Abstract
Processes controlling groundwater recharge have been a topic of pursuit in the hydrological research community. The groundwater recharge in hard-rock aquifers is significantly impacted by rainfall patterns, aquifer characteristics, weathering/soil conditions, topography, land use, and land cover. Analysis of the recharge process in tropical semi-arid hard-rock aquifer regions of southern India is crucial due to several factors, including (a) a heavily tailed monsoon system prevailing in the region, which is characterized by very few episodic storm events; (b) heterogeneity of aquifers in terms of fractures; and (c) the presence of several man-made irrigation lakes/tanks along with the drainage network. This study uses a lumped unconfined aquifer model to estimate the groundwater recharge for nine locations in Gundlupet taluk and 150 locations in Berambadi Experimental Watershed (EWS) in the south Indian state of Karnataka. Analysis of estimated recharge factors identifies 30 high-episodic recharge events out of 292 observations (around 10%) in Gundlupet taluk and 80 out of 150 locations in 2017 in Berambadi EWS. Partial information correlation (PIC) analysis is used to select the significant predictors out of potential predictors based on rainfall intensity distribution and climatological indices. PIC analysis reveals that the number of rainfall events with 15–30 mm daily rainfall intensity are most significant for normal recharge events in Gundlupet taluk and Berambadi EWS. The combined information on daily rainfall distribution, daily rainfall events of 20–40 mm, and the number of La Niña months in a particular year can explain the variability of high-episodic recharge events in Gundlupet taluk. These high-intensity rainfall events can be potential sources of alternate recharge pathways resulting in faster indirect recharge, which dominates the diffused recharge and results in high-episodic recharge events. Rainfall intensity distribution and climatological indices contain the potential information required to disaggregate normal and high-episodic recharge factors for future rainfall projections, which is useful for future groundwater level projections.
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