Revisiting the Terrestrial Water Storage Changes in the Northeastern Tibetan Plateau Using GRACE/GRACE-FO at Different Spatial Scales Considering the Impacts of Large Lakes and Reservoirs

Abstract

Highlights: What are the main findings? The constrained forward modeling (CFM) method is effective for correcting leakage errors in terrestrial water storage (TWS) changes in regions where large lakes and reservoirs co-exist. Lake and reservoir water storage and groundwater storage contribute over 85% to TWS changes during 2003–2022. What is the implication of the main finding? Level-2 spherical harmonic coefficients combined with CFM enhance the detection of multi-scale TWS changes and abrupt hydrological events. Provides a perspective for water resources monitoring and climate-driven studies in regions where lakes and reservoirs co-exist. The large lakes and reservoirs of the northeastern Tibetan Plateau play a key role in regional water resources, yet their influence on terrestrial water storage (TWS) changes at different spatial scales remains unclear. This study employed the constrained forward modeling (CFM) method to correct leakage errors in level-2 spherical harmonic (SH) coefficients from the Gravity Recovery and Climate Experiment and its follow-on missions (GRACE/GRACE-FO) at three spatial scales: two circular regions covering 90,000 km2 and 200,000 km2, respectively, and a 220,000 km2 region based on the shape of mass concentration (Mascon). TWS changes derived from SH solutions after leakage correction through CFM were compared with level-3 Mascon solutions. Individual water storage components, including lake and reservoir water storage (LRWS), groundwater storage (GWS), and soil moisture storage (SMS), were quantified, and their relationships with precipitation were assessed. From 2003 to 2022, the CFM method effectively mitigated signal leakage, revealing an overall upward trend in TWS at all spatial scales. Signals from Qinghai Lake and Longyangxia Reservoir dominated the long-term trend and amplitude variations of LRWS, respectively. LRWS explained more than 47% of the TWS changes, and together with GWS, accounted for over 85% of the changes. Both CFM-based and Mascon-based TWS changes indicated a consistent upward trend from January 2003 to September 2012, followed by declines from November 2012 to May 2017 and October 2018 to December 2022. During the decline phases, GWS contributions increased, while LRWS contributions and component exchange intensity decreased. LRWS, SMS, and TWS changes were significantly correlated with precipitation, with varying time lags. These findings underscore the value of GRACE/GRACE-FO data for monitoring multiscale TWS dynamics and their climatic drivers in lake- and reservoir-dominated regions.