This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH4 emission model (MeSMOD) for estimating CH4 emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH4 sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH4 flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH4 emissions using satellite soil moisture of 10 km (33 Tg CH4 yr−1) and hydrological model soil moisture of 10 km (39 Tg CH4 yr−1) compared with previous studies using random forest technique for upscaling (29.5 Tg CH4 yr−1), LPJ-wsl process model (30 Tg CH4 yr−1), and CH4 CAMS inversion (34 Tg CH4 yr−1). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH4 emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH4 emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH4 emissions, ultimately reducing uncertainties in global CH4 budgets.

An important uncertainty in the modeling of methane (CH4) emissions from natural wetlands is the wetland area. It is difficult to model wetlands’ CH4 emissions because of several factors, including its spatial heterogeneity on a large range of scales. In this study, we investigate the impact of model resolution on the simulated wetland methane emission for the Fennoscandinavian Peninsula. This is carried out using a high-resolution wetland map (100 × 100 m2) and soil carbon map (250 × 250 m2) in combination with a highly simplified CH4 emission model that is coarsened in five steps from 0.005° to 1°. We find a strong relation between wetland emissions and resolution, which is sensitive, however, to the sub-grid treatment of the wetland fraction. In our setup, soil carbon and soil moisture are positively correlated at a high resolution, with the wetland location leading to increasing CH4 emissions with increasing resolution. Keeping track of the wetland fraction reduces the impact of resolution. However, uncertainties in CH4 emissions remain high because of the large uncertainty in the representation of wetland the area, as demonstrated using the output of the WetChimp intercomparison over our study domain. Because of wetland mapping uncertainties, existing models are unlikely to realistically represent the correlation between soil moisture and soil carbon availability. The correlation is positive in our simplified model but may be different in more complex models depending on their method of representing substrate availability. Therefore, depending on the correlation, CH4 emissions may be over- or underestimated. As increasing the model resolution is an effective approach to mitigate the problem of accounting for the correlation between soil moisture and soil carbon and to improve the accuracy of models, the main message of this study is that increasing the resolution of global wetland models, and especially the input datasets that are used, should receive high priority.

Abstract. An important uncertainty in the modelling of methane (CH4) emissions from natural wetlands is the wetland area. It is difficult to model wetlands CH4 emissions because of several factors, including its spatial heterogeneity on a large range of scales. In this study, we investigate the impact of model resolution on the simulated wetland methane emission for the Fenno-Scandinavian Peninsula. This is done using a high-resolution wetland map (100 × 100m2) and soil carbon map (250 × 250m2) in combination with a highly simplified CH4 emission model that is coarsened in six steps from 0.005° to 1°. We find a strong relation between wetland emissions and resolution, which is sensitive, however, to the sub-grid treatment of the wetland fraction. In our setup, soil carbon and soil moisture are positively correlated at high-resolution with wetland location leading to increasing CH4 emissions with increasing resolution. Keeping track of wetland fraction reduces the impact of resolution. However, uncertainties in CH4 emissions remain high because of the large uncertainty in the representation of wetland area, as demonstrated using the output of the WetChimp intercomparison over our study domain. Because of wetlands mapping uncertainties, the existing models are unlikely to realistically represent the correlation between soil moisture and soil carbon availability. The correlation is positive in our simplified model, but maybe different in more complex models depending on their method of representing substrate availability. Therefore, depending on the correlation, CH4 emissions may be over or underestimated. As increasing the model resolution is an effective approach to mitigate the problem of accounting for the correlation between soil moisture and soil carbon and improve the accuracy of models, the main message of this study is that increasing the resolution of global wetland models, and especially the input datasets that are used, should receive high priority.

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