the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Oct 2021
How to use empirical data to improve transportation infrastructure risk assessment
Abstract. Rainfall-induced hazards, such as landslides, debris flows, and floods cause significant damage to transportation infrastructure. However, an accurate assessment of rainfall-induced hazard risk to transportation infrastructure is limited by the lack of regional and asset-tailored vulnerability curves. This study aims to use multi-source empirical damage data to generate vulnerability curves and assess the risk of transportation infrastructure to rainfall-induced hazards. The methodology is exemplified through a case study for the Chinese national railway infrastructure. In doing so, regional and national-level vulnerability curves are derived based on historical railway damage records. This is combined with daily precipitation data and the railway infrastructure market value to estimate regional- and national-level risk. The results show large variations in the shape of the vulnerability curves across the different regions. The railway infrastructure in Northeast and Northwest China is more vulnerable to rainfall-induced hazards due to low protection standards. The expected annual damage (EAD) ranges from 1.88 to 5.98 billion RMB for the Chinese railway infrastructure, with a mean value of 3.91 billion RMB. However, the risk of railway infrastructure in China shows high spatial differences due to the spatially uneven precipitation characteristics, exposure distribution, and vulnerability curves. The South, East and Central provinces have a high risk to rainfall-induced hazards, resulting in an average EAD of 184 million RMB, 176 and 156 million RMB, respectively, whereas the risk in the Northeast and Northwest provinces are estimated to be relatively lower. The usage of multi-source empirical data offer opportunities to perform risk assessments that include spatial detail among regions. These risk assessments are highly needed in order to make effective decisions to make our infrastructure resilient.
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Status: closed
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RC1: 'Comment on nhess-2021-277', Anonymous Referee #1, 05 Dec 2021
Railway system is essential for the functioning of modern society, especially in China. In recent years, we have seen an increase in the frequency of disruption of railway system caused by extreme rainfall in the context of climate change. Therefore, an accurate assessment of rainfall-induced hazard risk to railway infrastructure is of great importance. This work proposes a multi-source data-based vulnerability curves to explore the risk of railway infrastructure to rainfall-induced hazards. Thus, this research is worth publishing. And I have some minor comments:
- Please indicate the source or credit of the photo in Figure 1.
- What do the values in Table 3 mean? For example, does 5-8 in the Table 3 indicate the estimated value of the 95% confidence interval or what? Need to explain. If you only need to use the average value, then some columns may not be listed
- 5 and M1-5d have different physical meanings, you need to unify them in Equation 1, Figure 4 and the corresponding text description.
- I did not see clearly or understand the part about the moving average method. I am especially curious about how to use the moving average method to get multiple values under the same rainfall intensity in Figure 5. Finally, the statistics get the highest, lowest and average value.
- Reading through the full text, this article does a risk assessment of the railway. I suggest replacing the transportation with railway.
Citation: https://doi.org/10.5194/nhess-2021-277-RC1 -
AC4: 'Reply on RC1', Kai Liu, 30 Mar 2022
We thank the reviewer for the insightful comments and detailed suggestions on how to improve the manuscript. We found the comments to be very helpful and have incorporated them into the revised manuscript. In the following, the texts with blue font are the reviewer's original comments, the texts with normal font are authors' responses and the texts with italic font are authors' responses in the revised manuscript. Our detailed responses can be found in the supplement material.
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CC1: 'Comment on nhess-2021-277', Lu Yang, 22 Dec 2021
Regional railway vulnerability curves are potential and rare for risk assessment, especially in China. Therefore, the regional curves generated in this work are valuable for managers also for researchers. If you can list the parameters for each equation of curves, there must be clearer and more convenient for others.
Citation: https://doi.org/10.5194/nhess-2021-277-CC1 - AC3: 'Reply on CC1', Kai Liu, 30 Mar 2022
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RC2: 'Comment on nhess-2021-277', Anonymous Referee #2, 08 Feb 2022
The paper “How to use empirical data to improve transportation infrastructure risk assessment” concerns an interesting research topic demanding the use of huge amount of data carefully collected and presented. Unfortunately, despite the good intentions, the paper does not cope with the problem with the necessary accuracy to reach reliable results.
The paper shows numerous lacks; the main deficiencies concern:
- The unclear and not described methodological approach
- The unconventional data gathering, merging sources that describe damage without the date of the occurrence, news, and rain of different length all together and finally merged to monetary values of infrastructures.
- The creation of damage curves without explain the type of data used and the methodological approach
- Impossibility of replication of the approach because it is described in a confused manner.
- Figures not explicative, with low graphical standard.
For all these reasons, the paper results not understandable, and it and cannot be published on NHESS
best regards
Citation: https://doi.org/10.5194/nhess-2021-277-RC2 -
AC5: 'Reply on RC2', Kai Liu, 30 Mar 2022
We thank the reviewer for reading our article and the comments. In the following, the texts with blue font are the reviewer's original comments, the texts with normal font are authors' responses and the texts with italic font are authors' responses in the revised manuscript. Our detailed responses can be found in the supplementary material.
Status: closed
-
RC1: 'Comment on nhess-2021-277', Anonymous Referee #1, 05 Dec 2021
Railway system is essential for the functioning of modern society, especially in China. In recent years, we have seen an increase in the frequency of disruption of railway system caused by extreme rainfall in the context of climate change. Therefore, an accurate assessment of rainfall-induced hazard risk to railway infrastructure is of great importance. This work proposes a multi-source data-based vulnerability curves to explore the risk of railway infrastructure to rainfall-induced hazards. Thus, this research is worth publishing. And I have some minor comments:
- Please indicate the source or credit of the photo in Figure 1.
- What do the values in Table 3 mean? For example, does 5-8 in the Table 3 indicate the estimated value of the 95% confidence interval or what? Need to explain. If you only need to use the average value, then some columns may not be listed
- 5 and M1-5d have different physical meanings, you need to unify them in Equation 1, Figure 4 and the corresponding text description.
- I did not see clearly or understand the part about the moving average method. I am especially curious about how to use the moving average method to get multiple values under the same rainfall intensity in Figure 5. Finally, the statistics get the highest, lowest and average value.
- Reading through the full text, this article does a risk assessment of the railway. I suggest replacing the transportation with railway.
Citation: https://doi.org/10.5194/nhess-2021-277-RC1 -
AC4: 'Reply on RC1', Kai Liu, 30 Mar 2022
We thank the reviewer for the insightful comments and detailed suggestions on how to improve the manuscript. We found the comments to be very helpful and have incorporated them into the revised manuscript. In the following, the texts with blue font are the reviewer's original comments, the texts with normal font are authors' responses and the texts with italic font are authors' responses in the revised manuscript. Our detailed responses can be found in the supplement material.
-
CC1: 'Comment on nhess-2021-277', Lu Yang, 22 Dec 2021
Regional railway vulnerability curves are potential and rare for risk assessment, especially in China. Therefore, the regional curves generated in this work are valuable for managers also for researchers. If you can list the parameters for each equation of curves, there must be clearer and more convenient for others.
Citation: https://doi.org/10.5194/nhess-2021-277-CC1 - AC3: 'Reply on CC1', Kai Liu, 30 Mar 2022
-
RC2: 'Comment on nhess-2021-277', Anonymous Referee #2, 08 Feb 2022
The paper “How to use empirical data to improve transportation infrastructure risk assessment” concerns an interesting research topic demanding the use of huge amount of data carefully collected and presented. Unfortunately, despite the good intentions, the paper does not cope with the problem with the necessary accuracy to reach reliable results.
The paper shows numerous lacks; the main deficiencies concern:
- The unclear and not described methodological approach
- The unconventional data gathering, merging sources that describe damage without the date of the occurrence, news, and rain of different length all together and finally merged to monetary values of infrastructures.
- The creation of damage curves without explain the type of data used and the methodological approach
- Impossibility of replication of the approach because it is described in a confused manner.
- Figures not explicative, with low graphical standard.
For all these reasons, the paper results not understandable, and it and cannot be published on NHESS
best regards
Citation: https://doi.org/10.5194/nhess-2021-277-RC2 -
AC5: 'Reply on RC2', Kai Liu, 30 Mar 2022
We thank the reviewer for reading our article and the comments. In the following, the texts with blue font are the reviewer's original comments, the texts with normal font are authors' responses and the texts with italic font are authors' responses in the revised manuscript. Our detailed responses can be found in the supplementary material.
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