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Introduction Flooding is a natural phenomenon, but human activity has significantly altered the natural drainage processes thereby occasionally causing greater flood risk. Urban flooding has become more frequent due to a number of factors including climate change, urban growth and an increase in paved surfaces. Pluvial flooding results from heavy rainfall when water that does not infiltrate into the ground ponds in hollows or flows over the ground. In flood damage estimation, the concept of damage curves or damage functions is applied. Such functions give the building damage due to inundation. Most damage assessment models have in common that the direct monetary damage is obtained from the type of the element at risk and the inundation depth. Problem definition Flood damage assessment models do not focus solely on pluvial flood damage estimation. In addition, the existing flood damage models and developed depth-damage curves have not been tested for application of pluvial flood events. Research This study is carried out with the main objective to test the flood damage assessment model HOWAD-PREVENT in a case study in Rotterdam and to evaluate the uncertainty and sensitivity of this model. The model applicability and sensitivity was tested by running the model with two building type files together with three water level files.

In deze studie is gekeken naar de invloed van het tijdstip van neerslagpieken op inboedel- en opstalclaims. Deze invloed zou verklaard kunnen worden door de variaties in neerslag over de dag, maar ook de mogelijkheid van mensen om schadebeperkende maatregelen te treffen. Als mensen slapen of niet thuis zijn kan het zijn dat er vaker of meer schade optreed. Hiertoe zijn neerslaggegevens van 26 KNMI-weerstations over een periode van 1992 tot 2010 bekeken, waaruit onafhankelijke buien zijn ge¨extraheerd. Per bui is het uurvak waarin de hoogste intensiteit werd waargenomen aangemerkt als het referentietijdstip van de bui, met een bijbehorende datum en locatie van het weerstation. Per referentietijdstip zijn de verzekeringsgegevens, binnen een straal van 10 km rond het weerstation en van de desbetreffende datum, samengevoegd voor verdere analyse. De gebruikte verzekeringsgegevens zijn het relatieve aantal claims (aantal claims / aantal polissen), gemiddelde claimhoogte (totale claimhoogte / aantal claims) en relatieve claimhoogte (totale claimhoogte / aantal polissen). Uit de analyse van de neerslaggegevens blijkt dat in de zomer ’s ochtends minder neerslagpieken voorkomen. Uurvak 8 (07:00-08:00 UT) bevat met 21% minder dan het daggemiddelde het minste aantal pieken. Deze pieken hebben ook een lagere intensiteit, tijdens uurvak 9 is de gemiddelde intensiteit 16% lager dan het daggemiddelde. In de namiddag komen vaker neerslagpieken voor en deze hebben ook een hogere intensiteit dan het daggemiddelde, respectievelijk 30% en 20%. Voor de winterperiode is ook gevonden dat het aantal pieken ’s ochtends lager (10%, uurvak 9) is en in de namiddag hoger (25%, uurvak 18), maar de intensiteit van deze pieken wijken niet significant af van het gemiddelde. Uit de analyse van de verzekeringsgegevens komt naar voren dat tijdens de zomerperiode het aantal inboedel- en opstalclaims, behorende bij neerslagpieken in de ochtend, lager uitvalt dan het daggemiddelde. Het relatieve aantal inboedelclaims is 38% lager tijdens uurvak 8, voor opstalclaims is dit 33% tijdens uurvak 10. Voor de relatieve claimhoogte is dit respectievelijk 52% tijdens uurvak 7 en 40% tijdens uurvak 8. Dit lag in lijn der verwachting aangezien het gemiddeld gezien minder hard regent in deze periode. Hoewel het in de namiddag gemiddeld harder regent, is dit niet terug te zien in een verhoging in het aantal claims. Voor de winterperiode zijn deze afwijkingen niet gevonden. Voor zowel de zomer- als winterperiode is geen variatie in de tijd gevonden voor de gemiddelde claimhoogte. Op basis van deze studie is niet te zeggen of de mogelijkheid tot het nemen van maatregelen bijdraagd aan het verband, of dat dit alleen op neerslagvariaties berust. Door middel van een andere aanpak in het indentificeren van buien, een betere ruimtelijke resolutie van neerslag, en nauwkeurigere verzekeringsgegevens zou dit in een vervolgonderzoek verder onderzocht kunnen worden.

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.

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 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.

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.