Investigation of 2020–2022 extreme floods and droughts in Sichuan Province of China based on joint inversion of GNSS and GRACE/GFO data

Abstract

The Global Navigation Satellite System (GNSS) surface deformation measurement and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO) satellite gravimetry provide complementary strengths in estimating terrestrial water storage (TWS) changes, which helps quantify the evolutions of extreme floods and droughts. We implemented a joint inversion framework that only incorporates the contributions of GNSS and GRACE/GFO to recover the TWS changes for monitoring extreme floods and droughts in the Sichuan Province of China (SCP). The performance of TWS changes from the joint inversion is evaluated through closed-loop simulation and independent hydrometeorological data (i.e., precipitation (P), evapotranspiration (ET), and runoff (R)) based on the water balance equation (named P-ET-R) in the spatio-temporal domains. The simulation analysis results show that joint inversion presents better performance in recovering TWS changes than GNSS-only and GRACE/GFO-only solutions in the spatio-temporal scales. The measured joint inversion results reveal more spatial details of TWS changes than GNSS-only and GRACE/GFO-only solutions and present better correlations with GLDAS- and ERA5-derived P-ET-R (0.77 vs. 0.79) than GNSS-only (0.49 vs. 0.54) and GRACE/GFO-only (0.61 vs. 0.68) in the spatial domain. Meanwhile, the joint inversion results effectively characterize the spatio-temporal dynamics of extreme floods and droughts in SCP during 2020–2022, the average TWS surpluses are 33.61 and 32.86 mm in 2020 and 2022 flood events and the average TWS deficits are −36.78 and −63.87 mm in 2021 and 2022 drought events. In addition, the drought propagation time generally presents an increasing trend from southwestern to northeastern SCP. Furthermore, the average and minimum recovery time of drought and flood in the Sichuan basin is longer than those in the western mountains during 2020–2022. The proposed approach provides an effective estimation strategy for recovering reliable TWS changes for the investigation of extreme floods and droughts in regions where GNSS stations are distributed.