Spatio-Temporal Simulation of the Productivity of Four Typical Subtropical Forests: A Case Study of the Ganjiang River Basin in China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper presents the results of modelling the productivity of four subtropical forests. The authors used the Biome-BGC model to predict GPP and NPP values. The topic of the article is relevant, but the presentation of the material requires revision.

1. The Introduction provides almost no information about the models used to predict forest productivity. The overview of models should be significantly expanded - what types of models exist, references to their application in other studies. Why was the Biome-BGC model chosen, what makes it better suited for this specific case?
2. The initial data needs to be described in more detail. Figure 1b shows that there were a small number of sampling points, just over 20. Was the model run only on this data or was some spatial interpolation used? What specific data were taken from the vegetation map of China (species, age, something else)?
3. In Section 2.2.3 there is a reference to Table 1 which lists model parameters, but the table is missing from the text.
4. You write that most of the model parameters were selected from the relevant literature. However, references to these sources are not provided. Please add reference to these sources to support the parameter selection.
5. Section 2.3 is too brief and does not clearly explain the methodology. Describe the modelling process in more detail. You write that total biomass was calculated using regression models, but these models are not specified in the text. The used carbon content coefficient in tree species should also be explicitly stated.
6. In the Discussion in subsection 4.1.3 you say that one of the causes of data uncertainty is the different spatial and temporal resolution of remote sensing data. However, it is unclear whether remote sensing data were actually used in the study. Satellite data are not mentioned earlier in the paper, so this point needs clarification.
7. Figure 1b lacks a legend to explain the abbreviations (for MIX, BF, ENF...)

Author Response

1. The Introduction provides almost no information about the models used to predict forest productivity. The overview of models should be significantly expanded - what types of models exist, references to their application in other studies. Why was the Biome-BGC model chosen, what makes it better suited for this specific case?

Reply:

We agree with the Reviewer's view. We have added the textual content of the productivity prediction in the third paragraph of the Introduction. 
See lines 62-68 of the revised manuscript for details.
In addition, we have added an overview of the model, detailing the applicability and scientific validity of the Biome-BGC model in our case.
See lines 69-80 of the revised manuscript for details.

2. The initial data needs to be described in more detail. Figure 1b shows that there were a small number of sampling points, just over 20. Was the model run only on this data or was some spatial interpolation used? What specific data were taken from the vegetation map of China (species, age, something else)?

Reply:

We apologize for the oversimplified description of the empirical data in our original manuscript, which led to ambiguity in this study by the reviewer. For this reason, we have added detailed textual descriptions of the measured sampling data and vegetation type maps.
See lines 143-166 of the revised manuscript for details.

3. In Section 2.2.3 there is a reference to Table 1 which lists model parameters, but the table is missing from the text.

Reply:

We are very sorry that our original manuscript lost the parameter description table for the Biome-BGC model. For this reason, the table of model parameters has been added in the Supplementary Material.
See line 155 of the revised manuscript, as well as the Supplementary Material of the paper, for details.

4. You write that most of the model parameters were selected from the relevant literature. However, references to these sources are not provided. Please add reference to these sources to support the parameter selection.

Reply:

Thanks for reviewer's comments, we have added references for the model parameters in the Supplementary Material.
See the Supplementary Material of the manuscript for details.

5. Section 2.3 is too brief and does not clearly explain the methodology. Describe the modelling process in more detail. You write that total biomass was calculated using regression models, but these models are not specified in the text. The used carbon content coefficient in tree species should also be explicitly stated.

Reply:

Thanks to the reviewers' comments, we have described the model and its computational methods in detail in the revised manuscript.
See lines 217-249 of the revised manuscript for details.

6. In the Discussion in subsection 4.1.3 you say that one of the causes of data uncertainty is the different spatial and temporal resolution of remote sensing data. However, it is unclear whether remote sensing data were actually used in the study. Satellite data are not mentioned earlier in the paper, so this point needs clarification.

Reply:

We are very sorry for the ambiguity in the original presentation of our manuscript. Instead of remote sensing data, our input to the model should be a raster data product. Therefore, we have revised the presentation of the relevant text.
See lines 614-621 of the revised manuscript for details.

7. Figure 1b lacks a legend to explain the abbreviations (for MIX, BF, ENF...)

Thanks to the reviewers' comments, we have added a figure note to Figure 1.
See lines 139-140 of the revised manuscript for details.

Reviewer 2 Report

Comments and Suggestions for Authors

 

The review on the paper

“Spatio-temporal Simulation of the Productivity of Four Typical  Subtropical Forests: A case study of the Ganjiang River Basin  in China” by Zhiliang Wen et.al

In the study, the authors present the results of applying the Biome-BGC model to simulate forest carbon productivity in the Ganjiang River Basin.

To calibrate the mole, the authors used direct measurements of the parameters that have a significant impact on vegetation productivity, such as carbon-nitrogen ratios of leaves, roots, and tree cores, and specific leaf area .Also the authors used information on climate and forest distribution from literature and open data sources.

The comparison of the simulated and the measured carbon NPP in Figure 2 shows that the simulation results are quite adequate.

The authors present comparative results according to different scenarios: depending on the type of forest, season or growing area

The results are shown in Figures 3, 4, 6-8.  Qualitatively, they do not contradict the known data on the carbon GPP and NPP of forests (for example Malhi, Baldocchi, Jarvis, Plant, Cell and Environment (1999) 22, 715-740) and, given the demonstrated adequacy of the model, can be used to assess the quantitative values of GPP and NPP of different kinds of forests.

The methodology is described  clearly.

The references are appropriate.

Notes regarding the Figures:

1. In Figure 1, the scale needs to be adjusted: the non-realistic value of 100 meters below sea level is striking.
2. There is no Figure 5 in the text.
3. It is not clear where in Figure 8 the data on the GPP are presented.

Author Response

1. In Figure 1, the scale needs to be adjusted: the non-realistic value of 100 meters below sea level is striking.

Reply:

Thanks to the reviewers' comments, we have revised the legend of the DEM in Fig. 1.
See lines 139-140 of the revised manuscript for details.

2. There is no Figure 5 in the text.

Reply:

We are very sorry that Figure 5 was missing from the original manuscript and we have added it in the revised manuscript.
See lines 379-380 of the revised manuscript for details.

3. It is not clear where in Figure 8 the data on the GPP are presented.

Reply:

Thanks to the reviewers' comments, we have redrawn Figure 8 in the revised manuscript.
See line 576 of the revised manuscript for details.

Reviewer 3 Report

Comments and Suggestions for Authors

 The abstract states that the study provides a "scientific basis for protection and management," but it lacks a direct mention of how the findings would influence policy-making or forest conservation strategies in practical terms.
 The introduction spends excessive space summarizing general concepts about carbon cycles and forest productivity. While informative, a more concise background would be preferable. Although the study mentions that "relatively few studies have been conducted on subtropical forest ecosystems," it does not substantiate this claim with references.
The research objectives are broadly mentioned but lack precision in defining the specific hypotheses or expected outcomes.
While it describes the Ganjiang River Basin, it does not highlight unique climatic or ecological factors that may have influenced forest productivity in this specific region.
The study does not explain why this particular river basin was chosen over others with similar climatic conditions.
The study relies on datasets such as ERA5-Land, but it does not explain whether or how these datasets were cross-validated with local meteorological data to ensure accuracy.
There is no discussion on potential errors in vegetation physiological parameter data and how these might impact the results.
The study assumes that parameters such as carbon-nitrogen ratios are spatially consistent, which may not be valid given the potential variations in soil types and forest composition.
While this is a well-established model, the study does not critically evaluate its limitations or compare it with alternative modeling approaches.
The methods do not specify whether locally measured parameters were used to refine the Biome-BGC model or if default settings were used, which affects reproducibility.
The model validation is based solely on linear regression between simulated and measured NPP, which is not sufficient to assess model accuracy. Additional metrics like RMSE or bias analysis should have been included.
The validation is performed at the study-wide level, but there is no assessment of whether the model performs differently in different forest types.
The discussion on trends (e.g., increasing productivity over time) lacks an explanation of how land-use changes, afforestation, or extreme weather events might have influenced these results.
The study does not account for factors like deforestation, pest outbreaks, or human intervention that could have affected the productivity trends.
While the study describes spatial variations, it does not adequately explore why certain areas exhibit higher or lower productivity. More correlation analysis with environmental variables.
There is no sensitivity analysis to determine how much variation in key parameters would impact productivity estimates.
 While the study acknowledges uncertainties, it does not attempt to quantify them. Uncertainty ranges should have been provided for key results.
The authors state that the Biome-BGC model simplifies some processes, but they do not discuss whether alternative models.
The study mainly attributes productivity variation to temperature and precipitation, neglecting other critical factors such as soil nutrients, disturbances, or species composition.
The discussion lacks any cross-validation with remote sensing-based productivity estimates .
The conclusions summarize the results well but lack a strong statement on how these findings contribute to policy, forest management, or climate change mitigation strategies.
 A major shortcoming is the absence of a dedicated section discussing the study's limitations, which would provide transparency about the potential weaknesses of the methodology. 

Author Response

1. The abstract states that the study provides a "scientific basis for protection and management," but it lacks a direct mention of how the findings would influence policy-making or forest conservation strategies in practical terms. The conclusions summarize the results well but lack a strong statement on how these findings contribute to policy, forest management, or climate change mitigation strategies.

Reply:

We agree with the reviewer's comments. The relevance of our findings to the management and policy development aspects of forests is weak. Therefore, we have revised the last sentence of the abstract.
See lines 27-30 of the revised manuscript for details.

2. The introduction spends excessive space summarizing general concepts about carbon cycles and forest productivity. While informative, a more concise background would be preferable. Although the study mentions that "relatively few studies have been conducted on subtropical forest ecosystems," it does not substantiate this claim with references.

Reply:

We agree with the reviewer's comments. We have made changes and additions to the relevant text of the Abstract.
See lines 82-87 of the revised manuscript for details.

3. The research objectives are broadly mentioned but lack precision in defining the specific hypotheses or expected outcomes.
While it describes the Ganjiang River Basin, it does not highlight unique climatic or ecological factors that may have influenced forest productivity in this specific region. The study does not explain why this particular river basin was chosen over others with similar climatic conditions.

Reply:

We agree with the reviewer's comments. We detail the representativeness and typicality of using the Ganjiang River Basin as a study area in the Introduction section.
See lines 92-106 of the revised manuscript for details.

4. The study relies on datasets such as ERA5-Land, but it does not explain whether or how these datasets were cross-validated with local meteorological data to ensure accuracy.

Reply:

We thank the reviewer for his comments. We have added a textual description of this data product being widely used in Biome-BGC modeling in the Data Introduction section. In addition, in the Discussion section, we have also elaborated on the limitations of the meteorological data that were not cross-validated.
See lines188; 618-621 of the revised manuscript for details.

5. There is no discussion on potential errors in vegetation physiological parameter data and how these might impact the results.

Reply:

Many thanks to the reviewers' comments, we have added the uncertainty effects of vegetation physiological parameters on model assessment in the revised manuscript.
See lines 587-605; 614-629 of the revised manuscript for details.

6. The study assumes that parameters such as carbon-nitrogen ratios are spatially consistent, which may not be valid given the potential variations in soil types and forest composition.

Reply:

We are very sorry for the ambiguity of the reviewer about this study owing to the lack of clarity in the explication of our previous manuscript. In fact, soil attributes as well as forest components are among the input parameters of the model, i.e., the spatial heterogeneity of these factors is taken into account in this study. We have added this aspect in the revised manuscript.
See lines 196-203 of the revised manuscript for details.

7. While this is a well-established model, the study does not critically evaluate its limitations or compare it with alternative modeling approaches.

Reply:

Thanks to the reviewer's suggestion, we have added Table 2 in the revised manuscript, which is a comparative analysis between the data obtained from different models.
See lines 258-273 of the revised manuscript for details.

8. The methods do not specify whether locally measured parameters were used to refine the Biome-BGC model or if default settings were used, which affects reproducibility.

Reply:

We are very sorry for the ambiguity the reviewer had about this study owing to our lack of clarity in the previous manuscript. In fact, we localized the key sensitive parameters in the Biome-BGC model instead of using the model default parameters. We have explained this in detail in the revised manuscript.
See lines 143-166 of the revised manuscript for details.

9. The model validation is based solely on linear regression between simulated and measured NPP, which is not sufficient to assess model accuracy. Additional metrics like RMSE or bias analysis should have been included.

Reply:

We agree with the reviewer's comments, and therefore, in the revised manuscript, we have added the value of RMSE to Figure 2.
See line 256 of the revised manuscript for details.

10. The validation is performed at the study-wide level, but there is no assessment of whether the model performs differently in different forest types.

Reply:

Thanks to the reviewers' comments, we have added model accuracy validation for different forest types in Table 2 of the revised manuscript.
See lines 258-273 of the revised manuscript for details.

11. The discussion on trends (e.g., increasing productivity over time) lacks an explanation of how land-use changes, afforestation, or extreme weather events might have influenced these results. The study does not account for factors like deforestation, pest outbreaks, or human intervention that could have affected the productivity trends.

Reply:

Thanks to the reviewers' comments, we have added the impacts of land use change, deforestation, climate extremes, and pest and disease outbreaks on the uncertainty of model assessments in the Discussion section of the revised manuscript.
See lines 659-665 of the revised manuscript for details.

12. While the study describes spatial variations, it does not adequately explore why certain areas exhibit higher or lower productivity. More correlation analysis with environmental variables.

Reply:

Thanks to the reviewers' comments, we have expanded the text of the Discussion section of the paper to cover the analysis of the relationship between productivity and environmental variables.
See the Discussion section of the revised manuscript for details.

13. There is no sensitivity analysis to determine how much variation in key parameters would impact productivity estimates.

Reply:

Thanks to the reviewers' comments, we have added the impact of the sensitivity analysis of the parameters on the uncertainty of the model estimation in the Discussion section of the revised manuscript.
See lines 587-605 of the revised manuscript for details.

14. While the study acknowledges uncertainties, it does not attempt to quantify them. Uncertainty ranges should have been provided for key results. The authors state that the Biome-BGC model simplifies some processes, but they do not discuss whether alternative models.

Reply:

Thanks to the reviewers' comments, we have added data comparison of the results of forest productivity assessment by different models in the revised manuscript and analyzed the consistency of the estimated data from the Biome-BGC model used in this study with those from other models.
See lines 258-273 of the revised manuscript for details.

15. The study mainly attributes productivity variation to temperature and precipitation, neglecting other critical factors such as soil nutrients, disturbances, or species composition.

Reply:

Thanks to the reviewers' comments, we have, on the one hand, in the Data and Methods section of the revised manuscript, added that the data for the model are inclusive of soil properties and vegetation properties. On the other hand, in the Discussion section, we have also added the soil, vegetation, and anthropogenic disturbances to the uncertainty influence on the model evaluation.
See the Data section, Methods section, and Discussion section of the revised manuscript for more details.

16. The discussion lacks any cross-validation with remote sensing-based productivity estimates .
Reply:

Many thanks to the reviewer's comments, we have added Table 2 in the revised draft, which is for data cross-validation.
See lines 258-273 of the revised manuscript for details.

17. A major shortcoming is the absence of a dedicated section discussing the study's limitations, which would provide transparency about the potential weaknesses of the methodology. 

Reply:

We are very grateful to the reviewer's comments, and we have added the limitations of this study in detail in the revised manuscript.
See lines 587-605; 614-629; 643-665 of the revised manuscript for details.

Reviewer 4 Report

Comments and Suggestions for Authors

I have completed the review of manuscript by Wen et al. Authors have investigated spatio-temporal distribution of four forest types using Biome-BGC model. The presented work is worthy of publication. However, I have few concerns, particularly regarding the methods section. I could not get the details of the methods used for collecting data from field plots.

What parameters and how were they determined?

What was the size of the plots?

How many samples were analyzed?

How were they processed?

Did authors normalize the data before analysis/simulation using Biome BGC Model?

Since most of the physiological parameters were not determined, would it not affect the validity of the model keeping in mind the role of climate change in influencing physiology of trees? A robust explanation is required.

In my opinion, authors need to discuss the limitations of the model in view of these observations.

Author Response

1. What parameters and how were they determined?

Reply:

We are very sorry that our original manuscript presented the parameters of the Biome-BGC model too briefly. Therefore, we have provided a detailed description of the parameter settings of the model in the revised manuscript.
For details, see lines 154-166; 217-249 of the revised manuscript; and table S1 of the Supplementary Material.

2. What was the size of the plots?

Reply:

We thank the reviewer for his comments. We have added a sample plot description of the field measurements in the revised manuscript.
See lines 149-153 of the revised manuscript for details.

3. How many samples were analyzed?

Reply:

We thank the reviewer for his comments. We have added a description of the number of field sampling sites in the revised manuscript.
See lines 143-148 of the revised manuscript for details.

4. How were they processed? and did authors normalize the data before analysis/simulation using Biome BGC Model?

Reply:

We are very sorry that our original manuscript presented the method section of the model too briefly. We have added details in the revised manuscript.
See lines 143-166; 196-203; 217-249 of the revised manuscript for details.

5. Since most of the physiological parameters were not determined, would it not affect the validity of the model keeping in mind the role of climate change in influencing physiology of trees? A robust explanation is required.

Reply:

We are very sorry that our original manuscript presented the method section of the model too briefly. We have measured and localized the key parameters that have a large impact on the accuracy of the model.
See lines 154-162 of the revised manuscript, and Table S1 in Supplementary Materials for details.

6. In my opinion, authors need to discuss the limitations of the model in view of these observations.

Reply:

We agree with the reviewer's comments. We have added details of the limitations of this study in the revised manuscript.
See lines 587-605; 614-629; 643-665 of the revised manuscript for details.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have done a great job with the manuscript and have corrected all my comments. I noticed only one small typo: lines 157-158 in the sentence "Measured data from 22 forest sample plots..." a verb is missing or it should be part of another sentence.

Reviewer 3 Report

Comments and Suggestions for Authors

I appreciate the author's effort in incorporating my comments and suggestions to enhance the quality of the paper.

Reviewer 4 Report

Comments and Suggestions for Authors

Authors have revised the manuscript as per the suggestions of reviewers. It is now ready for publication.