Flood Risk Assessment Methodology for Complex Railway Networks: Application to the National Rail Network in Great Britain

Abstract

Rail networks comprise complex interdependent systems with extensive trackside and off-track assets that are exposed to extreme weather-related hazards. In this paper, a methodology is presented for flood risk assessment of large rail networks and is demonstrated on the national rail network in Great Britain, which is analyzed for the impact of river flood hazards, with the disruptive impacts measured in terms of direct asset damages, disruptions to trains and passengers, and economic losses. Our analysis of the national rail network finds that trackside railway assets are most vulnerable to flood hazards with about a third of all track assets in Great Britain exposed to river flood hazards. London and the Southeast regions are the most affected by train and passenger disruptions, with both regions incurring about half of all train and passenger disruptions for river flood hazards nationally. Relatively frequent flood events are found to make the greatest contribution to overall risk, with 20-year return period flood events resulting in 48% of all economic losses on the rail network nationally, most of which occurred in London and the southeast, with the northwest of England having the next highest losses. Our methodology and analysis findings can provide infrastructure managers and decision makers with evidence for prioritizing investments for network resilience. Rail networks are complex systems that can be disrupted by weather conditions, including floods and storms. This paper develops a system modeling methodology to identify flood risk to the rail network in Great Britain. The systems analysis has identified the railway assets that are most exposed to flooding and the potential impacts of their failure. We show that the risk is consequence of a combination of factors, including the rail asset reliability, the intensity of rail usage, and the network structure. The method will enable better prioritization of actions to enhance the resilience of railway systems.