the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Oct 2025
Modelling long-term soil organic carbon sequestration under varying environmental drivers and internal protection mechanisms – towards a digital twin
Abstract. Soil organic carbon (SOC) plays a large role in sustainable soil management and climate change mitigation. To understand the potential of soils to sequester additional carbon requires detailed knowledge of the underlying processes and drivers. In this study, we use soil evolution model SoilGen3.8.2 to assess the effects of environmental drivers (bioclimate, erosion level and land use) and four protection mechanisms on long-term SOC dynamics.
The protection mechanisms (aggregation, clay mineralogy, microporosity and metal oxyhydroxides (MOOHs)) showed large differences with different temporal patterns, where aggregation and clay mineralogy dominated during 10 ka of pedogenesis and MOOHs had a negligible effect. Ranking internal and external controls on SOC stocks revealed a decreasing influence of bioclimate > land use > erosion > time > protection mechanism.
Topsoil and subsoil SOC recovery after agricultural use revealed different dynamics, controlled by the history of environmental drivers and pedogenesis. Natural SOC recovery showed lowest rates for subsoils and highest rates for topsoils, with a strong control of erosion and pedogenetic history. The addition of ground rock of different mineralogies to enhance SOC sequestration had some effect, mainly for goethite, montmorillonite and a temporary effect of calcite. Our simulations demonstrate how SoilGen can improve understanding of soil processes, while also highlighting knowledge gaps, such as missing experimental insights in key SOC stabilization mechanisms.
Our study shows that soil models such as SoilGen cannot act as full digital twins of a soil, as not all processes and parameters of the complex soil system are represented. These models can, however, form the basis of topical digital twins, targeting specific processes or properties. We provide a roadmap for developing such topical digital twins and recommend to start from a complex model that accounts for pedogenetic history.
Competing interests: At least one of the (co-)authors serves as topic editor for the special issue to which this paper belongs.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
(1980 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5077', Anonymous Referee #1, 25 Nov 2025
-
RC2: 'Comment on egusphere-2025-5077', Anonymous Referee #2, 25 Nov 2025
In their manuscript, titled 'Modelling long-term soil organic carbon sequestration under varying environmental drivers and internal protection mechanisms – towards a digital twin', van der Meij and Finke describe their result of a study on the relation between long-term soil organic carbon (SOC) dynamics and soil development, using the SoilGen model.
The main aim was to assess the effect of three environmental drivers (bioclimate, soil erosion and land use) and four SOC protection mechanisms (soil aggregation, clay mineralogy, microporosity and metal oxyhydroxides) on long-term SOC dynamics. To do so, the authors ran SoilGen for artificial European loess soils in two different climatic settings (a dry natural steppe vegetation and a wet natural deciduous forest) for a time period of 10,000 years, with an additional 2,000 years to simulate recovery.
Their model results showed that the most important protection mechanism of SOC varied temporally depending on how soil development proceeded, with stabilization of SOC in aggregates initially being the most important protection mechanism. Their simulations showed that bioclimate had the largest influence on SOC, while protection mechanisms the lowest. In addition, the model showed that the subsoil recovered slower than the topsoil after an intense agricultural phase.
It is well-know that there is an important link between climate, soil development, mineralogy and soil organic matter (SOM) dynamics. Therefore, linking these processes in numerical models is an important step towards improving the simulation of SOM at the landscape scale, or over long time scales. This study is therefore timely, and tackles an important topic that has largely been overlooked in modelling studies.
The manuscript is well written and the results are clearly presented. I think it is an important contribution to the field over SOC – soil development modelling. I hope my feedback can improve the quality of the manuscript, and make some aspects more clear to the reader.
My main feedback concerning the present manuscript is the following:
- The statements the authors make about feedbacks between soil development and soil organic carbon can be more nuanced or more extensively compared to literature, as these are based on model results that have not been validated.
- Reporting of the turnover times of different simulated SOC pools would be valuable information for the reader to interpret the results.
- The description of how the RothC pools are linked to the protection mechanisms should be more detailed. Which RothC pools could be protected by aggregates, MOOHs, clay minerals and pores? This information is necessary for the reader to understand how the model functions.
- Some information about the methods is missing, for example:
- Was there a different parameterization for processes in the topsoil and subsoil?
- Did SOC turn over slower in the subsoil? If so, how was this simulated?
- Which depth layers were simulated?
- See more detailed aspects below.
- More information on the statistical analyses is needed for the reader to understand how these were performed.
- Why was a linear model used? Were all relations linear?
- What was regressed against what?
- Which data was exactly used in the statistics? Data for every simulated year?
- The figures can be made more clear:
- Avoid using abbreviations in the titles, or explain them in the caption. For example, mention the different erosion intensities, instead of E0, E1, E1E2E3, etc.
- A box around each plot would make it easier to distinguish between plots.
In my feedback, I mention certain published articles. These have been chosen based on their scientific relevance, and I leave it up to the authors whether they want to include these in their manuscript or not.
Specific comments
--- Introduction ---
Title: Why is the term ‘internal’ protection mechanisms used, also throughout the manuscript? As this term isn’t commonly used in the SOM literature, perhaps just talk about ‘protection mechanisms’?
L23-24: as the terms ‘full digital twins’ and ‘topical digital twins’ have not been introduced, the reader will not know what you mean by this.
L31-32: The study by Minasny et al. (2017) led to a large scientific debate in the literature, as many scientists did not agree with their outcomes. I would encourage the authors to also mention some of these studies, to show both sides of this debate (for example: Schlesinger and Amundson, 2019 (https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.14478); van Groenigen et al., 2017 (https://doi.org/10.1021/acs.est.7b01427); de Vries, 2018 (https://doi.org/10.1016/j.geoderma.2017.05.023); VandenBygaart, 2018 (https://doi.org/10.1016/j.geoderma.2017.05.024); Poulton et al., 2018 (https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.14066))
L39: The term ‘digital twin’ can be described more extensively here, so the reader knows what you mean.
L55: it would be useful to have a better introduction on soil evolution models. Are there alternatives to SoilGen? How about trade-offs between process representation in a complex model like SoilGen, and data availability? How well constrained are the parameters of SoilGen based on previous studies
L67: define what you mean by ‘long timescales’.
--- Methods ---
L83: Were cover crops simulated?
L87-91: it would be useful for the reader to mention in which climates and for which soils these previous SoilGen analyses were performed. That way, the reader knows if that’s relevant for this study or not.
Section 2.1.3: it would be good to also explain what the ‘no protection scenario’ is, and how the reader should interpret this.
Eq 1: it would be better to provide intuitive letters, instead of abcd
Caption Table 1: Why are the rate modifiers limited to values between 0.8 and 1? Please explain this in the text
Table 1: it would be much more intuitive for the reader if graphs are provided that show how these equations affect the rate modifiers. This is very difficult based on the equations alone, and as this is central to the performed simulations, the reader would benefit from a better understanding of this.
L137-140: Can the values for NPP that were used in the simulations be provided?
L143: It would be good to explain how the C inputs were vertically distributed during the simulations.
L145-146: Please explain how soil material was mixed through bioturbation. Was this magnitude constant with depth, or exponentially decreasing? What was the ploughing depth?
Table 2: was CaCO3 added to the soil surface, or vertically distributed along the soil profile?
L154: What were the values of the average July and January temperatures used in the simulations?
L173: Please mention the thickness of this topsoil layer
--- Results ---
L198: I would rather talk about ‘higher compared to the base scenario’, instead of ‘increase’, as the latter implies an increase over time when talking about SOC dynamics.
L220-221: it would be more useful to mention if NPP was the same between these sites, in addition to that they have the same vegetation
L221: It would be useful to explain why the decrease in SOC after 8000 years is larger in the dry compared to the wet scenarios.
L223: it would be useful to already mention this frequency of layer removal in the methods, when erosion in described.
Figure 3: It is not clear based on which data points the box plots were constructed. Were these the C values for every year in the same simulation between 9,900 – 10,000?
L247: Is this because the simulations were started with very little C, which increased over time? And since there were different forcings at different points in time, would time not by definition have an effect on SOC stocks? This needs more explanation.
L264-265: It would be good to mention the turnover rates of the pools that were used, as these values are determined by those parameters.
Caption Fig. 4: would be good to mention that the lines connect dots at the same erosion rate
Figure 4: How are topsoil and subsoil defined? This is not mentioned in the manuscript?
Figure 5: It would be more intuitive if all y-axes had the same range
--- Discussion ---
L297-299: You state that ‘results revealed surprising insights into SOC dynamics’ and ‘the model can advance our understanding of these feedback and drivers’. However, the model results have not been confronted to data, and it seems you relied on model parameters determined in previous studies. Please support these statements by explaining why you have confidence in the correctness of these results.
L304: Can you say that aggregation is the dominant protection mechanism? Because this protection mechanism is only ca. 10 % larger than the ‘no protection’ scenario in Fig. 1. Does this mean that ca. 90 % of SOC is in the soil because of the chemical composition of OC, which is the basis on which RothC simulates differences in turnover between the pools? Please explain this in the manuscript.
L307: ‘liming restored aggregation as the dominant protection mechanism’: did this take place over the entire soil profile? Or was the CaCO3 only applied at the topsoil and aggregation improved there? It would be useful to the reader to make this clear.
L313-317: This reasoning is not clear to me. What does it mean when ‘erosion keeps up with the calcite depletion rate’, and why does this ensure that sufficient calcite remains available during the entire simulation? Is there evidence from experiments that minor soil erosion can lead to larger C stocks compared to a non-eroding site? Or is this a model artifact?
L318-319: ‘agreeing with observations […]’: would be good to have a reference here to back this statement up
L332: please explicitly state which soil depths you considered to be subsoil
L334-335: Please state, here or in the methods, which portion of OC inputs under natural vegetation enters the system as litter, versus as belowground biomass. From these lines it seems as if this was mainly aboveground litter, which is not realistic for forest soils, where C originating from aboveground litter is generally mainly detected in the upper 20 or 30 cm of the soil.
L335-336: ‘agricultural crops contribute more organic carbon directly into the subsoil’: do you mean compared to natural forest vegetation? Would you say it’s realistic to find larger subsoil SOC stocks under agriculture compared to natural forest vegetation in loess soils?
L340: why ‘arbitrary’ and ‘loosely-based’? Please explain
L340: You mention the turnover rates, but don’t mention these in the manuscript. However, this information is needed for the reader to assess if these were realistic, and to know how these changed along the soil profile in the simulations. Please provide this information. Further, in L342, you say that these ‘do not directly represent real-world recovery rates’. How should the reader interpret these? And if this is the case, how can you have confidence in the correctness of the SOC recovery rates you found? Please clarify this in the manuscript.
L347: ‘are controlled by external controls’ => ‘are controlled by external controls in the simulations’
L351-354: you state rates of changes in SOC upon land use change or erosion. But higher up, you state that these ‘do not directly represent real-world recovery rates’. How does this reconcile? Or am I missing something here?
L364-374: A comparison with literature would be valuable here to assess model performance.
L376: Please better discuss why such a digital twin is necessary, and would be preferable to current model approaches. Also, how feasible is this, given the large spatial heterogeneity of the soil system, but spatially and temporarily?
L401-402: ‘this phase ensures that the model can accurately simulate soil development up to the point where the monitoring starts’. However, above you state that ‘the timeline of interest often extends far beyond the period of monitoring’. Please explain how you can have confidence in simulations that are performed without having the necessary measurements to obtain reliable model parameters.
L405: Please explain what you mean by monitoring data: which data should be monitored, at which spatial and temporal resolution?
--- Conclusion ---
L424-426: Before stating the results, it would be good to make it clear that these are model results that have not been validated using measurements.
Technical comments
L47: something seems to be missing after ‘environmental’
L48: > 25 cm => below 25 cm depth
L50: Pries et al. 2023 => Hicks Pries et al. 2023
L52 : ‘There is still a lot unknown’ => ‘A lot it still unknown’
L87: over => for?
Caption Table 1: ‘protection mechanisms’ => ‘SOC protection mechanisms’
L132: output => outputs
Caption Figure 2: bold => thick
L246: decrease => were lower; increasing => higher
L312: recognized as degrading process => recognized as a degrading process
L409: addition > additional
Citation: https://doi.org/10.5194/egusphere-2025-5077-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 292 | 39 | 21 | 352 | 13 | 12 |
- HTML: 292
- PDF: 39
- XML: 21
- Total: 352
- BibTeX: 13
- EndNote: 12
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This paper presents the results of scenario simulations with the latest version of the soil evolution model SoilGen, which considers four different mechanisms that influence the strength of protection of soil organic carbon (SOC) against microbial decomposition. Many properties that are considered as static and therefore described with constant parameter values in almost all other models of transport processes in soil, are treated as dynamic variables in the SoilGen model. This paper therefore fits very well to the topic of the special issue in SOIL (“Advances in dynamic soil modelling across scales”)
The simulations can be seen as a kind of sensitivity analysis to investigate the relative importance of these different stabilization mechanisms, along with environmental factors like climate and land use, for soil organic carbon sequestration in a very long-term perspective (millenia). Although strict validation of such model predictions has not been attempted (it seems very difficult for such temporal scales), the authors have tried to do some “reality checks” in section 4.1.3, for example with respect to the effects of erosion and afforestation on C-stocks. Could this be further developed? I was thinking, for example, of the estimates of C-stock loss due to the development of intensive agriculture over longer time spans than centuries, given by Lal (2013) and Sanderman et al (2017).
The description of the model itself is very brief. Some more detail would help the reader, particularly on the organic carbon turnover model. For example, in equation 1, could you explain how the “crop cover” modifier is estimated? What aspects of crop cover influence SOC decomposition? How does it relate to the land use scenarios shown in table 2? Apart from re-distributing SOC in the plough layer, does tillage also affect decomposition rates?
The authors discuss at length in section 4.2. the potential of SoilGen to evolve into a “digital twin”, that is to say a model that is data-aware, one that can be updated on receiving new data from the real world. I have to admit that I am a little sceptical about this. I can’t see how a model that makes predictions of slow processes for thousands of years into the future could really be a suitable prospect for digital twinning. The gap in time-scales between data collection and model projections seems just too large. So far, dynamic twinning between data and models has only been attempted in short-term studies, as the authors acknowledge at lines 391-395. It would be good if the authors could briefly discuss the disparity in time-scales in the text that follows at lines 396-422.
On the whole, the paper is well written and nicely presented. However, I think it would be better for the reader if section 2.3 came before the parameterization is presented in 2.2. The scenarios are currently mentioned in 2.2 before they are defined and explained.
Specific comments
Line 47: there seems to be a word missing after “environmental”
Line 49: replace “seem to” by “may”. I am not convinced there should be any difference in the mechanisms (only in how strongly they are expressed).
Line 51: for the same reasons as at line 49, replace “plays” with “may play”
Line 60: Please update this reference to the final published article: Coucheney, E., Herrmann, A., Jarvis, N. 2025. A simple model of the turnover of organic carbon in a soil profile: model test, parameter identification and sensitivity. SOIL 11, 715-733.
Line 75: perhaps the heading should be: “Model overview”
Line 79: replace “that include” by “of”
Line 82: “seldom” not “seldomly”
Line 88: “have undergone” not “underwent”
Line 105: Instead of “default rate”, it would be better to write “reference rate constant”. Strictly, R is a rate constant, not a rate. It would be good to make this clear by writing the units of R here (as well as the other terms in equation 1).
Line 132: Comparison of model output with what? This should be clarified.
Lines 177-179: Is this really a model artefact?
Lines 353: This comparison seems a little dubious to me because no-till can affect SOC stocks through several other processes, not just by reducing rates of erosion. Surface crop residues can modify the soil thermal regime, which in the decadal time perspective that is relevant here, could be just as important as erosion. Tillage also enhances decomposition by disrupting soil aggregates. It’s not clear to me whether SoilGen considers these processes or not. This should be clarified.
Lines 365-366: “ground” or “crushed” not “grinded”
References
Lal, R. 2013. Intensive agriculture and the soil carbon pool. Journal of Crop Improvement, 27, 735-751.
Sanderman, J., Hengl, T., Fiske, G. 2017. Soil carbon debt of 12,000 years of human land use. Proc. Natl. Acad. Sci. USA. 114, 9575-9580.