Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

Abstract

• CH 4 emission from Finnish wetlands was simulated by CH4MOD wetland .• We recalibrated the vegetation parameters of graminoids, shrubs and Sphagnum.• Simulated CH 4 variations agreed well with the eddy-covariance observations. • Annual CH 4 emissions were reasonably simulated in different years and sites.• Parameterization of different vegetation process was essential in long-term CH 4 estimates. Boreal/arctic wetlands are dominated by diverse plant species, which vary in their contribution to CH 4 production, oxidation and transport processes. Earlier studies have often lumped the processes all together, which may induce large uncertainties into the results. We present a novel model, which includes three vegetation classes and can be used to simulate CH 4 emissions from boreal and arctic treeless wetlands. The model is based on an earlier biogeophysical model, CH4MOD wetland . We grouped the vegetation as graminoids, shrubs and Sphagnum and recalibrated the vegetation parameters according to their different CH 4 production, oxidation and transport capacities. Then, we used eddy-covariance-based CH 4 flux observations from a boreal (Siikaneva) and a subarctic fen , which was consistent with the observations (7-22 g m −2 yr −1 G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o). However, there are some discrepancies between the simulated and observed daily CH 4 fluxes for the Siikaneva site (RMSE = 50.0%) and the Lompolojänkkä site (RMSE = 47.9%). Model sensitivity analysis showed that increasing the proportion of the graminoids would significantly increase the CH 4 emission levels. Our study demonstrated that the parameterization of the different vegetation processes was important in estimating long-term wetland CH 4 emissions.

Temporal Variation of Ecosystem Scale Methane Emission From a Boreal Fen in Relation to Temperature, Water Table Position, and Carbon Dioxide Fluxes

“…In such models the methane production typically depends on the available carbon pool development (Li et al, 2016;Oikawa et al, 2017;Raivonen et al, 2017;Tian et al, 2010;Walter & Heimann, 2000;Wania et al, 2010;Watts et al, 2013;Zhuang et al, 2004), in line with our findings. In such models the methane production typically depends on the available carbon pool development (Li et al, 2016;Oikawa et al, 2017;Raivonen et al, 2017;Tian et al, 2010;Walter & Heimann, 2000;Wania et al, 2010;Watts et al, 2013;Zhuang et al, 2004), in line with our findings.…”

“…Knox et al (2016) have presented a 6.5-year time series measured at a Californian rice paddy, and Li et al (2016) used 5 years of data from Lompolojänkkä, Finland, and 6 years from Siikaneva, Finland, to evaluate methane model performance. Knox et al (2016) have presented a 6.5-year time series measured at a Californian rice paddy, and Li et al (2016) used 5 years of data from Lompolojänkkä, Finland, and 6 years from Siikaneva, Finland, to evaluate methane model performance.…”

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

“…Again, we assumed that autotrophic respiration contributes 50 % to the GPP (Gifford, 1994) and the contribution of Sphagnum was estimated to be 10 %, based on the biomass values reported for Siikaneva and Lompolojänkkä (Li et al, 2016).…”

Small spatial variability in methane emission measured from a wet patterned boreal bog

“…However, recent studies based on spatial (Turetsky et al, 2014) and temporal variation (Rinne et al, 2018) indicate maximum fluxes at intermediate WT positions. Vegetation has recently been included in the process models as a controlling factor of methane fluxes from peatlands (Li et al, 2016;Raivonen et al, 2017). However, these models do not yet take into account the impact of its spatial heterogeneity on methane fluxes.…”