Details

Autor(en) / Beteiligte

• Tangdamrongsub, Natthachet
• Han, Shin-Chan
• Yeo, In-Young
• Dong, Jianzhi
• Steele-Dunne, Susan C.
• Willgoose, Garry
• Walker, Jeffrey P.

Titel

Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates

Ist Teil von

• Advances in water resources, 2020-01, Vol.135, p.103477, Article 103477

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2020

Link zum Volltext

Quelle

ScienceDirect

Beschreibungen/Notizen

• •Development of multivariate data assimilation with GRACE, SMOS, and SMAP data.•Elaborating the impacts of univariate and multivariate DA on state estimates.•Multivariate DA leads to simultaneous improved groundwater and soil moisture estimates. 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.