## 1 Introduction  
Hydrological models are essential tools for the reliable nowcasting and forecasting of terrestrial water resources. Acquiring realistic forecasts and sufficiently accurate estimates of the hydrological variables under various future climate and land-use change scenarios requires a rigorous calibration process [1, 2], which in turn relies on a proper dataset of hydrological observations, either in situ or from remote sensing. Ground monitoring networks for surface waters (i.e., river discharge monitoring stations, precipitation gauges and lake levels) are present in many parts of the world like North America and Europe and provide precious hydrological information. However, they lack the necessary spatial density in large areas like Africa, South America and Asia, to capture the heterogeneity of hydrological variability [3]. Even less information is available for groundwater resources, especially in modern, i.e., less than 50 years old, groundwater, which is particularly exposed to climate change impacts [4]. Remote sensing has been providing unprecedented hydrological information globally over the last few decades, at spatial and temporal resolutions of practical use [3], which can be assimilated into hydrological models. The Gravity Recovery and Climate Experiment (GRACE) is a joint mission of the National Aeronautics and Space Administration (NASA) and the Deutsches Zentrum fur Luft und Raumfahrt (DLR). GRACE satellites have been detecting changes in the Total Water Storage (TWS) from 2002 until present, with global coverage every 30 days. The initial mission, scheduled for a five-year operation, continued until October 2017, with data release being interrupted at increasing intervals during the last years of operation as a result of the energy-saving operation plan for the degraded power system [5]. Its successor mission, GRACE-Follow On (GRACE-FO), was launched in May 2018 and is still operating successfully at this time. Long-term ice mass changes and sea level rise monitoring are perhaps the most well-known applications of GRACE observations [6; 7]. GRACE observations have been increasingly used to monitor TWSA (i.e., changes in the sum of surface water, snow cover, soil moisture and groundwater) in large basins all over the world [8; 9] to estimate changes in different water storage components [10; 11; 12; 13]. Meanwhile, the advantages of assimilating GRACE information into hydrological models have also been highlighted in previous works [14; 15; 16], thus indicating the vast potential of GRACE to contribute to practical applications such as drought and flood monitoring and forecasting [17; 18; 19], assessment of groundwater sustainability [20] and the allocation of drinking water supplies [8]. GRACE data have been combined with modeling information to estimate groundwater depletion at the global scale [21], whereas a methodology to assimilate GRACE within WaterGAP Global Hydrology Model and improve its performance using an ensemble-based Kalman filter was presented in [22]. Besides the coarse spatial resolution of GRACE observations, which presents a challenge while assimilating GRACE data into hydrological models, many works have shown the potential of GRACE information to improve spatiotemporal characterization of the various hydrological variables like groundwater levels [23], changes in TWS [24] or indices like renewable groundwater stress [12; 25] and groundwater abstractions [26]. The main disadvantage of GRACE data is the coarse spatial resolution which limits its applicability to large basins. Therefore, GRACE Level 2 products at a spatial resolution of \(\sim\)3\({}^{\circ}\) are processed to release gridded Level 3 products at grid cell of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) or 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\).