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.

The Aquifer Risk Map Web Tool contains all archived maps, including this 2023 Aquifer Risk Map.The Aquifer Risk Map is developed to fulfill requirements of SB-200 (Monning, 2019) and is intended to help prioritize areas where domestic wells and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the map is made available to the public and updated annually starting January 1, 2021. This web map is part of the 2023 Aquifer Risk Map. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding.

This web map includes the following layers:Water Quality Risk: water quality risk estimates per square mile section for all contaminants with an MCL. Water quality risk is listed as “high” (average or recent concentration in section is above MCL for one or more contaminants), “medium” (average or recent concentration in section is between 80% - 100% of MCL for one or more contaminants), “low” (average or recent concentration in section is less than 80% of MCL for all measured contaminants) or “unknown” (no water quality data available in section).Individual Contaminant Risk: water quality risk estimates for nitrate, arsenic, 1,2,3-trichloropropane, hexavalent chromium, and uranium per square mile section.State Small Water Systems (DDW): state small water systems (5-14 connections) location from the Division of Drinking Water joined with water quality risk section estimates from the 2023 Aquifer Risk Map.Domestic Well Records (OSWCR): the approximate count and location of domestic well completion reports submitted to the Department of Water Resources. This is used as a proxy to identify domestic well locations.Public Water System Boundaries (DDW): the approximate boundaries of public drinking water systems, from the Division of Drinking Water. For reference only.Census Areas: Census block groups and census tract boundaries containing demographic information from the 2021 American Community Survey (B19013 Median Household Income and B03002 race/ethnicity) joined with summarized water quality risk estimates from the 2023 Aquifer Risk Map (count of high risk domestic wells and state small water systems per census area).Reference Boundaries: Various geographic boundaries including counties, basins, GSA’s, CV-SALTS basin prioritization status, Disadvantaged Community (DAC) status, and legislative boundaries. For reference only.CalEnviroScreen 4.0: CalEnviroScreen scores from OEHHA. For reference only.Groundwater Level Percentiles (DWR): Groundwater depth in various monitoring wells compared to the historic average at that well. For reference only.

The water quality risk is based on depth-filtered, de-clustered water quality results from public and domestic supply wells. The methodology used to determine water quality risk is outlined here. For more information about the SAFER program, please email SAFER@waterboards.ca.gov. For technical questions or feedback on the map please email GAMA@waterboards.ca.gov.

The Aquifer Risk Map is developed to fulfill requirements of SB-200 (Monning, 2019) and is intended to help prioritize areas where domestic wells and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the map is made available to the public and updated annually starting January 1, 2021. This layer is part of the 2023 Aquifer Risk Map. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding.This layer displays water quality risk estimates per square mile section.The water quality risk is based on depth-filtered, de-clustered water quality results from public and domestic supply wells. To provide comments or feedback on this map, please email SAFER@waterboards.ca.gov or GAMA@waterboards.ca.gov.

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 is the map image layer. The feature layer is available here.

The Aquifer Risk Map is developed to fulfill requirements of SB-200 (Monning, 2019) and is intended to help prioritize areas where domestic wells and state small water systems may be accessing groundwater that does not meet primary drinking water standards (maximum contaminant level or MCL). In accordance with SB-200, the map is made available to the public and updated annually starting January 1, 2021. This layer is part of the 2023 Aquifer Risk Map. The Fund Expenditure Plan states the risk map will be used by Water Boards staff to help prioritize areas for available SAFER funding.

This layer contains summarized water quality risk per square mile section for five individual contaminants (nitrate, arsenic, 1,2,3-trichloropropane, hexavalent chromium, and uranium).

The water quality risk is based on depth-filtered, de-clustered water quality results from public and domestic supply wells. To provide comments or feedback on this map, please email SAFER@waterboards.ca.gov or GAMA@Waterboards.ca.gov.