This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.

We suggest a generic classification of early warning systems for natural hazards, which distinguishes alarm, warning, and forecasting systems. On the basis of this classification, we developed a three-step framework for evaluating the effectiveness of such systems and illustrate its applicability using case studies. Our results will support practitioners in comparing the effectiveness of early warning systems with those of structural mitigation measures.