کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4376225 | 1617492 | 2013 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty](/preview/png/4376225.png)
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.
► Bayesian inference and MCMC were combined to quantify model uncertainties.
► The Metropolis–Hastings sampling was investigated regarding various proposal distributions.
► Procedures for implementation of multinormal proposal distribution were suggested.
Journal: Ecological Modelling - Volume 253, 24 March 2013, Pages 97–106