The files and folders in this data release contain the input and output files and MATLAB algorithms used for simulations described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x). The algorithms implement a data-driven, mechanistic model of vertical infiltration through the unsaturated zone and recharge to the water table that is developed from water-balance concepts. The model of infiltration and recharge is defined in terms of observed states (such as, the water-table altitude) and unobserved states (such as, fluxes through the unsaturated zone and recharge to the water table) and includes both diffuse and preferential flow through the unsaturated zone to the water table. Estimates of the daily contributions to recharge at the water table from diffuse and preferential flow are performed by interpreting daily time-series records of observations of water-table altitude and meteorological inputs (such as, the liquid precipitation rate, snowmelt rate, and the Potential Evapotranspiration (PET) rate). The modeling approach used here is an extension of concepts of modeling infiltration and rapid recharge originally presented in Shapiro and Day-Lewis (2021) https://doi.org/10.1029/2020WR029110 and Shapiro and others (2022) (https://doi.org/10.1111/gwat.13206). The model of infiltration and recharge to the water table is applied to daily records available at 32 U.S. Geological Survey (USGS) Climate Response Network (CRN) wells located in the Delaware River Basin (DRB) in the eastern United States from January 1, 2005, through December 31, 2021. The daily water-table altitude and the meteorological records described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x) are included as input files to the MATLAB algorithms described in this data release.

This set of data includes four potential annual recharge grids for the State of Maine that were simulated using the Maine Soil-Water-Balance model for 1991 through 2015. The files include a grid representing the uncertainty in the potential recharge and a grid showing the annual average precipitation from the climate dataset that the simulation is based on. A 25-year simulation of potential recharge to groundwater from the Maine Soil-Water-Balance model for the years 1991 to 2015 produced annual results from which the four potential recharge grids were derived. The four are: 25-year mean, median, maximum, and minimum simulated annual potential. A data exclusion zone (see Scientific Investigations Report 2019–5125) has been applied to the recharge datasets, resulting in a dataset that covers most, but not all, of the State of Maine. The potential recharge grids are given in units of inches per year, with a raster grid cell size of 250 meters. The uncertainty in the simulated grid values is the standard deviation grid, which represents the standard deviation of the simulated median recharge grid. The precipitation data used in the 25-year simulation are from DayMet version 3 daily data. The average annual precipitation grid is the calculated annual average from those data. Further details about the generation and application of the data can be found in Scientific Investigations Report 2019–5125.

This set of data includes four potential annual recharge grids for the State of Maine that were simulated using the Maine Soil-Water-Balance model for 1991 through 2015. The files include a grid representing the uncertainty in the potential recharge and a grid showing the annual average precipitation from the climate dataset that the simulation is based on. A 25-year simulation of potential recharge to groundwater from the Maine Soil-Water-Balance model for the years 1991 to 2015 produced annual results from which the four potential recharge grids were derived. The four are: 25-year mean, median, maximum, and minimum simulated annual potential. A data exclusion zone (see Scientific Investigations Report 2019–5125) has been applied to the recharge datasets, resulting in a dataset that covers most, but not all, of the State of Maine. The potential recharge grids are given in units of inches per year, with a raster grid cell size of 250 meters. The uncertainty in the simulated grid values is the standard deviation grid, which represents the standard deviation of the simulated median recharge grid. The precipitation data used in the 25-year simulation are from DayMet version 3 daily data. The average annual precipitation grid is the calculated annual average from those data. Further details about the generation and application of the data can be found in Scientific Investigations Report 2019–5125.

Groundwater recharge is an important part of water budget estimation and is a critical data component used in creating and calibrating groundwater flow models such as MODFLOW. Soil Water Balance Models (SWB) can be used to estimate potential groundwater recharge across spatial domains and through time. This metadata record describes an SWB archive for parts of the Coastal Plain of North Carolina, South Carolina, and Georgia, eastern United States. The model was run for various land cover build out scenarios and several downscaled climate models (SWB) The model’s pixel resolution is 609.9-meters (m) and it was run for the for the period 1979 - 2060. The SWB model executable code is detailed in the report SWB—A Modified Thornthwaite-Mather Soil-Water-Balance Code for Estimating Groundwater Recharge; Chapter 31 of Section A, Groundwater, of Book 6, Modeling Techniques By S.M. Westenbroek, V.A. Kelson,W.R. Dripps,R.J. Hunt, and K.R. Bradbury (https://pubs.usgs.gov/tm/tm6-a31/) The SWB model was not calibrated; however, various water budget components from the model output compared reasonably well with other estimates. Due to size limitations climate data used in the production of this model are not included in this archive, URLs to locate the climate data are included.

Groundwater recharge is an important part of water budget estimation and is a critical data component used in creating and calibrating groundwater flow models such as MODFLOW. Soil Water Balance Models (SWB) can be used to estimate potential groundwater recharge across spatial domains and through time. This metadata record describes an SWB archive for parts of the Coastal Plain of North Carolina, South Carolina, and Georgia, eastern United States. The model was run for various land cover build out scenarios and several downscaled climate models (SWB) The model’s pixel resolution is 609.9-meters (m) and it was run for the for the period 1979 - 2060. The SWB model executable code is detailed in the report SWB—A Modified Thornthwaite-Mather Soil-Water-Balance Code for Estimating Groundwater Recharge; Chapter 31 of Section A, Groundwater, of Book 6, Modeling Techniques By S.M. Westenbroek, V.A. Kelson,W.R. Dripps,R.J. Hunt, and K.R. Bradbury (https://pubs.usgs.gov/tm/tm6-a31/) The SWB model was not calibrated; however, various water budget components from the model output compared reasonably well with other estimates. Due to size limitations climate data used in the production of this model are not included in this archive, URLs to locate the climate data are included.

A soil-water balance model (SWB) was developed to estimate potential recharge to the groundwater system in Brunswick, New Hanover, and Pender counties North Carolina 1980 through 2016 to support a regional groundwater flow model being produced for the surficial, Castle Hayne, and Peedee Aquifer System. The SWB model was not calibrated; however, various water budget components from the model output compared reasonably well with other estimates including evapotranspiration rates reported by NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) satellite platform. This USGS data release contains all the input and output files for the simulations described in this data release.

A soil-water balance model (SWB) was developed to estimate potential recharge to the groundwater system in Brunswick, New Hanover, and Pender counties North Carolina 1980 through 2016 to support a regional groundwater flow model being produced for the surficial, Castle Hayne, and Peedee Aquifer System. The SWB model was not calibrated; however, various water budget components from the model output compared reasonably well with other estimates including evapotranspiration rates reported by NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) satellite platform. This USGS data release contains all the input and output files for the simulations described in this data release.

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).