Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study

摘要

The performance of the joint assimilation in a land surface model of a SoilWetness Index (SWI) product provided by an exponential filter together withLeaf Area Index (LAI) is investigated. The data assimilation is evaluatedwith different setups using the SURFEX modeling platform, for a period ofseven years (2001–2007), at the SMOSREX grassland site in southwesternFrance. The results obtained with a Simplified Extended Kalman Filterdemonstrate the effectiveness of a joint data assimilation scheme when bothSWI and Leaf Area Index are merged into the ISBA-A-gs land surface model.The assimilation of a retrieved Soil Wetness Index product presents severalchallenges that are investigated in this study. A significant improvement ofaround 13 % of the root-zone soil water content is obtained by assimilatingdimensionless root-zone SWI data. For comparison, the assimilation of insitu surface soil moisture is considered as well. A lower impact on the rootzone is noticed. Under specific conditions, the transfer of the informationfrom the surface to the root zone was found not accurate. Also, our resultsindicate that the assimilation of in situ LAI data may correct a number ofdeficiencies in the model, such as low LAI values in the senescence phaseby using a seasonal-dependent error definition for background andobservations. In order to verify the specification of the errors for SWIand LAI products, a posteriori diagnostics are employed. This approachhighlights the importance of the assimilation design on the quality of theanalysis. The impact of data assimilation scheme on CO₂ fluxes is alsoquantified by using measurements of net CO₂ fluxes gathered at the SMOSREXsite from 2005 to 2007. An improvement of about 5 % in terms of rms error isobtained.