A systematic impact assessment of GRACE error correlation on data assimilation in hydrological models

Recently, ensemble Kalman filters (EnKF) have found increasing application for merging hydrological models with total water storage anomaly (TWSA) fields from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Previous studies have disregarded the effect of spatially correlated errors of GRACE TWSA products in their investigations. Here, for the first time, we systematically assess the impact of the GRACE error correlation structure on EnKF data assimilation into a hydrological model, i.e. on estimated compartmental and total water storages and model parameter values. Our investigations include (1) assimilating gridded GRACE-derived TWSA into the WaterGAP Global Hydrology Model and, simultaneously, calibrating its parameters; (2) introducing GRACE observations on different spatial scales; (3) modelling observation errors as either spatially white or correlated in the assimilation procedure, and (4) replacing the standard EnKF algorithm by the square root analysis scheme or, alternatively, the singular evolutive interpolated Kalman filter. Results of a synthetic experiment designed for the Mississippi River Basin indicate that the hydrological parameters are sensitive to TWSA assimilation if spatial resolution of the observation data is sufficiently high. We find a significant influence of spatial error correlation on the adjusted water states and model parameters for all implemented filter variants, in particular for subbasins with a large discrepancy between observed and initially simulated TWSA and for north–south elongated sub-basins. Considering these correlated errors, however, does not generally improve results: while some metrics indicate that it is helpful to consider the full GRACE error covariance matrix, it appears to have an adverse effect on others. We conclude that considering the characteristics of GRACE error correlation is at least as important as the selection of the spatial discretisation of TWSA observations, while the choice of the filter method might rather be based on the computational simplicity and efficiency.