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Abstract

Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which are essential for better understanding our climate system. Recently, we have developed a self-supervised data assimilation algorithm to combine the advantages of the TWSAs obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission and the WaterGAP Global Hydrology Model (WGHM) model, resulting in a high-quality TWSA product with a spatial resolution of 0.5 degrees. In this presentation, we discuss the potential applications and contributions of the downscaled TWSAs, from understanding long-term changes in water storage to monitoring short-term climate extremes. Benefiting from the strong performance in retaining the long-term trends, the downscaled TWSAs allow us to study the water changes on a local scale. For example, the significant negative trends in the High Plains aquifer are partially smoothed out in the GRACE TWSAs since the positive trends in the neighbouring cells caused by progress from dry to wet periods average them out. These signals can be better separated from the downscaled TWSAs. In the aspect of short-term variations, we can derive the well-established GRACE-based indices, such as the flooding potential index and drought severity index, using downscaled TWSAs. The high-resolution indices visually agree with the GRACE-derived ones on larger scales while opening the window to monitor extreme environmental events with higher spatial resolution. As a result, hazard monitoring and prevention could be better targeted and more efficiently conducted by different organisations and governments.