Comparison of Data-Driven Groundwater Recharge Estimates with a Process-Based Model for a River Basin in the Southeastern USA
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 7
Abstract
Reliable estimates of aquifer recharge have the potential to help develop sustainable groundwater management policies. Despite its importance, quantifying this flux continues to be a challenge and remains one of the most uncertain components of the hydrological cycle. Here, we obtain a spatially explicit estimate of recharge using a semi-distributed hydrologic model for a major river basin in the Southeastern United States. A comparison of these process-based estimates with a data-driven recharge product (developed by USGS), which was obtained using a set of empirical regression equations, shows good agreement at the basin scale, but significant discrepancies at finer spatial resolutions. Overall, the semi-distributed model shows a higher degree of spatial heterogeneity across the basin than the USGS study results, which likely indicates that the empirical relationships modeled at the basin scale by the USGS empirical equations might not hold at smaller spatial scales. However, more ground-truthing recharge datasets are necessary to properly evaluate subbasin-scale models and reduce the uncertainty of estimates at these scales.
Practical Applications
Groundwater recharge information at local scales is essential for various tasks: It is critical in the assessment of groundwater contamination from point sources, determining rates of change in response to pumping, quantifying local scale climate-induced storage change effects, assessing climate impacts on land cover changes and water supply, to name a few (Scanlon and Cook 2002) (Reitz et al. 2017). Because precipitation, pumping rates, land cover changes, and other important factors that affect groundwater recharge can vary significantly at a local scale (on the order of 1 to ), having recharge estimates at a similarly fine scale will be useful for groundwater managers to evaluate the effectiveness of various practices that impact different stakeholders within the basin, and use this information to develop more effective water management plans.
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Data Availability Statement
All data that support the findings of this study, such as SWAT model and code, are available from the corresponding author upon reasonable request.
Acknowledgments
Funding for this project was provided in part by the National Science Foundation Grants OIA 2019561 and OIA 1854631, and by CIROH Grant GR29007 awarded through the NOAA’s Cooperative Agreement with The University of Alabama, NA22NWS4320003.
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Published In
Journal of Hydrologic Engineering
Volume 28 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 22, 2022
Accepted: Jan 31, 2023
Published online: Apr 24, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 24, 2023
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