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).

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).

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).

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).

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).

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).

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).

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to be included as explanatory variables. The ML approach integrated output from a groundwater-flow model and water-quality data to predict salinity, and the approach can be applied to other aquifers to provide context for the long-term availability of groundwater resources. The Mississippi embayment includes two principal regional aquifer systems; the surficial aquifer system, dominated by 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, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were developed to predict SC and Cl 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). TDS maps were created using the correlation between SC and TDS. 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). Prediction intervals were calculated for SC and Cl 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).

Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to be included as explanatory variables. The ML approach integrated output from a groundwater-flow model and water-quality data to predict salinity, and the approach can be applied to other aquifers to provide context for the long-term availability of groundwater resources. The Mississippi embayment includes two principal regional aquifer systems; the surficial aquifer system, dominated by 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, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Boosted regression tree (BRT) models (Elith and others, 2008; Kuhn and Johnson, 2013) were developed to predict SC and Cl 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). TDS maps were created using the correlation between SC and TDS. 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). Prediction intervals were calculated for SC and Cl 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).

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).