Optimal design under uncertainty of a passive defense structure against snow avalanches: from a general Bayesian framework to a simple analytical model

Abstract. For snow avalanches, passive defense structures are generally designed by considering high return period events. In this paper, taking inspiration from other natural hazards, an alternative method based on the maximization of the economic benefit of the defense structure is proposed. A general Bayesian framework is described first. Special attention is given to the problem of taking the poor local information into account in the decision-making process. Therefore, simplifying assumptions are made. The avalanche hazard is represented by a Peak Over Threshold (POT) model. The influence of the dam is quantified in terms of runout distance reduction with a simple relation derived from small-scale experiments using granular media. The costs corresponding to dam construction and the damage to the element at risk are roughly evaluated for each dam height-hazard value pair, with damage evaluation corresponding to the maximal expected loss. Both the classical and the Bayesian risk functions can then be computed analytically. The results are illustrated with a case study from the French avalanche database. A sensitivity analysis is performed and modelling assumptions are discussed in addition to possible further developments.

[1]  Christian Wilhelm Wirtschaftlichkeit im Lawinenschutz , 1996 .

[2]  Nicolas Eckert,et al.  Revisiting statistical–topographical methods for avalanche predetermination: Bayesian modelling for runout distance predictive distribution , 2007 .

[3]  Christopher J. Keylock,et al.  Snow avalanche impact pressure: vulnerability relations for use in risk assessment , 2001 .

[4]  P. A. Chernouss,et al.  Application of statistical simulation for avalanche-risk evaluation , 2001, Annals of Glaciology.

[5]  Kristján Jónasson,et al.  Avalanche hazard zoning in Iceland based on individual risk , 2004, Annals of Glaciology.

[6]  P. Müller Simulation Based Optimal Design , 2005 .

[7]  Roman Krzysztofowicz,et al.  The case for probabilistic forecasting in hydrology , 2001 .

[8]  Nicolas Eckert,et al.  Bayesian stochastic modelling for avalanche predetermination: from a general system framework to return period computations , 2008 .

[9]  Nicolas Eckert,et al.  Bayesian optimal design of an avalanche dam using a multivariate numerical avalanche model , 2009 .

[10]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[11]  Michael Bründl,et al.  Damage Potential and Losses Resulting from Snow Avalanches in Settlements of the Canton of Grisons, Switzerland , 2005 .

[12]  David M. McClung,et al.  Statistical and geometrical definition of snow avalanche runout , 1987 .

[13]  Maurice Meunier,et al.  Computing extreme avalanches , 2004 .

[14]  C. Ancey,et al.  Towards a conceptual approach to predetermining long-return-period avalanche run-out distance , 2004 .

[15]  Adrienne Grêt-Regamey,et al.  Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS , 2006 .

[16]  Margreth Keiler,et al.  Development of the damage potential resulting from avalanche risk in the period 1950-2000, case study Galtür , 2004 .

[17]  David M. McClung Extreme avalanche runout in space and time , 2000 .

[18]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[19]  Ian Jordaan Decisions under Uncertainty: Probabilistic Analysis for Engineering Decisions , 2005 .

[20]  Andreas Paul Zischg,et al.  The long-term development of avalanche risk in settlements considering the temporal variability of damage potential , 2005 .

[21]  Sven Fuchs,et al.  The net benefit of public expenditures on avalanche defence structures in the municipality of Davos , Switzerland , 2005 .

[22]  Eric Parent,et al.  The deductive phase of statistical analysis via predictive simulations: test, validation and control of a linear model with autocorrelated errors representing a food process , 2004 .

[23]  Christophe Ancey,et al.  Dynamique des avalanches , 2006 .

[24]  Peter Gauer,et al.  Overrun length of avalanches overtopping catching dams: Cross‐comparison of small‐scale laboratory experiments and observations from full‐scale avalanches , 2008 .

[25]  T. E. Lang,et al.  Modeling of snow flow , 1980 .

[26]  James S. Clark,et al.  Why environmental scientists are becoming Bayesians , 2004 .

[27]  Christopher J. Keylock,et al.  CONCLUSIONS FROM A RECENT SURVEY OF AVALANCHE COMPUTATIONAL MODELS , 1998 .

[28]  Christopher J. Keylock,et al.  Avalanche risk mapping by simulation , 1999 .

[29]  Christophe Ancey,et al.  L"approche conceptuelle pour l"étude des avalanches , 2004 .

[30]  Eric J. Johnson,et al.  Decisions Under Uncertainty: Psychological, Economic, and Neuroeconomic Explanations of Risk Preference , 2009 .

[31]  F. Savi,et al.  Risk assessment in avalanche-prone areas , 2004, Annals of Glaciology.

[32]  Roman Krzysztofowicz Why should a forecaster and a decision maker use Bayes Theorem , 1983 .

[33]  Jacques Bernier,et al.  Bayesian POT modeling for historical data , 2003 .

[34]  Christopher J. Keylock,et al.  An alternative form for the statistical distribution of extreme avalanche runout distances , 2005 .

[35]  Kristján Jónasson Estimation of avalanche risk , 1999 .

[36]  J. Bernier,et al.  Décisions et comportement des décideurs face au risque hydrologique , 2003 .

[37]  Christian P. Robert,et al.  Bayesian-Optimal Design via Interacting Particle Systems , 2006 .

[38]  Mohamed Naaim,et al.  Experimental study of dense snow avalanches: velocity profiles in steady and fully developed flows , 2004, Annals of Glaciology.

[39]  F. Mallor,et al.  An introduction to statistical modelling of extreme values. Application to calculate extreme wind speeds , 2009 .

[40]  Laurent Bélanger,et al.  Projects for Past Avalanche Observation and Zoning in France, After 1999 Catastrophic Avalanches , 2004 .

[41]  Philippe Berthet-Rambaud Structures rigides soumises aux avalanches et chutes de blocs : modélisation du comportement mécanique et caractérisation de l'interaction "phénomène-ouvrage" , 2004 .

[42]  Rudolf Sailer,et al.  Empirical Estimate Of Vulnerability RelationsFor Use In Snow Avalanche Risk Assessment , 2004 .

[43]  Betty Sovilla,et al.  Impact pressures and flow regimes in dense snow avalanches observed at the Vallée de la Sionne test site , 2008 .

[44]  B. Merz,et al.  Estimation uncertainty of direct monetary flood damage to buildings , 2004 .

[45]  J. Pickands Statistical Inference Using Extreme Order Statistics , 1975 .

[46]  J. Berger Statistical Decision Theory and Bayesian Analysis , 1988 .

[47]  Johann Stötter,et al.  Development of avalanche risk between 1950 and 2000 in the Municipality of Davos, Switzerland , 2004 .

[48]  Mohamed Naaim,et al.  Dense snow avalanche modeling: flow, erosion, deposition and obstacle effects , 2004 .

[49]  T. Faug,et al.  Varying Dam Height to Shorten the Run-Out of Dense Avalanche Flows: Developing a Scaling Law from Laboratory Experiments , 2003 .

[50]  D. Mcclung,et al.  Extreme avalanche runout: a comparison of empirical models , 2001 .