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Abstract. The effectiveness of disaster risk management and financing mechanisms depends on an accurate assessment of current and future hazard exposure. The increasing availability of detailed data offers policy makers and the insurance sector new opportunities to understand trends in risk, and to make informed decisions on ways to deal with these trends. In this paper we show how comprehensive property level information can be used for the assessment of exposure to flooding on a national scale, and how this information provides valuable input to discussions on possible risk financing practices. The case study used is the Netherlands, which is one of the countries most exposed to flooding globally, and which is currently undergoing a debate on strategies for the compensation of potential losses. Our results show that flood exposure has increased rapidly between 1960 and 2012, and that the growth of the building stock and its economic value in flood-prone areas has been higher than in non-flood-prone areas. We also find that property values in flood-prone areas are lower than those in non-flood-prone areas. We argue that the increase in the share of economic value located in potential flood-prone areas can have a negative effect on the feasibility of private insurance schemes in the Netherlands. The methodologies and results presented in this study are relevant for many regions around the world where the effects of rising flood exposure create a challenge for risk financing.

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Abstract. A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input-output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of them in combination with noneconomic methods. While both IO and CGE models are widely used, they are mainly compared on theoretical grounds. Few studies have compared disaster impacts of different model types in a systematic way and for the same geographical area, using similar input data. Such a comparison is valuable from both a scientific and policy perspective as the magnitude and the spatial distribution of the estimated losses are born likely to vary with the chosen modelling approach (IO, CGE, or hybrid). Hence, regional disaster impact loss estimates resulting from a range of models facilitate better decisions and policy making. Therefore, this study analyses the economic consequences for a specific case study, using three regional disaster impact models: two hybrid IO models and a CGE model. The case study concerns two flood scenarios in the Po River basin in Italy. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of 7 between the three models, depending on the type of recovery path. Total economic impact, comprising all Italian regions, is negative in all models though.

This study investigates the short‐ and long‐run impact on population dynamics of the major flood in the Netherlands in 1953. A dynamic difference‐in‐differences analysis reveals that the flood had an immediate negative impact on population growth, but limited long‐term effects. In contrast, the resulting flood protection program (Deltaworks), had a persisting positive effect on population growth. As a result, there has been an increase in population in flood‐prone areas. Our results suggest a moral hazard effect of flood mitigation leading to more people locating in flood‐prone areas, increasing potential disaster costs.