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Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty

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  • Wang, Gangsheng
  • Chen, Shulin

Abstract

We combined the Bayesian inference and the Markov Chain Monte Carlo (MCMC) technique to quantify uncertainties in the process-based soil greenhouse gas (GHG) emission models. The Metropolis–Hastings sampling was examined by comparing four univariate proposal distributions (UPDs: symmetric/asymmetric uniform and symmetric/asymmetric normal) and one multinormal proposal distribution (MPD). Almost all the posterior parameter ranges from the MPD could be reduced to 1 order of magnitude. The simulation errors in CO2 fluxes were much greater than those in N2O fluxes, which resulted in a greater importance in model structure than in model parameters for CO2 simulations. We suggested deriving the covariance matrix of parameters for MPD from the sampling results of a UPD; and generating a Markov chain by updating a single parameter rather than updating all parameters at each time. The method addressed in this paper can be used to evaluate uncertainties in other GHG emission models.

Suggested Citation

  • Wang, Gangsheng & Chen, Shulin, 2013. "Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty," Ecological Modelling, Elsevier, vol. 253(C), pages 97-106.
    • Handle: RePEc:eee:ecomod:v:253:y:2013:i:c:p:97-106
    DOI: 10.1016/j.ecolmodel.2012.09.010
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    References listed on IDEAS

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    1. Tiana, G. & Sutto, L. & Broglia, R.A., 2007. "Use of the Metropolis algorithm to simulate the dynamics of protein chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 241-249.
    2. Gary Yohe & Michael Oppenheimer, 2011. "Evaluation, characterization, and communication of uncertainty by the intergovernmental panel on climate change—an introductory essay," Climatic Change, Springer, vol. 108(4), pages 629-639, October.
    3. Ogle, Stephen M. & Breidt, F. Jay & Easter, Mark & Williams, Steve & Paustian, Keith, 2007. "An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils," Ecological Modelling, Elsevier, vol. 205(3), pages 453-463.
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    1. Boulange, Julien & Watanabe, Hirozumi & Akai, Shinpei, 2017. "A Markov Chain Monte Carlo technique for parameter estimation and inference in pesticide fate and transport modeling," Ecological Modelling, Elsevier, vol. 360(C), pages 270-278.

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