A sound understanding of flood risk drivers (hazard, exposure and vulnerability) is essential for the effective and efficient implementation of risk-reduction strategies. In this paper, we focus on 'exposure' and study the influence of different methods and parameters of flood exposure analyses in Switzerland. We consider two types of exposure indicators and two different spatial aggregation schemes: the density of exposed assets (exposed numbers per km2) and the ratios of exposed assets (share of exposed assets compared to total amount of assets in a specific region) per municipality and per grid cells of similar size as the municipalities. While identifying high densities of exposed assets highlights priority areas for cost-efficient strategies, high exposure ratios can suggest areas of interest for strategies focused on the most vulnerable regions, i.e. regions with a low capacity to cope with a disaster. In Switzerland, the spatial distribution of high exposure densities and exposure ratios tend to be complementary. With regards to the methods, we find that the spatial cluster analysis provides more information for the prioritization of flood protection measures than 'simple' maps of spatially aggregated data represented in quantiles. In addition, our study shows that the data aggregation scheme influences the results. It suggests that the aggregation based on grid cells supports the comparability of different regions better than aggregation based on municipalities and is, thus, more appropriate for nationwide analyses.

A coevolutionary perspective is adopted to understand the dynamics of exposure to mountain hazards in the European Alps. A spatially explicit, object-based temporal assessment of elements at risk to mountain hazards (river floods, torrential floods, and debris flows) in Austria and Switzerland is presented for the period from 1919 to 2012. The assessment is based on two different data sets: (1) hazard information adhering to legally binding land use planning restrictions and (2) information on building types combined from different national-level spatial data. We discuss these transdisciplinary dynamics and focus on economic, social, and institutional interdependencies and interactions between human and physical systems. Exposure changes in response to multiple drivers, including population growth and land use conflicts. The results show that whereas some regional assets are associated with a strong increase in exposure to hazards, others are characterized by a below-average level of exposure. The spatiotemporal results indicate relatively stable hot spots in the European Alps. These results coincide with the topography of the countries and with the respective range of economic activities and political settings. Furthermore, the differences between management approaches as a result of multiple institutional settings are discussed. A coevolutionary framework widens the explanatory power of multiple drivers to changes in exposure and risk and supports a shift from structural, security-based policies toward an integrated, risk-based natural hazard management system.

Despite an increasing number of people exposed to flood risks in Europe, flood risk perception remains low and effective flood risk management policies are rarely implemented. It becomes increasingly important to understand how local governments can design effective flood risk management policies to address flood risks. In this article, we study whether high flood exposure and flood risk perception correlate with the demand for a specific design of flood risk management policies. We take the ideal case of Switzerland and analyze flood risk management portfolios in 18 flood-prone municipalities along the Aare River. We introduce a novel combination of risk analysis and public policy data: we analyze correlations between recorded flood exposure data and survey data on flood risk perception and policy preferences for selected flood risk management measures. Our results indicate that local governments with high flood risk perception tend to prefer non-structural measures, such as spatial planning and ecological river restoration, to infrastructure measures. In contrast, flood exposure is neither linked to flood risk perception nor to policy preferences. We conclude that flood risk perception is key: it can decisively affect local governments’ preferences to implement specific diversified policy portfolios including more preventive or integrated flood risk management measures. These findings imply that local governments in flood-prone areas should invest in raising their population’s awareness capacity of flood risks and keep it high during periods without flooding.

Quantitative flood risk analyses support decisions in flood management policies that aim for cost efficiency. Risk is commonly calculated by a combination of the three quantified factors: hazard, exposure and vulnerability. Our paper focuses on the quantification of exposure, in particular on the relevance of building value estimation schemes within flood exposure analyses on regional to national scales. We compare five different models that estimate the values of flood-exposed buildings. Four of them refer to individual buildings, whereas one is based on values per surface area, differentiated by land use category. That one follows an approach commonly used in flood risk analyses on regional or larger scales. Apart from the underlying concepts, the five models differ in complexity, data and computational expenses required for parameter estimations and in the data they require for model application.The model parameters are estimated by using a database of more than half a million building insurance contracts in Switzerland, which are provided by 11 (out of 19) cantonal insurance companies for buildings that operate under a monopoly within the respective Swiss cantons. Comparing the five model results with the directly applied spatially referenced insurance data suggests that models based on individual buildings produce better results than the model based on surface area, but only if they include an individual building's volume.Applying the five models to all of Switzerland produces results that are very similar with regard to the spatial distribution of exposed-building values. Therefore, for spatial prioritizations, simpler models are preferable. In absolute values, however, the five model results differ remarkably. The two simplest models underestimate the overall exposure, and even more so the extreme high values, upon which risk management strategies generally focus. In decision-making processes based on cost-efficiency, this underestimation would result in suboptimal resource allocation for protection measures. Consequently, we propose that estimating exposed-building values should be based on individual buildings rather than on areas of land use types. In addition, a building's individual volume has to be taken into account in order to provide a reliable basis for cost–benefit analyses. The consideration of other building features further improves the value estimation. However, within the context of flood risk management, the optimal value estimation model depends on the specific questions to be answered. The concepts of the presented building value models are generic. Thus, these models are transferable, with minimal adjustments according to the application's purpose and the data available. Within risk analyses, the paper's focus is on exposure. However, the findings also have direct implications for flood risk analyses as most risk analyses take the value of exposed assets into account in a linear way.