Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China

Round 1

Reviewer 1 Report

This study investigated the temporal and spatial evolution of meteorological drought (MD) and hydrological drought (HD) in the JRB under different climate emission scenarios using the SWAT model and downscaled climate data sets. The main findings were: (1) Drought frequency decreased with an increase in time scale and MD trend was more significant under the RCP8.5 scenario on monthly and seasonal scales, but the opposite on annual scale. (2) The JRB would have significant differences in spatial drought characteristics under different emission scenarios, affected by uneven precipitation distribution. MD under RCP4.5 was more intense in the middle reaches of JRB and more significant on an annual scale compared to RCP8.5. HD affected a larger area under RCP4.5, but was more concentrated and severe under RCP8.5, making recovery difficult. (3) The joint distribution model of drought duration and severity showed that MD severity would increase in the future under both scenarios. The probability of an S0 event (no correlation between MD and HD events) increased under RCP4.5, but the probability of S2 and S3 events (numerous and short-duration MD events causing long-duration HD events) increased under RCP8.5, making recovery difficult. 

Overall, the study is well-written, but I could not find any novel ideas or specifics to explain why two different climatic scenarios (RCP4.5 and RCP8.5) caused various types of drought. Can the author provide specifics regarding RCP4.5 and RCP8.5? What are the parameters and assumptions of the climate model for these two seniors?

In this article, the SWAT model was used to simulate runoff, although the authors did not provide an explanation of the model's parameters. In table 1, for instance, model parameter descriptions must be specified in full. Moreover, when the runoff was generated, land use conditions, such as impervious ratio, soil moisture, and storage, were significant factors that influenced the runoffs. How did the author view these apportionments during climatic impacts? Third, monthly runoff observation data from Zhangjiashan Hydrological Station to calibrate and validate the SWAT model parameters. I believe that the data on water depth is insufficient for calibrating the model if the river's cross section is altered.

Overall, the purpose of this study is solely to present the results, with no discussion of the scientific question behind them.

Author Response

Reply: We are very grateful to the reviewer for his comments on this manuscript. In response to the reviewer's valuable comments, we have made detailed revisions to help improve the readability, aesthetics, and normative nature of the manuscript. According to the requirements, the revised content has been marked in the manuscript (‘Blue Track Changes’ function), and the specific revisions are as follows:

Point 1: Overall, the study is well-written, but I could not find any novel ideas or specifics to explain why two different climatic scenarios (RCP4.5 and RCP8.5) caused various types of drought. Can the author provide specifics regarding RCP4.5 and RCP8.5? What are the parameters and assumptions of the climate model for these two seniors?

Reply: We are very grateful to the reviewer for pointing out the details in the manuscript, and we have added explanations about RCP 4.5 and RCP 8.5 in the corresponding paragraphs. The following paragraph in lines  is added to explain this:

‘The RCP4.5 and RCP8.5 climate scenarios represent low and high levels of greenhouse gas emissions, respectively, meaning that the carbon dioxide concentration will reach 650 and 1370 ppm, respectively.’

Point 2: In this article, the SWAT model was used to simulate runoff, although the authors did not provide an explanation of the model's parameters. In table 1, for instance, model parameter descriptions must be specified in full.

Reply: We fully agree with the reviewer's suggestion, and we have added the meaning of the SWAT model parameters in Table 1, please see Table 2 for details:

Table2. Optimization parameters of SWAT model in the JRB

Point 3: Moreover, when the runoff was generated, land use conditions, such as impervious ratio, soil moisture, and storage, were significant factors that influenced the runoffs. How did the author view these apportionments during climatic impacts?

Reply: We are very grateful and agree with the reviewer for his valuable comments, which will add to the logicality of the article. However, in this study, we did not considered the Land use change, because the subject of this manuscript is discussing the evolution of MD and HD characteristics under the natural conditions in the future. As can be seen in lines 544-545, next further step should consider the impact of human activities and subsurface changes on drought.

‘Due to the limited data collected, this study only explored the meteorological and hydrological drought characteristics of the JRB under natural conditions. ’

Point 4: Third, monthly runoff observation data from Zhangjiashan Hydrological Station to calibrate and validate the SWAT model parameters. I believe that the data on water depth is insufficient for calibrating the model if the river's cross section is altered.

Reply:  We are very sorry for the inconvenience caused to our dear reviewers, because of our mistake resulted in the translation of parts of the manuscript being incorrect. We have corrected the relative part, it is not water depth data while runoff data. please see lines 172-186 for details:

‘2.3.4. Drought index

The Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) are used to describe the occurrence of meteorological and hydrological drought in the JRB. The SPI was proposed by McKee et al. It has been applied to meteorological and hydrological drought assessment globally [31]. The calculation of SPI includes 3 steps: (1) accumulation of precipitation series according to a specific time scale (1, 3 and 12 months were used to represent the monthly, seasonal, and annual condition in this study); (2) selection of optimal probability distribution to fit cumulative precipitation series; (3) transformation of the optimal probability distribution to a standard normal distribution. The calculation of SRI is similar to SPI. The Gamma distribution is confirmed to be the optimal choice for SPI and SRI calculation in most regions of the world [  ], so the Gamma distribution is applied to fit precipitation and runoff series in this study. The wet-ness/dryness levels of SPI/SRI are ≥ 0.5, -0.5 to 0.5, −0.5 to −1.0, −1.5 to −1.0, −1.5 to −2.0, and ≤ −2.0, which correspond to wet, normal, mild dry, moderate dry, severe dry, and extreme dry, respectively.’

Author Response File: Author Response.docx

Reviewer 2 Report

SPI and SRI drought indices are evaluated in the study. RCP4.5 and RCP8.5 datasets in CMIP5 are used for the climate scenarios. Jinghe River Basin in the Loess Plateau is selected for the application. The subject is very important and the study is valuable in terms of climate change & drought forecasting but the flowchart of the methodology is missing. Some suggestions and comments to the authors are presented below:

1. A basic flowchart of the suggested methodology should be presented in the paper. Thus, the readers can easily follow the application procedures.

2. Literature part is looking weak. Give new and last updated examples from literature about “drought characteristics (duration, severity, and intensity)” as

doi.org/10.1080/02626667.2021.1934473

doi.org/10.3390/rs15020337

3. The performance metrics are missing in the paper. Some metrics can be calculated to evaluate the application results.

4. Some statistical properties as coefficient of variation, confidence intervals, distribution characteristics, min and median, etc. of used data (SPI or precipitation data for study areas) should be given in a table, additionally Table 1.

5. Spatial efficiency metric (SPAEF) can be used to compare two raster maps of SPI and SRI or RCP4.5 and RCP8.5 scenarios.

6. Conclusions part can be improved in the paper. Here is presented in a general concept.

7. How did the authors select the gauging stations for calibration and validation procedures? Is there any rule or criteria at this step or randomly? Shortly, are there any criteria for the calibration and validation procedures in the model?

8. Is the used methodology in the paper valid for all areas or is there any limitation or classification for the application?

9. The resolution of the spatial maps are very low.

Author Response

Reply: We thank the reviewers for his valuable and detailed comments, which greatly contributed to the rigor and scientific of the manuscript. According to the requirements, the revised content has been marked in the manuscript (‘Blue Track Changes’ function), and the specific revisions are as follows:

Point 1: A basic flowchart of the suggested methodology should be presented in the paper. Thus, the readers can easily follow the application procedures.

Reply: Great thanks for this comment. According to the comments of our dear reviewers, and we have added a flowchart to illustrate the input datasets, methods, and output results in this manuscript.

‘In order to reflect the main research content, we have drawn a flowchart (Figure 2), which mainly illustrates the input datasets, methods, and output results of this study. ’

Figure 2. Flowchart including the input dataset, method, and output result.

Point 2: Literature part is looking weak. Give new and last updated examples from literature about “drought characteristics (duration, severity, and intensity)” as

doi.org/10.1080/02626667.2021.1934473

doi.org/10.3390/rs15020337

Reply: We fully agree with the reviewer's suggestion. and we have added the relevant literature on "drought characteristics" in the corresponding paragraph. please see lines 188-190.

‘In this study, the run theory is applied to identify drought and derive drought characteristics based on SPI/SRI with multiple time scales. Drought severity, duration, and intensity [36-37] are the most important features to characterize a drought event ’

Point 3: The performance metrics are missing in the paper. Some metrics can be calculated to evaluate the application results.

Reply: Great thanks to our dear reviewer, for his thoughtful reminder, we have added performance metrics to the lines 240-249 in order to verify the simulation degree of SWAT model.

‘2.3.7. Performance evaluation criteria

In order to evaluate the SWAT model in this study, Root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R2) were used to evaluate the model performance:

                          (17)

                                     (18)

                               (16)

where N represents the number of samples;  represents the observed data of variable i;  represents the simulated data of variable i;  and  represent the average values of the observed and simulated data, respectively. ’

Point 4: Some statistical properties as coefficient of variation, confidence intervals, distribution characteristics, min and median, etc. of used data (SPI or precipitation data for study areas) should be given in a table, additionally Table 1.

Reply: We thanks dear reviewer for his nice comments, and we have added the statistical Table to give the climate environment in the JRB. As can be seen from the Table 1.

‘A statistical analysis of the annual average climate factors at JRB was showed in Table 1’

Table1. A statistical analysis of the annual average precipitation and temperature

Point 5: Spatial efficiency metric (SPAEF) can be used to compare two raster maps of SPI and SRI or RCP4.5 and RCP8.5 scenarios.

Reply: We are very grateful to our dear reviewers for their innovative comments. Due to the limited length of our manuscript, we will not do much research in this paper at this time. However, we have made enhancements to the spatial raster diagram, as detailed in the revised manuscript.

Point 6: Conclusions part can be improved in the paper. Here is presented in a general concept.

Reply: We thanks dear reviewer for his critical comments, which greatly contributed to the rigor and scientific of the manuscript, and we have improved our conclusions part. Please see lines 557-593 for details.

‘5. Conclusions

This study explored the temporal and spatial evolution of meteorological drought and hydrological drought in the JRB at different time scales by driving the SWAT model and downscale climate data sets for two representative future emission scenarios. Moreover, the Copula joint distribution function was used to reveal the relationship between different drought characteristic values and the joint distribution characteristic law. The main conclusions were as follows:

(1) With the increase of the time scale, the frequency of drought events gradually decreased. The meteorological drought and hydrological drought of JRB displayed complex periodic change trends of drought and flood succession. The patterns were influenced by the characteristics of precipitation distribution. Importantly, JRB meteorological and hydrological drought experienced effects from aggravation to mitigation under the RCP8.5 scenario. However, a periodic change pattern of drought-flood-drought-flood occurred under the RCP4.5 scenario.

(2) In the future period, the JRB would have significant differences under different emission scenarios of spatial drought characteristic scale, due to it affected by an uneven precipitation distribution. The duration and severity of MD under the RCP4.5 emission scenario were more intense in the middle reaches of JRB. As for JRB HD, the area affected by drought under the RCP4.5 scenario was larger than that under the RCP8.5 scenario, which exceeds 50% of the whole area. However, under the RCP8.5 scenario, the areas where JRB will be suffered from serious drought, the drought was more concentrated, the drought duration was longer, it was very difficult to recover from this type of drought.

(3) It can be seen from the future joint distribution model of drought duration and severity that the future MD severity of JRB would increase both under the two emission scenarios. Compared with the historical period, the overall runoff of the JRB in the future period would be increased and the HD events would be mitigated. In contrast, HD duration of the JRB was significantly prolonged under the RCP4.5 scenario but reduced under the RCP8.5 scenario.

It is worth noting that the combined return period of JRB drought duration and severity of MD as well as HD in the future were slightly larger than those of the co-occurrence return period, indicating that when one drought characteristic variable occurred, the probability of another drought variable occurring simultaneously was higher. The numerous and short-duration MD events causing long-duration HD events would increase. In that case, the HD caused by this situation would last relatively longer, and the drought severity would be high, making the recovery thereof difficult. Although the area affected by HD under the RCP8.5 scenario was smaller than that under the RCP4.5 scenario, some of its local areas were vulnerable to long duration and high se-verity HD events would have unfavorable effects after the occurrence. ’

Point 7: How did the authors select the gauging stations for calibration and validation procedures? Is there any rule or criteria at this step or randomly? Shortly, are there any criteria for the calibration and validation procedures in the model?

Reply: Great thanks for this comment. We have explained in the corresponding lines why this hydrographic station was used. In addition, section 2.3.7 of this paper calibrate and validate the results of the SWAT model for this study. Please see lines 275-277 and 240-249 for details.

‘The Zhangjiashan Hydrological Station is the main outlet hydrographic station of the JRB and controls more than 95% of the total area of the basin.’

‘2.3.7. Performance evaluation criteria

In order to evaluate the SWAT model in this study, Root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R²) were used to evaluate the model performance:

                          (10)

                                     (11)

                               (12)

where N represents the number of samples;  represents the observed data of variable i;  represents the simulated data of variable i;  and  represent the average values of the observed and simulated data, respectively. ’

Point 8: Is the used methodology in the paper valid for all areas or is there any limitation or classification for the application?

Reply: We are very grateful to dear reviewers for their detailed comments on this manuscript, and we have added the limitations for this paper, please see lines 543-555 for details.

‘4.3 Limitations

Due to the limited data collected, this study only explored the meteorological and hydrological drought characteristics of the JRB under natural conditions. However, the occurrence of hydrological drought is not only influenced by natural meteorological elements, but also closely related to local underlying surface conditions and anthropo-genic disturbances [add]. Future studies should also consider the evolution of hydro-logical drought patterns under the combined influence of natural and anthropogenic factors.

Furthermore, different GCM models have different simulations for precipitation and temperature in different regions [add]. Although the GFDL-CM3 model is better used on the JRB, more GCM models should be considered in the other regions. Moreover, the CMIP6 model data is already widely used and can be further investigated [add]. Therefore, it is encouraged to use this climate projection product for drought projections in future studies. ’

Point 8: The resolution of the spatial maps are very low.

Reply: We fully agree with the reviewer's suggestion that it helps to improve the recognition of images of the manuscript. Please see all spatial maps for details.

Author Response File: Author Response.docx

Reviewer 3 Report

The article is an interesting, though not new, approach to analyzing system response to drought using CMIP5 data. There are several typos that the authors should correct. I also have some comments regarding the hypothesis and analysis. In general, some of the figures are not easy to read because the font is too small, or a legend is missing. The methodology is not clearly described, but the execution of the analysis and the discussion are somewhat difficult to follow.

I am very concerned about the use of CMIP satellite products (5 or 6) in estimating the extent of drought but always accompanied by their evaluation with observed measurements. I bet you that these estimates have many sources of uncertainty and associated errors that have not been shown here, so the authors should describe them. Or they should provide a justification if this can be used without correction. 

The authors should elaborate further on the selection of CMIP5 products selected in the study and how they prepared these data for the SWAT model. What kind of data and what resolutions? There are more than 24 regional climate models in CMIP5: which model and why?

1.      The title is not faithful to the content of the article

2.      Review the affiliations: a lot of redundancy, see the website of the newspaper

3.      Why CMIP5? there is CMIP6 which is more efficient

4.      In the summary, it is not clear if you are working on the past or the future.

5.      The abstract is weak and doesn't really reflect the content of the article. To be reviewed. Thank you.

6.      Line 48 - 58: you have to start from a global context and then come to the case of China and then talk about the case of the study basin.

7.      Line 89-90: research contains one main objective and from it at least two specific objectives.

8.      For the introduction, it is necessary to add the state of the art on previous studies: SWAT, meteorological indicators, drought

9.      Study area: figure 1: unclear legend

10.   Add a table for the description of the data: types, origins, spatial and temporal resolution

11.   Trend analysis: from lines 136-142: very weak.

12.   Z ? what's Z ? you need to add more explanation about the MK test.

13.   Add a flowchart General of the methodology

14.   The figures in the results part are illegible and the legend is sometimes bigger than the map! Definitely need to be reviewed. The quality is very poor unfortunately  

15.   How did you process the CMIP5 data and what are the parameters used? And how did you format them according to SWAT? Add details and algorithms if developed.

Proposed references :

[1–4]

1.          Seyoum, W.M. Characterizing Water Storage Trends and Regional Climate Influence Using GRACE Observation and Satellite Altimetry Data in the Upper Blue Nile River Basin. J. Hydrol. 2018, 566, 274–284, doi:10.1016/j.jhydrol.2018.09.025.

2.          Hamdi, M.; Goïta, K. Investigating Terrestrial Water Storage Response to Meteorological Drought in the Canadian Prairies. Sustain. 2022, 14, doi:10.3390/su142013216.

3.          Cui, T.; Li, C.; Tian, F. Evaluation of Temperature and Precipitation Simulations in CMIP6 Models Over the Tibetan Plateau. Earth Sp. Sci. 2021, 8, 1–20, doi:10.1029/2020EA001620.

4.          Mpelasoka, F.; Awange, J.L.; Goncalves, R.M. Accounting for Dynamics of Mean Precipitation in Drought Projections: A Case Study of Brazil for the 2050 and 2070 Periods. Sci. Total Environ. 2018, 622–623, 1519–1531, doi:10.1016/j.scitotenv.2017.10.032.

Recommendation: serious major corrections.

Author Response

Reply: The authors are very grateful to reviewer for their recognition of this research. The valuable comments put forward by reviewer are of great help to improving and enhancing the quality of this article and highlighting the significance of the research. As dear reviewer saying goes, it has many uncertain in this manuscript, we have added the Limitations in 4.3 section. According to the requirements, the revised content has been marked in the manuscript (‘Blue Track Changes’ function), and the specific revisions are as follows:

Point 1: The title is not faithful to the content of the article.

Reply: We strongly agree with the reviewers' valuable comments that the original title was inappropriate and did not convey the main research content of the manuscript. Therefore, we have made a change to the title of the manuscript.

‘Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China’

Point 2: Review the affiliations: a lot of redundancy, see the website of the newspaper.

Reply: We fully agree with the reviewer's suggestion that it helps to enrich the content and increase the readability of the manuscript, and we have improved the affiliations. Please see the introduction for details.

Point 3: Why CMIP5? there is CMIP6 which is more efficient.

Reply: We are very grateful to the reviewers for their thoughtful consideration of our manuscript. It is indeed that the CMIP6 climate model data is relatively new, but allow us to explain that the CMIP5 climate model data has been widely used by scholars and is more mature. Therefore, the reasons for the atmospheric model used in this manuscript are explained in section 3.1 and its simulation with several other commonly used models in the Jing River basin are compared using Taylor diagrams. Please see lines 251-271 for details:

‘3.1. GCM Simulation Assessment

Different CMIP5 models shows different simulation performance for different climatic elements in different regions, therefore, five commonly used CMIP5 models were selected in this study for comparing and analyzing the simulation effects of different climate models under 2 RCPs on precipitation in JRB (Figure 3). the reasonableness of the model selection is comprehensively examined in terms of correlation coefficient, ratio of standard deviation, and centralization root mean square error by using Taylor diagrams [add].

The simulation shows that the GFDL-CM3 climate models are better than other 4climate models, which spatial correlation coefficients nearly to 0.8. In general, the simulation of precipitation under the RCP8.5 emission scenario is better than the RCP4.5 scenario.

The performance of the statistical downscaling model (SDSM) was evaluated by three elements: daily average precipitation, daily average maximum temperature, and daily average minimum temperature from the 14 weather stations. Accordingly, the performance of the downscaling model was evaluated using standard statistical methods (R², RMSE, and NSE) [changed]. The results shows that the atmospheric downscaling model had the highest simulation accuracy and could be used to simulate the drought characteristics under future climate change scenarios in the JRB. ’

Figure 4. Taylor diagrams of precipitation under different RCPs scenario: (a) RCP4.5 and (b) RCP8.5.

Point 4: In the summary, it is not clear if you are working on the past or the future.

Reply: Great thanks for this comment. The theme of this manuscript is to explore the evolution and patterns of meteorological and hydrological drought in the Jing River Basin in future periods, however, in section 3.5, when discussing the joint distribution patterns of drought characteristics, historical periods have been included to make comparisons.

Point 5: The abstract is weak and doesn't really reflect the content of the article. To be reviewed. Thank you.

Reply: Great thanks for dear reviewer's valuable comments. We have enhanced and improved the abstract section of this manuscript. Please see lines 20-36 for details:

‘Abstract: Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts, and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) series were used to represent the meteorological drought and hydrological drought with monthly runoff generated by SWAT model. In addition, the evolution laws of JRB in the future based on Copula functions are discussed. Results showed that: (1) The meteorological drought and hydrological drought of JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different Spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase of the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of the future climate change, as well as the early warning of hydrological drought. ’

Point 6: Line 48 - 58: you have to start from a global context and then come to the case of China and then talk about the case of the study basin.

Reply: We fully agree with the reviewer's suggestion that it will help enrich the content and increase the logicality and readability of the manuscript. Please see lines 41-45 for details:

‘Global warming and increased frequency of extreme climate events have led to frequent water shortages in most areas in recent years caused by large-scale drought events [1–4]. The spatial and temporal distribution of precipitation in China is uneven [5]. The Loess Plateau is an important area for western development in China, especially in northwestern China. ’

Point 7: Line 89-90: research contains one main objective and from it at least two specific objectives.

Reply: Great thanks for dear reviewer's valuable comments. We have changed the original lines 87-101 to lines 83-90.

‘The main purpose of this study was to evaluate the spatial and temporal evolution laws of the meteorological and hydrological drought characteristics in the JRB under climate change in the future. The research contents of this study include (i) To obtain the future spatial and temporal evolution of precipitation and runoff; (ii) To identify the meteorological and hydrological drought spatiotemporal evolution characteristics of JRB under different future emission scenarios; (iii) To reveal the joint distribution of the meteorological and hydrological drought characteristics under different periods ; (iv) To explore the probabilities of different types of drought propagation events in the future for the JRB. ’

Point 8: For the introduction, it is necessary to add the state of the art on previous studies: SWAT, meteorological indicators, drought.

Reply: We strongly agree with the reviewer's suggestion because it will help enrich the content and increase the logicality and readability of the manuscript. Therefore, we added some contents to describe the relevant previous studies. Please see lines 51-54 and lines 79-82 for details:

‘The standardized precipitation index (SPI) is recommended by the World Meteorological Organization (WMO). Liu et al. [13] applied the standardized precipitation-evapotranspiration index (SPEI) to the Tibetan Plateau (TP) region, which is sensitive to climate change.’

‘The SWAT model is a semi-distributed hydrological model developed by the American Center for Agricultural Research. The model is widely used to simulate hydrological processes under the influence of climate change and human activities due to its relatively complete consideration of physical mechanisms [26-27]. ’

Point 9: Study area: figure 1: unclear legend.

Reply: Great thanks for dear reviewer's valuable comments, and we have improved the study area figure.

Point 10: Add a table for the description of the data: types, origins, spatial and temporal resolution.

Reply: We are very grateful and agree with the reviewer for his valuable comments, which will add to the logicality of the article. The table that describes the data type, source and spatial-temporal resolution can be seen in Table 2.

Table2. SWAT model input data and hydrometeorological data

Point 11: Trend analysis: from lines 136-142: very weak.

Reply: Great thanks for dear reviewer's valuable comments, we have added some details to illustrate this part, so please see lines 135-140 for details:

‘The MK test statistic Z is generally used to identify the degree to which a trend is consistently decreasing or increasing [31-32]. Positive values of the Z statistic indicate upward trends over the whole time series, whereas negative values of the Z statistic indicate downward trends over the whole time series. In this study, α = 0.01, α = 0.05, and α = 0.1 significance levels were considered, and the corresponding value of Z_1−α/2 were 2.58, 1.96, and 1.64, respectively. ’

Point 12: Z? what's Z? you need to add more explanation about the MK test.

Reply: We are very grateful to dear reviewers for their detailed comments on this

manuscript, same as the last reply, we have explained the meaning of Z. Thanks to dear reviewer.

Point 13: Add a flowchart General of the methodology.

Reply: Great thanks for dear reviewer's valuable comments, and we have added a flowchart to illustrate the input, methods, and output in the new manuscript in accordance with the reviewers' comments. Please see lines 167-168 and Figure 2 for details:

‘In order to reflect the main research content, we have drawn a flowchart (Figure 3), which mainly illustrates the input datasets, methods, and output results of this study. ’

Figure 2. Flowchart including the input dataset, method, and output result.

Point 14: The figures in the results part are illegible and the legend is sometimes bigger than the map! Definitely need to be reviewed. The quality is very poor unfortunately.

Reply: We thanks the reviewers for their critical comments, which greatly helped to enhance the quality of the maps in this manuscript. Please see the Figures for details. Thank you.

Point 15: How did you process the CMIP5 data and what are the parameters used? And how did you format them according to SWAT? Add details and algorithms if developed.

Reply: Great thanks for dear reviewer's valuable comments, please let us to explain the process about the CMIP5 data. First, we selected the best fit GCM model based on Taylor diagram, then we used the SDSM method to input the climate factors into the SWAT model, then we used the well-constructed model to simulate the future runoff in JRB.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Thank you for updating the draft based on my review comments. I appreciate your efforts in incorporating the feedback provided.

However, I still have major concerns with the SWAT model used in this study. The model only considers meteorological factors, but the impact of runoff is significant and influenced by various other factors such as evaporation, land use condition, and soil type. Thus, I do not see any improvement in the SRI index for future conditions.

Additionally, the impact of urbanization and human activities such as runoff mitigation (e.g., ponds, LID in the urban area and pipe systems) cannot be ignored. These factors are crucial in affecting the SRI index, yet they are not considered in your draft. This lack of consideration leads to a lack of novel ideas to tackle these issues and raises questions about the validity of your conclusions.

Therefore, based on the current limitations of the model and the lack of consideration of key factors, I regret to inform you that I do not recommend publishing this draft in its current form.

I encourage you to consider incorporating these important factors and make necessary revisions to improve the overall quality of the study. I am happy to assist with any further questions or comments you may have.

Author Response

Dear reviewer 1:
First of all, we are very grateful to reviewer 1 for providing us with such comprehensive comments.
Secondly, please allow us to explain that our authors has made corresponding research in the Jinghe River basin and applied the Markov model to simulate the land use situation of the Jinghe River basin in the next 40 years. The results shows that the contribution rate of land use change to meteorological drought and hydrological drought only accounts for less than 1%. Therefore, this manuscript will not be considered. According to the comments of the reviewer1, human activities will be included in our next further steps.The main theme of this manuscript is to study the impact of future climate change on hydrological drought. For such references, please refer to:
doi.org/10.1155/2016/2905198.
doi.org /10.1016/j.jhydrol.2022.128889.

We thank you for understanding,

Kind regards.

Reviewer 2 Report

I suggest accepting the manuscript. The authors carefully revised the paper by answering each comment from the first round.

Author Response

Dear reviewer 2:
We are very grateful to dear reviewer 2 for his approval of our research content, which will help us to further improve. Thank you again.

Best wishes.

Reviewer 3 Report

I thank you for the corrections made in the new version.

One last thing I propose before giving my final opinion is to add in the discussion part a paragraph about the comparison of CMIP5 and CMIP6 products and also add some perspectives.

Thanks

Author Response

Reply: The authors are very grateful to reviewer for their recognition of this research. The valuable comments put forward by reviewer are of great help to improving and enhancing the quality of this article and highlighting the significance of the research. As our dear reviewer saying goes, this manuscript lacks the comparison and discussion of CMIP5 and CMIP6 models. Therefore, we listen to the opinions of our dear reviewers, and add the detailed description and perspectives of this part in the Sect4.3 ‘Limitation and Extension section’ of this manuscript, in order to improve the quality of the manuscript, please see lines 555-569 for details:

‘Many studies indicated that there are some improvements from the CMIP5 to CMIP6 GCMs in simulating the mean and extreme temperature and precipitation globally and in China as a whole [49-50]. Compared with CMIP5, the resolution of CMIP6 for atmosphere and ocean is improved. Because it includes new and more com-plex processes, as well as more complex surface processes, ice sheets and permafrost, these processes can restore hydrological processes more comprehensively [51].

Guo et.al evaluated and compared the performance of CMIP6 and CMIP5 models in simulating the runoff on global-scale and eight large-scale basins, and found that: CMIP6 models have less uncertainty on the global scale when compared with CMIP5 models, but it has not made outstanding progress on the basin scale [52]. Moreover, Cui et.al evaluated the mean and extreme surface air temperature and precipitation in the CMIP6 multimodel ensemble simulations over the Tibetan Plateau [53], and it shows that CMIP6 models continue to suffer from cold bias in temperature and wet bias in precipitation similar as its predecessor CMIP5. Therefore, the runoff simulation capability at the basin scale needs to be further improved to some extent.’

Author Response File: Author Response.docx