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

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.