the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Dec 2025
Drought-Induced Soil Carbon Dynamics in Subtropical Forests: Emergent Divergence from Model Structures
Abstract. Accurately quantifying drought impacts on terrestrial carbon cycling is essential for advancing predictions of climate-carbon feedbacks. However, current biogeochemical models exhibit limited capability in simulating drought-induced transformations of soil organic carbon (SOC), particularly regarding microbial processes. Here, we conducted a systematic comparative evaluation of three prevailing SOC modeling structures, including conventional three-pool partitioning scheme (SM1), mineral and particulate- associated carbon partitioning scheme (SM2) and Michaelis-Menten regulated carbon-stabilization scheme (SM3), to elucidate their capacity in simulating soil carbon dynamics under decadal drought scenarios in a subtropical forest. We found divergent effects of drought in soil C input (SM1, 66%; SM2, 10%; SM3, -4%) and mean residence time (MRT; SM1, -31%; SM2, -14%; SM3, 65%), which lead to the predicted SOC substantial accumulation for both SM1 and SM3 (+39.5% and +56.9%, respectively) and moderate depletion (-6.1%) for SM2. The different C input directly affect the passive SOC (SM1) and mineral-associated organic carbon (SM2 and SM3). In comparison, the drought effects on passive SOC (SM1), microbe biomass (SM2) and MAOC (SM2 and SM3), lead to notable spread in MRT. These findings highlight critical model structural dependencies in simulating drought-affected soil carbon dynamics and emphasize the necessity for models to integrate microbial-physicochemical interactions for improved climate-carbon coupling projections.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(2399 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2399 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-5037', Anonymous Referee #1, 21 Jan 2026
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AC1: 'Reply on RC1', Zhenggang Du, 14 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5037/egusphere-2025-5037-AC1-supplement.pdf
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AC1: 'Reply on RC1', Zhenggang Du, 14 Apr 2026
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RC2: 'Comment on egusphere-2025-5037', Anonymous Referee #2, 23 Mar 2026
This study focus on the scientific issue of divergent model simulations of soil carbon dynamics under drought stress in subtropical forests. The research findings provide guidance for improving climate-carbon coupling projections. While the results are innovative and meaningful, several points need to be further modified before publication. Below please find my concerns.
Main concerns:
First, although the discussion identifies carbon pool partitioning and enzyme-catalyzed processes as key factors underlying the simulation differences, it provides only a general mechanistic explanation for how drought regulates carbon inputs and mean residence times of different carbon pools via plant–microbe–mineral interactions.It lacks targeted analyses incorporating the ecological characteristics of the study area, such as the mineral composition of red and yellow soils, the carbon allocation strategies of evergreen tree species, and the fungal-dominated shift in microbial communities under drought.In addition, the consistent result of increased particulate organic carbon (POC) under drought across all three models is not thoroughly explained in terms of its ecological mechanisms and model representation.Second, SM3 incorporated β-1,4-glucosidase (BG), polyphenol oxidase (PPO), and cellobiohydrolase (CBH), but does not clarify the basis for enzyme parameter values (Vmax.enzy, fenzy, KM.enzy) — for example, whether they were calibrated using in-situ measured enzyme activity data from the research station or directly adopted from published literature. The rationale for selecting these three enzymes is not provided, nor is it explained why other enzymes potentially involved indirectly in carbon decomposition were not included.
Specific comments:
Line 48: You can cited a more recent version of the IPCC here.
Line 145: You may consider providing a clear definition of "decadal drought".
Lines 186-189: Please pay attention to the subscripts here.Â
Citation: https://doi.org/10.5194/egusphere-2025-5037-RC2 -
AC2: 'Reply on RC2', Zhenggang Du, 14 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5037/egusphere-2025-5037-AC2-supplement.pdf
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AC2: 'Reply on RC2', Zhenggang Du, 14 Apr 2026
Peer review completion
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-5037', Anonymous Referee #1, 21 Jan 2026
This work addresses a critical question in models: how different model structures simulate soil carbon responses to drought. The integration of long-term experimental data from the Tiantong Forest with a multi-model comparison and traceability analysis is a clear strength of the manuscript. Below, I offer some feedback aimed at further strengthening the scientific novelty and rigor of this manuscript.
Â
First, the introduction effectively establishes the problem but could more sharply define the specific knowledge gap that this study uniquely fills. It stated that models have limited capability and studies are scarce, but what is the conceptual novelty? I think the authors need to convey the message: how does this comparison of different model structure actually contribute to our capability to more accurately/realistically simulate SOC responses to drought?
Second, is it possible to illustrate some details of the vegetation model? It does seem that coupling of the vegetation model with the three different soil modules is a significant undertaking worths of highlighting. The flexibility of this vegetation model is a strength. So I would recommend some brief text about the coupling.
Third, Â the differences between models are clear qualitatively. Is it possible to add a simple metric to quantitatively underscore the magnitude of model-structure-induced uncertainty? This could be a powerful summary statistic.
Fourth, in the discussion sections, is it possible to discuss what these divergences imply for future model development and large-scale projections? Should all terrestrial models move towards SM2/SM3 structures for drought simulation? What are the trade-offs (complexity, data needs, computational cost)?
Citation: https://doi.org/10.5194/egusphere-2025-5037-RC1 -
AC1: 'Reply on RC1', Zhenggang Du, 14 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5037/egusphere-2025-5037-AC1-supplement.pdf
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AC1: 'Reply on RC1', Zhenggang Du, 14 Apr 2026
-
RC2: 'Comment on egusphere-2025-5037', Anonymous Referee #2, 23 Mar 2026
This study focus on the scientific issue of divergent model simulations of soil carbon dynamics under drought stress in subtropical forests. The research findings provide guidance for improving climate-carbon coupling projections. While the results are innovative and meaningful, several points need to be further modified before publication. Below please find my concerns.
Main concerns:
First, although the discussion identifies carbon pool partitioning and enzyme-catalyzed processes as key factors underlying the simulation differences, it provides only a general mechanistic explanation for how drought regulates carbon inputs and mean residence times of different carbon pools via plant–microbe–mineral interactions.It lacks targeted analyses incorporating the ecological characteristics of the study area, such as the mineral composition of red and yellow soils, the carbon allocation strategies of evergreen tree species, and the fungal-dominated shift in microbial communities under drought.In addition, the consistent result of increased particulate organic carbon (POC) under drought across all three models is not thoroughly explained in terms of its ecological mechanisms and model representation.Second, SM3 incorporated β-1,4-glucosidase (BG), polyphenol oxidase (PPO), and cellobiohydrolase (CBH), but does not clarify the basis for enzyme parameter values (Vmax.enzy, fenzy, KM.enzy) — for example, whether they were calibrated using in-situ measured enzyme activity data from the research station or directly adopted from published literature. The rationale for selecting these three enzymes is not provided, nor is it explained why other enzymes potentially involved indirectly in carbon decomposition were not included.
Specific comments:
Line 48: You can cited a more recent version of the IPCC here.
Line 145: You may consider providing a clear definition of "decadal drought".
Lines 186-189: Please pay attention to the subscripts here.Â
Citation: https://doi.org/10.5194/egusphere-2025-5037-RC2 -
AC2: 'Reply on RC2', Zhenggang Du, 14 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5037/egusphere-2025-5037-AC2-supplement.pdf
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AC2: 'Reply on RC2', Zhenggang Du, 14 Apr 2026
Peer review completion
Journal article(s) based on this preprint
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Fengfeng Du
Lianjun Feng
Lingyan Zhou
Zhizhuang Gu
Yaqi Zhang
Xuhui Zhou
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2399 KB) - Metadata XML
This work addresses a critical question in models: how different model structures simulate soil carbon responses to drought. The integration of long-term experimental data from the Tiantong Forest with a multi-model comparison and traceability analysis is a clear strength of the manuscript. Below, I offer some feedback aimed at further strengthening the scientific novelty and rigor of this manuscript.
Â
First, the introduction effectively establishes the problem but could more sharply define the specific knowledge gap that this study uniquely fills. It stated that models have limited capability and studies are scarce, but what is the conceptual novelty? I think the authors need to convey the message: how does this comparison of different model structure actually contribute to our capability to more accurately/realistically simulate SOC responses to drought?
Second, is it possible to illustrate some details of the vegetation model? It does seem that coupling of the vegetation model with the three different soil modules is a significant undertaking worths of highlighting. The flexibility of this vegetation model is a strength. So I would recommend some brief text about the coupling.
Third, Â the differences between models are clear qualitatively. Is it possible to add a simple metric to quantitatively underscore the magnitude of model-structure-induced uncertainty? This could be a powerful summary statistic.
Fourth, in the discussion sections, is it possible to discuss what these divergences imply for future model development and large-scale projections? Should all terrestrial models move towards SM2/SM3 structures for drought simulation? What are the trade-offs (complexity, data needs, computational cost)?