Benefits and challenges of daily GRACE(-FO) satellite Data Assimilation (DA) for predicting fast-evolving hydrological processes.

Terrestrial Water Storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE, 2002-2017) and its Follow-On mission (GRACE-FO, 2018-now) reflects a vertical summation of large-scale mass changes, globally. Better-than-monthly temporally resolved gravity solutions, such as daily GRACE(-FO) data, have the potential to reflect fast evolving hydrometeorological events. In the DansK-LSM project, supported by the Independent Research Fund Denmark (DFF), we will assess for the first time the benefits and challenges of daily GRACE(-FO) TWS Data Assimilation (DA) into a water-balance model (the modified 10 km resolution World-Wide Water Resources Assessment model, W3RA) for fast-evolving flood monitoring. Therefore, our particular interest is to assess to what extent a remotely sensed TWS data, which has a low spatial resolution, can help improving hydrological modelling during flood events. This experiment is performed within the region of the Ganges-Brahmaputra Delta for major floods occurred in 2004, 2007 and 2008. The daily DA is implemented in-house through a daily Ensemble Kalman Filter (EnKF) along with localization. When compared to the monthly solution, the daily TWS DA succeeds at transferring the High-Frequency (HF) GRACE TWS signal into the model (correlation coefficients of 0.97 between GRACE and daily DA TWS). However, the filter encounters some difficulties at accurately disaggregating the TWS into the different vertical water storage compartments (namely affecting the soil water) as well as horizontal grid cells. The observed irregularities are attributed to the very intensive use of ensemble statistics when the DA step is performed on the daily basis. To address this issue, a few possibilities for more stable filters and regularizations are explored and assessed. In this presentation, we will explore the spatial and temporal impacts of these choices.