Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

C Albergel,A L Barbu,J.-L Roujean,J.-C Calvet,S Lafont,J.-F Mahfouf,J P Walker,M Crapeau,J.-P Wigneron,C Rüdiger

doi:10.5194/hess-14-1109-2010

Abstract

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

Highlights

Soil moisture is a key variable in short- and medium-range meteorological modelling as well as in climate and hydrological studies
Global Surface Soil Moisture (SSM) products are operationally available from microwave spaceborne instruments such as ASCAT (Advanced Scatterometer onboard METOP, Wagner et al, 2007), the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor (Njoku et al, 2003, for the official product and Owe et al, 2001, 2008, for a new retrieval product), or will be available from the recently launched SMOS (Soil Moisture and Ocean Salinity, ESA/Centre National d’Etudes Spatiales (CNES), Kerr et al, 2001, 2007) satellite dedicated to the observation of the microwave brightness temperature (TB) at L-band, and from SMAP (Soil Moisture Active Passive, Entekhabi et al, 2004) which is scheduled for launch in 2015
Following Sabater et al (2008), the present study focuses on the assimilation of SSM and Leaf Area Index (LAI) into ISBA-A-gs, at the SMOSREX experimental site

Summary

Introduction

Soil moisture is a key variable in short- and medium-range meteorological modelling as well as in climate and hydrological studies. Continuous land surface processes such as the evolution of soil moisture, plant transpiration and soil evaporation can be modelled with Land Surface Models (LSM). While microwave remote sensing provides global maps of SSM (Schmugge, 1983), combining this information with LSM simulations through a Land Data Assimilation System (LDAS) allows the root zone soil moisture (w2) to be retrieved as demonstrated by several authors (Entekhabi et al, 1994; Houser et al, 1998; Walker et al, 2001; Ragab, 1995; Sabater et al, 2007).

Methods

Results

Discussion

Conclusion