The application of numerical debris flow modelling for the generation of physical vulnerability curves

C. J. van Westen,B. Quan Luna,T. W. J. van Asch,S. O. Akbas,S. Sterlacchini,J. Blahut

doi:10.5194/nhess-11-2047-2011

Abstract

Abstract. For a quantitative assessment of debris flow risk, it is essential to consider not only the hazardous process itself but also to perform an analysis of its consequences. This should include the estimation of the expected monetary losses as the product of the hazard with a given magnitude and the vulnerability of the elements exposed. A quantifiable integrated approach of both hazard and vulnerability is becoming a required practice in risk reduction management. This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results can then be applied to consequence analyses and risk calculations. On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy). One of the largest debris flows events occurred in a village called Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. The Selvetta event was modelled with the FLO-2D program, an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The behaviour and run-out of the flow was reconstructed. The significance of calculated values of the flow depth, velocity, and pressure were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which was calculated as the ratio between the monetary loss and the reconstruction value. Three different empirical vulnerability curves were obtained, which are functions of debris flow depth, impact pressure, and kinematic viscosity, respectively. A quantitative approach to estimate the vulnerability of an exposed element to a debris flow which can be independent of the temporal occurrence of the hazard event is presented.

Highlights

The increase in population and resulting demand for resources has given rise to a continuous pressure to settle in places were the interaction between humans and continuous land processes becomes a potential risk (Nadim and Kjekstad, 2009)
This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model
On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy)

Summary

Introduction

The increase in population and resulting demand for resources has given rise to a continuous pressure to settle in places were the interaction between humans and continuous land processes becomes a potential risk (Nadim and Kjekstad, 2009) For this reason, it is essential to analyze the possible damage that the hazard process can yield in the affected sectors. A quantifiable integrated approach of both hazard and risk is becoming a required practice in risk reduction management (Fell and Hartford, 1997; Duzgun and Lacasse, 2005) This quantitative assessment should include the expected losses as the product of the hazard with a given magnitude, the costs of the elements at risk, and their vulnerability (Uzielli et al, 2008). Several authors have proposed different methods to quantify the risk by estimating the hazard in a heuristic-empirical or statistical manner; while assessing the vulnerability of the affected elements in a qualitative method (Liu and Lei, 2003; Remondo et al, 2008; Zezere et al, 2008).

Objectives

Methods

Findings

Conclusion