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

[摘要] Understanding the dynamics of peatland methane ( CH 4 ) emissions and quantifying sources of uncertainty in estimating peatland CH 4 emissions are critical for mitigating climate change. The relative contributions of CH 4 emission pathways through ebullition, plant-mediatedtransport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH 4 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 CH 4 emissions. To improve modelsimulations of CH 4 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 CH 4 flux and concentration data collected in a peatlandin northern Minnesota, USA. When model parameters were constrained using observed CH 4 fluxes, the CH 4 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 CH 4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBGmodel with both CH 4 flux and concentration data in model–data fusion, uncertainty of the modeled CH 4 concentration profiles wasreduced by 78 % to 86 % in comparison to constraints based on CH 4 flux data alone. The improved model capability was attributed tothe well-constrained parameters regulating the CH 4 production and emission pathways. Our results suggest that the EBG modeling approachbetter characterizes CH 4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH 4 flux andconcentration data are required to constrain model parameterization.

[学科分类] 大气科学