Journal article
Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty
Ecological modelling, Vol.253, pp.97-106
03/24/2013
Handle:
https://hdl.handle.net/2376/111494
Abstract
► 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.
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.
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Details
- Title
- Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty
- Creators
- Gangsheng Wang - Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USAShulin Chen - Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USA
- Publication Details
- Ecological modelling, Vol.253, pp.97-106
- Publisher
- Elsevier B.V
- Identifiers
- 99900582330301842
- Language
- English
- Resource Type
- Journal article