Improving Groundwater Recharge Estimation Using Remote Sensing Information in a Multiobjective Calibration.

This study aims to enhance groundwater spatiotemporal recharge estimation by incorporating three different sources of remote sensing information into a hydrological model. Traditional approaches for model calibration using flow rates often encounter equifinality issues, as aggregated variables may not adequately represent the spatial behavior of the watershed. To address these limitations, we hypothesized that including spatial information in the calibration process could lead to improved estimations.

The TETIS model was implemented in the Lebrija river watershed, located in the Magdalena middle valley of Colombia. R and Ostrich were used to couple the model with remote sensing data in a multiobjective calibration process with the Pareto archived dynamically dimensioned search algorithm. Subsequently, four calibration scenarios were executed, with the first one as a control scenario using only flow rates. The other three scenarios progressively integrated evapotranspiration and soil moisture remote sensing information. As a validation step, GRACE information was used to calculate recharge and compared with the simulations.

The inclusion of remote sensing information improved the model spatial behavior in 47.9%. And comparations with GRACE also show an improvement representation of groundwater recharge in 31.9%. In conclusion, the incorporation of remote sensing data in the calibration process significantly increased the reliability of groundwater recharge estimations in the model.