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

作者：Wang Gangsheng^*; Chen Shulin

来源：Ecological Modelling, 2013, 253: 97-106.

DOI：10.1016/j.ecolmodel.2012.09.010

• Bayesian inference
• Greenhouse gas (GHG)
• Markov Chain Monte Carlo (MCMC)
• Metropolis-Hastings algorithm
• Model uncertainty

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.

• 出版日期2013-3-24