Representing explicit microbial processes enhances methane modeling under oxygen fluctuation

The dynamics of soil carbon (C) emissions along with their biogeochemical and environmental control have garnered increasing attention. However, the role of methane (CH₄) in soil organic carbon (SOC) modelling has been relatively underexplored compared to carbon dioxide (CO₂), and the omission of microbial processes may prevent us from accurately modelling CH₄ dynamics under environmental changes. Here, we incorporated an explicit microbial CH₄ module into the Microbial-ENzyme Decomposition (MEND) model and evaluated it against a sub-version with the first-order kinetics (First-order) and the previous MEND (MEND_old) model. We conducted a rigorous calibration and validation of MEND with high-resolution CO₂ and CH₄ efflux observations across two soil types and five different oxygen (O₂) fluctuation conditions. Beyond precisely capturing soil CO₂ and CH₄ effluxes, the model could also effectively simulate the relative contents of microbial biomass and enzymes. The multi-model comparison further revealed that the inclusion of new processes did not necessarily enhance model performance if microbes were not perceived as explicit state variables. Our results demonstrated that adopting microbial functional groups as drivers of soil CH₄ cycle could provide a basis for testing hypotheses on microbially mediated CH₄ processes and their responses to environmental changes. With the availability of diverse data and the development of genetic technologies, our modelling framework present here will empower ecologists and governments to perceive and intervene in global warming from underlying biogeochemical mechanisms rather than predictions.