论文推荐|中山大学姜中山副教授：追踪加州陆地水储量变化对大气河流的动态响应

California is highly vulnerable to extreme precipitation events due to the dense landfall of atmospheric rivers (ARs) during the winter months, often resulting in catastrophic consequences such as widespread floods, mudslides, and landslides. This study focuses on the recovery of daily variations in AR-driven terrestrial water storage (TWS), which produces geodetically detectable ground subsidence. We invert GNSS vertical positions to obtain daily estimates of equivalent water height (EWH) through a variational Bayesian principal component analysis (vbPCA) based inversion scheme and track significant water gains during record-setting winter months in four water years (WYs) 2011, 2017, 2019, and 2023. These precipitation extremes have resulted in a substantial short-term increase in water storage, as evidenced by the multi-source EWH datasets (GNSS, GRACE, and NLDAS). Notably, WY 2023 experienced the highest snowfall due to the landfalls of high-density, high-category ARs, while WY 2017 recorded the highest precipitation totals, driven by the most frequent occurrence of hazardous ARs. Our findings further highlight that GNSS can accurately detect exceptionally wet hydrological events on short time scales, benefiting from an improved signal-to-noise ratio due to substantial increase in water storage. The results also indicate that while these extreme water years can help alleviate surface subsidence in the Central Valley caused by groundwater overexploitation, it is insufficient to alter California’s heavy reliance on groundwater for its intensive agricultural activities. Our findings demonstrate that GNSS is successful in tracking prodigious water increases from short-term precipitation extremes that are weaker than powerful hurricanes, illuminating the prospect of GNSS in supporting water management and flood preparedness.