Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Abstract

Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region.