Genome‐Enabled Parameterization Enhances Model Simulation of CH4 Cycling in Four Natural Wetlands

Abstract

Abstract Microbial processes are crucial in producing and oxidizing biological methane (CH4) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH4 cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH4 flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH4 flux. Specifically, the coefficient of determination (R2) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop in R2 was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH4 flux, additional constraints of functional gene data led to better CH4 seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH4 production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH4 flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH4 models to better understand microbial roles in CH4 in the era of climate change.

Keywords

• methane
• CLM‐Microbe model
• wetlands
• microbe
• functional gene