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

A L Barbu,C Albergel,J.-F Mahfouf,J.-C Calvet,S Lafont

doi:10.5194/bg-8-1971-2011

Abstract

Abstract. The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI) product provided by an exponential filter together with Leaf Area Index (LAI) is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001–2007), at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO2 fluxes is also quantified by using measurements of net CO2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained.

Highlights

The objective of data assimilation is to combine optimally data from different sources that bring complementary information on a geophysical system
This work is a first attempt to assimilate a Soil Wetness Index (SWI) derived from the exponential filter method in a Land Surface Models (LSM)
A posteriori diagnostics are employed for the first time in order to verify the specification of the errors for SWI and Leaf Area Index (LAI)

Summary

Introduction

The objective of data assimilation is to combine optimally data from different sources that bring complementary information on a geophysical system. The development of Land Surface Models (LSM) able to simulate photosynthesis processes, surface carbon fluxes and vegetation biomass allows the joint assimilation of soil moisture data together with Leaf Area Index (LAI) estimates. The Leaf Area Index is an important factor controlling surface evapo-transpiration, as it impacts the exchange of water vapor and CO2 between the vegetation canopy and the atmosphere. Several studies (Jarlan et al, 2008; Sabater et al, 2008) have shown the potential of assimilating LAI to estimate the vegetation characteristics and to reduce model uncertainties. Soil moisture is a key variable to be initialized in meteorological models since the partition between sensible and latent heat fluxes depends on the quantity of water in the soil available in the root zone. As the near-surface soil moisture (wg) is reasonably well correlated with the profile soil moisture content under specific circumstances, the retrieval of root-zone soil moisture (w2) using surface observations is possible (Calvet and Noilhan, 2000)

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Conclusion