Representing dynamic grass density in the land surface model ORCHIDEE r9010

Xu, Siqing; Luyssaert, Sebastiaan; Balkanski, Yves; Ciais, Philippe; Viovy, Nicolas; Wan, Liang; Sciare, Jean

doi:10.5194/egusphere-2025-3382

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https://doi.org/10.5194/egusphere-2025-3382

Preprints

25 Sep 2025

| 25 Sep 2025

Representing dynamic grass density in the land surface model ORCHIDEE r9010

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Abstract. In semi-arid regions, grasses and shrubs often form spatial heterogeneous patterns interspersed with bare soil, optimizing resource use and productivity. Accurately representing the matrix of vegetation and bare soil in global land surface models is essential for advancing the understanding of the carbon, water, and dust cycles. This study focuses on grasslands using the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms), which originally assumes a globally fixed maximum grass density. This assumption, referred to as the fixed maximum density approach, limits the model’s ability to capture grassland responses to environmental changes, resulting in unsustainable productivity and unrealistically frequent mortality events, particularly in resource-limited regions. To address these limitations, we introduced a dynamic density approach that simulates grassland density based on indicators of vegetation growth, such as reserve and labile carbon content in the grass. The emerging positive correlation between precipitation and simulated grass density supported the validity of the approach. Compared to the fixed maximum density approach, the new approach substantially reduced simulated mortality events, raised the aridity threshold for frequent mortality, and maintained realistic grassland productivity in regions where the presence of grassland is indicated by the observed leaf area index (LAI). This study not only demonstrates that simulating grass density as a function of carbon availability improves ORCHIDEE’s capacity to capture grassland dynamics under environmental variability, but also provides a promising foundation for investigating land–atmosphere feedbacks in (semi-)arid regions.

Received: 15 Jul 2025 – Discussion started: 25 Sep 2025

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Journal article(s) based on this preprint

05 Jan 2026

Representing dynamic grassland density in the land surface model ORCHIDEE r9010

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Geosci. Model Dev., 19, 1–25, https://doi.org/10.5194/gmd-19-1-2026,https://doi.org/10.5194/gmd-19-1-2026, 2026

Short summary

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3382', Anonymous Referee #1, 30 Sep 2025

Overall assessment
This manuscript presents a physiology-based dynamic grass density approach for the ORCHIDEE land surface model that addresses key limitations of the fixed-density representation.
Here is this reviewer’s understanding: By linking vegetation density to reserve and labile carbon (C) pools, the model adjusts dynamically to resource availability. This mechanism reduces unrealistic mortality events, produces a more realistic emergent slope between precipitation and density, and generates the bare soil fraction directly rather than prescribing it. Together, these advances increase ecological realism, provide a basis for dust emission modelling and improve IPSL-CM performance across major grassland biomes. The study appears timely, well designed and methodologically sound, but several revisions that could strengthen its impact.

Validation and ecological realism
Validation relies mainly on indirect proxies such as LAI and precipitation correlations. Including regional case studies that use field-based estimates of grass density or bare soil cover would allow for more direct evaluation and strengthen confidence in the model’s realism.
The mortality–recruitment scheme, based on C pool trade-offs, is elegant in its simplicity but assumes asexual recruitment. Explicitly discussing the limitations of this assumption would help readers understand how the approach may underperform in ecosystems dominated by seed banks or sexual reproduction.
Parameter recalibration, particularly for C4 grasslands, improves outcomes, yet the robustness of these changes remains uncertain. Providing an additional sensitivity analysis, for example in supplementary material, to show how density responds to parameter variation would bolster confidence in the results.

External uncertainties and broader implications
The paper also acknowledges uncertainties in prescribed plant functional type (PTF) maps, including the unrealistic placement of grasses in hyper-arid zones. Quantifying the extent to which such mapping errors contribute to remaining mortality artefacts would help distinguish external sources of error from limitations internal to the model.
Although dust flux simulations are planned for future work, the manuscript would benefit from a conceptual schematic linking dynamic density, emergent bare soil fractions and dust emission potential. Such a figure would highlight the broader significance of the study.

Presentation and minor issues
Presentation could be improved through more consistent terminology, particularly in distinguishing “density” from “cover” and in clarifying what constitutes “an individual” in the model. Although the manuscript explains that “density” differs from “plant cover”, it sometimes uses “density” in a way that resembles “cover”, e.g. in the statement “… whereas grassland density reflects grass and bare soil fractions within the grassland PFT” (Line 285), which conflicts with the earlier definition of “the number of individuals per unit area” (line 60).
Likewise, the methods section states that “each individual is assumed to occupy 1 m²” (lines 104-105), yet discussions of biomass allocation and asexual reproduction obscure the line between a biological plant and an abstract unit, potentially confusing readers. For example, in “This approach for increasing grassland density reflects grass recruitment through asexual means, which is a suitable method for representing perennial plants” (lines 153-155), it should be clarified that the “individual” is a conceptual unit, not a physical plant.
The distinction between vegetation type fraction - “a value for its fraction (V_fra), line 91 - and “density” is also sometimes unclear, with “density” referring to surface coverage rather than actual counts of individuals., e.g. “... land cover map represents the fraction of vegetation type (V_fra) for each PFT within one grid cell, whereas grassland density represents grass and bare soil fractions within the grassland PFT” (lines 284–285).
A schematic showing C redistribution during density adjustments would help readers follow the mechanism, and adding explicit mortality thresholds to figure annotations (e.g. Fig. 7) would improve interpretability.

Minor grammatical polishing would further smooth the narrative. For example, awkward phrasing, such as “… the mortality in ORCHIDEE should be infrequent and primarily …” (line 443) would flow better as “… mortality in ORCHIDEE should occur infrequently and mainly …”, or “… grassland dies in the ORCHIDEE model and …” (lines 174-175) would be better if worded as “… the grassland is considered dead in ORCHIDEE, and …”.
Using the simple present tense to model descriptions would also enhance the writing, e.g. changing “Adding to these limitations, a fixed density fails to respond to changes in resource availability, hindering the possibility of studying the response of dust emissions …” (lines 71-72) to “In addition, a fixed density does not respond to resource availability, which hinders the study of dust emission responses …”.
Removing phrases such as "Note that" and "including" from “Note that the carbon of other compartments (including leaf, aboveground stem, root and fruit) in each individual remains …” (lines 124-125) would allow for the following: “The carbon in other compartments (leaf, stem, root, fruit) remains …”. Likewise, “Both of the events …” (line 191) could simply be shortened to “The events …”.

Finally, unit notation should follow SI conventions, with spaces before unit symbols and negative exponents for “per” relationships. For example, “gC m^-2 per day” should be written as “g C m^-2 d^-1”, denoting grams of carbon per square meter per day. Likewise, the unit “m² gC^-1” is ambiguous and could be misread as “square meters times grams per carbon”. To remove this confusion, it should be rewritten as “m² g^-1 C”, which distinctly indicates square meters per gram of carbon.

Summary and recommendation
This study represents a significant methodological advance for ORCHIDEE and makes an important contribution to Earth system modelling. Strengthening validation, clarifying demographic simplifications and refining presentation would further enhance its impact. With these minor revisions, the manuscript will be a valuable and timely addition.

Citation: https://doi.org/10.5194/egusphere-2025-3382-RC1
- AC1: 'Reply on RC1', Siqing Xu, 20 Nov 2025
  
  Dear Reviewer#1,
  Thank you very much for taking the time to review our manuscript and for providing insightful and constructive comments. Your feedback has been highly valuable in helping us to improve the clarity, completeness, and overall quality of our paper.
  We have carefully considered all the points raised in your review and have incorporated all suggestions comprehensively into the revised manuscript and its Supplementary Information.
  A detailed, point-by-point response to all your comments is provided in the attachment.
  Thank you again for your time and expertise.
  Best regards,
  
  Siqing Xu, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-3382-AC1
RC2:
'Comment on egusphere-2025-3382', Anonymous Referee #2, 31 Oct 2025

This manuscript presents a new implementation of a dynamic grassland density scheme within the ORCHIDEE land surface model. The proposed approach allows grass density to vary in response to physiological carbon reserves, enabling a more flexible and ecologically realistic representation of vegetation cover, particularly under semi-arid conditions. The overall scheme is well justified. The authors evaluate this scheme using multiple lines of evidence, including relationships between precipitation and grass density, frequency of grassland mortality events, and comparisons with satellite-derived LAI products. The proposed dynamic density scheme effectively mitigates key limitations of the original model in simulating grassland dynamics under semi-arid conditions, thereby offering substantial value for model development and holding considerable potential for broader scientific impact. The manuscript is well written. However, the current version of the manuscript requires some improvements in the rigor and comprehensiveness of the model evaluation.
(1) The evaluation relies primarily on indirect indicators (e.g., LAI, mortality frequency) without sufficient direct evidence that the model accurately reproduces observed spatial patterns of grass density or vegetation coverage. Comparison against datasets such as vegetation coverage (e.g., vegetation fractional coverage data) may provide insights in the model improvements?
(2) The simulated LAI was compared with MODIS and Sentinel-2 LAI. However, it does not convincingly show how the dynamic density scheme improves the LAI simulation. The differences in LAI between the dynamic and fixed approaches are illustrated (e.g., Fig. 8), while there is little quantitative assessment of the improvement. The figures do not clearly highlight regions where the new scheme reduces model–data mismatches. Without clearer metrics or spatial diagnostics, the added value of the new scheme remains ambiguous. Furthermore, it is not clear whether the seasonality of LAI is improved due to the new scheme.
Minor remarks:
Figure 2 is unnecessary. The processes are quite simple and can be well understand with text only.

Citation: https://doi.org/10.5194/egusphere-2025-3382-RC2
- AC2: 'Reply on RC2', Siqing Xu, 20 Nov 2025
  
  Dear Reviewer#2,
  Thank you very much for taking the time to review our manuscript and for providing insightful and constructive comments. Your feedback has been highly valuable in helping us to improve the clarity, completeness, and overall quality of our paper.
  We have carefully considered all the points raised in your review and have incorporated all suggestions comprehensively into the revised manuscript and its Supplementary Information.
  A detailed, point-by-point response to all your comments is provided in the attachment.
  Thank you again for your time and expertise.
  Best regards,
  
  Siqing Xu, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-3382-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-3382', Anonymous Referee #1, 30 Sep 2025

Overall assessment
This manuscript presents a physiology-based dynamic grass density approach for the ORCHIDEE land surface model that addresses key limitations of the fixed-density representation.
Here is this reviewer’s understanding: By linking vegetation density to reserve and labile carbon (C) pools, the model adjusts dynamically to resource availability. This mechanism reduces unrealistic mortality events, produces a more realistic emergent slope between precipitation and density, and generates the bare soil fraction directly rather than prescribing it. Together, these advances increase ecological realism, provide a basis for dust emission modelling and improve IPSL-CM performance across major grassland biomes. The study appears timely, well designed and methodologically sound, but several revisions that could strengthen its impact.

Validation and ecological realism
Validation relies mainly on indirect proxies such as LAI and precipitation correlations. Including regional case studies that use field-based estimates of grass density or bare soil cover would allow for more direct evaluation and strengthen confidence in the model’s realism.
The mortality–recruitment scheme, based on C pool trade-offs, is elegant in its simplicity but assumes asexual recruitment. Explicitly discussing the limitations of this assumption would help readers understand how the approach may underperform in ecosystems dominated by seed banks or sexual reproduction.
Parameter recalibration, particularly for C4 grasslands, improves outcomes, yet the robustness of these changes remains uncertain. Providing an additional sensitivity analysis, for example in supplementary material, to show how density responds to parameter variation would bolster confidence in the results.

External uncertainties and broader implications
The paper also acknowledges uncertainties in prescribed plant functional type (PTF) maps, including the unrealistic placement of grasses in hyper-arid zones. Quantifying the extent to which such mapping errors contribute to remaining mortality artefacts would help distinguish external sources of error from limitations internal to the model.
Although dust flux simulations are planned for future work, the manuscript would benefit from a conceptual schematic linking dynamic density, emergent bare soil fractions and dust emission potential. Such a figure would highlight the broader significance of the study.

Presentation and minor issues
Presentation could be improved through more consistent terminology, particularly in distinguishing “density” from “cover” and in clarifying what constitutes “an individual” in the model. Although the manuscript explains that “density” differs from “plant cover”, it sometimes uses “density” in a way that resembles “cover”, e.g. in the statement “… whereas grassland density reflects grass and bare soil fractions within the grassland PFT” (Line 285), which conflicts with the earlier definition of “the number of individuals per unit area” (line 60).
Likewise, the methods section states that “each individual is assumed to occupy 1 m²” (lines 104-105), yet discussions of biomass allocation and asexual reproduction obscure the line between a biological plant and an abstract unit, potentially confusing readers. For example, in “This approach for increasing grassland density reflects grass recruitment through asexual means, which is a suitable method for representing perennial plants” (lines 153-155), it should be clarified that the “individual” is a conceptual unit, not a physical plant.
The distinction between vegetation type fraction - “a value for its fraction (V_fra), line 91 - and “density” is also sometimes unclear, with “density” referring to surface coverage rather than actual counts of individuals., e.g. “... land cover map represents the fraction of vegetation type (V_fra) for each PFT within one grid cell, whereas grassland density represents grass and bare soil fractions within the grassland PFT” (lines 284–285).
A schematic showing C redistribution during density adjustments would help readers follow the mechanism, and adding explicit mortality thresholds to figure annotations (e.g. Fig. 7) would improve interpretability.

Minor grammatical polishing would further smooth the narrative. For example, awkward phrasing, such as “… the mortality in ORCHIDEE should be infrequent and primarily …” (line 443) would flow better as “… mortality in ORCHIDEE should occur infrequently and mainly …”, or “… grassland dies in the ORCHIDEE model and …” (lines 174-175) would be better if worded as “… the grassland is considered dead in ORCHIDEE, and …”.
Using the simple present tense to model descriptions would also enhance the writing, e.g. changing “Adding to these limitations, a fixed density fails to respond to changes in resource availability, hindering the possibility of studying the response of dust emissions …” (lines 71-72) to “In addition, a fixed density does not respond to resource availability, which hinders the study of dust emission responses …”.
Removing phrases such as "Note that" and "including" from “Note that the carbon of other compartments (including leaf, aboveground stem, root and fruit) in each individual remains …” (lines 124-125) would allow for the following: “The carbon in other compartments (leaf, stem, root, fruit) remains …”. Likewise, “Both of the events …” (line 191) could simply be shortened to “The events …”.

Finally, unit notation should follow SI conventions, with spaces before unit symbols and negative exponents for “per” relationships. For example, “gC m^-2 per day” should be written as “g C m^-2 d^-1”, denoting grams of carbon per square meter per day. Likewise, the unit “m² gC^-1” is ambiguous and could be misread as “square meters times grams per carbon”. To remove this confusion, it should be rewritten as “m² g^-1 C”, which distinctly indicates square meters per gram of carbon.

Summary and recommendation
This study represents a significant methodological advance for ORCHIDEE and makes an important contribution to Earth system modelling. Strengthening validation, clarifying demographic simplifications and refining presentation would further enhance its impact. With these minor revisions, the manuscript will be a valuable and timely addition.

Citation: https://doi.org/10.5194/egusphere-2025-3382-RC1
- AC1: 'Reply on RC1', Siqing Xu, 20 Nov 2025
  
  Dear Reviewer#1,
  Thank you very much for taking the time to review our manuscript and for providing insightful and constructive comments. Your feedback has been highly valuable in helping us to improve the clarity, completeness, and overall quality of our paper.
  We have carefully considered all the points raised in your review and have incorporated all suggestions comprehensively into the revised manuscript and its Supplementary Information.
  A detailed, point-by-point response to all your comments is provided in the attachment.
  Thank you again for your time and expertise.
  Best regards,
  
  Siqing Xu, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-3382-AC1
RC2:
'Comment on egusphere-2025-3382', Anonymous Referee #2, 31 Oct 2025

This manuscript presents a new implementation of a dynamic grassland density scheme within the ORCHIDEE land surface model. The proposed approach allows grass density to vary in response to physiological carbon reserves, enabling a more flexible and ecologically realistic representation of vegetation cover, particularly under semi-arid conditions. The overall scheme is well justified. The authors evaluate this scheme using multiple lines of evidence, including relationships between precipitation and grass density, frequency of grassland mortality events, and comparisons with satellite-derived LAI products. The proposed dynamic density scheme effectively mitigates key limitations of the original model in simulating grassland dynamics under semi-arid conditions, thereby offering substantial value for model development and holding considerable potential for broader scientific impact. The manuscript is well written. However, the current version of the manuscript requires some improvements in the rigor and comprehensiveness of the model evaluation.
(1) The evaluation relies primarily on indirect indicators (e.g., LAI, mortality frequency) without sufficient direct evidence that the model accurately reproduces observed spatial patterns of grass density or vegetation coverage. Comparison against datasets such as vegetation coverage (e.g., vegetation fractional coverage data) may provide insights in the model improvements?
(2) The simulated LAI was compared with MODIS and Sentinel-2 LAI. However, it does not convincingly show how the dynamic density scheme improves the LAI simulation. The differences in LAI between the dynamic and fixed approaches are illustrated (e.g., Fig. 8), while there is little quantitative assessment of the improvement. The figures do not clearly highlight regions where the new scheme reduces model–data mismatches. Without clearer metrics or spatial diagnostics, the added value of the new scheme remains ambiguous. Furthermore, it is not clear whether the seasonality of LAI is improved due to the new scheme.
Minor remarks:
Figure 2 is unnecessary. The processes are quite simple and can be well understand with text only.

Citation: https://doi.org/10.5194/egusphere-2025-3382-RC2
- AC2: 'Reply on RC2', Siqing Xu, 20 Nov 2025
  
  Dear Reviewer#2,
  Thank you very much for taking the time to review our manuscript and for providing insightful and constructive comments. Your feedback has been highly valuable in helping us to improve the clarity, completeness, and overall quality of our paper.
  We have carefully considered all the points raised in your review and have incorporated all suggestions comprehensively into the revised manuscript and its Supplementary Information.
  A detailed, point-by-point response to all your comments is provided in the attachment.
  Thank you again for your time and expertise.
  Best regards,
  
  Siqing Xu, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-3382-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Siqing Xu on behalf of the Authors (21 Nov 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (21 Nov 2025) by Nathaniel Chaney

RR by Anonymous Referee #1 (23 Nov 2025)

Suggestions for revision or reasons for rejection

The reviewer praises the authors for their thorough and well-reasoned responses to the comments offered during the review. The revisions have significantly improved the contribution, clarifying the manuscript, strengthening the methodology and deepening the conceptual framing. The study is very timely and robust and represents a clear and major contribution to the emerging global dynamic vegetation modelling arena. The central idea of linking grass population density to the carbon economy of plants is simultaneously elegant and ecologically well-grounded. The process is rigorous and the multi-pronged validation, which incorporates direct field comparisons alongside global satellite products, is compelling. Overall, the revisions have effectively elevated the work from a technically sound study to a polished, compelling scientific article.

While the manuscript is now excellent, the following minor points could be considered for future work or final polishing:
1. Interpretation of the LAI-Mortality Trade-off - The analysis in Fig. 9 is insightful. To enhance its immediate accessibility, a brief plain-language summary in the main text - stating that the model achieves a substantial reduction in mortality for only a minor cost in LAI across most grid cells - would help readers quickly grasp the ecological significance of this result.
2. Justification for C₄ recalibration parameters - The recalibration of C₄ grasslands is well-documented. For completeness, a succinct justification for the selected parameter values (e.g. the scaling factor of 1.5 and α=8) could be added to the main text, noting that they were chosen to introduce realistic spatial heterogeneity without causing a widespread collapse of grassland populations.
3. Emphasis on PFT map implications - The discussion in Section 4.5 on the implications for Plant Functional Type (PFT) maps is excellent. The point that the dynamic density approach could eventually render a prescribed “bare soil” PFT obsolete in grasslands is a profound long-term implication that could be emphasized more strongly.
4. Terminological accuracy in Figure 1 - In the conceptual diagram (Figure 1c), the phrase “sufficient wind erosivity” could be refined for terminological accuracy. In geomorphology, erosivity is a property of the wind, while erodibility is a property of the surface. A more accurate phrasing, such as “… when the surface is exposed to sufficient wind strength” or “… when the exposed surface is subject to sufficient wind force”, would enhance accuracy without altering the scientific meaning. This is a minor linguistic adjustment.

Finally, the authors' use of the “conceptual individual” is clearly defined and justified as a necessary abstraction for global-scale modelling. While one could note that a 1 m² unit is coarse for sparse grasslands, this acknowledged trade-off between realism and computational feasibility is well-argued and does not detract from the methodological advance.

All four suggestions are minor editorial or proof-stage improvements and do not require further peer review.

Recommendation: accept

Hide

RR by Anonymous Referee #2 (08 Dec 2025)

ED: Publish as is (08 Dec 2025) by Nathaniel Chaney

AR by Siqing Xu on behalf of the Authors (15 Dec 2025) Author's response Manuscript

Journal article(s) based on this preprint

05 Jan 2026

Representing dynamic grassland density in the land surface model ORCHIDEE r9010

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Geosci. Model Dev., 19, 1–25, https://doi.org/10.5194/gmd-19-1-2026,https://doi.org/10.5194/gmd-19-1-2026, 2026

Short summary

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

Supplement

https://doi.org/10.5194/egusphere-2025-3382-supplement

Model code and software

ORCHIDEE code for the submitted paper: Representing dynamic grass density in the land surface model ORCHIDEE r9010 Siqing Xu https://doi.org/10.5281/zenodo.15723740

Siqing Xu, Sebastiaan Luyssaert, Yves Balkanski, Philippe Ciais, Nicolas Viovy, Liang Wan, and Jean Sciare

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Prescribing a fixed maximum grass density in the ORCHIDEE model limits its ability to capture grassland dynamics, leading to unrealistic mortality, especially in semi-arid grasslands. We proposed a dynamic density approach where a positive density-precipitation relationship emerges. This method significantly reduces mortality, sustains productivity, and raises the aridity threshold above which frequent mortality events occur in grasslands.


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