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

Boreal/arctic wetlands are dominated by diverse plant species, which vary in their contribution to CH₄ 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 CH₄ 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 CH₄ production, oxidation and transport capacities. Then, we used eddy-covariance-based CH₄ flux observations from a boreal (Siikaneva) and a subarctic fen (Lompolojänkkä) in Finland to validate the model. The results showed that the recalibrated model could generally simulate the seasonal patterns of the Finnish wetlands with different plant communities. The comparison between the simulated and measured daily CH₄ fluxes resulted in a correlation coefficient (R²) of 0.82 with a slope of 1.0 and an intercept of -0.1mgm^-2h^-1 for the Siikaneva site (n=2249, p<0.001) and an R² of 0.82 with a slope of 1.0 and an intercept of 0.0mgm^-2h^-1 for the Lompolojänkkä site (n=1826, p<0.001). Compared with the original model, the recalibrated model in this study significantly improved the model efficiency (EF), from -5.5 to 0.8 at the Siikaneva site and from -0.4 to 0.8 at the Lompolojänkkä site. The simulated annual CH₄ emissions ranged from 7 to 24gm^-2yr^-1, which was consistent with the observations (7-22gm^-2yr^-1). However, there are some discrepancies between the simulated and observed daily CH₄ 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 CH₄ emission levels. Our study demonstrated that the parameterization of the different vegetation processes was important in estimating long-term wetland CH₄ emissions.