Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin.

The integration of satellite-based observations into hydrological models contributes to achieving more precise simulations, thus supporting hazard mitigation and policy-making especially in poorly gauged basins. Sub-monthly Terrestrial Water Storage (TWS) observations derived from the Gravity Recovery and Climate Experiment (GRACE) mission have been shown to contain useful information for the prediction and monitoring of sub-monthly water storage anomalies such as floods. This study assesses, for the first time, the benefits and challenges of integrating sub-monthly TWS into a large-scale hydrological model during flood events. The experiment is carried out for the Brahmaputra River Basin and the integration is performed through the state-of-the-art of sequential Data Assimilation (DA) with the aim of improving model water storage estimates. The results indicate that the daily TWS DA, based on the Ensemble Kalman Filter (EnKF), successfully introduces the observed sub-monthly TWS variability into the model (differences below 10 mm with daily GRACE TWS). The daily TWS DA spatially and vertically downscales storage updates with precise timing and distribution. Especially, it modifies the river storage compartment, where sub-monthly variations are expected during floods. In contrast, the updates of monthly TWS DA, implemented through both an EnKF and an Ensemble Kalman Smoother (EnKS), introduce undesired peaks in the TWS time series. Choosing an adequate model covariance localization is found to be crucial for daily TWS DA. Finally, the statistical characteristics of the daily TWS DA and the translation of water storage updates into river discharge are investigated, and recommendations for future developments are provided.