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As a consequence of the ongoing growth in demand for houses in the Netherlands over recent decades, the fraction of the total surface used for residential purposes has expanded rapidly. The location patterns of new residential construction are the result of various forces: government intervention aiming at the preservation of open space via zoning, new-town, and 'compact-city' policies, and market forces reflecting preferences on the demand side (households and real-estate developers). The main factors influencing the location of residential construction are analysed by means of a statistical analysis. The most significant variables appear to be the proximity of a location to existing residential areas, location in new towns receiving government support, the accessibility of workplaces, distance to railway stations, and, to a lesser extent, the accessibility of nature, surface water, and recreational areas.

Abstract. We present an approach for flood damage simulations through the creation of a comparatively large number of inundation scenarios for a polder area, using a high-resolution digital elevation model. In particular, the method could be used for detailed scenario studies of the impact of future socioeconomic and climatic developments on flood risks. The approach is applied to a case-study area in the south of the Netherlands along the river Meuse. The advantage of our approach is that a large number of potential flood events can be created relatively fast without hydrodynamical calculations, and that it can be applied to high-resolution elevation models and for large areas. The large number of flood scenarios and the high horizontal resolution reduces at least part of the uncertainties encountered in flood loss modelling. The approach with a low horizontal-resolution (100-m) for loss modelling results in an overestimation of losses by up to 22% for high density urban areas, and underestimation of 100% for infrastructure, compared to the high-resolution (25-m). Loss modelling at 5-m horizontal resolution shows that aggregate losses may be overestimated by some 4.3%, compared to the 25-m resolution. The generation of a large variety of inundation scenarios provides a basis for constructing loss probability curves. The calculated range and expected values of damages compare reasonably well with earlier independent estimates.