Drought Vulnerability Curves Based on Remote Sensing and Historical Disaster Dataset

Round 1

Reviewer 1 Report

remotesensing-2177372

General Comments: I would like to thank the editor of Remote Sensing for providing me with an opportunity to review the paper. The title of the study seems interesting, the study is focused on a quantitative assessment of drought vulnerability at a regional scale. No doubt, the study is aligned with the scope of the journal and provides some interesting insights into the literature. However, for scientific contribution to the literature, the study needs quality improvement. Below are my comments and suggestion that the authors need to incorporate.

Main Points:

1.     The abstract of the paper is too weak and does not provide any significance for the finding of the study.

2.     In the abstract, before the layout of your purpose, try to add a sentence at the beginning with a brief background information description. There are too many simple conclusive statements in the abstract, which are well-known to people who are involved in this area. You should first lay out a background information description.

3.     I found a weakness in the identification of the research gap identification paragraph. There are many studies cited in the papers have already been published in the literature. How the author distinguished this research has significance.

4.     The description of the study area has been written too long, I should conceive, and the location of the study area should be included.

5.     Author needs to explain Figure 1 (a & d), Why these figures show different results.

6.     Results are well written; however, I suggest using reader-friendly language as the reader can easily understand and get more benefit from the research output.

7.     Conclusion section is too large and needed to be precise.

Minor Points:

I found various grammatical mistakes in the manuscript.

It is recommended that a native speaker must review the manuscript before publication. Moreover, the author may forget to cite recent studies on a similar topic

https://doi.org/10.1007/s11069-022-05650-y

10.1016/j.ecolind.2021.108207

Author Response

General Comments: I would like to thank the editor of Remote Sensing for providing me with an opportunity to review the paper. The title of the study seems interesting; the study is focused on a quantitative assessment of drought vulnerability at a regional scale. No doubt, the study is aligned with the scope of the journal and provides some interesting insights into the literature. However, for scientific contribution to the literature, the study needs quality improvement. Below are my comments and suggestion that the authors need to incorporate.

Reply:

We appreciate for the reviewer’ warm review work earnestly. The comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Once again, thank you very much for your comments and suggestions.

Main Points:

1. The abstract of the paper is too weak and does not provide any significance for the finding of the study.

Reply:

We greatly appreciate the reviewers' comment. It is really true as reviewer suggested that the abstract of the paper is too weak. So we have modified the part of abstract and provided more significance for the finding of the study according to the Reviewer’s suggestion (Page 1, line 13-19; line 30-32; line 36-41).

2. In the abstract, before the layout of your purpose, try to add a sentence at the beginning with a brief background information description. There are too many simple conclusive statements in the abstract, which are well-known to people who are involved in this area. You should first lay out a background information description.

Reply:

Special thanks to you for your good comments. Following the reviewer’s comment, we have added some sentences at the beginning with a brief background information description in the revised manuscript (Page 1, line 13-17). The core background information added is as follows: A vulnerability curve is the key to risk assessment of various disasters, connecting analysis of hazard and risk. To date, the research on vulnerability curves of earthquakes, floods and typhoons is relatively mature. However, there are few studies on the drought vulnerability curve, and its application value needs to be further confirmed and popularized.

3. I found a weakness in the identification of the research gap identification paragraph. There are many studies cited in the papers have already been published in the literature. How the author distinguished this research has significance.

Reply:

We greatly appreciate the reviewers' comment. Thank you for pointing this out. By identifying research gaps, the significance of this study may be reflected in the following three aspects:

(1) The traditional drought monitoring and vulnerability analysis are mostly based on the observation records of meteorological stations. In this study, multiple drought remote sensing monitoring indexes are selected as the disaster causing factors to realize the quantitative characterization of large-scale drought conditions;

(2) The disaster data in this study is based on the authoritative historical drought event records released by the government, which links up the Natural Disaster Statistics System, and can provide scientific basis for the implementation of national drought disaster emergency rescue response;

(3) The formation mechanism and impact of drought in different regions are different. To date, most studies are limited to the applicability of individual drought indexes in individual regions. Thereby, this paper constructs the vulnerability curves of typical drought regions in China, and systematically compares the regional applicability of different drought indexes.

We have made explanations and a unified adjustment in the revised manuscript (Page 3, line 110-113; Page 3, line 124-125; Page 9, line 351-354).

4. The description of the study area has been written too long, I should conceive, and the location of the study area should be included.

Reply:

We agree with this comment. We have added a location map of the study area in the revised manuscript (Figure 1; Page 4, line 176-177).

5. Author needs to explain Figure 1 (a & d), Why these figures show different results.

Reply:

We agree with this comment. Thank you for pointing this out. It is really true as reviewer pointed out that different result of Figure 1 should be explained. We have added the result explanations according to the Reviewer’s suggestion (Page 2, line 274-284).

6. Results are well written; however, I suggest using reader-friendly language as the reader can easily understand and get more benefit from the research output.

Reply:

We greatly appreciate the reviewers' encouragement. Some adjustments and modifications have been made in the Results part according to the Reviewer’s suggestion (Page 2, line 263-265; Page 7, line 335-337; Page 9, line 343-345).

7. Conclusion section is too large and needed to be precise.

Reply:

We agree with this comment. We have simplified the Conclusion according to the Reviewer’s suggestion (Page 11, line 434-449).

Minor Points:

I found various grammatical mistakes in the manuscript. It is recommended that a native speaker must review the manuscript before publication.

Reply:

With the help of Charlesworth Author Services ([email protected]), a native English speaker has rechecked the language carefully throughout. The editing certificate can be provided in the attachment. We believe that the presentation of this revised manuscript has been greater improved, especially the English writing. Many syntactical errors in the whole text have been carefully corrected (Page 1-20).

Moreover, the author may forget to cite recent studies on a similar topic

https://doi.org/10.1007/s11069-022-05650-y

10.1016/j.ecolind.2021.108207

Reply:

We greatly appreciate the reviewers' comment. These two references have been added to the revised manuscript according to the Reviewer’s suggestion (Page 2, line 47; Page 2, line 49; Page 12, References 2-3). Similarly, some relevant references have also been added (Page 3, line 110-114). Thank you for your kind reminding and thoughtful suggestions. The references provided by the reviewer are as follows,

1. Jehanzaib, M.; Shah, S.A.; Kim, J.E.; et al. Exploring spatio-temporal variation of drought characteristics and propagation under climate change using multi-model ensemble projections. Nat Hazards 2022, https://doi.org/10.1007/s11069-022-05650-y.
2. Zhou, K.; Wang, Y.M.; Chang, J.X.; et al. Spatial and temporal evolution of drought characteristics across the Yellow River basin. Ecological Indicators 2021,131, 108207.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper entitled Drought Vulnerability Curves Based on Remote Sensing and Historical Disaster Dataset, the authors selected three drought remote sensing indexes as disaster-causing factors, the drought at-risk populations as the drought disaster indicator, by using the cumulative distribution function and the probability density function to analyze drought vulnerability.

1. This paper is mainly about the evaluates statistical probability density distribution for drought remote sensing indexes. My concern is whether is it meaningful for statistical analysis of the remote sensing drought index.

NDVI, EVI, and TVDI were selected to conduct statistics, however, NDVI and EVI were based on band reflectivity, TVDI was influenced by LST and NDVI, the authors need to explain why they chose the above three indices.

2. droughts are categorized into different types: meteorological droughts, agricultural droughts, hydrological droughts et al. the authors need to clarify the conceptual statement in their paper, which type of drought was discussed?

3. What does random variable X mean in this paper?

4. Many kinds of literature point out that NDVI and EVI response to changes in hydrothermal conditions (such as precipitation, temperature, et al) has a time lag effect, moreover, the hydrothermal conditions have directly reflected the droughts, how did the authors explain the time lag effect in remote sensing drought index statistics?

5. in figure 1 (a),(c),(e), what does N stand for?

6. I am confused about the drought vulnerability curve, how did the authors get the conclusion in lines 263-273?

Author Response

The paper entitled Drought Vulnerability Curves Based on Remote Sensing and Historical Disaster Dataset, the authors selected three drought remote sensing indexes as disaster-causing factors, the drought at-risk populations as the drought disaster indicator, by using the cumulative distribution function and the probability density function to analyze drought vulnerability.

1. This paper is mainly about the evaluates statistical probability density distribution for drought remote sensing indexes. My concern is whether is it meaningful for statistical analysis of the remote sensing drought index. NDVI, EVI, and TVDI were selected to conduct statistics, however, NDVI and EVI were based on band reflectivity, TVDI was influenced by LST and NDVI, the authors need to explain why they chose the above three indices.

Reply:

We greatly appreciate the reviewers' comment. Thank you for pointing this out. Remote sensing drought index overcomes the disadvantages of using vegetation index or land surface temperature data alone to monitor drought in traditional methods. They are closely related to vegetation physiology and growth, and are common important indicators for agricultural drought monitoring. The soil moisture, soil temperature and ground surface temperature involved in agricultural drought are important land surface parameters, and their spatiotemporal characteristics of each layer have not been comprehensively displayed in detail.

The NDVI index has the advantage of using satellite data to monitor the vegetation health related to drought events, with very high resolution and large spatial coverage. The disadvantage is that it is greatly affected by the soil background, which has lower sensitivity to high vegetation area.

EVI can minimize the impact of vegetation canopy background and maintain high sensitivity under dense vegetation conditions. The disadvantage is that the stress of plant canopy may be caused by factors other than drought, and it is difficult to identify using EVI only.

The advantage of TVDI is that it combines visible and near infrared data, has clear physical meaning and is easy to operate. It does not rely on any atmospheric or surface data or any special land surface model. The disadvantage is that the applicability in different climatic regions will vary due to different vegetation conditions and soil temperatures.

Each index has its own advantages and disadvantages. Taking data availability into consideration primarily, the above three indexes are selected in this study. Some explanations have been made in the revised manuscript (Page 2, line 212-224).

2. Droughts are categorized into different types: meteorological droughts, agricultural droughts, hydrological droughts et al. the authors need to clarify the conceptual statement in their paper, which type of drought was discussed?

Reply:

Special thanks to you for your good comments. Agricultural drought relates to agricultural production. The people with difficulties in drinking water concerned in this study can reflect the scope and degrees of drought impact on agriculture. Therefore, agricultural drought was discussed in our study. Some clarifications have been made in the revised manuscript (Page 3, line 128-131).

3. What does random variable X mean in this paper?

Reply:

We greatly appreciate the reviewers' comment. X is only a random variable, which describes the probability statistics method of this study, and has no special meaning (Page 2, line 231).

4. Many kinds of literature point out that NDVI and EVI response to changes in hydrothermal conditions (such as precipitation, temperature, et al) has a time lag effect, moreover, the hydrothermal conditions have directly reflected the droughts, how did the authors explain the time lag effect in remote sensing drought index statistics?

Reply:

We agree with this comment. Thank you for pointing this out. After the occurrence of meteorological drought, there may be a few days or more before the occurrence of agricultural drought, followed by the loss of farmland and drinking water. Indeed, as the reviewer said, the remote sensing drought index has a lag effect. This study focuses on the correlation between hazard and risk. That is to say, after mastering the lag time rule, this relationship can also be used for analysis and application. We also have other articles analyzing the drought legacies. For the published papers, please see:

He, YHZ ; Chen, F; Jia, HC ; Wang, L ; Bondur, VG. Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region. Remote Sens. 2020, 12(11), 1700; doi:10.3390/rs12111700.

In the future, our research team will conduct in-depth analysis based on lag and vulnerability. Thank the reviewers for your guidance and suggestions.

5. in figure 1 (a),(c),(e), what does N stand for?

Reply:

We greatly appreciate the reviewers' comment. N represents the number of samples. N in this study represents the number of data excluding the abnormal value of drought index variance ratio and corresponding loss of zero (DRP=0). Some explanations have been made in the revised manuscript (Page 2, line 264-265).

6. I am confused about the drought vulnerability curve, how did the authors get the conclusion in lines 263-273?

Reply:

We greatly appreciate the reviewers' comment. A vulnerability curve is the key to risk assessment of drought disaster, connecting analysis of hazard and risk. Vulnerability can typically reflect the damage or loss of exposure by hazard. The authors get the conclusion in lines 263-273 from Figure 5. The analysis of lines 263-273 is that if the value of the selected horizontal axis is 0.5, the standardized DRP values corresponding to the five regions of EVI, NDVI and TVDI are compared and the comparison results are obtained (Page 7, line 323-334).

We appreciate for the reviewer’ warm review work earnestly. The comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Once again, thank you very much for your comments and suggestions.

Author Response File: Author Response.pdf

Reviewer 3 Report

Review report for paper

Paper entitled “

Drought Vulnerability Curves Based on Remote Sensing and 2 Historical Disaster Dataset ‘’

General remarks:

·      Moderate English changes is required

·      Quality of all figures should be improved (600dpi is proposed)

Specific remarks

·      The methodology section should be more explained; you can add a chart explaining all steps of calculation of NDVI, EVI et TDVI, the satellite images resolution and corrections

·      The main objective and the utility of each indicator in drought evaluation should be explained in the methodology part

·      Figure 2 should be improved; the text in the figure is not clear and for its quality 600dpi is proposed

Author Response

General remarks:

Moderate English changes is required.

Quality of all figures should be improved (600dpi is proposed).

Reply:

Special thanks to you for your good comments. With the help of Charlesworth Author Services ([email protected]), a native English speaker has rechecked the language carefully throughout. The editing certificate can be provided in the attachment. We believe that the presentation of this revised manuscript has been greater improved, especially the English writing. Many syntactical errors in the whole text have been carefully corrected (Page 1-20). Quality of almost all figures has been improved into 600dpi (Page 4, Figure1; Page 5, Figure 3-4; Page 6, Figure 5; Page 7, Figure 6).

Specific remarks

The methodology section should be more explained; you can add a chart explaining all steps of calculation of NDVI, EVI et TDVI, the satellite images resolution and corrections

Reply:

We greatly appreciate the reviewers' comment. More explanations have been added in the methodology section in the revised manuscript (Page 2, line 212-224; line 242-249). We also have added a flow chart explaining all the steps in the revised manuscript (Page 2, Figure 2).

The main objective and the utility of each indicator in drought evaluation should be explained in the methodology part

Reply:

We greatly appreciate the reviewers' comment. Thank you for pointing this out. Each index has its own advantages and disadvantages. Taking data availability into consideration primarily, the three indexes are selected in this study. More explanations of each indicator in drought evaluation have been added in the methodology part in the revised manuscript (Page 2, line 212-224).

Figure 2 should be improved; the text in the figure is not clear and for its quality 600dpi is proposed

Reply:

We agree with this comment. We have improved the quality of Figure 2 into 600dpi in the revised manuscript (Figure 4; Page 5).

We appreciate for the reviewer’ warm review work earnestly. The comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Once again, thank you very much for your comments and suggestions.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have put a lot of energy to revise the manuscript. The revised manuscript seems much improved than the first draft submitted, the current form of the manuscript is recommended for publication. 

Reviewer 2 Report

The paper has been developed.  I am recommending it for publication.