Kenneth Belitz

A random forest classification (RFC) model was developed to predict hydrochemical facies (HCFs) of groundwater in three dimensions across the conterminous United States (CONUS). Major‐ion data from 152,673 sites were used to categorize groundwater into one of six HCFs (CaMg‐HCO ₃ , NaK‐HCO ₃ , CaMg‐SO ₄ , NaK‐SO ₄ , Cl, or Mixed). These six HCFs were used as targets for RFC modeling. Model features that represent relevant geochemical processes and/or physical conditions were derived from previously published data. Additional model features were specifically engineered to support this analysis: elevation of the bottom of a well relative to the base of drinking water (ERDW) and six flags that relate geologic units to HCFs. The most important model feature was ERDW. The model was used to map HCFs at a 1‐km ² resolution across CONUS and to depths of 400 m below the base of drinking water (which varies from 22 m to 2 km). Model predictions are consistent with expectations. CaMg‐HCO ₃ is predicted to occur near the water table in more humid settings, and areas underlain by carbonate or crystalline rocks. At depths below the base of drinking‐water supplies, the model predicts a rapid transition from HCO ₃ HCFs to Cl. Model predictions are accurate based on point data, and data averaged across hydrogeologic regions and with depth. Model predictions of HCFs could be used for multiple purposes, including the mapping of salinity and other groundwater characteristics.

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking‐water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble‐tree machine‐learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two‐variable partial‐dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process‐informed data, such as MODFLOW results and airborne geophysics, into a machine‐learning model improves model interpretability. Incorporation of process‐rich information into machine‐learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States.Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records).Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons.Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale.Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions.Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.