Downscaling total water storage anomalies by fusing GRACE measurements and hydrological models using deep learning algorithms
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2022-10-19
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Abstract
The Gravity Recovery And Climate Experiment (GRACE) satellite mission provides a unique opportunity to monitor the monthly global gravity variations, which can be converted to total water storage anomalies (TWSAs). Monitoring TWSAs is an essential way to study the hydrological cycle, which is crucial in many fields, such as climate change, ecological systems, and water resource management. However, the coarse resolution of GRACE TWSAs impedes its regional application. In this study, we develop a deep learning model to fuse GRACE measurements and WaterGAP Hydrological Model (WGHM) simulations. Besides, additional hydrological data from the Global Land Data Assimilation System (GLDAS) are considered as features. The proposed deep learning model combines the principles of convolutional neural networks, residual learning, and an encoder-decoder structure. A novel loss function is designed to maximize the similarities to the WGHM TWSAs while minimizing the average deviations from the GRACE TWSAs within an area. The result is a global model that can produce TWSAs over all the land areas except Greenland with a resolution of 0.5 degrees. Our validations prove that the downscaled TWSAs have a better agreement with the GRACE TWSAs on larger scales while keeping a high spatial correlation to the WGHM TWSAs. Compared to the GRACE TWSAs, the spatial correlation with the WGHM TWSAs is improved from 0.43 to 0.71. On the other hand, the basin- and continent-wise average TWSAs of our downscaled solutions agree better with the GRACE TWSAs with RMS errors lower than 20 mm in most continents, equal to improvements of more than 50% compared to the WGHM TWSAs. Moreover, our method can estimate the trends and seasonal signals in the TWSA time series more accurately without sensing any temporal information. The correlation with the GRACE TWSA trends has been improved from 0.34 (WGHM TWSAs) to 0.80 (our model).
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Frontiers of Geodetic Science 2022 (FROGS 2022)
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09707 - Soja, Benedikt / Soja, Benedikt
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Conference lecture held on October 19, 2022.