Multi-scale debris flow vulnerability assessment and direct loss estimation of buildings in the Eastern Italian Alps

Abstract

Vulnerability assessment, as a component of the consequence analysis, represents a fundamental stage in the risk assessment process because it relates the hazard intensity to the characteristics of the built environment that make it susceptible to damage and loss. The objective of this work is to develop a quantitative methodology for vulnerability and loss assessment of buildings exposed to debris flows and apply it to a study area in NE Italy at local and regional scale. Using existing conceptual models of vulnerability and loss, this paper seeks to identify solutions for maximizing the information gained from limited observational damage data and a heterogeneous building data set. Two vulnerability models are proposed: Model 1 is based on the generation of empirical vulnerability curves using observed intensities; Model 2 takes into account multiple resistance characteristics of buildings and uses modeled debris flow intensities. The process intensity descriptor in both cases is debris flow height. The vulnerability values obtained with the local (Model 1) and regional (Model 2) models are further multiplied with the building value to calculate the minimum and maximum loss for each building in the study area. Loss is also expressed as cumulative probability calculated with Model 1 using a Monte Carlo sampling technique. The methodology is applied in the Fella River valley (northeastern Italian Alps), a region prone to multiple mountain hazards. Uncertainties are expressed as minimum and maximum values of vulnerability, market values and loss. The

Multi-hazard risk assessment for roads: Probabilistic versus deterministic approaches

“…The use of vulnerability parameters or lethality values as a function of process-specific intensities is often based on incomplete or insufficient statistical data resulting from missing event documentation (Fuchs et al, 2013). As discussed in Kappes et al (2012a), Papathoma-Köhle et al (2011, 2017 and Ciurean et al (2017) with respect to mountain hazards, potential sources of uncertainty in vulnerability assessment are independent of the applied assessment method. The amplitude in data is considerably high in continuous vulnerability curves or functions, but also in discrete (minimum and maximum) vulnerability values referred to as matrices (coefficients), and in indicator-/index-based methods used to calculate the cumulative probability of loss.…”

“…The amplitude in data is considerably high in continuous vulnerability curves or functions, but also in discrete (minimum and maximum) vulnerability values referred to as matrices (coefficients), and in indicator-/index-based methods used to calculate the cumulative probability of loss. Associated with the uncertainty in vulnerability matrices, Ciurean et al (2017) suggested a fully probabilistic simulation in order to quantify the propagation of errors between the different stages of analysis by substituting the range of minimum-maximum values with a probability distribution for each variable in the model. listed potential sources of uncertainties in risk assessment models and classified uncertainties into aleatory and epistemic uncertainties.…”

Debris Flow Risk Assessment Based on a Water–Soil Process Model at the Watershed Scale Under Climate Change: A Case Study in a Debris-Flow-Prone Area of Southwest China

“…In this study, vulnerability was expressed as a comprehensive index estimated through principal component analysis (PCA) and set as a range from 1 to 100. the construction of an evaluation index system is a commonly used method to integrate different categories of affected elements, to synthesize the vulnerability index by statistical methods or other mathematical methods, and to represent the relative degree of vulnerability of the assessment unit [54][55][56][57]. Considering the availability and consistency of socio-economic data in debris flow watersheds which normally correspond to the township scale, 11 variables (Table 1) were included and were mostly inferred from previous representative studies [58][59][60]. The procedures of PCA are described by Jolliffe [61].…”

Assessment of the physical vulnerability of buildings affected by slow-moving landslides

“…Slow-moving landslides are observed worldwide in many countries, e.g. Italy (Cascini et al, 2008;Antronico et al, 2015;Uzielli et al, 2015a;Borrelli et al, 2018;Ferlisi et al, 2019), Canada (Clifton et al, 1986;Brooker and Peck, 1993;Moore et al, 2006;Barlow, 2000), China (Chen et al, 2016;Zhang et al, 2018;Dong et al, 2018;Wang et al, 2018), the USA (Esser, 2000), and Australia (Jworchan et al, 2008). Fell et al (2008) suggested the estimation of the physical vulnerability of elements at risk for various landslide types.…”