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

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Susiluoto , J , Raivonen , M , Backman , L , Laine , M , Makela , J , Peltola , O , Vesala , T & Aalto , T 2018 , ' Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC ' , Geoscientific Model Development , vol. 11 , no. 3 , pp. 1199-1228 . https://doi.org/10.5194/gmd-11-1199-2018

Title: Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC
Author: Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif; Laine, Marko; Makela, Jarmo; Peltola, Olli; Vesala, Timo; Aalto, Tuula
Contributor organization: Institute for Atmospheric and Earth System Research (INAR)
Viikki Plant Science Centre (ViPS)
Ecosystem processes (INAR Forest Sciences)
Micrometeorology and biogeochemical cycles
Date: 2018-03-29
Language: eng
Number of pages: 30
Belongs to series: Geoscientific Model Development
ISSN: 1991-959X
DOI: https://doi.org/10.5194/gmd-11-1199-2018
URI: http://hdl.handle.net/10138/234426
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 uncertain-ties 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.
Subject: CO2 EXCHANGE
METROPOLIS ALGORITHM
NATURAL WETLANDS
ECOSYSTEM MODEL
VASCULAR PLANTS
BOREAL WETLANDS
CLIMATE SYSTEM
FEN ECOSYSTEM
LEAF-AREA
CARBON
1172 Environmental sciences
114 Physical sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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