| description 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. 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 10 km2), 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. | |
