 Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertaintyGangsheng Wang and Shulin Chen Ecological Modelling, 2013, vol. 253, issue C, 97-106 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. Keywords: Bayesian inference; Greenhouse gas (GHG); Markov Chain Monte Carlo (MCMC); Metropolis–Hastings algorithm; Model uncertainty (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:253:y:2013:i:c:p:97-106 DOI: 10.1016/j.ecolmodel.2012.09.010 Access Statistics for this article Ecological Modelling is currently edited by Brian D. Fath More articles in Ecological Modelling from Elsevier |
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