Abstract
AbstractSnow avalanche simulation software is a commonly used tool for hazard estimation and mitigation planning. In this study a depth-averaged flow model, combining a simple entrainment and friction relation, is implemented in the software SamosAT. Computational results strongly depend on the simulation input, in particular on the employed model parameters. A long-standing problem is to quantify the influence of these parameters on the simulation results. We present a new multivariate optimization approach for avalanche simulation in three-dimensional terrain. The method takes into account the entire physically relevant range of the two friction parameters (Coulomb friction, turbulent drag) and one entrainment parameter. These three flow model parameters are scrutinized with respect to six optimization variables (runout, matched and exceeded affected area, maximum velocity, average deposition depth and mass growth). The approach is applied to a documented extreme avalanche event, recorded in St Anton, Austria. The final results provide adjusted parameter distributions optimizing the simulation–observation correspondence. At the same time, the degree of parameter–variable correspondence is determined. We show that the specification of optimal values for certain model parameters is near-impossible, if corresponding optimization variables are neglected or unavailable.
Highlights
Snow avalanche simulation tools are used for hazard estimations and protection planning (Sampl and Zwinger, 2004; Christen and others, 2010a)
Besides the obvious relations, which reflect the meaning of the parameters in the employed flow model, the quantification allows for a relative evaluation
The method allows us to optimize multiple model parameters using a multivariate evaluation by comparing simulation results with field data based on objective, well-defined optimization variables
Summary
Snow avalanche simulation tools are used for hazard estimations and protection planning (Sampl and Zwinger, 2004; Christen and others, 2010a). Back calculation of real avalanche events requires modification of these parameter suggestions to include physical processes such as flow regime transitions (Issler and Gauer, 2008; Bartelt and others, 2012) as well as the effects of snow temperature and entrainment (Naaim and others, 2013; Vera Valero and others, 2015) Is it possible to reproduce observed runout, flow velocities, impact pressures and deposition depths. To which the proposed method is similar, do not explicitly consider model uncertainties (McMillan and Clark, 2009) They are based on a more arbitrary function to quantify the correspondence between simulation results and observation. The main focus of the presented optimization concept is to provide adjusted parameter distributions employing a systematic, multivariate comparison of simulation results with field observations and their related uncertainties. This yields a locally orthogonal coordinate system. i 1⁄4 1 is in the direction of the surface-parallel velocity vector, i 1⁄4 2 is surface-parallel and orthogonal to the velocity vector and i 1⁄4 3 appears naturally normal to the surface z: dV 1⁄4 dðA hÞ 1⁄4 q_ A, ð1Þ dt dt
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