Total water storage anomalies reconstruction using noise-augmented u-shaped network: A case study in the Yangtze River Basin

The satellite mission of Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) have characterized global total water storage anomalies (TWSA) with unprecedented accuracy. However, the data gap between GRACE and GRACE-FO from July 2017 to May 2018 represents challenges for interpreting long-term water storage changes. In this study, we present a deep learning model, a noise-augmented u-shaped network (NA-UNet), to bridge the gap over the Yangtze River Basin (YRB). This model has been trained on precipitation, temperature, and hydrological model data. We show that the NA-UNet model achieves a state-of-the-art TWSA reconstruction, outperforming the standard UNet model and previous studies. At the basin scale, the NA-UNet model agrees favorably well with GRACE observations, with a correlation coefficient (CC) of 0.99, Nash-Sutcliff efficient (NSE) of 0.97, and normalized root-mean-square error (NRMSE) of 0.04 during the testing period. At the grid-cell scale, our model has a much more stable performance with median CC/NSE/NRMSE values of 0.99/0.96/0.05. This significant improvement in the gap-filling ability addresses the issue with discontinuous TWSA observations while laying the foundation for predicting future changes in total water storage.