Estimating flood damage potentials

According to EM-DAT floods are the leading cause of economic damages from natural disasters in Austria, accounting for almost 70 % of total damages from natural disasters in the period 1990 to 2015. In terms of economic consequences, the summer floods of 1997, 2002, 2005 and 2013 rank among the most severe events. Due to the comparatively high threat of damages due to flood events in Austria, information on the current and future damage potential is of great importance not only for sustainable flood risk management, but also for public finances as the Austrian risk transfer system currently in place mainly relies on federal grants (Catastrophe Fund).

Worldwide, actuarial (i.e. insurance mathematical) estimations of damage potentials and the resulting calculations of insurance premiums are often based on damage experience comprising comparatively short time periods and few data points. When long-term climatological damage potentials are to be estimated, these limited time periods and data points are a problem for two reasons: (i) extreme – or so called “fat-tail” – events (i.e. events of low probability, but with high impact) are very likely to be underrepresented in these damage samples and (ii) estimating damage functions based on such limited time series usually requires the assumption of stationarity due to methodological matters. Both reasons give rise to significant uncertainty, also as regards the present and future flood risk damage potential for Austria.

The overall aim of FloodRisk-7000 – a two-year research project that started in March 2016 – is to provide improved estimations on flood damage potential in Northern Austria for past, current and future climatic conditions by making use of and merging different kinds of data sets. These data sets include, amongst others, damage data from surveys conducted by provinces and municipalities in the context of granting aid, 7000 years of paleoflood records derived from Lake Mondsee sediments, and cyclon tracks with their related precipitation totals.