Correlations between rainfall data and insurance damage data related to sewer flooding for the case of Aarhus, Denmark

Sewer flooding due to extreme rainfall may result in considerable damage. Damage data to quantify costs of cleaning, drying, and replacing materials and goods are rare in literature. In this study, insurance claim data related to property damages were analysed for the municipality of Aarhus, Denmark. The aim of this paper was to study the extent to which rainfall data can be used to explain variations in insurance claim data. In particular, the paper addresses the issue of time-lag between claim date and time of the damaging rainfall event, which may, if not taken into account, lead to underestimations of correlations between rainfall and damage variables. Rainfall data from two rain gauges were used to extract rainfall characteristics. From cross correlations between time series of rainfall and claim data, it can be concluded that rainfall events induce claims mostly on the same day, but also on the three days after. A linear model that takes into account rainfall data from previous days slightly improves correlations between rainfall and damage variables compared to a simple linear model. Best correlation coefficients were found between maximum hourly rainfall intensity and daily number of claims (0.47-0.57) and daily total damage (0.43-0.53).