Abstract
Abstract. Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.
Highlights
Methane is the third most important gas in the atmosphere in terms of its capacity to warm the climate, after water vapor and carbon dioxide, currently with the radiative forcing of 0.97 W m−2 (IPCC, 2013)
Three different parameter estimates obtained from the posterior distribution were used to look at its features and fluxes: the maximum a posteriori (MAP) estimate, posterior mean estimate, and a non-hierarchical posterior mean estimate, where the mean values of the parameters ζexu (–) and Q10 (–) over the different years were used
A leave-one-out cross validation (LOO-CV; see, e.g., Gelman et al, 2013) of the regression modeling was performed by optimizing the hierarchical parameters with respect to the cost function in Eq (24) leaving one year at a time out, calculating the estimates for the hierarchical parameters based on the results obtained for other years, and predicting the CH4 emissions for the year that was left out
Summary
Methane is the third most important gas in the atmosphere in terms of its capacity to warm the climate, after water vapor and carbon dioxide, currently with the radiative forcing of 0.97 W m−2 (IPCC, 2013). Methane models typically use measured values from field campaigns and parameters estimated from those studies where applicable (Lai, 2009b; Walter and Heimann, 2000; Tang et al, 2010; Riley et al, 2011), and, when needed, include extra tuning parameters for processes (Walter and Heimann, 2000) This is a practical and much-used route, as information regarding all of the needed parameters is not available at all sites (van Huissteden et al, 2009; Walter and Heimann, 2000). As a part of this work, knowledge about how the methane and carbon dioxide flux data are able constrain the parameters and processes is obtained
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