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

C. Albergel (1) , J.-C. Calvet (1) , J.-F. Mahfouf (1) , C. Rüdiger (1) , A. L. Barbu (1) , S. Lafont (1) , Jean-Louis Roujean (1) , J. P. Walker (2) , M. Crapeau (3, 4) , J.-P. Wigneron (3)

1 CNRM - Centre national de recherches météorologiques
2 Department of Civil and Environmental Engineering [Melbourne]
3 EPHYSE - Écologie fonctionnelle et physique de l'environnement
4 EUMETSAT - European Organisation for the Exploitation of Meteorological Satellites

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 ISBAA-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.

Keywords: Précipitations; Humidité du sol en surface; Données de végétation; Assimilation des données; Modèle d'humidité de surface terrestre; Prévision; Flux d'eau; Flux de carbone

Domains: Atmospheric and Oceanic Physics [physics.ao-ph]; Hydrology; Global Changes; Meteorology

C. Albergel, J.-C. Calvet, J.-F. Mahfouf, C. Rüdiger, A. L. Barbu, et al.. Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France. Hydrology and Earth System Sciences Discussions, 2010, 14 (14), pp.1109-1124. ⟨10.5194/hess-14-1109-2010⟩. ⟨meteo-00983057⟩