Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data

Several previous studies have shown the significance of representing shallow groundwater in land surface model (LSM) simulations. However, optimal methods for parameter estimation in order to realistically simulate water table depth have received little attention. The recent availability of Gravity Recovery and Climate Experiment (GRACE) water storage data provides a unique opportunity to constrain LSM simulations of terrestrial hydrology. In this study, we incorporate both GRACE (storage) and estimated base flow (flux) data in the calibration of LSM parameters, and demonstrate the advantages gained from this approach using a Monte Carlo simulation framework. This approach improves parameter estimation and reduces the uncertainty of water table simulations in the LSM. Using the optimal parameter set identified from the multiobjective calibration, water table simulation can be improved due to close dependence of both base flow and total subsurface water storage on the water table depth. Moreover, it is shown that parameters calibrated from short-term (2003-2005) GRACE and base flow data can be validated using simulations for the periods of 1984-1998 and 2006-2007, which implies that the proposed multiobjective calibration strategy is robust. More important, this study has demonstrated the potential for the joint use of routinely available GRACE water storage data and streamflow records to constrain LSM simulations at the global scale.