Abstract. 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][2004][2005][2006][2007][2008][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-R 2 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.

Abstract. Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities.The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998-2011. The databases include claims of waterrelated damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households.Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22-26 % of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18 % of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Abstract. Flooding is assessed as the most important natural hazard in Europe, causing thousands of deaths, affecting millions of people and accounting for large economic losses in the past decade. Little is known about the damage processes associated with extreme rainfall in cities, due to a lack of accurate, comparable and consistent damage data. The objective of this study is to investigate the impacts of extreme rainfall on residential buildings and how affected households coped with these impacts in terms of precautionary and emergency actions. Analyses are based on a unique dataset of damage characteristics and a wide range of potential damage explaining variables at the household level, collected through computer-aided telephone interviews (CATI) and an online survey. Exploratory data analyses based on a total of 859 completed questionnaires in the cities of Mün-ster (Germany) and Amsterdam (the Netherlands) revealed that the uptake of emergency measures is related to characteristics of the hazardous event. In case of high water levels, more efforts are made to reduce damage, while emergency response that aims to prevent damage is less likely to be effective. The difference in magnitude of the events in Mün-ster and Amsterdam, in terms of rainfall intensity and water depth, is probably also the most important cause for the differences between the cities in terms of the suffered financial losses. Factors that significantly contributed to damage in at least one of the case studies are water contamination, the presence of a basement in the building and people's awareness of the upcoming event. Moreover, this study confirms conclusions by previous studies that people's experience with damaging events positively correlates with precautionary behaviour. For improving future damage data acquisition, we recommend the inclusion of cell phones in a CATI survey to avoid biased sampling towards certain age groups.

Abstract. Rainstorm damage caused by the malfunction of urban drainage systems and water intrusion due to defects in the building envelope can be considerable. Little research on this topic focused on the collection of damage data, the understanding of damage mechanisms and the deepening of data analysis methods. In this paper, the relative contribution of different failure mechanisms to the occurrence of rainstorm damage is investigated, as well as the extent to which these mechanisms relate to weather variables. For a case study in Rotterdam, the Netherlands, a property level home insurance database of around 3100 water-related damage claims was analysed. The records include comprehensive transcripts of communication between insurer, insured and damage assessment experts, which allowed claims to be classified according to their actual damage cause. The results show that roof and wall leakage is the most frequent failure mechanism causing precipitation-related claims, followed by blocked roof gutters, melting snow and sewer flooding. Claims related to sewer flooding were less present in the data, but are associated with significantly larger claim sizes than claims in the majority class, i.e. roof and wall leakages. Rare events logistic regression analysis revealed that maximum rainfall intensity and rainfall volume are significant predictors for the occurrence probability of precipitation-related claims. Moreover, it was found that claims associated with rainfall intensities smaller than 7-8 mm in a 60-min window are mainly related to failure processes in the private domain, such as roof and wall leakages. For rainfall events that exceed the 7-8 mm h −1 threshold, the failure of systems in the public domain, such as sewer systems, start to contribute considerably to the overall occurrence probability of claims. The communication transcripts, however, lacked information to be conclusive about to which extent sewer-related claims were caused by overloading of sewer systems or failure of system components.

Abstract. Flooding is assessed as the most important natural hazard in Europe, causing thousands of deaths, affecting millions of people and accounting for large economic losses in the past decade. Little is known about the damage processes associated with extreme rainfall in cities, due to a lack of accurate, comparable and consistent damage data. The objective of this study is to investigate the impacts of extreme rainfall on residential buildings and how affected households coped with these impacts in terms of precautionary and emergency actions. Analyses are based on a unique dataset of damage characteristics and a wide range of potential damage explaining variables at the household level, collected through computer-aided telephone interviews (CATI) and an online survey. Exploratory data analyses based on a total of 859 completed questionnaires in the cities of Münster (Germany) and Amsterdam (the Netherlands) revealed that the uptake of emergency measures is related to characteristics of the hazardous event. In case of high water levels, more efforts are made to reduce damage, while emergency response that aims to prevent damage is less likely to be effective. The difference in magnitude of the events in Münster and Amsterdam in terms of rainfall intensity and water depth, is probably also the most important cause for the differences between the cities in terms of the suffered financial losses. Factors that significantly contributed to damage in at least one of the case studies are water contamination, the presence of a basement in the building and people's awareness of the upcoming event. Moreover, this study confirms conclusions by previous studies that people's experience with damaging events positively correlates with precautionary behaviour. For improving future damage data acquisition, we recommend to include cell-phones in a CATI survey to avoid biased sampling towards certain age groups.