Incorporating Soil Organic Carbon Dynamics into Global Hydrogen Uptake Models: A Focus on Microbial Activity

Abstract. Molecular hydrogen is a secondary greenhouse gas that indirectly contributes to climate forcing by extending the atmospheric lifetime of methane through competition for hydroxyl radicals. Soil serves as a major sink for atmospheric hydrogen, making accurate estimation of soil hydrogen uptake essential for understanding its role in atmospheric chemistry. Most existing process-based models of hydrogen uptake focus primarily on abiotic controls, such as soil temperature and moisture, while either neglecting or oversimplifying the role of biotic factors, particularly microbial activity. In this study, we refine four widely used hydrogen uptake models by integrating microbial activity rate modifiers and machine learning derived soil porosity. The microbial activity rate modifiers are derived from the decomposability of soil organic carbon, which is assumed to be a proxy for potential microbial activity. This leverages simulations of soil organic matter turnover provided by well-established and tested models of soil organic matter decomposition. This simple approach enables application of hydrogen uptake models from field to global scales. We have integrated our simulations of microbial activity into four widely used hydrogen uptake models. Model performance is evaluated against empirical datasets from four detailed studies of soil hydrogen uptake. Results show that replacing traditional texture-based porosity with machine learning derived estimates significantly improved physical transport modelling, particularly for the Bertagni and Ehhalt frameworks. Furthermore, incorporating the coupled climate-carbon microbial activity rate modifier consistently strengthened model performance, producing larger reductions in prediction error and more pronounced increases in correlation than using microbial activity alone, thereby providing a more realistic representation of soil microbial processes. These findings highlight the importance of including biologically relevant factors in atmospheric hydrogen modelling and offer a more mechanistic framework for predicting soil–atmosphere hydrogen exchange under diverse environmental conditions.