having water-level altitudes below the bottom of the MRVA when digitally working with the Torak and Painter (2019) surface of the MRVA bottom. These 18 records are retained through the workflow described herein to avoid potential scientific interpretation of hydrogeologic framework. In summary, for each of the 4,855 well-water-level records (or rather in detail, each unique well identifier), the visGWDBmrva software isolated all water levels for the MAP/MRVA from USGS (2023) within 16 kilometers radial distance. This means that the driller's dataset is being internally compared to itself and USGS MAP/MRVA data. The visGWDBmrva software computed a "pseudo water level" from a blending of GAM and SVM model predictions for the date of the driller's recorded water level. These computations are all created on-the-fly. A residual was computed from the pseudo water level (as altitude) to that water-level altitude reported for the well-water-level record of the driller's dataset. These statistical results are listed the file titled "LADNR_retained_levels.csv" (4,744 records) for which records were retained LADNR_drillers_working.csv if the absolute value of the residual of the well-water-level record and the pseudo water level was less than or equal to 20 feet. This threshold resulted from exploratory review of the statistical computations and is consistent with Smith and others (2020) and Weber and others (2021) for a similar driller's reported dataset for the Missouri part of the MAP/MRVA. The results listed in file LADNR_retained_levels.csv are deemed especially suitable for greater statistical modeling of groundwater levels in the MRVA (Asquith and Killian, 2022).

This dataset contains all publicly-available analytical data collected by the U.S. Geological Survey (USGS) at each location within the Pennsylvania Groundwater Monitoring Network (GWMN) from 2015 to present. Additional analytical data will be appended following the conclusion of each sampling season, and all data is made publicly available in the online National Water Inventory System (NWIS). This dataset serves as a repository for data used by the Pennsylvania Groundwater Monitoring Network map, found here: https://rconnect.chs.usgs.gov/PA_GWMN_map/

This data set describes wells (excluding observation wells) that are completed in the Arikaree aquifer and that were used to create a map of the generalized potentiometric surface of the Arikaree aquifer in the Pine Ridge Indian Reservation and Bennett County. Water levels in the wells were measured by a variety of people including the well drillers and U.S. Geological Survey personnel. The water-level data for this data set are based on the most recent measured value for the well through 2006. The range n water-level data for this data set was from 1929 to 2006. All water levels were retrieved from the U.S. Geological Survey's NWISWeb database at http://nwis.waterdata.usgs.gov/sd/nwis/gwlevels.

This data set describes springs issuing from the Arikaree aquifer or overlying alluvium that were used to create a map of the generalized potentiometric surface of the Arikaree aquifer in the Pine Ridge Indian Reservation and Bennett County. All data were retrived from the U.S. Geological Survey's NWISWeb database at http://nwis.waterdata.usgs.gov/sd/nwis/.

In 1995, the U.S. Geological Survey (USGS) began a series of studies to monitor several major creeks and rivers that discharge freshwater into northeastern Florida Bay and the southwest coast of Everglades National Park (ENP). These studies provide water-level, flow, salinity, and temperature data for model development and calibration and also serve as a long term data set to assist in detecting change in hydrology, as well as other physical, biological and chemical studies being conducted in these areas. These studies are being done as part of the USGS Greater Everglades Priority Ecosystems Science program (PES), which is an effort by the USGS to provide earth science information needed to resolve land-use and water issues. Additional support is provided by the U.S. Army Corps of Engineers and Everglades National Park (ENP) for PES. As part of these studies, a network of 34 hydrologic monitoring stations is already in place and historical data is currently available through the USGS South Florida Information Access (SOFIA) web page at URL: http://sofia.er.usgs.gov/. Real time information is available at the USGS National Water Information Systems URL: http://waterdata.usgs.gov/fl/nwis/rt. In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network, Map Activity 3.1.3.3. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for those MAP activities. Future funding for the northeastern Florida Bay and southwest coast estuarine studies is expected to continue from the USGS PES program in order support the larger integrated monitoring network. The MAP funding of monitoring stations within the Coastal Gradients network is a direct benefit to the overall integrated network and supplies critical hydrologic information where none previously existed.

Purpose and Scope

The purpose of this project is to operate and maintain ten (10) established hydrologic and water quality data collection platforms (DCP’s) in the coastal and fresh water marsh environments of the Everglades in order to support a larger integrated monitoring network (Fig. 1). The hydrologic and water quality information from this network is available for the development and calibration of hydrodynamic and water quality models of the Everglades, Florida Bay, and adjacent marine systems. Data will also provide information to evaluate impacts from project level CERP activity such as the C-111 Spreader Canal, the Combined Structural and Operational Plan (CSOP), and ModWaters.

The network of DCP’s collect information at points of interest along transects that represent major flow paths from the Everglades wetlands to the southern estuaries. The continuous data collected from surface and ground water along with nutrient loading computations is summarized in subsequent sections of this report. This long-term monitoring network spans the major flow paths from the Everglades wetlands to the southern estuaries which help provide a system-wide understanding of the ecosystem responses seen in the Everglades due to changes in water management practices and climatic variability. This data set contributes to the success of CERP by: a. Providing pre-CERP (baseline) and concurrent data on hydrologic and water quality parameters available for comparison during and after CERP modifications from projects such as the C-111 Spreader Canal, CSOP, and ModWaters. b. The ability to perform scientific investigations with physical data in order to increase ecosystem understanding. c. Having real-time and historic data available to detect unexpected responses within the ecosystem due to CERP activities.

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey).

This map is for Weber County, Utah.

The scope and purpose of NWIS is defined on the web site:

http://water.usgs.gov/public/pubs/FS/FS-027-98/

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey). This map is for Wayne County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/

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).

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey). This map is for Boxelder County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey). This map is for Iron County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey). This map is for Duchesne County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/