Development of a process-based modeling framework for harnessing hydrometeorological datasets to estimate groundwater recharge

A better understanding of groundwater recharge rates is needed for sustainably managing freshwater resources. The goal of this effort is to develop a calibration-free, simple, process-based modeling framework for estimating groundwater recharge rates at a yearly timescale. To accomplish this, we performed water balance calculations to estimate recharge as the difference between precipitation and the combined losses due to evapotranspiration and runoff. The SCS method was used to compute runoff at the daily timescale and at the HUC-12 spatial scale and was later mapped over the desired 800-m grid scale. The Penman-Monteith model was used to estimate the ET value for every grid at the daily scale. The daily values of runoff, ET, and precipitation were then averaged to estimate annual averages, and these values were then used to obtain groundwater recharge at the annual timescale. We compared our results with the USGS recharge product for the Tombigbee-Black-Warrior River Basin located in the Southeastern United States. Results show that at the annual timescale, about 55 % of the average annual rainfall that fell over the catchment is lost as evapotranspiration and 24 % as runoff, thus yielding about 21 % as groundwater recharge over the entire river basin. The study provides a simple water balance approach for computing recharge values using publicly available hydrometeorological data and demonstrates its application for a large river basin in Alabama, USA.