Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates
Natthachet Tangdamrongsub (NASA Goddard Space Flight Center, University of Maryland)
Shin Chan Han (The University of Newcastle, Australia)
In Young Yeo (The University of Newcastle, Australia)
Jianzhi Dong (USDA-ARS Hydrology and Remote Sensing Laboratory)
Susan C. Steele-Dunne (TU Delft - Water Resources)
Garry Willgoose (The University of Newcastle, Australia)
Jeffrey P. Walker (Monash University)
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Abstract
Assimilating remote sensing observations into land surface models has become common practice to improve the accuracy of terrestrial water storage (TWS) estimates such as soil moisture and groundwater, for understanding the land surface interaction with the climate system, as well as assessing regional and global water resources. Such remote sensing observations include soil moisture information from the L-band Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, and TWS information from the Gravity Recovery And Climate Experiment (GRACE). This study evaluates the benefit of assimilating them into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The evaluation is conducted in the Goulburn River catchment, South-East Australia, where various in situ soil moisture and groundwater level data are available for validating data assimilation (DA) approaches. It is found that the performance of DA mainly depends on the type of observations that are assimilated. The SMOS/SMAP-only assimilation (SM DA) improves the top soil moisture but degrades the groundwater storage estimates, whereas the GRACE-only assimilation (GRACE DA) improves only the groundwater component. Assimilating both observations (multivariate DA) results in increased accuracy of both soil moisture and groundwater storage estimates. These findings demonstrate the added value of multivariate DA for simultaneously improving different model states, thus leading to a more robust DA system.
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