1Institute of Earth and Environmental Science, University of Potsdam,
Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany
2ÖBB-Infrastruktur AG, Nordbahnstrasse 50, 1020 Vienna, Austria
Received: 03 Aug 2016 – Discussion started: 10 Aug 2016
Abstract. Experience has shown that river floods can significantly hamper the reliability of railway networks and cause extensive structural damage and disruption. As a result, the national railway operator in Austria had to cope with financial losses of more than EUR 100 million due to flooding in recent years. Comprehensive information on potential flood risk hot spots as well as on expected flood damage in Austria is therefore needed for strategic flood risk management. In view of this, the flood damage model RAIL (RAilway Infrastructure Loss) was applied to estimate (1) the expected structural flood damage and (2) the resulting repair costs of railway infrastructure due to a 30-, 100- and 300-year flood in the Austrian Mur River catchment. The results were then used to calculate the expected annual damage of the railway subnetwork and subsequently analysed in terms of their sensitivity to key model assumptions. Additionally, the impact of risk aversion on the estimates was investigated, and the overall results were briefly discussed against the background of climate change and possibly resulting changes in flood risk. The findings indicate that the RAIL model is capable of supporting decision-making in risk management by providing comprehensive risk information on the catchment level. It is furthermore demonstrated that an increased risk aversion of the railway operator has a marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies.
Revised: 19 Oct 2016 – Accepted: 20 Oct 2016 – Published: 10 Nov 2016
Kellermann, P., Schönberger, C., and Thieken, A. H.: Large-scale application of the flood damage model RAilway Infrastructure Loss (RAIL), Nat. Hazards Earth Syst. Sci., 16, 2357-2371, doi:10.5194/nhess-16-2357-2016, 2016.