Vulnerability curves vs. vulnerability indicators: application of an indicator-based methodology for debris-flow hazards

Abstract

Abstract. The assessment of the physical vulnerability of elements at risk as part of the risk analysis is an essential aspect for the development of strategies and structural measures for risk reduction. Understanding, analysing and, if possible, quantifying physical vulnerability is a prerequisite for designing strategies and adopting tools for its reduction. The most common methods for assessing physical vulnerability are vulnerability matrices, vulnerability curves and vulnerability indicators; however, in most of the cases, these methods are used in a conflicting way rather than in combination. The article focuses on two of these methods: vulnerability curves and vulnerability indicators. Vulnerability curves express physical vulnerability as a function of the intensity of the process and the degree of loss, considering, in individual cases only, some structural characteristics of the affected buildings. However, a considerable amount of studies argue that vulnerability assessment should focus on the identification of these variables that influence the vulnerability of an element at risk (vulnerability indicators). In this study, an indicator-based methodology (IBM) for mountain hazards including debris flow ) is applied to a case study for debris flows in South Tyrol, where in the past a vulnerability curve has been developed. The relatively "new" indicator-based method is being scrutinised and recommendations for its improvement are outlined. The comparison of the two methodological approaches and their results is challenging since both methodological approaches deal with vulnerability in a different way. However, it is still possible to highlight their weaknesses and strengths, show clearly that both methodologies are necessary for the assessment of physical vulnerability and provide a preliminary "holistic methodological framework" for physical vulnerability assessment showing how the two approaches may be used in combination in the future.

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

“…Still, Fig. 4 shows that a local impact indicator can be suitable to evaluate the hydrostatical forces of this type of hazard, which, in addition to the characteristics of the element at risk, might allow vulnerability estimations such as performed by Papathoma-Köhle (2016).…”

Multi-variable flood damage modelling with limited data using supervised learning approaches

“…Similar approaches are also applied to other natural hazards, for example for landslides (Papathoma-Köhle et al, 2015), and the software package HAZUS can be used for floods, earthquakes and hurricanes (Scawthorn et al, 2006). Alternative approaches to calculate flood risk also exist, such as vulnerability indicators (Papathoma-Köhle, 2016).…”

Establishment and characteristics analysis of a crop-drought vulnerability curve: a case study of European winter wheat

“…The Di1 value is positively correlated with relative humidity and elevation, and the correlation coefficient is 0.41 and 0.40, respectively. That is, in areas with high relative humidity or altitude, only when the drought develops to a rather serious extent does it begin to have a significant impact on winter wheat From the perspective of an influencing mechanism, when the soil sandy content is high, the soil drainage ability is high, and the crop is more vulnerable to drought, exhibiting low Di1, Di2, and Di3 values and a high CLr value in the vulnerability curve (Reid et al, 2006;Papathoma-Köhle, 2016). The cause-effect 25 relationship between the temperature and the characteristic parameters cannot be defined, although the spatial distributions of the two have a certain correlation.…”

“…Crop model simulations are based on the crop growth and development mechanism, using mathematical physics methods and computer technology to quantitatively describe the crop growth and yield formation process in specific environments, which can solve the problem of 25 insufficient samples or limited precision in observational or statistical data to some extent (Palosuo et al, 2011;Challinor et al, 2009). This method can provide ideas for the study of disaster-causing mechanisms and help to improve risk prediction (Papathoma-Köhle, 2016). However, because the curve is infinite dimensional data (James and Sugar, 2003), it is difficult to directly express the vulnerability and analyse the regional differences.…”