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Risk Management and Physical Modelling for Mountainous Natural Hazards

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Extreme Events in Nature and Society

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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Summary

Population growth and climate change cause rapid changes in mountainous regions resulting in increased risks of floods, avalanches, debris flows and other natural hazards. Xevents are of particular concern, since attempts to protect against them result in exponentially growing costs. In this contribution, we suggest an integral risk management approach to dealing with natural hazards that occur in mountainous areas. Using the example of a mountain pass road, which can be protected from the danger of an avalanche by engineering (galleries) and/or organisational (road closure) measures, we show the advantage of an optimal combination of both versus the traditional approach, which is to rely solely on engineering structures. Organisational measures become especially important for Xevents because engineering structures cannot be designed for those events. However, organisational measures need a reliable and objective forecast of the hazard. Therefore, we further suggest that such forecasts should be developed using physical numerical modelling. We present the status of current approaches to using physical modelling to predict snow cover stability for avalanche warnings and peak runoff from mountain catchments for flood warnings. While detailed physical models can already predict peak runoff reliably, they are only used to support avalanche warnings. With increased process knowledge and computer power, current developments should lead to a enhanced role for detailed physical models in natural mountain hazard prediction.

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© 2006 Center for Frontier Sciences

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Lehning, M., Wilhelm, C. (2006). Risk Management and Physical Modelling for Mountainous Natural Hazards. In: Albeverio, S., Jentsch, V., Kantz, H. (eds) Extreme Events in Nature and Society. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28611-X_13

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