General comments:

The presented work is a good contribution to improving the reactive transport modeling approach. It develops a representative reaction network based on substrate-explicit thermodynamic modeling and completes a biogeochemical simulation with a reactive transport model.

However, I think the current version cannot be considered for publication in GMD. There are some possible reasons:

(a) Poor model validation. A strict validation checks whether a new model approach reflects the actual or desired physical and biochemical behavior, considering using a reactive transport model. In this study, I noticed that there are very limited observations. For example, the work only involved limited observed O2 (Fig.4a), CO2 (Fig.5a), and total organic carbon datasets (Fig.5b). I also noticed that the model cannot capture observed total organic carbon (Fig.5b).

(b) Incomplete simulation experiments. The work just considered two test cases (oxygen depletion and respiration). Too small a test size may lack sufficient statistical power to detect new model capabilities.

(c) Poor analysis. The work shows limited biogeochemical analysis. An advantage of RTM is its ability to elucidate complex biogeochemical dynamics. For example, How do oxygen dynamics and total carbon influence the variations of pH and C, N, and P species over time (Fig.5a)? Although I understand this work is technical, the justification for these time-series variations should be carefully discussed.

This manuscript describes a new workflow for integrating detailed organic matter data from FTICR-MS into a reactive transport modeling framework for simulating biogeochemical interactions. The workflow includes sensitivity analysis and parameter estimation capabilities. I think this has the potential to be a highly valuable tool for connecting biogeochemical models with organic matter measurements at a level of chemical detail that has historically been a major challenge for biogeochemical model-data integration. The workflow covers many of the pain points for this type of model-data connection, including aggregating large numbers of compounds into a smaller, tractable set of representative compounds, assigning meaningful model parameters to those compounds, and converting the resulting reaction network into a reactive transport model simulation configuration automatically.

The manuscript describes the underlying assumptions and theoretical framework in clear terms and acknowledges important caveats such as the lack of quantitative information about the relative amounts of different compounds. The steps for using the workflow are also clearly described, which fits with the goal of making the workflow accessible and reproducible for other scientists. The two provided test cases are useful concrete examples of the workflow, important parameters, and useful data types for parameter estimation.

I did think that the results shown from the test cases did not fully make the case for how the FTICR-MS data add value to the resulting simulations. While the comparison of the full reaction network to simulations using multiple compounds does show a difference in overall predicted concentrations relative to a simplified reaction network with only one organic matter compound, it is difficult to see how the high resolution of chemical compounds, which is the hallmark of the FTICR-MS analysis, specifically contributes to the simulation results. It would be helpful to include a visualization of the distribution of compounds, how they translate to reaction networks with different properties, and how that ultimately affects the model results. For example, is the reaction network derived from the Test Case 1 FTICR-MS measurements meaningfully different from that of Test Case 2 in terms of its stoichiometry or distribution of reaction rates? Does the specific FTICR-MS data from each experiment yield better model results than FTICR-MS data from a different experiment? What if the null hypothesis was 10 compounds derived from a different set of measurements, rather than 1 generic compound? This comparison could more directly show the value of those measurements from a particular experiment.

Other comments:

Line 97: is y_OM here the same as y_OC in Equation 2?

Line 101: I think “1000” is missing from this sentence

Line 123: HS should be HS^-

Line 192: Given the importance of specifying parameters to be estimated for the workflow, I think it would be helpful to list all the relevant parameters in one table, with explanations of what they mean and perhaps with suggestions for what kind of measurements could constrain them or what would be a good context for including them in the parameter estimation and what reasonable ranges of values might be.

Section 2.3.4: When discussing the parameter optimization, it would be helpful to start with some explanation of what kind of observations are useful for comparing with the model. While examples are provided in the test cases, I think some general explanation earlier on would be helpful as well.

Line 256: I’m curious how accurate the assumption of equal division of C mass across bins is. Do any data exist that could inform this? The approach might be quite sensitive to this assumption.

Figure 3: I was confused why k_deg and C_inhibit are shown in this figure, even though they aren’t part of the parameters being tested. And I did not understand the significance of the time dimension. Does this indicate whether model ensemble members are diverging or converging over the course of the simulation? It could use some more explanation of how to interpret that part.

Figure 5: The model generally fails to reproduce the observed total carbon concentrations. I might guess that the assumption of how much carbon gets solubilized from the solid state over time, or the characteristics of that soluble carbon, are not correct for this system. I would not necessarily expect a model to get every aspect of the system right, especially if it relates to a steady state process involving solid organic matter, which this framework is not designed to replicate. But I think this should be at least acknowledged and explained in the text and currently it feels like this part of the result is ignored.