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Among all remote sensing missions, the Gravity Recovery and Climate Experiment (GRACE) was unique as it measured the change in total water content across all terrestrial water storages (TWS) including subsurface, deep soil moisture, and groundwater. However, its coarse resolution is a major challenge for practical applications. Ensemble Kalman filters (EnKFs) are useful tools to combine observations with models to reduce prediction errors. But due to the coarse resolution of the GRACE products, the EnKF does not work well in its usual form. Accordingly, different EnKF structures have been proposed and employed but a comparison between them has not yet been attempted. Here we assessed these structures using a synthetic problem. Alternative structures were formed using different increment calculation and updating strategies, observation operators, and the types of observation fed to the filter. It was found that all available structures led to an improvement in model performance when measured against a synthetic reference. However, the degree of improvement was strongly dependent on the assimilation strategy. Assimilating absolute TWS values (the summation of the TWS anomalies and an unbiased baseline) gave the best model performance when combined with an increment calculation strategy in which the increments are calculated and applied to all days of the month. However, without an unbiased baseline, assimilating TWS changes still leads to an acceptable improvement in model performance. Among the observation operators, those that predict the observations as an average of multiple days had the best performance.