Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges

作者：Bailing Li, Matthew Rodell, Sujay V. Kumar, H. K. Beaudoing, Augusto Getirana, Benjamin F. Zaitchik, L. G. de Goncalves, Camila Cossetin, Soumendra N. Bhanja, Abhijit Mukherjee, Siyuan Tian, Natthachet Tangdamrongsub, Di Long, J. Nanteza, Jejung Lee, F. Policelli, Ibrahim Baba Goni, D. Djoret, Mohammed Bila, Gabriëlle De Lannoy, David M. Mocko, Susan Steele‐Dunne, Himanshu Save, Srinivas Bettadpur · 发表于：Water Resources Research · 年份：2019 · DOI：10.1029/2018wr024618 · 被引用次数：641 · 研究领域：Geophysics and Gravity Measurements、Solar and Space Plasma Dynamics、GNSS positioning and interference

Abstract The scarcity of groundwater storage change data at the global scale hinders our ability to monitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications. Evaluation using in situ data from nearly 4,000 wells shows that GRACE data assimilation improves the simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improved by 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed in regions with large interannual variability in precipitation, where simulated groundwater responds too strongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are further demonstrated using observed low‐flow data. CLSM and GRACE data assimilation performance is also examined across different permeability categories. The evaluation reveals that GRACE data assimilation fails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes to simulating realistic groundwater variations in regions with intensive groundwater abstraction. CLSM‐simulated groundwater correlates strongly with 12‐month precipitation anomalies in low‐latitude a...