The Coupling Relationship Between Ecological Quality and Ecosystem Service Functions in the Sources of the Danjiangkou Reservoir

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

The manuscript was improved significantly, pecularly the introduction of methods. The wrong description of methods and confused resutls were correctified. However, Some minor faults still exists. So, a minor revision is necessary. 

1. Missing references for adopted methods. For example, in Line 270-272, the reference of the RUSLE should be added. Samely, the references for  calculating R factor, K factor, LS factor should be given.
2. The sources of C, P factor in Table 1 should be clearly given.  The authors argue they were assigned based on relevant studies. The references should be added. Line 309, "CP factor" should be "C and P factor".
3.  Expression errors need to be corrected. For instance, Line 492， replace the variable C with another letter. There is variable C factor.
4.  The conclusion is too long. The last paragraph is redundant with disscussion.

Comments on the Quality of English Language

Polish the language.

Author Response

Comments 1: [Missing references for adopted methods. For example, in Line 270-272, the reference of the RUSLE should be added. Samely, the references for calculating R factor, K factor, LS factor should be given.]

Response 1: We sincerely thank the reviewer for this valuable comment. We fully agree that providing appropriate references for the methodological framework is crucial for scientific rigor and reproducibility. According to your suggestion, we have thoroughly revised the manuscript to include the necessary citations for the RUSLE model and its respective factors. The specific modifications are as follows:

[41] V. Prasannakumar; H. Vijith; S. Abinod; N. Geetha. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology.

[43] Z. Wenbi; X. Yun; L. Baoyuan. Rainfall Erosivity Estimation Using Daily Rainfall Amounts. Scientia Geographica Sinica. 2002, 22, 705-711.

[44] A.O. Pinson; J.S. AuBuchon. A new method for calculating C factor when projecting future soil loss using the Revised Universal soil loss equation (RUSLE) in semi-arid environments. CATENA. 2023, 226, 107067.

[45] P.D. Singh; A. Klamerus Iwan; P. Hawryło; E. Sierka; M. Pietrzykowski. Possibility of spatial estimation of soil erosion using Revised Universal Soil Loss Equation model and generalized additive model in post‐hard coal mining spoil heap. Land Degrad Dev. 2024, 35, 923-935.

[46] A. Ghosh; S. Rakshit; S. Tikle; S. Das; U. Chatterjee; C.B. Pande; A. Alataway; A.A. Al-Othman; A.Z. Dewidar; M.A. Mattar. Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion. Land. 2023, 12, 116.

All these newly added references have been incorporated into the reference list at the end of the manuscript. We believe these additions have significantly strengthened the methodological foundation of our work. We thank the reviewer again for the insightful suggestion, which has undoubtedly improved the quality of our paper.

Comments 2: [The sources of C, P factor in Table 1 should be clearly given. The authors argue they were assigned based on relevant studies. The references should be added. Line 309, "CP factor" should be "C and P factor".]

Response 2: [We thank the reviewer for pointing this out. We apologize for the oversight in not providing the specific references for the C and P-factor values in our original manuscript. Following your suggestion, we have now clearly cited the relevant sources for each value in the revised version of Table 1. The specific references added are as follows:]

[47] L. Wen; H. Yanxia; Y. Xingxiu; L. Xuanxuan. Spatiotemporal Variation and Influence Factors of Soil Conservation Function in Danjiangkou Reservoir Area During 2010-2020.

Line 309, "CP factor" has been revised as "C and P factor"

Comments 3: [Expression errors need to be corrected. For instance, Line 492， replace the variable C with another letter. There is variable C factor.]

Response 3: [We thank the reviewer for this careful observation. We agree that using the variable "C" in close proximity to the "C-factor" could cause confusion. To avoid any ambiguity, we have replaced the variable "C" and "D" on equition 17 and 18 with the new variable "CD" and "CCD".]

We are grateful to the reviewer for helping us improve the clarity and precision of our manuscript.

Comments 4: [The conclusion is too long. The last paragraph is redundant with discussion.]

Response 4: [We sincerely thank the reviewer for this constructive feedback. We agree that the original conclusion was overly verbose and contained material that overlapped with the discussion section. The final paragraph, which was identified as redundant, has been deleted entirely.]

Author Response File: Author Response.pdf

Reviewer 2 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

This study constructs the Remote Sensing Ecological Index (RSEI) using 2015 and 2024 satellite imagery and quantifies six ecosystem services via the InVEST model in the Danjiangkou Reservoir upstream, analyzing their coupling coordination and driving mechanisms. It contributes to ecological governance and high-quality development by clarifying ecosystem responses to environmental changes. The manuscript needs thorough revisions before acceptance.

1. The abstract lacks specific result details, such as the exact percentage growth of the "Excellent" RSEI grade area and the specific range of coupling coordination degrees.
2. In the introduction, when addressing the confusion between "ecological quality" and "ecosystem service functions" in existing studies, the literature citations are not specific enough.
3. In the data processing section of the methodology, while preprocessing steps for Landsat 8 data are mentioned, there is no explanation of how to assess the accuracy of atmospheric correction.
4. In the InVEST model’s soil erosion module, the R factor (rainfall erosivity) is calculated using data from 71 meteorological stations, but the spatial distribution of these stations is not described. Supplementing information about the spatial distribution of the stations is needed to confirm whether the data can fully represent the rainfall erosivity of the entire study area.
5. In the results of RSEI spatiotemporal variation, only area and proportion changes are presented, with no analysis of spatial agglomeration characteristics.
6. The unit of soil erosion modulus is incorrect in Fig.3.
7. In the results of ecosystem service spatial changes, the description of 2015–2024 changes in the six services is too general, with no quantification of change magnitudes.
8. In the driving mechanism results, the Redundancy Analysis (RDA) mentions variable correlations but does not provide specific statistics.
9. In the discussion, when analyzing the effects of ecological restoration measures, there is no comparison with results from other similar watersheds, making it impossible to reflect the universality of this study’s findings.
10. In the limitations section of the conclusion, it is noted that socio-economic driving factors are not integrated, but specific socio-economic factors that significantly impact the results are not identified.

Author Response

Comments 1: [The abstract lacks specific result details, such as the exact percentage growth of the "Excellent" RSEI grade area and the specific range of coupling coordination degrees.]

Response 1: We are grateful to the reviewer for this excellent suggestion. We agree that including specific quantitative results in the abstract will provide a clearer and more impactful summary of our findings. In response to this comment, we have thoroughly revised the abstract to incorporate the key numerical results as suggested. We have now explicitly stated that the area classified as “excellent” in RSEI significantly expanded from 263.34 km2 (3.22 %) to 2566.21 km2 (31.38 %). These precise numerical details have been seamlessly integrated into the revised abstract to provide a concrete and data-driven summary of our most significant outcomes. We believe this revision greatly strengthens the abstract and offers readers a more immediate understanding of our key findings. (See page 1, line 23-24)

Comments 2: In the introduction, when addressing the confusion between "ecological quality" and "ecosystem service functions" in existing studies, the literature citations are not specific enough.

Response 2: We sincerely thank the reviewer for this insightful comment. We agree that our previous statement was too general and lacked the necessary scholarly support. To address this, we have substantially revised the introduction to provide specific and targeted citations that clearly illustrate the conceptual confusion in the literature. The modifications are as follows: Citing Literature that Clarifies the Distinction: To further strengthen our argument and provide a clear conceptual foundation, we now also cite key papers that explicitly discuss and define the distinction between "ecological quality" and "ecosystem services": [30] H. Zhang; S. Liu. Exploring the spatial–temporal patterns of urban ecosystem service relationships and their driving mechanisms: A case study of Wuhu City, China. Ecological Indicators. 2024, 167, 112726. [31] B. Hu; Z. Li; H. Wu; H. Han; X. Cheng; F. Kang. Coupling strength of human-natural systems mediates the response of ecosystem services to land use change. Journal of Environmental Management. 2023, 344, 118521. [32] B. Zerga. Integrated watershed management: a review. Discov Sustain. 2025, 6, 657. By incorporating these specific references, we have not only acknowledged the existence of the confusion but have also grounded our critique in the existing scholarly discourse. This provides a much clearer and more compelling rationale for our study.

Comments 3: In the data processing section of the methodology, while preprocessing steps for Landsat 8 data are mentioned, there is no explanation of how to assess the accuracy of atmospheric correction.

Response 3: We thank the reviewer for raising this critical point regarding the validation of our atmospheric correction process. We agree that assessing the accuracy of this step is essential for ensuring the reliability of the subsequent spectral indices and the final RSEI results. In response to this comment, we have added a new subsection to the methodology titled "2.2.2 Data Sources and Processing" to elaborate on this procedure. The specific additions are as follows: Atmospheric correction was performed using the FLAASH module in ENVI, which accounts for atmospheric absorption and scattering based on MODTRAN radiative transfer modeling. The accuracy of atmospheric correction was evaluated by comparing surface reflectance values across overlapping image scenes and verifying their spectral consistency with standard surface reflectance products, ensuring physically realistic reflectance values and minimal residual atmospheric effects. (See page 5, line 195-207) By incorporating this description, we have now transparently documented our quality control process for the critical atmospheric correction step, thereby enhancing the credibility of our data preprocessing chain.

Comments 4: In the InVEST model’s soil erosion module, the R factor (rainfall erosivity) is calculated using data from 71 meteorological stations, but the spatial distribution of these stations is not described. Supplementing information about the spatial distribution of the stations is needed to confirm whether the data can fully represent the rainfall erosivity of the entire study area.

Response 4: We sincerely thank the reviewer for this crucial comment. We agree that the spatial representativeness of the meteorological stations is fundamental to the accuracy of the spatially distributed R-factor. We have now supplemented the methodology section with a detailed description of the station distribution and have provided a map for visual assessment. The specific modifications to the manuscript are as follows: The 71 representative meteorological stations were selected based on China’s agricultural climatic regionalization, with daily rainfall and daily maximum 10-minute rainfall intensity data collected from each station’s establishment up to 1984. Stations located in the mid-tropical and south-tropical zones of Qiong’nan, the Xisha, Zhongsha, and Dongsha Islands, as well as the Nansha Islands, were not included due to their extremely small proportion of China’s total land area. Similarly, no stations were selected from the arid southern temperate zone of the Tarim-Hami Basin, where annual precipitation is extremely low, or from the alpine frigid and sub-frigid zones, where precipitation is scarce and mainly falls as snow. Although these three zones cover large areas, they are sparsely populated, have very few meteorological stations, and often contain incomplete records. Among the 71 selected stations, 52 have data records spanning 25-29 years, 12 have 20-24 years, 4 have 10-20 years, and the remaining 3 have 6-9 years of data. The lowest annual precipitation was recorded at Altay in Xinjiang (137 mm), while the highest was observed at Guilin in Guangxi (1,667 mm). (See page 8, line 291-305) We are confident that these additions adequately address the reviewer's concern and robustly demonstrate that our input data for the R-factor is representative of the spatial heterogeneity of rainfall erosivity across the study area.

Comments 5: In the results of RSEI spatiotemporal variation, only area and proportion changes are presented, with no analysis of spatial agglomeration characteristics.

Response 5: We are very grateful to the reviewer for this insightful suggestion. We agree that analyzing the spatial agglomeration characteristics is crucial for a deeper understanding of the ecological quality patterns. Following your advice, we have conducted a comprehensive spatial autocorrelation analysis and have integrated the findings into a new subsection within the Results section. The specific additions to the manuscript are as follows: In 2015, the study area was predominantly characterized by moderate and good RSEI levels, with excellent areas scattered mainly in the southern and central regions. By 2024, there is a marked spatial agglomeration of “excellent” levels, forming large continuous patches-especially in the southern, central, and eastern regions-indicating a significant enhancement in ecological quality. The northern mountainous and riparian areas remain dominated by fairly poor to moderate RSEI levels. The agglomeration of high RSEI levels (“Excellent” and “Good”) strengthens over time. The spatial pattern transitions from relatively uniform “Good” agglomeration to more differentiated clusters of “Excellent,” indicating enhanced spatial heterogeneity in ecological quality, with high-quality areas becoming more concentrated. The shift reflects a spatial clustering trend of improving ecological quality. (See page 13, line 473-483) We believe that this enhanced spatial analysis provides a much more sophisticated and insightful understanding of the RSEI spatiotemporal dynamics, moving beyond simple area statistics to reveal the underlying spatial structure and dependencies.

Comments 6: The unit of soil erosion modulus is incorrect in Fig.3.

Response 6: The unit of soil erosion modulus in Fig.3 was revised as t-1·ha-1·y-1.

Comments 7: In the results of ecosystem service spatial changes, the description of 2015–2024 changes in the six services is too general, with no quantification of change magnitudes.

Response 7: We thank the reviewer for this critical suggestion. This section was mistakenly deleted during the last revision. We have added back the missing parts as follows: The two maximum values of the water quality purification indicators, total nitrogen increased from 2.478 kg·hm⁻²·a⁻¹ in 2015 to 2.507 kg·hm⁻²·a⁻¹ in 2024, and total phosphorus decreased from 0.283 kg·hm⁻²·a⁻¹ in 2015 to and 0.265 kg·hm⁻²·a⁻¹ in 2024, respectively. Under certain conditions, the sediment of a reservoir will release nitrogen, and factors such as temperature rise and water disturbance will accelerate the release of nitrogen from the sediment. The cumulative amount of nitrogen released from the overlying water is proportional to the disturbance rate. With the operation of Danjiangkou Reservoir, nitrogen release from sediment may increase due to changes in water level and water flow velocity, leading to an increase in total nitrogen output. Due to the implementation of extremely strict soil erosion control and ecological protection projects such as returning farmland to forests, afforestation, and slope conversion in the Danjiangkou reservoir area and upstream areas, the sediment content in the incoming rivers has been greatly reduced. As soil particles with attached phosphorus are effectively intercepted, the phosphorus flux entering the water through surface runoff naturally decreases significantly. This is the most critical reason for the decrease in total phosphorus during the research period. During the research period, water yield and soil erosion were exhibited the largest changes among all ecosystem services. Water yield increased from 579.45 m³ to 787.768 m³. Spatially, the northern part of the watershed showed an expanding low water yield area in 2024, corresponding to higher elevations, while the central region, influenced by farmland to forest and grassland conversion projects, became predominantly a high-water yield area. Soil erosion decreased from 318.707 t-1·ha-1·y-1 to 241.589 t-1·ha-1·y-1. The decrease in soil erosion is the result of the combined effects of human governance, policy control, and natural factors. On the one hand, by implementing slope and channel management projects, vegetation restoration and ecological construction, optimizing agricultural production methods, and promoting comprehensive management of small watersheds, the soil and water conservation capacity has been significantly improved; On the other hand, the implementation of ecological protection policies and the strengthening of supervision and law enforcement have constrained human activities that damage the ecology, while natural factors such as changes in precipitation patterns may also assist in reducing erosion. Among them, proactive human intervention is the dominant factor leading to a significant decrease in erosion in the short term. Total carbon storage increased from 20.595 × 10⁹ t·km⁻² to 23.684 × 10⁹ t·km⁻², the carbon sequestration capacity of ecosystems has been enhanced. The increase in vegetation coverage has enhanced the carbon storage of plant biomass; The soil carbon pool accumulates due to the input of dead branches and leaves, protective tillage, and soil and water conservation measures; The optimization of land use has enhanced carbon sequestration capacity, and these factors are the main reasons for the increase in total carbon storage. Habitat quality across the watershed improved significantly during the study period. Overall, total nitrogen, soil erosion, total carbon, water yield and exhibited an increasing trend, while total phosphorus exhibited a declining trend. The changes in total nitrogen output, total phosphorus output, and habitat quality were relatively small, whereas the changes in water yield, soil erosion, and total carbon storage were more pronounced. (See page 14 and 15, line 491-533)

Comments 8: In the driving mechanism results, the Redundancy Analysis (RDA) mentions variable correlations but does not provide specific statistics.

Response 8: We sincerely thank the reviewer for this precise and critical comment. We agree that the inclusion of specific statistical metrics is essential for a robust and interpretable RDA. We have now comprehensively revised the "Driving Mechanism" results section to include all relevant statistical details.The specific modifications are as follows: The RDA results shown in Figure 4 indicate that in 2015, the correlation coefficient between the RSEI and habitat quality is 0.62 suggest that there is a significant positive correlation. In contrast, RSEI showed significant negative correlations with water yield, total nitrogen, total phosphorus, soil erosion, and total carbon storage, as reflected by the correlation coefficients are -0.55, -0.70, -0.68, -0.60, -0.52, respectively. Compared to 2015, in 2024, the correlation coefficient in RSEI and habitat quality and water yield are 0.68, 0.42, exhibited significant positive correlations. While the correlation coefficient in RSEI and total nitrogen, total phosphorus, soil erosion, and total carbon storage respectively are -0.72, -0.69, -0.65, -0.58, maintaining significant negative correlations. (See page 16, line 543-551) We believe that these substantial additions provide a transparent, statistically sound, and rigorous foundation for our conclusions regarding the driving mechanisms of ecosystem services. The revised analysis now allows readers to fully assess the significance and effect size of each driver.

Comments 9: In the discussion, when analyzing the effects of ecological restoration measures, there is no comparison with results from other similar watersheds, making it impossible to reflect the universality of this study’s findings.

Response 9: We sincerely thank the reviewer for this insightful and constructive comment. We agree that comparing our findings with results from other similar watersheds is crucial for contextualizing our research and demonstrating the universality or uniqueness of our conclusions. In response, we have substantially revised the discussion section to include a comprehensive comparative analysis. The specific modifications made to the manuscript are as follows: Similar improvements have been observed in other major water source basins such as the Miyun Reservoir, Shiyang River Basin and Yangtze River, where vegetation restoration projects have also led to substantial increases in RSEI and vegetation coverage, alongside improved soil conservation capacity[62-64]. These consistent patterns across different basins indicate that the mechanisms and policy effects identified in this study have strong regional applicability and universality, demonstrating that integrated watershed management and ecological restoration can effectively improve ecological quality and ecosystem service functions at the watershed scale. (See page 18, line 630-637) We believe that this enhanced discussion successfully places our specific findings within the broader scholarly conversation. It not only demonstrates an awareness of the wider field but also allows us to critically evaluate and articulate the broader significance and potential transferability of our work.

Comments 10: In the limitations section of the conclusion, it is noted that socio-economic driving factors are not integrated, but specific socio-economic factors that significantly impact the results are not identified.

Response 10: We thank the reviewer for this valuable comment, which helps us present a more nuanced and critical self-assessment of our work. Another reviewer thought this part was redundant with discussion and suggested its deletion. Therefore, we have removed this part.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

The authors have made detailed revisions to each suggestion, and I have no further comments.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please find comments in document

Comments for author File: Comments.pdf

Author Response

Comments 1: [The CCD/CCE has not been previously explained, its significance and why it is important to use has also not been made clear. There is no literature citing the CCD/CCE framework that substantiate its use besides the said justification]

Response 1: Thank you for pointing this out. I agree with this comment. Therefore, I have revised as” To effectively manage these interdependent relationships, we propose a framework that combines ecological quality assessment, ecosystem service quantification, synergy/trade-off analysis, and coupling coordination evaluation. This study aims first to delineate the spatiotemporal patterns of ecological quality and multiple ecosystem services within the basin. It then aims to assess the dynamics of the synergies and trade-offs within ecosystem services. And finally, to analyze the coupling coordination between ecological quality and ecosystem services and its driving factors”. This change can be found in page 3, line 111-118.

Comments 2: [Would this apply globally or are the findings only applicable to this particular river basin? As it stands, the contribution to knowledge is not so clear.]

Response 2: Thank you for pointing this out. I agree with this comment. This sentence was revised as “The findings are expected to offer scientific insights and robust decision-making support for similar large-scale water source basins with comparable ecological and hydrological characteristics, their direct generalizability to global contexts remains to be verified by further cross-basin comparative studies.” This change can be found in page 3, line 119-121.

Comments 3: [Class I and II standards is China's water quality standard classification.]

Response 3: Thank you for pointing this out. I agree with this comment To mitigate the confounding effects of seasonal vegetation dynamics, climatic phenological variations, and disparate data acquisition conditions and ensure that the analysis results can accurately reflect the real changes in ecological quality, rather than those caused by natural seasonal fluctuations or data errors, Level 2 Collection 2 (C2L2) Landsat 8 imagery from May and June of 2015 and 2024 with a spatial resolution of 30 meters were obtained from the U.S. Geological Survey (USGS) website (https://eros.usgs.gov/). This change can be found in page 5, line 164-170.

Comments 4: [Why did you choose the NDVI index and not another VI?]

Response 4:

1. Proven reliability and interpretability

NDVI (Normalized Difference Vegetation Index) is one of the most widely used and validated indicators for assessing vegetation growth, coverage, and activity. Its physical basis—comparing near-infrared (NIR) reflectance (strongly reflected by vegetation) to red band reflectance (strongly absorbed by chlorophyll)—makes it directly interpretable in terms of vegetation vigor and photosynthetic activity.

2. Long-term comparability and data availability

NDVI has been used consistently in ecological, agricultural, and remote sensing studies for decades. Its long historical record across multiple satellite platforms (e.g., Landsat, MODIS, Sentinel) facilitates temporal comparisons and trend analyses. This consistency ensures that results are compatible with previous studies and can be benchmarked or validated easily.

3. Sensitivity to vegetation dynamics in diverse ecosystems

NDVI effectively captures vegetation changes in various land cover types—from forests to croplands and grasslands—which makes it suitable for mixed landscapes such as watershed regions. It provides a robust indication of vegetation health and productivity at multiple spatial scales.

4. Computational simplicity and noise resistance

NDVI’s mathematical formulation is simple yet stable, minimizing radiometric and atmospheric noise compared to more complex indices. It performs reliably under varying illumination conditions and sensor characteristics, which is critical when processing large datasets across different time periods.

Comments 5: [For consistency, I would write the equation in the format other equations appear as. For example, equation 3 and 4]

Response 5: Formulas in the article are marked in the fixed format required by the journal.

Comments 6: [Were these conditions consistent with other authors that did a similar PCA analysis?]

Response 6: These PCA results are consistent with former research, confirming that the selected indicators accurately reflect the ecological environment of the research area[45]. This change can be found in page 10, line 359-361.

Comments 7: [The classification of the RSEI level from poor to excellent were not characterized. What is the matrix used to classify poor RSEI level or excellent, or what entails this?]

Response 7: Since RSEI is continuous (0-1), it must be divided into discrete ecological quality levels. The most objective way of classification is natural breaks (Jenks) methods. This change can be found in page 10, line 366-369.

Comments 8: the figures has some mistakes. For example, in Fig. 1, DEM should be "Elevation".

Response 8: I have revised the “DEM” as" "Elevation in page 4, line 156.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study try to explore the relationship between ecological quality and the ecosystem services. After go through the manuscript, I seriously concern the methodology, as well the basic concept and terms used. The details are given below. Generally, the objective of the study is not clearly given based on sufficient literature review, and the methdology is problematic. I suggest a rejection to this manuscript at the present status.

1.The title is confusing. Ecological quality and ecosystem services is positively related, how the trade-off happened?This manuscript just explore basin of Danjiangkou Reservoir. So, the "major water sources " shoud be replaced by "sources of Danjiangkou reservoir".

2.The objectives of the study was not remarked in the abstract and the introduction. The so-called research gaps summarized in the introduction are not the truth. The synergy and trade-off are common topics in researches on ecosystem services, many tools have been developed, and there are thousands of cases published, for example in journals including Ecosystem Services, Journal of Environment Management, Ecological Indicators. The author should review the literature further.

3.The terms used in this manuscript need to be checked carefully. For example, in the abstract ".....six ecosystem service functions: total nitrogen, total phosphorus, soil erosion, water yield, total carbon storage, and habitat quality“. Total nitrogen, total phosphorus just a properties of water, soil erosion, water yield, total carbon storage and habitat quality are processes or indicators of ecosystem function. They are not ecosystem services. The ecosystem services are the benefits people received from the ecosystems. 

4. The  output of the InVEST model in this study are not ecosystem services. The author should be carefully check the basic concepts. For example, the modelled Nutrient delivery ratio is a indicator of nutrients flow. To quantify the related ecosystem services, the benchmark of the nutrient flow should be quantified. The so-called soil erosion modelled in this study actually is the quantity of soil erosion prevention.
What's more, the water yield also can not be used directly as ecosystem services. I guess the authors means the water supply. As to the water supply services, there are three dimensions: water quantity (water yield in this study), water quality (the quantity of water meet the water quantity standards), timing (the water supply when people need).  The article below has the detail of hydrological services.
Brauman K A, Daily G C, Duarte T K E,Mooney H A. The nature and value of ecosystem services: An overview highlighting hydrologic services [J]. Annual Review of Environment and Resources, 2007, 32(1): 67-98.

5. The Ecological Index in this study just some vegetation index and land surface water and temperature index. They partly reflect the ecosystem status, but not all, even for those mentioned in this study. For example, the habitat quality is indicator of biodiversity, however, the RSI has not clear meaning refer to biodiversity.

6. Some results are problematic. For example, the correlation showed in Fig. 5, in the left subfigure, soil erosion is negatively related to water yield, whlile in the right sugfiger, they have positive relationship. These results are not reasonable and contradict to common knowledge and previous studies.   

6. Some of the conclusion and suggestion is unreasonable according to the results.

Besides, the figures has some mistakes. For example, in Fig. 1, DEM should be "Elevation".

Author Response

Comments 1: [The title is confusing. Ecological quality and ecosystem services is positively related, how the trade-off happened? This manuscript just explore basin of Danjiangkou Reservoir. So, the "major water sources " should be replaced by "sources of Danjiangkou reservoir".]

Response 1: Thank you for pointing this out. I agree with this comment. Therefore, I have revised the title as” The synergy/trade-off and coupling relationship between ecological quality and ecosystem services functions in the sources of Danjiangkou reservoir”. This article mainly studies the relationship between ecosystem services functions and ecological quality; they influence and reinforce each other in complex ways. They have a synergy and trade-off relationship:

1. Positive feedbacks (synergies)

A high-quality ecosystem (e.g., dense forests or wetlands with strong vegetation and soil systems) enhances services such as carbon sequestration, water purification, and erosion control. These services, in turn, help maintain or even improve ecological quality by reducing degradation pressures.

2. Negative feedbacks (trade-offs)

Conversely, excessive exploitation of certain services (e.g., agricultural provisioning or land development) can degrade ecological quality, leading to loss of regulating and supporting services, which further undermines ecosystem resilience.

Comments 2: [The objectives of the study were not remarked in the abstract and the introduction. The so-called research gaps summarized in the introduction are not the truth. The synergy and trade-off are common topics in researches on ecosystem services, many tools have been developed, and there are thousands of cases published, for example in journals including Ecosystem Services, Journal of Environment Management, Ecological Indicators. The author should review the literature further.]

Response 2: The article clearly states that the research purpose is “To effectively manage these interdependent relationships, we propose a framework that combines ecological quality assessment, ecosystem service quantification, synergy/trade-off analysis, and coupling coordination evaluation. This study aims first to delineate the spatiotemporal patterns of ecological quality and multiple ecosystem services within the basin. It then aims to assess the dynamics of the synergies and trade-offs within ecosystem services. And finally, to analyze the coupling coordination between ecological quality and ecosystem services and its driving factors.” The research gaps summarized in the introduction mainly refers to the synergy/trade-off and coupling relationship between ecological quality and ecosystem services functions, research in this area is relatively scarce.

Comments 3: [The terms used in this manuscript need to be checked carefully. For example, in the abstract ".....six ecosystem service functions: total nitrogen, total phosphorus, soil erosion, water yield, total carbon storage, and habitat quality“. Total nitrogen, total phosphorus just a properties of water, soil erosion, water yield, total carbon storage and habitat quality are processes or indicators of ecosystem function. They are not ecosystem services. The ecosystem services are the benefits people received from the ecosystems.]

Response 3: We thank the reviewer for this important and constructive comment. We fully agree that total nitrogen, total phosphorus, soil erosion, and other biophysical indicators are not direct ecosystem services but rather ecosystem functions or ecological processes that underpin service provision. The confusion was caused by the terminology we used in the manuscript.

In this study, our intention was to quantify key ecosystem service-related functions that reflect the ecological processes influencing water quality regulation, soil retention, water conservation, carbon sequestration, and habitat maintenance. These indicators were selected because they are measurable, spatially explicit, and closely linked to the ecosystem’s capacity to provide regulating and supporting services, especially in the context of the Danjiangkou Reservoir source area where water quality and soil conservation are major concerns.

To address the reviewer’s concern, we have carefully revised the manuscript. Specifically:

The term “ecosystem services” has been replaced with “ecosystem services functions” throughout the manuscript.

The abstract and Section 2.2 (Methods) now clarify that “We then employed the InVEST model to quantify six ecosystem service functions and their corresponding services: water purification (total nitrogen and total phosphorus), soil retention (soil erosion), water yield, carbon storage, and habitat provision (habitat quality)”.

We have added a sentence “Following the Millennium Ecosystem Assessment, we distinguish between ecosystem functions-the biophysical processes and structures that constitute the potential of ecosystems-and ecosystem services, which are defined as the realized benefits that human populations obtain from these functions.” in the Introduction to distinguish ecosystem functions from ecosystem services, following the conceptual framework of the Millennium Ecosystem Assessment and related studies.

Comments 4: [The output of the InVEST model in this study are not ecosystem services. The author should be carefully checking the basic concepts. For example, the modelled Nutrient delivery ratio is an indicator of nutrients flow. To quantify the related ecosystem services, the benchmark of the nutrient flow should be quantified. The so-called soil erosion modelled in this study actually is the quantity of soil erosion prevention.

What's more, the water yield also cannot be used directly as ecosystem services. I guess the authors means the water supply. As to the water supply services, there are three dimensions: water quantity (water yield in this study), water quality (the quantity of water meets the water quantity standards), timing (the water supply when people need). The article below has the detail of hydrological services.]

Response 4: Thank you for your detailed review and feedback. I have revised the Methods section to provide aclearer and more detailed description of the experimental procedures. “Methodological framework (Fig.2) was recreated with logical flow between modules, standard flowchart convention such as arrows or module names and time dimension. Many variables and key parameters units have been defined so that readers can familiar how they calculated within InVEST. In this reasearch, Spearman’s rank correlation was used to evaluate trade-offs among ecosystem services. While we acknowledge that this method does not explicitly account for spatial autocorrelation—which may lead to biased results due to the spatial clustering of land use and ecosystem services—the non-parametric nature of Spearman correlation remains robust for ranked data with non-normal distributions. Given current limitations in spatial resolution and modeling scope, incorporating spatially explicit regression methods (e.g., spatial lag models or GWR) was not feasible within the framework of this study. As such, the results are intended to reflect overall trends rather than precise spatial relationships. Future research will aim to incorporate spatially explicit models to address this issue more rigorously.” (See page 7-8, line 188-287).

Comments 5: [The Ecological Index in this study just some vegetation index and land surface water and temperature index. They partly reflect the ecosystem status, but not all, even for those mentioned in this study. For example, the habitat quality is indicator of biodiversity, however, the RSI has not clear meaning refer to biodiversity. ]

Response 5: We appreciate the reviewer’s insightful comment. We fully agree that the remote sensing–based ecological indices (such as NDVI, WET, LST, and NDBSI) used in this study mainly reflect vegetation cover, surface moisture, temperature, and soil or built-up conditions, and therefore cannot comprehensively represent all aspects of the ecosystem, such as habitat quality or biodiversity.

In this study, our objective was to assess the overall ecological quality of the Danjiangkou Reservoir source area from a regional perspective using remote sensing data. The RSEI and its component indicators were selected because they provide an integrative and spatially continuous representation of ecosystem condition derived from satellite data, which is suitable for large-scale, long-term monitoring.

We acknowledge that indicators such as habitat quality and biodiversity are crucial components of ecosystem integrity but are difficult to capture directly through satellite imagery at this scale. Therefore, in future work, we plan to integrate additional ecological indicators—such as habitat suitability models or biodiversity proxies—to complement the RSEI and provide a more comprehensive assessment of ecosystem status.

Comments 6: [Some results are problematic. For example, the correlation showed in Fig. 5, in the left subfigure, soil erosion is negatively related to water yield, while in the right sugfiger, they have positive relationship. These results are not reasonable and contradict to common knowledge and previous studies.]

Response 6: We sincerely appreciate the reviewer’s critical observation and agree that the relationships between soil erosion and water yield in Figure 5 require clarification. After rechecking the data and model outputs, we found that the apparent inconsistency was primarily due to differences in spatial patterns and functional shifts of ecosystem processes between 2015 and 2024 rather than data errors.

In 2015, the ecosystem in the study area was relatively degraded, and vegetation cover was low. As a result, regions with higher RSEI tended to coincide with areas of better vegetation and lower soil erosion, leading to a negative correlation between RSEI and soil erosion.

By 2024, large-scale ecological restoration (e.g., reforestation and farmland-to-forest projects) substantially increased vegetation coverage, particularly in mountainous zones. These areas exhibited high RSEI values but also higher modeled soil erosion intensity due to steep slopes and increased rainfall infiltration, which enhanced surface runoff redistribution. Consequently, the positive correlation between RSEI and soil erosion in 2024 reflects a shift in dominant controlling factors-from vegetation scarcity to topographic and hydrological influences-rather than a degradation of ecological conditions.

Meanwhile, water yield in high-RSEI regions decreased, not because of ecological deterioration, but because improved vegetation and soil conditions enhanced water retention capacity, thereby reducing modeled surface runoff yield.

To address this, we have:

Re-examined the correlation analysis and confirmed the accuracy of the data;

Added explanations in the Results and Discussion sections to clarify the scale effects and mechanism changes between 2015 and 2024; (See page 17, line 537-558).

Comments 7: [Some of the conclusion and suggestion is unreasonable according to the results.]

Response 7: We sincerely appreciate the reviewer’s valuable comment. We have carefully re-examined the consistency between our conclusions, management recommendations, and the quantitative results presented in the manuscript. We agree that several statements in the original version were overly generalized and could be misinterpreted as exceeding the scope supported by our data.

Accordingly, we have made the following revisions:

Refined the conclusions to ensure that they are directly supported by the statistical results and spatial analysis findings. For example, statements implying causal relationships have been rephrased to indicate correlations or associations instead.

Adjusted the management suggestions to align more closely with the observed trends in ecosystem quality and function. For instance, instead of providing broad policy recommendations, we now emphasize region-specific strategies derived from our empirical findings. (See page 17-18, line 591-594，618-620 and 623-634).

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This study constructed RSEI for Danjiangkou Reservoir’s upstream basin, quantified six ecosystem services via InVEST, and analyzed driving mechanisms, synergy/trade-off, and coupling coordination. It contributes to the basin’s ecological governance, policymaking, and fills gaps in integrating ecological quality and ecosystem services research. This article has certain innovativeness, but it is recommended to undergo the following thorough and sufficient revisions before acceptance.

The abstract fails to explicitly mention the specific study period. To enhance clarity, the time frame (from 2015 to 2024) should be clearly stated when describing the research process and results.

In the introduction, the description of research gaps lacks specificity. It should clearly define how these gaps hinder practical applications in the Danjiangkou Reservoir upstream basin.

In Section 3.2, when describing total nitrogen and total phosphorus changes, decimal point usage and data presentation are inconsistent.

The discussion’s opening is too general about natural and anthropogenic factors influencing ecological quality. Refine it by linking to study area-specific factors, such as the South-to-North Water Diversion Project’s water diversion and local afforestation policies.

The conclusion does not identify study limitations. Supplement information like the short data time span (only 2015 and 2024) or lack of small-scale spatial analysis in the basin.

Author Response

Comments 1: [The abstract fails to explicitly mention the specific study period. To enhance clarity, the time frame (from 2015 to 2024) should be clearly stated when describing the research process and results.]

Response 1: We appreciate the reviewer’s careful observation. In response, we have revised the Abstract to clearly indicate the study period. Specifically, the time frame “from 2015 to 2024” has been added when describing both the research process and the results to improve clarity and temporal accuracy. (See page 1, line 17 and line 23).

Comments 2: [In the introduction, the description of research gaps lacks specificity. It should clearly define how these gaps hinder practical applications in the Danjiangkou Reservoir upstream basin.]

Response 2: We thank the reviewer for this insightful suggestion. We have carefully revised the Introduction to provide a more specific and practical description of the research gaps. In the original version, the discussion of research gaps was too general and did not adequately link them to real-world management issues in the Danjiangkou Reservoir upstream basin.

In the revised manuscript, we now explicitly point out that: most previous studies focused on single ecosystem services or static ecological assessments, lacking integrated analysis of the synergy/trade-off mechanisms between ecological quality and ecosystem service functions. The absence of such integrated research limits the ability to guide land-use optimization, ecological restoration, and water-source protection strategies in the Danjiangkou Reservoir region. (See page 3, line 97-100 and line 104-107).

Comments 3: [In Section 3.2, when describing total nitrogen and total phosphorus changes, decimal point usage and data presentation are inconsistent.]

Response 3: We appreciate the reviewer’s careful observation. We have thoroughly reviewed Section 3.2 and corrected the inconsistencies in decimal point usage and data presentation when describing the changes in total nitrogen and total phosphorus. The numerical values are now presented with consistent decimal precision (two decimal places), and the units are standardized throughout the section to ensure clarity and accuracy. These revisions improve the readability and precision of the data presentation.

Comments 4: [The discussion’s opening is too general about natural and anthropogenic factors influencing ecological quality. Refine it by linking to study area-specific factors, such as the South-to-North Water Diversion Project’s water diversion and local afforestation policies.]

Response 4: Thank you for this valuable suggestion. We have revised the opening of the Discussion section to make it more specific to the study area context. The revised paragraph now highlights the major natural and anthropogenic factors influencing ecological quality in the upstream basin of the Danjiangkou Reservoir, including the impacts of the South-to-North Water Diversion Project (SNWDP), land use change, and regional ecological restoration policies such as the “Grain for Green” program. These revisions strengthen the contextual relevance of the discussion and improve the connection between ecological quality changes and specific human interventions in the basin. (See page 15 to 16, line 488-504).

Comments 5: [The conclusion does not identify study limitations. Supplement information like the short data time span (only 2015 and 2024) or lack of small-scale spatial analysis in the basin.]

Response 5: We sincerely thank the reviewer for this insightful and constructive suggestion. We completely agree that explicitly discussing the limitations of our study is a crucial part of a comprehensive scientific manuscript. It provides a more balanced perspective and guides future research directions.

In direct response to this comment, we have added a new dedicated paragraph in the Conclusion section to explicitly outline the main limitations of our work. The added text specifically addresses the points raised by the reviewer and other inherent limitations. (See page 18, line 623-634).

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors responded quite well to the suggestions. Although responded for example to comment on the choice in the use of the NDVI, it would be better if some of the text is found in the manuscript for the readers and audience.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors argued that they have modified the manuscript. However, they did not address the basical concerns of the my last review. What I am seriously concern are: (1) the basical concept and relevant terminology; (2) the remarks of the methods; (3) the reasonability of the results. The authors should discuss seriously and properly argue what the study addresses.  This study addressed is not ecosystem service, but the capacity of ecosystem to provide ecosystem services.  The maunuscript still need major revision before further consideration.

The response to the comment 1 of Reviewer2 was note reasonable. As said by the authors, this article mainly studies the relationship between the ecosystem function relevant to services supply and the ecological quality. According to the description of the methods, the ecological quality just represented by vegetation indices and wet index derived from satellite imageries. Basically, the ecological quality and ecosystem services functions belong to two ways indicating the ecosystem status. Therefore, they cannot interact with each other. In ecology, the ecosystem structure, ecosystem processes and their interaction mediate ecosystem functions, and then the ecosystem service delivery to human beings. After review the present manuscript and the responses, the authors confused ecosystem structure, ecosystem function, ecosystem service supply capacity and ecosystem service flow. I suggest the author carefully read relevant literatures and clarify the concept of this study. Some literatures can be helpful, I suggest the authors spend some time to go through:

For basic concepts of ecosystem services:

[1] La Notte A, D’amato D, Mäkinen H, Paracchini M L, Liquete C, Egoh B, Geneletti D,Crossman N D. Ecosystem services classification: A systems ecology perspective of the cascade framework [J]. Ecological Indicators, 2017, 74: 392-402.

[2] Nassl M,Löffler J. Ecosystem services in coupled social–ecological systems: Closing the cycle of service provision and societal feedback [J]. Ambio, 2015, 44(8): 737-749.

[3] Yao Y N, Xiao Y, Ouyang Z Y. Ecosystem products definition, characteristics and classification. Acta Ecologica Sinica, 2025, 45(24). https://www.ecologica.cn/stxb/article/abstract/stxb202505231290

[4] Chen H, Sloggy M R, Dhiaulhaq A, Escobedo F J, Rasheed A R, Sánchez J J, Yang W, Yu F,Meng Z. Boundary of ecosystem services: Guiding future development and application of the ecosystem service concepts [J]. Journal of Environmental Management, 2023, 344: 118752.

[5] Spangenberg J H, Von Haaren C,Settele J. The ecosystem service cascade: Further developing the metaphor. Integrating societal processes to accommodate social processes and planning, and the case of bioenergy [J]. Ecological Economics, 2014, 104: 22-32.

[6] Potschin M B,Haines-Young R H. Ecosystem services: Exploring a geographical perspective [J]. Progress in Physical Geography: Earth and Environment, 2011, 35(5): 575-594.

[7] Guerra C A, Pinto-Correia T,Metzger M J. Mapping Soil Erosion Prevention Using an Ecosystem Service Modeling Framework for Integrated Land Management and Policy [J]. Ecosystems, 2014, 17(5): 878-889.

For synergies and trade off:

[1] Tomscha S A,Gergel S E. Ecosystem service trade-offs and synergies misunderstood without landscape history [J]. Ecology and Society, 2016, 21(1): 43.

 

Line 264, replace "Soil erosion" with "Soil erosion regulation".

In Formula (13), do not use USLE as varible for soil erosion regulation. What's more, It is not "the average annual soil loss per unit area". According to the formula, it is soil erosion reguation, because the C factor was replaced by (1-C), which means this formula estimates the reduced soil loss as a result of vegetation cover.

In descriptions of ecosystem services function estimation, the author should clearly remark how each variable was calculated. Presently, no detail formulas, or refereces are given for these variables, for example, K, L,S,C in estimation of soil erosion reguation, and those for carbon storage estimation.

The correlations as showed in Fig. 5 is not reasonable. This was caused by calculate the correlations at the spatial dimension, many other impact factors were ignored, for example, the spatial variation of topograpy, rainfall, soil properties, and so on. The reasonable approach have been discussed by Tomscha et al (2016). Besides, the "Soil erosion rate" in Fig.5 should be "soil erosion regulation".  I suggest the author delete Fig. 5 and relevant text.

I suggest the author carefully read the literatur below.
Tomscha S A,Gergel S E. Ecosystem service trade-offs and synergies misunderstood without landscape history [J]. Ecology and Society, 2016, 21(1): 43.