Spatio-Temporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems in the Yellow River Basin
, Xiaoqian Zhu 1, Guangqing Zhai 1, Zebang Song 1,3 and Jialin Mao 1,3
Round 1
Reviewer 1 Report
In this paper, the temporal and spatial evolution of carbon storage in the terrestrial ecosystem of the Yellow River Basin and the prediction of carbon storage under different scenarios in the future are studied. In this study, the geographical weighted regression model is used to estimate carbon storage, which is novel in method. However, the discussion section of this article is not in-depth enough. In the revised version, the author must also consider the advantages of this method compared with other research methods and discuss it. This paper needs to be revised to reach the level of the journal. Here are the other comments:
1.On page 2, lines 58 to 65, it is necessary to provide more information on why a geographically weighted regression model was chosen for carbon storage estimation. The author must discuss its advantages and emphasize it.
2.In the formula in Table 1, the text representing the variable symbol should be italicized, please check the whole text.
3.On page 4, lines 134 to 136, Why do you choose precipitation, temperature, altitude, slope, population, GDP, NPP, terrain and other factors to study the factors affecting carbon storage? Please explain the reasons.
4.Please check the units in Figure 7.
5.More discussion and analysis of the method is necessary, and it is recommended to increase the comparison with the results of other estimation methods.
6.On page 11, lines 313，Please check the font of the quotation marks.
7.On page 10, lines 273 to 274，"The Yellow River Basin started to carry out ecological projects vigorously in the 21st century, and although the ecological land area continued to expand" added references.
The overall quality of the English language is fine.
Author Response
Dear reviewer,
Thank you for taking the time to review this paper, and thank the expert for giving the opportunity to revise the paper. Your comments have played an important role in the improvement of this article. We very much agree with your views and suggestions, and have learned a lot in the process of revision, as well as recognizing our own shortcomings in professional knowledge and article writing. At present, we have carefully revised the article according to the opinions of experts, and hope to get your approval. At the same time, we also hope that you can take the time to point out the problems existing in the revised article. We are very willing to continue to learn and improve, and hope to publish a logical and high-quality article in your journal. Finally, I would like to express my heartfelt thanks to all the experts. The following are my replies to the review comments one by one.
- On page 2, lines 58 to 65, it is necessary to provide more information on why a geographically weighted regression model was chosen for carbon storage estimation. The author must discuss its advantages and emphasize it.
Reply: We accept your suggestion and thank you very much for pointing out the problem. The revised result is as follows:
"In recent years, a geographically weighted regression model to explore the spatial characteristics of data came into being, which incorporated the spatial characteristics of data into the estimation of the model and considered the spatial heterogeneity of the data, and achieved good application results in the fields of meteorology, forestry and ecology[30-32]."
- In the formula in Table 1, the text representing the variable symbol should be italicized, please check the whole text.
Reply: We fully accept your suggestion and thank you very much for your guidance. All the variables in Table 1 have been checked and the variables involved (L, r, G, I, C1 and C2) have been changed to italics.
- On page 4, lines 134 to 136, Why do you choose precipitation, temperature, altitude, slope, population, GDP, NPP, terrain and other factors to study the factors affecting carbon storage? Please explain the reasons.
Reply: We fully accept your suggestion and thank you very much for your valuable advice. In the selection of impact factors, we considered the ecological geographical conditions, social development status and relevant reference materials of the study area, and finally selected 2 climate factors, 3 terrain factors, 1 planting factor and 2 social factors to study the impact factors.The modification results are as follows:
Change “The influence of factors such as precipitation, temperature, altitude, slope, population, GDP, NPP, and topography on carbon storage was analyzed using a geographic probe[32]. The different factors were graded according to the ecological geography of the study area (Table 2)” to “According to the ecological geography and socio-economic development of the study area, the influencing factors such as precipitation, temperature, altitude, terrain, slope, NPP, population, and GDP were selected for classification (Table 2)[32], and the influence of each factor on carbon storage was analyzed with geographical detectors (including factor_detector、risk_detector、ecological_detector、interaction_detector)”.
- Please check the units in Figure 7.
Reply: We fully accept your suggestion and thank you very much for your guidance. We have changed “Carbon density (kg C-m-2)” to “Carbon density (kg C·m-2)”. Check and correct the spelling of other units in the full text.
- More discussion and analysis of the method is necessary, and it is recommended to increase the comparison with the results of other estimation methods.
Reply: We fully accept your suggestion and thank you very much for your guidance. In Section 5.2, we discussed the conditions that affect the results of carbon storage in terms of methods, location of data collection, and timing of data collection. The results show that the high value area of carbon storage can be better found and calculated by using the geographical weighted regression model, and the distribution of sampling points affects the calculation results.
5.2 is revised as "The total carbon storage of the Yellow River Basin ecosystem estimated by the geographically weighted regression model in this study is higher than that estimated by the InVEST model (3.96×109 t) [22], and this difference is mainly due to the different methods of carbon storage estimation. It shows that the geographical weighted regression model can find and calculate the high value area of the ecosystem in the basin to a large extent. Also, compared with the average carbon density of ecosystems estimated in this study, the carbon density estimates of typical vegetation in the Yellow River Basin[13] and ecosystems in the western region[28] based on a large number of sample points were higher than those in this study, which may be because most previous studies estimated carbon density based on typical vegetation or a single region[36]. In addition, different sampling times may also lead to differences in the estimation results[37]. For example, with the implementation of a series of ecological restoration measures in the Yellow River basin, the vegetation cover such as grassland increased, the carbon sequestration capacity improved, and the carbon density increased[28].”
6.On page 11, lines 313，Please check the font of the quotation marks.
Reply: We fully accept your suggestion. Thank you for pointing out the problem. The font of quotation marks has been corrected and the font of the whole text has been checked.
7.On page 10, lines 273 to 274，"The Yellow River Basin started to carry out ecological projects vigorously in the 21st century, and although the ecological land area continued to expand" added references.
Reply: We fully accept your suggestion. Thank you for your valuable comments, reference 19,23,25,35 has been added.
[19]Bu, X. Y.; Cui, D.; Dong, S. C.; Mi, W. B.; Li, Y.; Li, Z. G.; Feng, Y. L. Effects of Wetland Restoration and Conservation Projects on Soil Carbon Sequestration in the Ningxia Basin of the Yellow River in China from 2000 to 2015. Sustainability, 2020, 12, 10284.
[23]Wang, J. F.; Li, L, F.; Li, Q.; Wang, S.; Liu, X. L.; Li, Y. The spatiotemporal evolution and prediction of carbon storage in the Yellow River Basin based on the major function-oriented zone planning. Sustainability, 2022, 14, 1-18.
[25]Fang, M.; Si, G.; Yu, Q.; Guo, H. Study on the relationship between topological characteristics of vegetation ecospatial network and carbon sequestration capacity in the Yellow River Basin, China. Remote Sensing, 2021, 13, 11-15.
[35]Li, X. Y.; Zhang, Z. Q.; Sun, A. Z. Study on the spatial-temporal evolution and influence factors of vegetation coverage in the Yellow River Basin during 1982-2021. Journal of Earth Environment, 2022, 13, 429-436.
The above is my answer to the questions raised by the expert, please edit teachers and review experts.
Kind regards,
Ms Sun
2023.08.01
Author Response File: 
Author Response.docx
Reviewer 2 Report
This manuscript emphasized the relationship between carbon sequestration and other components in the Yellow River basin. Basically, China's environmental management, the huge country that made a global impact, has been very important. I think the manuscript comprehensively analyzed the current situation, but need to improve some minor things.
Line 1: I am not sure that the title is appropriate. The word, "mechanism" should be changed to some other word.
Line 37: Sorry for aiming for the Chinese government, global carbon neutrality focused on the situation in 2050 rather than 2060 may need additional comments.
Line 65: The problem should be changed to the problem. Please check some capitalized words.
Line 85: About defining the basion, please refer to some global basin dataset (Hydro Basin) or local agencies dataset - the figure looks like an administrative boundary
Line 124: The change analysis method needs some reference. The change amount is used, but some are analyzed by the change matrix, so it needs to clarify the methods and approach before representing the results.
Line 138: The InVEST model is not enough for future prediction - the model is operated by the carbon pools table. Please add some more information to consist of future carbon pools. In addition, land use change should be also emphasized.
Line 149: The result looks understandable, but the resolution (DPI quality) of the figures should be improved.
Line 264: This part looks like one of the results, especially figure 7. However, the author can check some words and sentences to fit the discussions.
Author Response
Dear reviewer,
Thank you for taking the time to review this paper, and thank the expert for giving the opportunity to revise the paper. Your comments have played an important role in the improvement of this article. We very much agree with your views and suggestions, and have learned a lot in the process of revision, as well as recognizing our own shortcomings in professional knowledge and article writing. At present, we have carefully revised the article according to the opinions of experts, and hope to get your approval. At the same time, we also hope that you can take the time to point out the problems existing in the revised article. We are very willing to continue to learn and improve, and hope to publish a logical and high-quality article in your journal. Finally, I would like to express my heartfelt thanks to all the experts. The following are my replies to the review comments one by one.
Line 1: I am not sure that the title is appropriate. The word "mechanism" should be changed to some other word.
Reply: We fully accept your suggestion. Thank you for pointing out the problem. Considering that the main research contents of this paper include estimation of carbon stocks in the Yellow River Basin from 2000 to 2020, analysis of changes in carbon stocks, analysis of influencing factors of carbon stocks, and prediction of carbon stocks in 2030-2050, we have changed “Spatio-temporal change mechanism and prediction of carbon storage in terrestrial ecosystems in the Yellow River Basin” to “Spatio-temporal variation and prediction of carbon storage in terrestrial ecosystems in the Yellow River Basin”.
Line 37: Sorry for aiming for the Chinese government, global carbon neutrality focused on the situation in 2050 rather than 2060 may need additional comments.
Reply: We accept your suggestions in part and thank you for your valuable comments. The world is expected to achieve carbon neutrality by 2050, and given that China is still a developing country, the Chinese government has set a goal of "striving to achieve carbon peak by 2030 and carbon neutrality by 2060." Our modifications are as follows:
Change “In 2020,China proposed to peak its carbon emissions by 2030 and strive to achieve “carbon neutrality” by 2060 [2-3]” to “ In 2020, in response to the Paris Agreement, UN Secretary-General Antonio Guterres called on major emitters to target net-zero greenhouse gas emissions by 2050, the Chinese government has proposed to peak carbon emissions before 2030 and strive to achieve "carbon neutrality" by 2060[2-3]”.
Line 65: The problem should be changed to the problem. Please check some capitalized words.
Reply: We fully accept your suggestion. Thank you for pointing out the question, I have corrected here and revised the full text.
Line 85: About defining the basion, please refer to some global basin dataset (Hydro Basin) or local agencies dataset - the figure looks like an administrative boundary.
Reply: The boundary of the study area used in this paper is the physical geographical boundary. The basin range of the Yellow River basin used in this study is derived from the data of nine major river basins in China provided by the Resources and Environmental Data Center of the Chinese Academy of Sciences(https://www.resdc.cn/). In order to make it clear, we attach a comparison chart between the physical geographical boundary and the administrative boundary.
Line 124: The change analysis method needs some reference. The change amount is used, but some are analyzed by the change matrix, so it needs to clarify the methods and approach before representing the results.
Reply: We fully accept your suggestion. Thank you for pointing out the question. Trend statistical analysis was used to analyze the change of carbon stocks in different ecological geographical regions, and spin graph was used to express the change of carbon stocks in different ecosystems. We have added the methods and references for analyzing changes in carbon stocks, revised as follows:
“Based on the trend statistical analysis, the changes of carbon stocks in different ecogeographical regions of the Yellow River Basin were analyzed in both time and spac, and the changes of carbon stocks in different ecosystems were investigated by using spiral graph analysise[9,23].”
Line 138: The InVEST model is not enough for future prediction - the model is operated by the carbon pools table. Please add some more information to consist of future carbon pools. In addition, land use change should be also emphasized.
Reply: We are sorry that we cannot accept your suggestion, but thank you very much for your valuable advice, the inadequacies you raised about the InVEST model are the focus of this article's research. At present, the InVEST model is still used by many scholars in predicting carbon storage[1-3]. Its ease of operation and availability of data have great advantages. After reading a lot of literature and further in-depth discussion, due to the lack of sufficient data of future factors, such as future vegetation index, we do not consider further adding other information to the corrected carbon pool at present. This study used PLUS model and InVEST model to predict future carbon storage. The PLUS model predicts the future land use status from 2030 to 2050 (the model combines the trend of land use change from 2000 to 2020, the contribution rate of various influencing factors to land use change, the future development policy orientation and the future climate, social and economic development). Then, the average carbon density of each carbon pool from 2000 to 2020 was used to calculate the carbon storage of each carbon pool from 2030 to 2050 in the InVEST model.
Reference:
1.Li, Y.; Liu, Z.; Li, S.; Li, X. Multi-scenario simulation analysis of land use and carbon storage changes in changchun city based on FLUS and InVEST model. Land, 2022, 11, 1-17.
2.Wang, J. F.; Li, L, F.; Li, Q.; Wang, S.; Liu, X. L.; Li, Y. The spatiotemporal evolution and prediction of carbon storage in the Yellow River Basin based on the major function-oriented zone planning. Sustainability, 2022, 14, 1-18.
3.Wang, C. Y.; Guo, X. H.; Guo, L. Bai, L. F.; Xia, L. L.; Wang, C. B.; Li, T. Z. Land use change and its impact on carbon storage in northwest China based on FLUS-Invest: A case study of Hu-Bao-Er-Yu urban agglomeration. Ecology and Environmental Sciences, 2022, 31, 1667-1679.
Line 149: The result looks understandable, but the resolution (DPI quality) of the figures should be improved.
Reply: We fully accept your suggestion.Thank you for pointing out the question, we have raised the DPI of the pictures in the article to 600.
Dear reviewer,
Thank you for taking the time to review this paper, and thank the expert for giving the opportunity to revise the paper. Your comments have played an important role in the improvement of this article. We very much agree with your views and suggestions, and have learned a lot in the process of revision, as well as recognizing our own shortcomings in professional knowledge and article writing. At present, we have carefully revised the article according to the opinions of experts, and hope to get your approval. At the same time, we also hope that you can take the time to point out the problems existing in the revised article. We are very willing to continue to learn and improve, and hope to publish a logical and high-quality article in your journal. Finally, I would like to express my heartfelt thanks to all the experts. The following are my replies to the review comments one by one.
Line 264: This part looks like one of the results, especially figure 7. However, the author can check some words and sentences to fit the discussions.
Reply: We accept your suggestions in part and thank you for your valuable comments. We moved Picture 7 to Section 4.2 to add density analysis of different ecosystems. The reasons for the first decrease and then increase of carbon stocks are discussed in depth in Section 5.3. The main reasons are the decrease of carbon density from 2000 to 2010, and the significant increase of vegetation coverage and carbon density from 2010 to 2020. The following is our revised results:
Move Figure 7 to Section 4.2 and change to Figure 5.
Section 4.2 added "At the same time, changes in carbon stocks in different ecosystems are also directly related to changes in carbon density (Figure 5)."
Section 5.3 changed to “The total carbon storage in the Yellow River Basin showed a trend of decreasing and then increasing in the past 20 years, which is consistent with the results of previous studies[29]. The Yellow River Basin started to carry out ecological projects vigorously in the 21st century, and although the ecological land area continued to expand[21,23-24], the vegetation cover of the new ecological land was low and the carbon density did not increase significantly (Figure 5), so that the total carbon storage in the Yellow River Basin was decreasing from 2000 to 2010. From 2010 to 2020, the results of multi-year afforestation and ecological restoration began to show, the vegetation cover of the watershed increased significantly[38-39], the carbon density of ecosystems such as forests, grasslands, and thickets became larger, and the total carbon storage increased, especially the above-ground biological carbon storage in the key ecological restoration areas such as the eastern and western parts of the high plains of Inner Mongolia[40], the plains of northern China[41], the highland hills of the Gandong plateau in northern Shanxi and central Jin[10,42], and the Guanzhong basin in southern Jin[43] increased.”
The above is my answer to the questions raised by the expert, please edit teachers and review experts.
Kind regards,
Ms Sun
2023.08.01
Author Response File: Author Response.docx
Reviewer 3 Report
Overall, an interesting paper. However, it needs some improvement in clarity and more experimental/methodological details.
Line 32 - Semicolon should be period.
Lines 81-85 - This is a confusing run-on sentence. Please break up for clarity.
Lines 89-103 - Again, break into multiple sentences. Provide information about number, frequency, and quality control of remotely sensed data.
Equation 9 - "BIUE" should be "BLUE."
Section 3.1 - It's not clear how or if you are separately modeling different carbon pools. None of the performance metrics are presented in the results.
Section 3.3 - How is contribution of each factor measured? It's not clear why you create classes for each factor. This needs better explanation.
Lines 138-148. This is not a complete sentence.
Figure 4 is confusing and might be better presented as separate pie charts or removed.
Table 4 - It's not clear what these numbers mean, or how they were determined. Is this the number of regions where each factor passed some contribution threshold? How was that calculated?
Line 253 - How exactly did their methodology differ, and how did that difference explain the difference in results?
Conclusion - This is not typically presented as bullet points.
Lines 156-164. This should be split up for clarity.
This needs improvement in clarity. There are too many run-on or incomplete sentences.
Author Response
Dear reviewer,
Thank you for taking the time to review this paper, and thank the expert for giving the opportunity to revise the paper. Your comments have played an important role in the improvement of this article. We very much agree with your views and suggestions, and have learned a lot in the process of revision, as well as recognizing our own shortcomings in professional knowledge and article writing. At present, we have carefully revised the article according to the opinions of experts, and hope to get your approval. At the same time, we also hope that you can take the time to point out the problems existing in the revised article. We are very willing to continue to learn and improve, and hope to publish a logical and high-quality article in your journal. Finally, I would like to express my heartfelt thanks to all the experts. The following are my replies to the review comments one by one.
Line 32 - Semicolon should be period.
Reply: We fully accept your suggestion. Thank you for pointing out the question, we have changed the semicolon to a period, and the full text correction has been checked.
Change “To cope with climate change and stop the trend of global warming, the United Nations formulated the United Nations Framework Convention on Climate Change in 1992 to reduce emissions to control the rise of global temperature; the Paris Agreement proposed to control the increase of global average temperature within 2 degrees Celsius and strive to limit it to 1.5 degrees Celsius [1];” to “To cope with climate change and stop the trend of global warming, the United Nations formulated the United Nations Framework Convention on Climate Change in 1992 to reduce emissions to control the rise of global temperature. The Paris Agreement proposed to control the increase of global average temperature within 2 degrees Celsius and strive to limit it to 1.5 degrees Celsius [1].”
Lines 81-85 - This is a confusing run-on sentence. Please break up for clarity.
Reply: We fully accept your suggestion. Thank you very much for the language problem you pointed out, we have revised it as follows:
Change “This study takes the Yellow River Basin as the study area, and by collecting a large number of measured sample data from different ecosystems and combining various remote sensing indices, we establish carbon density models with spatial heterogeneity for different ecosystems, estimate the carbon storage from 2000 to 2020, explore the spatial and temporal changes of carbon storage and the driving factors, predict the carbon sequestration potential from 2030 to 2050, and provide a scientific basis for the management decision of carbon sequestration and sink enhancement in the Yellow River Basin terrestrial ecosystems.” to “In this study, the Yellow River Basin was taken as the study area, and the spatial heterogeneity model of carbon density of different ecosystems was established by using the geographical weighted regression model, combining a large number of sample data and a variety of remote sensing indicators. In this paper, we estimated carbon stocks from 2000 to 2020, determined the spatial and temporal distribution of carbon stocks and their drivers, and predicted the carbon sequestration potential from 2030 to 2050. This study can provide scientific basis for the evaluation of carbon sequestration benefits and management decisions of terrestrial ecosystems in the Yellow River Basin.”
Lines 89-103 - Again, break into multiple sentences. Provide information about number, frequency, and quality control of remotely sensed data.
Reply: We fully accept your suggestion. Thank you very much for pointing out the problem.We have modified the language expression and increased the resolution of the data, and the results are as follows:
The land use data is derived from the China 30-meter Land Cover Product (CLCD)(80% accuracy), which is based on LANDSAT images, combined with automatic stabilization samples of existing products and visual interpretation samples. Vegetation type data were derived from the MCD12Q1 product (spatial resolution 500 m). Geomorphology, population, GDP, road traffic, night lights, and ecogeographic regionalization data were obtained from the Resources and Environmental Sciences and Data Center (resolution 1000 m). Precipitation and temperature data from the National Earth System Science Data Center (spatial resolution 30 m). The carbon density data was derived from the measured data in the published literature, including 703 above-ground biological samples, 708 underground biological samples, and 878 soil samples. Soil organic matter data is derived from the spatiotemporal tripolar environment big data platform (spatial resolution 1000 m). Evapotranspiration data were obtained from GLEAM v3 (spatial resolution 0.25°). Vegetation index, topography and slope data were obtained by atmospheric correction and band calculation of LANDSAT images (resolution 30 m). NPP data acquisition using MOD17A3HGF products (resolution 250 m). GDP and population data for future climate change scenarios are based on the Shared Socio-economic Pathways (SSPs) Population and Economy lattice dataset (resolution 1000 m ).
Equation 9 - "BIUE" should be "BLUE."
Reply: We fully accept your suggestion. Thank you very much for pointing out the problem, we have corrected it, and check to correct other variables for spelling errors.
Section 3.1 - It's not clear how or if you are separately modeling different carbon pools. None of the performance metrics are presented in the results.
Reply: This paper estimates the aboveground, underground and soil carbon pools. In this paper, geographical weighted regression model was used to model three carbon pools in different ecosystems by using remote sensing vegetation index and carbon density sample point data. The carbon density data was derived from the measured data in the published literature, including 703 above-ground biological samples, 708 underground biological samples, and 878 soil samples.
For estimates, please see sections 4.1, figure 2 and figure 3: it includes the spatial distribution of carbon stocks in different carbon pools, the changes of carbon stocks in different carbon pools in time.
Figure 2. Carbon storage distribution in the Yellow River Basin in 2020.
Figure 3. Changes of carbon storage in the Yellow River Basin from 2000 to 2020.
Section 3.3 - How is contribution of each factor measured? It's not clear why you create classes for each factor. This needs better explanation.
Reply: Thanks for the question pointed out by the expert. Combining the ecological and geographical conditions, social development status and relevant references of the study area, 2 climate factors, 3 terrain factors, 1 planting factor and 2 social factors were selected to study the impact factors. The geographical detector was used to detect the spatial differentiation, reveal the driving force behind it, analyze the relationship between carbon storage and each influencing factor, and explore the contribution rate of each factor to carbon storage. The question is revised as follows:
Change “The influence of factors such as precipitation, temperature, altitude, slope, population, GDP, NPP, and topography on carbon storage was analyzed using a geographic probe[32]. The different factors were graded according to the ecological geography of the study area (Table 2)” to “According to the ecological geography and socio-economic development of the study area, the influencing factors such as precipitation, temperature, altitude, terrain, slope, NPP, population, and GDP were selected for classification (Table 2)[32], and the influence of each factor on carbon storage was analyzed with geographical detectors”.Combining the ecological and geographical conditions, social development status and relevant references of the study area, 2 climate factors, 3 terrain factors, 1 planting factor and 2 social factors were selected to study the impact factors. The geographical detector was used to detect the spatial differentiation, reveal the driving force behind it, analyze the relationship between carbon storage and each influencing factor, and explore the contribution rate of each factor to carbon storage.
Lines 138-148. This is not a complete sentence.
Reply: We fully accept your suggestion. Thanks for the shortcomings you pointed out, we have made the following modifications:
Change “Combined with the land use prediction model PLUS (Patch-generating Land Use Simulation) and Markov model to predict the future development scenarios of land use changes in the Yellow River basin, and further by InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to estimate the carbon storage in the Yellow River Basin for 2030-2050[22]. To meet different future development needs, four future development scenarios[21-23,29] were set by considering the land use transfer in the Yellow River Basin from 2000 to 2020 and the Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin, and by combining the middle road scenario of socioeconomic development and the medium level of greenhouse gas emissions (SSP245) (Table 3)” to “Using Patch-generating Land Use Simulation (PLUS) and Markov model to predict future development scenarios of land use change in the Yellow River Basin during 2030-2050, InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) was further used to estimate carbon reserves in the Yellow River Basin during 2030-2050 [12]. In order to meet different future development needs, this paper sets four future development scenarios (Table 3), which comprehensively consider the land use transfer situation in the Yellow River Basin from 2000 to 2020, the Outline of Ecological Protection and High-quality Development Plan in the Yellow River Basin, the middle road scenario of socio-economic development, and the moderate level of greenhouse gas emissions (SSP245).”
Figure 4 is confusing and might be better presented as separate pie charts or removed.
Reply: We are sorry that we cannot accept your suggestion, but thank you very much for your valuable advice. In order to better express the amount and direction of carbon storage transfer in each ecosystem, Figure 4 is retained in this paper. Figure 4 shows the relationship between multiple ecosystems through string diagram, which is suitable for analyzing the relationship between complex data and the flow of data. The line segment connecting any two points on the circle is called string, and the string (the line between two points) represents the relationship between the two.
Table 4 - It's not clear what these numbers mean, or how they were determined. Is this the number of regions where each factor passed some contribution threshold? How was that calculated?
Reply: We accept your suggestions in part and thank you for your valuable comments.Table 4 is obtained by using Risk_detector of geographic detector. There are four kinds of geographical detectors, namely Factor_detector, Risk_detector, Ecological_detector and Interaction_detector. Risk_detector is used to determine whether there is a significant difference between the property values of each two subareas. In this study, 8 influence factors were selected and graded according to the current situation of the study area (Table 2), and the Risk_detector of geographic detector was used to determine which level of the factors had the greatest significance with the high-value carbon storage area, thus obtaining Table 4.For example, the table 4 highlights: The high-value areas of total carbon storage in the Yellow River Basin are mainly within the climate range of -5~0℃, precipitation >400 mm, elevation >3500 m, slope >25°, NPP 3000~4000 kg∙m-2, population 0~100 persons∙km-2, GDP 0~1 million yuan∙km-2, and middle rolling hill.
We followed your suggestion, In 3.3 added "The influence of each factor on carbon storage was analyzed with geographical detectors (including factor_detector, risk_detector, ecological_detector, interaction_detector).”
Table 2. Impact factor classification
|
Factor |
Classification Level |
||||
|
1 |
2 |
3 |
4 |
5 |
|
|
Elevation /m |
0~200 |
200~500 |
500~1500 |
1500~3500 |
＞3500 |
|
Landforms |
Plain |
Terrace |
Hilly |
Small undulating hills |
Middle rolling hills |
|
GDP /yuan∙km-2 |
0~1 million |
1~2 million |
2~5 million |
5~10 million |
＞10 million |
|
NPP /kg∙m-2 |
0~1000 |
1000~2000 |
2000~3000 |
3000~4000 |
＞4000 |
|
Precipitation /mm |
0~200 |
200~400 |
400~600 |
600~800 |
＞800 |
|
Population /persons∙km-2 |
0~100 |
100~200 |
200~500 |
500~1000 |
＞1000 |
|
Slope /° |
0~5 |
5~15 |
15~25 |
25~35 |
＞35 |
|
Temperature /℃ |
<-5 |
-5~0 |
0~5 |
5~10 |
＞10 |
Table 4. Distribution range of each factor in the high-value area of carbon storage in the study area
|
Factor |
Total carbon pool |
Above-ground biogenic carbon pool |
Below-ground biogenic carbon pool |
Soil carbon pool |
|
Elevation |
5 |
2-3 |
5 |
5 |
|
Landforms |
5 |
5 |
5 |
5 |
|
GDP |
1 |
4-5 |
1 |
1 |
|
NPP |
4 |
5 |
2-4 |
4-5 |
|
Precipitation |
3-5 |
4-5 |
2-5 |
3-4 |
|
Population |
1 |
5 |
1 |
1 |
|
Slope |
4-5 |
5 |
5 |
3-4 |
Line 253 - How exactly did their methodology differ, and how did that difference explain the difference in results?
Reply: The problem you raised is exactly the focus of this study. This section discusses the conditions affecting the results of carbon storage from the aspects of method, data collection location and data collection time. It is found that the geographical weighted regression model can better find and calculate the high-value area of carbon storage, and the distribution of sampling points affects the calculation results to a great extent. After repeated reading of a large number of literatures and further in-depth discussion, the problem is revised as follows:
5.2 is revised as "The total carbon storage of the Yellow River Basin ecosystem estimated by the geographically weighted regression model in this study is higher than that estimated by the InVEST model (3.96×109 t) [22], and this difference is mainly due to the different methods of carbon storage estimation. It shows that the geographical weighted regression model can find and calculate the high value area of the ecosystem in the basin to a large extent. Also, compared with the average carbon density of ecosystems estimated in this study, the carbon density estimates of typical vegetation in the Yellow River Basin[13] and ecosystems in the western region[28] based on a large number of sample points were higher than those in this study, which may be because most previous studies estimated carbon density based on typical vegetation or a single region[36]. In addition, different sampling times may also lead to differences in the estimation results[37]. For example, with the implementation of a series of ecological restoration measures in the Yellow River basin, the vegetation cover such as grassland increased, the carbon sequestration capacity improved, and the carbon density increased[28].” This section discusses the conditions affecting the results of carbon storage from the aspects of method, data collection location and data collection time. It is found that the geographical weighted regression model can better find and calculate the high-value area of carbon storage, and the distribution of sampling points affects the calculation results to a great extent
Conclusion - This is not typically presented as bullet points.
Reply: We fully accept your suggestion. Thanks for the shortcomings you pointed out. We have removed the bullet points. The problem is revised as follows:
“This paper used the method of geographical weighted regression to estimate carbon stocks in the Yellow River Basin from 2000 to 2020, analyzed the change trend of carbon stocks, discussed the factors affecting the change of carbon stocks, and predicted the carbon stocks from 2030 to 2050. The main conclusions are as follows:
The total carbon storage in the Yellow River Basin is about 8.84×109 t, with above-ground biogenic carbon storage, below-ground biogenic carbon storage, and soil carbon storage accounting for 6.39%, 5.07%, and 89.70%. Temporally, the total carbon storage in the Yellow River Basin decreased and then increased from 2000 to 2020, with a total decrease of 0.67×109 t (the average carbon density decreased by 6.80 kg C∙m-2). Spatially, the total carbon storage mainly exists in the western and southern parts of the basin, while the carbon storage in the northern part of the east is relatively small. In terms of different ecosystems, the area and carbon density of each ecosystem varied significantly, and forest ecosystems were the main contributor to the increase of carbon storage in the Yellow River Basin.
Precipitation, temperature, and altitude are important factors affecting the spatial pattern of carbon storage in the Yellow River Basin. The range of high-value carbon storage area in the Yellow River Basin is the annual average temperature of -5~0℃, precipitation in the climate range of >400 mm, altitude >3500 m, slope in the range of topographic factors of >25°, NPP in the range of 3000~4000 kg∙m-2, population 0~100 persons∙km-2, GDP in the range of 0~1 million yuan∙km-2, and the landform of medium undulating mountains.
From the four future development scenarios, the ecological conservation scenario has the best carbon gain effect, followed by the urban restricted development scenario, the natural development scenario, and the arable land conservation scenario is the worst.”
Lines 156-164. This should be split up for clarity.
Reply: We fully accept your suggestion. Thanks for the question pointed out by the expert, the question is revised as follows:
Change “The estimated value of terrestrial carbon storage in the Yellow River Basin in 2020 is 8.84×109 t. The spatial distribution of carbon density ranges from 0.02 to 62.27 kg C∙m-2, with an average value of 8.81 kg C∙m-2, showing an overall pattern that the western part of the basin is larger than the eastern part of the basin, and the southern part of the basin is larger than the northern part of the basin. The total carbon storage mainly exists in the Qilian Mountains of Qingdong (20.15%) and the uplands of Gandong in Jinzhong and Shaanxi Province (19.83%), mainly due to the large proportion of the basin area and high carbon density in this area; above-ground biogenic carbon storage mainly exists in the Guanzhong Basin of Jinnan (35.00%) and the Guanzhong Basin of Jinnan (17.00%); Below-ground biogenic carbon storage mainly exists in the Guanzhong Basin of Jinnan and the Qilian Mountains of Qingdong, accounting for 20.89% of the total aboveground biogenic carbon storage, respectively. Soil carbon storage is mainly distributed in the western and southern parts of the basin, such as the Qilian Mountains in Qingdong and the Naqu Mounded Plateau in Guoluo (Figure 2).” to “The estimated terrestrial carbon storage in the Yellow River Basin in 2020 is 8.84×109 t, and the spatial distribution of carbon density ranges from 0.02 to 62.27 kg C·m-2, with an average value of 8.81 kg C·m-2. The carbon storage in western basin is larger than that in eastern basin, and that in southern basin is larger than that in northern basin. Soil carbon storage is greater than biological carbon storage. The total carbon reserves mainly exist in the Qingdong Qilian mountains (20.15%) and the highlands in the Shaanxi Gandong plateau hills (19.83%), mainly because these regions occupy a large proportion of the basin area and have a high carbon density. Aboveground biological carbon reserves mainly exist in the Jinzhong North Shaanxi Gandong plateau hills (35.00%) and the Jinan Guanzhong basin (17.00%). The underground biological carbon reserves mainly exist in the the Jinzhong North Shaanxi Gandong plateau hillse and the Qingdong Qilian mountains, accounting for 20.89% and 20.36% of the total abovemeal biological carbon pool respectively. Soil carbon storage was mainly distributed in the western and southern parts of the basin, especially in the Qingdong Qilian mountains and the Guoluo Naqu hill-like plateau (Figure 2).”
The above is my answer to the questions raised by the experts, please edit teachers and review experts.
Kind regards,
Ms Sun
2023.07.30
Author Response File: Author Response.docx
