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

Abstract

Abstract. Estimating methane (CH 4 ) 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 CH 4 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 CH 4 production from anaerobic respiration, CH 4 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 CH 4 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.

Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model

“…HB approach has recently gained attention for the calibration of process-based models (Thomas et al, 2017;Susiluoto et al, 2018;Tian et al, 2020), however, hierarchical processbased model calibrations are still very limited. One reason why a HB approach may not have yet been widely adopted by the process-based modeling community is that Bayesian calibration is already computationally costly for process-based models (Fer et al, 2018).…”

The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990–2018

“…https://doi.org/10.5194/essd-2020-367 The model framework, JSBACH-HIMMELI (Raivonen et al, 2017;Susiluoto et al, 2018) is used to estimate wetland and mineral soil emissions, and an empirical model is used to estimate the emissions from inland water bodies.…”

“…HIMMELI (HelsinkI Model of MEthane buiLd-up and emIssion for peatlands) has been developed especially 1095 for estimating CH4 production and transport in northern peatlands. It simulates both CH4 and CO2 fluxes and can be used as a module within different modelling environments (Raivonen et al, 2017;Susiluoto et al, 2018). HIMMELI is driven with soil temperature, water table depth, the leaf area index and anoxic respiration.…”

“…Bradbury et al, 1993Coleman., 1996Jenkinson., 1977, 1987Smith et al, 1996, 2010a BU DayCent N2O emissions from direct agricultural soils avg. 2011-2015 EC-JRC Orgiazzi et al, 2018Lugato et al, 2018, 2017Quemada et al, 2020 CH4 and N2O bottom-up natural BU JSBACH-HIMMELI CH4 emissions from peatlands 2005-2017 FMI Raivonen et al, 2017Susiluoto et al, 2018 https://doi.org/10.5194/essd-2020-367 Maavara et al, 2017Lauerwald et al, 2019Deemer et al, 2016Del Sontro et al, 2018Mccauley et al, 1989 BU Geological emissions, including marine and land geological)…”

“…Tg CH4 yr -1 , or 48 % of the total natural CH4 emissions (sum of lakes and reservoirs, geological and peatlands emissions). Peatlands (Raivonen et al 2017 andSusiluoto et al 2018) account for 1.38 Tg CH4 yr -1 , i.e. 27 % of the total natural CH4 emissions, and geological sources sum up to 1.27 Tg CH4 yr -1 , i.e.…”