the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 May 2024
Future response of ecosystem water use efficiency to CO2 effects in the Yellow River Basin, China
Abstract. Ecosystem Water Use Efficiency (WUE) is pivotal for understanding the carbon-water cycle interplay. Current research seldom addresses how WUE might change under future elevated CO2 concentrations, limiting understanding of regional ecohydrological effects. We present a land-atmosphere attribution framework for WUE in the Yellow River Basin (YRB), integrating the Budyko model with global climate models (GCMs) to quantify the impacts of climate and underlying surface changes induced by CO2. Additionally, we further quantitatively decoupled the direct and secondary impacts of CO2 radiative and biogeochemical effects. Attribution results indicate that WUE in the YRB is projected to increase by 0.36–0.84 gC·kg-1H2O in the future, with climate change being the predominant factor (relative contribution rate of 77.9–101.4 %). However, as carbon emissions intensify, the relative importance of land surface changes becomes increasingly important (respective contribution rates of -1.4 %, 14.9 %, 16.9 %, and 22.1 % in SSP126, SSP245, SSP370, SSP585). Typically, WUE is considered a reflection of an ecosystem's adaptability to water stress. Thus, we analyzed the response of WUE under different scenarios and periods and various drought conditions. The results show a distinct "two-stage" response pattern of WUE to drought in the YRB, where WUE increases under moderate-severe drought conditions but decreases as drought intensifies across most areas. Furthermore, GCM projections suggest that plant adaptability to water stress may improve under higher carbon emission scenarios. Our findings enhance understanding of regional ecohydrological processes and provide insights for future predictions of drought impacts on terrestrial ecosystems.
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RC1: 'Comment on hess-2024-145', Anonymous Referee #1, 06 Jun 2024
The authors developed a new land-atmosphere attribution framework to quantify the impacts of climate and underlying surface changes on WUE. This paper gives a rigorous mathematical proof and physically meaningful explanation for this framework and applies it to the Yellow River Basin (YRB), China. The results explained the future WUE trends and attribution outcomes in the YRB. In addition, the paper found an interesting two-stage response pattern of WUE to drought as well as future changes in the pattern. The findings of this study can be very helpful for ecosystem monitoring and health management.
The paper is well written and of high interest to the HESS readership. Overall, I think it has the potential to become a very interesting contribution, and I believe that it deserves to be published after some minor corrections. Below I list some comments that hopefully help to strengthen the paper.
General comments
- Introduction:
Why was TWSA-DSI chosen as a drought indicator? Does it have any advantages over other drought indicators? I suggest the authors to explain accordingly in the introduction section.
- Introduction: I suggest the authors add a paragraph introducing the structure of this paper.
- In L22-23 you defined the WUE as the ratio of GPP and ET. I suggest authors write the defining mathematical expression for WUE.
- Section 3.1: Since trend-preserving bias correction is not well-known to readers, please add more details about trend-preserving bias correction.
- L13: ‘be-comes’ should be ‘becomes’.
- L55: ‘there has been’ should be ‘there have been’.
- In L96 ‘complementary relationship’ appears only once in the text, and I recommend that there is no need to write out its abbreviation ‘CR’.
- L100: ‘The GPP dataset were …’ should be ‘The GPP dataset was …’.
- L123-124: I would like to see these variables abbreviated in CMIP6 to make it easier for readers to search to get this data.
- The Equation 2 is very basic for the following derivation. However, it popped up without enough details, and should be derived step by step.
- Section 4.4: The article chooses the same time span for calculating future drought response, but in theory does not eliminate the effect of long-term trends on WUE and TWSA. If the authors have done other measures to avoid the effect of the long-term trend, they should make a statement here.
- Section 5.3: The authors explain how the ‘two-stage response pattern’ will change in the future through stomatal conductance. In fact, the data comes from models in CMIP6, and I suggest that the authors explain what effect CO2 will have on stomatal conductance through the parameter settings of the model.
- L134: ‘Series’ should be ‘series’.
- L169: Is ‘Equation (3)’ a clerical error? In the context of what follows, it should be ‘Equation (2)’.
- L175: As in the previous entry, here should be ‘Equation (2)’ instead of ‘Equation (3)’.
- L212: ‘Equation (4) and (8)’ should be ‘Equation (3) and (7)’.
- L217: ‘From Equation (14-16)’ should be ‘From Equation (13-15)’.
- L219: ‘Section 0’ should be ‘Section 2.2.4’.
- L251: ‘Section 0’ should be ‘Section 3.1’.
- L288: Why is there a right bracket after ‘SSPs’?
- L291: ‘appear’ should be ‘appeared’.
- L354: ‘slight’ should be ‘slightly’.
- L368: ‘act’ should be ‘acted’.
- L403: ‘decrease’ should be ‘decreased’.
- L414: ‘Section 0’ should be ‘Section 4.2’.
- L448: ‘Equation (3)’ should be ‘Equation (2)’.
- L474: ‘Zhao, Wu, et al., 2022’ should be ‘Zhao et al., 2022’
- L502: ‘ecosystems’ should be ‘ecosystem’.
- L515: ‘forecasts’ should be ‘forecast’
- L543: ‘The NDVI date …’ should be ‘The NDVI data …’
Citation: https://doi.org/10.5194/hess-2024-145-RC1 -
AC1: 'Reply on RC1', Siwei Chen, 08 Aug 2024
We thank the referees for your kind evaluation of our manuscript and for your insightful comments, which will be a great help in improving the quality of our paper. We will carefully revise the manuscripts according to your comments and suggestions.
Below, you will find a point-by-point response to the comments from Reviewer 1, with reviews in italic and our commentaries in normal font.
The authors developed a new land-atmosphere attribution framework to quantify the impacts of climate and underlying surface changes on WUE. This paper gives a rigorous mathematical proof and physically meaningful explanation for this framework and applies it to the Yellow River Basin (YRB), China. The results explained the future WUE trends and attribution outcomes in the YRB. In addition, the paper found an interesting two-stage response pattern of WUE to drought as well as future changes in the pattern. The findings of this study can be very helpful for ecosystem monitoring and health management.
The paper is well written and of high interest to the HESS readership. Overall, I think it has the potential to become a very interesting contribution, and I believe that it deserves to be published after some minor corrections. Below I list some comments that hopefully help to strengthen the paper.
Answer: Thank you very much for reviewing our manuscript and for your valuable insights. We have carefully considered each of your suggestions and have made corresponding revisions to the manuscript.
General comments
1.Introduction:Why was TWSA-DSI chosen as a drought indicator? Does it have any advantages over other drought indicators? I suggest the authors to explain accordingly in the introduction section.
Answer: Thanks to your suggestion. We will explained the advantages of TWSA-DSI in Section 1 Introduction by comparing several different drought indicators.
2. Introduction: I suggest the authors add a paragraph introducing the structure of this paper.
Answer: Thanks to your suggestion. We will add chapter introductions at the end of Section 1.
3. In L22-23 you defined the WUE as the ratio of GPP and ET. I suggest authors write the defining mathematical expression for WUE.
Answer: Thanks to your suggestion. We will label the formulas in the appropriate places to make it easier for the reader to understand them.
4. Section 3.1: Since trend-preserving bias correction is not well-known to readers, please add more details about trend-preserving bias correction.
Answer: Thanks for your suggestion. In order to balance the brevity and clarity of the language of the article, we will add the formula in the appropriate places to explain the core idea of this method. We will also provide further clarification on the use of this method.
5. L13: ‘be-comes’ should be ‘becomes’.
Answer: Thanks for the heads up. We will correct it.
6. L55: ‘there has been’ should be ‘there have been’.
Answer: Thanks for that. We will correct it.
7. In L96 ‘complementary relationship’ appears only once in the text, and I recommend that there is no need to write out its abbreviation ‘CR’.
Answer: Thanks for that. We will delete the abbreviation ‘CR’ in L96.
8. L100: ‘The GPP dataset were …’ should be ‘The GPP dataset was …’.
Answer: Thanks. We will correct it.
9. L123-124: I would like to see these variables abbreviated in CMIP6 to make it easier for readers to search to get this data.
Answer: Thanks to your suggestion. We will add the variables abbreviated in CMIP6.10. The Equation 2 is very basic for the following derivation. However, it popped up without enough details, and should be derived step by step.
Answer: Actually, the linear formula was proposed by previous study (Cheng et al., 2011; Fang et al., 2020). We apologize for any confusion caused by unclear expressions, leading to misunderstandings among the readers. We will revise the original text to present the formula in a more logical and clear manner.
11. Section 4.4: The article chooses the same time span for calculating future drought response, but in theory does not eliminate the effect of long-term trends on WUE and TWSA. If the authors have done other measures to avoid the effect of the long-term trend, they should make a statement here.
Answer: Thank you for your insightful comment regarding the potential influence of long-term trends on WUE and TWSA calculations in Section 4.4. In response to your concern, we would like to clarify the approach taken to mitigate these effects.
To address the impact of long-term trends on the analysis, we have adopted a method where the mean and standard deviation of WUE and TWSA are calculated for different time periods separately. This approach allows us to derive anomaly values of WUE and the standardized TWSA value (TWSA-DSI), effectively normalizing the data to remove the influence of long-term trends. By calculating anomalies relative to the respective time periods, we ensure that the analysis focuses on variability and anomalies within those periods, rather than being skewed by broader temporal trends.
This method provides a more accurate reflection of the specific conditions and variations within each studied period, allowing for a robust comparison across different time frames and scenarios without the confounding effects of overarching trends. We will amend the statement in the original text accordingly.12. Section 5.3: The authors explain how the ‘two-stage response pattern’ will change in the future through stomatal conductance. In fact, the data comes from models in CMIP6, and I suggest that the authors explain what effect CO2 will have on stomatal conductance through the parameter settings of the model.
Answer: Thank you for your insightful comment. In response, we will clarify the mechanisms through which CO2 influences stomatal conductance in the CMIP6 models used in our analysis, particularly focusing on the different parameter settings of each model's land surface model. We will also draw a table for this purpose and make some revisions in Section 5.3.
13. L134: ‘Series’ should be ‘series’.
Answer: Thank you. We will correct it.
14. L169: Is ‘Equation (3)’ a clerical error? In the context of what follows, it should be ‘Equation (2)’.
Answer: Yes, we made a mistake. We will correct it.
15. L175: As in the previous entry, here should be ‘Equation (2)’ instead of ‘Equation (3)’.
Answer: Thanks for that. It’s a mistake and We will correct it.
16. L212: ‘Equation (4) and (8)’ should be ‘Equation (3) and (7)’.
Answer: Thank you. We have corrected it.17. L217: ‘From Equation (14-16)’ should be ‘From Equation (13-15)’.
Answer: That’s right. We will amend it.
18. L219: ‘Section 0’ should be ‘Section 2.2.4’.
Answer: There were some errors in the cross-reference process. Thanks for your reminding.
19. L251: ‘Section 0’ should be ‘Section 3.1’.
Answer: We will correct it.
20. L288: Why is there a right bracket after ‘SSPs’?
Answer: It was a clerical error. We will delete it.
21. L291: ‘appear’ should be ‘appeared’.
Answer: Thanks. We will change the expression.
22. L354: ‘slight’ should be ‘slightly’.
Answer: Thank you. We will altere the wording.
23. L368: ‘act’ should be ‘acted’.
Answer: Thanks for that. We will change the tenses.
24. L403: ‘decrease’ should be ‘decreased’.
Answer: Thanks for that. We will change the tenses according your suggestion.
25. L414: ‘Section 0’ should be ‘Section 4.2’.
Answer: We will correct it like the comment 17&18.
26. L448: ‘Equation (3)’ should be ‘Equation (2)’.
Answer: Yes. We will correct it.
27. L474: ‘Zhao, Wu, et al., 2022’ should be ‘Zhao et al., 2022’
Answer: Thanks a lot. We will correct and check the citations are correct again.
28. L502: ‘ecosystems’ should be ‘ecosystem’.
Answer: Thanks. We will correct it.
29. L515: ‘forecasts’ should be ‘forecast’
Answer: Thank you. We will amend it.
30. L543: ‘The NDVI date …’ should be ‘The NDVI data …’
Answer: Thank you. We will correct it.
Citation: https://doi.org/10.5194/hess-2024-145-AC1
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RC2: 'Comment on hess-2024-145', Anonymous Referee #2, 02 Aug 2024
This is a very interesting piece of work in which the authors explore what factors will drive and change one important eco-hydrological index --- water use efficiency, under future climate change. The land-atmosphere attribution framework proposed in the article is quite novel and has been well validated in the Yellow River Basin in China. I think this piece of work answers some of the much-asked questions about eco-hydrological processes and gives quite interesting results. The proposed method can also be used in other similar river basins with deteriorating ecosystem.
In general, this article not only presents an interesting approach but also provides very useful conclusions. Therefore, I recommend publication of this work after moderate revision.
Here are some of my specific comments that I hope will help improve the quality and validity of the article:
- The authors should check again the labelling in their papers when quoting from the article's own sections, e.g., 'Section 0' in L219 and L326. These are just a few examples I found, and there may be other lapses of the same kind in the manuscript. Please check all through the manuscript.
- The authors used GPP data based on NIRv in their study. Why use NIRv? GPP data often have large uncertainty. If possible, add some discussion on this aspect.
- L104: Has any previous study done a similar restriction to 'GPP > 10 gC∙m-2 and ET > 10 mm’? If so, I suggest the authors make a citation.
- L113: Why did the authors choose the TWSA dataset only from 1997-2006? I think the length of time is somewhat inadequate.
- L138: Since the author mentions the Penman–Monteith equation, I think it would be helpful to add specific expressions after this paragraph for easy access by the reader.
- L196: What’s the meaning of ‘R’ here? Is what the author is trying to say actually O?
- L275: ‘SSP470’ should be ‘SSP370’.
- L385: It is obvious from Figure 8 ‘the most region of YRB’ show a two-stage character, but I personally think that the authors could have calculated the proportion of regions to quantify the ‘the most region’.
- L417: Δμ is not defined before.
- Figure 7a: What do the numbers (0.30, 0.54, 0.75,0.34) mean? Can you clarify them in Figure subtitle? What are 101.4%, 14.9% etc.? Please make them more clearly defined in this figure. Figures are self-independent.
Figure 8: Why did the authors only consider moderate-severe drought and extreme-exceptional drought? Please explain more clearly in the text
Citation: https://doi.org/10.5194/hess-2024-145-RC2 -
AC2: 'Reply on RC2', Siwei Chen, 08 Aug 2024
We thank the referees for your kind evaluation of our manuscript and for your insightful comments, which will be a great help in improving the quality of our paper. We will carefully revise the manuscripts according to your comments and suggestions.
Below, you will find a point-by-point response to the comments from Reviewer 2, with reviews in italic and our commentaries in normal font.
This is a very interesting piece of work in which the authors explore what factors will drive and change one important eco-hydrological index --- water use efficiency, under future climate change. The land-atmosphere attribution framework proposed in the article is quite novel and has been well validated in the Yellow River Basin in China. I think this piece of work answers some of the much-asked questions about eco-hydrological processes and gives quite interesting results. The proposed method can also be used in other similar river basins with deteriorating ecosystem.
In general, this article not only presents an interesting approach but also provides very useful conclusions. Therefore, I recommend publication of this work after moderate revision.
Here are some of my specific comments that I hope will help improve the quality and validity of the article:Answer: Thank you very much for your review and the insightful suggestions provided. We will make revisions in accordance with each of your suggestions.
1. The authors should check again the labelling in their papers when quoting from the article's own sections, e.g., 'Section 0' in L219 and L326. These are just a few examples I found, and there may be other lapses of the same kind in the manuscript. Please check all through the manuscript.
Answer: Thank you for your suggestions. Upon review, we discover that errors occurred during cross-referencing. We will correct these issues accordingly.
2. The authors used GPP data based on NIRv in their study. Why use NIRv? GPP data often have large uncertainty. If possible, add some discussion on this aspect.
Answer: Thank you for your suggestions. We chose the NIRv data because it is an advanced global GPP dataset that meets the needs of our analysis. Compared to other datasets like GMMIS, FLUXCOM, LUE, etc., the NIRv dataset offers advantages in terms of data precision and accuracy. In light of your advice, we will add discussion on GPP datasets in Section 5.1.
3. L104: Has any previous study done a similar restriction to 'GPP > 10 gC∙m-2 and ET > 10 mm’? If so, I suggest the authors make a citation.
Answer: Yes, our approach follows the method used by Naeem et al. (2023) to avoid uncertainties introduced by extremely small values. To facilitate reference for readers, we will appropriately cite this in our manuscript.
4. L113: Why did the authors choose the TWSA dataset only from 1997-2006? I think the length of time is somewhat inadequate.
Answer: We apologize for the typographical error. In fact, the TWSA dataset we used in this study covers the period from 1997-2016, spanning twenty years. We believe this duration is sufficient to reflect the trends and various conditions.
5. L138: Since the author mentions the Penman–Monteith equation, I think it would be helpful to add specific expressions after this paragraph for easy access by the reader.
Answer: Thank you for your suggestion. We will add the expression of Penman–Monteith equation and the variable explanation in the corresponding position.
6. L196: What’s the meaning of ‘R’ here? Is what the author is trying to say actually O?
Answer: Sorry, it is a clerical error. It should be the residual term Ο. We will correct it.
7. L275: ‘SSP470’ should be ‘SSP370’.
Answer: Thank you for your reminding. We will correct it.8. L385: It is obvious from Figure 8 ‘the most region of YRB’ show a two-stage character, but I personally think that the authors could have calculated the proportion of regions to quantify the ‘the most region’.
Answer: Thanks for your insightful suggestion. We acknowledge that our initial analysis lacks a quantitative assessment to specify the applicability of the two-stage response model across the basin. Following your suggestion, we will calculate and quantify the proportion of regions that exhibit this two-stage character and will update the manuscript accordingly to include these results.
9. L417: Δμ is not defined before.
Answer: Thank you for your observation regarding the undefined term on Line 417. The symbol is defined to denote the difference in the mean (μ) values between different drought severity levels within the same period. We will includ this definition clearly in the manuscript to ensure proper understanding of the context and to avoid any potential confusion. We apologize for the oversight and appreciate your attention.
10. Figure 7a: What do the numbers (0.30, 0.54, 0.75,0.34) mean? Can you clarify them in Figure subtitle? What are 101.4%, 14.9% etc.? Please make them more clearly defined in this figure. Figures are self-independent.
Answer: Thank you for your queries regarding Figure 7a. This figure consists of two parts: the stacked chart represents the relative contribution rates of climate change and underlying change to WUE variations under different scenarios, with the percentages on either side indicating the specific values of these contributions; the line graph depicts the average WUE changes across the basin under various scenarios, where the numbers 0.30, 0.54, 0.75, and 0.84 represent the changes in average WUE under respective scenarios. To avoid confusion and enhance clarity, we will unify the colors of the line and the right axis. Additionally, we will includ detailed explanations in the figure subtitle to further clarify these elements.
11. Figure 8: Why did the authors only consider moderate-severe drought and extreme-exceptional drought? Please explain more clearly in the text.
Answer: Thank you for your suggestion regarding our focus on specific drought categories in Figure 8. We primarily followed Yin et. al (2022)'s classification method which resampled the WUE responses under more severe drought events to identify universally applicable patterns. In fact, we excluded drought levels with a TWSA-DSI less than -0.8 with consideration that such mild droughts are unlikely to have a significant impact on the ecosystem's WUE. We consolidated drought categories D1-D4 (Table 2) into two levels to ensure a sufficient sample of events to support our conclusions. This approach has resulted in clearer and more persuasive findings, highlighting the eco-hydrological processes under drought levels that require more urgent attention.
Citation: https://doi.org/10.5194/hess-2024-145-AC2
Status: closed
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RC1: 'Comment on hess-2024-145', Anonymous Referee #1, 06 Jun 2024
The authors developed a new land-atmosphere attribution framework to quantify the impacts of climate and underlying surface changes on WUE. This paper gives a rigorous mathematical proof and physically meaningful explanation for this framework and applies it to the Yellow River Basin (YRB), China. The results explained the future WUE trends and attribution outcomes in the YRB. In addition, the paper found an interesting two-stage response pattern of WUE to drought as well as future changes in the pattern. The findings of this study can be very helpful for ecosystem monitoring and health management.
The paper is well written and of high interest to the HESS readership. Overall, I think it has the potential to become a very interesting contribution, and I believe that it deserves to be published after some minor corrections. Below I list some comments that hopefully help to strengthen the paper.
General comments
- Introduction:
Why was TWSA-DSI chosen as a drought indicator? Does it have any advantages over other drought indicators? I suggest the authors to explain accordingly in the introduction section.
- Introduction: I suggest the authors add a paragraph introducing the structure of this paper.
- In L22-23 you defined the WUE as the ratio of GPP and ET. I suggest authors write the defining mathematical expression for WUE.
- Section 3.1: Since trend-preserving bias correction is not well-known to readers, please add more details about trend-preserving bias correction.
- L13: ‘be-comes’ should be ‘becomes’.
- L55: ‘there has been’ should be ‘there have been’.
- In L96 ‘complementary relationship’ appears only once in the text, and I recommend that there is no need to write out its abbreviation ‘CR’.
- L100: ‘The GPP dataset were …’ should be ‘The GPP dataset was …’.
- L123-124: I would like to see these variables abbreviated in CMIP6 to make it easier for readers to search to get this data.
- The Equation 2 is very basic for the following derivation. However, it popped up without enough details, and should be derived step by step.
- Section 4.4: The article chooses the same time span for calculating future drought response, but in theory does not eliminate the effect of long-term trends on WUE and TWSA. If the authors have done other measures to avoid the effect of the long-term trend, they should make a statement here.
- Section 5.3: The authors explain how the ‘two-stage response pattern’ will change in the future through stomatal conductance. In fact, the data comes from models in CMIP6, and I suggest that the authors explain what effect CO2 will have on stomatal conductance through the parameter settings of the model.
- L134: ‘Series’ should be ‘series’.
- L169: Is ‘Equation (3)’ a clerical error? In the context of what follows, it should be ‘Equation (2)’.
- L175: As in the previous entry, here should be ‘Equation (2)’ instead of ‘Equation (3)’.
- L212: ‘Equation (4) and (8)’ should be ‘Equation (3) and (7)’.
- L217: ‘From Equation (14-16)’ should be ‘From Equation (13-15)’.
- L219: ‘Section 0’ should be ‘Section 2.2.4’.
- L251: ‘Section 0’ should be ‘Section 3.1’.
- L288: Why is there a right bracket after ‘SSPs’?
- L291: ‘appear’ should be ‘appeared’.
- L354: ‘slight’ should be ‘slightly’.
- L368: ‘act’ should be ‘acted’.
- L403: ‘decrease’ should be ‘decreased’.
- L414: ‘Section 0’ should be ‘Section 4.2’.
- L448: ‘Equation (3)’ should be ‘Equation (2)’.
- L474: ‘Zhao, Wu, et al., 2022’ should be ‘Zhao et al., 2022’
- L502: ‘ecosystems’ should be ‘ecosystem’.
- L515: ‘forecasts’ should be ‘forecast’
- L543: ‘The NDVI date …’ should be ‘The NDVI data …’
Citation: https://doi.org/10.5194/hess-2024-145-RC1 -
AC1: 'Reply on RC1', Siwei Chen, 08 Aug 2024
We thank the referees for your kind evaluation of our manuscript and for your insightful comments, which will be a great help in improving the quality of our paper. We will carefully revise the manuscripts according to your comments and suggestions.
Below, you will find a point-by-point response to the comments from Reviewer 1, with reviews in italic and our commentaries in normal font.
The authors developed a new land-atmosphere attribution framework to quantify the impacts of climate and underlying surface changes on WUE. This paper gives a rigorous mathematical proof and physically meaningful explanation for this framework and applies it to the Yellow River Basin (YRB), China. The results explained the future WUE trends and attribution outcomes in the YRB. In addition, the paper found an interesting two-stage response pattern of WUE to drought as well as future changes in the pattern. The findings of this study can be very helpful for ecosystem monitoring and health management.
The paper is well written and of high interest to the HESS readership. Overall, I think it has the potential to become a very interesting contribution, and I believe that it deserves to be published after some minor corrections. Below I list some comments that hopefully help to strengthen the paper.
Answer: Thank you very much for reviewing our manuscript and for your valuable insights. We have carefully considered each of your suggestions and have made corresponding revisions to the manuscript.
General comments
1.Introduction:Why was TWSA-DSI chosen as a drought indicator? Does it have any advantages over other drought indicators? I suggest the authors to explain accordingly in the introduction section.
Answer: Thanks to your suggestion. We will explained the advantages of TWSA-DSI in Section 1 Introduction by comparing several different drought indicators.
2. Introduction: I suggest the authors add a paragraph introducing the structure of this paper.
Answer: Thanks to your suggestion. We will add chapter introductions at the end of Section 1.
3. In L22-23 you defined the WUE as the ratio of GPP and ET. I suggest authors write the defining mathematical expression for WUE.
Answer: Thanks to your suggestion. We will label the formulas in the appropriate places to make it easier for the reader to understand them.
4. Section 3.1: Since trend-preserving bias correction is not well-known to readers, please add more details about trend-preserving bias correction.
Answer: Thanks for your suggestion. In order to balance the brevity and clarity of the language of the article, we will add the formula in the appropriate places to explain the core idea of this method. We will also provide further clarification on the use of this method.
5. L13: ‘be-comes’ should be ‘becomes’.
Answer: Thanks for the heads up. We will correct it.
6. L55: ‘there has been’ should be ‘there have been’.
Answer: Thanks for that. We will correct it.
7. In L96 ‘complementary relationship’ appears only once in the text, and I recommend that there is no need to write out its abbreviation ‘CR’.
Answer: Thanks for that. We will delete the abbreviation ‘CR’ in L96.
8. L100: ‘The GPP dataset were …’ should be ‘The GPP dataset was …’.
Answer: Thanks. We will correct it.
9. L123-124: I would like to see these variables abbreviated in CMIP6 to make it easier for readers to search to get this data.
Answer: Thanks to your suggestion. We will add the variables abbreviated in CMIP6.10. The Equation 2 is very basic for the following derivation. However, it popped up without enough details, and should be derived step by step.
Answer: Actually, the linear formula was proposed by previous study (Cheng et al., 2011; Fang et al., 2020). We apologize for any confusion caused by unclear expressions, leading to misunderstandings among the readers. We will revise the original text to present the formula in a more logical and clear manner.
11. Section 4.4: The article chooses the same time span for calculating future drought response, but in theory does not eliminate the effect of long-term trends on WUE and TWSA. If the authors have done other measures to avoid the effect of the long-term trend, they should make a statement here.
Answer: Thank you for your insightful comment regarding the potential influence of long-term trends on WUE and TWSA calculations in Section 4.4. In response to your concern, we would like to clarify the approach taken to mitigate these effects.
To address the impact of long-term trends on the analysis, we have adopted a method where the mean and standard deviation of WUE and TWSA are calculated for different time periods separately. This approach allows us to derive anomaly values of WUE and the standardized TWSA value (TWSA-DSI), effectively normalizing the data to remove the influence of long-term trends. By calculating anomalies relative to the respective time periods, we ensure that the analysis focuses on variability and anomalies within those periods, rather than being skewed by broader temporal trends.
This method provides a more accurate reflection of the specific conditions and variations within each studied period, allowing for a robust comparison across different time frames and scenarios without the confounding effects of overarching trends. We will amend the statement in the original text accordingly.12. Section 5.3: The authors explain how the ‘two-stage response pattern’ will change in the future through stomatal conductance. In fact, the data comes from models in CMIP6, and I suggest that the authors explain what effect CO2 will have on stomatal conductance through the parameter settings of the model.
Answer: Thank you for your insightful comment. In response, we will clarify the mechanisms through which CO2 influences stomatal conductance in the CMIP6 models used in our analysis, particularly focusing on the different parameter settings of each model's land surface model. We will also draw a table for this purpose and make some revisions in Section 5.3.
13. L134: ‘Series’ should be ‘series’.
Answer: Thank you. We will correct it.
14. L169: Is ‘Equation (3)’ a clerical error? In the context of what follows, it should be ‘Equation (2)’.
Answer: Yes, we made a mistake. We will correct it.
15. L175: As in the previous entry, here should be ‘Equation (2)’ instead of ‘Equation (3)’.
Answer: Thanks for that. It’s a mistake and We will correct it.
16. L212: ‘Equation (4) and (8)’ should be ‘Equation (3) and (7)’.
Answer: Thank you. We have corrected it.17. L217: ‘From Equation (14-16)’ should be ‘From Equation (13-15)’.
Answer: That’s right. We will amend it.
18. L219: ‘Section 0’ should be ‘Section 2.2.4’.
Answer: There were some errors in the cross-reference process. Thanks for your reminding.
19. L251: ‘Section 0’ should be ‘Section 3.1’.
Answer: We will correct it.
20. L288: Why is there a right bracket after ‘SSPs’?
Answer: It was a clerical error. We will delete it.
21. L291: ‘appear’ should be ‘appeared’.
Answer: Thanks. We will change the expression.
22. L354: ‘slight’ should be ‘slightly’.
Answer: Thank you. We will altere the wording.
23. L368: ‘act’ should be ‘acted’.
Answer: Thanks for that. We will change the tenses.
24. L403: ‘decrease’ should be ‘decreased’.
Answer: Thanks for that. We will change the tenses according your suggestion.
25. L414: ‘Section 0’ should be ‘Section 4.2’.
Answer: We will correct it like the comment 17&18.
26. L448: ‘Equation (3)’ should be ‘Equation (2)’.
Answer: Yes. We will correct it.
27. L474: ‘Zhao, Wu, et al., 2022’ should be ‘Zhao et al., 2022’
Answer: Thanks a lot. We will correct and check the citations are correct again.
28. L502: ‘ecosystems’ should be ‘ecosystem’.
Answer: Thanks. We will correct it.
29. L515: ‘forecasts’ should be ‘forecast’
Answer: Thank you. We will amend it.
30. L543: ‘The NDVI date …’ should be ‘The NDVI data …’
Answer: Thank you. We will correct it.
Citation: https://doi.org/10.5194/hess-2024-145-AC1
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RC2: 'Comment on hess-2024-145', Anonymous Referee #2, 02 Aug 2024
This is a very interesting piece of work in which the authors explore what factors will drive and change one important eco-hydrological index --- water use efficiency, under future climate change. The land-atmosphere attribution framework proposed in the article is quite novel and has been well validated in the Yellow River Basin in China. I think this piece of work answers some of the much-asked questions about eco-hydrological processes and gives quite interesting results. The proposed method can also be used in other similar river basins with deteriorating ecosystem.
In general, this article not only presents an interesting approach but also provides very useful conclusions. Therefore, I recommend publication of this work after moderate revision.
Here are some of my specific comments that I hope will help improve the quality and validity of the article:
- The authors should check again the labelling in their papers when quoting from the article's own sections, e.g., 'Section 0' in L219 and L326. These are just a few examples I found, and there may be other lapses of the same kind in the manuscript. Please check all through the manuscript.
- The authors used GPP data based on NIRv in their study. Why use NIRv? GPP data often have large uncertainty. If possible, add some discussion on this aspect.
- L104: Has any previous study done a similar restriction to 'GPP > 10 gC∙m-2 and ET > 10 mm’? If so, I suggest the authors make a citation.
- L113: Why did the authors choose the TWSA dataset only from 1997-2006? I think the length of time is somewhat inadequate.
- L138: Since the author mentions the Penman–Monteith equation, I think it would be helpful to add specific expressions after this paragraph for easy access by the reader.
- L196: What’s the meaning of ‘R’ here? Is what the author is trying to say actually O?
- L275: ‘SSP470’ should be ‘SSP370’.
- L385: It is obvious from Figure 8 ‘the most region of YRB’ show a two-stage character, but I personally think that the authors could have calculated the proportion of regions to quantify the ‘the most region’.
- L417: Δμ is not defined before.
- Figure 7a: What do the numbers (0.30, 0.54, 0.75,0.34) mean? Can you clarify them in Figure subtitle? What are 101.4%, 14.9% etc.? Please make them more clearly defined in this figure. Figures are self-independent.
Figure 8: Why did the authors only consider moderate-severe drought and extreme-exceptional drought? Please explain more clearly in the text
Citation: https://doi.org/10.5194/hess-2024-145-RC2 -
AC2: 'Reply on RC2', Siwei Chen, 08 Aug 2024
We thank the referees for your kind evaluation of our manuscript and for your insightful comments, which will be a great help in improving the quality of our paper. We will carefully revise the manuscripts according to your comments and suggestions.
Below, you will find a point-by-point response to the comments from Reviewer 2, with reviews in italic and our commentaries in normal font.
This is a very interesting piece of work in which the authors explore what factors will drive and change one important eco-hydrological index --- water use efficiency, under future climate change. The land-atmosphere attribution framework proposed in the article is quite novel and has been well validated in the Yellow River Basin in China. I think this piece of work answers some of the much-asked questions about eco-hydrological processes and gives quite interesting results. The proposed method can also be used in other similar river basins with deteriorating ecosystem.
In general, this article not only presents an interesting approach but also provides very useful conclusions. Therefore, I recommend publication of this work after moderate revision.
Here are some of my specific comments that I hope will help improve the quality and validity of the article:Answer: Thank you very much for your review and the insightful suggestions provided. We will make revisions in accordance with each of your suggestions.
1. The authors should check again the labelling in their papers when quoting from the article's own sections, e.g., 'Section 0' in L219 and L326. These are just a few examples I found, and there may be other lapses of the same kind in the manuscript. Please check all through the manuscript.
Answer: Thank you for your suggestions. Upon review, we discover that errors occurred during cross-referencing. We will correct these issues accordingly.
2. The authors used GPP data based on NIRv in their study. Why use NIRv? GPP data often have large uncertainty. If possible, add some discussion on this aspect.
Answer: Thank you for your suggestions. We chose the NIRv data because it is an advanced global GPP dataset that meets the needs of our analysis. Compared to other datasets like GMMIS, FLUXCOM, LUE, etc., the NIRv dataset offers advantages in terms of data precision and accuracy. In light of your advice, we will add discussion on GPP datasets in Section 5.1.
3. L104: Has any previous study done a similar restriction to 'GPP > 10 gC∙m-2 and ET > 10 mm’? If so, I suggest the authors make a citation.
Answer: Yes, our approach follows the method used by Naeem et al. (2023) to avoid uncertainties introduced by extremely small values. To facilitate reference for readers, we will appropriately cite this in our manuscript.
4. L113: Why did the authors choose the TWSA dataset only from 1997-2006? I think the length of time is somewhat inadequate.
Answer: We apologize for the typographical error. In fact, the TWSA dataset we used in this study covers the period from 1997-2016, spanning twenty years. We believe this duration is sufficient to reflect the trends and various conditions.
5. L138: Since the author mentions the Penman–Monteith equation, I think it would be helpful to add specific expressions after this paragraph for easy access by the reader.
Answer: Thank you for your suggestion. We will add the expression of Penman–Monteith equation and the variable explanation in the corresponding position.
6. L196: What’s the meaning of ‘R’ here? Is what the author is trying to say actually O?
Answer: Sorry, it is a clerical error. It should be the residual term Ο. We will correct it.
7. L275: ‘SSP470’ should be ‘SSP370’.
Answer: Thank you for your reminding. We will correct it.8. L385: It is obvious from Figure 8 ‘the most region of YRB’ show a two-stage character, but I personally think that the authors could have calculated the proportion of regions to quantify the ‘the most region’.
Answer: Thanks for your insightful suggestion. We acknowledge that our initial analysis lacks a quantitative assessment to specify the applicability of the two-stage response model across the basin. Following your suggestion, we will calculate and quantify the proportion of regions that exhibit this two-stage character and will update the manuscript accordingly to include these results.
9. L417: Δμ is not defined before.
Answer: Thank you for your observation regarding the undefined term on Line 417. The symbol is defined to denote the difference in the mean (μ) values between different drought severity levels within the same period. We will includ this definition clearly in the manuscript to ensure proper understanding of the context and to avoid any potential confusion. We apologize for the oversight and appreciate your attention.
10. Figure 7a: What do the numbers (0.30, 0.54, 0.75,0.34) mean? Can you clarify them in Figure subtitle? What are 101.4%, 14.9% etc.? Please make them more clearly defined in this figure. Figures are self-independent.
Answer: Thank you for your queries regarding Figure 7a. This figure consists of two parts: the stacked chart represents the relative contribution rates of climate change and underlying change to WUE variations under different scenarios, with the percentages on either side indicating the specific values of these contributions; the line graph depicts the average WUE changes across the basin under various scenarios, where the numbers 0.30, 0.54, 0.75, and 0.84 represent the changes in average WUE under respective scenarios. To avoid confusion and enhance clarity, we will unify the colors of the line and the right axis. Additionally, we will includ detailed explanations in the figure subtitle to further clarify these elements.
11. Figure 8: Why did the authors only consider moderate-severe drought and extreme-exceptional drought? Please explain more clearly in the text.
Answer: Thank you for your suggestion regarding our focus on specific drought categories in Figure 8. We primarily followed Yin et. al (2022)'s classification method which resampled the WUE responses under more severe drought events to identify universally applicable patterns. In fact, we excluded drought levels with a TWSA-DSI less than -0.8 with consideration that such mild droughts are unlikely to have a significant impact on the ecosystem's WUE. We consolidated drought categories D1-D4 (Table 2) into two levels to ensure a sufficient sample of events to support our conclusions. This approach has resulted in clearer and more persuasive findings, highlighting the eco-hydrological processes under drought levels that require more urgent attention.
Citation: https://doi.org/10.5194/hess-2024-145-AC2
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