Abstract

By utilizing Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) and remote sensing precipitation data, Flood Potential Index (FPI) has been widely used in large-scale flood monitoring. However, divergent post-processing dynamics of different GRACE solutions result in substantial uncertainties in GRACE TWSA and thus affecting predictive skills of FPI. To overcome this, this study develops an Integrated Flood Potential Index (IFPI) by linking the FPI derived from six GRACE products. The Gaussian copula is employed to establish the joint distribution of FPI from six spherical harmonic (SH) products and mass concentration blocks solutions. One of the most flood-prone regions, Yangtze River basin in China, is selected as a case study. We have identified and characterized the floods with different intensities using IFPI, which is evaluated against standardized discharge observations as well as the Total Storage Deficit Index (TSDI), Water Storage Deficit Index (WSDI) and Combined Climatologic Deviation Index (CCDI). Results show that the area under curve (AUC) values of IFPI for different levels of floods are generally greater than FPIs and their ensemble mean, implying the better predictive skill for the large-scale flood events. During the three severest floods in 2010, 2015, and 2016, IFPI captures the flood variability exhibited by TSDI, WSDI, and CCDI, as well as hydrological observations. This proposed approach might provide reference for flood monitoring and from multi-mission satellite data.

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