Abstract. Models for estimating flood losses to infrastructure are rare and their reliability is seldom investigated although infrastructure losses might contribute considerably to the overall flood losses. In this paper, an empirical modelling approach for estimating direct structural flood damage to railway infrastructure and associated financial losses is presented. Via a combination of event data, i.e. photodocumented damage on the Northern Railway in Lower Austria caused by the March River flood in 2006, and simulated flood characteristics, i.e. water levels, flow velocities and combinations thereof, the correlations between physical flood impact parameters and damage occurred to the railway track were investigated and subsequently rendered into a damage model. After calibrating the loss estimation using recorded repair costs of the Austrian Federal Railways, the model was applied to three synthetic scenarios with return periods of 30, 100 and 300 years of March River flooding. Finally, the model results are compared to depth-damagecurve-based approaches for the infrastructure sector obtained from the Rhine Atlas damage model and the Damage Scanner model. The results of this case study indicate a good performance of our two-stage model approach. However, due to a lack of independent event and damage data, the model could not yet be validated. Future research in natural risk should focus on the development of event and damage documentation procedures to overcome this significant hurdle in flood damage modelling.

Abstract. Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterized by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight international research groups, the objective of this study was to compare, in a blind-validation test, the performances of different models for the assessment of the direct flood damage to the residential sector at the building level (i.e. microscale). The test consisted of a common flood case study characterized by high availability of hazard and building data but with undisclosed information on observed losses in the implementation stage of the models. The nine selected models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the modelling approaches, and heterogeneity of the original calibration context in relation to both hazard and vulnerability features. By avoiding possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the reliability of their estimations, especially regarding the potentials of local and multivariable models. From another perspective, the exercise allowed us to increase awareness of strengths and limits of flood damage modelling, which are summarized in the paper in the form of take-home messages from a modeller's perspective.

Losses due to floods have dramatically increased over the past decades, and losses of companies, comprising direct and indirect losses, have a large share of the total economic losses. Thus, there is an urgent need to gain more quantitative knowledge about flood losses, particularly losses caused by business interruption, in order to mitigate the economic loss of companies. However, business interruption caused by floods is rarely assessed because of a lack of sufficiently detailed data. A survey was undertaken to explore processes influencing business interruption, which collected information on 557 companies affected by the severe flood in June 2013 in Germany. Based on this data set, the study aims to assess the business interruption of directly affected companies by means of a Random Forests model. Variables that influence the duration and costs of business interruption were identified by the variable importance measures of Random Forests. Additionally, Random Forest-based models were developed and tested for their capacity to estimate business interruption duration and associated costs. The water level was found to be the most important variable influencing the duration of business interruption. Other important variables, relating to the estimation of business interruption duration, are the warning time, perceived danger of flood recurrence and inundation duration. In contrast, the amount of business interruption costs is strongly influenced by the size of the company, as assessed by the number of employees, emergency measures undertaken by the company and the fraction of customers within a 50 km radius. These results provide useful information and methods for companies to mitigate their losses from business interruption. However, the heterogeneity of companies is relatively high, and sector-specific analyses were not possible due to the small sample size. Therefore, further sector-specific analyses on the basis of more flood loss data of companies are recommended.

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.