Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 12, issue 9
Biogeosciences, 12, 2809–2829, 2015
https://doi.org/10.5194/bg-12-2809-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 12, 2809–2829, 2015
https://doi.org/10.5194/bg-12-2809-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 May 2015

Research article | 13 May 2015

Bayesian inversions of a dynamic vegetation model at four European grassland sites

J. Minet1, E. Laloy2, B. Tychon1, and L. François3 J. Minet et al.
  • 1Université de Liège, Arlon Campus Environnement, Avenue de Longwy 185, 6700 Arlon, Belgium
  • 2Belgian Nuclear Research Centre (SCK-CEN), Boerentang 200, 2400 Mol, Belgium
  • 3Université de Liège, UMCCB, Allée du six août 17, 4000 Liège, Belgium

Abstract. Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m−2 day−1 and 0.50 to 1.28 mm day−1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics.

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We probabilistically invert the CARAIB dynamic vegetation model using a Markov chain Monte Carlo sampler, considering both homoscedastic and heteroscedastic eddy covariance residual errors with variances either fixed a priori or jointly inferred with the model parameters. A model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors result in more robust posterior parameter distributions.
We probabilistically invert the CARAIB dynamic vegetation model using a Markov chain Monte Carlo...
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