Covariance Analysis and Sensitivity Studies for GRACE Assimilation into WGHM

Abstract

An ensemble Kalman filter approach for improving the WaterGAP Global Hydrology Model (WGHM) has been developed, which assimilates Gravity Recovery And Climate Experiment (GRACE) data and calibrates the model parameters, simultaneously. The method uses the model-derived states and satellite measurements and their error information to determine updated water storage states. However, due to the fact that hydrological models do not provide any error information, an empirical covariance matrix needs to be calculated. In this paper, therefore, we analyse the combined state and parameter covariance matrix of WGHM. We found that high correlations of up to 0.75 exist between calibration parameters and storage compartments, and that these allow for an efficient calibration. In addition, a sensitivity analysis is performed to identify those parameters that the water compartments are most sensitive to. The performed analysis is important, since GRACE cannot observe the model parameters directly. We found that those parameters, which the water storage is most sensitive to, differ not only regionally, but also with respect to the water compartments. Not unexpected, some climate input multipliers implemented in our model version have an overall strong influence. We also found that the degree of sensitivity changes temporally, e.g. between 0 (in summer) and 0.5 (in winter) for the snow storage.