Estimating the Aquifer’s Renewable Water to Mitigate the Challenges of Upcoming Megadrought Events

In arid and semi-arid regions of the world, the occurrence of prolonged drought events (megadroughts) associated with climate change can seriously affect the balance between water supply and demand, thereby severely increasing the susceptibility of such regions to adverse impacts. In this study, a simple framework is introduced to estimate renewable water volumes (RW) to mitigate the challenges of megadrought events by managing the groundwater resources. The framework connects a weighted annual hydrological drought index (wSPEI) to RW, based on the short time-scale precipitation volume. The proposed framework, which was in a proof-of-concept case study applied to the Neishaboor watershed in the semi-arid part of Iran, showed that developing the weighted drought index can be valuable to estimate RW. The results suggested that the wSPEI, aggregating hydrological drought index (HSPEI) with the time scale k = 5 days and the regional coefficient s = 1.3 can be used to estimate RW with reasonable accuracy (R2 = 0.73, RMSE = 11.5 mm year−1). This indicates that in the Neishaboor watershed, the best estimation of RW can be determined by precipitation volumes (or the lack thereof) falling over 5-day aggregation periods rather than by any other time scales. The accuracy of the relationship was then investigated by cross validation (leave-one-out method). According to the results, the proposed framework performed fairly well for the estimation of RW, with R2 = 0.75 and RMSE = 12.2 mm year−1 for k = 5 days. The Overall agreement between the wSPEI, the RW derived from water balance calculations, and the estimated RW by the proposed framework was also assessed for a period of 34 years. It showed that the annual RW followed closely the wSPEI, indicating a reasonable relationship between wSPEI and the annual RW. Accordingly, the proposed framework is capable to estimate the renewable water of a given watershed for different climate change scenarios.