Evaluating the disaster risk levels associated with &ldquo;Dragon Boat Water&rdquo; in Guangdong China

Hu, Yamin; Jiang, Xiaocen; Wang, Juanhuai; Zhao, Liang; Huang, Guanrong

doi:https://doi.org/10.5194/egusphere-2024-957

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https://doi.org/10.5194/egusphere-2024-957

Preprints

15 Apr 2024

| 15 Apr 2024

Evaluating the disaster risk levels associated with “Dragon Boat Water” in Guangdong China

Yamin Hu, Xiaocen Jiang, Juanhuai Wang, Liang Zhao, and Guanrong Huang

Abstract. Dragon Boat Water (DBW) is a period characterized by abundant precipitation and concentrated precipitation process in Guangdong province, China. The period often leads to severe flood disasters, resulting in substantial losses to both livelihoods and production. However, a comprehensive assessment of disaster risk during DBW has been lacking.

In this study, we utilized daily precipitation data spanning the years from 1995 to 2020, coupled with related disaster data from Guangdong. The Precipitation Comprehensive Intensity Index (PCII) and the DBW Comprehensive Disaster Index (CDI) were established. PCII and CDI were categorized into three levels using the percentile method. Moreover, we delved into the intricate relationship between these two indexes.

The results revealed that PCII falls into the three levels: extreme heavy, heavy, and normal, representing 19.2 %, 30.8 %, and 50 %, respectively, with the peak occurring in 2008. Meanwhile, CDI comprises three levels: high, medium and low, accounting for 19.2 %, 30.8 % and 50.0 %, respectively, with peak years closely aligning with those of PCII. Our calculations demonstrated a positive correlation between PCII and CDI, as well as between PCII and the occurrence of five types of disasters. Notably, heavier PCII levels had a more pronounce impact on crop damage, affected population numbers, and the direct economic loss, with the most substantial influence observed on the affected population. Though disaster data for 2022 has yet to be fully collated, based on PCII and CDI, we determined that 2022 experienced the fourth-highest precipitation and fifth-highest disaster level since 1995. This assessment concurs with the actual conditions observe in 2022. As a result, we propose that in the future, PCII and CDI can be invaluable tools for pre-disaster risk assessment, ongoing disaster monitoring, and expedited post-disaster evaluation.

Received: 29 Mar 2024 – Discussion started: 15 Apr 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Yamin Hu, Xiaocen Jiang, Juanhuai Wang, Liang Zhao, and Guanrong Huang

Status: closed

RC1:
'Comment on egusphere-2024-957', Anonymous Referee #1, 09 May 2024

This study establishes two indices, PCII and CDI, and assesses the disaster risk level of DBW in recent decades. The key focus of this article lies in the rationality, applicability, and superiority of these two indices, yet from their definitions, they seem to lack uniqueness compared to traditional indices and fail to demonstrate clear advantages in assessing DBW risks. I believe this is the most pressing issue that this article should address. Therefore, further improvement of these indices to reflect their unique advantages in assessing and predicting DBW risks should be the author's next step. Other specific suggestions are provided below.

1) It needs to be clarified whether the disaster loss data is compiled annually or specifically during the DBW periods. This includes the data mentioned later in the text. If the data source is annual, the author should explain how they obtained disaster data during the DBW period, especially considering that the author has autonomously defined the DBW period.

2) In lines 131-133, the author introduces the PCII index to amplify precipitation. What is the rationale behind using the product of precipitation (PI) and the square root of the number of stations (S)? I would like to understand the relationship between precipitation (PI) and the number of stations (S). If the sole purpose is to amplify PI, why utilize the square root of S instead of multiplying by a constant or directly by S? What are the unique advantages of PCII in assessing DBW? I question the superiority of this index if PCII merely amplifies precipitation to a certain extent.

3) In L145-150, is it reasonable to assign equal weights to the five disaster indices? Please provide the range of values for CDI.

4) The author attributes the relatively low severity of disasters from 2015 to 2020 to interannual variations in precipitation, advancements in forecasting techniques, strengthened defense measures, and enhanced disaster response in lines 220-224. This conclusion appears overly simplistic and general, requiring further supplementation with corresponding data support.

5) Please explain the reason for the absence of data for certain years in Figure 6.

6) Why is there an upward trend in the affected population from lines 240-243? Please explain the reason for this phenomenon. Ideally, with the improvement of disaster prevention measures, the proportion of the affected population should decrease.

7) In lines 302-303, the author mentions the potential role of PCII in assessing disaster risk. In fact, the use of precipitation alone can also serve a similar purpose. From the composition of this index, it simply involves the simple multiplication of precipitation and the number of stations. Or it can be seen as merely amplifying precipitation to a certain extent. Throughout the entire text, the unique role of PCII and its distinction from precipitation have not been fully elaborated.

8) In the whole text, the author lacks necessary explanations regarding the advantages and uniqueness of the CDI and PCII. The analysis in the Results section only demonstrates the usability of these two indices but fails to adequately illustrate their effectiveness, rationality, and advancement. For example, it does not sufficiently elucidate in which aspects these indices can provide new insights and advantages in evaluation and prediction compared to current methods.

9) The structure of the article seems somewhat illogical. There is an abundance of analytical content in the Results section, which would be more appropriately placed in the Discussion section. Additionally, placing the Conclusion before the Discussion is unconventional; typically, the Conclusion should come at the end of the article. This layout gives the article a peculiar feel.

Citation: https://doi.org/10.5194/egusphere-2024-957-RC1
- AC1: 'Reply on RC1', Xiaocen Jiang, 24 May 2024
  
  Thanks for your advice. This study primarily conducts pre-disaster assessment analysis from a climatic perspective, considering the ability to assess risks and disaster situations that meteorological causative factors may pose in the future based on cumulative precipitation and spatial distribution forecasts at a quarterly or monthly scale. This information is intended to assist decision-making departments in scheduling production and arranging daily life.
  
  In designing the indices, we selected the total precipitation and the number of precipitation stations during the Dragon Boat Water (DBW) period at each station, primarily considering the predictability of climate and the need for rapid pre-disaster assessment. As mentioned in the introduction, previous assessments of heavy rain and flood disasters mainly focused on short-term weather processes lasting several days. In the design of precipitation indices, factors such as the duration and amount of heavy rain, and even hourly rainfall, may be considered.
  However, the DBW period spans 31 days, and the design of precipitation indices needs to consider the predictability of climate. Previous index designs focused on factors such as the duration and intensity of heavy rain, or even shorter time scales of rainfall impacts, which are more challenging to predict in terms of climate forecasting, making it difficult to achieve rapid pre-disaster risk and situation assessment. Regarding rainfall amount, although disasters are often triggered by heavy precipitation, the total precipitation during the DBW period encompasses precipitation of all levels. In Section 2.2.1 of the manuscript, we also verified that total precipitation can represent heavy rainfall above a certain threshold.
  
  Therefore, the design of PCII in the manuscript (Due to the flat design of PCII, we have changed PCII into PI in the revised manuscript), whether in terms of climate predictability or disaster assessment, has a certain level of rationality, representativeness, and applicability. In the previous sections of the manuscript, we did not provide a detailed and explicit explanation of the above content in the index design. We appreciate your suggestions, and modifications have been made in Section 2.2.2 and the conclusion section of the manuscript, now.
  1) It needs to be clarified whether the disaster loss data is compiled annually or specifically during the DBW periods. This includes the data mentioned later in the text. If the data source is annual, the author should explain how they obtained disaster data during the DBW period, especially considering that the author has autonomously defined the DBW period.
  1)Thank you for the reminder. We have added explanation in the revised manuscript that "the disaster data we selected occurred during DBW period". For example, in 2019, the DBW period was from May 23rd to June 22nd. The disaster data provided in the "Guangdong Province Disaster Reduction Yearbook" mainly occurred from May 23rd to 31st and from June 10th to 13th. Therefore, the overall disaster situation during the 2019 DBW period is the aggregation of the disaster statistics from these two periods.
  2) In lines 131-133, the author introduces the PCII index to amplify precipitation. What is the rationale behind using the product of precipitation (PI) and the square root of the number of stations (S)? I would like to understand the relationship between precipitation (PI) and the number of stations (S). If the sole purpose is to amplify PI, why utilize the square root of S instead of multiplying by a constant or directly by S? What are the unique advantages of PCII in assessing DBW? I question the superiority of this index if PCII merely amplifies precipitation to a certain extent.
  Here we mainly refer to China local standard and national standard documents: "Heavy Rain Process Comprehensive Intensity Assessment Method DB45/T 2281-2021" and "GB/T 33680-2017 Heavy Rain Disaster Level"
  
  S is the total number of precipitation stations, and PI is the total precipitation during DBW period divided by the total number of stations S. Meteorologically, it can represent the intensity of precipitation, taking into account the influence of total precipitation and precipitation range to a certain extent. In the previous heavy rain disaster standards, by multiplying the square root of S and PI, not only can the calculated value not be too small, but it can also solve the problem that it is difficult to classify the levels more precisely because the value is too small.
  
  Considering the flat design of the rainfall intensity index, in the revised paper we changed the "comprehensive precipitation intensity index" to "precipitation intensity index", and changed the original abbreviation PCII to PI.
  3) In L145-150, is it reasonable to assign equal weights to the five disaster indices? Please provide the range of values for CDI.
  Assigning equal weights to the five disaster indices is based on the standards set by the Chinese meteorological industry and local regulations in Guangdong, which is considered reasonable. This approach mainly involves treating various disasters with equal weights while considering the normalization of variables. The rationale behind this decision is derived from the national standard: GB/T 33680-2017 Classification of rainstorm disasters.
  
  Table 2 provides the calculated annual range of CDI values, ranging from a minimum of -0.624 to a maximum of 2.991.
  4) The author attributes the relatively low severity of disasters from 2015 to 2020 to interannual variations in precipitation, advancements in forecasting techniques, strengthened defense measures, and enhanced disaster response in lines 220-224. This conclusion appears overly simplistic and general, requiring further supplementation with corresponding data support.
  From the total rainfall of Guangdong DBW from 1995 to 2020, it can be found that the precipitation during DBW period in Guangdong from 2015 to 2020 is in the background of less interdecadal, which is consistent with the change of precipitation intensity index in Figure 2 in the paper. We also added the trend line of PI in Figure 2 in the paper.
  The total rainfall of Guangdong DBW from 1995 to 2020.
  We also added the data support of the impact of prediction improvement on disasters in the paper as follows:
  
  At the same time, the change in precipitation prediction score in Guangdong indicates that since 2014, the level of precipitation forecasts in Guangdong has significantly improved (Fig.6). This may also be one of the reasons for the relatively low disaster levels during DBW period from 2015 to 2020.
  5) Please explain the reason for the absence of data for certain years in Figure 6.
  In Figure 6, there were no disasters in 2020, 2002, 2000, and 1999. Because the precipitation during the DBW period in these years was abnormally low, Guangdong did not suffer various disasters caused by precipitation. For details, see Tables 2 and 4.
  6) Why is there an upward trend in the affected population from lines 240-243? Please explain the reason for this phenomenon. Ideally, with the improvement of disaster prevention measures, the proportion of the affected population should decrease.
  The x-axis of Figure 8 represents the intensity of precipitation, not time. Therefore, the implication here is that as the intensity of precipitation increases, the number of affected population generally increases. To avoid misunderstanding, we have modified the relevant content as follows:
  
  " Notably, it is observed that as the PI increases, the number of affected population generally tends to increase, aligning with the observation that the affected population contributes the largest proportion among the different disaster indicators in the disaster-impacted years, as discussed in Section 3.2.”
  
  This phenomenon can be attributed to the fact that higher precipitation intensity is often associated with more severe impacts on human settlements and infrastructure, leading to a higher number of affected individuals.
  7) In lines 302-303, the author mentions the potential role of PCII in assessing disaster risk. In fact, the use of precipitation alone can also serve a similar purpose. From the composition of this index, it simply involves the simple multiplication of precipitation and the number of stations. Or it can be seen as merely amplifying precipitation to a certain extent. Throughout the entire text, the unique role of PCII and its distinction from precipitation have not been fully elaborated.
  This study focuses on the DBW period, characterized by a continuous 31-day period of concentrated rainfall. The prediction of rainfall during this period involves monthly-scale climate forecasting rather than short-term weather forecasting lasting only a few days. It primarily considers the predictability of climate, the severity of disasters, and the aim of rapid pre-disaster assessment of different disaster situations. The index design here is simplified.
  
  When designing the index, we also consulted industry standards and relevant literature. For severe rainfall events, they should last for a long time, affect a wide area, and maintain a strong intensity. However, if the duration is deemed too long or the area too wide, the average intensity may be too weak. There is a balance among these three factors, as discussed in detail in "Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects."
  
  Reference:
  
  [1] GB/T 24438.1-2009 Statistics of natural disaster losses-Part 1: Basic indices.
  
  [2] GB/T 33680-2017 Classification of rainstorm disasters.
  
  [3] Lu E, Zhao W, Zou X K, et al. Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects. Journal of Climate, 2017, 30(16): 6123-6132, doi: 10.1175/JCLI-D-17-0105.1.
  
  [4] Lu E, Zhao W, Gong L Q, et al. Determining starting time and duration of extreme precipitation events based on intensity. Climate Res, 2015, 63(1): 31-41, doi: 10.3354/cr01280.
  8) In the whole text, the author lacks necessary explanations regarding the advantages and uniqueness of the CDI and PCII. The analysis in the Results section only demonstrates the usability of these two indices but fails to adequately illustrate their effectiveness, rationality, and advancement. For example, it does not sufficiently elucidate in which aspects these indices can provide new insights and advantages in evaluation and prediction compared to current methods.
  Previous studies often select weather scales based on daily or hourly precipitation data, and calculate various elements such as precipitation in the preceding three days, average precipitation in the precipitation area, maximum precipitation during the event, maximum daily precipitation, maximum hourly rainfall intensity, total duration of the event, average duration at stations, number of stations with precipitation during the event, and number of stations with heavy rainfall during the event. It is challenging to provide predictions at the seasonal and monthly scales using these elements. The flattened design of the comprehensive precipitation intensity index in this paper primarily considers the predictability of climate forecasting. Currently, climate forecasting still faces significant challenges in predicting extreme precipitation events and their intensity. Therefore, it is essential to conduct pre-disaster assessments based on forecastable quantities as much as possible while also facilitating rapid disaster assessment work.
  
  Thank you for the suggestions. We have added explanations regarding the rationality and applicability of the index design in Section 2.2.2 and the conclusion section of the manuscript.
  9) The structure of the article seems somewhat illogical. There is an abundance of analytical content in the Results section, which would be more appropriately placed in the Discussion section. Additionally, placing the Conclusion before the Discussion is unconventional; typically, the Conclusion should come at the end of the article. This layout gives the article a peculiar feel.
  Following your suggestions, we have adjusted the order of the conclusion and discussion sections.
  
  Citation: https://doi.org/10.5194/egusphere-2024-957-AC1
RC2:
'Comment on egusphere-2024-957', Anonymous Referee #2, 10 May 2024

The article evaluates disaster risk levels associated with “Dragon Boat Water” (DBW) in Guangdong, China, a period known for heavy rainfall and flood disasters. Utilizing daily precipitation data (1995-2020) and disaster data from Guangdong, the study introduces the Precipitation Comprehensive Intensity Index (PCII) and the DBW Comprehensive Disaster Index (CDI), categorized into three levels. A positive correlation was found between PCII and CDI, with heavier PCII levels significantly impacting crop damage, affected population, and economic loss. The study suggests PCII and CDI as valuable tools for pre-disaster risk assessment, ongoing disaster monitoring, and post-disaster evaluation. I found the study interesting but the structure is a little confusing, my specific comments are below:

1. My primary concern is why not use indices other than CDI and PCII, what is the motivation behind using these particular indices?
2. Does implementing other indices change the conclusion?
3. I find the structure of the article unconventional, I do not understand the utility of the Conclusion section before the Discussion section.
4. Line: 95 the text can be represented as the equation for better readability.
5. In "Figure 6. The proportion of the five disasters of DBW from 1995 to 2020." what is the reason behind the mission data?

Citation: https://doi.org/10.5194/egusphere-2024-957-RC2
- AC2: 'Reply on RC2', Xiaocen Jiang, 24 May 2024
  
  1. My primary concern is why not use indices other than CDI and PCII, what is the motivation behind using these particular indices?
  
  We designed these two sets of indices, CDI and PCII, based on literature research and the requirements of the technology. The purpose of our design takes into account two main aspects. Firstly, we considered the perspective of pre-disaster assessment. Currently, climate predictions still face significant challenges in forecasting extreme precipitation events and precipitation intensity. Therefore, we only considered daily precipitation amounts. Secondly, our design aims to facilitate the rapid assessment of disasters.
  
  Reference:
  
  [1] GB/T 24438.1-2009 Statistics of natural disaster losses-Part 1: Basic indices.
  
  [2] GB/T 33680-2017 Classification of rainstorm disasters.
  
  [3] Lu E, Zhao W, Zou X K, et al. Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects. Journal of Climate, 2017, 30(16): 6123-6132, doi: 10.1175/JCLI-D-17-0105.1.
  
  [4] Lu E, Zhao W, Gong L Q, et al. Determining starting time and duration of extreme precipitation events based on intensity. Climate Res, 2015, 63(1): 31-41, doi: 10.3354/cr01280.
  2. Does implementing other indices change the conclusion?
  
  We had previously devised several alternative approaches, but they did not significantly affect the conclusion.
  3. I find the structure of the article unconventional, I do not understand the utility of the Conclusion section before the Discussion section.
  
  Thanks for your advice. We have adjusted the structure of the paper according to your recommendations.
  4. Line: 95 the text can be represented as the equation for better readability.
  We have added the equation as your suggestion.(Refer to the supplement)
  5. In "Figure 6. The proportion of the five disasters of DBW from 1995 to 2020." what is the reason behind the mission data?
  The missing data is due to the need for comparing the occurrences of disasters in different years. The low precipitation during the Dragon Boat Festival periods in 2020, 2002, 2000, and 1999 is consistent with the actual situation. Guangdong did not suffer from various disasters caused by precipitation. Specific details can be found in Tables 2 and 4.
  
  Citation: https://doi.org/10.5194/egusphere-2024-957-AC2

Status: closed

RC1:
'Comment on egusphere-2024-957', Anonymous Referee #1, 09 May 2024

This study establishes two indices, PCII and CDI, and assesses the disaster risk level of DBW in recent decades. The key focus of this article lies in the rationality, applicability, and superiority of these two indices, yet from their definitions, they seem to lack uniqueness compared to traditional indices and fail to demonstrate clear advantages in assessing DBW risks. I believe this is the most pressing issue that this article should address. Therefore, further improvement of these indices to reflect their unique advantages in assessing and predicting DBW risks should be the author's next step. Other specific suggestions are provided below.

1) It needs to be clarified whether the disaster loss data is compiled annually or specifically during the DBW periods. This includes the data mentioned later in the text. If the data source is annual, the author should explain how they obtained disaster data during the DBW period, especially considering that the author has autonomously defined the DBW period.

2) In lines 131-133, the author introduces the PCII index to amplify precipitation. What is the rationale behind using the product of precipitation (PI) and the square root of the number of stations (S)? I would like to understand the relationship between precipitation (PI) and the number of stations (S). If the sole purpose is to amplify PI, why utilize the square root of S instead of multiplying by a constant or directly by S? What are the unique advantages of PCII in assessing DBW? I question the superiority of this index if PCII merely amplifies precipitation to a certain extent.

3) In L145-150, is it reasonable to assign equal weights to the five disaster indices? Please provide the range of values for CDI.

4) The author attributes the relatively low severity of disasters from 2015 to 2020 to interannual variations in precipitation, advancements in forecasting techniques, strengthened defense measures, and enhanced disaster response in lines 220-224. This conclusion appears overly simplistic and general, requiring further supplementation with corresponding data support.

5) Please explain the reason for the absence of data for certain years in Figure 6.

6) Why is there an upward trend in the affected population from lines 240-243? Please explain the reason for this phenomenon. Ideally, with the improvement of disaster prevention measures, the proportion of the affected population should decrease.

7) In lines 302-303, the author mentions the potential role of PCII in assessing disaster risk. In fact, the use of precipitation alone can also serve a similar purpose. From the composition of this index, it simply involves the simple multiplication of precipitation and the number of stations. Or it can be seen as merely amplifying precipitation to a certain extent. Throughout the entire text, the unique role of PCII and its distinction from precipitation have not been fully elaborated.

8) In the whole text, the author lacks necessary explanations regarding the advantages and uniqueness of the CDI and PCII. The analysis in the Results section only demonstrates the usability of these two indices but fails to adequately illustrate their effectiveness, rationality, and advancement. For example, it does not sufficiently elucidate in which aspects these indices can provide new insights and advantages in evaluation and prediction compared to current methods.

9) The structure of the article seems somewhat illogical. There is an abundance of analytical content in the Results section, which would be more appropriately placed in the Discussion section. Additionally, placing the Conclusion before the Discussion is unconventional; typically, the Conclusion should come at the end of the article. This layout gives the article a peculiar feel.

Citation: https://doi.org/10.5194/egusphere-2024-957-RC1
- AC1: 'Reply on RC1', Xiaocen Jiang, 24 May 2024
  
  Thanks for your advice. This study primarily conducts pre-disaster assessment analysis from a climatic perspective, considering the ability to assess risks and disaster situations that meteorological causative factors may pose in the future based on cumulative precipitation and spatial distribution forecasts at a quarterly or monthly scale. This information is intended to assist decision-making departments in scheduling production and arranging daily life.
  
  In designing the indices, we selected the total precipitation and the number of precipitation stations during the Dragon Boat Water (DBW) period at each station, primarily considering the predictability of climate and the need for rapid pre-disaster assessment. As mentioned in the introduction, previous assessments of heavy rain and flood disasters mainly focused on short-term weather processes lasting several days. In the design of precipitation indices, factors such as the duration and amount of heavy rain, and even hourly rainfall, may be considered.
  However, the DBW period spans 31 days, and the design of precipitation indices needs to consider the predictability of climate. Previous index designs focused on factors such as the duration and intensity of heavy rain, or even shorter time scales of rainfall impacts, which are more challenging to predict in terms of climate forecasting, making it difficult to achieve rapid pre-disaster risk and situation assessment. Regarding rainfall amount, although disasters are often triggered by heavy precipitation, the total precipitation during the DBW period encompasses precipitation of all levels. In Section 2.2.1 of the manuscript, we also verified that total precipitation can represent heavy rainfall above a certain threshold.
  
  Therefore, the design of PCII in the manuscript (Due to the flat design of PCII, we have changed PCII into PI in the revised manuscript), whether in terms of climate predictability or disaster assessment, has a certain level of rationality, representativeness, and applicability. In the previous sections of the manuscript, we did not provide a detailed and explicit explanation of the above content in the index design. We appreciate your suggestions, and modifications have been made in Section 2.2.2 and the conclusion section of the manuscript, now.
  1) It needs to be clarified whether the disaster loss data is compiled annually or specifically during the DBW periods. This includes the data mentioned later in the text. If the data source is annual, the author should explain how they obtained disaster data during the DBW period, especially considering that the author has autonomously defined the DBW period.
  1)Thank you for the reminder. We have added explanation in the revised manuscript that "the disaster data we selected occurred during DBW period". For example, in 2019, the DBW period was from May 23rd to June 22nd. The disaster data provided in the "Guangdong Province Disaster Reduction Yearbook" mainly occurred from May 23rd to 31st and from June 10th to 13th. Therefore, the overall disaster situation during the 2019 DBW period is the aggregation of the disaster statistics from these two periods.
  2) In lines 131-133, the author introduces the PCII index to amplify precipitation. What is the rationale behind using the product of precipitation (PI) and the square root of the number of stations (S)? I would like to understand the relationship between precipitation (PI) and the number of stations (S). If the sole purpose is to amplify PI, why utilize the square root of S instead of multiplying by a constant or directly by S? What are the unique advantages of PCII in assessing DBW? I question the superiority of this index if PCII merely amplifies precipitation to a certain extent.
  Here we mainly refer to China local standard and national standard documents: "Heavy Rain Process Comprehensive Intensity Assessment Method DB45/T 2281-2021" and "GB/T 33680-2017 Heavy Rain Disaster Level"
  
  S is the total number of precipitation stations, and PI is the total precipitation during DBW period divided by the total number of stations S. Meteorologically, it can represent the intensity of precipitation, taking into account the influence of total precipitation and precipitation range to a certain extent. In the previous heavy rain disaster standards, by multiplying the square root of S and PI, not only can the calculated value not be too small, but it can also solve the problem that it is difficult to classify the levels more precisely because the value is too small.
  
  Considering the flat design of the rainfall intensity index, in the revised paper we changed the "comprehensive precipitation intensity index" to "precipitation intensity index", and changed the original abbreviation PCII to PI.
  3) In L145-150, is it reasonable to assign equal weights to the five disaster indices? Please provide the range of values for CDI.
  Assigning equal weights to the five disaster indices is based on the standards set by the Chinese meteorological industry and local regulations in Guangdong, which is considered reasonable. This approach mainly involves treating various disasters with equal weights while considering the normalization of variables. The rationale behind this decision is derived from the national standard: GB/T 33680-2017 Classification of rainstorm disasters.
  
  Table 2 provides the calculated annual range of CDI values, ranging from a minimum of -0.624 to a maximum of 2.991.
  4) The author attributes the relatively low severity of disasters from 2015 to 2020 to interannual variations in precipitation, advancements in forecasting techniques, strengthened defense measures, and enhanced disaster response in lines 220-224. This conclusion appears overly simplistic and general, requiring further supplementation with corresponding data support.
  From the total rainfall of Guangdong DBW from 1995 to 2020, it can be found that the precipitation during DBW period in Guangdong from 2015 to 2020 is in the background of less interdecadal, which is consistent with the change of precipitation intensity index in Figure 2 in the paper. We also added the trend line of PI in Figure 2 in the paper.
  The total rainfall of Guangdong DBW from 1995 to 2020.
  We also added the data support of the impact of prediction improvement on disasters in the paper as follows:
  
  At the same time, the change in precipitation prediction score in Guangdong indicates that since 2014, the level of precipitation forecasts in Guangdong has significantly improved (Fig.6). This may also be one of the reasons for the relatively low disaster levels during DBW period from 2015 to 2020.
  5) Please explain the reason for the absence of data for certain years in Figure 6.
  In Figure 6, there were no disasters in 2020, 2002, 2000, and 1999. Because the precipitation during the DBW period in these years was abnormally low, Guangdong did not suffer various disasters caused by precipitation. For details, see Tables 2 and 4.
  6) Why is there an upward trend in the affected population from lines 240-243? Please explain the reason for this phenomenon. Ideally, with the improvement of disaster prevention measures, the proportion of the affected population should decrease.
  The x-axis of Figure 8 represents the intensity of precipitation, not time. Therefore, the implication here is that as the intensity of precipitation increases, the number of affected population generally increases. To avoid misunderstanding, we have modified the relevant content as follows:
  
  " Notably, it is observed that as the PI increases, the number of affected population generally tends to increase, aligning with the observation that the affected population contributes the largest proportion among the different disaster indicators in the disaster-impacted years, as discussed in Section 3.2.”
  
  This phenomenon can be attributed to the fact that higher precipitation intensity is often associated with more severe impacts on human settlements and infrastructure, leading to a higher number of affected individuals.
  7) In lines 302-303, the author mentions the potential role of PCII in assessing disaster risk. In fact, the use of precipitation alone can also serve a similar purpose. From the composition of this index, it simply involves the simple multiplication of precipitation and the number of stations. Or it can be seen as merely amplifying precipitation to a certain extent. Throughout the entire text, the unique role of PCII and its distinction from precipitation have not been fully elaborated.
  This study focuses on the DBW period, characterized by a continuous 31-day period of concentrated rainfall. The prediction of rainfall during this period involves monthly-scale climate forecasting rather than short-term weather forecasting lasting only a few days. It primarily considers the predictability of climate, the severity of disasters, and the aim of rapid pre-disaster assessment of different disaster situations. The index design here is simplified.
  
  When designing the index, we also consulted industry standards and relevant literature. For severe rainfall events, they should last for a long time, affect a wide area, and maintain a strong intensity. However, if the duration is deemed too long or the area too wide, the average intensity may be too weak. There is a balance among these three factors, as discussed in detail in "Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects."
  
  Reference:
  
  [1] GB/T 24438.1-2009 Statistics of natural disaster losses-Part 1: Basic indices.
  
  [2] GB/T 33680-2017 Classification of rainstorm disasters.
  
  [3] Lu E, Zhao W, Zou X K, et al. Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects. Journal of Climate, 2017, 30(16): 6123-6132, doi: 10.1175/JCLI-D-17-0105.1.
  
  [4] Lu E, Zhao W, Gong L Q, et al. Determining starting time and duration of extreme precipitation events based on intensity. Climate Res, 2015, 63(1): 31-41, doi: 10.3354/cr01280.
  8) In the whole text, the author lacks necessary explanations regarding the advantages and uniqueness of the CDI and PCII. The analysis in the Results section only demonstrates the usability of these two indices but fails to adequately illustrate their effectiveness, rationality, and advancement. For example, it does not sufficiently elucidate in which aspects these indices can provide new insights and advantages in evaluation and prediction compared to current methods.
  Previous studies often select weather scales based on daily or hourly precipitation data, and calculate various elements such as precipitation in the preceding three days, average precipitation in the precipitation area, maximum precipitation during the event, maximum daily precipitation, maximum hourly rainfall intensity, total duration of the event, average duration at stations, number of stations with precipitation during the event, and number of stations with heavy rainfall during the event. It is challenging to provide predictions at the seasonal and monthly scales using these elements. The flattened design of the comprehensive precipitation intensity index in this paper primarily considers the predictability of climate forecasting. Currently, climate forecasting still faces significant challenges in predicting extreme precipitation events and their intensity. Therefore, it is essential to conduct pre-disaster assessments based on forecastable quantities as much as possible while also facilitating rapid disaster assessment work.
  
  Thank you for the suggestions. We have added explanations regarding the rationality and applicability of the index design in Section 2.2.2 and the conclusion section of the manuscript.
  9) The structure of the article seems somewhat illogical. There is an abundance of analytical content in the Results section, which would be more appropriately placed in the Discussion section. Additionally, placing the Conclusion before the Discussion is unconventional; typically, the Conclusion should come at the end of the article. This layout gives the article a peculiar feel.
  Following your suggestions, we have adjusted the order of the conclusion and discussion sections.
  
  Citation: https://doi.org/10.5194/egusphere-2024-957-AC1
RC2:
'Comment on egusphere-2024-957', Anonymous Referee #2, 10 May 2024

The article evaluates disaster risk levels associated with “Dragon Boat Water” (DBW) in Guangdong, China, a period known for heavy rainfall and flood disasters. Utilizing daily precipitation data (1995-2020) and disaster data from Guangdong, the study introduces the Precipitation Comprehensive Intensity Index (PCII) and the DBW Comprehensive Disaster Index (CDI), categorized into three levels. A positive correlation was found between PCII and CDI, with heavier PCII levels significantly impacting crop damage, affected population, and economic loss. The study suggests PCII and CDI as valuable tools for pre-disaster risk assessment, ongoing disaster monitoring, and post-disaster evaluation. I found the study interesting but the structure is a little confusing, my specific comments are below:

1. My primary concern is why not use indices other than CDI and PCII, what is the motivation behind using these particular indices?
2. Does implementing other indices change the conclusion?
3. I find the structure of the article unconventional, I do not understand the utility of the Conclusion section before the Discussion section.
4. Line: 95 the text can be represented as the equation for better readability.
5. In "Figure 6. The proportion of the five disasters of DBW from 1995 to 2020." what is the reason behind the mission data?

Citation: https://doi.org/10.5194/egusphere-2024-957-RC2
- AC2: 'Reply on RC2', Xiaocen Jiang, 24 May 2024
  
  1. My primary concern is why not use indices other than CDI and PCII, what is the motivation behind using these particular indices?
  
  We designed these two sets of indices, CDI and PCII, based on literature research and the requirements of the technology. The purpose of our design takes into account two main aspects. Firstly, we considered the perspective of pre-disaster assessment. Currently, climate predictions still face significant challenges in forecasting extreme precipitation events and precipitation intensity. Therefore, we only considered daily precipitation amounts. Secondly, our design aims to facilitate the rapid assessment of disasters.
  
  Reference:
  
  [1] GB/T 24438.1-2009 Statistics of natural disaster losses-Part 1: Basic indices.
  
  [2] GB/T 33680-2017 Classification of rainstorm disasters.
  
  [3] Lu E, Zhao W, Zou X K, et al. Temporal-spatial monitoring of an extreme precipitation event: determining simultaneously the time period it lasts and the geographic region it affects. Journal of Climate, 2017, 30(16): 6123-6132, doi: 10.1175/JCLI-D-17-0105.1.
  
  [4] Lu E, Zhao W, Gong L Q, et al. Determining starting time and duration of extreme precipitation events based on intensity. Climate Res, 2015, 63(1): 31-41, doi: 10.3354/cr01280.
  2. Does implementing other indices change the conclusion?
  
  We had previously devised several alternative approaches, but they did not significantly affect the conclusion.
  3. I find the structure of the article unconventional, I do not understand the utility of the Conclusion section before the Discussion section.
  
  Thanks for your advice. We have adjusted the structure of the paper according to your recommendations.
  4. Line: 95 the text can be represented as the equation for better readability.
  We have added the equation as your suggestion.(Refer to the supplement)
  5. In "Figure 6. The proportion of the five disasters of DBW from 1995 to 2020." what is the reason behind the mission data?
  The missing data is due to the need for comparing the occurrences of disasters in different years. The low precipitation during the Dragon Boat Festival periods in 2020, 2002, 2000, and 1999 is consistent with the actual situation. Guangdong did not suffer from various disasters caused by precipitation. Specific details can be found in Tables 2 and 4.
  
  Citation: https://doi.org/10.5194/egusphere-2024-957-AC2

Yamin Hu, Xiaocen Jiang, Juanhuai Wang, Liang Zhao, and Guanrong Huang

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Short summary

Dragon Boat Water (DBW) in Guangdong, China, brings heavy rain, often causing floods. Yet, disaster risk during DBW lacks assessment. Using 1995–2020 data, we established the Precipitation Comprehensive Intensity Index (PCII) and DBW Comprehensive Disaster Index (CDI). Correlating PCII with CDI and disasters, we found heavier PCII levels notably impacted crops, populations, and caused economic losses. PCII and CDI could aid pre-disaster risk assessment and disaster management.


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