Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges. Issue 9 (4th September 2019)

• Record Type: Journal Article
• Authors: Li, Bailing; Rodell, Matthew; Kumar, Sujay; Beaudoing, Hiroko Kato; Getirana, Augusto; Zaitchik, Benjamin F.; de Goncalves, Luis Gustavo; Cossetin, Camila; Bhanja, Soumendra; Mukherjee, Abhijit; Tian, Siyuan; Tangdamrongsub, Natthachet; Long, Di; Nanteza, Jamiat; Lee, Jejung; Policelli, Frederick; Goni, Ibrahim B.; Daira, Djoret; Bila, Mohammed; de Lannoy, Gabriëlle; Mocko, David; Steele‐Dunne, Susan C.; Save, Himanshu; Bettadpur, Srinivas
• Is Part Of: Water resources research. Volume 55:Issue 9(2019)
• Page Start: 7564
• Page End: 7586
• Publication Date: 2019-09-04
• Subjects: global GRACE data assimilation -- groundwater storage estimates -- global groundwater drought monitoring -- groundwater temporal variability -- groundwater drought; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR024618
• Languages: English
• ISSNs: 0043-1397

Abstract

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 and midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generally agrees with other regional‐scale drought indicators, with discrepancies mainly in their estimated drought severity. Key Points: A mascon‐based GRACE terrestrial water storage product is assimilated into the Catchment model at the global scale GRACE data assimilation improved RMSE and correlation of simulated groundwater storage with in situ data at point and regional scales Uncertainty in model estimated water storage is strongly related to interannual variability of precipitation