How to deal properly with a natural catastrophe database – analysis of flood losses

Abstract. Global reinsurer Munich Re has been collecting data on losses from natural disasters for almost four decades. Together with EM-Dat and sigma, Munich Re's NatCatSERVICE database is currently one of three global databases of its kind, with its more than 30 000 datasets. Although the database was originally designed for reinsurance business purposes, it contains a host of additional information on catastrophic events. Data collection poses difficulties such as not knowing the exact extent of human and material losses, biased reporting by interest groups, including governments, changes over time due to new findings, etc. Loss quantities are often not separable into different causes, e.g., windstorm and flood losses during a hurricane, or windstorm, hail and flooding during a severe storm event. These difficulties should be kept in mind when database figures are analysed statistically, and the results have to be treated with due regard for the characteristics of the underlying data. Comparing events at different locations and on different dates can only be done using normalised data. For most analyses, and in particular trend analyses, socio-economic changes such as inflation or growth in population and values must be considered. Problems encountered when analysing trends are discussed using the example of floods and flood losses.