A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil

The efficacy of mathematical modeling as a tool for estimating greenhouse gas (GHG) emissions from soil depends on the uncertainty. Systematic evaluation of various sources of uncertainties in GHG emission models is limited. This paper reviews the state-of-the-art knowledge on the parameterization and uncertainty analysis of soil GHG emission models. Major recommendations and conclusions from this work include: (a) uncertainties due to model parameters and structure can be quantified by combining the Bayesian theorem and the Markov Chain Monte Carlo (MCMC) method; (b) uncertainty due to event-based model input may also be assessed by regarding each event as a latent variable; however, the necessity of the simultaneous evaluation of uncertainties from model input, parameters, and structure might be negotiable because strong correlations may exist between input errors and model parameters; (c) uncertainty analysis is essential for a successful model parameterization by reducing both the number of undetermined parameters and the parameter space; and (d) model parameterization (calibration) should be conducted on multiple sites towards multiple objectives. Case studies were presented for comparing the model uncertainties of the denitrification components of four models, DAYCENT, DNDC, ECOSYS, and COMP. The methods discussed in this paper can help to evaluate model uncertainties and performances, and to offer a critical guidance for model selection and parameterization.

► Model should be calibrated on multiple sites towards multiple objectives.
► Uncertainty analysis is essential for a successful model parameterization.
► Model uncertainties can be quantified by combining Bayesian theorem and MCMC.
► Case studies are presented for model comparison through uncertainty analysis.
► We suggest developing a model library including various model structures.