Pierrick Nicolet

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 results are compared with relevant published vulnerability curves and historical damage reports.

A Simple Method to Include Uncertainties in Cost-Benefit Analyses

Cost-benefit analysis is a widely used tool in the field of risk induced by natural hazards in order to (1) compare different mitigation options (2) determine if a protection measure is worth to subsidize and (3) prioritize mitigation measures over a region. These analyses are affected by many uncertainties, although the most uncertain parameter in risk assessment is generally the event frequency. As a result, when cost-benefit analyses are compared over different sites, potentially studied by diverse specialists, with different uncertainties and/or errors on the frequency, an unfortunate decision is likely to result from these analyses, especially as the results looks precise and objective. This study proposes to include an uncertainty on the input parameters, by means of triangular distributions, in order to include the experts uncertainty, as well as the natural variability. Special emphasis is laid on the simplicity of the procedure, since assessing all parameters of the distributions would be time-consuming and difficult. A discussion on the relevance of correlating the random variables used for the sampling process is also presented.

Evaporite sinkhole risk for a building portfolio

Karst-related hazard can be a problem for buildings, especially in the case of evaporite karst. This study aims at evaluating the risk posed by evaporite karst for a building portfolio in western Switzerland, using a susceptibility map and an event inventory. Since the inventory is not complete, different corrections aim at obtaining a frequency of sinkhole events damaging a building as close as possible to the actual frequency. These corrections account for the variation of the building stock during the inventory period, the varying inventory quality among the municipalities and the partial knowledge, even in the best case. This approach is preferred here to estimating spatially the hazard, since the amount of information on the frequency and magnitude is insufficient to draw a proper hazard map. The distribution of loss ratios is also retrieved from the inventory, thanks to the estimated or actual price of the remedial works. Annual losses are then estimated using a Monte Carlo approach, which consists in sampling for a number of damaged buildings from a Poisson distribution, for a distribution of loss ratios and for a building value. Different exceedance curves relying on different hypotheses are presented, and the mean annual loss that the public insurance company might have to compensate is estimated at CHF 0.8–1.5 million.