Articles | Volume 12, issue 9
https://doi.org/10.5194/bg-12-2809-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-12-2809-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Bayesian inversions of a dynamic vegetation model at four European grassland sites
J. Minet
CORRESPONDING AUTHOR
Université de Liège, Arlon Campus Environnement, Avenue de Longwy 185, 6700 Arlon, Belgium
Belgian Nuclear Research Centre (SCK-CEN), Boerentang 200, 2400 Mol, Belgium
B. Tychon
Université de Liège, Arlon Campus Environnement, Avenue de Longwy 185, 6700 Arlon, Belgium
L. François
Université de Liège, UMCCB, Allée du six août 17, 4000 Liège, Belgium
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11 citations as recorded by crossref.
- A Bayesian alternative for multi-objective ecohydrological model specification Y. Tang et al. https://doi.org/10.1016/j.jhydrol.2017.07.040
- Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework H. Yang et al. https://doi.org/10.3390/rs11111335
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- Towards a More Realistic Simulation of Plant Species with a Dynamic Vegetation Model Using Field-Measured Traits: The Atlas Cedar, a Case Study A. Hambuckers et al. https://doi.org/10.3390/f13030446
- A high-resolution monitoring approach of urban CO2 fluxes. Part 2 – surface flux optimisation using eddy covariance observations S. Stagakis et al. https://doi.org/10.1016/j.scitotenv.2023.166035
- A High-Resolution Monitoring Approach of Urban Co2 Fluxes. Part 2 - Optimisation Framework Using Eddy Covariance Observations S. Stagakis et al. https://doi.org/10.2139/ssrn.4172740
- Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa M. Dury et al. https://doi.org/10.3390/f9110722
Saved (final revised paper)
Latest update: 12 Jun 2026
Short summary
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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