Preprints
https://doi.org/10.5194/nhess-2021-277
https://doi.org/10.5194/nhess-2021-277
 
19 Oct 2021
19 Oct 2021
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

How to use empirical data to improve transportation infrastructure risk assessment

Weihua Zhu1,2, Kai Liu1,2, Ming Wang1,2, Sadhana Nirandjan3, and Elco Koks3 Weihua Zhu et al.
  • 1School of national security and emergency management, Beijing Normal University, Beijing 100875, China
  • 2Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • 3Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands

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.

Weihua Zhu et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-277', Anonymous Referee #1, 05 Dec 2021
    • AC4: 'Reply on RC1', Kai Liu, 30 Mar 2022
  • CC1: 'Comment on nhess-2021-277', Lu Yang, 22 Dec 2021
    • AC3: 'Reply on CC1', Kai Liu, 30 Mar 2022
  • RC2: 'Comment on nhess-2021-277', Anonymous Referee #2, 08 Feb 2022
    • AC5: 'Reply on RC2', Kai Liu, 30 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-277', Anonymous Referee #1, 05 Dec 2021
    • AC4: 'Reply on RC1', Kai Liu, 30 Mar 2022
  • CC1: 'Comment on nhess-2021-277', Lu Yang, 22 Dec 2021
    • AC3: 'Reply on CC1', Kai Liu, 30 Mar 2022
  • RC2: 'Comment on nhess-2021-277', Anonymous Referee #2, 08 Feb 2022
    • AC5: 'Reply on RC2', Kai Liu, 30 Mar 2022

Weihua Zhu et al.

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Short summary
We use multi-source empirical damage data to generate vulnerability curves and assess the risk of transportation infrastructure to rainfall-induced hazards. The results show large variations in the shape of the vulnerability curves and risk of railway infrastructure in China across the different regions. The usage of multi-source empirical data offer opportunities to perform risk assessments that include spatial detail among regions.
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