The Development of Agricultural Drought Monitoring and Drought Limit Water Level Assessments for Plateau Lakes in Central Yunnan Based on MODIS Remote Sensing: A Case Study of Qilu Lake

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Some advices:

Through innovative multi-index fusion and localization weight optimization, this research provides a scientific tool for drought resistance decision-making in the plateau lake basin area, and its integrated framework of "monitoring-early warning-scheduling" has important promotion value. However, it is necessary to deepen research in data fusion, dynamic modeling and multi-objective optimization, and strengthen the coupling of model verification and social economy, so as to further improve the precision and systematization of drought response.

VCI and TCI rely on limited satellite data resources (such as MODIS or Landsat), which are susceptible to cloud cover and sensor errors, and may lead to insufficient spatial and temporal continuity and accuracy of drought monitoring. Additional complementary microwave remote sensing data (such as Sentinel-1) can be considered to reduce insufficient cloud interference.

 This study focused on VHI and water level scheduling, but did not thoroughly analyze the specific effects of drought on lake hydrological processes (e. g., groundwater recharge, evaporation loss), which may simplify the drought transmission mechanism. It is suggested to introduce hydrological models (e. g. SWAT) to simulate the lake water balance and enhance the coupling between VHI and hydrological processes.

The selected VCI and TCI weight optimization is based on historical data, which does not consider the change of drought frequency and intensity in the context of future climate warming, and the long-term applicability of the model is in doubt. Climate change trends can be analyzed in combination with machine learning (e. g., random forest, LSTM) to achieve adaptive updating of VHI weights. At the same time, it is also necessary to increase the actual effect verification of the weight optimization results and the scheduling scheme, and extend the model to other plateau lake basins (such as Dianchi Lake and Erhai Lake) to test its universality.

Author Response

Comment 1: VCI and TCI rely on limited satellite data resources (such as MODIS or Landsat), which are susceptible to cloud cover and sensor errors, and may lead to insufficient spatial and temporal continuity and accuracy of drought monitoring. Additional complementary microwave remote sensing data (such as Sentinel-1) can be considered to reduce insufficient cloud interference.
Response 1:  Regarding the suggestions raised by the reviewer，The use of MODIS data is affected by the cloud error, but a series of operations can be used to eliminate the cloud, which will not have a great impact on the final result of the data. VCI and TCI need long-term time series. Due to the short time series of sentinel series satellites and GF series satellites in this study, they may not be able to meet the needs of specific use. After accumulating a certain amount of remote sensing data from GF satellites in China in the future, it is necessary to carry out exploration in this regard.

Response 2:  This study focused on VHI and water level scheduling, but did not thoroughly analyze the specific effects of drought on lake hydrological processes (e. g., groundwater recharge, evaporation loss), which may simplify the drought transmission mechanism. It is suggested to introduce hydrological models (e. g. SWAT) to simulate the lake water balance and enhance the coupling between VHI and hydrological processes.
Response 2：Regarding the suggestions raised by the reviewer，The impact of drought on hydrological processes such as groundwater recharge and evaporation losses is indeed significant, and these factors play a key role in understanding the mechanisms of drought propagation. We agree that introducing distributed hydrological models like SWAT for water balance simulation would help deepen the coupled analysis of VHI and hydrological processes. However, this study mainly focuses on quickly revealing the macroscopic response relationship between VHI and water level scheduling. The coupling process of groundwater-evaporation involves simulating physically meaningful watershed-scale water cycle mechanisms, which requires more detailed basic data, more complex model building, and computational adjustments.

Comment 3: The selected VCI and TCI weight optimization is based on historical data, which does not consider the change of drought frequency and intensity in the context of future climate warming, and the long-term applicability of the model is in doubt. Climate change trends can be analyzed in combination with machine learning (e. g., random forest, LSTM) to achieve adaptive updating of VHI weights. At the same time, it is also necessary to increase the actual effect verification of the weight optimization results and the scheduling scheme, and extend the model to other plateau lake basins (such as Dianchi Lake and Erhai Lake) to test its universality.
Response 3: Regarding the suggestions raised by the reviewer，The current VCI and TCI weight optimization based on historical data does not fully take into account the changes in drought frequency and intensity under future climate change conditions, which indeed limits the long-term applicability of the model. Moreover, this study only investigates one lake and does not consider other plateau lakes. Therefore, it is necessary to supplement the discussion with: The weight optimization of VCI and TCI in VHI is only carried out under the existing historical data, and the change of drought frequency and intensity under the background of future climate warming is not fully considered, which limits the long-term applicability of the model. The study area is within the water supply scope of the Central Yunnan Water Diversion Project, and the water resources regulation ability of the basin has been significantly improved after water supply. Existing studies have shown that although Dianchi Lake, Erhai Lake, Qilu Lake, Yangzonghai Lake, Fuxian Lake, and Yilong Lake on the central Yunnan Plateau belong to different watershed systems, they are basically located in the convergence area of the warm and humid airflows from the summer southeast monsoon and southwest monsoon. The trends of precipitation and runoff show similar patterns of increase or decrease. Therefore, after the water diversion from the Central Yunnan Water Diversion Project to the Qilu Lake watershed, it is necessary to reassess and adjust the drought limit water levels of these plateau lakes. Considering the improvement of the watershed's water resource carrying capacity, it is important to comprehensively consider factors such as climate change, changes in water demand, and ecological protection goals, so as to appropriately raise the drought limit water levels of the lakes. There are more than 50 natural lakes on the Yunnan-Guizhou Plateau, and this method can also be applied to establish drought limit water levels for other plateau lake watersheds. This not only helps to test the general applicability but also provides valuable management strategy references for these regions.

Reviewer 2 Report

Comments and Suggestions for Authors

The abstract can point out important results with key data, such as VCI, TCI, VHI index values, and water level values at different frequencies.

The title is too long, and is the 'Drought Monitoring Assessment', a mandatory part of the title?

There is Chinese labeling in equations 6~7?

Can the correlation coefficient exceed 1 when the maximum value of the vertical coordinate of Figure 3 exceeds 1?

What are the meanings of (1)~(3) on the right side of Fig. 7?

Author Response

Comments 1: The abstract can point out important results with key data, such as VCI, TCI, VHI index values, and water level values at different frequencies.
Response 1: Regarding the questions raised by the reviewer，additional information has been provided in the text:
The method is most effective in assessing agricultural drought in the Qilu Lake basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short-term drought limit water levels and long-term drought limit water levels. The short-term drought limited water level is still divided into drought warning water level and drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, frequency P=75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, frequency P=95%) ranges from1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels

Comments 2: The title is too long, and is the 'Drought Monitoring Assessment', a mandatory part of the title?
Response 2: Regarding the questions raised by the reviewer，the title has been revised to: Development of Agricultural Drought Monitoring and Drought Limit Water Levels Assessment for Plateau Lakes in Central Yunnan Based on MODIS Remote Sensing: A Case Study of Qilu Lake

Comments 3: There is Chinese labeling in equations 6~7?
Response 3: Regarding the questions raised by the reviewer, modifications have been made

Comments 4: Can the correlation coefficient exceed 1 when the maximum value of the vertical coordinate of Figure 3 exceeds 1?
Response 4: Regarding the questions raised by the reviewer，In previous studies, the distribution of both TCI and VCI in the calculation of the VHI index ranged from 0 to 1, with no scenarios where the coefficient exceeded 1. Similarly, an analysis and trial calculation were conducted for situations where the coefficient was greater than 1. It was found that the correlation coefficient during Pearson correlation analysis was relatively small, so it was not taken into account.

Comments 5: What are the meanings of (1)~(3) on the right side of Fig. 7?
Response 5: Regarding the questions raised by the reviewer，The (1)~(3) on the right side of Figure Seven serve as criteria for adopting different water restriction measures when facing drought in the future. These have been specifically reflected in lines 466-485 of the text:
As illustrated in interval (1) of Figure 7, there will be no restrictions on water supply to the lake. 2. When the lake water level is below the drought alarm water level but above the drought conservation water level: As shown in interval (2) of Figure 7, water consumption across various sectors within the watershed should be reduced according to a lower supply adjustment coefficient.3. When the lake water level is below the drought conservation water level: As depicted in interval (3) of Figure 7, water consumption across various sectors should be curtailed based on a higher supply adjustment coefficient.

Reviewer 3 Report

Comments and Suggestions for Authors

The research shows us how to determine the proportion of VCI and TCI and they also ploted the water levels under multiple scenario levels. The result is interesting and is helpful for setting water level for some lakes or rivers. But there are several issues in the paper. I hope the research can improve them before the paper goes further.

 

1. The paper did not displayed what some data were from. The whole paper mentioned a lot about P = 95% and P = 75% but little about P = 50%. In Figure 5 and Figure 7, suitable ecological water level curves abruptly appeared. In Equation 8, the phase “when y is more than 1795.17” is confusing. Where can I find “1795.17”?

 

2. Some key processing or index are missing. Figure 2 shows some possible drought indices like SPI-1, SPI-3 and SPI-6. You need to tell me why you excluded the other (like SPI-6) with some quantitative support (That processing is missing). Then you chose the ratio as 0.4. What is the p value? Is the p value less than 0.05? I recommend one paper with a good approach to identify the optimum ratio (Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018).

 

3. More details can be added, which can be easy for the readers to understand the whole process. In Figure 1, you can pinpoint the locations of the meteorological stations. How many station did you get your data from? In Line 150, you mentioned the application of Gaofen-6 satellite. How do you use the satellite images? What are the spatial resolution of the images?

 

4. Language should be improved. I hope the authors can check the language after the Chinese were directly translated with some apps like Chat-GPT or others. In Figure 2 the long-term ?? what??. Equation 7 had many Chinese words. Line 224 “for mild, moderate, severe, and extreme drought levels” and Line 289 “of light drought, moderate drought…..” are not consistent. The axis X of Figure 5, Line 242 and Line 250 also have grammar problems. Please read through the whole paper carefully and correct the grammar issues.

Comments on the Quality of English Language

Language should be improved. I hope the authors can check the language after the Chinese were directly translated with some apps like Chat-GPT or others. In Figure 2 the long-term ?? what??. Equation 7 had many Chinese words. Line 224 “for mild, moderate, severe, and extreme drought levels” and Line 289 “of light drought, moderate drought…..” are not consistent. The axis X of Figure 5, Line 242 and Line 250 also have grammar problems. Please read through the whole paper carefully and correct the grammar issues.

Author Response

Comments 1: The paper did not displayed what some data were from. The whole paper mentioned a lot about P = 95% and P = 75% but little about P = 50%. In Figure 5 and Figure 7, suitable ecological water level curves abruptly appeared. In Equation 8, the phase “when y is more than 1795.17” is confusing. Where can I find “1795.17”?
Response 1:Regarding the questions raised by the reviewer：The research in this paper mainly focuses on two parts: the hydrological frequency P=75% (medium drought year) and P=95% (special drought year). The appropriate ecological water level curve of P=50% (normal year) only appears as a reference value in the article. 1795.17 is the water level calculated according to Formula (8) under the water supply restriction condition when the agricultural drought index VHI is 0, and the hydrological frequency equals P=75%.

Comments 2: Some key processing or index are missing. Figure 2 shows some possible drought indices like SPI-1, SPI-3 and SPI-6. You need to tell me why you excluded the other (like SPI-6) with some quantitative support (That processing is missing). Then you chose the ratio as 0.4. What is the p value? Is the p value less than 0.05? I recommend one paper with a good approach to identify the optimum ratio (Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018).
Response 2:Regarding the questions raised by the reviewer，In the study, the correlation coefficients between SPI-1 and SPI-6 with VHI are lower than that between SPI-3 and VHI, so the other two indices are excluded. In the correlation monitoring, the P value is 0.632, and the correlation is significant at the 0.01 level.

	0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1
SPI-1	0.294	0.389	0.408	0.419	0.437	0.402	0.387	0.364	0.336	0.296	0.249
SPI-3	0.319	0.445	0.513	0.593	0.632	0.617	0.572	0.49	0.466	0.45	0.374
SPI-6	0.253	0.368	0.418	0.427	0.456	0.515	0.453	0.424	0.397	0.327	0.222

Comments 3: More details can be added, which can be easy for the readers to understand the whole process. In Figure 1, you can pinpoint the locations of the meteorological stations. How many station did you get your data from? In Line 150, you mentioned the application of Gaofen-6 satellite. How do you use the satellite images? What are the spatial resolution of the images?
Response 3:Regarding the questions raised by the reviewer，Since the GF-6 satellite began operations only in 2018, and due to the frequent overcast and rainy weather during the summer and autumn seasons in the study area, which affected the effective acquisition of remote sensing data, there are relatively few GF-6 satellite data available. During the initial stage of writing, these data were used only as a reference for MODIS data, and unfortunately could not be utilized in the text. The relevant content related to GF-6 satellite data has been removed from the manuscript.

Comments 4: Language should be improved. I hope the authors can check the language after the Chinese were directly translated with some apps like Chat-GPT or others. In Figure 2 the long-term ?? what??. Equation 7 had many Chinese words. Line 224 “for mild, moderate, severe, and extreme drought levels” and Line 289 “of light drought, moderate drought…..” are not consistent. The axis X of Figure 5, Line 242 and Line 250 also have grammar problems. Please read through the whole paper carefully and correct the grammar issues.
Response 4:Regarding the questions raised by the reviewer, the language problems mentioned have been modified.

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript is devoted to the investigation of various indices calculated from multispectral imagery that help determine the effects of seasonal droughts. The manuscript contains a great literature review, indicating the authors' deep immersion in the drought issue. However, there are several points that require clarification:

1. The precipitation characterizing the Drought period could be added to Table 1.
2. The text of lines 242-258 should be moved to the Results and Analysis section.

3. The authors use Pearson correlation analysis to reassess the weights of TCI and VCI in the VHI, aiming to balance the relationship between VCI, TCI, and VHI during the study period (L. 169). However, the Pearson correlation coefficient assumes a linear relationship model.
4. By comparing the Pearson correlation coefficients of VHI-VCI and VHI-TCI, it is found that the correlation of VHI-VCI is lower than that of VHI-TCI, indicating that TCI has a more significant impact on VHI. Perhaps TCI does not have a more significant effect on VHI, but TCI has a greater sensitivity than VHI.

5. When discussing Figure 4 “Monthly Variation of SPI, VHI and water levels in the Qilu Lake Basin”, it is necessary to explain the discrepancy between the dynamics of VHI and SPI-3 and the dynamics of Water Levels.

6. It is not clear how the calculated “minimum spatial requirement for fish survival is established at Z3=1793.92 m”. What happened to fish in 2013? Please describe this point in more detail.
7. Sections 4.3.3 and 4.3.4 have the same title (Calculation of Inflow Runoff to the Lake).

8. The conclusion needs improvement. For example «The growth of vegetation is affected by a multitude of conditions, which introduces certain limitations. Further research is required to uncover these complexities and integrate them into the determination of drought limit water levels» is more in line with the abstract than the Conclusion.

9. There are grammatical and punctuation errors in the text. L. 195 “limi water levels is ”should be “limit water levels are”. Also L. 419, 424, 452, 479, 499, 500 and others.

Comments on the Quality of English Language

The reviewer is not a native speaker, but grammatical and punctuation errors were found during the review process.

Author Response

Comments 1: The precipitation characterizing the Drought period could be added to Table 1
Response 1: Regarding suggestions put forward by reviewers，supplements have been made.

Category	SPI	VHI	Precipitation characterizing(mm/ month)
wet	≥-0.5	≥0.4	≥43.4
Mild drought	-1~-0.5	0.3~0.4	26.7~43.4
Moderate drought	-1.5~-1	0.2~0.3	15.7~27.6
Severe drought	-2~-1.5	0.1~0.2	7.54~15.7
Extreme drought	<-2	<0.1	<7.5

Comments 2: The text of lines 242-258 should be moved to the Results and Analysis section
Response 2: Regarding suggestions put forward by reviewers，the content of lines 242-258 should be the section on rationality analysis and correction. The two parts have been modified and merged into: Analysis of the Rationality of Drought Limit Water Levels. Adjustment and Determination of Drought Warning Water Levels. The establishment of drought warning water levels requires not only a foundational calculation but also necessary adjustments that account for external inflow from surrounding watersheds, as well as the specific management criteria associated with the lake's operational requirements. Fundamentally, the drought warning water level must remain within defined parameters: it cannot exceed the flood control level established for the wet season, nor can it fall below the minimum operational water level necessary for sustaining ecological balance and resource management. In the analysis of the rationality of drought limit water levels, it is essential to incorporate actual drought statistical data and water allocation rules for a reasonable analysis and optimization adjustment of the drought limit water levels. A comparative analysis should be conducted to evaluate the improvement in water supply during typical drought years before and after the establishment of the drought limit water levels.

Comments 3: The authors use Pearson correlation analysis to reassess the weights of TCI and VCI in the VHI, aiming to balance the relationship between VCI TCI, and VHI during the study period (L.169).However, the Pearson correlation coefficient assumes a linear relationship model.
Response 3: Regarding the questions raised by the reviewers，In the initial calculation of VCI and TCI, the allocation of 0.5 and 0.5 for both did not make a difference. However, further studies have found that in different study areas, the allocation may affect the monitoring effect. The literature Quantification of interactions among agricultural drought indices within Köppen–Geiger climate zones in Bangladesh, and Remote Sensing Monitoring of Agricultural Drought and Vegetation Sensitivity Analysis in the Middle and Lower Reaches of the Yangtze River from 2001 to 2019 both have demonstrated this. This paper aims to calculate the correlation coefficients based on the existing research by using the Pearson correlation coefficient.

Comments 4: By comparing the Pearson correlation coefficients of VHI-VCI and VHI-TCI, it is found that the correlation of VHI-VCI is lower than that of VHI-TCI, indicating that TCI has a more significant impact on VHI. Perhaps TCI does not have a more significant effect on VHI, but TCI has a greater sensitivity than VHI.
Response 4: Regarding the questions raised by the reviewer，the research results reflect the relative significance of TCI within the VHI framework. The VHI is constructed through a weighted combination of TCI and VCI, comprehensively reflecting heat stress and vegetation growth status; while TCI is only based on temperature data and is more sensitive to temperature changes, making it suitable for rapid monitoring of heat stress. By contrast, the VHI needs to take into account the influence of vegetation greenness when assessing heat stress, thus better reflecting the long-term health status of vegetation. Therefore, under the comprehensive consideration of temperature and vegetation status, the sensitivity of TCI is relatively higher than that of VHI.

Comments 5: When discussing Figure 4 “Monthly Variation of SPI, VHI and water levels in the Qilu Lake Basin", it is necessary to explain the discrepancy between the dynamics of VHI and SPI-3 and the dynamics of Water Levels.
Response 5: Regarding the questions raised by the reviewer，Add the discrepancy between the dynamics of VHI and SP-3 and the dynamics of Water Levels：Under normal conditions without drought, the water level of Qilu Lake follows an annual variation pattern: the water level increases month by month from June to October, reaching its highest point, then decreases month by month until it reaches its lowest point in May, forming an annual cycle. The two significant decreases in the water level of Qilu Lake occurred during 2009-2013 and 2019-2020, both of which were due to extremely severe droughts within the watershed, with relatively low levels of VHI and SPI-3.

Comments 6: It is not clear how the calculated "minimum spatial requirement for fish survival is established at Z3=1793.92 m". What happened to fish in 2013? Please describe this point in more detail.
Response 6: Regarding the questions raised by the reviewer，In the ecological water demand of aquatic organisms, fish have the highest requirement for water level. In the extreme case, the ecological water level obtained by adding 1.0m to the bottom elevation of the lake is the minimum spatial demand method for organisms. However, no relevant elevation data for the bottom of Qilu Lake was found. Therefore, the lowest operating water level that can meet the needs of lake water ecological restoration, watershed water resource allocation planning, and water supply safety guarantee was adopted. At this time, the lake water depth is greater than 1793.92m, which is the ecological water level that can meet the needs of aquatic organisms.

Comments 7: Sections 4.3.3 and 4.3.4 have the same title (Calculation of Inflow Runoff to the Lake).
Response 7: Regarding the questions raised by the reviewer, the modifications have been made.

Comments 8: The conclusion needs improvement. For example «The growth of vegetation is affected by a multitude of conditions, which introduces certain limitations. Further research is required to uncover these complexities and integrate them into the determination of drought limit water levels» is more in line with the abstract than the Conclusion
Response 8: Regarding the questions raised by the reviewer，The growth of vegetation is affected by a multitude of conditions, which introduces certain limitations. Further research is required to uncover these complexities and integrate them into the determination of drought limit water levels. This part of the content belongs to the under-researched section, and it has now been marked in the text for discussion.

Comments 9: There are grammatical and punctuation errors in the text. L 195 "limit water levels is "should be "limit water levels are" Also L.419,424,452,479,499, 500 and others.
Response 9: Regarding the questions raised by the reviewer, the language problems mentioned have been modified.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I think the authors have given me the feedback on my concerns.

Comments on the Quality of English Language

I can still find the traces of AI language apps, please check through the whole paper.

Author Response

Comment: I can still find the traces of AI language apps, please check through the whole paper.

Response: We have utilized the author services feature on the platform to polish language, which has being uploaded as revised manuscript