Constraining a global ecosystem model with multi-site eddy-covariance data

Sylvain Kuppel*, P. Peylin, F. Chevallier, C. Bacour, F. Maignan, A. D. Richardson

*Corresponding author for this work

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO 2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (R eco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO 2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI).

Original languageEnglish
Pages (from-to)3757-3776
Number of pages20
JournalBiogeosciences
Volume9
Issue number10
DOIs
Publication statusPublished - 5 Oct 2012

Fingerprint

eddy covariance
ecosystems
ecosystem
ecosystem respiration
carbon
flux measurement
latent heat flux
carbon flux
parameter
temperate forest
deciduous forest
terrestrial ecosystem
data assimilation
leaf area index
NDVI
simulation
remote sensing
primary productivity
satellite data
data analysis

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Ecology, Evolution, Behavior and Systematics

Cite this

Kuppel, S., Peylin, P., Chevallier, F., Bacour, C., Maignan, F., & Richardson, A. D. (2012). Constraining a global ecosystem model with multi-site eddy-covariance data. Biogeosciences, 9(10), 3757-3776. https://doi.org/10.5194/bg-9-3757-2012

Constraining a global ecosystem model with multi-site eddy-covariance data. / Kuppel, Sylvain; Peylin, P.; Chevallier, F.; Bacour, C.; Maignan, F.; Richardson, A. D.

In: Biogeosciences, Vol. 9, No. 10, 05.10.2012, p. 3757-3776.

Research output: Contribution to journalArticle

Kuppel, S, Peylin, P, Chevallier, F, Bacour, C, Maignan, F & Richardson, AD 2012, 'Constraining a global ecosystem model with multi-site eddy-covariance data', Biogeosciences, vol. 9, no. 10, pp. 3757-3776. https://doi.org/10.5194/bg-9-3757-2012
Kuppel S, Peylin P, Chevallier F, Bacour C, Maignan F, Richardson AD. Constraining a global ecosystem model with multi-site eddy-covariance data. Biogeosciences. 2012 Oct 5;9(10):3757-3776. https://doi.org/10.5194/bg-9-3757-2012
Kuppel, Sylvain ; Peylin, P. ; Chevallier, F. ; Bacour, C. ; Maignan, F. ; Richardson, A. D. / Constraining a global ecosystem model with multi-site eddy-covariance data. In: Biogeosciences. 2012 ; Vol. 9, No. 10. pp. 3757-3776.
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