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.

Monthly rollup of the discrete and daily-aligned groundwater levels were created from Robinson, Asquith, and Seanor (2020) data products with removal of the paired groundwater and surface-water sites listed by Robinson, Killian, and Asquith (2020). The monthly rollup is composed of (1) computed monthly "mean" values regardless of whether a well had one measurement in the month or up to about 30 days of daily-mean values, (2) standard deviation of the water levels within the month (sample size is generally just one day but for recorder sites could be up to about 30 days), (3) the last water level in the month, and (4) monthly counts of water levels. The algorithm is available within the sources of visGWDBmrva (Asquith and others, 2019). A comment is made that the string 1980-01-01_2019-12-31 is retained in the file naming to parallel that for Robinson, Asquith, and Seanor (2020) files although the day of the month has no meaning for a monthly rollup. There are 18,736 unique wells of statistics; 18,736 wells in the metadata; and 107,568 year-month entries in the monthly rollup product. References: Asquith, W.H., Seanor, R.C., McGuire, V.L. (contributor), and Kress, W.H. (contributor), 2019, Source code in R to quality assure, plot, summarize, interpolate, and extend groundwater-level information, visGWDB—Groundwater-level informatics with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9W004O6.

Monthly rollup of the discrete and daily-aligned groundwater levels were created from Robinson, Asquith, and Seanor (2020) data products with removal of the paired groundwater and surface-water sites listed by Robinson, Killian, and Asquith (2020). The monthly rollup is composed of (1) computed monthly "mean" values regardless of whether a well had one measurement in the month or up to about 30 days of daily-mean values, (2) standard deviation of the water levels within the month (sample size is generally just one day but for recorder sites could be up to about 30 days), (3) the last water level in the month, and (4) monthly counts of water levels. The algorithm is available within the sources of visGWDBmrva (Asquith and others, 2019). A comment is made that the string 1980-01-01_2019-12-31 is retained in the file naming to parallel that for Robinson, Asquith, and Seanor (2020) files although the day of the month has no meaning for a monthly rollup. There are 18,736 unique wells of statistics; 18,736 wells in the metadata; and 107,568 year-month entries in the monthly rollup product. References: Asquith, W.H., Seanor, R.C., McGuire, V.L. (contributor), and Kress, W.H. (contributor), 2019, Source code in R to quality assure, plot, summarize, interpolate, and extend groundwater-level information, visGWDB—Groundwater-level informatics with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9W004O6.

The Mississippi Alluvial Plain (MAP) has become one of the most important agricultural regions in the US, and it relies heavily on a groundwater system that is poorly understood and shows signs of substantial change. The heavy use of the available groundwater resources has resulted in significant groundwater-level declines and reductions in base flow in streams within the MAP. These impacts are limiting well production and threatening future water-availability for the region. This product will help not only scientists in our center, but also at a national level. This product will also be part of a larger study encompassing the Mississippi Alluvial Plain region. The Mississippi Alluvial Plain extent was delineated using GIS tools to represent the geographic extent of the Mississippi Alluvial Aquifer through incorporation of elevation information, geomorphology knowledge, ecological region extent, and previously published extents for part of the MAP region. The current MAP extent represents version 1.0. Future changes to the MAP extent will be tracked through increasing version numbers.

The Mississippi Alluvial Plain (MAP) has become one of the most important agricultural regions in the US, and it relies heavily on a groundwater system that is poorly understood and shows signs of substantial change. The heavy use of the available groundwater resources has resulted in significant groundwater-level declines and reductions in base flow in streams within the MAP. These impacts are limiting well production and threatening future water-availability for the region. This product will help not only scientists in our center, but also at a national level. This product will also be part of a larger study encompassing the Mississippi Alluvial Plain region. The Mississippi Alluvial Plain extent was delineated using GIS tools to represent the geographic extent of the Mississippi Alluvial Aquifer through incorporation of elevation information, geomorphology knowledge, ecological region extent, and previously published extents for part of the MAP region. The current MAP extent represents version 1.0. Future changes to the MAP extent will be tracked through increasing version numbers.

This dataset contains the contours, in feet, of the potentiometric-surface, spring 2020, Mississippi River Valley alluvial aquifer (MRVA). The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The contours were derived from most of the available groundwater-altitude (GWA) data from wells and surface-water-altitude (SWA) data from streamgages, measured in for spring 2020. The potentiometric contours ranged from 10 to 340 feet (3 to 104 meters) above NAVD 88. The regional direction of groundwater flow was generally towards the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flows into the depressions, and near rivers, where groundwater flow generally parallels the flow in the rivers.

This dataset contains the contours, in feet, of the potentiometric-surface, spring 2018, Mississippi River Valley alluvial (MRVA) aquifer. The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The contours were derived from most of the available groundwater-altitude data from wells and surface-water-altitude data from streamgages, measured in for spring 2018. The potentiometric contours ranged from 10 to 340 feet (3 to 104 meters) above NAVD 88. The regional direction of groundwater flow was generally towards the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flows into the depressions, and near rivers, where groundwater flow generally parallels the flow in the rivers.

This dataset contains the contours, in feet, of the potentiometric-surface, spring 2020, Mississippi River Valley alluvial aquifer (MRVA). The contours are referenced to the North American Vertical Datum of 1988 (NAVD 88). The contours were derived from most of the available groundwater-altitude (GWA) data from wells and surface-water-altitude (SWA) data from streamgages, measured in for spring 2020. The potentiometric contours ranged from 10 to 340 feet (3 to 104 meters) above NAVD 88. The regional direction of groundwater flow was generally towards the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flows into the depressions, and near rivers, where groundwater flow generally parallels the flow in the rivers.

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).