Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2017-66
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model evaluation paper
21 Apr 2017
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
Calibrating a wetland methane emission model with hierarchical modeling and adaptive MCMC
Jouni Susiluoto1,3,4, Maarit Raivonen2, Leif Backman1,2, Marko Laine1, Jarmo Mäkelä1,4, Olli Peltola2, Timo Vesala2,5, and Tuula Aalto1 1Finnish Meteorological Institute, Erik Palmenin Aukio 1, 00560 Helsinki, Finland
2University of Helsinki, Department of Physics, Finland
3Lappeenranta University of Technology, School of Science, Finland
4University of Helsinki, Department of Mathematics and Statistics, Finland
5University of Helsinki, Department of Forest Sciences, Finland
Abstract. Methane (CH4) emission estimation for natural wetlands is complex and the estimates contain large uncertainties. The models used for the task are typically heavily parametrized and the parameter values are not well known. In this study we perform a Bayesian model calibration for a new wetland CH4 model to improve 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 MCMC techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in Southern Finland.

The model parameters are calibrated using six different modeled peat column depths, and the hierarchical modeling allows us to assess the effect of the parameters on an annual basis. The results of the calibration and their cross validation suggest that the early spring net primary production and soil temperatures could be used to predict the annual methane emissions. The modeled peat column depth has an effect on how much the plant transport pathway dominates the gas transport, and the optimization moved most of the gas transport from the diffusive pathway to plant transport. This is in line with other research, highlighting the usefulness of algorithmic calibration of biogeochemical models.

Modeling only 70 cm of the peat column gives the best flux estimates at the flux measurement site, while the estimates are worse for a column deeper than one meter or shallower than 50 cm. The posterior parameter distributions depend on the modeled peat depth. At the process level, the flux measurement data is able to constrain CH4 production and gas transport processes, but for CH4 oxidation, which is an important constituent of the total CH4 emission, the determining parameter is not identifiable.


Citation: Susiluoto, J., Raivonen, M., Backman, L., Laine, M., Mäkelä, J., Peltola, O., Vesala, T., and Aalto, T.: Calibrating a wetland methane emission model with hierarchical modeling and adaptive MCMC, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2017-66, in review, 2017.
Jouni Susiluoto et al.
Jouni Susiluoto et al.
Jouni Susiluoto et al.

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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 difficult to estimate. We analyze the functioning and performance of a wetland emission computer model with mathematical algorithms and data from a wetland in Southern Finland. The analysis provides insight into how wetland emission modeling can be improved and how uncertainties in the emission estimates can be reduced in future studies.
Methane is an important greenhouse gas and methane emissions from wetlands contribute to the...
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