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. Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediatedtransport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to beoxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. Inparticular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve modelsimulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach(EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatlandin northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBGapproach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated asmaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improvedsimulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBGmodel with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles wasreduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed tothe well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approachbetter characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux andconcentration data are required to constrain model parameterization.

Evaluating alternative ebullition models for predicting peatland methane emission and its pathways via data–model fusion