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

Abstract

The scarcity of groundwater storage change data at the global scale hinders our ability tomonitor groundwater resources effectively. In this study, we assimilate a state‐of‐the‐art terrestrialwater storage product derived from Gravity Recovery and Climate Experiment (GRACE) satelliteobservations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal ofgenerating 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 improvesthe simulation of groundwater, with estimation errors reduced by 36% and 10% and correlation improvedby 16% and 22% at the regional and point scales, respectively. The biggest improvements are observed inregions with large interannual variability in precipitation, where simulated groundwater responds toostrongly to changes in atmospheric forcing. The positive impacts of GRACE data assimilation are furtherdemonstrated using observed low‐flow data. CLSM and GRACE data assimilation performance is alsoexamined across different permeability categories. The evaluation reveals that GRACE data assimilationfails to compensate for the lack of a groundwater withdrawal scheme in CLSM when it comes tosimulating realistic groundwater variations in regions with intensive groundwater abstraction.CLSM‐simulated groundwater correlates strongly with 12‐month precipitation anomalies in low‐latitudeand midlatitude areas. A groundwater drought indicator based on GRACE data assimilation generallyagrees with other regional‐scale drought indicators, with discrepancies mainly in their estimateddrought severity.