Characterizing Spatiotemporal Patterns of Terrestrial Water Storage Variations Using GNSS Vertical Data in Sichuan, China
We invert daily GNSS vertical position time series for terrestrial water storage (TWS) changes and characterize their spatiotemporal patterns using various geodetic and hydrological data in Sichuan which features complex climatic and geographical conditions. Our inversion strategy depends on a variational Bayesian principal component analysis (VBPCA), which recovers the equivalent water height (EWH) changes using three principal components that explain 82% of the data variance. Various data sets from GNSS, GRACE, and GLDAS indicate an increasing seasonal water variability from northeast to southwest Sichuan. The GNSS modeling results reveal significant annual EWH amplitudes (up to 300 mm), which are largely underestimated in both GRACE-EWH (∼120 mm) and GLDAS-EWH (∼100 mm) products. The GNSS-EWH also shows considerable seasonal changes in the high-altitude plateau but subtle seasonal oscillations in the lowland basin, whereas this sharp contrast is invisible in GRACE- and GLDAS-EWH primarily due to coarse spatial resolution. The GNSS inferred water products are used to determine the scaling factor for GRACE, with a value of 2.1 in Sichuan. The regional average precipitation peaks in July, about 1-2 months ahead of the maximum of monthly water heights. A bimodal feature in daily precipitation data is most notable in the low-elevation Sichuan Basin and results in two peaks in daily GNSS-EWH time series. Our results demonstrate that GNSS can serve as an independent tool for measuring water storage with high spatiotemporal resolution and provides additional constraints for understanding hydrological dynamics.