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

Abstract

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.

The Flow of the Yangtze River Inverted From a Continuous Global Navigation Satellite System Station

“…The Yangtze River, which is the longest river in China and the third longest river in the world, originates on the Tibetan Plateau, flows through 11 provinces and cities, and flows approximately 6,300 km eastward to the East China Sea (Xie et al., 2022). The Yangtze River basin covers a vast area and plays an important role in China's economic development, agricultural production and animal and plant activities.…”

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

“…The FPI characterizes the effective water storage capacity of the basin. A daily scale flood potential index (NDFPI) is developed on the basis of FPI [20].…”

“…Although high discharge does not necessarily lead to flooding, it indicates a high risk of flooding. Many studies have used discharge to identify flood levels [20,24,54]. In this study, we use the in situ daily streamflow from Datong station for the 90th percentile flood to evaluate the early warning capability of MWI and NDFPI for the flood event in the YRB in 2020.…”

“…However, the release of ITSG−Grace2018 daily solution has a delay of several months, and the coverage of gPhone is limited. Recently, Xie et al [20] used machine learning techniques to downscale the GRACE/GRACE-FO TWSA from a monthly scale to a daily scale and further established a normalized daily flood potential index (NDFPI) based on downscaled TWSA. The results showed that NDFPI could robustly and reliably characterize severe flood events at submonthly scales.…”

Spatiotemporal Variability of Current and Future Sub‐Daily Rainfall in Japan Using State‐Of‐The‐Art High‐Quality Data Sets

“…These increases, combined with multiple anthropogenic factors, such as urbanization and land‐use change, would likely lead to more frequent and intense rapidly developing (flash) flood events. This prospect of extreme events that occur with little warning can consequentially cause substantial damage to nature and people, including food reduction, infrastructure damage, economic decline, and more water‐related diseases (Fowler et al., 2021; He et al., 2021; IPCC AR6, 2022; Lesk et al., 2020; Xie et al., 2022).…”