Monitoring Water Area Dynamics in Kashgar (2003–2023) Using Multi-Source Remote Sensing Data
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis manuscript offers a timely and methodologically sound analysis of surface water dynamics in the Kashgar region over a 20-year period. By leveraging multi-source remote sensing data (Landsat and Sentinel-2), the authors successfully map and analyze spatiotemporal changes in both seasonal and permanent water bodies, while identifying climate and anthropogenic drivers of such changes. The use of the Random Forest classifier, validation metrics, and statistical analyses (Theil-Sen, Mann-Kendall tests, and kernel density) demonstrates methodological rigor. The study’s insights have important implications for sustainable water resource management in arid regions. However, some areas require improvement, particularly in the interpretation of correlations, the clarity of data visualization references, and the discussion of uncertainty. Below is a section-by-section review with recommendations.
The introduction provides an appropriate background to the study, clearly outlining the importance of monitoring surface water bodies in arid zones such as Kashgar, and the implications for ecosystem stability, agricultural productivity, and policy-making. The literature review is detailed and contextualizes the current research within a broader body of work on water monitoring using remote sensing. However, while prior studies are well cited, the novelty of this specific study is not sufficiently emphasized. It would be beneficial for the authors to clearly state how their research addresses specific limitations of previous efforts—whether in terms of spatial resolution, temporal continuity, or methodological innovation. Moreover, the objectives of the paper, while implied throughout, would benefit from a more concise statement toward the end of the introduction section.
The methodological framework of the study is comprehensive and well-structured, integrating remote sensing data processing, classification algorithm evaluation, and temporal trend analysis. The use of Random Forest (RF) as the preferred classification method is adequately justified through comparative performance metrics against other classifiers (e.g., SVM, Mahalanobis Distance, Maximum Likelihood). The combination of NDWI and EVI indices to minimize misclassification caused by shadow effects in mountainous terrain is appropriate and well-implemented. However, certain methodological choices require additional elaboration. For example, while the rationale for using RF is empirically supported, the study could further benefit from a discussion of why RF outperforms other classifiers in this specific environmental context. Additionally, although the use of water inundation frequency (WIF) to distinguish between seasonal and permanent water bodies is well explained, the decision to use the 25th and 75th percentile thresholds appears somewhat arbitrary and would benefit from justification—perhaps by referencing precedent studies or cross-validation procedures. Finally, the authors effectively combine non-parametric statistical tools (Theil-Sen estimator, Mann-Kendall test) with visual and spatial analyses (kernel density), although these analytical steps could be better tied back to the specific research objectives.
The results are generally clear, logically presented, and supported by relevant figures and tables. The temporal analysis of water area from 2003 to 2023 is particularly insightful, capturing not only interannual variability but also distinctions between permanent and seasonal water bodies. The identification of a statistically significant downward trend in total and seasonal water areas, coupled with a slight increase in permanent water bodies, is important and well-supported by trend statistics. The spatial analysis further reveals nuanced insights into clustering and the distributional shifts of lakes and rivers, although the referencing of figures could be more precise throughout the section to help the reader better follow the argument. For example, transitions between decadal maps and corresponding kernel density figures could be accompanied by stronger explanatory links within the text. The temporal analysis of climatic variables—precipitation, temperature, and evapotranspiration—is thorough, though the interpretation of weak correlations (e.g., R² < 0.35) might be overstated in terms of causality. It is commendable that the authors acknowledge these limitations in the discussion, but care should be taken not to overstate the explanatory power of such statistical relationships.
The discussion section does a good job of synthesizing results and linking them back to the broader socio-environmental context of Kashgar. The identification of precipitation in surrounding mountainous regions (Taxkorgan and Yecheng Counties) as a key driver is well-founded and supported by statistical evidence. Additionally, the authors appropriately acknowledge that socioeconomic development, land conversion, and infrastructure projects contribute to long-term water area shrinkage. However, the discussion could benefit from a more detailed critique of the limitations inherent in satellite-based water detection, especially concerning mixed pixels, cloud cover, and the accuracy of seasonal classification. While a subsection is dedicated to uncertainties, it remains too brief. The authors could expand this section to include discussions on the limitations of MODIS-based evapotranspiration estimates or the effect of spectral confusion in arid environments. Also, while the discussion acknowledges weak correlations with temperature and evapotranspiration, it misses an opportunity to explore more complex interactions or propose hypotheses for future research. Additionally, more attention could be given to the lag effects between precipitation events and surface water response, which is particularly relevant in glacier-fed hydrological systems.
The conclusion is a good summary of the key findings, stating that total and seasonal water areas in Kashgar have generally declined over the past two decades, while permanent water bodies have increased slightly. The identification of precipitation (especially in adjacent counties) as the primary driver, with temperature as a secondary factor, is clearly stated. The conclusion effectively conveys the implications for regional water management and ecological planning. The conclusion would be strengthened by highlighting concrete recommendations for policy or future research, especially in terms of data integration (e.g., coupling remote sensing with hydrological models), addressing data gaps, or improving detection accuracy. Moreover, the authors should briefly reiterate the methodological strengths of the study—such as the combination of RF classification and long-term time series analysis—to emphasize the contribution to the literature.
Author Response
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Response to Reviewer 1 Comments
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1. Summary |
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Thank you for your careful review and valuable comments on our paper. We are very grateful for your affirmation of our research methods and results, and have carefully considered all your suggestions. We have made corresponding revisions to the manuscript in response to your comments. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
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2. Questions for General Evaluation |
Reviewer’s Evaluation |
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Does the introduction provide sufficient background and include all relevant references? |
Yes/Can be improved/Must be improved/Not applicable |
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Are all the cited references relevant to the research? |
Yes/Can be improved/Must be improved/Not applicable |
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Is the research design appropriate? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the methods adequately described? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the results clearly presented? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the conclusions supported by the results? |
Yes/Can be improved/Must be improved/Not applicable |
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3. Point-by-point response to Comments and Suggestions for Authors |
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Comments 1: [The introduction provides an appropriate background to the study, clearly outlining the importance of monitoring surface water bodies in arid zones such as Kashgar, and the implications for ecosystem stability, agricultural productivity, and policy-making. The literature review is detailed and contextualizes the current research within a broader body of work on water monitoring using remote sensing. However, while prior studies are well cited, the novelty of this specific study is not sufficiently emphasized. It would be beneficial for the authors to clearly state how their research addresses specific limitations of previous efforts—whether in terms of spatial resolution, temporal continuity, or methodological innovation. Moreover, the objectives of the paper, while implied throughout, would benefit from a more concise statement toward the end of the introduction section.]
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Response 1: [Thank you very much for your affirmation of the research background and significance in the introduction. Based on your opinion, we fully realize the lack of innovative discussion in this study, so we discussed the problems of surface water extraction, such as the single image, and reflected the advantages of multi-source remote sensing images. In addition, in response to your valuable opinions, we have supplemented the research objectives at the end of the introduction.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ Surface water area exhibits significant interannual and intra-annual variability. Consequently, delineating water bodies solely based on single remote sensing images introduces considerable uncertainty [13]. This uncertainty stems from two primary factors. First, seasonal fluctuations in water extent make it challenging to capture the full range of annual variability using a single image acquisition. Determining the optimal acquisition time to meet specific research objectives is also problematic, as images acquired during the rainy season will reflect maximum water extent, whereas those acquired during the dry season will primarily depict permanent water bodies. To mitigate this uncertainty, utilizing a full-year time series of imagery is recommended. Second, interannual variability in water area can further complicate trend analysis. Even when using images acquired on the same date across multiple years, accurately capturing long-term trends can be difficult, potentially leading to inconsistencies in both water area and the number of water bodies [14]. Accurately determining the temporal position of a single image within a year, as well as the corresponding temporal position of images acquired on the same date in different years, is crucial for minimizing uncertainty. Therefore, a comprehensive understanding of surface water area dynamics necessitates the analysis of a substantial dataset of remote sensing imagery spanning multiple seasons and years. – 2, 2, and 68-84. Our findings provide valuable insights for policymakers and stakeholders to formulate effective water resource management strategies and promote sustainable development, ultimately contributing to the improvement of the local ecological environment. – 3,1, and 107-109] |
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Comments 2: [The methodological framework of the study is comprehensive and well-structured, integrating remote sensing data processing, classification algorithm evaluation, and temporal trend analysis. The use of Random Forest (RF) as the preferred classification method is adequately justified through comparative performance metrics against other classifiers (e.g., SVM, Mahalanobis Distance, Maximum Likelihood). The combination of NDWI and EVI indices to minimize misclassification caused by shadow effects in mountainous terrain is appropriate and well-implemented. However, certain methodological choices require additional elaboration. For example, while the rationale for using RF is empirically supported, the study could further benefit from a discussion of why RF outperforms other classifiers in this specific environmental context. Additionally, although the use of water inundation frequency (WIF) to distinguish between seasonal and permanent water bodies is well explained, the decision to use the 25th and 75th percentile thresholds appears somewhat arbitrary and would benefit from justification—perhaps by referencing precedent studies or cross-validation procedures. Finally, the authors effectively combine non-parametric statistical tools (Theil-Sen estimator, Mann-Kendall test) with visual and spatial analyses (kernel density), although these analytical steps could be better tied back to the specific research objectives.]
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Response 2: [Thank you very much for your affirmation of the research method framework. Based on your opinion, we fully realize the insufficiency of this study in discussing the superiority of RF over other classifiers in specific environments, so we added a discussion on method comparison in 4.1. First, we analyze the advantages of RF compared with Mahalanobis distance method, minimum distance method and maximum likelihood method based on indicators such as overall accuracy and Kappa coefficient, and then demonstrate the advantages of RF in Kashgar area by using the fact that the data preprocessing process of support vector machine is relatively complex and not suitable for long time series and large-scale water body information extraction. In addition, in response to your valuable suggestions, we have used 25% and 75% as the segmentation thresholds for water body frequency based on the literature [Zou, Zhenhua, et al. "Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016." Proceedings of the National Academy of Sciences 115.15 (2018): 3810-3815.] and [Olthof, Ian. "Mapping seasonal inundation frequency (1985–2016) along the St-John River, New Brunswick, Canada using the Landsat archive." Remote Sensing 9.2 (2017): 143.]] Thank you for pointing this out. We agree with this comment. Therefore, we have…. [4.1. Water Extraction Methodologies in the Kashgar Region This study evaluated the performance of five water body extraction methods: Mahalanobis distance, minimum distance, maximum likelihood, support vector machine (SVM), and random forest (RF) algorithms, within the Kashgar region. Results indicated that the Mahalanobis distance, minimum distance, and maximum likelihood methods yielded suboptimal overall accuracy, user accuracy, and Kappa coefficients, primarily due to misclassification of water bodies. These methods exhibited limitations in distinguishing water bodies from spectrally similar features such as shadows and glaciers. While SVM demonstrated relatively higher accuracy, its requirement for feature normalization during data preprocessing rendered it computationally intensive for the study's extensive temporal and spatial scales. In contrast, the RF algorithm achieved a favorable balance between accuracy and processing efficiency, proving to be the most suitable method for large-scale water body extraction in the Kashgar region. – 16, 1, and 536-548. To mitigate noise artifacts arising from cloud cover, shadowing, and resolution constraints, the 25th and 75th percentiles of water inundation frequency (WIF) are adopted as primary segmentation thresholds, consistent with established methodologies [6] [38]. Pixels with WIF values below the 25th percentile were classified as non-water bodies. Pixels exceeding the 25th percentile were further categorized into two classes: permanent water bodies (characterized by year-round presence and insensitivity to seasonal variations, with WIF values ranging from 0.75 to 1) and seasonally fluctuating water bodies (exhibiting inundation extent variations across seasons, with WIF values ranging from 0.25 to 0.75). – 6,1, and 237-245] |
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Comments 3: [The results are generally clear, logically presented, and supported by relevant figures and tables. The temporal analysis of water area from 2003 to 2023 is particularly insightful, capturing not only interannual variability but also distinctions between permanent and seasonal water bodies. The identification of a statistically significant downward trend in total and seasonal water areas, coupled with a slight increase in permanent water bodies, is important and well-supported by trend statistics. The spatial analysis further reveals nuanced insights into clustering and the distributional shifts of lakes and rivers, although the referencing of figures could be more precise throughout the section to help the reader better follow the argument. For example, transitions between decadal maps and corresponding kernel density figures could be accompanied by stronger explanatory links within the text. The temporal analysis of climatic variables—precipitation, temperature, and evapotranspiration—is thorough, though the interpretation of weak correlations (e.g., R² < 0.35) might be overstated in terms of causality. It is commendable that the authors acknowledge these limitations in the discussion, but care should be taken not to overstate the explanatory power of such statistical relationships.] |
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Response 3: [Thank you very much for your affirmation of the results and charts of this study. Based on your opinions, we fully realize the shortcomings of this study in the analysis of influencing factors. We introduced regional climate as one of the influencing factors for correlation analysis. The results show that the correlation coefficients between precipitation in Taxkorgan County and Yecheng County and the change of water area in Kashgar area are 0.5687 and 0.5461 respectively, which fully demonstrates that the correlation is high.] Thank you for pointing this out. We agree with this comment. Therefore, we have…. [The source of surface water recharge in Kashgar remains a subject of ongoing debate within the scientific community, with no definitive consensus reached. This study aims to elucidate the relationship between surface water recharge in Kashgar and regional climate change by investigating the correlation between changes in Kashgar's water area and climatic factors in its surrounding regions. This analysis seeks to provide a scientific foundation for a deeper understanding of the region's hydrological cycle characteristics. Given Kashgar's topography, which is characterized by higher elevations in the southwest and lower elevations in the northeast, with most rivers and lakes originating from the southwestern mountainous areas and distributed along the slopes, this study selected annual average precipitation data from Taxkorgan County and Yecheng County, located in Kashgar's southwestern mountainous region. The objective is to analyze the potential relationship between the trends in these climatic variables and changes in Kashgar's water area. Figure 14(a) reveals a statistically significant positive correlation between changes in Kashgar's surface water area and the average annual precipitation in Taxkorgan County (R² = 0.5687, p = 2.4 × 10-33 < 0.05). This strong correlation indicates that precipitation in Taxkorgan County is a significant driver of surface water area fluctuations in Kashgar. Similarly, Figure 14(b) demonstrates a significant positive correlation between changes in Kashgar's surface water area and average annual precipitation in Yecheng County (R² = 0.5461, p = 1.2 × 10-33 < 0.05). This finding further supports the conclusion that precipitation in Yecheng County plays a crucial role in influencing surface water dynamics in Kashgar. This study also investigated the relationship between changes in Kashgar's water area and average annual temperature and evaporation in Taxkorgan and Yecheng Counties. However, these analyses did not reveal statistically significant correlations (p > 0.05), suggesting that temperature and evaporation in these surrounding areas are not primary drivers of surface water area fluctuations in Kashgar. In contrast, precipitation in the surrounding areas emerged as the key factor influencing changes in Kashgar's water area. – 15, 1, and 505-533.]
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Comments 4: [The discussion section does a good job of synthesizing results and linking them back to the broader socio-environmental context of Kashgar. The identification of precipitation in surrounding mountainous regions (Taxkorgan and Yecheng Counties) as a key driver is well-founded and supported by statistical evidence. Additionally, the authors appropriately acknowledge that socioeconomic development, land conversion, and infrastructure projects contribute to long-term water area shrinkage. However, the discussion could benefit from a more detailed critique of the limitations inherent in satellite-based water detection, especially concerning mixed pixels, cloud cover, and the accuracy of seasonal classification. While a subsection is dedicated to uncertainties, it remains too brief. The authors could expand this section to include discussions on the limitations of MODIS-based evapotranspiration estimates or the effect of spectral confusion in arid environments. Also, while the discussion acknowledges weak correlations with temperature and evapotranspiration, it misses an opportunity to explore more complex interactions or propose hypotheses for future research. Additionally, more attention could be given to the lag effects between precipitation events and surface water response, which is particularly relevant in glacier-fed hydrological systems.]
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Response 4: [Thank you very much for your affirmation of the impact of some socio-economic aspects on the change of water area in Kashgar region discussed in this study. Based on your opinion, we fully realize that this study has not discussed enough about the accuracy of multi-source remote sensing images. This revision discussed the limitation of high-resolution images due to the long revisit period, and explained that subsequent research can be combined with ground data to improve the accuracy of water extraction; this study is based on the limitations of MODIS calculation of evaporation you mentioned. This discussion explains that Kashgar is an arid area, and it is difficult to distinguish between soil evaporation and vegetation transpiration due to low vegetation coverage, and proposes that subsequent research can be combined with drones or Sentinel-2 for more accurate evaporation calculations; Regarding the glacier recharge you mentioned, this discussion explains that the model accuracy should be improved in subsequent research to better distinguish between ice and snow and water bodies, and fully explain the impact of glacier recharge on precipitation. star border] Thank you for pointing this out. We agree with this comment. Therefore, we have…. [Due to the extensive temporal and spatial scale of this study, the utilization of high spatial resolution satellite imagery, while desirable, was constrained by the limitations of revisit frequency, hindering the continuous monitoring of dynamic water body changes. The 30m spatial resolution employed in this study resulted in the omission of narrow water bodies, such as ponds and ditches. Future research should integrate optical imagery with ground-based observations to enhance the accuracy of water body delineation. Given the arid nature of the Kashgar region, characterized by complex oasis and desert landscapes, MODIS-derived evapotranspiration estimates may exhibit inherent biases. The sparse vegetation cover in arid environments complicates the differentiation between soil evaporation and vegetation transpiration. Subsequent studies should leverage unmanned aerial vehicles (UAVs) or Sentinel-2 data for more precise evapotranspiration estimations. This study encountered challenges in effectively distinguishing ice and snow from water bodies. Future research should refine model accuracy and comprehensively analyze the influence of ice and snow melt on local water area variations. – 16,5, and 566-580.] |
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We believe these revisions have significantly improved the quality and clarity of the paper. Thank you again for your valuable comments and suggestions, and we look forward to your further feedback.
Sincerely.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for Authors1. Figure 13 and its approach should be placed in the results section.
2. Item 4.3 addresses the absence of hydrochemical analyses, but other factors were also not included in the study that may be significant, such as an analysis of land use and occupation and the dynamics between urbanized and agricultural areas.
3. Validation with field data is another missing factor; data on reservoir levels could be collected to compare and validate the analyses performed, providing a more robust and reliable research design.
4. A significant p may indeed indicate some reliability in the results, but conclusions should be based on more statistical parameters. The inclusion of additional analyses in the conclusion could strengthen the interpretation of the data and provide a more comprehensive view of the relationships observed in the study. The results indicate a statistically significant correlation, but with low explanatory power for temperature and precipitation (R² < 0.35, R² < 0.25), which suggests that other factors, possibly not considered in the study, are influencing the observed variations. I recommend that the text discuss these limitations in more depth and, if possible, consider additional variables.
Author Response
For research article
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Response to Reviewer 2 Comments
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1. Summary |
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Thank you for your careful review and valuable comments on our paper. We are very grateful for your affirmation of our research methods and results, and have carefully considered all your suggestions. We have made corresponding revisions to the manuscript in response to your comments. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
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2. Questions for General Evaluation |
Reviewer’s Evaluation |
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Does the introduction provide sufficient background and include all relevant references? |
Yes/Can be improved/Must be improved/Not applicable |
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Are all the cited references relevant to the research? |
Yes/Can be improved/Must be improved/Not applicable |
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Is the research design appropriate? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the methods adequately described? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the results clearly presented? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the conclusions supported by the results? |
Yes/Can be improved/Must be improved/Not applicable |
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3. Point-by-point response to Comments and Suggestions for Authors |
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Comments 1: [Figure 13 and its approach should be placed in the results section.]
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Response 1: [Thank you very much for your valuable comments. We have adjusted Section 4.1 and moved it to the Results section to further improve our conclusions.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ The source of surface water recharge in Kashgar remains a subject of ongoing de-bate within the scientific community, with no definitive consensus reached. This study aims to elucidate the relationship between surface water recharge in Kashgar and re-gional climate change by investigating the correlation between changes in Kashgar's water area and climatic factors in its surrounding regions. This analysis seeks to pro-vide a scientific foundation for a deeper understanding of the region's hydrological cy-cle characteristics. Given Kashgar's topography, which is characterized by higher ele-vations in the southwest and lower elevations in the northeast, with most rivers and lakes originating from the southwestern mountainous areas and distributed along the slopes, this study selected annual average precipitation data from Taxkorgan County and Yecheng County, located in Kashgar's southwestern mountainous region. The ob-jective is to analyze the potential relationship between the trends in these climatic var-iables and changes in Kashgar's water area. Figure 14(a) reveals a statistically significant positive correlation between changes in Kashgar's surface water area and the average annual precipitation in Taxkorgan County (R² = 0.5687, p = 2.4 × 10-33 < 0.05). This strong correlation indicates that precip-itation in Taxkorgan County is a significant driver of surface water area fluctuations in Kashgar. Similarly, Figure 14(b) demonstrates a significant positive correlation be-tween changes in Kashgar's surface water area and average annual precipitation in Yecheng County (R² = 0.5461, p = 1.2 × 10-33 < 0.05). This finding further supports the conclusion that precipitation in Yecheng County plays a crucial role in influencing sur-face water dynamics in Kashgar. This study also investigated the relationship between changes in Kashgar's water area and average annual temperature and evaporation in Taxkorgan and Yecheng Counties. However, these analyses did not reveal statistically significant correlations (p > 0.05), suggesting that temperature and evaporation in these surrounding areas are not primary drivers of surface water area fluctuations in Kashgar. In contrast, precipitation in the surrounding areas emerged as the key factor influencing changes in Kashgar's water area. – 15, 1, and 505-530.]
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Comments 2: [Item 4.3 addresses the absence of hydrochemical analyses, but other factors were also not included in the study that may be significant, such as an analysis of land use and occupation and the dynamics between urbanized and agricultural areas.] |
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Response 2: [Thank you very much for your valuable comments. This study is based on the literature [Maimaitiaili, Ayisulitan, et al. "Monitoring and analysing land use/cover changes in an arid region based on multi-satellite data: The Kashgar Region, Northwest China." Land 7.1 (2018): 6.] and the literature [Ran H, Ma Y, Xu Z. Evaluation and Prediction of Land Use Ecological Security in the Kashgar Region Based on Grid GIS[J]. Sustainability, 2022, 15(1): 40.], which discusses the increase in cultivated land area in the Kashgar region and the intensification of urbanization, which has led to a decrease in forest and water area, and in turn caused changes in snow and ice runoff. Through the discussion of ecological security in the Kashgar region, the future ecological prospects of the Kashgar region are relatively optimistic.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ Literature findings [45] indicate that since 1972, cultivated land in Kashgar has ex-panded continuously, urbanization has intensified, and the area of forests and water bodies has shown a downward trend. Snow cover has also been subject to fluctuations driven by both climate change and anthropogenic activities. These changes, in turn, influence snow and ice melt and runoff patterns, ultimately impacting water body dy-namics across the entire Kashgar region. According to [46], the ecological security of the Kashgar region is transitioning from a state of "overall security" to "relative security" and ultimately towards "security." This trajectory suggests a positive outlook for the protection and enhancement of local wa-ter security. The future ecological prospects for the Kashgar region appear relatively optimistic. – 16, 3, and 554-563.]
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Comments 3: [Validation with field data is another missing factor; data on reservoir levels could be collected to compare and validate the analyses performed, providing a more robust and reliable research design.] |
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Response 3: [Thank you very much for your valuable advice. This experiment does lack verification based on field data. Based on the data from the Kuruklanggan (III) hydrological station, we compared the water level change trend of the upper Yarkand River with the water area change trend in the Kashgar region. The results showed that the change trends of the two were basically consistent. This consistency proves the accuracy and reliability of the experimental results. However, due to the large amount of missing water level data of many large reservoirs in the Kashgar region over the years, we have already explained in the discussion section that field monitoring devices should be installed in the future for long-term water level observation.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ To validate the experimental findings, the upper reaches of the Yarkand River, a representative water body in the Kashgar region, were selected as the validation site. Situated in the high-altitude mountainous terrain west of Kashgar, this area receives the majority of the river's recharge, making it an ideal location for result verification. Water level data for the upper Yarkand River from 2003 to 2023 were obtained from the Kuruklangan (III) hydrological station, located in Datong Township, Taxkorgan County. As illustrated in Figure 5, the water level data from this station exhibits a strong correlation with the temporal trends in water body area change observed across Kashgar. This concordance lends credence to the accuracy and reliability of the exper-imental results, providing robust data support for local policymakers. By elucidating the dynamics of water body change in Kashgar, these findings can inform the devel-opment of evidence-based policies that promote sustainable development and safe-guard the region's ecological integrity. – 9, 2, and 365-376. During the water level data collection process, inconsistencies were identified in the water level records of several reservoirs. Future studies should implement in situ moni-toring devices for long-term water level observations. – 17, 2, and 580-583.]
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Comments 4: [A significant p may indeed indicate some reliability in the results, but conclusions should be based on more statistical parameters. The inclusion of additional analyses in the conclusion could strengthen the interpretation of the data and provide a more comprehensive view of the relationships observed in the study. The results indicate a statistically significant correlation, but with low explanatory power for temperature and precipitation (R² < 0.35, R² < 0.25), which suggests that other factors, possibly not considered in the study, are influencing the observed variations. I recommend that the text discuss these limitations in more depth and, if possible, consider additional variables.] |
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Response 4: [Thank you very much for your valuable comments. This influencing factor analysis experiment introduces climate change in the surrounding areas of Kashgar. Due to the terrain characteristics of Kashgar, this study analyzes the correlation between precipitation changes in Taxkorgan County and Yecheng County and changes in water area in Kashgar. The correlation coefficients are 0.5687 and 0.5461, respectively, and the p values ​​are less than 0.05, which shows that the two have a high correlation and significance. In addition, this discussion part discusses the limitations of MODIS in estimating evaporation in arid areas and the limitations of the model in identifying ice and snow and water bodies. Subsequent research should be combined with ground data to improve the accuracy of the model, so as to more accurately analyze the impact of precipitation, temperature, evaporation, and ice and snow melting on water changes in Kashgar.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ The source of surface water recharge in Kashgar remains a subject of ongoing de-bate within the scientific community, with no definitive consensus reached. This study aims to elucidate the relationship between surface water recharge in Kashgar and re-gional climate change by investigating the correlation between changes in Kashgar's water area and climatic factors in its surrounding regions. This analysis seeks to pro-vide a scientific foundation for a deeper understanding of the region's hydrological cy-cle characteristics. Given Kashgar's topography, which is characterized by higher ele-vations in the southwest and lower elevations in the northeast, with most rivers and lakes originating from the southwestern mountainous areas and distributed along the slopes, this study selected annual average precipitation data from Taxkorgan County and Yecheng County, located in Kashgar's southwestern mountainous region. The ob-jective is to analyze the potential relationship between the trends in these climatic var-iables and changes in Kashgar's water area. Figure 14(a) reveals a statistically significant positive correlation between changes in Kashgar's surface water area and the average annual precipitation in Taxkorgan County (R² = 0.5687, p = 2.4 × 10-33 < 0.05). This strong correlation indicates that precip-itation in Taxkorgan County is a significant driver of surface water area fluctuations in Kashgar. Similarly, Figure 14(b) demonstrates a significant positive correlation be-tween changes in Kashgar's surface water area and average annual precipitation in Yecheng County (R² = 0.5461, p = 1.2 × 10-33 < 0.05). This finding further supports the conclusion that precipitation in Yecheng County plays a crucial role in influencing sur-face water dynamics in Kashgar. This study also investigated the relationship between changes in Kashgar's water area and average annual temperature and evaporation in Taxkorgan and Yecheng Counties. However, these analyses did not reveal statistically significant correlations (p > 0.05), suggesting that temperature and evaporation in these surrounding areas are not primary drivers of surface water area fluctuations in Kashgar. In contrast, precipitation in the surrounding areas emerged as the key factor influencing changes in Kashgar's water area. – 15, 1, and 505-530. Given the arid nature of the Kashgar region, characterized by complex oasis and desert landscapes, MODIS-derived evapotranspiration estimates may exhibit inherent biases. The sparse vegetation cover in arid environments complicates the differentia-tion between soil evaporation and vegetation transpiration. Subsequent studies should leverage unmanned aerial vehicles (UAVs) or Sentinel-2 data for more precise evapo-transpiration estimations. This study encountered challenges in effectively distinguishing ice and snow from water bodies. Future research should refine model accuracy and comprehensively ana-lyze the influence of ice and snow melt on local water area variations. – 17, 1, and 572-580.] We believe these revisions have significantly improved the quality and clarity of the paper. Thank you again for your valuable comments and suggestions, and we look forward to your further feedback. Sincerely.
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Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsRecommendation made to the Editor.
Author Response
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1. Summary |
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Thank you for your careful review and valuable comments on our paper. We are very grateful for your affirmation of our research methods and results, and have carefully considered all your suggestions. We have made corresponding revisions to the manuscript in response to your comments. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.
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2. Questions for General Evaluation |
Reviewer’s Evaluation |
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Does the introduction provide sufficient background and include all relevant references? |
Yes/Can be improved/Must be improved/Not applicable |
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Are all the cited references relevant to the research? |
Yes/Can be improved/Must be improved/Not applicable |
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Is the research design appropriate? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the methods adequately described? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the results clearly presented? |
Yes/Can be improved/Must be improved/Not applicable |
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Are the conclusions supported by the results? |
Yes/Can be improved/Must be improved/Not applicable |
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3. Point-by-point response to Comments and Suggestions for Authors |
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Comments 1: [The changes made by the authors have improved the robustness of the work, however there is some confusion regarding the statistical evaluation - significant p.The significant p is a factor that determines the statistical robustness of the results in a study. The authors are using this factor to conclude a strong relationship between precipitation and temperature and the flooded area, a fact not evidenced by the results. I suggest that the authors remove this statement from the conclusions and consider a more detailed analysis of the results to avoid erroneous interpretations.] |
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Response 1: [Thank you very much for your affirmation on the improvement of the robustness of the research after the revision of the paper. Based on your opinion, we had a full discussion and felt that the statement of significant p-value in the conclusion should be deleted. We wrote the positive correlation between water area and precipitation and temperature and the negative correlation between evaporation into the conclusion.] Thank you for pointing this out. We agree with this comment. Therefore, we have….[ This study investigated the spatiotemporal dynamics of water bodies in Kashgar, Xinjiang, China, from 2003 to 2023, utilizing Landsat and Sentinel-2 imagery. Water bodies were delineated using a random forest algorithm. Results revealed a fluctuating trend in total water area, exhibiting a slight overall decrease. Seasonal variations mirrored this trend, displaying a fluctuating downward trajectory. Conversely, permanent water bodies demonstrated an upward trend. Statistical analysis indicated a positive correlation between water area and both precipitation and temperature, and a negative correlation with evaporation. Notably, precipitation in Taxkorgan and Yecheng counties, influenced by topographic factors, exerted a significant impact on water area changes, exhibiting strong correlations (R² = 0.5687 and 0.5461, respectively). This highlights the crucial role of regional climatic factors in shaping the overall dynamics of water bodies in Kashgar. These findings provide a robust scientific foundation for developing effective regional water resource management policies and ecological conservation strategies. – 17, 3, and 585-597.] |
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We believe these revisions have significantly improved the quality and clarity of the paper. Thank you again for your valuable comments and suggestions, and we look forward to your further feedback. Sincerely.
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Author Response File: Author Response.docx
