Improved assessment of rainfall-induced railway infrastructure risk in China using empirical data

Weihua Zhu, Kai Liu (), Ming Wang, Sadhana Nirandjan and Elco E. Koks
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Weihua Zhu: Beijing Normal University
Kai Liu: Beijing Normal University
Ming Wang: Beijing Normal University
Sadhana Nirandjan: Vrije Universiteit Amsterdam
Elco E. Koks: Vrije Universiteit Amsterdam

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 2, No 22, 1525-1548

Abstract: Abstract Rainfall-induced hazards, such as landslides, debris flows, and floods, cause significant damage to railway infrastructure. However, an accurate assessment of rainfall-induced hazard risk to railway infrastructure is limited by the lack of regional and asset-tailored vulnerability curves. This study aims to use multisource empirical damage data to generate vulnerability curves and assess the risk of rainfall-induced hazards to railway infrastructure. The methodology is exemplified through a case study of the Chinese national railway infrastructure. Regional- and national-level vulnerability curves are derived based on historical railway damage records. These curves are combined with the 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 to railway infrastructure in China shows high spatial differences due to the spatially variations of precipitation characteristics, exposure distribution, and vulnerability curves. The South, East, and Central provinces have a high risk of rainfall-induced hazards, resulting in the average EADs of 184 million RMB, 176 million RMB, and 156 million RMB, respectively, whereas the risks in the Northeast and Northwest provinces are estimated to be relatively lower. The usage of multisource empirical data enables risk assessments that include spatial details for each region. These risk assessments are highly necessary for effective decision making to achieve infrastructure resilience.

Keywords: Multisource empirical data; Vulnerability curve; Risk estimate; Damage length factor (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05605-3

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