Abstract. Over the last ten years, a risk-based approach to manage natural hazards -termed the risk concept -has been introduced to the management of natural hazards in Switzerland. Large natural hazard events, new political initiatives and limited financial resources have led to the development and introduction of new planning instruments and software tools that should support natural hazard engineers and planners to effectively and efficiently deal with natural hazards. Our experience with these new instruments suggests an improved integration of the risk concept into the community of natural hazard engineers and planners. Important factors for the acceptance of these new instruments are the integration of end-users during the development process, the knowledge exchange between science, developers and end-users as well as training and education courses for users. Further improvements require the maintenance of this knowledge exchange and a mindful adaptation of the instruments to case-specific circumstances.
IFKIS-Hydro is an information and warning system for hydrological hazards in small-and medium-scale catchments. The system collects data such as weather forecasts, precipitation measurements, water level gauges, discharge simulations and local observations of event-specific phenomena. In addition, IFKIS-Hydro incorporates a web-based information platform, which serves as a central hub for the submission and overview of data. Special emphasis is given to local information. This is accomplished particularly by human observers. In medium-scale catchments, discharge forecast models have an increasing importance in providing valuable information. IFKIS-Hydro was developed in several test regions in Switzerland and the first results of its application are available now. The system is constantly extended to additional regions and may become the standard for warning systems in smaller catchments in Switzerland.
Quantitative risk assessments of debris flows and other hydrogeological hazards require the analyst to predict damage potentials. A common way to do so is by use of proportional loss functions. In this paper, we analyze a uniquely rich dataset of 132 buildings that were damaged in one of five large debris flow events in Switzerland. Using the double generalized linear model, we estimate proportional loss functions that may be used for various prediction purposes including hazard mapping, landscape planning, and insurance pricing. Unlike earlier analyses, we control for confounding effects of building characteristics, site specifics, and process intensities as well as for overdispersion in the data. Our results suggest that process intensity parameters are the most meaningful predictors of proportional loss sizes. Cross-validation tests suggest that the mean absolute prediction errors of our models are in the range of 11%, underpinning the accurateness of the approach
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.