the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2026
A process-based modeling of soil organic matter physical properties for land surface models – Part 2 : Global land surface simulations and mineral soil compaction adjustment
Abstract. In the companion paper, Decharme (2025) developed a process-based framework using soil mixture theory to represent the effects of soil organic matter on soil physical properties in land surface models. The present study extends this work by testing the framework in global land surface simulations with the ISBA-CTRIP land surface modeling system. The approach derives the volumetric organic matter fraction and phase-specific densities from soil organic carbon and bulk density using mass volume relationships, and computes hydraulic and thermal parameters using mixing rules consistent with the model physics. We also introduce an optional mineral soil compaction adjustment, under the assumption that texture-based pedotransfer functions are calibrated on weakly compacted samples, whereas gridded bulk density products mostly reflect in situ conditions that include varying degrees of compaction. We examine the effects of both developments in multidecadal global offline simulations forced by a standard meteorological dataset and driven by SoilGrids soil inputs. Four configurations are compared, a mineral-only control, a previous empirical scheme, the new process-based scheme, and its compaction-adjusted variant. The evaluation combines site-scale constraints on porosity and hydraulic behavior with large-scale benchmarks of the terrestrial water and energy cycles, including terrestrial water storage variations, river discharge, evapotranspiration, soil temperature, and active layer thickness. Overall, the global experiments suggest that the new process-based scheme produces more consistent large-scale hydrothermal responses than the previous empirical scheme, whereas the compaction adjustment plays a secondary role and mainly acts as a local modulator.
Competing interests: At least one of the (co-)authors serves as topic editor for the special issue to which this paper belongs.
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Status: open (until 03 Jun 2026)
- RC1: 'Comment on egusphere-2026-860', Anonymous Referee #1, 15 Apr 2026 reply
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RC2: 'Comment on egusphere-2026-860', Anonymous Referee #2, 30 Apr 2026
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This manuscript investigates how the revised representation of soil organic matter, together with a mineral compaction adjustment, influences land surface processes within the ISBA-CTRIP modeling system. The study addresses an important and often overlooked inconsistency in compaction status between pedotransfer-function-based soil properties and bulk density estimates derived from SoilGrids, and proposes a more physically consistent framework to account for compaction effects. Overall, the manuscript makes a valuable contribution by quantifying the impact of these processes on global land simulations.
Major comments
- The manuscript discusses the analytical formulation used to diagnose organic matter bulk density (ρbom), noting that the function may become numerically unstable when ρb approaches ρbms, potentially leading to divergence or nonphysical values. To mitigate this issue, a lower bound is applied to fmom in the computation of ρbom. However, the implementation in Part 1 (Decharme, 2025) uses the uncorrected fmom, which introduce an inconsistency between the theoretical formulation and its numerical application. The relatively large values of DE25c compared to DE25 in Figure 3 suggest that this bound does indeed exert a strong influence. It would be helpful if the authors could provide a spatial diagnostic showing where the lower bound is active (i.e., grid cells where fmom is constrained), in order to better assess the robustness of this bound. In addition, a brief sensitivity analysis of key results using the corrected fmom would help evaluate how much the prescribed lower bound affects the main conclusions, and whether this numerical treatment could alter any of the conclusions presented in Part 1.
- The mineral compaction parameterization introduced in Section 2.2.3 is an important component of the proposed framework, but it appears to be formulated primarily as a function of soil texture, without explicitly considering the thermodynamic state of the soil. In practice, soil compaction processes may depend on temperature-dependent properties such as liquid water and ice content. Frozen soils, for instance, are generally less compressible than unfrozen soils, which could affect the expression for compacted bulk density, particularly the values of the empirical parameters involved. It would therefore be helpful if the authors could clarify the assumptions behind the current formulation, especially whether it is intended to represent long-term equilibrium conditions. A brief discussion of the potential implications of ignoring temperature- or phase-dependent effects would strengthen the physical interpretation of the scheme. If possible, even a qualitative sensitivity discussion of this simplification would be valuable for assessing its robustness across climate regimes.
- In several parts of the manuscript, the model evaluation is done primarily against the CTL experiment, rather than against independent observations. While such relative comparisons are useful for isolating the effects of the proposed parameterization changes, they do not necessarily tell us whether the modified configurations are more realistic. Including observational or reanalysis-based benchmarks would therefore strengthen the manuscript. For example, in Figure 5, comparing total water content against observational datasets would help assess whether the simulated changes are genuine improvements. In Figure 10, where clear differences in soil temperature over the northern high latitudes appear between DE25 and DE25c, incorporating observed data would help determine which configuration performs more realistically. Also, the interpretation of standard deviation in Figures 5, 9, and 10 is not entirely clear, as reduced variability does not necessarily imply improved performance. Clarifying the diagnostic purpose would be helpful.
Minor comments
- L125–140: the sentences describing SoilGrids 2.0 and the Princeton Global Forcing dataset read more like a methods section. It might work better to move part of this description to Section 2 to improve the flow and readability. The remaining text could be trimmed into shorter, more focused sentences.
- In Figure 4, the uncertainty ranges (e.g., red versus pink shading) overlap heavily, making it hard to tell the different configurations apart. Adjusting the color scheme would improve interpretability.
- L678–680: the manuscript attributes the soil cooling in Figure 10 to increased total water content (Figure 5) and reduced thermal conductivity (Supplementary Figures S13–S14). However, the vertical patterns in Figure 5 and Figure 10 do not seem fully consistent. In Figure 5, DE16 shows increasing soil water content with depth relative to CTL, while DE25 and DE25c show differences that decrease with depth. In contrast, Figure 10 indicates that all configurations (DE16, DE25, DE25c) become increasingly colder with depth relative to CTL, a pattern more consistent with the reduced thermal conductivity shown in the supplementary figures S13–S14. It would therefore be helpful if the authors could clarify how soil moisture and thermal conductivity work together to produce the simulated vertical temperature structure.
Citation: https://doi.org/10.5194/egusphere-2026-860-RC2
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Summary
In a companion paper, a new framework for the parametrization of soil was proposed, accounting for the presence of soil organic matter (SOM) using the soil mixture theory. Here, this framework is tested in the context of global climatological simulations, using the ISBA-CTRIP land surface modelling scheme. Additionally, a correction for the compaction of mineral soil was evaluated as well. The results indicate that the new scheme has an overall modest, yet positive impact on the terrestrial water and energy cycle. Furthermore, it reveals some inconsistencies in the input soil maps (soilgrids).
General comments
This work is a significant and qualitative contribution to the land surface modelling community. Soil organic matter and its impact on soil properties is highly complex to account for at global scale, and is often overlooked. This follow-up to the companion paper is showing the impact of accounting for SOM with a (pragmatic) process-based scheme, and sheds light on the associated uncertainties and challenges. It is clearly presented and contains all the details to reproduce the results.
Here are some general comments:
- Overall, the metrics used to evaluate the new framework show a modest improvement of the model. Most of these metrics are dominated by the global climatological behaviour of the model, but the temporal variability, seasonal cycles, and anomalies are largely masked. Perhaps this could be considered out of scope, but the presented results leaves the reader wondering what the impact is on temporal dynamics from this new framework, especially since some soil parameters are modified rather drastically.
- As the authors indicate, the methodology relies on correct input values from soilgrids. Since it is known that there is a substantial uncertainty associated with bulk density of organic soils, it might be reasonable to constrain the bulk density of organic matter more strongly. See below.
Overall, this is an excellent work. I only have a few minor remarks.
Specific comments
- Litter layer: is it only activated for forest soils? Is there a risk that the impact of soil organic matter in the topsoil is double accounted for in the soil properties?
- To justify the mineral soil compaction correction, it is stated that the soil samples used in the development of the Cosby 1984 PTF were “not explicitly subjected to mechanical compaction”, whereas the samples used to build the soilgrids dataset are assumed to reflect “closer to compacted than to ideal, noncompacted conditions”. While it is appreciated that subsoil compaction is taken in consideration, these assumptions and the logic should probably be rephrased or clarified. A large fraction of the dataset used in Cosby 1984 are subsoil (B and even C-horizon) samples, mostly from agricultural fields, so a certain degree of compaction can be assumed. Furthermore, it is not clear why the soilgrids samples are assumed to have a stronger degree of compaction. This might be true for deeper subsoil samples, but is probably not the case for topsoil layers. Finally, the correction used here is actually the computation of the packing density, which is a correction to allow comparison of bulk densities (and eliminate the influence of soil texture). To my knowledge, using these equations to compute a “compacted” bulk density is not their intended use (at least, that is not how it is used in the cited references either). Although it is demonstrated in the results that this correction seems to improve the estimated porosity, the justification of this approach needs some work.
- A detail, but it could be mentioned that this is the white-sky albedo (I think?)
- Fig 3: top row plots (low-organic soil): It is at first sight surprising that the compacted soil (with substantially higher mineral bulk density) has roughly the same total porosity as the non-compacted soil, despite the low organic material content. This is due to the compensation in the organic bulk density (which is much lower for the compacted soil), yielding a higher volumetric fraction of organic material. Due to the freedom in bulk density of organic matter, the consequence of accounting for compaction is that more weight is given to the organic matter in the estimation of soil properties. The same can be observed in the other rows and in Fig. 4. It could be highlighted in the manuscript that compacting the mineral soil doesn’t necessarily result in a more compacted soil, but rather gives more weight to the organic component of the soil.
- Associated to the previous remark, the reference to optimum degree of compactness seems not fully appropriate here. First, this does not refer to the mineral fraction only, but to the total bulk density (which is unaffected by the correction). Second, it is the ratio between the bulk density and the reference bulk density, and is an indication for the degree of compaction. In this manuscript, it is the ratio between the mineral bulk density and the compacted mineral bulk density, where the latter is supposedly representing field conditions more accurately. It is not clear how to match these two approaches.
- Why do we see only small wsat differences in fig 3 and 4, whereas they are more evident in Fig. 2? Is it due to the imposed limit of 1g/cm3 for the bulk density of organic matter?
- Fig 4: Some important differences between DE16 and DE25(c) are observed in the bulk density of organic matter and the air entry value. DE16 was based on idealized profiles from literature (L203). It is known that properties of organic matter can be highly variable and strongly depend on peat type (eg Liu and Lennartz (2018)). It would be relevant to elaborate a bit on the differences between DE16 and DE25, and what they reflect.
- As discussed in the manuscript, DE25 is prone to inconsistencies in the input. Could it be made more robust by more strongly constraining the bulk density of organic matter? Or would this be detrimental to capture the spatial variability of OM? Given the uncertainty associated with bulk density in organic soils from soilgrids, it might be reasonable to constrain the organic matter bulk density to correct potential errors in the bulk density from soilgrids.
- Consider to exchange Figure 5 (water content) with Figure S15 (saturation degree). The latter seems more informative to understand differences in evaporation/runoff. (though this figure links well with Figure 10)
- The impact on some components of the water cycle could be elaborated on: Impact on deep drainage? baseflow? Bare soil evaporation/transpiration partitioning?
- L676, it seems to me that the anomalies are larger in the deeper layers?
- A side-note: On a more local scale, OM is strongly dependent on land cover. I wonder whether the impact is notable at this scale.