Premium
Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges
Author(s) -
Li Bailing,
Rodell Matthew,
Kumar Sujay,
Beaudoing Hiroko Kato,
Getirana Augusto,
Zaitchik Benjamin F.,
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,
Lannoy Gabriëlle,
Mocko David,
SteeleDunne Susan C.,
Save Himanshu,
Bettadpur Srinivas
Publication year - 2019
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr024618
Subject(s) - groundwater , data assimilation , environmental science , precipitation , hydrology (agriculture) , assimilation (phonology) , groundwater flow , streamflow , proxy (statistics) , drainage basin , climatology , meteorology , geology , aquifer , geography , linguistics , philosophy , geotechnical engineering , cartography , machine learning , computer science
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.