| 23 Mar 2026
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.
Karbin et al. presents a revision of the four major H2 uptake models to improve model performance. To improve accuracy, they modify the models to include a (1) microbial activity rate modification, and (2) ML-derived soil porosity. While the results are presented clearly, some of the interpretations can be more thoroughly discussed.
First, there is no discussion of the broader implications of the revised model formulations. Since the total sink strength is held constant by recalibrating k'max, the model refinements are actually change is the regional (and seasonal ?) distribution of uptake. The key question then is whether the revised models produce a more realistic spatial pattern of H₂ uptake.
specific comments:
L239-240: The revised value of k’max,SD essentially serves to redistribute the total uptake spatially, so that the global mean remains unchanged. But is the new pattern of uptake more realistic than the previous? Which is difficult to test since the actual datasets available for validation are from a geographically limited set of sites. Could the authors comment on the validity of the revised k’max,SD estimates?
L268-269: If modifying Kmax by mCMAC leads to similar sensitivities as using NPP as a proxy for SOC-modulation, what’s the value gained by running RothC in non-desert soils? Also, since NPP is largely a proxy for labile C input from vegetation, I wonder if using DPM and BIO alone in estimating mCMAC would give identical results to the full mCMAC. Not suggesting that the authors do this, but just wondering whether the HUM and RPM fractions contribute anything significant at all.
Lines 344-345 and L300-301: the authors state that in the Ehhalt and Bertagni models, the revised k’max values reflecting an increase of 25% and 46%, respectively, are indicative of the “added response of the model(s) to microbial activity” – but is this categorically true? Both of these models include explicit parameterizations of soil porosity, and it’s not clear if the revised value of k’max was estimated using just the microbial modifier or a simultaneous revision of the porosity inputs as well. If the latter, attributing the change in k’max estimates completely to the microbial rate modifier is potentially inaccurate.
In Table 3, and elsewhere throughout the manuscript: Can the authors discuss if the ML-derived porosity estimates are in fact more reliable than texture-based estimates, outside of the model prediction comparisons? In all 5 cases, ML-based estimates seem to be higher than the texture-based estimates, but these results are never really validated using actual measurements of porosity. How realistic is to have loamy sand soils from Harvard forest to have a porosity of 0.55? Naively assuming a particle density of ~2.65 g/cm3 for these soils, porosity can be calculated from the measured bulk density as 0.36. This is lower than both texture-based and ML-derived estimates, and I’m finding it hard to decide which estimate is more reliable.
Regarding ML-based porosity estimation: it looks like the authors essentially used the SoilGrids database to estimate porosity, but the way this is presented in the manuscript is somewhat confusing/misleading: as far as I can tell, this paper did not derive porosity using ML methods, but rather calculated it from ML-derived soil properties available in SoilGrids. To improve clarity, I suggest reframing “ML-derived porosity” to something like “porosity estimated using ML-predicted saturation water content”.
Related to the above, it’s also stated that the authors used water content at saturation as a proxy for porosity. Could the authors discuss potential problems with this method?
L666-667: Another place where the apparent claim is that the “ML-derived” values are inherently better to texture-based approximations, but crucially, this claim is not validated.
800-801: This conclusion is never really examined in the Discussion as far as I can tell. Why exactly would the Ehhalt model remain unaffected with the addition of a coupled-microbial rate modifier?
Minor: Note that Coleman and Jenkinson (1996) is not cited in the References.