Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling effort was focused on the MRVA, Middle Claiborne aquifer (MCAQ), and Lower Claiborne aquifer (LCAQ)of the Mississippi embayment aquifer system. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were used to predict pH to 1-km raster grid cells of the National Hydrologic Grid (Clark and others, 2018). Predictions were made for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework used in a regional groundwater flow model (Hart and others, 2008). Explanatory variables for the BRT models included attributes associated with well position and construction, surficial variables, and variables extracted from a MODFLOW groundwater flow model for the MISE (Haugh and others, 2020a,b). For a full description of modeling workflow see Knierim and others (2020).

A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United States. Employing two machine-learning techniques offered the opportunity to generate model and statistical error and covariance between them to estimate total uncertainty. Potentiometric surface predictions were made at the 1-kilometer grid scale using the National Hydrogeologic Grid (Clark and others, 2018). Results produced by the mmlMRVAgen1 software have been condensed into thirteen themes each containing a multi-banded GeoTIFF raster with 504 layers, corresponding to each month for the available years described in the metadata. The themes include the final predicted monthly water-level altitudes, in feet North American Vertical Datum of 1988 (NAVD88), for the study area (pol), which were computed by pooling through weighted-mean averaging by cell the even and odd year predictions for that month. The depth to water was predicted in feet (nhgd2w), utilizing the NHG cell altitude as the land surface datum. Model errors were evaluated using both the normal error (modnorerr) in standard deviations of feet and the polynomial-density-quantile4 distribution (PDQ4)-error model (modpdqerr) without the inclusion of land-surface variation of the NHG. The equivalent standard deviations of these error models were calculated both with and without the inclusion of land-surface variation of the NHG (norerr, norerrnhg, pdqerr, pdqerrnhg). The lower and upper bounds (in feet) of the 90-percent prediction limits for both model error forms were computed (norlwr, norupr, pdqlwr, pdqupr). Lastly, the ratio of model error to total error (modtotrat) was also computed. Complementing each of the GeoTIFFs are `.json` extensions to each file. These provide additional multi-band support information. This double-file representation stems from the native GeoTIFF drivers within the terra R package underpinning the operations. Overall, the model objects created by the mmlMRVAgen1 from about 156,000 water-level records for about 58,000 wells report (1) a normalized Nash−Sutcliffe Efficiency (NNSE) of about 0.997, (2) a root-mean-square error (RMSE) of about 4.15 feet, and (3) a bias prior to computing the NNSE and RMSE of about 0.0963 feet before its subsequent removal (see mmlMRVAgen1 software diagnostics associated with "MRVA_MML_CONSTANTS"). The model objects also report for the 156,000 water-level records (1) a mean percent ratio of model error to total error of about 69.2 percent and (2) a mean width of about 12.05 feet for the 90-percent prediction bounds from the PDQ4 error framework (see mmlMRVAgen1 software diagnostics associated with "genMML/03step.R"). The model objects were used in post-model creation to predict each of the rasters provided in this data release. (Note, the results herein are associated with the "April 21, 2024" model run, see mmlMRVAgen1/model_archive/README.md.) For a full description of covariate assemblage and hydrograph modeling, see Asquith and Killian (2022) (covMRVAgen1 software). For a full description of multiple machine-learning modeling, see Asquith and Killian (2024) (mmlMRVAgen1 software).

A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United States. Employing two machine-learning techniques offered the opportunity to generate model and statistical error and covariance between them to estimate total uncertainty. Potentiometric surface predictions were made at the 1-kilometer grid scale using the National Hydrogeologic Grid (Clark and others, 2018). Results produced by the mmlMRVAgen1 software have been condensed into thirteen themes each containing a multi-banded GeoTIFF raster with 504 layers, corresponding to each month for the available years described in the metadata. The themes include the final predicted monthly water-level altitudes, in feet North American Vertical Datum of 1988 (NAVD88), for the study area (pol), which were computed by pooling through weighted-mean averaging by cell the even and odd year predictions for that month. The depth to water was predicted in feet (nhgd2w), utilizing the NHG cell altitude as the land surface datum. Model errors were evaluated using both the normal error (modnorerr) in standard deviations of feet and the polynomial-density-quantile4 distribution (PDQ4)-error model (modpdqerr) without the inclusion of land-surface variation of the NHG. The equivalent standard deviations of these error models were calculated both with and without the inclusion of land-surface variation of the NHG (norerr, norerrnhg, pdqerr, pdqerrnhg). The lower and upper bounds (in feet) of the 90-percent prediction limits for both model error forms were computed (norlwr, norupr, pdqlwr, pdqupr). Lastly, the ratio of model error to total error (modtotrat) was also computed. Complementing each of the GeoTIFFs are `.json` extensions to each file. These provide additional multi-band support information. This double-file representation stems from the native GeoTIFF drivers within the terra R package underpinning the operations. Overall, the model objects created by the mmlMRVAgen1 from about 156,000 water-level records for about 58,000 wells report (1) a normalized Nash−Sutcliffe Efficiency (NNSE) of about 0.997, (2) a root-mean-square error (RMSE) of about 4.15 feet, and (3) a bias prior to computing the NNSE and RMSE of about 0.0963 feet before its subsequent removal (see mmlMRVAgen1 software diagnostics associated with "MRVA_MML_CONSTANTS"). The model objects also report for the 156,000 water-level records (1) a mean percent ratio of model error to total error of about 69.2 percent and (2) a mean width of about 12.05 feet for the 90-percent prediction bounds from the PDQ4 error framework (see mmlMRVAgen1 software diagnostics associated with "genMML/03step.R"). The model objects were used in post-model creation to predict each of the rasters provided in this data release. (Note, the results herein are associated with the "April 21, 2024" model run, see mmlMRVAgen1/model_archive/README.md.) For a full description of covariate assemblage and hydrograph modeling, see Asquith and Killian (2022) (covMRVAgen1 software). For a full description of multiple machine-learning modeling, see Asquith and Killian (2024) (mmlMRVAgen1 software).

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data products described herein. A sweep through the combined dataset (the "database") was made to isolate duplicate observations, or observations for the same well and on the same day. If a discrete value was present, it was retained as authoritative for the day and in descending order of priority daily-mean, daily-maximum, and daily minimum. Therefore, only a single record for a well and day are present in the dataset. The duplicate search removed 876 records and 31 wells were involved; in total, this is about 0.3 percent of the database. References: Asquith, W.H., Seanor, R.C., 2019, infoGW2visGWDB—An R groundwater data-processing utility for manipulating, checking the veracity, and converting an "infoGW" object to the "GWmaster" object for the visGWDB software with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9MK0B6L. Painter, J.A., and Westerman, D.A., 2018. Mississippi Alluvial Plain extent, November 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F70R9NMJ. U.S. Geological Survey, 2020, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed April 2, 2020, at https://doi.org/10.5066/F7P55KJN.