摘要
Many regions worldwide are threatened by multiple natural hazards with the potential to cause high damages and losses. However, the modeling of multiple hazards in a joint analysis scheme is still in the early stages of development as a range of serious challenges emerges in the multihazard context such as differing modeling approaches in use for contrasting hazards; the time- and data-demanding conduct of each single preparative, intermediate and analysis step; and the clear visualization of the modeling outcome. Under consideration of these difficulties, a regional multihazard exposure analysis concept is developed for five natural hazards: debris flows, rock falls, shallow landslides, avalanches, and river floods, complemented by a visualization scheme to present the modeling outcome. An automation of the two schemes resulted in a beta version of the MultiRISK modeling and the MultiRISK visualization software tool forming together the MultiRISK platform. To test the analysis scheme and the software implementation of MultiRISK a case study is performed in the Barcelonnette basin in France with a worst-case parameterization of the models on the basis of extensive literature reviews. Experiences from this case study offered many insights into the multihazard topic and even more questions, e.g. with respect to coherent multihazard model parameterization, validation or the comparability and interpretation of single-hazard modeling results, respectively. Although analysis schemes can be proposed and software tools can be provided to facilitate many steps, a well-conceived and reflective approach to multihazard settings is essential. The worst-case analysis based on literature values apparently leads to an overestimation of the susceptible areas and the number of exposed elements. Nevertheless, depending on the data situation of an area, especially in areas without any information on past events, this approach may offer the determination of general hazard distributions, overlaps, and areas of potential risk without data-demanding calibration.