Iron concentration rasters of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers
Dates
Publication Date
2020-12-14
Start Date
1960
End Date
2019
Citation
Knierim, K.J., Kingsbury, J.A., and Haugh, C.J., 2020, Machine-learning model predictions and rasters of dissolved oxygen probability, iron concentration, and redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers: U.S. Geological Survey data release, https://doi.org/10.5066/P9N108JM.
Summary
Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some areas by high concentrations of trace elements. The concentration of trace elements in groundwater is largely driven by oxidation-reduction (redox) processes. Redox processes are a group of biotically driven reactions in which energy is derived from the exchange of electrons. In groundwater, this commonly occurs through decomposition of organic matter (carbon) by microbes, which consumes dissolved oxygen (DO). Under low DO conditions, iron (Fe), manganese, and arsenic can dissolve from coatings on aquifer sediments and be released into groundwater. Therefore, predictions of [...]
Summary
Groundwater is a vital resource in the Mississippi embayment physiographic region (Mississippi embayment) of the central United States and can be limited in some areas by high concentrations of trace elements. The concentration of trace elements in groundwater is largely driven by oxidation-reduction (redox) processes. Redox processes are a group of biotically driven reactions in which energy is derived from the exchange of electrons. In groundwater, this commonly occurs through decomposition of organic matter (carbon) by microbes, which consumes dissolved oxygen (DO). Under low DO conditions, iron (Fe), manganese, and arsenic can dissolve from coatings on aquifer sediments and be released into groundwater. Therefore, predictions of redox conditions (using DO and Fe) are important in the Mississippi embayment for a better understanding of the potential zones of high trace elements in drinking-water aquifers. The Mississippi embayment includes two principal regional aquifer systems: the Quaternary Mississippi River Valley Alluvial aquifer (MRVA) and the Mississippi embayment aquifer system, which includes deeper Tertiary aquifers and confining units. Based on the distribution of groundwater use for drinking water, the modeling focused on the MRVA, the middle Claiborne aquifer (MCAQ), and the lower Claiborne aquifer (LCAQ).
Machine learning was used to predict redox conditions—including the probability of exceeding a DO concentration of 1 milligram per liter (mg/L) and Fe concentrations—across the MRVA, MCAQ, and LCAQ. Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were developed to predict DO probability and Fe concentration to 1-kilometer (km) raster grid cells of the National Hydrologic Grid (Clark and others, 2018) for 7 aquifer layers (1 MRVA, 4 MCAQ, 2 LCAQ) following the hydrogeologic framework of Hart and others (2008). Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as soils and land use), and variables extracted from a MODFLOW groundwater flow model for the Mississippi embayment (Haugh and others, 2020a; Haugh and others, 2020b). Output from DO and Fe models were used to classify redox zones, including anoxic, mixed anoxic, mixed oxic, and oxic conditions. Oxic conditions included areas where the probability of exceeding a DO concentration of 1 mg/L was greater than 80 percent and iron was less than 1,000 µg/L. Anoxic conditions included areas where the probability of exceeding a DO concentration of 1 mg/L was less than 10 percent. Mixed conditions include anywhere that the predicted DO probability was greater than or equal to 10 percent and less than or equal to 80 percent, and either less than 500 µg/L iron (mixed oxic) or greater than or equal to 500 µg/L iron (mixed anoxic). Prediction intervals were calculated for DO and Fe by bootstrapping raster-cell predictions following methods from Ransom and others (2017). For a full description of modeling workflow and final model selection see Knierim and others (2020).
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Purpose
The machine-learning model predictions and groundwater quality rasters support the Scientific Investigations Map by Knierim and others (2020). This child item includes input files and an R script used to create machine-learning models of iron (Fe) concentration. This child item also includes output files as groundwater-quality prediction rasters and prediction intervals of Fe concentration.
