the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Jun 2022
Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China
Abstract. Flood (EF) disasters in China are characterized by large influence range, high frequency, strong burst and uneven distribution in time and space. Once the EF disaster occurs, it will pose a great threat to the people’s life safety, economic, natural and social environment. Compared with the hazards and exposure factors of EF, the vulnerability of disaster regions shows great differences due to China’s vastness and complex social and environmental background of disasters, which leads to less large-scale study at provincial-level on EF vulnerability. This study calculated the vulnerability to EF from the favorable and unfavorable factors of flood resistance of four aspects including life, economy, environment and society. The Cloud-improved Entropy Method is used to calculate the index weight, and the Fuzzy Variable Theory is used to calculate the comprehen-sive vulnerability grads. The vulnerability ranking of 31 provinces or regions in China was made according to the differences of population, social structure, economy and environment among these regions. Furthermore, synthesizing disaster science and geographic mapping, the spatial distribution map of vulnerability to EF in China was generated, which shows that vul-nerability to EF in most regions of China is in “moderate” or “severe” grade. The spatial distri-bution of the EF risk vulnerability shows (1) a decreasing trend from the regions with high pop-ulation density to regions with low population density, (2) a decreasing trend from economical-ly developed regions to economically backward regions, (3) a decreasing trend from the eastern coastal regions to the central agricultural provinces and then to the southwest, northwest and northeast regions in China. The outcome of this study maybe one of the first efforts providing research database for vulnerability to EF in large scale of China, and it is useful for future regional research and risk management.
- Preprint
(589 KB) - Metadata XML
- BibTeX
- EndNote
Status: closed
-
CC1: 'Comment on nhess-2022-136', xixi Hong, 13 Jun 2022
This study is of great significance for flood disaster risk assessment at regional scale.
Citation: https://doi.org/10.5194/nhess-2022-136-CC1 -
CC3: 'Reply on CC1', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC3
-
CC3: 'Reply on CC1', Wei Li, 15 Jul 2022
-
CC2: 'Comment on nhess-2022-136', Te Wang, 15 Jun 2022
An interesting idea combined with a scientific method has inspired me a lot
Citation: https://doi.org/10.5194/nhess-2022-136-CC2 -
CC4: 'Reply on CC2', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC4 -
CC5: 'Reply on CC2', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC5
-
CC4: 'Reply on CC2', Wei Li, 15 Jul 2022
-
RC1: 'Comment on nhess-2022-136', Anonymous Referee #1, 02 Jul 2022
I have read with great interest the manuscript entitled "Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China" The manuscript is very interesting and has a substantial impact on extreme flood disaster assessment and management.
The manuscript have both theoretical and practical significance. For this reason, I consider that this manuscript is suitable for publication in "Natural Hazards and Earth System Sciences" after considering some minor revisions.
# 1:
The provincial importance index (PI) and social disaster recovery index (SI) are not listed in Table 1.
# 2:
Lines 172-175, it should be “the 14 to 65 year-old is considered as the labor force.”
# 3:
In Table 5, the mean value of the results of the four parameter combinations is taken. Whether the combination of these four parameters is of practical significance or just the needs of theoretical calculation.
# 4:
Lines 224-230 describe that the social disaster recovery index (SI) reflects the disaster resistance, disaster relief and recovery capacity of provinces and cities. What is the difference between SI and vulnerability?
Citation: https://doi.org/10.5194/nhess-2022-136-RC1 -
AC1: 'Reply on RC1', Wei Li, 15 Jul 2022
I have read with great interest the manuscript entitled "Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China" The manuscript is very interesting and has a substantial impact on extreme flood disaster assessment and management.
The manuscript have both theoretical and practical significance. For this reason, I consider that this manuscript is suitable for publication in "Natural Hazards and Earth System Sciences" after considering some minor revisions.
Response:
Thank you for your hard work and recognition of the manuscript, which is very important to encourage us to further improve the model.
# 1:
The provincial importance index (PI) and social disaster recovery index (SI) are not listed in Table 1.
Response:
According to your comment, the provincial importance index (PI) and social disaster recovery index (SI) are added in Table 1.
# 2:
Lines 172-175, it should be “the 14 to 65 year-old is considered as the labor force.”
Response:
According to your comment, the relevant contents have been corrected, as shown in lines 178-179 in the manuscript marked with changes.
# 3:
In Table 5, the mean value of the results of the four parameter combinations is taken. Whether the combination of these four parameters is of practical significance or just the needs of theoretical calculation.
Response:
Thanks for your professional and valuable comments. The combination of four parameters is the need of calculation and has certain theoretical significance. Through four different variable model parameters, it better reflects the correlation and dynamic variability of fuzzy concepts, and avoids the problem of static membership function in traditional fuzzy set theory.
# 4:
Lines 224-230 describe that the social disaster recovery index (SI) reflects the disaster resistance, disaster relief and recovery capacityy of provinces and cities. What is the difference between SI and vulnerability.
Response:
SI mainly reflects the vulnerability of provinces from the perspective of social impact. The vulnerability of this manuscript also comprehensively considers the impact of economy, life and environment.
Citation: https://doi.org/10.5194/nhess-2022-136-AC1
-
AC1: 'Reply on RC1', Wei Li, 15 Jul 2022
-
RC2: 'Comment on nhess-2022-136', Anonymous Referee #2, 07 Jul 2022
There was a great job of explaining what models have been done before, which gave this manuscript good research significance. In addition, the manuscript has a clear structure, proper language expression, and it is pleasant to read. I have only a few suggestions for the manuscript.
(1) Add more explanations on the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method to reflect the necessity of using improved methods.
(2) Whether the vulnerability of provinces and the probability of EF can be combined in further research to improve the guiding significance for the government.
(3) Some contents need to be added to the manuscript: PI and SI indicators in Table 1, and explanations of a and p in Table 5.
All in all, I think this manuscript can be published after some minor modifications.
Citation: https://doi.org/10.5194/nhess-2022-136-RC2 -
AC2: 'Reply on RC2', Wei Li, 15 Jul 2022
There was a great job of explaining what models have been done before, which gave this manuscript good research significance. In addition, the manuscript has a clear structure, proper language expression, and it is pleasant to read. I have only a few suggestions for the manuscript.
Response:
Thank you for your recognition of the manuscript, which is of great significance to help and encourage us to further conduct in-depth research.
(1) Add more explanations on the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method to reflect the necessity of using improved methods.
Response:
According to your comment, the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method have been supplemented, as shown in lines 99-109 in the manuscript marked with changes.
(2) Whether the vulnerability of provinces and the probability of EF can be combined in further research to improve the guiding significance for the government.
Response:
Thank you for your professional advice. As you said, it is more instructive to combine probability with vulnerability, which is the direction of our next in-depth research.
(3) Some contents need to be added to the manuscript: PI and SI indicators in Table 1, and explanations of a and p in Table 5. All in all, I think this manuscript can be published after some minor modifications.
Response:
Thank you for your valuable comments and recognition of the manuscript. According to your comment, the relevant contents have been added in the manuscript marked with changes.
Citation: https://doi.org/10.5194/nhess-2022-136-AC2
-
AC2: 'Reply on RC2', Wei Li, 15 Jul 2022
-
RC3: 'Comment on nhess-2022-136', Anonymous Referee #3, 17 Jul 2022
The authors used the Cloud-improved Entropy Method and the Fuzzy Variable Theory to calculate the vulnerability of Chinese provinces to extreme floods. The trend and law of vulnerability distribution are analyzed, which provides a reference for regional risk management. This manuscript is very interesting and valuable. I do not have strong suggestions, only the following small suggestions.
(1) Add relevant contents of index SI and PI in Table 1.
(2) In section 2.1, it is written that the subjectivity and objectivity of weights are considered. Where are the subjectivity and objectivity embodied respectively?
(3) In section 2.2.1, RP is defined as the population at risk. In fact, it is more appropriate to call it population density.
(4) The method proposed in this study is to conduct flood vulnerability research and assessment on a provincial basis. Compared with the research on City, county and district scale, does it have more advantages in some aspects?
(5) The severity of disasters caused by floods is largely related to the intensity of floods. Are the results of this study applicable to flood analysis on any scale? When the flood intensity is different, how does it affect the final evaluation result?Citation: https://doi.org/10.5194/nhess-2022-136-RC3 -
AC3: 'Reply on RC3', Wei Li, 22 Jul 2022
The authors used the Cloud-improved Entropy Method and the Fuzzy Variable Theory to calculate the vulnerability of Chinese provinces to extreme floods. The trend and law of vulnerability distribution are analyzed, which provides a reference for regional risk management. This manuscript is very interesting and valuable. I do not have strong suggestions, only the following small suggestions.
(1) Add relevant contents of index SI and PI in Table 1.response:
According to your comment, the index SI and PI have been added in Table 1.
(2) In section 2.1, it is written that the subjectivity and objectivity of weights are considered. Where are the subjectivity and objectivity embodied respectively?response:
Considering the expectation and entropy of the Cloud Model, the subjective judgment of experts is treated by the Cloud Model as a key representative parameter representing uncertainty, which reflects subjectivity. Entropy weight Method has the characteristics of strong objectivity. It reflects objectivity by using entropy weight method to deal with the differences between indicators.
(3) In section 2.2.1, RP is defined as the population at risk. In fact, it is more appropriate to call it population density.response:
According to your comment, the PD (population density) has been substituted for RP (risk population).
(4) The method proposed in this study is to conduct flood vulnerability research and assessment on a provincial basis. Compared with the research on City, county and district scale, does it have more advantages in some aspects?response:
We believe that the study of each scale has its advantages and necessity The distribution of population, economy and natural environment among different provinces in China has strong regional heterogeneity. Carrying out provincial-level research can provide a research basis for the overall risk management of the country, which cannot be macro grasped by other scales.
(5) The severity of disasters caused by floods is largely related to the intensity of floods. Are the results of this study applicable to flood analysis on any scale? When the flood intensity is different, how does it affect the final evaluation result?response:
On the one hand, the severity of a regional disaster is related to the intensity of floods, on the other hand, it is related to its ability to resist floods, that is, the vulnerability mentioned in this paper. Therefore, the vulnerability assessment results are not affected by the flood intensity, and can be applied to extreme floods of any scale.
Citation: https://doi.org/10.5194/nhess-2022-136-AC3
-
AC3: 'Reply on RC3', Wei Li, 22 Jul 2022
-
RC4: 'Comment on nhess-2022-136', Anonymous Referee #4, 20 Sep 2022
The authors present the paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China”. They apply Cloud-improved Entropy Method to calculate used indexes weight, and the Fuzzy Variable Theory to calculate the total vulnerability. A small-scale map of China vulnerability was produced.
Below some considerations and suggestion.
- Vulnerability is an essential component of risk analysis and, as such, it has to be deeply investigated. In the manuscript the terms “risk” and “vulnerability”, are often used as synonymous and in other cases they are used together (see e.g. row 20: “…spatial distribution of the EF risk vulnerability…”, row 99: (… “risk indicator of EF vulnerability...”), row 237: (… “factors of flood risk vulnerability”), and many others. I suggest to review the terminology.
- In the flow diagram of Fig. 1 reference is made to “Stability analysis”. What does it refer to? To the stability of adopted model? This analysis process should be descripted in the manuscript.
- The matrix (2) is formally incorrect (see last row and last column).
- “H”, listed for each province/city in table 5, is described from the authors (equation 7, row 148) as “level eigenvalue of the evaluation sample”. From these values, the vulnerability map (fig. 5) is derived. If “H” is the vulnerability, why call it “level eigenvalue”? if not, how do the authors get the map from value of H? The map derivation process should be better explained in the text.
- The vulnerability is divided into four levels: “mild”, “moderate”, “severe” and “extremely severe”, passing from numerical analysis to a purely qualitative data. In the text should be indicated the criterion of numerical thresholds choosing and, the thresholds value indicated.
- Conclusions are sparse and, in many respects, obvious. They should be integrated.
- Both to complete and improve the paper, and to support the reliability of the procedure adopted, a validation of the vulnerability map produced, could be useful.
In attached file other minor notifications are reported.
-
AC4: 'Reply on RC4', Wei Li, 20 Nov 2022
The author present the paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China”. They apply Cloud-improved Entropy Method to calculate used indexes weight, and the Fuzzy Variable Theory to calculate the total vulnerability. A small-scale map of China vulnerability was produced.
Below some considerations and suggestion.
Comment 1:
Vulnerability is an essential component of risk analysis and, as such, it has to be deeply investigated. In the manuscript the terms “risk” and “vulnerability”, are often used as synonymous and in other cases they are used together (see e.g. row 20: “…spatial distribution of the EF risk vulnerability…”, row 99:(…“risk indicator of EF vulnerability…”), row 237:(…“factors of flood risk vulnerability”), and many others. I suggest to review the terminology.
Response:
Sorry for the inconvenience caused by our writing habits. What we want to express is just vulnerability. Thank you for your suggestion. We have removed "risk" from the word “risk vulnerability”.
Comment 2:
In the flow diagram of Fig. 1 reference is made to “Stability analysis”. What does it refer to? To the stability of adopted model? This analysis process should be descripted in the manuscript.
Response:
Thank you for your suggestion. Stability analysis is aimed at variable fuzzy model. When using the variable fuzzy model, the general formula has four changes (a=1, p=1; a=2, p=1; a=1, p=2; a=2, p=2). When the calculation results of the four parameter combinations differ greatly, it means that the stability of the calculation results is poor, and the model needs to be adjusted. When the calculation results are similar, it is considered that the stability meets the requirements and the calculation results of vulnerability have good applicability. The process of stability analysis is to compare whether the results of four parameter combinations are similar.
Comment 3:
The matrix (2) is formally incorrect (see last row and last column).
Response:
According to your suggestions, we have modified matrix (2).
Comment 4:
“H”, listed for each province/city in table 5, is described from the authors (equation 7, row 148) as “level eigenvalue of the evaluation sample”. From these values, the vulnerability map (fig.5) is derived. If “H” is the vulnerability, why call it “level eigenvalue”? If not, how do the authors get the map from value of H?
Response:
Thank you for your hard work. As written in Table 5, H is the average characteristic value of the evaluation sample, and it is the premise and basis for classifying the vulnerability level. After an average and second average analysis of H, we get the vulnerability level, which is why we do not directly call H vulnerability.
Average and secondary average analysis is a commonly used mathematical method, which can solve the problem that individual data is too scattered, resulting in the evaluation results being too concentrated and falling into a certain level, and the grading is unreasonable and not smooth. For example, the data [1, 2, 3, 4, 5, 21] are divided into three grades: slight, moderate and serious. If the traditional grading method is used to divide the thresholds into [1, 7], [8, 14] and [15, 21], then the evaluation results are basically "slight", and the differences between them cannot be effectively distinguished, making the evaluation ineffective. Therefore, we use the average and second average analysis to solve this problem. As it is an existing method and not the focus of our research, so we only pointed out the method used and explained its role, but did not write its application steps in the manuscript.
Comment 5:
The vulnerability is divided into four levels: “slight”, “moderate”, “severe” and “extremely serious”, passing from numerical analysis to a purely qualitative data. In the text should be indicated the criterion of numerical thresholds choosing and, the thresholds value indicated.
Response:
Thank you for your professional advice. The reason why we did not specify the threshold value of the grade standard is that it is not a fixed value and has no practical significance. Just like a cake, we can divide it into four smaller portions, but when the size of the whole cake changes, the size of each smaller portion will also change, so we only indicate the division method (average and second average analysis), but not the threshold value.
Comment 6:
Conclusions are sparse and, in many respects, obvious. They should be integrated.
Response:
According to your suggestion, we have integrated the results in the discussion and supplemented the rationality analysis of the results.
Comment 7:
Both to complete and improve the paper, and to support the reliability of the procedure adopted, a validation of the vulnerability map produced, could be useful.
Response:
According to your suggestion, we have added the analysis of the rationality of the results. The revised contents are as follows：
Although Beijing, Shanghai Tianjin, Guangdong and Jiangsu have strong comprehensive disaster reduction and prevention capabilities, but their economies are the most developed and their population density is very high (their GDP per capita and population density are among the top seven in China all the year round), which leads to their extremely serious vulnerability. Provinces in the Yangtze River, Yellow River and Huaihe River basins (Hebei, Henan, Shandong, Sichuan, Chongqing, Hunan, Hubei, Anhui, Zhejiang and Fujian) are the main producing areas of grain in China, with relatively developed economy and large population, so their vulnerability is severe. In the northwest, southwest and northeast of China, although the traffic network density is low, once a serious EF disaster occurs, it is difficult to transfer the affected people and property, and may cause indirect losses due to ineffective relief and slow recovery. However, due to the terrain and geographical location, its population density is low, it is a gathering area of most ethnic minorities in China, and its economy is underdeveloped, so the vulnerability level of these provinces (Jilin, Liaoning, Inner Mongolia, Shanxi, Shanxi, Ningxia, Qinghai, Gansu, Xinjiang, Tibet, Yunnan, Guizhou, Jiangxi and Guangxi) is moderate. Heilongjiang province has become the only province with slight vulnerability to EF disasters. The reasons include low population density (bottom three in China all the year round), high proportion of labor force (top six in China all the year round), and strong disaster prevention and relief capacity (Heilongjiang, known as the "Great Granary of China", is the location of the Great Xing'an Mountains and the Small Xing'an Mountains. It is rich in forests, minerals, animals and plants. Therefore, the local government attaches great importance to disaster prevention). The vulnerability assessment results are highly consistent with the distribution laws of population, economy and natural environment in China, which verifies the rationality of the results.
Citation: https://doi.org/10.5194/nhess-2022-136-AC4
-
RC5: 'Comment on nhess-2022-136', Anonymous Referee #5, 18 Oct 2022
-
AC5: 'Reply on RC5', Wei Li, 20 Nov 2022
The paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China” concerns an interesting research topic that is the spatial variability of vulnerability to floods. Nevertheless, despite the good intentions, the paper does not face the problem with the necessary accuracy to reach reliable results, and does not explain enough neither methodology nor results.
I believe that this manuscript needs strong improvement to bring it up to an international level, in terms of both language and scientific approach, and from my point of view it should be rejected.
Main problems
Comment 1:
Results and method presented in the paper are merged to results already published in literature, and the reader cannot understand what is literature and what is a result of this research.
Response:
I'm sorry, what we want to explain here is that the Variable Fuzzy Set theory and Cloud-improved Entropy weighting method we quoted are the previous research results of our team. In addition, I would like to explain our differences from the existing vulnerability researches in combination with the introduction.
First of all, it was proved by literatures that vulnerability research is a scientific method of disaster management and a hot spot of current research. Secondly, through the study and summary of a large number of literatures, we founded that under the specific scale, the vulnerability of different regions may be quite different, and it is necessary to study the regional vulnerability. Finally, it was founded that in the existing regional vulnerability researches, there almost no one has studied the vulnerability from the provincial scale, so we have carried out relevant research to help government decision-makers manage disasters at the macro level.
Comment 2:
The methodology is not clearly explained neither in terms of data nor procedures and calculations, and it is impossible its replication in another study area.
Response:
We have selected eight representative indicators from the four levels of environment, economy, life and society to reflect the vulnerability of disaster victims. Then reliable data were obtained from various official sources. Finally, the vulnerability is calculated by a scientific theoretical method. I'm sorry that the introduction of theory in the manuscript may be not sufficient, which makes you feel that there is no clear explanation, and the method cannot be copied. Therefore, we have supplemented the theoretical content in the manuscript.
Comment 3:
There is an unappropriated use of words as vulnerability and risk that already have specific definitions in literature. Authors combine them in unintelligible neologisms that generate a cascading effect of misunderstandings throughout the entire paper. According to IPCC (Intergovernmental Panel on Climate Change), the "Determinants of Risk are: Hazard, Exposure, and Vulnerability". Then, vulnerability is part of the risk, and terms as “flood risk vulnerability” and "Extreme flood riskvulnerability" for me are obscure.
Response:
Thank you for your professional advice. Other experts also raised this issue. I'm sorry for the inconvenience caused by our writing mistakes. According to your suggestion, we have deleted the word "risk" from the word "risk vulnerability".
Comment 4:
The language used is not adequate to an international scientific journal and does not allow to readers to understand the topic.
Response:
According to your suggestion, we have carefully checked the language expression of the manuscript.
Comment 5:
Figures are not explicative, and show low graphical standard.
Response:
According to your suggestion, we have replaced the high-quality figures and supplemented the description of the figures.
ABSTRACT
Comment 6:
It is not clear. After a section related to flood damage and a not understandable description of the methodological approach, the results are presented as:
“The spatial distribution of the EF risk vulnerability shows (1) a decreasing trend from the regions with high population density to regions with low population density, (2) a decreasing trend from economically developed regions to economically backward regions, (3) a decreasing trend from the eastern coastal regions to the central agricultural provinces and then to the southwest, northwest and northeast regions in China”.
First of all, I cannot understand what is the “EF risk vulnerability”, and secondly it is unclear what are the “factors of flood resistance” and the “four aspects including life, economy, environment and society” listed in lines 13-16.
Response:
According to the previous suggestions, we have revised the writing of EF risk vulnerability. Ge et al. (2020) points out that the impact of EF can be divided into four aspects: economy, life, environment and society (Title: Status and development trend of research on risk consequences caused by dam breach; DOI: 10. 14042 /j. cnki. 32. 1309. 2020. 01. 015). Among them, life mainly refers to the loss of life in the flooded area caused by factors such as water flow impact, inundation and cold; Economic losses mainly refer to the direct economic losses of houses, furniture, materials, agriculture, etc. And indirect economic losses caused by affecting the transportation and normal production of factories and mining enterprises; The environmental impact mainly refers to the changes in river morphology, human landscape and major pollution caused by floods, which are specifically reflected in the water environment, soil environment, ecological environment and human settlements. The social impact mainly refers to the change of people's original life style, quality of life, psychological state and the impact on the political system and cultural level.
INTRODUCTION
Comment 7:
It is confused and pertains papers of sectors not inherent to the research topic. The authors used here several unclear words (for example: L65 Country-scale regions (country region or scale region?); L47 vulnerability of life loss; L56 vulnerability of EF…!) The authors do not relate their work to the broader literature, and then the reader cannot understand the context, what others have done, and what is the novelty of the submitted paper
Response:
We are sorry that our writing method has caused you great trouble in reading. In line 65, what we want to express is that Zeng, the author, has conducted a vulnerability study at the county scale. In line 47, what we want to express is that Ziegler, the author, only carried out the research on the vulnerability of EF to people life, excluding the consideration of economic, environmental and social impacts. In line 56, what we want to express is that Adikari, the author, believes global changes, domestic migration patterns, development practices, political instability and other factors have a great impact on vulnerability. According to your suggestions, we also checked and revised the language problems in the rest of the manuscript.
Comment 8:
From L90 to 94 the authors tried to explain the aim of the work, even if this explanation in not clear enough. Introduction ends with the 4th aim of the paper that is:
”It will provides important decision-making basis for flood control, disaster reduction, disaster relief and disaster reduction, and provides reference for similar research in the future”.
In my opinion, this is not an objective of the paper, maybe an application of results, with the repetition of “disaster reduction”.
Response:
In lines 90 to 94, we gave a more specific description of the 4th aim of the manuscript: to provide the government decision-makers with a macro understanding of the vulnerability at the provincial scale, and to provide reference for the subsequent research on the vulnerability at the provincial scale.
We believe that providing an important decision-making basis for flood prevention, disaster reduction, disaster relief and disaster reduction is the ultimate goal of all disaster assessment researches, but different studies have different ways to achieve this goal. Our research on vulnerability at the provincial scale can help government decision-makers grasp vulnerability at the provincial scale.
MATERIALS AND METHODS
Comment 9:
From L99-105, the authors talk about the “evaluation method”. But the sentence explaining it is unclear:
“In this manuscript, we make full use of the expectation of cloud model and cloud entropy parameters, learn from the processing method of entropy weight method for index differences, take into account the subjectivity and objectivity of weight, and scientifically reflect the importance of risk factors”.
The description becomes more intricate in INDEX SYSTEM section, where it is unclear the difference between quoted literature and the work carried out by the authors.
Basing on this premises, from here on I found great difficulties in reading the paper, and especially the “Calculating Model” resulted completely obscure to me.
Response:
I'm sorry for the inconvenience. In lines 99-105, the references we cited are the results of our team's previous research (Li et al., 2018, 2019; Ge et al., 2020). We applied them to the current manuscript to ensure the scientificity of the calculation results. The specific information of the three references is as follows:
Li, Z.; Li, W.; Ge, W. Weight analysis of influencing factors of dam break risk consequences. Nat. Hazards Earth Syst. Sci. 2018, 18(12), 3355-3362. https://doi.org/10.5194/nhess-18-3355-2018
Li, Z., Li, W., Ge, W., Xu, H, Dam breach environmental impact evaluation based on set pair analysis-variable fuzzy set coupling model. Journal of tianjin university (science and technology). 2019, 52(03), 49-56. https://doi.org/10.11784/tdxbz201807030
Ge, W.; Li, Z.; Li, W.; Wu, M.; Li, J.; Pan, Y. Risk evaluation of dam-break environmental impacts based on the set pair analysis and cloud model. Nat. Hazards. 2020, 104, 1641-1653. https://doi.org/10.1007/s11069-020-04237-9
Comment 10:
“Evaluation indexes are constructed in the same type of data source for the consistency of data caliber”. I cannot understand the meaning.
Response:
What we want to express is that, due to the lack of statistical data, we try our best to ensure that various data are same or similar in years.
Comment 11:
L156: “2.2.1 Index value basis and its standard”: also here, it is unclear the title of the section and absolutely obscure the entire section. The use of index (singular) means that the authors used a single index. Section starts declaring that “disaster’s impact on a region has a significant correlation with its population density”. It seems quite obvious.
Response:
According to your suggestion, we have changed the title to “Indexes value basis and their standards”. We believe that indicators must be typical and representative when selecting indicators, so the more obvious the indicators are, the more they meet the selection criteria.
Comment 12:
L168: why POPULATION DENSITY is called Rp (risk population)??
Response:
According to the suggestions of other experts, we have modified it to population density (PD).
Comment 13:
L170: the authors stated that: “The young and middle-aged populations are physically stronger, so they tend to have the ability to rescue themselves and others when disasters occur. Therefore, they are regarded as the main social force forresuming production and life after a disaster. According to the China and international labor force classification standards”. This is an opinion: why this opinion is reported here?
Response:
Because it divides the young and middle-aged people, the ability of self rescue can be calculated according to this view
Comment 14:
L178 “Regional and urban-rural disparities are the important factors leading to regional disparities in disaster prevention in China. In terms of economic development, the eastern regions are the most developed, the central regions the second, and the western regions are the least developed. The economic development gap among these three regions is obvious. From the perspective of losses caused by disasters, the more intensive social and economic activities of an area, the more its social assets exposed to disasters due to the concentration of social wealth”. Once damaged by disasters, the area will suffer greater economic losses”. It seems that the authors already know the results that they are looking for, even before try to assess them…
Response:
Thank you for your comments to help us further improve the level of the manuscript. On the one hand, the more intensive social and economic activities are, the more likely they will cause serious losses. This is a qualitative description. The problem is how to quantitatively analyze this problem. For example, the more severe the flood, the more serious the economic loss. We also know this rule or result, but we will still analyze it. On the other hand, the difference in economic level is one of the important factors leading to different vulnerabilities, but it is not the only factor. We comprehensively assess vulnerability through four aspects: economy, life loss, environment and society, and then compare vulnerability among provinces.
Comment 15:
The section Discussion is merely made by one text line and one figure. Conclusions, according to the style of the paper, describe things that the reader is unable to assess if they represent a result of the paper or an opinion of the authors in some way confirmed by the intricate combination of not well-defined parameters presented in the paper.
Response:
The vulnerability is comprehensively affected by many factors. On the basis of calculating the vulnerability of each province and city, we intuitively express the degree of vulnerability in the form of images through classification methods (average and second average analysis), and analyze the distribution law of China's vulnerability according to the figure. These contents are the results of the paper and also represent our views. In addition, in order to verify the scientificity of the results, we also added the rationality analysis of the results as follows:
Although Beijing, Shanghai Tianjin, Guangdong and Jiangsu have strong comprehensive disaster reduction and prevention capabilities, but their economies are the most developed and their population density is very high (their GDP per capita and population density are among the top seven in China all the year round), which leads to their extremely serious vulnerability. Provinces in the Yangtze River, Yellow River and Huaihe River basins (Hebei, Henan, Shandong, Sichuan, Chongqing, Hunan, Hubei, Anhui, Zhejiang and Fujian) are the main producing areas of grain in China, with relatively developed economy and large population, so their vulnerability is severe. In the northwest, southwest and northeast of China, although the traffic network density is low, once a serious EF disaster occurs, it is difficult to transfer the affected people and property, and may cause indirect losses due to ineffective relief and slow recovery. However, due to the terrain and geographical location, its population density is low, it is a gathering area of most ethnic minorities in China, and its economy is underdeveloped, so the vulnerability level of these provinces (Jilin, Liaoning, Inner Mongolia, Shanxi, Shanxi, Ningxia, Qinghai, Gansu, Xinjiang, Tibet, Yunnan, Guizhou, Jiangxi and Guangxi) is moderate. Heilongjiang province has become the only province with slight vulnerability to EF disasters. The reasons include low population density (bottom three in China all the year round), high proportion of labor force (top six in China all the year round), and strong disaster prevention and relief capacity (Heilongjiang, known as the "Great Granary of China", is the location of the Great Xing'an Mountains and the Small Xing'an Mountains. It is rich in forests, minerals, animals and plants. Therefore, the local government attaches great importance to disaster prevention). The vulnerability assessment results are highly consistent with the distribution laws of population, economy and natural environment in China, which verifies the rationality of the results.
Citation: https://doi.org/10.5194/nhess-2022-136-AC5
-
AC5: 'Reply on RC5', Wei Li, 20 Nov 2022
Status: closed
-
CC1: 'Comment on nhess-2022-136', xixi Hong, 13 Jun 2022
This study is of great significance for flood disaster risk assessment at regional scale.
Citation: https://doi.org/10.5194/nhess-2022-136-CC1 -
CC3: 'Reply on CC1', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC3
-
CC3: 'Reply on CC1', Wei Li, 15 Jul 2022
-
CC2: 'Comment on nhess-2022-136', Te Wang, 15 Jun 2022
An interesting idea combined with a scientific method has inspired me a lot
Citation: https://doi.org/10.5194/nhess-2022-136-CC2 -
CC4: 'Reply on CC2', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC4 -
CC5: 'Reply on CC2', Wei Li, 15 Jul 2022
Thank you for your recognition of the manuscript
Citation: https://doi.org/10.5194/nhess-2022-136-CC5
-
CC4: 'Reply on CC2', Wei Li, 15 Jul 2022
-
RC1: 'Comment on nhess-2022-136', Anonymous Referee #1, 02 Jul 2022
I have read with great interest the manuscript entitled "Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China" The manuscript is very interesting and has a substantial impact on extreme flood disaster assessment and management.
The manuscript have both theoretical and practical significance. For this reason, I consider that this manuscript is suitable for publication in "Natural Hazards and Earth System Sciences" after considering some minor revisions.
# 1:
The provincial importance index (PI) and social disaster recovery index (SI) are not listed in Table 1.
# 2:
Lines 172-175, it should be “the 14 to 65 year-old is considered as the labor force.”
# 3:
In Table 5, the mean value of the results of the four parameter combinations is taken. Whether the combination of these four parameters is of practical significance or just the needs of theoretical calculation.
# 4:
Lines 224-230 describe that the social disaster recovery index (SI) reflects the disaster resistance, disaster relief and recovery capacity of provinces and cities. What is the difference between SI and vulnerability?
Citation: https://doi.org/10.5194/nhess-2022-136-RC1 -
AC1: 'Reply on RC1', Wei Li, 15 Jul 2022
I have read with great interest the manuscript entitled "Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China" The manuscript is very interesting and has a substantial impact on extreme flood disaster assessment and management.
The manuscript have both theoretical and practical significance. For this reason, I consider that this manuscript is suitable for publication in "Natural Hazards and Earth System Sciences" after considering some minor revisions.
Response:
Thank you for your hard work and recognition of the manuscript, which is very important to encourage us to further improve the model.
# 1:
The provincial importance index (PI) and social disaster recovery index (SI) are not listed in Table 1.
Response:
According to your comment, the provincial importance index (PI) and social disaster recovery index (SI) are added in Table 1.
# 2:
Lines 172-175, it should be “the 14 to 65 year-old is considered as the labor force.”
Response:
According to your comment, the relevant contents have been corrected, as shown in lines 178-179 in the manuscript marked with changes.
# 3:
In Table 5, the mean value of the results of the four parameter combinations is taken. Whether the combination of these four parameters is of practical significance or just the needs of theoretical calculation.
Response:
Thanks for your professional and valuable comments. The combination of four parameters is the need of calculation and has certain theoretical significance. Through four different variable model parameters, it better reflects the correlation and dynamic variability of fuzzy concepts, and avoids the problem of static membership function in traditional fuzzy set theory.
# 4:
Lines 224-230 describe that the social disaster recovery index (SI) reflects the disaster resistance, disaster relief and recovery capacityy of provinces and cities. What is the difference between SI and vulnerability.
Response:
SI mainly reflects the vulnerability of provinces from the perspective of social impact. The vulnerability of this manuscript also comprehensively considers the impact of economy, life and environment.
Citation: https://doi.org/10.5194/nhess-2022-136-AC1
-
AC1: 'Reply on RC1', Wei Li, 15 Jul 2022
-
RC2: 'Comment on nhess-2022-136', Anonymous Referee #2, 07 Jul 2022
There was a great job of explaining what models have been done before, which gave this manuscript good research significance. In addition, the manuscript has a clear structure, proper language expression, and it is pleasant to read. I have only a few suggestions for the manuscript.
(1) Add more explanations on the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method to reflect the necessity of using improved methods.
(2) Whether the vulnerability of provinces and the probability of EF can be combined in further research to improve the guiding significance for the government.
(3) Some contents need to be added to the manuscript: PI and SI indicators in Table 1, and explanations of a and p in Table 5.
All in all, I think this manuscript can be published after some minor modifications.
Citation: https://doi.org/10.5194/nhess-2022-136-RC2 -
AC2: 'Reply on RC2', Wei Li, 15 Jul 2022
There was a great job of explaining what models have been done before, which gave this manuscript good research significance. In addition, the manuscript has a clear structure, proper language expression, and it is pleasant to read. I have only a few suggestions for the manuscript.
Response:
Thank you for your recognition of the manuscript, which is of great significance to help and encourage us to further conduct in-depth research.
(1) Add more explanations on the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method to reflect the necessity of using improved methods.
Response:
According to your comment, the advantages of Variable Fuzzy Set theory and Cloud-improved Entropy weighting method have been supplemented, as shown in lines 99-109 in the manuscript marked with changes.
(2) Whether the vulnerability of provinces and the probability of EF can be combined in further research to improve the guiding significance for the government.
Response:
Thank you for your professional advice. As you said, it is more instructive to combine probability with vulnerability, which is the direction of our next in-depth research.
(3) Some contents need to be added to the manuscript: PI and SI indicators in Table 1, and explanations of a and p in Table 5. All in all, I think this manuscript can be published after some minor modifications.
Response:
Thank you for your valuable comments and recognition of the manuscript. According to your comment, the relevant contents have been added in the manuscript marked with changes.
Citation: https://doi.org/10.5194/nhess-2022-136-AC2
-
AC2: 'Reply on RC2', Wei Li, 15 Jul 2022
-
RC3: 'Comment on nhess-2022-136', Anonymous Referee #3, 17 Jul 2022
The authors used the Cloud-improved Entropy Method and the Fuzzy Variable Theory to calculate the vulnerability of Chinese provinces to extreme floods. The trend and law of vulnerability distribution are analyzed, which provides a reference for regional risk management. This manuscript is very interesting and valuable. I do not have strong suggestions, only the following small suggestions.
(1) Add relevant contents of index SI and PI in Table 1.
(2) In section 2.1, it is written that the subjectivity and objectivity of weights are considered. Where are the subjectivity and objectivity embodied respectively?
(3) In section 2.2.1, RP is defined as the population at risk. In fact, it is more appropriate to call it population density.
(4) The method proposed in this study is to conduct flood vulnerability research and assessment on a provincial basis. Compared with the research on City, county and district scale, does it have more advantages in some aspects?
(5) The severity of disasters caused by floods is largely related to the intensity of floods. Are the results of this study applicable to flood analysis on any scale? When the flood intensity is different, how does it affect the final evaluation result?Citation: https://doi.org/10.5194/nhess-2022-136-RC3 -
AC3: 'Reply on RC3', Wei Li, 22 Jul 2022
The authors used the Cloud-improved Entropy Method and the Fuzzy Variable Theory to calculate the vulnerability of Chinese provinces to extreme floods. The trend and law of vulnerability distribution are analyzed, which provides a reference for regional risk management. This manuscript is very interesting and valuable. I do not have strong suggestions, only the following small suggestions.
(1) Add relevant contents of index SI and PI in Table 1.response:
According to your comment, the index SI and PI have been added in Table 1.
(2) In section 2.1, it is written that the subjectivity and objectivity of weights are considered. Where are the subjectivity and objectivity embodied respectively?response:
Considering the expectation and entropy of the Cloud Model, the subjective judgment of experts is treated by the Cloud Model as a key representative parameter representing uncertainty, which reflects subjectivity. Entropy weight Method has the characteristics of strong objectivity. It reflects objectivity by using entropy weight method to deal with the differences between indicators.
(3) In section 2.2.1, RP is defined as the population at risk. In fact, it is more appropriate to call it population density.response:
According to your comment, the PD (population density) has been substituted for RP (risk population).
(4) The method proposed in this study is to conduct flood vulnerability research and assessment on a provincial basis. Compared with the research on City, county and district scale, does it have more advantages in some aspects?response:
We believe that the study of each scale has its advantages and necessity The distribution of population, economy and natural environment among different provinces in China has strong regional heterogeneity. Carrying out provincial-level research can provide a research basis for the overall risk management of the country, which cannot be macro grasped by other scales.
(5) The severity of disasters caused by floods is largely related to the intensity of floods. Are the results of this study applicable to flood analysis on any scale? When the flood intensity is different, how does it affect the final evaluation result?response:
On the one hand, the severity of a regional disaster is related to the intensity of floods, on the other hand, it is related to its ability to resist floods, that is, the vulnerability mentioned in this paper. Therefore, the vulnerability assessment results are not affected by the flood intensity, and can be applied to extreme floods of any scale.
Citation: https://doi.org/10.5194/nhess-2022-136-AC3
-
AC3: 'Reply on RC3', Wei Li, 22 Jul 2022
-
RC4: 'Comment on nhess-2022-136', Anonymous Referee #4, 20 Sep 2022
The authors present the paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China”. They apply Cloud-improved Entropy Method to calculate used indexes weight, and the Fuzzy Variable Theory to calculate the total vulnerability. A small-scale map of China vulnerability was produced.
Below some considerations and suggestion.
- Vulnerability is an essential component of risk analysis and, as such, it has to be deeply investigated. In the manuscript the terms “risk” and “vulnerability”, are often used as synonymous and in other cases they are used together (see e.g. row 20: “…spatial distribution of the EF risk vulnerability…”, row 99: (… “risk indicator of EF vulnerability...”), row 237: (… “factors of flood risk vulnerability”), and many others. I suggest to review the terminology.
- In the flow diagram of Fig. 1 reference is made to “Stability analysis”. What does it refer to? To the stability of adopted model? This analysis process should be descripted in the manuscript.
- The matrix (2) is formally incorrect (see last row and last column).
- “H”, listed for each province/city in table 5, is described from the authors (equation 7, row 148) as “level eigenvalue of the evaluation sample”. From these values, the vulnerability map (fig. 5) is derived. If “H” is the vulnerability, why call it “level eigenvalue”? if not, how do the authors get the map from value of H? The map derivation process should be better explained in the text.
- The vulnerability is divided into four levels: “mild”, “moderate”, “severe” and “extremely severe”, passing from numerical analysis to a purely qualitative data. In the text should be indicated the criterion of numerical thresholds choosing and, the thresholds value indicated.
- Conclusions are sparse and, in many respects, obvious. They should be integrated.
- Both to complete and improve the paper, and to support the reliability of the procedure adopted, a validation of the vulnerability map produced, could be useful.
In attached file other minor notifications are reported.
-
AC4: 'Reply on RC4', Wei Li, 20 Nov 2022
The author present the paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China”. They apply Cloud-improved Entropy Method to calculate used indexes weight, and the Fuzzy Variable Theory to calculate the total vulnerability. A small-scale map of China vulnerability was produced.
Below some considerations and suggestion.
Comment 1:
Vulnerability is an essential component of risk analysis and, as such, it has to be deeply investigated. In the manuscript the terms “risk” and “vulnerability”, are often used as synonymous and in other cases they are used together (see e.g. row 20: “…spatial distribution of the EF risk vulnerability…”, row 99:(…“risk indicator of EF vulnerability…”), row 237:(…“factors of flood risk vulnerability”), and many others. I suggest to review the terminology.
Response:
Sorry for the inconvenience caused by our writing habits. What we want to express is just vulnerability. Thank you for your suggestion. We have removed "risk" from the word “risk vulnerability”.
Comment 2:
In the flow diagram of Fig. 1 reference is made to “Stability analysis”. What does it refer to? To the stability of adopted model? This analysis process should be descripted in the manuscript.
Response:
Thank you for your suggestion. Stability analysis is aimed at variable fuzzy model. When using the variable fuzzy model, the general formula has four changes (a=1, p=1; a=2, p=1; a=1, p=2; a=2, p=2). When the calculation results of the four parameter combinations differ greatly, it means that the stability of the calculation results is poor, and the model needs to be adjusted. When the calculation results are similar, it is considered that the stability meets the requirements and the calculation results of vulnerability have good applicability. The process of stability analysis is to compare whether the results of four parameter combinations are similar.
Comment 3:
The matrix (2) is formally incorrect (see last row and last column).
Response:
According to your suggestions, we have modified matrix (2).
Comment 4:
“H”, listed for each province/city in table 5, is described from the authors (equation 7, row 148) as “level eigenvalue of the evaluation sample”. From these values, the vulnerability map (fig.5) is derived. If “H” is the vulnerability, why call it “level eigenvalue”? If not, how do the authors get the map from value of H?
Response:
Thank you for your hard work. As written in Table 5, H is the average characteristic value of the evaluation sample, and it is the premise and basis for classifying the vulnerability level. After an average and second average analysis of H, we get the vulnerability level, which is why we do not directly call H vulnerability.
Average and secondary average analysis is a commonly used mathematical method, which can solve the problem that individual data is too scattered, resulting in the evaluation results being too concentrated and falling into a certain level, and the grading is unreasonable and not smooth. For example, the data [1, 2, 3, 4, 5, 21] are divided into three grades: slight, moderate and serious. If the traditional grading method is used to divide the thresholds into [1, 7], [8, 14] and [15, 21], then the evaluation results are basically "slight", and the differences between them cannot be effectively distinguished, making the evaluation ineffective. Therefore, we use the average and second average analysis to solve this problem. As it is an existing method and not the focus of our research, so we only pointed out the method used and explained its role, but did not write its application steps in the manuscript.
Comment 5:
The vulnerability is divided into four levels: “slight”, “moderate”, “severe” and “extremely serious”, passing from numerical analysis to a purely qualitative data. In the text should be indicated the criterion of numerical thresholds choosing and, the thresholds value indicated.
Response:
Thank you for your professional advice. The reason why we did not specify the threshold value of the grade standard is that it is not a fixed value and has no practical significance. Just like a cake, we can divide it into four smaller portions, but when the size of the whole cake changes, the size of each smaller portion will also change, so we only indicate the division method (average and second average analysis), but not the threshold value.
Comment 6:
Conclusions are sparse and, in many respects, obvious. They should be integrated.
Response:
According to your suggestion, we have integrated the results in the discussion and supplemented the rationality analysis of the results.
Comment 7:
Both to complete and improve the paper, and to support the reliability of the procedure adopted, a validation of the vulnerability map produced, could be useful.
Response:
According to your suggestion, we have added the analysis of the rationality of the results. The revised contents are as follows：
Although Beijing, Shanghai Tianjin, Guangdong and Jiangsu have strong comprehensive disaster reduction and prevention capabilities, but their economies are the most developed and their population density is very high (their GDP per capita and population density are among the top seven in China all the year round), which leads to their extremely serious vulnerability. Provinces in the Yangtze River, Yellow River and Huaihe River basins (Hebei, Henan, Shandong, Sichuan, Chongqing, Hunan, Hubei, Anhui, Zhejiang and Fujian) are the main producing areas of grain in China, with relatively developed economy and large population, so their vulnerability is severe. In the northwest, southwest and northeast of China, although the traffic network density is low, once a serious EF disaster occurs, it is difficult to transfer the affected people and property, and may cause indirect losses due to ineffective relief and slow recovery. However, due to the terrain and geographical location, its population density is low, it is a gathering area of most ethnic minorities in China, and its economy is underdeveloped, so the vulnerability level of these provinces (Jilin, Liaoning, Inner Mongolia, Shanxi, Shanxi, Ningxia, Qinghai, Gansu, Xinjiang, Tibet, Yunnan, Guizhou, Jiangxi and Guangxi) is moderate. Heilongjiang province has become the only province with slight vulnerability to EF disasters. The reasons include low population density (bottom three in China all the year round), high proportion of labor force (top six in China all the year round), and strong disaster prevention and relief capacity (Heilongjiang, known as the "Great Granary of China", is the location of the Great Xing'an Mountains and the Small Xing'an Mountains. It is rich in forests, minerals, animals and plants. Therefore, the local government attaches great importance to disaster prevention). The vulnerability assessment results are highly consistent with the distribution laws of population, economy and natural environment in China, which verifies the rationality of the results.
Citation: https://doi.org/10.5194/nhess-2022-136-AC4
-
RC5: 'Comment on nhess-2022-136', Anonymous Referee #5, 18 Oct 2022
-
AC5: 'Reply on RC5', Wei Li, 20 Nov 2022
The paper “Spatial Distribution of Vulnerability to Extreme Flood: in provincial scale of China” concerns an interesting research topic that is the spatial variability of vulnerability to floods. Nevertheless, despite the good intentions, the paper does not face the problem with the necessary accuracy to reach reliable results, and does not explain enough neither methodology nor results.
I believe that this manuscript needs strong improvement to bring it up to an international level, in terms of both language and scientific approach, and from my point of view it should be rejected.
Main problems
Comment 1:
Results and method presented in the paper are merged to results already published in literature, and the reader cannot understand what is literature and what is a result of this research.
Response:
I'm sorry, what we want to explain here is that the Variable Fuzzy Set theory and Cloud-improved Entropy weighting method we quoted are the previous research results of our team. In addition, I would like to explain our differences from the existing vulnerability researches in combination with the introduction.
First of all, it was proved by literatures that vulnerability research is a scientific method of disaster management and a hot spot of current research. Secondly, through the study and summary of a large number of literatures, we founded that under the specific scale, the vulnerability of different regions may be quite different, and it is necessary to study the regional vulnerability. Finally, it was founded that in the existing regional vulnerability researches, there almost no one has studied the vulnerability from the provincial scale, so we have carried out relevant research to help government decision-makers manage disasters at the macro level.
Comment 2:
The methodology is not clearly explained neither in terms of data nor procedures and calculations, and it is impossible its replication in another study area.
Response:
We have selected eight representative indicators from the four levels of environment, economy, life and society to reflect the vulnerability of disaster victims. Then reliable data were obtained from various official sources. Finally, the vulnerability is calculated by a scientific theoretical method. I'm sorry that the introduction of theory in the manuscript may be not sufficient, which makes you feel that there is no clear explanation, and the method cannot be copied. Therefore, we have supplemented the theoretical content in the manuscript.
Comment 3:
There is an unappropriated use of words as vulnerability and risk that already have specific definitions in literature. Authors combine them in unintelligible neologisms that generate a cascading effect of misunderstandings throughout the entire paper. According to IPCC (Intergovernmental Panel on Climate Change), the "Determinants of Risk are: Hazard, Exposure, and Vulnerability". Then, vulnerability is part of the risk, and terms as “flood risk vulnerability” and "Extreme flood riskvulnerability" for me are obscure.
Response:
Thank you for your professional advice. Other experts also raised this issue. I'm sorry for the inconvenience caused by our writing mistakes. According to your suggestion, we have deleted the word "risk" from the word "risk vulnerability".
Comment 4:
The language used is not adequate to an international scientific journal and does not allow to readers to understand the topic.
Response:
According to your suggestion, we have carefully checked the language expression of the manuscript.
Comment 5:
Figures are not explicative, and show low graphical standard.
Response:
According to your suggestion, we have replaced the high-quality figures and supplemented the description of the figures.
ABSTRACT
Comment 6:
It is not clear. After a section related to flood damage and a not understandable description of the methodological approach, the results are presented as:
“The spatial distribution of the EF risk vulnerability shows (1) a decreasing trend from the regions with high population density to regions with low population density, (2) a decreasing trend from economically developed regions to economically backward regions, (3) a decreasing trend from the eastern coastal regions to the central agricultural provinces and then to the southwest, northwest and northeast regions in China”.
First of all, I cannot understand what is the “EF risk vulnerability”, and secondly it is unclear what are the “factors of flood resistance” and the “four aspects including life, economy, environment and society” listed in lines 13-16.
Response:
According to the previous suggestions, we have revised the writing of EF risk vulnerability. Ge et al. (2020) points out that the impact of EF can be divided into four aspects: economy, life, environment and society (Title: Status and development trend of research on risk consequences caused by dam breach; DOI: 10. 14042 /j. cnki. 32. 1309. 2020. 01. 015). Among them, life mainly refers to the loss of life in the flooded area caused by factors such as water flow impact, inundation and cold; Economic losses mainly refer to the direct economic losses of houses, furniture, materials, agriculture, etc. And indirect economic losses caused by affecting the transportation and normal production of factories and mining enterprises; The environmental impact mainly refers to the changes in river morphology, human landscape and major pollution caused by floods, which are specifically reflected in the water environment, soil environment, ecological environment and human settlements. The social impact mainly refers to the change of people's original life style, quality of life, psychological state and the impact on the political system and cultural level.
INTRODUCTION
Comment 7:
It is confused and pertains papers of sectors not inherent to the research topic. The authors used here several unclear words (for example: L65 Country-scale regions (country region or scale region?); L47 vulnerability of life loss; L56 vulnerability of EF…!) The authors do not relate their work to the broader literature, and then the reader cannot understand the context, what others have done, and what is the novelty of the submitted paper
Response:
We are sorry that our writing method has caused you great trouble in reading. In line 65, what we want to express is that Zeng, the author, has conducted a vulnerability study at the county scale. In line 47, what we want to express is that Ziegler, the author, only carried out the research on the vulnerability of EF to people life, excluding the consideration of economic, environmental and social impacts. In line 56, what we want to express is that Adikari, the author, believes global changes, domestic migration patterns, development practices, political instability and other factors have a great impact on vulnerability. According to your suggestions, we also checked and revised the language problems in the rest of the manuscript.
Comment 8:
From L90 to 94 the authors tried to explain the aim of the work, even if this explanation in not clear enough. Introduction ends with the 4th aim of the paper that is:
”It will provides important decision-making basis for flood control, disaster reduction, disaster relief and disaster reduction, and provides reference for similar research in the future”.
In my opinion, this is not an objective of the paper, maybe an application of results, with the repetition of “disaster reduction”.
Response:
In lines 90 to 94, we gave a more specific description of the 4th aim of the manuscript: to provide the government decision-makers with a macro understanding of the vulnerability at the provincial scale, and to provide reference for the subsequent research on the vulnerability at the provincial scale.
We believe that providing an important decision-making basis for flood prevention, disaster reduction, disaster relief and disaster reduction is the ultimate goal of all disaster assessment researches, but different studies have different ways to achieve this goal. Our research on vulnerability at the provincial scale can help government decision-makers grasp vulnerability at the provincial scale.
MATERIALS AND METHODS
Comment 9:
From L99-105, the authors talk about the “evaluation method”. But the sentence explaining it is unclear:
“In this manuscript, we make full use of the expectation of cloud model and cloud entropy parameters, learn from the processing method of entropy weight method for index differences, take into account the subjectivity and objectivity of weight, and scientifically reflect the importance of risk factors”.
The description becomes more intricate in INDEX SYSTEM section, where it is unclear the difference between quoted literature and the work carried out by the authors.
Basing on this premises, from here on I found great difficulties in reading the paper, and especially the “Calculating Model” resulted completely obscure to me.
Response:
I'm sorry for the inconvenience. In lines 99-105, the references we cited are the results of our team's previous research (Li et al., 2018, 2019; Ge et al., 2020). We applied them to the current manuscript to ensure the scientificity of the calculation results. The specific information of the three references is as follows:
Li, Z.; Li, W.; Ge, W. Weight analysis of influencing factors of dam break risk consequences. Nat. Hazards Earth Syst. Sci. 2018, 18(12), 3355-3362. https://doi.org/10.5194/nhess-18-3355-2018
Li, Z., Li, W., Ge, W., Xu, H, Dam breach environmental impact evaluation based on set pair analysis-variable fuzzy set coupling model. Journal of tianjin university (science and technology). 2019, 52(03), 49-56. https://doi.org/10.11784/tdxbz201807030
Ge, W.; Li, Z.; Li, W.; Wu, M.; Li, J.; Pan, Y. Risk evaluation of dam-break environmental impacts based on the set pair analysis and cloud model. Nat. Hazards. 2020, 104, 1641-1653. https://doi.org/10.1007/s11069-020-04237-9
Comment 10:
“Evaluation indexes are constructed in the same type of data source for the consistency of data caliber”. I cannot understand the meaning.
Response:
What we want to express is that, due to the lack of statistical data, we try our best to ensure that various data are same or similar in years.
Comment 11:
L156: “2.2.1 Index value basis and its standard”: also here, it is unclear the title of the section and absolutely obscure the entire section. The use of index (singular) means that the authors used a single index. Section starts declaring that “disaster’s impact on a region has a significant correlation with its population density”. It seems quite obvious.
Response:
According to your suggestion, we have changed the title to “Indexes value basis and their standards”. We believe that indicators must be typical and representative when selecting indicators, so the more obvious the indicators are, the more they meet the selection criteria.
Comment 12:
L168: why POPULATION DENSITY is called Rp (risk population)??
Response:
According to the suggestions of other experts, we have modified it to population density (PD).
Comment 13:
L170: the authors stated that: “The young and middle-aged populations are physically stronger, so they tend to have the ability to rescue themselves and others when disasters occur. Therefore, they are regarded as the main social force forresuming production and life after a disaster. According to the China and international labor force classification standards”. This is an opinion: why this opinion is reported here?
Response:
Because it divides the young and middle-aged people, the ability of self rescue can be calculated according to this view
Comment 14:
L178 “Regional and urban-rural disparities are the important factors leading to regional disparities in disaster prevention in China. In terms of economic development, the eastern regions are the most developed, the central regions the second, and the western regions are the least developed. The economic development gap among these three regions is obvious. From the perspective of losses caused by disasters, the more intensive social and economic activities of an area, the more its social assets exposed to disasters due to the concentration of social wealth”. Once damaged by disasters, the area will suffer greater economic losses”. It seems that the authors already know the results that they are looking for, even before try to assess them…
Response:
Thank you for your comments to help us further improve the level of the manuscript. On the one hand, the more intensive social and economic activities are, the more likely they will cause serious losses. This is a qualitative description. The problem is how to quantitatively analyze this problem. For example, the more severe the flood, the more serious the economic loss. We also know this rule or result, but we will still analyze it. On the other hand, the difference in economic level is one of the important factors leading to different vulnerabilities, but it is not the only factor. We comprehensively assess vulnerability through four aspects: economy, life loss, environment and society, and then compare vulnerability among provinces.
Comment 15:
The section Discussion is merely made by one text line and one figure. Conclusions, according to the style of the paper, describe things that the reader is unable to assess if they represent a result of the paper or an opinion of the authors in some way confirmed by the intricate combination of not well-defined parameters presented in the paper.
Response:
The vulnerability is comprehensively affected by many factors. On the basis of calculating the vulnerability of each province and city, we intuitively express the degree of vulnerability in the form of images through classification methods (average and second average analysis), and analyze the distribution law of China's vulnerability according to the figure. These contents are the results of the paper and also represent our views. In addition, in order to verify the scientificity of the results, we also added the rationality analysis of the results as follows:
Although Beijing, Shanghai Tianjin, Guangdong and Jiangsu have strong comprehensive disaster reduction and prevention capabilities, but their economies are the most developed and their population density is very high (their GDP per capita and population density are among the top seven in China all the year round), which leads to their extremely serious vulnerability. Provinces in the Yangtze River, Yellow River and Huaihe River basins (Hebei, Henan, Shandong, Sichuan, Chongqing, Hunan, Hubei, Anhui, Zhejiang and Fujian) are the main producing areas of grain in China, with relatively developed economy and large population, so their vulnerability is severe. In the northwest, southwest and northeast of China, although the traffic network density is low, once a serious EF disaster occurs, it is difficult to transfer the affected people and property, and may cause indirect losses due to ineffective relief and slow recovery. However, due to the terrain and geographical location, its population density is low, it is a gathering area of most ethnic minorities in China, and its economy is underdeveloped, so the vulnerability level of these provinces (Jilin, Liaoning, Inner Mongolia, Shanxi, Shanxi, Ningxia, Qinghai, Gansu, Xinjiang, Tibet, Yunnan, Guizhou, Jiangxi and Guangxi) is moderate. Heilongjiang province has become the only province with slight vulnerability to EF disasters. The reasons include low population density (bottom three in China all the year round), high proportion of labor force (top six in China all the year round), and strong disaster prevention and relief capacity (Heilongjiang, known as the "Great Granary of China", is the location of the Great Xing'an Mountains and the Small Xing'an Mountains. It is rich in forests, minerals, animals and plants. Therefore, the local government attaches great importance to disaster prevention). The vulnerability assessment results are highly consistent with the distribution laws of population, economy and natural environment in China, which verifies the rationality of the results.
Citation: https://doi.org/10.5194/nhess-2022-136-AC5
-
AC5: 'Reply on RC5', Wei Li, 20 Nov 2022
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 674 | 252 | 46 | 972 | 11 | 16 |
- HTML: 674
- PDF: 252
- XML: 46
- Total: 972
- BibTeX: 11
- EndNote: 16
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1