Quantitative risk and optimal design approaches in the snow avalanche field: Review and extensions

Standard engineering procedures, such as adopting high return periods as reference events, are a simplified means of handling the complex and multivariate nature of snow avalanches. Furthermore, such methods do not explicitly take into account the elements at risk and/or possible budgetary constraints. In recent years, many authors from a variety of fields have tried to overcome these limitations with quantitative risk evaluations including cost–benefit analyses. Their proposals are based on different modelling assumptions, and often on different definitions for certain important concepts including scenarios, vulnerability relations and time effects. The first goal of this paper is to propose a state of the art, and to discuss the common points, advantages and drawbacks of the various proposals within a unified formal framework based on decision theory. Most of the applications already in use concern long term risk assessment in land use planning and traffic road regulation, but some potential also exists for short term problems including risk assessment to back-country skiing. In a second time, new extensions of a simple decisional model for the optimal design of an avalanche dam are proposed to illustrate the key point of the place of uncertainty in risk analyses. Finally, to stimulate further research efforts, other important outlooks including computational issues, multivariate optimal design and measures of risk alternative to the standard expected loss minimisation are discussed.

► Half of the paper reviews existing risk and optimal design approaches in the snow avalanche field. ► They are discussed within a clarified formal framework based on statistical decision theory. ► Emphasis is given to key points: time, randomness in hazard and vulnerability, uncertainty. ► From this basis, half of the paper expands a decisional model for an avalanche dam. ► It illustrates how uncertainty can be processed consistently up to the engineering decision.