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

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)(2002)(2003)(2004)(2005)(2006)(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.

Use of agricultural statistics to verify the interannual variability in land surface models: a case study over France with ISBA-A-gs

“…This option is useful for climate change impact studies , and allows the sequential assimilation of satellite LAI estimates. The latter was demonstrated at the local scale, for an unmanaged grassland site (Sabater et al, 2008;Albergel et al, 2010;Barbu et al, 2011). These studies show that representing LAI observation errors is not easy.…”

Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE

“…In order to use this information into LSMs, data assimilation techniques were implemented and tested (Demarty et al, 2007;Sabater et al, 2008;Albergel et al, 2010a;Barbu et al, 2011). Data assimilation techniques aim at improving the model simulations, which are affected by uncertainties, mainly caused by the lack of knowledge of some biophysical processes, together with errors in the atmospheric variables used as input to the models (Szczypta et al, 2011;Zhao et al, 2011).…”

Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data

“…Four statistical indices are tabulated for each dataset: the global bias, the root mean square error (RMS), the correlation coefficient and the Nash index (Nash and Suttcliffe, 1970), which definition is given in Eq. (14) and a short interpretation can be found in Albergel et al (2010): where y iest and y iobs are respectively estimated and observed values, y i obs is the mean of observations and n is the sample size.…”

“…Wang et al, 2007) to the most complex models (e.g. Rodell et al, 2004;Albergel et al, 2010) have been investigated and applied to a variety of spatial scales, from local and regional (Bastiaanssen et al, 1998;Su, 2002;Stisen et al, 2008;Miglietta et al, 2009) to global scales (Jiménez et al, 2009). Most of these studies are made for research purposes and use only selected datasets.…”