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Insurance databases form a promising data source that can be used to improve pluvial flood damage estimations. This paper describes the key characteristics of an insurance database on water related damages to private buildings and content in the Netherlands that has been made available for research. The paper presents preliminary results of a case study where insurance data are explored to find relationships between rainfall characteristics and pluvial flood damage. The results show that variations in damage are partly related to rainfall characteristics. More research on rainfall characteristics and other explanatory variables of flood damage is needed to capture the processes causing damage.

Urban pluvial flood management requires detailed spatial and temporal information on flood characteristics and damaging consequences. There is lack of quantitative field data on pluvial flooding resulting in large uncertainties in urban flood model calculations and ensuing decisions for investments in flood protection. In this paper four different data sources are discussed, based on literature and expert consultation, that are believed to be of value for the acquisition of quantitative data on pluvial flooding. Data assembled by insurance agencies on flood damage, call databases held by water authorities and emergency services and remote sensing images cover years of observational data that can be mined to obtain data on flood characteristics and occurrence. Flood monitoring using sensor technology can be effective to collect additional pluvial flood data, that is not captured by existing data sources.

In this paper, a database of water-related insurance damage claims related to private properties and content was analysed. The aim was to investigate whether the probability of occurrence of rainfall-related damage was associated with the intensity of rainfall. Rainfall data were used for the period of 2003–2009 in the Netherlands based on a network of 33 automatic rain gauges operated by the Royal Netherlands Meteorological Institute. Insurance damage data were aggregated to areas within 10-km range of the rain gauges. Through a logistic regression model, high claim numbers were linked to maximum rainfall intensities, with rainfall intensity based on 10-min to 4-h time windows. Rainfall intensity proved to be a significant damage predictor; however, the explained variance, approximated by a pseudo-R2 statistic, was at most 34% for property damage and at most 30% for content damage. When directly comparing predicted and observed values, the model was able to predict 5–17% more cases correctly compared to a random prediction. No important differences were found between relations with property and content damage data. A considerable fraction of the variance is left unexplained, which emphasizes the need to study damage generating mechanisms and additional explanatory variables.

The objective of this paper is to establish relationships between rainfall extremes and damage data from Dutch insurance industry. Rainfall data are based on a network of 33 automatic rain gauges held by the Royal Netherlands Meteorological Institute. Rainfall characteristics, such as peak rainfall intensity and rainfall volume, are correlated with damage statistics of claims in the vicinity of the rain gauges. The results show that rainfallrelated damage mainly occurs during summer seasons. There is a weak relationship between property damage and rainfall intensities and between property damage and rainfall volumes for summer events. More data is needed to confirm these relationships. In a subsequent study this will be investigated by using weather radar data to obtain a higher spatial rainfall resolution and thus be able to include more insurance data.

In this paper, a database of water-related insurance damage claims related to private properties and content was analysed. The aim was to investigate whether high numbers of damage claims were associated with high rainfall intensities. Rainfall data were used for the period of 2003–2010 in the Netherlands based on a network of 33 automatic rain gauges operated by the Royal Netherlands Meteorological Institute. Insurance damage data were aggregated to areas within 10-km range of the rain gauges. Through a logistic regression model, high claim numbers were linked to maximum rainfall intensities, with rainfall intensity based on 10-min to 4-h time windows. Rainfall intensity proved to be a significant damage predictor; however, the explained variance, approximated by a pseudo-R2 statistic, was at most 34% for property damage and at most 30% for content damage. When directly comparing predicted and observed values, the model was able to predict 5–17% more cases correctly compared to a random prediction. No important differences were found between relations with property and content damage data. A considerable fraction of the variance is left unexplained, which emphasizes the need to study damage generating mechanisms and additional explanatory variables.