Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)

Ninomiya, Hideki; Kato, Tomomichi; Végh, Lea; Wu, Lan

doi:https://doi.org/10.5194/egusphere-2022-835

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https://doi.org/10.5194/egusphere-2022-835

Preprints

05 Oct 2022

| 05 Oct 2022

Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)

Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu

Abstract. Forest dynamics need to be considered when estimating the global carbon budget. The alteration of forest structure and function under a changing climate and expanding human activity could lead to a reduction of forest canopy cover and a spread of lower-biomass ecosystems in warm and dry regions. Non-structural carbohydrate (NSC) acts as a storage buffer between carbon supplied by assimilation and carbon consumed by, inter alia, respiration, reproduction, and pests. Estimation of NSC concentrations in a tree is very important for accurate projection of future forest dynamics. We developed a new NSC module for incorporation into a spatially explicit, individual-based, dynamic global vegetation model (SEIB-DGVM) to validate the simulated NSC dynamics with observations. NSC pools were simulated in three plant organs: leaves, trunk, and roots. The seasonal dynamics of the NSCs varied among plant species, and the sizes of the NSC pools inferred from observations differed between the boreal, temperate, and tropical climates. The NSC models were therefore validated for each of the three climatic regions at both point and global scales to assess the performance of the models. The modeled NSCs showed good agreement in seasonality with the observed NSCs at four sites – Canada (boreal), Austria and Switzerland (temperate), and Panama (tropical) – and in mean values for three climate zones derived from the global NSC dataset. The SEIB-DGVM-NSCv1.0 is expected to enable simulation of biome shifts caused by the changes of NSC dynamics worldwide. These dynamics will contribute to changes of not only the global carbon cycle but also of forest structure and demography at a global scale.

Received: 25 Aug 2022 – Discussion started: 05 Oct 2022

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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.

Journal article(s) based on this preprint

25 Jul 2023

Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)

Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu

Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023,https://doi.org/10.5194/gmd-16-4155-2023, 2023

Short summary

Hideki Ninomiya et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-835', Anonymous Referee #1, 02 Nov 2022

The study addresses an interesting and additional angle in vegetation modelling - NSC dynamics across different organs. As such, the study could make a worthwhile contribution to readers of EGUsphere, I believe.

That being said, the modelling exercise shows several logical flaws and conceptual inconsistencies, at least in my eyes. While I understand that a model 'validation' based on observations is an imporrant procedure to show that the model can reproduce reality to some degree, the main findings, i.e. the modelled NSCs showed good agreement in seasonality with the observed NSCs at four sites, are a matter of model parameterization and not a proof of model precision. Being an ecophysiologist, not a modeller, I may be wrong, but it appears to be logic that a model constrained by field data will produce simulations that fall within the range of that data.

Even more important, I believe that the central piece of the study, the model validation, is a red herring and does not provide show that this augmented model produces more realistic simulations than simpler ones. In particular, it would be really interesting to see whether the model can achieve what the authors claim in the discussion, i.e. "the model has a high potential to simulate various biotic effects on terrestrial ecosystems". Several studies during the last decade or so (e.g., Stich et al. 2008 GCB, Wu et al. 2018 J Climate, Hartmann et al. 2022 ARPB) have shown that large-scale vegetation models have substantial difficulties in simulating stress responses to drought and heat, and cannot reproduce impacts of biotic disturbances on vegetation dynamics. If, as the authors clain, this model has a high potential to do just that, it would be great if their study could show it. This would then really be a substantial contribution to modelling science.

I also found that authors appear to confound NSC with carbon storage, which are not interchangeable terms. Some NSC, in particular starch, play a role in carbon storage, whereas other NSC, like soluble sugars, are primary metabolites of immediate use. They are needed in physiological functions and are thus not really storage that is put aside for later use. Maybe this confusion also led to the somewhat uncommon allocation scheme shown in Fig. 2. Whether my confusion about the carbon flow from trunk to leaves to roots shown in that figure comes from the lack of a thorough explanation in the text of figure caption, I cannot say, but I am concerned that this is a documentation of the authors' misunderstanding of C allocation in real plants.

Please find more comments in the attached annotated pdf document.

Citation: https://doi.org/10.5194/egusphere-2022-835-RC1
- AC1: 'Reply on RC1', Hideki Ninomiya, 27 Apr 2023
  
  I really appreciate you for reading the manuscript and providing me with your comments.
  First of all, you said that “it appears to be logic that a model constrained by field data will produce simulations that fall within the range of that data.” As you can imagine, whether a model can achieve the initially planned concept depends heavily on parameterization in the case of process-based material cycling modeling. Unlike a large amount of observational data, such as eddy correlation fluxes, NSC, which is the focus of this paper, is not accumulated a lot, making it difficult to model. However, to predict tree mortality caused by future events such as drought, an explicit calculation of NSC in DGVM that can handle process-based and population dynamics is necessary, and we expect the model accuracy to improve as observational data accumulates in the future. This paper provides the basic concept and initial validation of the model and is expected to provide a foundation for future use.
  Secondly, regarding the term “NSC” used in the paper, I know that the term NSC refers to two types of NSCs in plants: soluble sugar and starch. Starch plays a role in long-term carbon storage, while soluble sugar is used for immediate energy needs. In this paper, we use the term NSC to refer to the mixture of both soluble sugar and starch. I apologize if this caused any confusion. Each type of NSC has a different role but we observed that the mixture of both types of NSC are explicitly accumulated by using assimilated carbon through photosynthesis, and the stored NSC is used for metabolism, bud flush, and other processes. We chose to use the NSC data in our study because we had more data on the mixed NSC than on each type separately. I used the term “carbon storage” to refer to NSC in previous manuscript, but I realize that this may have confused readers. Therefore, in the new manuscript, I will only use the term NSC to avoid any ambiguity.
  Thirdly Figure 1 shows the NSC pool model to represent explicit NSC accumulation. The concept of a "pool model" is often used in modeling research. The amount of NSC fluctuates in the pool, increasing up to the maximum (determined by eq. (10) and (11)) by the assimilated carbon through photosynthesis and sometimes decreasing due to metabolism and bud flush. I apologize for any confusion caused by the lack of caption in Figure 1. Perhaps you misunderstood from Figure 1 that the NSC in trunks feeds into NSC in other organs. This understanding is incorrect. The carbon from the trunk to the leaf is not in the form of NSC. The assimilated carbon satisfies the NSC trunk pool, and then the rest of assimilated carbon satisfies the next NSC leaf pool. The outflow of carbon as NSC is only used for metabolism and bud flush, as shown in the square below the NSC pool.
  The inflow and outflow of NSC are represented by the pool model, where each leaf, trunk, and root has its own NSC pool as Figure 1 shows. The maximum pool size depends on the biomass of each organ. However, I agree with you, the NSC pool model does not represent all the processes of carbon allocation in real plants. In reality, trees allocate leaf-generated soluble sugar to other organs to support their physiological activity, and NSC in one organ may flow into another. In SEIB-DGVM ver 1.0, we did not consider the translocation because, although leaves have a high soluble sugar concentration, they account for a small proportion of the total NSC amount of trees and trunk account for the largest NSCs pool due to the largest biomass, so the flow of NSC from leaves to trunk is small compared to the NSC in trunks (Cho et al., 2022, Ecol. Inform.). In ver 2.0, I will try to include this process. However, in ver 1.0, this process does not affect the results much, and the key point of the study is to develop an explicit NSC pool model for each organ that matches the observed seasonality and total NSC. The carbon flow within organs may be important for further model development.
  Please find more comments in the attached pdf document.
  
  Citation: https://doi.org/10.5194/egusphere-2022-835-AC1
RC2:
'Comment on egusphere-2022-835', Anonymous Referee #2, 16 Dec 2022

Comments to the Authors

First of all, I really enjoyed to read the manuscript and found it very interesting!!

The authors developed a new non-structural carbohydrate (NSC) module and incorporated it into a spatially explicit individual-based dynamic global vegetation model SEIB-DVGM. In recent years, the magnitude and frequency of extreme weather events have been increasing. Further increases in magnitude and frequency are also predicted. To accurately understand or predict tree responses to extreme weather events, an understanding of NSC dynamics is essential. Therefore, I believe that this study contributes significantly to understandings and future projections of forest dynamics under significant climate change.

This study certainly has merit, though I’ve got some concerns list below. The main issue in my mind is lack of explanation of the simulation scheme. Specifically, with regard to the comparison with observational NSC dynamics, is the age and/or tree size of the simulated forests same or similar with the observations? Also, it was unclear to me how the coefficients of NSCmax (a and b) were determined for the global-scale validation.

If the authors could consider above, I believe that it would improve the readability of this manuscript, and I would strongly encourage them to provide the consideration in a revised version of this manuscript.

Specific Comments:

Introduction

Line 42: What do the” spatial and temporal drivers” describe?

Line 80-95: The authors explain the indirect impacts first, but it seems easier to understand if the direct impacts are explained first.

Line 118-120: It would be easier to understand if it were stated after line 110, which describes the advantages of individual-based SEIB-DGVM.

Line 126-129: To demonstrate the superiority of the enhanced model (SEIB-DVGM-NSC), I think a comparison of the enhanced model with the original model (SEIB-DGVM) in discussion section would be of more interest to the readers. For example, regarding the comparison with the observation of NSC dynamics, is there a significant increase in similarity in the enhanced model over the original model that only considers the stock to the trunk? how much difference is observed when Fig5 is drawn in the original model compared to FIg5 in the enhanced model?

Model

Line 158-160: It would be better to have an explanation of the process of carbon stocking in the trunk in the original model.

Line 257-258: I did not understand how the authors determined the coefficients in Table 2. Please add a little explanation.

Line 260-262: What happens if the total carbon stock is insufficient?

Line 292-294: With regard to the comparison with observations of NSC dynamics, is the age and/or tree size of the simulated forests same or similar with the observations? This point may be important since it is mentioned in this manuscript that tree size is important for carbon allocation.

Line 405-406: Why decide from January percentage of NSC and biomass?

Line 411-413: It is unclear to me how the coefficients of NSCmax (a and b) were determined for the global-scale validation.

Results

Discussion

To demonstrate the superiority of the enhanced model (SEIB-DVGM-NSC), I think a comparison of the enhanced model with the original model (SEIB-DGVM) in discussion section would be of more interest to the readers. For example, regarding the comparison with the observation of NSC dynamics, is there a significant increase in similarity in the enhanced model over the original model that only considers the stock to the trunk? how much difference is observed when Fig5 is drawn in the original model compared to FIg5 in the enhanced model?

Line 620-622: The sentence is not clear. What did the authors want to say?

Line 626-627 :The sentence is not clear. What did the authors want to say?

Citation: https://doi.org/10.5194/egusphere-2022-835-RC2
- AC2: 'Reply on RC2', Hideki Ninomiya, 27 Apr 2023
  
  I really appreciate you for reading the manuscript and providing me with your comments.
  I'm glad to hear that you are interested in my manuscript. As you know, understanding the dynamics of NSC is crucial for studying how trees respond to extreme weather events, but there is still limited research on modeling these dynamics.
  Regarding your concerns, I have modified the simulation scheme accordingly. I totally agree with you. It is better to compare with observation which age/tree size is similar with the simulated forests. However, I did not consider them in the model honestly. This is because the age and tree size data were not measured simultaneously with the NSC data. It is challenging to obtain these data at the same time since measuring NSC involves cutting trees in field sites and extracting the NSC content after making them dry.The trees used for measuring NSC are often located in parks and other field sites that are not well managed for research. As a result, I could not find NSC data with age/size information from trees managed by universities or research institutes. Instead, the NSC data used in this study are presented as relative values of NSC to total dry woody biomass. To compare the model with observations, we used the simulated relative values of NSC to total simulated dry woody biomass to avoid the influence of age and size.
  I added the comparison as you suggested between the new model and the original SEIB-DGVM. Since the original SEIB-DGVM only calculates NSC in the trunk, I did not compare the NSC seasonality at a point scale. Instead, I compared the total NSC across climate zones and biome types on a global scale. I also made modifications to Figure 5 to show the differences between the original and new model. The additions to the Results and Discussion section demonstrate the superiority of the new model.
  Please find more comments in the attached pdf document.
  
  Citation: https://doi.org/10.5194/egusphere-2022-835-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-835', Anonymous Referee #1, 02 Nov 2022

The study addresses an interesting and additional angle in vegetation modelling - NSC dynamics across different organs. As such, the study could make a worthwhile contribution to readers of EGUsphere, I believe.

That being said, the modelling exercise shows several logical flaws and conceptual inconsistencies, at least in my eyes. While I understand that a model 'validation' based on observations is an imporrant procedure to show that the model can reproduce reality to some degree, the main findings, i.e. the modelled NSCs showed good agreement in seasonality with the observed NSCs at four sites, are a matter of model parameterization and not a proof of model precision. Being an ecophysiologist, not a modeller, I may be wrong, but it appears to be logic that a model constrained by field data will produce simulations that fall within the range of that data.

Even more important, I believe that the central piece of the study, the model validation, is a red herring and does not provide show that this augmented model produces more realistic simulations than simpler ones. In particular, it would be really interesting to see whether the model can achieve what the authors claim in the discussion, i.e. "the model has a high potential to simulate various biotic effects on terrestrial ecosystems". Several studies during the last decade or so (e.g., Stich et al. 2008 GCB, Wu et al. 2018 J Climate, Hartmann et al. 2022 ARPB) have shown that large-scale vegetation models have substantial difficulties in simulating stress responses to drought and heat, and cannot reproduce impacts of biotic disturbances on vegetation dynamics. If, as the authors clain, this model has a high potential to do just that, it would be great if their study could show it. This would then really be a substantial contribution to modelling science.

I also found that authors appear to confound NSC with carbon storage, which are not interchangeable terms. Some NSC, in particular starch, play a role in carbon storage, whereas other NSC, like soluble sugars, are primary metabolites of immediate use. They are needed in physiological functions and are thus not really storage that is put aside for later use. Maybe this confusion also led to the somewhat uncommon allocation scheme shown in Fig. 2. Whether my confusion about the carbon flow from trunk to leaves to roots shown in that figure comes from the lack of a thorough explanation in the text of figure caption, I cannot say, but I am concerned that this is a documentation of the authors' misunderstanding of C allocation in real plants.

Please find more comments in the attached annotated pdf document.

Citation: https://doi.org/10.5194/egusphere-2022-835-RC1
- AC1: 'Reply on RC1', Hideki Ninomiya, 27 Apr 2023
  
  I really appreciate you for reading the manuscript and providing me with your comments.
  First of all, you said that “it appears to be logic that a model constrained by field data will produce simulations that fall within the range of that data.” As you can imagine, whether a model can achieve the initially planned concept depends heavily on parameterization in the case of process-based material cycling modeling. Unlike a large amount of observational data, such as eddy correlation fluxes, NSC, which is the focus of this paper, is not accumulated a lot, making it difficult to model. However, to predict tree mortality caused by future events such as drought, an explicit calculation of NSC in DGVM that can handle process-based and population dynamics is necessary, and we expect the model accuracy to improve as observational data accumulates in the future. This paper provides the basic concept and initial validation of the model and is expected to provide a foundation for future use.
  Secondly, regarding the term “NSC” used in the paper, I know that the term NSC refers to two types of NSCs in plants: soluble sugar and starch. Starch plays a role in long-term carbon storage, while soluble sugar is used for immediate energy needs. In this paper, we use the term NSC to refer to the mixture of both soluble sugar and starch. I apologize if this caused any confusion. Each type of NSC has a different role but we observed that the mixture of both types of NSC are explicitly accumulated by using assimilated carbon through photosynthesis, and the stored NSC is used for metabolism, bud flush, and other processes. We chose to use the NSC data in our study because we had more data on the mixed NSC than on each type separately. I used the term “carbon storage” to refer to NSC in previous manuscript, but I realize that this may have confused readers. Therefore, in the new manuscript, I will only use the term NSC to avoid any ambiguity.
  Thirdly Figure 1 shows the NSC pool model to represent explicit NSC accumulation. The concept of a "pool model" is often used in modeling research. The amount of NSC fluctuates in the pool, increasing up to the maximum (determined by eq. (10) and (11)) by the assimilated carbon through photosynthesis and sometimes decreasing due to metabolism and bud flush. I apologize for any confusion caused by the lack of caption in Figure 1. Perhaps you misunderstood from Figure 1 that the NSC in trunks feeds into NSC in other organs. This understanding is incorrect. The carbon from the trunk to the leaf is not in the form of NSC. The assimilated carbon satisfies the NSC trunk pool, and then the rest of assimilated carbon satisfies the next NSC leaf pool. The outflow of carbon as NSC is only used for metabolism and bud flush, as shown in the square below the NSC pool.
  The inflow and outflow of NSC are represented by the pool model, where each leaf, trunk, and root has its own NSC pool as Figure 1 shows. The maximum pool size depends on the biomass of each organ. However, I agree with you, the NSC pool model does not represent all the processes of carbon allocation in real plants. In reality, trees allocate leaf-generated soluble sugar to other organs to support their physiological activity, and NSC in one organ may flow into another. In SEIB-DGVM ver 1.0, we did not consider the translocation because, although leaves have a high soluble sugar concentration, they account for a small proportion of the total NSC amount of trees and trunk account for the largest NSCs pool due to the largest biomass, so the flow of NSC from leaves to trunk is small compared to the NSC in trunks (Cho et al., 2022, Ecol. Inform.). In ver 2.0, I will try to include this process. However, in ver 1.0, this process does not affect the results much, and the key point of the study is to develop an explicit NSC pool model for each organ that matches the observed seasonality and total NSC. The carbon flow within organs may be important for further model development.
  Please find more comments in the attached pdf document.
  
  Citation: https://doi.org/10.5194/egusphere-2022-835-AC1
RC2:
'Comment on egusphere-2022-835', Anonymous Referee #2, 16 Dec 2022

Comments to the Authors

First of all, I really enjoyed to read the manuscript and found it very interesting!!

The authors developed a new non-structural carbohydrate (NSC) module and incorporated it into a spatially explicit individual-based dynamic global vegetation model SEIB-DVGM. In recent years, the magnitude and frequency of extreme weather events have been increasing. Further increases in magnitude and frequency are also predicted. To accurately understand or predict tree responses to extreme weather events, an understanding of NSC dynamics is essential. Therefore, I believe that this study contributes significantly to understandings and future projections of forest dynamics under significant climate change.

This study certainly has merit, though I’ve got some concerns list below. The main issue in my mind is lack of explanation of the simulation scheme. Specifically, with regard to the comparison with observational NSC dynamics, is the age and/or tree size of the simulated forests same or similar with the observations? Also, it was unclear to me how the coefficients of NSCmax (a and b) were determined for the global-scale validation.

If the authors could consider above, I believe that it would improve the readability of this manuscript, and I would strongly encourage them to provide the consideration in a revised version of this manuscript.

Specific Comments:

Introduction

Line 42: What do the” spatial and temporal drivers” describe?

Line 80-95: The authors explain the indirect impacts first, but it seems easier to understand if the direct impacts are explained first.

Line 118-120: It would be easier to understand if it were stated after line 110, which describes the advantages of individual-based SEIB-DGVM.

Line 126-129: To demonstrate the superiority of the enhanced model (SEIB-DVGM-NSC), I think a comparison of the enhanced model with the original model (SEIB-DGVM) in discussion section would be of more interest to the readers. For example, regarding the comparison with the observation of NSC dynamics, is there a significant increase in similarity in the enhanced model over the original model that only considers the stock to the trunk? how much difference is observed when Fig5 is drawn in the original model compared to FIg5 in the enhanced model?

Model

Line 158-160: It would be better to have an explanation of the process of carbon stocking in the trunk in the original model.

Line 257-258: I did not understand how the authors determined the coefficients in Table 2. Please add a little explanation.

Line 260-262: What happens if the total carbon stock is insufficient?

Line 292-294: With regard to the comparison with observations of NSC dynamics, is the age and/or tree size of the simulated forests same or similar with the observations? This point may be important since it is mentioned in this manuscript that tree size is important for carbon allocation.

Line 405-406: Why decide from January percentage of NSC and biomass?

Line 411-413: It is unclear to me how the coefficients of NSCmax (a and b) were determined for the global-scale validation.

Results

Discussion

To demonstrate the superiority of the enhanced model (SEIB-DVGM-NSC), I think a comparison of the enhanced model with the original model (SEIB-DGVM) in discussion section would be of more interest to the readers. For example, regarding the comparison with the observation of NSC dynamics, is there a significant increase in similarity in the enhanced model over the original model that only considers the stock to the trunk? how much difference is observed when Fig5 is drawn in the original model compared to FIg5 in the enhanced model?

Line 620-622: The sentence is not clear. What did the authors want to say?

Line 626-627 :The sentence is not clear. What did the authors want to say?

Citation: https://doi.org/10.5194/egusphere-2022-835-RC2
- AC2: 'Reply on RC2', Hideki Ninomiya, 27 Apr 2023
  
  I really appreciate you for reading the manuscript and providing me with your comments.
  I'm glad to hear that you are interested in my manuscript. As you know, understanding the dynamics of NSC is crucial for studying how trees respond to extreme weather events, but there is still limited research on modeling these dynamics.
  Regarding your concerns, I have modified the simulation scheme accordingly. I totally agree with you. It is better to compare with observation which age/tree size is similar with the simulated forests. However, I did not consider them in the model honestly. This is because the age and tree size data were not measured simultaneously with the NSC data. It is challenging to obtain these data at the same time since measuring NSC involves cutting trees in field sites and extracting the NSC content after making them dry.The trees used for measuring NSC are often located in parks and other field sites that are not well managed for research. As a result, I could not find NSC data with age/size information from trees managed by universities or research institutes. Instead, the NSC data used in this study are presented as relative values of NSC to total dry woody biomass. To compare the model with observations, we used the simulated relative values of NSC to total simulated dry woody biomass to avoid the influence of age and size.
  I added the comparison as you suggested between the new model and the original SEIB-DGVM. Since the original SEIB-DGVM only calculates NSC in the trunk, I did not compare the NSC seasonality at a point scale. Instead, I compared the total NSC across climate zones and biome types on a global scale. I also made modifications to Figure 5 to show the differences between the original and new model. The additions to the Results and Discussion section demonstrate the superiority of the new model.
  Please find more comments in the attached pdf document.
  
  Citation: https://doi.org/10.5194/egusphere-2022-835-AC2

Peer review completion

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

AR by Hideki Ninomiya on behalf of the Authors (27 May 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (08 Jun 2023) by Hisashi Sato

RR by Anonymous Referee #2 (22 Jun 2023)

ED: Publish as is (22 Jun 2023) by Hisashi Sato

AR by Hideki Ninomiya on behalf of the Authors (26 Jun 2023) Author's response Manuscript

Journal article(s) based on this preprint

25 Jul 2023

Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)

Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu

Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023,https://doi.org/10.5194/gmd-16-4155-2023, 2023

Short summary

Hideki Ninomiya et al.

Model code and software

Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0) Hideki Ninomiya; Tomomichi Kato; Lea Végh; Lan Wu https://doi.org/10.5281/zenodo.7021459

Hideki Ninomiya et al.

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Short summary

Non-structural carbohydrates (NSCs) are important carbon storage in plants against the climate change. We added a new NSC module into an individual-based ecosystem model: SEIB-DGVM. The amounts of simulated NSCs and the general seasonality agree well with observed NSCs on a point scale and global scale. The model has a high potential to simulate the biotic effects resulting from insufficient NSC that are otherwise difficult to measure in terrestrial ecosystems globally.


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