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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 6
Hydrol. Earth Syst. Sci., 14, 1109–1124, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Earth observation and water cycle science

Hydrol. Earth Syst. Sci., 14, 1109–1124, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Jun 2010

29 Jun 2010

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

C. Albergel1, J.-C. Calvet1, J.-F. Mahfouf1, C. Rüdiger1,*, A. L. Barbu1, S. Lafont1, J.-L. Roujean1, J. P. Walker2,*, M. Crapeau3,**, and J.-P. Wigneron3 C. Albergel et al.
  • 1CNRM/GAME (Météo-France, CNRS), URA 1357, Toulouse, France
  • 2Department of Civil and Environmental Engineering, The University of Melbourne, Melbourne, Australia
  • 3Unité de recherche Ecologie fonctionnelle et Physique de l'Environnement (INRA), Villenave d'Ornon, France
  • *now at: Department of Civil Engineering, Monash University, Clayton, Australia
  • **now at: EUMETSAT, Darmstadt, Germany

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.

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