Reply on RC1

Abstract

Groundwater recharge is difficult to estimate, especially in fractured aquifers, because of the spatial variability of the soil properties and because of the lack of data at basin scale. A relevant method, known as the WTF method, consists in inferring recharge directly from the water table fluctuations (WTF) observed in boreholes. However, the WTF method neglects the impact of lateral groundwater redistribution in the aquifer, i.e. assumes that all the WTF are attributable to recharge. In this study, we developed the WTF approach in the frequency domain to better consider groundwater lateral flow, which quickly redistributes the inpulse of recharge and mitigates the link between WTF and recharge. First, we calibrated a 1D analytical groundwater model to estimate hydrodynamic parameters at each borehole. These parameters were defined from the WTF recorded for several years, independently of prescribed potential recharge. Second, calibrated models are reversed analytically in the frequency domain to estimate recharge fluctuations (RF) at weekly to monthly scales from the observed WTF. Models were tested on two twin sites with similar climate, fractured aquifer, and land use but different hydrogeologic settings: one has been operated as a pumping site for the last 25 years (Ploemeur, France) while the second has not been perturbed by pumping (Guidel). Results confirm the important role of rainfall temporal distribution to generate recharge. While all rainfall contribute to recharge, the ratio of recharge to rainfall minus potential evapotranspiration is frequency dependent, varying between 20–30 % at periods <10 days, 30–50 % at monthly scale, and reaching 75 % at seasonal time scales. We further show that the unsaturated zone thickness controls the intensity and timing of RF. Overall, this approach contributes to better assess recharge and enable to improve the representation of groundwater systems within hydrological models. In spite of the heterogeneous nature of aquifers, parameters controlling WTF can be inferred from WTF time series.