the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2024
Depth-dependence of soil organic carbon additional storage capacity in different soil types by the 2050 target for carbon neutrality
Abstract. Land planning projects aiming to maximise soil organic carbon (SOC) stocks are increasing in number and scope. In response, a rising number of studies assess SOC additional storage capacities over regional to global spatial scales. In order to provide realistic values transferrable beyond the scientific community, SOC storage capacity assessments should consider the timescales over which this capacity might be reached, considering the effects of C inputs, soil type and depth on soil C dynamics.
This research was conducted in a 320 km2 territory in North-eastern France where eight contrasted soil types have been identified, characterized and mapped thanks to a high density of fully-described soil profiles. Continuous profiles of SOC stocks were interpolated for each soil type and land use (cropland, grassland or forest). Depth-dependent estimates of maximum SOC additional storage capacity using the Hassink equation and a data-driven approach were compared. We used a novel method that uses the data-driven approach to constrain C inputs in a simple model of depth-dependent C dynamics to simulate SOC accrual over 25 years, and mapped the SOC stocks, maximum additional storage capacity and stock evolution.
SOC stocks and maximum additional storage capacities are highly heterogenous over the region of study. Median SOC stocks range from 78–333 tC ha-1. Data-driven maximum SOC additional storage capacities vary from 19 tC ha-1 in forested Leptosols to 197 tC ha-1 in grassland Gleysols. Estimations of SOC maximum additional storage capacities based on the Hassink approach led to unrealistic vertical distributions of SOC stock, with particular overestimation in the deeper layers. Crucially, the simulated SOC accrual over 25 years was five times lower than the maximum SOC additional storage capacity (0.57 and 2.5 MgC respectively). Further consideration of depth-dependent SOC dynamics in different soil types is therefore needed to provide targets of SOC storage over timescales relevant to public policies aiming to approach carbon neutrality by 2050.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1284', Anonymous Referee #1, 28 May 2024
This manuscript reports a study that simulated organic C accrual over 25 years in eight soils with contrasting properties of North-eastern France. The authors compared the Hassink equation and a novel data-driven approach to estimate soil organic C stocks and maximum soil organic C additional storage capacity. They found that the Hassink approach leads to unrealistic estimates and that the simulated soil organic C accrual over 25 years was five times lower than the maximum storage capacity.
The study has interest, and to the best of my knowledge, well conducted, although some assumptions need to be validated or better justified (see below). Also to the best of my knowledge, the results are well discussed and the conclusion are well supported. However, the study needs to be better introduced and some aspects of the approach need clarification.
My specific comments:
l. 46. This sentence is unclear. What do you mean by “the upper percentiles of the total carbon content in a large dataset”?
l. 50 The Hassink method needs to be introduced.
l. 50. It is unclear what data-driven approach the authors are referring to. This should be better introduced and explained.
l. 64. Additional to what? Please clarify.
l. 75. The last sentence seems to be disconnected from the rest of the paragraph.
l. 78. The model needs to be briefly introduced here.
l. 81-84. This text is unclear.
l. 107. What do the authors mean by data points?
l. 109. Sampling was conducted between and 2019. Do different starting points affect modeling results?
l. 130. The use of this pedotransfer function to estimate bulk density would need to be validated with data from this study.
l. 178-180. These estimates need justification and validation.
l. 307. “for reasons that will be detailed further in the Discussion section” can be removed.Citation: https://doi.org/10.5194/egusphere-2024-1284-RC1 - AC2: 'Reply on RC1', Clementine Chirol, 23 Aug 2024
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RC2: 'Comment on egusphere-2024-1284', Anonymous Referee #2, 24 Jul 2024
General CommentsIn this manuscript, Chirol et al. combine direct measurements of deep soil cores with process modelling to investigate the potential for soils in a region of North-eastern France to accrue SOC over 25 years under a scenario of increased C inputs. While the topic is timely and important, I found the logic of the paper hard to follow and the methods to be a bit convoluted. In my opinion, the manuscript needs a clearer storyline throughout, from the hypothesis or research question, to the approach, and through to the discussion. Why was this particular approach chosen, what are the strengths, and what exact gaps does it fill? While elements of this are there, it was really not clear enough to follow easily and I was left with many questions as I read it. The methods were presented in a bit of a circular way and were not well-justified, especially around the determination of potential SOC accrual and the input rates used (see below), and the AGM model, which is integral to the paper, is not well-described in the main text at all. I have four major recommendations for the manuscript:
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More clearly present the logic and reasoning behind comparing two approaches (Hassink vs. Chen et al.) for estimating potential SOC storage. First, there needs to be clearer description of and distinction between the idea of fine fraction saturation vs. maximum SOC accrual, which are two very different concepts (though saturation may be a constraint on maximum SOC accrual), and which relate directly to the conclusions and the implications of the work. I have several specific comments on this below. Second, how do these ideas and assumptions relate to the conclusions drawn from them (see #4)? It is critical to be careful in how one presents and interprets these "maximum values".
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Provide better rationale of the approach in terms of the scenarios modeled. The input rates were chosen based on what the model requires to maintain the theoretical upper limits of SOC accrual by land use at steady state, and then those input rates were simulated to see how much SOC accrued after 25 years. Why not instead use the accrual rates necessary to achieve theoretical maximum SOC storage to inform ideal inputs and see if they are achievable, or use more realistic input rates (perhaps ranges) and see how far from the maximum SOC accrual those are after 25 years?
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Provide more detail on the AGM model, especially whether/how it represents saturation and temperature sensitivity of carbon pools and how those relate to the concepts of MAOM and POM mentioned early in the manuscript. Why is this an appropriate model for this use, and what are the implications of the model structure (and perhaps its limitations) for the results and conclusions?
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The discussion can be much clearer about the implications of the approaches used and dig more into how different assumptions end up informing potential strategies for climate mitigation. How does considering depth change our understanding of how we might manage soils for climate? What about methods of determining maximum potential C storage (and trajectories for reaching it)? Should we even base management ideas around these maxima, or are they infeasible to reach on relevant timescales?
Specific CommentsLines 50-54: I find this hard to understand and perhaps misleading, as there are several issues that are being confounded, and depth is not quite relevant in the way it is said here. My reading of the Chen et al. (2018) paper was that their approach not only identified many soils that were undersaturated (as pointed out here), but also identified many soils that were "oversaturated" according to the Hassink equation. The latter is because the Hassink equation was developed for MAOM only and is a least-squares linear regression approach which results in many soils falling above the predicted saturation level and is precisely why boundary approaches (but still typically for MAOM only) have gained favor over the past decade (Feng et al., 2013). Further, given that the Hassink approach was not developed on whole soil profiles (top 10 cm only), applying it to a whole profile does not seem a relevant solution to the issue presented in lines 53-54. The actual issue at hand here is that these two methods are not compatible and should not be directly compared, as one pertains only to saturation of the fine fraction while the other looks at whole soil carbon stocks.Lines 55-65: While I agree that better understanding of subsoil C dynamics and its controls is important, it is doubtful whether saturation or storage capacity are important constraints on subsoil C accrual. This paragraph should better connect subsoils with the topic of saturation and ideally address why it may or may not be an important constraint on subsoil C accrual head-on. This point is very important to the justification, methods, and conclusions of the paper and can further clarify the aims and ideal application of the two contrasting methods (fine fraction saturation vs. whole C storage capacity). In general it should be pointed out that a saturation approach is not ideal for estimating the target of subsoil C accrual because fine fraction saturation is unlikely to be a relevant constraint; this approach may therefore lead to inflated expectations for C accrual.Lines 65-67: Perhaps the shortcomings of simple models could be briefly highlighted here, and at least one example of such a model provided in the text.Line 78: Can the model be introduced better here? Is it named? Why is this model appropriate for this use, etc?Lines 79-81: Is this sentence supposed to mention a time horizon? Because couldn't the target levels be reached with different input rates, just on different time scales? But see comment below for line 217.Line 163: Yet, this method does not necessarily identify the maximum possible SOC stocks, it is constrained by several factors including input rates (if they increased massively, higher SOC levels could be reached). It is actually the maximum *observed* SOC stock, not necessarily the maximum possible. This really should be clarified here and throughout.Line 165: The Hassink method is not meant to estimate maximum total SOC stock, only the fine fraction. This and the previous comment point to the real need to clarify these concepts throughout the paper.Line 168: I think using the term "data-driven approach" is misleading as the Hassink equation is data-driven. The distinction is more that one focuses on the fine fraction while the other does not, and that one was aiming to understand saturation (i.e. sought out particular soils with high fine fraction C contents) while the other is not (i.e., it is looking at maximum observed total C for a given dataset).Lines 169-174: Given the authors point out that improvements to the original Hassink equation have been suggested, what is the rationale for using the original Hassink equation? It is not clear.Lines 178-179: These choices of percent POM and MAOM seem extremely arbitrary and overly precise. Why not use ranges? Please explain the rationale (even if briefly) rather than just referring to the papers.Lines 198-191: It is good that the authors highlight that the choice of boundary line affects the conclusions, but it is unclear why they chose 75th as the focus and 88th as the highest quantile when higher quantiles are commonly used in the literature (e.g. 95th; Georgiou et al. 2022).Line 207: This is the first mention in the main text of the AMG model. Please define and explain what it is, ideally earlier in the manuscript. It is also important to describe how the model represents SOC pools of different character (including saturation behavior, temperature sensitivity) given the relevance of MAOM and POM to the overall interpretation.Line 217: I find this to be very unclear and perhaps arbitrary. "... the vertical repartition of annual inputs needed to reach and maintain this target stock" by when? The time scale chosen will determine this level of inputs needed to reach the target (I suppose the point is that it is not overshooting the target, but that gets to whether the model and the maximum SOC potentials are realistic which is an issue in and of itself). Perhaps this is just due to wording, and it would be clearer to use only "maintain the target stock at steady state according to the model" or similar. Adding some of the detail that is now in the Appendix would also potentially help to clarify this. Overall though, this seems to be more of a result in itself; would it make more sense to choose realistic levels of inputs (or low-high ranges) and see how long it would take to reach the maximum C levels at steady state?Line 229: The temperature aspect seems tacked on, yet I agree that it is an important issue for planning climate mitigation efforts. It could be more effectively introduced and discussed throughout the manuscript, including how the model handles temperature effects on SOC dynamics.Line 249: Please clarify what is meant by "non-spatialized" (I got lost here in the explanation)Line 363: In line with comments above, I do not think that use of the Hassink equation is "typical" as it has been surpassed by the boundary approaches mentioned in lines 169-174.Lines 400-403: I do not think it makes sense to directly compare the Chen et al. (2019) approach and the Georgiou et al. (2022) approach as they are very different in assumption and scope. Georgiou is assessing capacity of the fine fraction (i.e., saturation approach) while Chen is assessing ecosystem C level (i.e. potentially saturation-agnostic if saturation is not driving maximum total C levels). This gets at the general need to be clearer about the differences in these approaches throughout the paper, though I appreciate that this is done to some extent in lines 377-387.Paragraph beginning on line 449: I think this paragraph highlights the need to put these results into context of the input rates that were chosen, and what is realistic (see below). In theory, the determined maxima could be reached with very high input rates, and given that this paper does not include such scenarios it does not directly show that it is not achievable. But it does suggest so when one takes into account the input rates that would be required to do so, compared to those that were assumed in this scenario. This could be better explained here.Lines 458-469: This discussion of potential C inputs rates is not very thorough. Examples of input rates occurring in single studies is probably not representative of whether these can be achieved everywhere, especially when socioeconomic factors that limit changes to land management are considered. This part of the discussion should clearly acknowledge the difficulties in universally increasing C inputs to various lands, and ideally give ranges from the literature that demonstrate these realities. Further, as depth is such a key piece of this paper, this discussion should also touch on what is realistic in terms of inputs at depth (i.e. amount and vertical distribution), and whether there is evidence it can be achieved in different systems.Line 690 (Appendix 1): I think that a figure which describes the model structure could be helpful.Lines 691-699: Text is largely repeated from lines 200-209.Appendix equations: the terms in the equations need to be defined somewhere.Lines 740-753: This information is key to understanding the logic and should be included in the main text. Yet, I still find this a bit confusing and am not clear on how the input rates presented in the main text were determined (how do they depend on the maximum potential SOC storage and time to reach it?).Line 750: The assumption that there is no vertical redistribution of C inputs, especially downward, is very unrealistic. The implications of this assumption for the results at depth should be described in the main text.Technical CorrectionsLines 169 and 214 (also Fig. 2 and elsewhere): I suggest an alternate term for "stationary", maybe "constant", "current", "baseline", "steady-state", or "equilibrium" depending on what is most correct here.Line 391: change "ie." to "i.e.,"Citation: https://doi.org/10.5194/egusphere-2024-1284-RC2 - AC1: 'Reply on RC2', Clementine Chirol, 23 Aug 2024
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