Articles | Volume 11, issue 3
Geosci. Model Dev., 11, 1199–1228, 2018
Geosci. Model Dev., 11, 1199–1228, 2018

Model evaluation paper 29 Mar 2018

Model evaluation paper | 29 Mar 2018

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

Jouni Susiluoto1,3,4, Maarit Raivonen2, Leif Backman1,2, Marko Laine1, Jarmo Makela1,4, Olli Peltola2, Timo Vesala2,5, and Tuula Aalto1 Jouni Susiluoto et al.
  • 1Climate research, Finnish Meteorological Institute, P.O. Box 503, Helsinki, Finland
  • 2Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
  • 3Lappeenranta University of Technology, School of Science, Lappeenranta, Finland
  • 4University of Helsinki, Department of Mathematics and Statistics, Helsinki, Finland
  • 5Institute for Atmospheric and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland

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.

Short summary
Methane is an important greenhouse gas and methane emissions from wetlands contribute to the warming of the climate. Wetland methane emissions are also challenging to estimate. We analyze the performance of a new wetland emission computer model utilizing mathematical methods and using data from a wetland in southern Finland. The analysis helps to explain how wetlands produce methane and how emission modeling can be improved and uncertainties in the emission estimates reduced in future studies.