Global warming to increase flood risk on European railways

Abstract

For effective disaster risk management and adaptation planning, a good understanding of current and projected flood risk is required. Recent advances in quantifying flood risk at the regional and global scale have largely neglected critical infrastructure, or addressed this important sector with insufficient detail. Here, we present the first European-wide assessment of current and future flood risk to railway tracks for different global warming scenarios using an infrastructure-specific damage model. We find that the present risk, measured as expected annual damage, to railway networks in Europe is approx. €581 million per year, with the highest risk relative to the length of the network in North Macedonia, Croatia, Norway, Portugal, and Germany. Based on an ensemble of climate projections for RCP8.5, we show that current risk to railway networks is projected to increase by 255% under a 1.5 °C, by 281% under a 2 °C, and by 310% under a 3 °C warming scenario. The largest increases in risk under a 3 °C scenario are projected for Slovakia, Austria, Slovenia, and Belgium. Our advances in the projection of flood risk to railway infrastructure are important given their criticality, and because losses to public infrastructure are usually not insured or even uninsurable in the private market. To cover the risk increase due to climate change, European member states would need to increase expenditure in transport by €1.22 billion annually under a 3 °C warming scenario without further adaptation. Limiting global warming to the 1.5 °C goal of the Paris Agreement would result in avoided losses of €317 million annually.

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

“…In the last decade, the adverse effects of global warming have resulted in a higher frequency of floods in various regions around the globe [57][58][59]; therefore, new studies to develop better tools for flood prediction are highly necessary. In this research, we proposed a new modeling approach, named the HFPS-RSTree, for the spatial prediction of flash flood susceptibility, with a case study of a high-frequency torrential rainfall area.…”

Biases in national and continental flood risk assessments by ignoring spatial dependence

“…These large-scale assessments have often assumed spatially homogeneous flood scenarios, where each area within the large-scale region is subject to an event with the same exceedance probability or return period 12 . For instance, Ward et al 11 and Winsemius et al 2 at the global scale and Feyen et al 13 , Rojas et al 14 and Bubeck et al 15 at the European scale, and te Linde et al 16 at the scale of the Rhine basin estimate flood risk in terms of expected annual damage (EAD) and/or expected annual affected population (EAP) under the assumption of homogeneous return periods. Other studies quantify risk in terms of damage or affected population for specific return period floods.…”

Direct flood risk assessment of the European road network: an object-based approach

“…Following disruptive and costly European floods in the year 2000 and between 2009 and 2014, significant advances in continental (and global) scale flood risk modelling have been made (Dankers and Feyen, 2008;Hirabayashi et al, 2013;Kundzewicz et al, 2017;Ward et al, 2013). Although these give good estimates of total damage to all land use types, they do not accurately represent damage to 30 transport infrastructure (Jongman et al, 2012, Bubeck et al, 2019, Koks et al 2019. Flood damage to road infrastructure is still an underexplored, yet important issue (Doll et al, 2014;Merz et al, 2010).…”

“…tangible flood losses, usually in the order of 5-10% but in exceptional cases up to 50-60% (Bubeck et al, 2019;Jongman et al, 2012). 35…”

“…However, on the European scale, object data availability is more complete, allowing for a more 70 detailed approach. A comparable study on the European railway network has been done by Bubeck et al (2019), using OpenRailwayMap data and a highly stylized damage function implemented in the RAIL model.…”