Vulnerability of strategic infrastructure

- VERSION 2.0 -

- Vienna, 2014/07/25

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Vulnerability of strategic infrastructure

Bearbeitung Wissensstand Vulnerabilitätsanalysen unter Einbezug strategischer

Infrastruktur

- 2.0 -

Project: IAN Report 161

Authors: Sven FUCHS

Version 2.0 including positions 1-3 (revised) and 4-5

Institution: Institute of Mountain Risk Engineering, University of Natural Resources and Life Sciences

Date: 25.07.2014

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DELIVERABLE SUMMARY

PROJECT INFORMATION

Project acronym:

Project title:

Contract number:

Starting date:

Ending date:

Project website address:

Lead partner organisation:

Address:

Project manager:

E-mail:

DELIVERABLE INFORMATION

Title of the deliverable:

WP/activity WP4 – Creating a state-of-the-art

related to the deliverable: WP4 / Action 4.1 Gathering completion/benchmarking testing available good-practice pre-standards in hazard/risk assessment and mapping (for floods/debris flow/rockfall/landslides)

Type: Public

WP leader:

Activity leader:

Participating partner(s):

Author(s): Sven Fuchs, BOKU University of Natural Resources and Life Sciences

E-mail: sven.fuchs@boku.ac.at

Keywords: Vulnerability, infrastructure

This report should be cited in

bibliography as follows:

Fuchs, S. (2014): Vulnerability of strategic infrastructure, IAN Report 161, Institut für Alpine Naturgefahren, Universität für Bodenkultur, Wien (unveröffentlicht)

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CONTENT

1 Zusammenfassung 5

2 Introduction 6

3 The concept of structural vulnerability 8

3.1 Losses due to mountain hazards 8

3.2 Implications for vulnerability research 10

3.3 Semi-detailed assessment level 11

3.4 detailed assessment level 12

4 Vulnerability to flooding 14

4.1 Static flooding 14

4.2 Dynamic flooding 16

5 Vulnerability to Landslides 17

6 Vulnerability to strategic infrastructure 21

7 Gaps and challenges of current approaches 26

8 Conclusions 28

9 References 34

Appendix 42

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Vulnerability of strategic infrastructure

1 ZUSAMMENFASSUNG

Im vorliegenden Bericht wird ein Überblick zum Stand der Vulnerabilitätsforschung gegeben, mit einem

Schwerpunkt auf Ansätzen, die im Europäischen Alpenraum in den letzten Jahren entwickelt wurden. Aufbauend auf einem kurzen Überblick zu naturgefahreninduzierten Schäden wird die Skalenabhängigkeit der Vulnerabilität diskutiert, sowie die Einbettung unterschiedlicher Vulnerabilitätskonzepte (physisch, sozial und

ökonomisch) vorgestellt. In den weiteren Kapiteln wird auf die Ansätze zur Quantifizierung von physischer Vulnerabilität für Wassergefahren eingegangen. Im Wesentlichen existieren verschiedene, auf empirischen Datengrundlagen basierende Schadensfunktionen, die im Rahmen einer Risikoanalyse angewendet werden

können. Ein Ergebnis der Untersuchungen ist, dass zwischenzeitlich genügend Schadensereignisse ausgewertet werden konnten, um verlässliche statistische Beziehungen zwischen der Prozessintensität und der Höhe des potentiellen Schadens herzustellen.

In Bezug auf die Vulnerabilität strategischer Infrastruktureinrichtungen sind in den vergangenen Jahren zahlreiche Studien publiziert worden, die zu einem Großteil auf die Methodik der Graphentheorie zurückgreifen.

Bislang sind allerdings lediglich wenige Arbeiten durchgeführt worden, die sich explizit mit alpinen Naturgefahren befassen. Demzufolge besteht ein Nachholbedarf in Bezug auf die erforderlichen Grundlagendaten für derartige Untersuchungen, allen voran in Hinblick auf eine räumlich korrekte und

vollständige Repräsentation des zu untersuchenden Netzwerkes. Aufgrund der Tatsache, dass viele Infrastrukturdaten nur in aggregierter Form erhältlich sind, kann eine Untersuchung nur der Realität angenähert werden, diese jedoch nicht vollständig wiedergeben. Des Weiteren werden bei Anwendung netzwerkbasierter

Modelle etwaige externe, die Vulnerabilität beeinflussende Faktoren in der Regel nicht abgebildet.

Insgesamt sind netzwerkbasierte Methoden geeignet, die Vulnerabilität strategischer Infrastruktur abzubilden,

erfolgversprechend erscheint jedoch hier eine Kombination mit anderen räumlich (und zeitlich) fokussierenden Methoden zur Vulnerabilitätsanalyse, um die Komplexität der Thematik entsprechend gut abzubilden. Hierzu sind weitere Untersuchungen notwendig.

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2 INTRODUCTION

Extreme geophysical events, such as those which recently occurred in the United States (hurricane Katrina),

Europe and Pakistan (floods), New Zealand and Japan (earthquake and tsunami), have focused the attention of the global community to the topic of vulnerability to natural hazards. Why has there been so little progress in our ability to mitigate and adapt to natural hazards? White et al. (2001) summarised this paradox in an article

with the title ‘Knowing better and losing even more – the use of knowledge in hazard management’. While there are many reasons for this paradox, one might also state that truly interdisciplinary research appears to be necessary to tackle this problem as it allows for the analysis of the dynamics and multi-faceted characteristics

of vulnerability (Fuchs et al. 2011).

The concept of vulnerability is pillared by multiple disciplinary theories underpinning either a technical or a

social origin of the concept and resulting in a range of paradigms for either a qualitative or quantitative assessment of vulnerability. However, efforts to reduce susceptibility to hazards and to create disaster-resilient communities require intersections among these theories, since human activity cannot be seen independently

from the environmental setting. Acknowledging different roots of disciplinary paradigms, issues determining structural, economic, institutional and social vulnerability should be combined in order to be prepared for climate change and necessary adaptation. Boruff and Cutter (2007) remarked on the lack of agreement and

understanding concerning the methods or techniques for comparing hazard vulnerability within or between places, and they stated that a refinement of vulnerability assessment methods and the delineation of highly vulnerable hot spots (e.g., strategic infrastructure) may support stakeholders interested in vulnerability

reduction use their resources more efficiently.

It is argued that physical vulnerability of elements at risk such as buildings exposed results in considerable

economic vulnerability, generated by the institutional settings of dealing with natural hazards and the challenges of climate change, and shaped by the overall societal framework in the communities affected (Fuchs 2009). If vulnerability and its counterpart, resilience, is analyzed and evaluated by using such a comprehensive

approach, a better understanding of the vulnerability-influencing parameters could be achieved, taking into account the interdependencies and interactions between the multiple actors involved.

Place-based vulnerability can exist in a variety of configurations depending on the exposure to any type of hazards and on the characteristics of the exposed society. The spatial distribution of indicators framing societal characteristics of a given population, such as poverty and education, result in corresponding vulnerability

patterns, apart from inequalities in the capacities of individuals and groups to manage stress and to avoid, prepare for, and respond to disasters. This socioscientific fundament goes along with the engineering definition of vulnerability: A hotspot of natural hazard vulnerability may be a place where high exposure to meteorological

and geophysical hazards intersects with high population densities, a high density of elements at risk and associated (economic) values, inadequately-built infrastructure, hazardous industries, etc.

Following the convention that flood or landslide hazard risk is a function of hazard and consequences (e.g., Varnes 1984), the ability to determine vulnerability either quantitatively or qualitatively is essential for reducing these consequences and therefore natural hazard risk. The assessment of vulnerability requires an ability to

both identify and understand the susceptibility of elements at risk and – in a broader sense – of the society to these hazards (Birkmann et al. 2013). Studies related to vulnerability of human and natural systems to mountain hazards, and of the ability of these systems to adapt to dynamic changes, are a challenging field for policy

development that brings together experts from a wide range of disciplines, including natural sciences, development studies, disaster management, health, social sciences, and economics, to name only a few (Fuchs 2009). Experts from these fields bring their own conceptual models and methods to study vulnerability and

adaptation, models which often address similar problems and processes by using different languages (Brooks 2003). Therefore, basic terminology should be clarified so that every stakeholder involved can participate in the assessment, and to promote understanding of basic risk management terms and concepts in order to plan

preparation.

Taking the perspective of sciences, and neglecting any social implications arising from mountain hazards,

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vulnerability is considered as a functional relationship between process magnitude or intensity, the resulting

impact on structural elements at risk, and exposed values. With respect to the built environment, vulnerability is related to the susceptibility of physical structures and is defined as the expected degree of loss resulting from the impact of a certain (design) event on the elements at risk. Its assessment requires the evaluation of different

parameters and factors such as type of element at risk, resistance, and implemented protective measures (i.e., local structural protection). With respect to the hazardous processes, empirical parameters such as magnitude and frequency have to be evaluated based on probability theory. Thereby the magnitude-frequency concept

plays a key role. When the activity of different hazard processes is compared on a given timescale some processes appear to operate continuously while others operate only when specific conditions occur. The term episodicity was used by Crozier (2004) to refer to the tendency of processes to exhibit discontinuous behaviour

and to occur sporadically as a series of individual events. Episodicity appears when discontinuity is inherent in the forcing process, however, with respect to mountain hazards, the relationship between the initiating forcing process (e.g., intense but discontinuous rainfall) and the geomorphic response (e.g., formation of debris flows

as a result from erosion and mobilisation of solid particles in a channel bed) is not constant. Operationally, triggering thresholds are used instead to indirectly approach the probability of occurrence of a specific design event, and connectivity is assumed to deduce the behaviour of the hazard process from that of the triggering

factor itself (Keiler 2011).

Physical vulnerability to mountain hazards can be reduced by either structural or non-structural measures (Fig.

1). With respect to sustainability, but also because of economic constraints (Fuchs et al. 2007b), adaptation to or accommodation of such hazards should be preferred rather than structural and permanent mitigation measures (e.g., dams, levees, and floodwalls) to reduce vulnerability. In particular non-structural adjustments,

consisting of policy arrangements imposed by a governing body (local, regional, or national) to restrict the use of floodplains and areas prone to landslides, or flexible human adjustments to vulnerability that do not involve substantial investment in hazard control, still remain central with respect to a contemporary management of

hazards and risk (Holub and Fuchs 2008, 2009; Gibbs 2012). Following Hallegatte and Corfee-Morlot (2011), private and public adjustments to reduce vulnerability to such hazard events consist largely of fixed investments (flood control structures, torrential barriers, etc.), while other involve primarily recurrent expenses

for personnel (information and early warning, Fuchs et al. 2012a). While some adjustments are inherently public (hazard and risk zoning regulations, Fuchs and McAlpin 2005), others are private (local structural protection, Holub and Fuchs 2009). Some involve physical interference with the natural event (landslides: technical

protection in the starting zones), while others are merely attempts to reduce the effects of natural variations (floods: retention basins with grain-sorting outlet structures), and still others involve only the control of human society (evacuation).

Active Passive

Soil bio-engineering Spatial planning and land-use

Forestal measures Hazard mapping Permanent

Technical measures Local structural measures

Immediate measures Information and warning Temporarily

Exclusion zones and evacuation

Figure 1: Categories of mitigation measures to reduce vulnerability.

In the following section an overview on the concept of vulnerability is given, focusing on an engineering approach of physical or structural vulnerability as a starting point for the vulnerability circle composed from

physical and social vulnerability, and connected by the drivers of vulnerability emerging from economic and institutional constraints (Fuchs 2009).

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3 THE CONCEPT OF STRUCTURAL VULNERABILITY

By applying the concept of risk, the definition of vulnerability plays an important role in natural hazards

research within mountain environments (Fuchs et al. 2007b). Hence, from an engineering point of view, considerable areas in European regions are vulnerable to natural hazards. This is repeatedly stated in studies related to losses due to natural hazards (e.g., Rougier 2013; Fekete and Sakdapolrak 2014), and is therefore also

valid for European mountain regions. Hence, this topic is addressed in the following section in more detail.

3.1 LOSSES DUE TO MOUNTAIN HAZARDS

Data on hazardous events and associated losses for European mountain regions seems to be quite well

documented, particularly for Italy, Switzerland and Austria. However, as a result of duplicities in research efforts and administrative responsibili-ties, several bibliographies and databases concerning hazard inventories exist, which makes a comparison and assessment difficult. Nevertheless, apart from the resulting inconsistency and

incompleteness of individual inventories (which has already been reported with respect to mountain regions by e.g., Eisbacher and Clague (1984) for Europe and by Alger and Brabb (2001) focusing on the US), the general development of losses can be concluded and an overall conclusion for possible future needs can be drawn.

• For the Republic of Italy, data related to landslide occurrence focusing on fa-talities due to landslide-type events had been analysed by Guzzetti (2000), Calcaterra and Parise (2001), Guzzetti and

Tonelli (2004) and Guzzetti et al. (2005). Additionally, Catenacci (1992) estimated that at least 2,447 lives were lost in the period 1945-1990, while Guzzetti (2000) reported almost 10,000 lost lives due to landslides (including debris flows) in Italy, and a total of 50,593 harmed people due to landslides

and flooding in the last 725 years (Guzzetti et al. 2005). Thereby, the alpine regions of Italy have suffered twice as much missing people than the residual part of the country (Guzzetti 2000). Consid-erable efforts to establish a database on mountain hazards have been under-taken by

Zischg et al. (2007) in order to assess floods and torrent events since the mediaeval times for the Autonomous Province of Bolzano – Southern Tyrol in the alpine part of Italy. The major focus thereby was on a characterisation of events according to magnitude and frequency, and on the

establishment of chronologies for individual watersheds.

• Recently, a Swiss database was established by Hilker et al. (2009) based on comprehensive information

in Röthlisberger (1991) and Fraefel et al. (2004). In the period 1972-2007, 102 persons lost their lives due to floods, debris flows and landslides in Switzerland. Naturally triggered floods, debris flows, landslides and rockfall events have caused financial damage amounting to nearly € 8,000 million in

total within the last 36 years (taking inflation into ac-count, Hilker et al. 2009), which results in an average of approximately € 222 million per year. However, the data is not equally distributed, and distinct years with above-average loss (due to the category flood/inundation) were traceable (1978,

1987, 1993, 1999, 2000, 2005, and most recently 2007). Approximately 90 % of the costs originated from floods and inundations (including torrent processes such as hyperconcentrated flow and flooding with moderate bedload transport), while debris flows and landslides were only

responsible for 4 % and 6 % of the losses, respectively. Since 1972, nearly 50 % of the losses had been registered in the months of July and August, which clearly indicates the importance of stratiform (dynamic) precipitation and convective precipitation as triggering events (Hilker et al.

2009). However, between 1972 and 2007, a statistically significant trend with regard to an increase in the annual cost of damage could not be proven.

• In Austria, a database of destructive torrent events was established and ana-lysed concerning monetary losses by Oberndorfer et al. (2007). A total number of 4,894 damaging torrent events was reported between 1972 and 2004. For almost 4,300 events the process type could be determined

ex-post due to the event documentation carried out by the Austrian Torrent and Avalanche

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Control Service, resulting in a classification between floods (0.3 %), flooding with bedload

transport (21.8 %), hyperconcentrated flows (49.2 %), and debris flows (28.7 %). The average direct loss per event due to these 4,300 records amounted to approximately € 170,000 (in 2014 values), and annually losses due to torrent events amounted to around € 25 million. Approximately two

third of the losses could be ascribed to buildings, and one third to infrastructure facilities (Fuchs 2009). Within the period under investigation, 21 people were physically harmed and 49 people died1.

Figure 2: Data related to torrent events collected from the reports which were compiled during the implementation of hazard maps by the Austrian Torrent and Avalanche Control Service for the period 1950-2008. Even if a spatial concentration of events can be proven for the western part of Austria, the overall trend for both process categories is decreasing, which is in line with studies by Oberndorfer et al. (2007) even if their studies were based on another universal set of data. (Data source: Institute of Mountain Risk Engineering, University of Natural Resources and Applied Life Sciences, see also Fuchs et al. 2013).

The annual distribution of the losses showed that considerable cumulative damage exceeding € 1 million per event occurred in 1975, 1978, and 1991. In contrast, in 1976 and 1984, the average damage per event summed up to € 11,000 and € 16,000, respectively. A considerable number of events was reported from 1974, 1990, and

2002, leading to the conclusion that a high number of events does not necessarily result in high losses, and vice versa. An additional analysis of destructive torrent events between 1950 and 2008, derived from a reanalysis of

1 For comparison, during the same period 1972-2004, 1.48 million traffic accidents involving physical injury caused 53,576 fatal casualties in Austria (approximately 1,600 per year), 1.95 million persons were injured

(approximately 59,000 per year, Kuratorium für Verkehrssicherheit 2005); and even 92 persons committed suicide every year (Statistik Austria 2008) – obviously traffic risk is subject to other moral concepts than risk resulting from torrent processes, and potential suicidal tendency is even not a real issue in reducing population

vulnerability so far.

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written reports which were compiled during the implementation of hazard maps by the Austrian Torrent and

Avalanche Control Service had shown a decreasing trend related to the overall number [N = 9,852, annual mean = 167]. However, consid-erable events were observed in individual years, in particular in the western part of Austria (see Fig. 2).

3.2 IMPLICATIONS FOR VULNERABILITY RESEARCH

Even if the theory of vulnerability had been subject to extensive research and numerous practical application for

the last decades, considerable gaps still exist with respect to standardised functional relationships between impacting forces due to occurring hazard processes and the structural damage caused (Mazzorana et al. 2014). For a major part these gaps result from the overall lack of data, in particular concerning (1) losses caused by

mountain hazards as a result of outstanding empirical classifications of damages and (2) impact forces that caused these losses. Moreover, most of the studies undertaken so far were exclusively focusing on specific building categories, such as residential buildings or hotels and guest houses (Fuchs et al. 2007a; Totschnig et al.

2011; Totschnig and Fuchs 2013). Finally, due to the underlying empiricism of such vulnerability functions, the spread in the data is considerable in particular for process intensities exceeding 1.0-1.5 m (Fuchs et al. 2007a). The physics of the damage generating mechanisms for a well-defined element at risk with its peculiar geometry

and structural characteristics remain unveiled, and, as such, the applicability of the empirical approach for planning hazard-proof buildings is limited (Holub et al. 2012; Mazzorana et al. 2014). Consequently, possible losses due to future events can only be predicted so far at the basis of relatively sporadic empirical

classifications.

Although different approaches exist to describe the physical dimension of vulnerability, it is possible to make a

distinction between two groups,

• vulnerability indicators, developed to allow the vulnerability to be retraced and compared to locations

and societies, which is recommended for a semi-detailed analysis, and

• vulnerability models, developed to explain vulnerability and its dynamics on a detailed scale.

While the first are more suitable for regional-scale assessments, the latter is more suitable for local-scale assessments and requires more data for computation. The ultimate goal of vulnerability assessment should be

to measure vulnerability as quantitatively as possible, so that subsequent evaluations with respect to risk can be carried out to determine if it is being reduced or not. The physical vulnerability is a representation of the expected level of damage and its assessment requires an understanding of the interaction between the

hazardous event(s) and the elements at risk.

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Figure 3: Possible workflow for determining regional-scale physical vulnerability. Note that the weighting is subject to individual preferences.

3.3 SEMI-DETAILED ASSESSMENT LEVEL

The regional-scale assessment of vulnerability is suitable for lager regions and may provide an overview on the (physical) vulnerability towards mountain hazards. Tech-nically, within a Geographic Information System a spatial overlap is generated be-tween areas prone to hazards (e.g., in terms of a susceptibility map), and of

areas with a high value concentration such as city centers or with strategic importance such as main traffic corridors or power lines. A regional vulnerability assessment will result in a qualitative ranking of regions more or less prone to natural hazards, usually in terms of indicators, and as a consequence in areas of higher and

lower vulnerability. The advantage of such a procedure is the possibility for application in data-scarce areas and over lager regions, respectively. The procedure is applicable in order to cost-efficiently identify hotspots and elaborate detailed assessments only for the hotspot areas.

Areas with high vulnerability may include settlements with high population density and poor housing conditions, municipal centers and buildings of the public health sector exposed to hazards. To such areas a high

vulnerability value can be attributed by e.g. using a valuing scheme of indicators between 1 and 10 (1 = low, 10 = high), see Fig. 3. This procedure may be supported by local experts and decision makers during a round-table discussion or a scenario analysis (Mazzorana and Fuchs 2010b; Mazzorana et al. 2013).

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Figure 4: Example for an empirically-determined vulnerability function for torrential floods, based on absolute process intensities against the corresponding degree of loss for Austrian (blue dots) and Italian (green triangles) test sites (Totschnig and Fuchs 2013).

3.4 DETAILED ASSESSMENT LEVEL

The detailed assessment level is focusing on individual elements at risk and is relat-ed to the susceptibility of

physical structures. On this level, vulnerability is defined as the expected degree of loss resulting from the impact of a certain (design) event on the elements at risk. Regularly, the degree of loss is quantified using an economic approach by establishing a ratio between the loss and the reconstruction value of every individual

element at risk (building) exposed, if data on incurring losses is available (Apel et al. 2009; Totschnig and Fuchs 2013). Its assessment re-quires the evaluation of different parameters and factors such as type of element at risk, resistance, and implemented protective measures (i.e., local structural protec-tion). With respect to the

hazardous processes, empirical parameters such as magni-tude and frequency have to be evaluated and opposed to the damage pattern or the monetary loss. In a second set of calculations, this ratio obtained for every individual element at risk (or a group of elements with similar characteristics) is linked to the respective

process intensities which should be regularly documented ex-post by the respective responsible public authorities or their subcontractors. Otherwise, if such data is not available, process intensities may result from modelling exercises (Jakob et al. 2012), which, in turn, need careful interpretation and validation in order to

mirror the real-world conditions as precise as possible. As a result, scatterplots can be developed linking process intensities to the degree of loss, which is a proxy for object vulnerability values (Fuchs et al. 2007a, see Fig. 4). By using Geographic Information Systems quantitative information on the susceptibility of elements

exposed can be computed, and a detailed overview on a local level will be provided. The overall framework of the method applied to determine physical vulnerability is outlined in Fig. 5, and requires quantitative information on the local level.

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Damage ratio

Loss data

Loss proxy

oror

Reconstructionvalue

Reconstructionproxy

oror

Ex-postdocumentation

Process intensity

ModellingEx-postdocumentationEx-postdocumentation

Process intensity

ModellingModelling

Vulnerability function

oror

Figure 5: Framework for the deduction of vulnerability functions for torrent events (Fuchs et al. 2012c).

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4 VULNERABILITY TO FLOODING

Most of the flood vulnerability analyses focus on the estimation of direct, tangible damages which are (1) first-

order-consequences neglecting any indirect effects that emerge later and may be more difficult to attribute directly to a specific flood event (e.g., mental illness resulting from shock, long-term decrease in stock prices, etc.) and (2) effects where it is possible to assign reasonable monetary values (e.g., replacement of damaged

property). Tracing back to White (1945), who linked inundation depth to expected losses expressed as percentage or total damage (monetary value), the most frequently applied approach links flood intensity to estimated damages, as shown in Fig. 4. However, for use on the semi-detailed scale, classified flood information

may be used such as related to the severity of flood to express vulnerability (e.g., serious (< 1.5 m), disastrous (1.5-4 m) and catastrophic (> 4 m)). Subsequently the percentage of total potential damage for households, industrial assets, infrastructure, etc. and number of inhabitants, respectively, can be determined. More

quantitative so-called stage-damage functions are widely used in flood risk assessment; these functions need detailed information on the hazard magnitude (Figs. 4 and 6).

Figure 6: Different stage-damage functions used in flood risk management (Apel et al. 2009).

4.1 STATIC FLOODING

The use of stage-damage curves is usually restricted to static inundation (gently flowing water, flow velocities <

1 m/s) since faster flowing water bodies result with increasing likelihood in damages due to the dynamic load and erosive forces (Egli 2000). Walton et al. (2004) limited the applicability even further to slow-rising, low-silt and low-flow floods. While Kang et al. (2005), for example, presented such stage-damage functions for

residential buildings interrelating flow depth with total damage, Grünthal et al. (2006) obtained relative stage-damage curves estimating the damage ratio of buildings and contents for various economic sectors such as private housing, commerce, services, public infrastructure. Meyer (2008) used relative stage-damage functions

for vulnerability assessment of various asset categories such as residential, agricultural, or industrial areas. In addition to the economic as-sessment, this work considered also ecological (erosion, accumulation and inundation of oligotrophic biotopes) and social (spatial distribution of affected population, location of social

hot spots such as hospitals, schools, etc.) consequences.

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Figure 7: Stage-damage curves formulated for agriculture product damage estimation (Dutta et al. 2003).

By means of multi-criteria analysis, the individual sub-criteria were combined and the spatial allocation of

these monetary and non-monetary consequences was visualised as a standardised multi-criteria risk map (Meyer et al. 2012). Dutta et al. (2003) produced relative stage-damage curves for residential wooden structures, residential concrete structures, residential content, non-residential property and non-residential stocks.

Additionally, they developed relative damage curves for crops relating flood duration to relative damages for three inundation depth classes (Fig. 7). Merz et al. (2010) reported a review of damage functions for floods in a wider application of assessment methods for economic flood damage. They distinguished various vulnerability

functions in relative (used in the US HAZUS-MH model) and absolute (used in the UK and Australia), and summarised the respective challenges in the assessment procedure. For static inundation, the water depth

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may indeed be the dominating factor and sufficient for a vulnerability and risk analysis, but Merz et al. (2004)

criticised the limitation to this hazard indicator as too simplistic since still a considerable variety of further parame-ters may influence the quantity of losses, above all contamination (due to oil spill from the heating system in case of European studies) and flood duration (e.g., Büchele et al. 2006).

4.2 DYNAMIC FLOODING

For dynamic floods flow velocity is an important parameter, but still only few studies are available which include it into damage estimations. De Lotto and Testa (2000) analysed the effect of dam-break scenarios in an

alpine valley basing their analysis on water depth and flow velocity. By that time no velocity-damage function could be found thus, they adopted the pressure used as threshold of complete destruction of structures due to snow avalanches (30 kN/m2) following Frutiger et al. (1980). Fur-thermore the velocity of water on urban areas

outside the channel bed was consid-ered as the 50 % of the maximum velocity given by the model. Since for the elements at risk (1-3 floor buildings and the content) two damage values were obtained – one for flow depth and one for flow velocity – always the highest value was used and interactions were not taken into account. In

the US-centred HAZUS-MH (2013) a velocity-depth function is included indicating whether building collapse has to be assumed. If the threshold for collapse is reached or exceeded, the damage is set to 100 % while below this threshold the damage is estimated based on inundation levels only. The transferability of such a model

developed for the US market to another environmental and cultural setting, however, is limited due to different constructive and design principles, as outlined in Fuchs et al. (2012c). Furthermore, the effect of warning and associated damage reduction can be considered and assessed by a so-called day curve. Based on the time of the

warning before the event a maximum percentage of 35 % damage reduction can be achieved if a public response rate of 100 % can be assumed.

Based on a motion in the Swiss National Council (Nationalrat [Swiss National Council] 2000), the Swiss risk concept from PLANAT (2004) defines three intensity classes for an exposure analysis for buildings, based on flood depth and flow velocity (Fig. 8), which are also used as basis for spatial planning regulations (BWW et al.

1997).

Intensity class Flood

low df < 0.5 or vf ∙ df < 0.5

medium 2 > df > 0.5 or 2 > vf ∙ df > 0.5

high df > 2 or vf ∙ df > 2

Figure 1: Classification of intensity parameters according to BWW et al. (1997) based on flow depth df [m], and flow velocity vf [m/s].

The intensity classes are established according to their effect on human beings and buildings (BWW et al. 1997):

• High: persons inside and outside of buildings are at risk and the destruction of buildings is possible or events with a lower intensity occur but with higher frequency and persons outside of buildings are

at risk.

• Middle: persons outside of buildings are at risk and damage to buildings can occur while persons in

buildings are quite safe and sudden destruction of buildings is improbable.

• Low: persons are barely at risk and only low damages at buildings or disrup-tions have to be expected.

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5 VULNERABILITY TO LANDSLIDES

In this section, different approaches dealing with vulnerability functions for landslides are discussed, with a

particular focus on torrent processes.

Borter (1999) reported a comprehensive approach for risk analyses focusing mainly on gravitational mass

movements in the European Alps. Vulnerability functions were presented in this study for snow avalanches and rock fall processes. With respect to floods and debris flows, however, vulnerability values were only given semi-quantitatively for three classes (low, medium, high process intensity). The intensity parameters were quantified

according to BWW et al. (1997): the flood intensity was given as a combination between flow depth and flow velocity times flow depth and the debris flow intensity was given as a combination between deposition depth and flow velocity (Fig. 9)

Intensity class Debris flow

low Not assessed

medium dd < 1 or vf < 1

high dd > 1 and vf > 1

Figure 2: Classification of intensity parameters according to BWW et al. (1997) based on deposition depth dd [m] and flow velocity vf [m/s].

Romang (2004) compiled a study on the effectiveness and costs of torrent mitigation measures in Switzerland.

Flooding with an undefined amount of transported sediment was the considered process. Vulnerability data were based on the ratio between losses incurred and the reinstatement values of buildings at risk in order to calculate the degree of loss of buildings exposed to torrent processes. The respective data were provided by the

(mandatory) building insurer2. Due to the considerable range in the vulnerability data, Romang (2004) concluded that a vulnerability function cannot be derived and therefore, only mean vulnerability values for certain process intensity classes were presented. These intensity classes were defined according to the Swiss

guidelines (Fig. 7).

Fuchs et al. (2007a) presented a vulnerability function for debris flows based on the analyses of an event in the

Austrian Alps. Due to missing information on flow velocities, the deposition depth was taken as a proxy for the process intensity. Depo-sition depth directly adjacent to the damaged buildings was assessed during a field campaign following the incident and classified in steps of 0.5 m. The degree of loss was calculated as the ratio

between monetary damage and reconstruction value for each building which included brick masonry and concrete residential buildings. The losses were collected using information from the responsible public authorities. Since in Austria an obligatory building insurance against losses from natural hazards is not available

so far, property losses are partly covered by a governmental fund3. Consequently, these losses were collected on

2 In Switzerland, 19 of 26 cantons conduct a mandatory insurance system for buildings, underwriting natural hazards damage unlimited until the legally certified reinstatement values of the buildings (Fuchs et al. 2007b).

Those insurers are organised as independent public corporations based on can-tonal law, and cover approximately 80% of all Swiss buildings with an insured value of around € 1.2 billion. Within the individual canton, each insurer operates as a monopolist regulated by public law. Apart from the insurance policies, the

business segments include loss prevention and risk manage-ment. In this context, cantonal insurers perform a sovereign function, consulting municipalities in all concerns on building permits and spatial planning activities.

3 In Austria, natural hazards are not subject to compulsory insurance. Apart from the inclusion of losses resulting from hail, pressure due to snow load, rock fall and sliding processes in an optional storm damage insurance, no

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an object level immediately after an event by professional judges. The reconstruction values were calculated

using the volume of the buildings and averaged prices (€/m3) according to the type of building, as indicated by Keiler et al. (2006b). The resulting vulnerability curve was expressed by a second order polynomial function. Although based on a limited number of data points, Fuchs et al. (2007a) demonstrated the general applicability

of such an ap-proach to torrent processes.

Akbas et al. (2009) applied the approach outlined by Fuchs et al. (2007a) to a debris flow event in the Italian Alps.

Deposition depth as the intensity parameter and the degree of loss were derived similarly, and information regarding eleven damaged and two destroyed buildings was used to develop a vulnerability function as a second order polynomial function. Compared to the vulnerability function of Fuchs et al. (2007a), the

vulnerability function obtained in Akbas et al. (2009) showed a similar shape but a higher degree of loss. Overall, the vulnerability values derived by Fuchs et al. (2007a) were approximately 35 % smaller than the ones derived by Akbas et al. (2009). The limited number of data points, however, precludes a robust statement regarding the

uncertainties. Possible explanations could be differences in process characteristics and construction techniques or the inherent range of the applied method.

Calvo and Savi (2009) applied vulnerability functions within a debris flow risk as-sessment. Three different vulnerability functions were tested in this study: (a) a vul-nerability function for flood waves using flow depth as intensity parameter, (b) a vul-nerability function for avalanches based on impact pressure, and (c) vulnerability

functions developed by Faella and Nigro (2001a, b) for debris flows, taking into ac-count both hydrostatic and hydrodynamic forces. The latter is based on a combina-tion of flow depth and flow velocity as intensity parameter. The debris flow hazard was computed using a Monte Carlo procedure. Calvo and Savi (2009)

concluded that the vulnerability function developed for debris flows yielded the most reliable results. However, the main source of uncertainty in their debris flow risk assessment approach was the vulnerability assessment.

Tsao et al. (2010) presented a debris flow risk estimation approach for Taiwan (Re-public of China). For brick masonry and concrete buildings they used the vulnerability function presented in Fuchs et al. (2007a). A second vulnerability function was derived for wooden and sheet-metal buildings which represent a com-mon

construction type in Taiwan. As debris flows may damage the interior of a building, Tsao et al. (2010) recommended the use of an individual vulnerability curve for home interiors.

As outlined by Fuchs et al. (2007a), the second order polynomial functions used in these approaches have to be limited to an upper and lower threshold as they yield economic gains for very small process intensities and a degree of loss > 1 for high process intensities. To overcome these shortcomings, Totschnig et al. (2011) modi-

fied the approach by taking three torrent events characterised by fluvial sediment transport processes as an example. Instead of a second order polynomial function, cumulative distribution functions were used which define the degree of loss as a dependent variable in a confined interval between 0 and 1. In a first step,

deposition depth was used as the intensity parameter to characterise the hazard process. A so-called relative intensity was further introduced to consider the influence of different building heights (different number of storeys) on the degree of loss. This relative intensity was defined as a normalised parameter composed from a

standardised product is currently available on the national insurance market. Moreover, the terms of business

of this storm damage insurance explicitly exclude coverage of dam-age due to avalanches, floods and inundation, debris flows, earthquakes and similar extraordinary natural events (Holub and Fuchs 2009; Holub et al. 2011). Furthermore, according to the constitution of the Republic of Austria, catastrophes resulting from

natural hazards do not fall under the national jurisdiction. Thus, the responsibility for an aid to repair damage resulting from natural hazards general-ly rests with the Federal States. However, the Austrian government enacted a law for financial support of the Federal States in case of extraordinary losses due to natural hazards in

the aftermath of the avalanche winter in 1951. The so-called ‘law related to the catastrophe fund’ (Katastro-phenfondsgesetz) is the legal basis for the provision of national resources for (a) preventive actions to construct and maintain torrent and avalanche control measures, and (b) financial aids for the Federal States to enable

them to compensate individuals and private enterprises for losses due to natural hazards in Austria. The budget of the catastrophe fund originates from a defined percentage (since 1996: 1.1 %) of the federal share on the income taxes, capital gains taxes, and corporation taxes. The annually prescribed maximum reserves amount to

€ 29 million (Republik Österreich 1996).

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ratio between the deposition depth and the height of the affected building. The individual analysis of both

intensity parameters had shown that the application of a relative intensity pa-rameter improves the calculation.

Quan Luna et al. (2011) applied a numerical debris flow model to derive vulnerability functions. The

vulnerability values derived by Akbas et al. (2009) were related to dif-ferent intensity parameters using the software FLO-2D. Accumulation height, impact pressure, and kinematic viscosity were back-calculated as intensity parameters for each individual building on the torrent fan. The proposed vulnerability curves were

expressed by logistic functions, and had to be limited to an upper threshold. Within their extent of validity they obtained high coefficients of determination.

Lo et al. (2012) reported vulnerability functions for residential buildings affected by debris flows in Taiwan (Republic of China). Loss functions for the content and the structure of the building were separately calculated and subsequently merged to a general vulnerability function. Two types of buildings were distinguished based

on the construction material used (brick and reinforced brick), considering the different resistance against debris flow impacts. The content loss function was based on a synthetic approach taking the total values of fixtures and fittings as loss proxy when the process intensity (expressed as deposition depth) inside the building

affects the corresponding element. The structure loss was quantified using the deposition depth as intensity parameter. Loss values were estimated by using reconstruction expenses for the incurred damage, as no insurance data were available in Taiwan.

Papathoma-Köhle et al. (2012) highlighted the challenge of missing data for the de-duction of vulnerability curves. To overcome this gap, a methodology was presented to calculate the total loss of a building by summing

up expenses for the fixing costs (repair works) of different damage patterns. For example, the damage pattern “flood-ing of the basement” necessitates the following works: removal of furniture and equipment, drying, cleaning, re-plastering and painting of the inner walls, and potentially the installation of new doors and floors.

The damage patterns were identified from previous events in corresponding photo documentations. The reconstruction value, necessary for the calculation of the degree of loss, was estimated by using the footprint of the building and regional standard prices (€/m2) for different building sections such as living area, attic and

basement (Kaswalder 2009). The methodology was tested for a debris flow event in the Martell valley (Italian Alps). Deposition depth was applied as the intensity parameter, and since information regarding the monetary compensation of the losses was available for this event a visual validation of the deduced vulnerability curve

showed a satisfying consistency.

Totschnig and Fuchs (2012) compared vulnerability functions for fluvial sediment transport with vulnerability

functions deduced for debris flows. To compare different vulnerability curves, the approach outlined in Totschnig et al. (2011) was applied during the set of calculations. The resulting vulnerability curves for fluvial sediment transport processes and debris flows exhibited a mismatch due to a data gap related to high loss

values in case of debris flows. However, after complementing the debris flow data set with vulnerability values given in Akbas et al. (2009), the vulnerability curves for debris flow and fluvial sediment transport showed nearly the same shape. Hence, the authors concluded that there is no need to distinguish between different sediment-

laden torrent processes when assessing the physical vulnerability of resi-dential buildings.

Totschnig and Fuchs (2013) presented a GIS-based analysis of individual torrent events to compare vulnerability

values for different torrent processes as well as dif-ferent building types, and to obtain a joint vulnerability function for different torrent processes and different building types exposed. Their results suggest that there is no need to distinguish between different sediment-laden torrent processes (includ-ing debris floods) when

assessing physical vulnerability of residential buildings to-wards torrent processes. However, the differentiation between different types of pro-cesses is still necessary for the development of comprehensive mitigation concepts (Mazzorana and Fuchs 2010a; Mazzorana et al. 2012b) and might be necessary for the assessment of

the vulnerability of other elements at risk, such as persons or in-frastructure.

Vulnerability functions are only one way to assess the vulnerability of buildings. Using semi-quantitative

approaches, threshold values of impact pressure for different damage classes (Zanchetta et al. 2004; Hu et al. 2012) as well as qualitative intensity parameters for quantitative vulnerability values (Fell and Hartford 1997; Bell and Glade 2004) are suggested. Haugen and Kaynia (2008) adopted fragility curves developed for

earthquakes to debris flows assuming that ground vibrations from an earthquake cause similar damage to a building as vibratory forces from a debris flow impact. Jakob et al. (2012) suggested a damage probability

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matrix for debris flows based on 68 well-documented case studies worldwide. Four damage classes were related

to an intensity index composed of the product of the square of the maximum flow velocity and the maximum expected flow depth. The method was tested on a debris flow event in Italy and exemplarily used to predict the total loss of a 500-year debris flood in a Canadian test site.

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6 VULNERABILITY TO STRATEGIC INFRASTRUCTURE

Types of strategic or critical infrastructure may include, but are not limited to energy, transportation,

telecommunications, and even national monuments (Michel-Kerjan 2003). Often these infrastructures are interconnected, and damage to one network of critical infrastructure can have cascading effects upon other critical infrastructure networks, possibly causing major damage to a country’s national security and identi-ty.

The interconnectedness of these infrastructures not only extends to other types of critical infrastructure, but can also be extended across political boundaries; in many cases strategic infrastructures are dependent on international agreements and cross international borders, such as e.g. power networks and railway lines in the

European Alps. Therefore, the vulnerabilities of a specific strategic infrastructure are dependent on condition and decay, capacity and use, obsolescence, inter-dependencies, location and topology, disruptive threats, policy and political environment, as well as safeguards (Grubesic and Matisziw 2013).

Strategic infrastructure networks include the highly complex and interconnected systems that are so vital to a city or state that any sudden disruption can result in debilitating impacts on human life, the economy and the

society as a whole (Cavalieri et al. 2012). The vulnerability of a system is multidimensional (Yates and Sanjeevi 2012), a vector in mathematical terms. There are two major considerations for the efficacy of risk management in the context of infrastructure resilience and protection (Haimes 2006):

• One is the ability to control the states of the system by improving its resilience. Primarily, this is the ability to recover the desired values of the states of a sys-tem that has been attacked, within an

acceptable time period and at an ac-ceptable cost. Resilience may be accomplished, for example, through harden-ing the system by adding redundancy and robustness, or by simply construct-ing them hazard-proof if the exposure is obvious and quantitatively assessa-ble.

• The second consideration is to reduce the effectiveness of the threat by other actions that may or may not necessarily change the vulnerability of the system (i.e., do not necessarily change its state

variables). Such actions may include detection, prevention, protection, interdiction, containment, and attribution. Note that these actions (risk management options), while not necessarily changing the inherent states of the system, do change the level of the effectiveness of a potential threat.

With respect to European mountain regions, much less data are available regarding the vulnerability of infrastructure to natural hazards than for buildings. However, in many parts of the world the failure, disruption

or reduced functionality of infrastruc-ture is likely to have a larger impact on livelihoods and the local economy than dam-age to buildings (Jenkins et al. 2014). In some cases it can act as a catalyst to exist-ing economic, social or agronomic decline (e.g., Wilson et al. 2012) because of a high systemic vulnerability (interdependencies

between physical, economic and social systems).

The impacts of mountain hazards for infrastructure vary depending upon the hazard intensity but could include

disruption of electricity supplies, contamination of agricultural processing areas and sedimentation of surface water networks, requiring extensive and repeated clean-up (Bundesministerium für Land- und Forstwirtschaft 2006). Even if usually manifest on a local level, threats may result in cascading effects such as delays in

transport times which then are likely to compound any disruption and associated impacts. Transport loss of function due to locally deposited material on a road can potentially be mitigated through the use of engineered channels, dams and barriers or repeated clean-up in case of low intensity/high frequency events. However, the

diverse range of infrastructure system design, types and configurations make it very difficult, perhaps impossible, to reliably create generic infrastructure vulnerability curves. Therefore, analysing interdependencies between infrastructural systems and carrying out comprehensive local inventory surveys to

produce site-specific vulnerability functions is the most valid approach (Jenkins et al. 2014).

Using an indicator approach, Eidsvig et al. (2014) were presenting a qualitative rank-ing for vulnerability of

(strategic) infrastructures. The indicator used five classes and was based on (a) care facilities such as hospitals, schools, fire-fighting and police stations, (b) critical facilities such as large companies or production facilities where many people are located at the same time; chemical or other hazardous material fa-cilities, (c) lifelines

such as a railway network or station and/or major roads, tunnels and bridges in the hazard zone, which might

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serve as an evacuation route or provide major access to the community, (d) power stations (e.g., electric, gas)

located in the hazard zone were destruction would lead to an interruption of the power supply or an interruption of telecommunication which in turn affects early warning and emergency response. Additionally, (e) major water pipes or stations (e.g., tanks or pumping stations) were assessed since a destruction of these

would lead to an interruption of the water supply. The criteria for indicator ranking are shown in Fig. 10.

Indicator weight Criteria for indicator ranking

1 No critical care facilities and lifelines in

the hazard zone

2 Only a few critical care facilities and no

lifelines in the hazard zone

3 Several critical facilities and lifelines in

the hazard zone

4 Important care facilities, such as

hospitals, and major lifelines in the

hazard zone

5 All major critical care facilities and all

lifelines in the hazard zone

Figure 10: Qualitative ranking of strategic infrastructure. The proposed model assesses the level of vulnerability by a relative ranking scale (1-5) (modified from Eidsvig et al. 2014).

Jaedicke et al. (2014) were applying a small-scale susceptibility model with respect to landslides throughout

Europe. The intersection of the landslide hazard, population density and an infrastructure density map allowed for an identification of areas where potential landslide activity coincides with areas of higher population and/or infrastructure density, thus providing a first-pass estimate of landslide risk hotspots.

Recently numerous studies have applied complex network-based models to study the performance and vulnerability of infrastructure systems under various types of attacks and hazards (a major part of them is,

particularly after the 9/11 incident, related to terrorism attacks, Maliszewski and Horner 2010; Briggs 2012). Here, vul-nerability is generally defined as the performance drop of an infrastructure system under a given disruptive event (Ouyang et al. 2014). The performance can be meas-ured by different metrics, which

correspond to various vulnerability values.

Focusing on the Austrian Alps, Möderl and Rauch (2011) presented a regional-scale spatial risk assessment

method allowing for managing critical network infrastructure in urban areas under irregular and future conditions caused e.g., by terrorist attacks, natural hazards or climate change. For the spatial risk assessment, vulnerability maps for critical network infrastructure were merged with hazard maps for an interfering process.

Raster-based vulnerability maps subsequently result using a spatial sensitivity analysis of network transport models to evaluate performance decrease under the studied scenarios.

Scholars recently have applied complex network-based models to describe infra-structure topologies and then study their vulnerabilities from a topological perspec-tive. Purely topological models, which describe infrastructure systems as networks, with system components represented as nodes and component

relationships as edges, are related to the performance response of the networks under disruptive events without the consideration of spatiotemporal dynamics within the network. Empirical studies show that some infrastructure topologies have exponential degree distributions and are robust to the failures of both randomly

selected nodes and the most connected nodes; examples include bus-transport systems (Xu et al. 2007), railway systems (Sen et al. 2003), urban street networks (Porta et al. 2006), power grids (Amaral et al. 2000), or water distribution networks (Yazdani and Jeffrey 2011), while some infrastructure topologies have power-law degree

distributions and are robust to the failures of randomly selected nodes but very vulnerable to the failures of the

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most connected nodes, such as shown for airline networks (Bagler 2008), cargo ship networks (Hu and Zhu 2009;

Kaluza et al. 2010), internet (Vázquez et al. 2002), and power grids (Amaral et al. 2000; Chassin and Posse 2005). Besides the random failures and target attacks, scholars have studied the vulnerability of infrastructure systems under other hazards by using topology-based approach, such as the seismic vulnerability of interdependent

power, gas and water systems in Europe (Poljanšek et al. 2012) and the US (Dueñas-Osorio et al. 2007a, b), terrorism vulnerability (Patterson and Apostolakis 2007) and hurricane vulnerability (Winkler et al. 2011) of interdependent power, water, steam supply and gas systems in the US.

Artificial flow-based models considering spatiotemporal dynamics within the network require modelling the engineering properties of infrastructure systems as well as their components, such as generator productions,

load levels, line impedances in power grids, which are sometimes difficult to obtain due to security concerns. To overcome this challenge, the artificial flow models assume particles move along virtual routes to capture the flow transportation and possible redistribution in real infrastructure systems. Nodes, links, and endpoints are

just a small subset of the properties that can describe a flow model or graph. A few other common properties of a graph or its elements might include are degree, geodesic path, diameter, and betweenness. The degree of a node refers to the number of links that are connected to the node. The geodesic path describes the shortest

path through the network from one node to another. The diameter of the network is the number of links of the longest geodesic path between two nodes (Gross et al. 2013). Finally, betweenness indicates the number of links that pass through a node (Rocco et al. 2011). Some studies assumed the particles run along the shortest path

between a pair of vertices, and then used betweenness as a proxy for the amount of particles passing through a vertex or an edge, where betweenness is computed as the number of shortest paths that pass through every component when connecting vertices. A disruptive event can cause some component failures and alter the

infrastructure topology. In particular path length (network diameter) was suggested as a measure of network vulnerability because as more components fail nodes become more distant, which may indicate that flows within the network are inhibited (Hines et al. 2010). Depending on the operation mechanisms of the

infrastructures under consideration, some studies do not consider the flow or load redistribution, such as the railway systems (Ouyang et al. 2014), while some studies assumed that the altered infrastructure topologies further change betweenness of all components and cause some overload and failure of other components. This

type of models have been used to study the vulnerability of power grids in the US (Motter et al. 2002; Kinney et al. 2005) and Europe (Crucitti et al. 2004), trans-European gas networks (Carvalho et al. 2009), or transportation networks (Kurant and Thiran 2006).

Figure 11: An illustration of the difference between a topological nearest-neighbour model of cascading failure and one based on Kirchhoff’s laws. (a) Node 2 fails, which means that its power-load must be redistributed to functioning nodes. (b) In many topological models of cascading failure, e.g., Wang and Rong (2009), load from failed components is redistributed to nearest neigh-bours (nodes 1 and 3). (c) In an electrical network current re-routes by Kirchhoff’s laws, which in this case means that the power that previously traveled through node 2 is rerouted through nodes 5 and 6. In addition, by Kirchhoff’s laws, node 3 ends up with no power-flow (Hines et al. 2010).

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While graph approaches, such as the centrality metric betweenness, are useful and provide a great deal of

information about a grid and its interdependencies, there are shortcomings in only utilizing these approaches ( Kim and Obah 2007; Hines et al. 2010). One major shortcoming of graph-based approaches is the lack of data availa-bility and standardisation to support such analysis. Even in developed countries, data about strategic

infrastructure, especially the energy grid, may be proprietary and difficult to access. Additionally, critical infrastructure models often suffer from a Data Death Spiral: Whenever data is misused as the only means for making decisions, a death spiral begins. Initially, data are only available in an aggregated form, and the critical

infrastructure models were built to ingest these data. Eventually, models were built to perform simulations on parallel platforms and were able to answer new ques-tions; however, these new models require finer spatial resolution data. Such fine resolution data are not available because the models that were created never

required this level of granularity. It is a cycle that has limited the development of the granular infrastructure models that are really needed to understand critical infrastructure systems.

Another shortcoming of graph theoretic approaches was that they do not provide any great detail to geographic characteristics of the surrounding area that might contribute to a network’s vulnerability. Hines et al. (2010) indicate that while graph metrics provide information about the general vulnerability of the network, these

metrics are misleading when viewed alone and without ancillary information. First, graph and simulation approaches do not address infrastructure service areas, or the area with which the infrastructure serves. The attributes of the service area may make a node more or less vulnerable depending on the characteristics it

encompasses. For example, the loss of nodes (substations) “a” and “b” may each cut off energy from two additional nodes on each side of an electrical network (Fig. 11). If node “b” has a service area with very few clients (such as in a rural area), it may be less critical than node “a,” which serves more people or contains

critical facilities, such as a hospital. In this example, these two nodes might be ranked equally vulnerable by graph approaches that do not include characteristics of the population (absolute number, income, age, etc.) within the service area. Alternatively, the node that serves more people and/or contains medical facilities (such

as a hospital) may be regarded as more vulnerable and critical and should have a higher rank, indicative of higher vulnerability. If a natural disaster were to strike, and decision makers only looked at betweenness, substations “a” and “b” would have the same vulnerability, when in reality substation “a” is more vulnerable

due to the higher population and higher frequency of critical assets.

Figure 12: Factors shaping the risks faced by critical infrastructures (Kröger 2008).

Additional examples of the usefulness and importance of a geospatially integrated approach include the

occurrence of a natural disaster, where a controlling authority (e.g. state government, private utility) may want

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to divert a node’s power to serve only the areas with the greatest amount of people and the most critical

infrastructure, such as hospitals, fire departments, and shelters. In this case, the controlling authority may choose to shut down substations that serve fewer people and divert that power to a node with larger concentrations of critical infrastructure and population.

Kröger (2008) identified several factors that can shape the vulnerability to critical infrastructure. These factors are categorized by: societal, system-related, technologi-cal, natural, and institutional. Societal factors include

attractiveness for attack, public risk awareness, and demographics. System-related factors include the complexity and interconnectedness of the network. Technological factors include failure friendliness and infrastructure related operating principles. Natural factors include availability of resources and natural hazards.

Finally, institutional factors include historic structures, legislation, and market organization (see Fig. 12).

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7 GAPS AND CHALLENGES OF CURRENT APPROACHES

In this study, more than 40 vulnerability assessment methods were reviewed, focus-ing on (a) physical vulnerability, (b) hydrological processes and (c) other mountain hazards (see the Appendix). Most of the reviewed methods consider vulnerability to be the degree of loss of a specific element at risk to a hazard of a

given magnitude, following an engineering approach. The vast majority of the vulnerability assessment methods are quantitative, assigning vulnerability values from 0 to 1 to the elements at risk (e.g., Michael-Leiba et al. 2005; Fuchs et al. 2007a; Totschnig et al. 2011; Totschnig and Fuchs 2013), whereas, only a small

percentage of them are qualitative describing vulnerability as low, medium and high (e.g. Cardinali et al. 2002; Macquarie et al. 2004; Sterlacchini et al. 2007). This degree of loss is often expressed as monetary loss (reconstruction costs, building value, etc., e.g. Keylock and Barbolini 2001; Barbolini et al. 2004; Romang 2004;

Cappabianca et al. 2008; Totschnig et al. 2011; Totschnig and Fuchs 2013), in other cases it is expressed as damages (aesthetic, functional, structural, etc., e.g. Corominas et al. 2005; Sterlacchini et al. 2007; Mavrouli and Corominas 2010a, b). Finally, in some studies (e.g., Mejía-Navarro et al. 1994; Liu and Lei 2003; Papathoma-Köhle

et al. 2007), vulnerability is a combination of all these factors that contribute to the susceptibility of the building or the given element at risk. Moreover, for studies with a focus on human life, vulnerability is the probability of a life to be lost (e.g. Jónasson et al. 1999; Keylock and Barbolini 2001; Barbolini et al. 2004; Zhai et al. 2006).

As discussed in Douglas (2007), there are more vulnerability curves for other geo-hazards, such as earthquakes, rather than for landslides and flooding of (mountain) rivers, these hazards usually affect larger regions than

mountain hazards and have a higher frequency, leading to considerable economic loss. Moreover, in the cases where vulnerability functions are used the expected damages to the built environ-ment are not always expressed in relationship to the same characteristic of the haz-ardous phenomenon. For example, in the case of

debris flows, vulnerability is pre-sented in relationship to the intensity of the debris flow hazard, which is expressed as deposit height. Other properties of the phenomenon (e.g. flow velocity) are not taken into consideration (Totschnig and Fuchs 2013). In contrast, Jakob et al. (2012) published a study on the vulnerability

of buildings to debris flow impact were modelling approaches were used. They presented an intensity index as the product of maximum expected flow depth and the square of the maximum flow velocity. Very exceptionally, they wish to update their model if more data will become available, and as a consequence it was made openly

available to international researchers at http://chis.nrcan.gc.ca/QRA-EQR/index-eng.php.

In general, for river flooding (static inundation) there is a variety of vulnerability curves available in the

literature. The majority of the studies use vulnerability curves that demonstrate the relationship between expected damage and inundation depth. The large number of vulnerability curves in flood studies can be explained by the fact that floods (just like storms which are also hazards with very well developed vulnerability

curves) damage more buildings in a single event than other hazard types (Douglas 2007). Additionally, most of the methodologies have been applied in Europe or in countries with similar level of development, such as North America and Australia. However, the curves that are produced are mostly for a specific construc-tion type that is

common in the study area. Therefore, they cannot be used in another part of the world where the dominant construction type is different or where there is diversity in the quality or types of buildings, as extensively discussed in Fuchs et al. (2012c).

As pointed out by Papathoma-Köhle et al. (2011), the focus of the methodologies varies significantly. While the majority of the approaches are targeted at an assess-ment of buildings at risk, others include also potential

victims, infrastructure and life-lines such as the road network. Very few studies focus on the vulnerability of the environment or agricultural land, or the economic vulnerability of the affected community that can include the vulnerability of businesses, employment, tourism, etc.

A very limited number of the reviewed studies address the multi-dimensional nature of vulnerability ( Leone et al. 1996; Liu and Lei 2003; Sterlacchini et al. 2007; Fuchs 2009). As far as the scale of the study is concerned, the

majority of the studies, es-pecially the ones involving landslides, concern methodologies designed to be applied only on a local level (e.g., individual torrent fans), whereas only a few (Liu and Lei 2003; Galli and Guzzetti 2007) are applied on a regional scale. In the case of studies concerning river floods, the majority of them are carried

out on a regional scale (Grünthal et al. 2006; Meyer et al. 2008, etc). The regional vulnerability assess-ment is important for the central or the regional government in order to make decisions regarding funding allocations.

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However, as far as on-site emergency management and disaster planning is concerned in particular local

vulnerability assessment can provide the decision makers with useful information.

There are many difficulties in implementing the methodologies. The most common setback is the data

availability and the fact that some methods are time-consuming due to extensive field work and the detailed data that are required. The physics of the damage-generating mechanisms remains unveiled and restricts the applicability of the empirical approach for planning hazard-proof buildings. In fact, as outlined by Fuchs (2009)

and confirmed by Totschnig and Fuchs (2013), the analysis of empiri-cal data from torrent processes has shown that the vulnerability of buildings affected by medium hazard intensities (e.g. 1.00-1.50 m deposition height for torrent process-es) critically depends on the patterns of material intrusion through openings such as doors,

wells and windows. This points out that in addition to the intensity of the physical impact and the structural response of the considered element at risk also the geometry characterizing the individual building has to be carefully considered in vulnerability analyses (Mazzorana et al. 2014).

Vulnerability maps, which could give an overview of the spatial vulnerability pattern (Fuchs et al. 2012b), are often not provided. Although due to the goal of the study vulnerability maps are not always necessary, they may

be a valuable tool for emer-gency planning and decision making in disaster management (Fuchs et al. 2009; Meyer et al. 2012). In many cases the authors provide an inventory of the elements at risk but they do not provide information regarding their properties which is essen-tial for a vulnerability assessment (Fuchs et al.

2007a; Mazzorana et al. 2012a, 2014).

There is a significant number of risk assessment methodologies for critical infrastructures. In general the

approach that is used is rather common and linear, consisting of some main elements: Identification and classification of threats, identification of vulnerabilities and evaluation of impact. This is a well-known and established approach for evaluating risk and it is the backbone of almost all risk assessment methodologies

(Giannopoulos et al. 2012). However, there is a huge differentiation of risk assessment methodologies based on the scope of the methodology, the audience to which it is addressed (policy makers, decision makers, research institutes) and their domain of applicability (asset level, infrastructure/system level, system of systems level).

The methodologies reviewed, in general, fail to incorporate the social and organizational components into the analysis of physical infrastructures. This is arguably the most important deficiency found in the current methodological and empirical practices to measure vulnerability and resilience. The interdependencies among

physical and human components in infrastructure seem to be very strong and complex. With respect to strategic or critical infrastructure, the importance of geospatial information in the analysis of such infrastructure vulnerabilities has repeatedly been claimed. Simple, indicator-based approaches follow in principle guidelines

such as the Swiss (Borter 1999) or the Austrian (Bundesministerium für Land- und Forstwirtschaft 2005) approach and only allow for a qualitatively assessment of vulnerability by using e.g. a point classification scheme. Higher sophisticated models rely on the graph theory, and have been applied to a wide field of topics.

Despite the amount of work published, in particular the classical metrices in graph approaches, such as betweenness, may be misleading if vulnerability due to natural hazards should be assessed. Hines et al. (2010) indicated that one must be careful in viewing infrastructure vulnerabilities only in the context of the structure of

the graph, as ancillary data (e.g. population, land use, and critical facilities other than those of the infrastructure itself) are also important in analysing vulnerabilities in these networks. Moreover, the infrastructure context should also be established from another point of view. There are certain elements of each that can contribute to

the attenuation or amplification of the vulnerable areas. For instance, vulnerable groups that are distant from evacuation routes or downstream from a dam will be at greater risk. Overlaying the infrastructure over the place-vulnerability may yield valuable information for mitigation planning. Two procedures are involved in

establishing the infrastructure context: (1) the identi-fication and collection of special-needs population data, and (2) the determination of key infrastructure and lifelines.

An inclusive approach that incorporates physical, social, organizational, economic, and environmental variables in addition to empirical measurements and operationalization of resilience and vulnerability will help to improve the understanding and management of risk associated with threats to complex infrastructure systems.

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8 CONCLUSIONS

The notion of vulnerability emphasizes the exposure of a system to a hazard from the point of view of the nature of that system itself. Ideally such an account should include some of the systemic properties, particularly from the perspective of the resilience of the human-environment interfaces of the system under consideration.

Because vulnerability has often been regarded as a property, and not as an outcome of social relations and technological systems (Hilhorst and Bankoff 2004), the concept is easier to deal with than that of risk, as it does not exclusively emphasize a future event or system state, but also, and perhaps most obviously, certain actually

present qualities of a system. Vulnerability assessments cannot take place without attention to the hazard and thereby also to risk, however, the concept puts the emphasis on what an actor can directly affect rather than a threat from the outside, or a possible development in the future. One may say that the vulnerability, or

opposite, the resilience, of a system is a more ‘ontologically robust’ and ‘epistemologically accessible’ dimension than that of its exposure to risk (Hellström 2007).

The notions of dynamics in critical infrastructures can already be found in Wisner et al.’s (2004) ‘pressure and release’ (PAR) model. The PAR model was originally developed to account for socio-economic vulnerability to natural disasters in developing countries, and has the household as a main reference point. Because of its

comprehensiveness, it is well suited for analyzing vulnerability in larger socio-technical systems. The model depicts how underlying factors or root causes, which are deeply embedded in social and technological conditions give rise to dynamic pressures affecting specific areas of activity, and ultimately resulting in unsafe

conditions at different localities. When unsafe conditions generated by underlying factors and root cause are confronted with a hazard or trigger event, which in the present understanding could be a planned attack on a system as well as a intersystem conflict brought to the surface by an environmental fluctuation, the result of

which is an adverse event.

As such, infrastructure vulnerability and resilience are intricately related with community resilience. Research

conducted to enhance methodologies and practical measurements of vulnerability and resilience must try to include (in addition to the physical dimension) the social, organizational, economic, and natural environment dimensions.

Cutter et al. (2008) mention important gaps in the research field of vulnerability. There is still progress to make in the identification of standards and metrics for measuring disaster resilience; in the advancement of a

theoretical framework and practical applications of vulnerability; in the articulation of the relationship between vulnerability, resilience, and adaptive capacity; and in the explanation of the causal structure of vulnerability. It is not obvious what leads to resilience within coupled human-environment systems or what variables should be

utilized to measure it. There have been few attempts to combine all factors that contribute to vulnerability (compare Fuchs (2009) for a discussion related to mountain hazards). Conceptual models for hazard vulnerability fail to address the coupled human-environment system associated with the proximity to a hazard,

and they fail to include a temporal dimension that shows where vulnerability begins and ends.

As pointed out by Solano (2010), advancing the field of vulnerability and resilience assessment will also have

important policy implications. Arboleda et al. (2009) indicated that after a hazard event, infrastructure managers must prioritize the allocation of restoration resources to meet the demand at critical facilities. Similarly, Egan (2007) highlighted that larger technical systems create challenges for policymakers in the way of

negative externalities, the risks of failure and disasters, and problems with management, control, and coordination. He argues that an effective policy would be to establish liability rules based on the notion that organizations should internalize the costs of the risks they produce and that by internalizing them, they will

make wiser choices about the technologies they use.

Generally, the vulnerability assessment methodology should follow the listed objectives:

• To understand the overall mission of the system to be assessed (e.g., a power network differs from a

railroad network even if both cases could be viewed in terms of an abstracted network and

mathematically represented by e.g. the graph theory);

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• to identify the system-specific vulnerability and susceptibility to harm;

• to analyse the specific system design and operation in order to determine failure modes and associated

likelihoods;

• to identify the possible consequences of a system failure in terms of effects (interruption, down time, any

secondary or cascading effects on other systems, which may be quite obvious in case of power

interruption but maybe less obvious in case of railway interruption);

• and finally to recommend improvements to reduce vulnerability.

From a scientific perspective, two different approaches can be distinguished, (a) traditional and local-scale assessment approaches, and (b) modelling-based approaches which may be used in multiple-scale

applications. As indicated in the previous chapters, both approaches have pros and cons, and should be carefully evaluated when applied with respect to their practical implementation.

Obviously there is a great need to reduce the complexity inherent in deciding how administrative resources should be put to use for vulnerability reduction in critical infrastructures. In has to be assumed that critical infrastructures are essentially technological systems filling social functions, which are dependent for their

creation, utility and maintenance on human actors (Hellström 2007).

To add complexity in the assessment, however, such systems are commissioned at different times (a temporal

dimension) and different places (a spatial dimension), during and at which there are different interests and capabilities at play. Across sectors there will be a spectrum of judgments as to what are the cost-benefit trade-offs, and finally, if one takes enough of a birds-eye view it becomes apparent that all technological components

of a critical infrastructure are added and removed incrementally, and have differently paced cycles of adoption, maturation and death. These qualities of system components generate a number of key ‘interfoliations’ between technologies, social systems and interests, which together form an analytical framework for planning

of vulnerability reducing interventions (Mazzorana et al. 2012a). This framework refers back to the previous discussion on dynamics of vulnerability, and derives a number of analytical principles for vulnerability planning. Some guiding principles can be identified that are relevant with respect to an assessment of vulnerability of

strategic infrastructure:

Principle 1: Functional interlocking

A socio-technological system has a functional interlocking, i.e. its functionality is dependent on the functionalities of other systems according to the principle ‘get one get them all’. This quality means that cascading effects through a number of systems may occur even when these systems are not physically

connected. A system may be functionally dependent on another only on certain occasions, and when used for certain ends. Successfully reducing critical points of interlocking does not only depend on ‘architectural analysis’ of the interrelatedness of parts, but also on the dimensions of functionality, which in turn relate to

level of functional embeddedness and place of the interrelated technologies in their respective life-cycles. In effect such an analysis must also deal with the social life of the technology. A critical question for this type of analysis is how hard will it be to ‘challenge’ a technology which is part of normal life for many people in the

sense that it is integrated into more complex technological and social practices.

Principle 2: Temporal embeddedness

It is important to take account of how fractional additions and improvements (incremental innovation) relate to radical innovations or innovations in the technological system as a whole. For example, if a strategic infrastructure system is about to undergo a radical innovation, it makes sense to carefully identify the ‘depth’ of

this system in terms of how it depends on technologies and social practices that have been deeply embedded throughout a longer period of time. Hidden faults may emerge as a result of a radical change in the superstructure of a system. How, for instance is new aviation-navigation technology dependent on more sticky

operating procedures for air communication? Commissioning and decommissioning new technologies on top

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of old ones, may be a play of dice. Insofar as this is an embedded technology, are there competences available

for its successful decommissioning? Here it becomes important to critically assess what is a technology. Apart from well-known examples, such as sanitation and power supply, is an education system a technology?

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Principle 3: Critical socio-technical tipping-points

Strategic infrastructures are ‘critical’, not because they are important in general, but because they are strategically connected in such a way that they focus society’s total vulnerability to a few particular points in the

system. While from the perspective of risk analysis critical infrastructures must be conceived broadly enough to allow for an open minded assessment of the possible interaction effects between social and technological systems, from the perspective of vulnerability reduction and cost-effective interventions, these critical points

must take the centre stage. It is through eliminating pre-emptively (by for example pro-active technology assessment) and by protecting such critical points that we can effectively engage with the critical infrastructure without disrupting the wider functioning of society (cf. the fact that dynamic pressures are also often sources of

benefit for society, see e.g. Wisner et al. (2004)). The fact that these critical points are of a socio-technical nature, means that their management is not a technical question but a rather a question of finding an optimal ‘tipping-point’ where intervention in technology does not disrupt social functionality.

Principle 4: Dynamic and reversive effects

It is important to note that critical points of large socio-technical systems are dynamic

and fluid. The analysis of critical infrastructure failures, such as e.g., an ICT4 failure, in terms of root causes, dynamic pressures and unsafe conditions shows that one unsafe condition can become a dynamic pressure,

which is harder to remedy, and further that a few root causes and dynamic pressures can affect several unsafe conditions at once. Also, the model makes it clear that triggers of adverse events are not necessarily taking shape outside of the system itself, but are critically dependent on factors already present as vulnerabilities in

the system. It is rather the combination of vulnerabilities/pressures focused at some point of the system that ultimately realize the potential for an adverse outcome.

As such, the following recommendations with respect to an assessment of strategic infrastructure are given:

First of all, it is recommended that those public actors relevant for hazard mitigation related to strategic infrastructure share their individual experience, adjust their needs and expectations in the protection of

strategic infrastructure, and define what type and category of strategic infrastructure should be assessed at which level of detail (scale, but also with respect to temporal and spatial dynamics). This is the most important step before any related project can be initiated, and should be discussed on the broadest focus possible, but

also with clearly expressed targets directed towards the individual project to be defined. Therefore, the operating companies of the identified infrastructures must be involved in order to assure for support and also the allocation of data (and possible also financial contributions for the pilot study addressed below).

Secondly, it should be discussed whether or not an assessment will be undertaken taking a more traditional viewpoint, following e.g. the decision-making on the closure of high-alpine passroads during winter time

(Margreth et al. 2003). Even if such a procedure is not able to capture all possible system states, it may be appropriate because of its easy accessibility and therefore traceability also from a practitioner’s side.

Thirdly, a pilot project should be initiated for a more comprehensive assessment of infrastructure vulnerability by taking e.g. a graph or network theory (e.g. Hellström 2007; Kröger 2008, Hines et al. 2010). In order to be able to decide which approach to choose, a thorough literature review should be undertaken including the targeted

4 The ICT-system, i.e. Public Telecommunications Networks (PTNs), the Internet and an increasing number of Extranets, connects emergency services, financial networks, military command-control systems, gas and oil pipeline systems, transport and educational systems. Growing complexity and interdependence in particular in

the energy and communication infrastructure imply that even minor disturbances can cascade into, for example, regional outages. Technical complexity may also permit major disturbances to go unrecognised, and cumulate, until failure occurs. The most important vulnerability lies in the interdependency between PTNs and

the Internet, in the sense that the Internet depends heavily on PTNs, and the PTNs in turn depend on electrical power operations, satellite and optical cables. The ICT-systems connect in various ways to physical infrastructural systems, at which points these physical systems may be directly attacked through virtual means

or simply by natural hazard such as regional flooding.

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infrastructure network, but also with respect to other approaches which were successfully applied for different

types of strategic infrastructure (including those approaches that emerged in the aftermath of 9/11 focusing on terrorism). Only if such a pilot case study is accomplished it is useful and target-oriented to deduce general patterns of infrastructure vulnerability and an overall framework in terms of an overall applicable method.

Figure 13: Variability of elements at risk exposed on traffic corridors. Variability of daily traffic in 2010 for an alpine main access road (a) and variability of the hourly traffic on a diurnal basis in 2012 for a major road connection in the Khibiny mountains, Russian Federation (b) (Fuchs et al. 2013).

Finally, uncertainties and dynamics have to be taken into account. From a temporal point of view, not only the strategic infrastructure itself is variable, also elements at risk using an infrastructure line such e.g. a track

network or a road network, which in turn have a considerable influence on the overall vulnerability: The

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number of persons or the freight traffic potentially affected by mountain hazards is subject to high fluctuations

on different temporary scales. As a consequence, vulnerability (resulting from the respective average daily traffic during the period of investigation, the mean number of passengers per car and the mean value of good being transported, the speed of the vehicles crossing the endangered sections of the traffic corridor, their mean

widths, and the probability of death in vehicles, etc.) is variable with a high temporal resolution (Fuchs et al. 2013).

To provide an example, in Fig. 13a, an analysis of daily traffic in 2010 is shown for the main access road to the community of Davos, Switzerland (Wolfgang pass). The annual curve shows for both the daily and the weekly data, two minima in April and May as well as in October and November, and the maxima occurred during the

summer months (July and August) as well as during the winter months (November and December). In particular for the months of January and February, a considerable weekly peak is detectable during the weekend, when a large amount of tourists is using this connection to access the ski resorts. In contrast, in Fig. 13b, the mean daily

traffic is shown for a road connecting the city centre of Kirovsk, Russian Federation, with the mining facilities in the region. Since the mining industry is operating on a 24/7 basis, particular differences in traffic quantities are observable in accordance with the shift schedule of the mining company: While the overall traffic during night is

considerably below the daytime traffic, in the early morning, shortly after noon and in the early evening, traffic peaks are apparent. In order to assess the infrastructure vulnerability it makes a considerable difference when the considered threat will hit the respective road sections, how long they are disturbed or interrupted, and how

fast they could be recovered. The evolution of vulnerability due to socio-economic transformation, but also due to changes in the frequency and magnitude of processes varies remarkably on different temporal and spatial scales. Long-term changes are superimposed by short-term fluctuations, and both have to be considered when

evaluating vulnerability of strategic infrastructure resulting from mountain hazards.

Therefore – at the present stage – a standardization approach cannot be recommended because of the overall

lack in system delimitation, of the general scope of analysis and of the timeframe available for analysis.

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APPENDIX

The appendix is adapted from Fuchs et al. (2007a), Papathoma-Köhle et al. (2011), and Totschnig and Fuchs

(2013), and additionally extended for this study.

Source General information Vulnerability definition

used

Gaps and challenges of the

method

1 Akbas et

al. (2009)

Type of disaster: Debris

flow

Scale: Local

Location: Selvetta (Italian

Alps)

Research domain: Natural

science

Focus: Buildings,

infrastructure, population

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users: Local

authorities, planning

agencies, engineers

Vulnerability is considered

to be the expected degree

of loss to a given element

at risk resulting from the

occurrence of a hazard of a

given magnitude. It is

defined as the ratio

between the loss and the

individual reconstruction

value.

The authors suggest that, in

order to reach a higher

confidence level, there is a

need for more data

concerning not only the

resulting damage to

buildings but also intensity

measures if the event such

as deposition height and

velocity.

2 Alexander

(2005)

Type of disaster:

Landslides

Scale: local (multi-scale)

Location: N/A

Research domain: disaster

management

Focus: Buildings, human

lives, socio-economic

activities

Type of assessment:

qualitative

Hazard dependant: NO

Vulnerability curves: NO

Possible end users: Local

authorities, disaster

managers

“….with respect to the

elements at risk

vulnerability can be

considered either as

susceptibility to damage in

mass movements of given

types and sizes or in terms

of value…”

Required data should be

mainly collected by time-

consuming field survey.

3 Barbolini

et al.

(2004)

Type of disaster: Snow

avalanches

Scale: Local

Location: Italy

Vulnerability is defined as

the degree of loss and it is

expressed on a scale of 0

(no loss) to 1 (total loss).

For buildings the loss is the

More data are necessary in

order to assess the validity

of the method. Moreover,

the curves are created for

one type of construction

Section Title

43

Research domain: Natural

Science

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users: Civil

engineers, local

authorities, city planners

value of the property and

for people it is the

probability that a particular

life will be lost. In more

detail, the vulnerability of

buildings is defined as the

ratio between the cost of

repair and the building

value (SL: specific loss).

(alpine types of buildings),

which makes the

methodology difficult to be

applied in an area with

different types of buildings.

Finally, the vulnerability of

people is based to a limited

amount of data and many

assumptions.

4 Bell and

Glade

(2004)

Type of disaster:

Landslides

Scale: Local

Location: Iceland

Research domain: Natural

Science

Focus: Buildings and

people

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end users: Local

authorities, emergency and

civil protection services.

No definition of

vulnerability is provided.

No vulnerability map is

provided and no detailed

investigations of buildings is

carried out.

5 Bertrand

and

Naaim

(2010)

Type of disaster: Snow

avalanches

Scale: Local

Location: -

Research domain:

Engineering

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end users:

Engineers, construction

specialists

The vulnerability is the

degree of loss (from 0 to 1)

of a given element within

the threaten area.

The methodology is

developed only for one type

of building (unreinforced

masonry structures) and it is

time consuming if it is to be

applied to a large number of

buildings. Therefore, it is not

appropriate for emergency

planning and disaster

management or vulnerability

mapping.

6 Bründl et

al. (2009)

Type of disaster: Floods,

avalanches, debris flows,

rock fall, landslides,

earthquakes, storms, hail,

heat waves

Scale: regional

Characterisation of the

extent of

disturbance/damage an

object experiences due to

a specific process action.

Non-continuous approach,

well adapted classification

thresholds to the specific

situation in Switzerland

(especially to spatial

planning) but transferability

Section Title

44

Location: Switzerland

Research domain: Natural

science, engineering

Focus: Buildings,

infrastructure, people,

agricultural land

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end users: Natural

hazards experts and

decision makers at various

administrative levels

to other countries or other

applications may be difficult.

7 Büchele et

al. (2006)

Type of disaster: Floods

Scale: local

Location: Baden-

Württemberg, Germany

Research domain: Natural

science, engineering,

reinsurance sector

Focus: Buildings and

contents

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users: Public

authorities (communities),

spatial planners, house

owners and insurance

agencies.

“Stage-damage functions

for individual objects” .

Very site specific approach

with high amount of data

needed.

8 Borter

(1999)

Type of disaster: Debris

flow

Scale: Local

Location: Switzerland

Research domain: Natural

science, engineering

Focus: Infrastructure,

people, agricultural land,

farm animals

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

The authors do not give

any definition of

vulnerability. The degree

(or susceptibility) of loss

(from 0 to 1) is part of the

calculation for the damage

potential.

The degree of loss is more an

estimation due to only few

detailed event analyses. The

damage function is only

given for three intensity

classes that lead to over- and

underestimation,

respectively.

Section Title

45

Possible end users:

Regional and local

authorities, civil engineers,

insurance companies

9 Borter

(1999)

Type of disaster: Snow

avalanches

Scale: Local

Location: Switzerland

Research domain: Natural

science, engineering

Focus: Buildings,

infrastructure, people,

agricultural land

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users:

Regional and local

authorities, civil engineers,

insurance companies

The authors do not give

any definition of

vulnerability. The degree

(or susceptibility) of loss

(from 0 to 1) is part of the

calculation for the damage

potential.

The degree of loss is more an

estimation due to only few

detailed event analysis. The

damage function is only

given for three intensity

classes that lead to over- and

underestimation,

respectively.

10 Borter

(1999)

Type of disaster: Rock falls

Scale: Local

Location: Switzerland

Research domain: Natural

science, engineering

Focus: Buildings,

infrastructure, people,

agricultural land

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users:

Regional and local

authorities, civil engineers,

insurance companies

The authors do not give

any definition of

vulnerability. The degree

(or susceptibility) of loss

(from 0 to 1) is part of the

calculation for the damage

potential.

The degree of loss is more an

estimation due to only few

detailed event analysis. The

damage function is only

given for three intensity

classes that lead to over- and

underestimation,

respectively.

11 Cappabian

ca et al.

(2008)

Type of disaster: Snow

avalanches

Scale: Local

Location: Italian Alps

(Trento)

Research domain:

Engineering

Focus: Buildings and

No definition is given. It is

stated that for buildings,

vulnerability represents

the ratio between the cost

of repair and the building

value and for people, the

probability of being killed

inside a building.

For buildings the authors use

the vulnerability curve from

Wilhelm (1997) only for one

type of building (concrete).

Other building types and

other building characteristics

are excluded from the

vulnerability assessment.

Section Title

46

people

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end users:

Decision makers

12 Cardinali

et al.

(2002)

Type of disaster:

Landslides, debris flow,

rock falls

Scale: Local

Location: Umbria, Italy

Research domain: Natural

science

Focus: Buildings and

people

Type of assessment:

Qualitative (A,S,F)

Hazard dependant: YES

Vulnerability curves: NO

Possible end users: Town

officials, private

consultants involved in

land use and city planning.

No definition is given. They

suggest that a vulnerability

assessment should include

considerations of the type

of failure, the elements at

risk and the buildings

ability to survive the

expected landslide.

The authors proposed three

different types of damage

for different types of

landslides and magnitude

but they never quantified

vulnerability for the

elements at risk.

13 Corominas

et al.

(2005)

Type of disaster: Rock falls

Scale: Local

Location: Andorra

Research domain:

Engineering

Focus: Buildings and

people

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users:

Engineers, owner of

buildings, local authorities.

Vulnerability is the degree

of loss of an element at

risk.

A vulnerability score is

assigned to the buildings

according to the volume of

the impact block. The

characteristics of the

buildings are not taken into

consideration.

14 De Lotto

and Testa

(2000)

Type of disaster: Floods

Scale: regional

Location: Alpine valley in

Italy

Research domain:

Engineering

"A function that relates the

percentage of the value of

a property that could be

lost with the intensity of

the event".

Only the highest expected

damage value of depth and

velocity was used and

interactions were neglected.

Section Title

47

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Planners, emergency

managers and engineers

15 Dutta et

al. (2003)

Type of disaster: Floods

Scale: local and regional

Location: Ichinomiya river

basin, Japan

Research domain:

Engineering

Focus: Buildings, contents,

crops and infrastructure

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Insurance agencies,

engineers, authorities and

emergency managers.

Vulnerability as term not

mentioned.

Only stage-damage curves

are used, other parameters

neglected. High errors in

urbanised areas hinder the

applicability for the real

world.

16 FEMA

(2013)

Type of disaster: Floods

Scale: local

Location: USA

Research domain: Natural

hazards risk management

Focus: buildings, contents,

essential/high loss facility,

lifelines, vehicles, Human

casualties

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Federal, state, regional and

local governments, private

enterprises, emergency

preparedness, response

and recovery institutions.

The term is not explicitly

defined but dealt with as

the degree of loss a

particular element at risk

will suffer due to a certain

impact of a hazardous

process.

The method is mainly based

on flow depth, flow velocity

is only taken into account

with a threshold for building

collapse.

17 Fuchs et

al. (2007)

Type of disaster: Debris

flow

The vulnerability was

measured using an

economic approach.

The vulnerability assessment

method is designed only for

one kind of building which is

Section Title

48

Scale: Local

Location: Austrian Alps

Research domain: Natural

science

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Local

authorities, emergency

planners, building owners

Vulnerability was derived

from the quotient between

the loss and the individual

reinstatement value for

each element at risk in the

test site.

common in alpine countries

but the methodology and

the vulnerability curve could

not be transferred to areas

with different structural

characteristics.

18 Fuchs et

al. (2012b)

Type of disaster: Debris

flow and fluvial sediment

transport

Scale: Local

Location: Austrian Alps

Research domain: Natural

science

Focus: Buildings

Type of assessment:

Quantitative, spatial

statistics

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Local

authorities, emergency

planners, building owners

The vulnerability was

measured using an

economic approach.

Vulnerability was derived

from the quotient between

the loss and the individual

reinstatement value for

each element at risk in the

test site. The spatiality of

vulnerability was assessed

The vulnerability assessment

method is designed only for

two building categories

which are common in alpine

countries, hence, the

methodology and the results

could not be transferred to

areas with different

structural characteristics.

19 Galli and

Guzzetti

(2007)

Type of disaster:

Landslides

Scale: Regional

Location: Umbria, Italy

Research domain: Natural

science disaster

management

Focus: Buildings and roads

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Local

authorities, emergency

services

Vulnerability is the

probability of total loss to a

specific element given the

occurrence of the

landslide.

The resulting map is not easy

to read and to use for

planning due to the scale

(regional).

20 Grünthal

et al.

Type of disaster: Floods, Vulnerability assessment:

“evaluation how exposed

Only inundation depth is

taken into account. For

Section Title

49

(2006) storms, earthquakes

Scale: regional

Location: Cologne,

Germany

Research domain: Natural

science - engineering,

reinsurance

Focus: Buildings and

contents

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Disaster managers, urban

planners, insurers, regional

and local authorities etc.

assets will suffer by various

hazard events”.

concrete planning decisions

and emergency strategies

still more detailed analyses

might be needed.

21 Hooijer et

al. (2001)

Type of disaster: Floods

Scale: regional

Location: Hai River Basin,

China

Research domain:

Engineering

Focus: Agricultural

/industrial production,

industrial fixed assets,

households and people.

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users:

Decision makers for

planning of mitigation

measures

Instead of vulnerability the

term “loss rate” is used

which is defined as the

“percentage of total

potential damage and

number of inhabitants”.

Non-continuous approach,

only considering flood depth.

The data availability was too

low for the proposed

methodology and thus the

results are not sufficient for

flood management cost-

benefit analyses.

22 Jónasson

et al.

(1999)

Type of disaster: Snow

avalanches

Scale: local

Location: Iceland

Research domain: natural

science

Focus: people

Type of assessment:

Quantitative

Hazard dependant: YES

The term vulnerability is

not included in this study,

however, the survival

probability (which is

calculated in this study)

could be used as a

component of a

vulnerability assessment to

snow avalanches.

The method concerns only

Icelandic type of buildings

and it cannot be transferred

elsewhere.

Section Title

50

Vulnerability curves: NO

Possible end-users:

Emergency services

23 Kang et al.

(2005)

Type of disaster: Floods

Scale: local and regional

Location: Taipei, Taiwan

Research domain: Natural

science, Engineering

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Risk

managers and engineers.

The term vulnerability is

not used in this article.

Focus is put on the

damage. Stage-damage

curves establish the link

between flood depth and

total damage.

Only flow depth is

considered. Absolute

damage was calculated,

hindering transferability to

other locations and usability

in the future due to inflation

etc.

24 Kaynia et

al. (2008)

Type of disaster:

Landslides

Scale: Local

Location: Germany

Research domain: Natural

sciences, engineering

Focus: Buildings and

people

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Emergency planners, local

authorities

Vulnerability is defined in

terms of both the landslide

intensity and of the

susceptibility of the

elements at risk.

V= I x S

The method is too

sophisticated and the data

difficult to collect especially

for larger areas.

25 Keiler et

al. (2006a)

Type of disaster: Snow

avalanches

Scale: Local

Location: Austria

Research domain: Natural

Science

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: A

study target audience is

not identified in the study.

"The vulnerability of the

buildings is understood as

a degree of loss to a given

element within the area

affected by natural

hazards. A vulnerability

function for different

construction types of

buildings that depends on

avalanche pressure was

used to assess the degree

of loss."

The vulnerability of buildings

to avalanche impact

pressure has to be further

investigated since the

present study takes into

consideration a method

(Wilhelm, 1997), which could

only serve as a rough

estimation.

Section Title

51

However, the results could

be used by local

authorities, planners,

emergency services and

insurance companies.

26 Keylock

and

Barbolini

(2001)

Type of disaster: Snow

avalanches

Scale: Local

Location: Iceland

Research domain: Natural

science

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users:

Avalanche experts,

engineers, planners,

decision makers

Vulnerability is defined as

the degree of loss and it is

expressed on a scale of 0

(no loss) to 1 (total loss).

For property, the loss will

be the value of the

property and for persons

the probability that a

particular life could be lost.

Very simple relation for the

estimation of the

vulnerability (derived from

one event). Different

buildings types are not

regarded.

27 Leone et

al. (1996;

1995)

Type of disaster:

Landslides

Scale: Regional/ local

Location: -

Research domain: Natural

sciences

Focus: Multi-dimensional

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users: End

users are not defined but

local authorities and

emergency planners could

use the outcomes of this

study.

Vulnerability is defined as

the level of potential

damage (0 to 1) to a given

exposed element which is

subject to a possible or real

phenomenon of a given

intensity.

The potential damage level

for different elements at risk

is given in a table without

being explained or

connected with different

process intensities.

28 Liu and Lei

(2003)

Type of disaster: Debris

flow

Scale: Regional

Location: China

Research domain: Natural

sciences,

Disaster management

Vulnerability is defined as

the potential total

maximum loss due to a

potential damaging

phenomenon for a specific

area and for a reference

period.

The approach can be used

for funding allocation but

due to its regional scale and

the difficulty of the data to

be collected on a local scale,

cannot be used in a local

scale.

Section Title

52

Focus: Multi dimensional

physical, economic,

environmental

Type of assessment:

Quantitative

Hazard dependant: NO

Vulnerability curves: YES

Possible end-users:

Regional or central

government

29 Macquaire

et al.

(2004)

Type of disaster:

Landslides

Scale: Local

Location: Barcelonette,

Southeast France

Research domain: Natural

Sciences

Focus: Buildings and

people

Type of assessment:

Qualitative

Hazard dependant: NO

Vulnerability curves: NO

Possible end-users: Local

authorities

A vulnerability definition is

not given. Vulnerability is

considered to be related

with the interaction

between the exposed

element and the landslide

phenomenon.

The methodology has not

been validated and it has

been only been tested on a

specific built-up

environment (ski resort).

30 Mavrouli

and

Corominas

(2008)

Type of disaster: Rock falls

Scale: Local

Location: Andorra

Research domain:

Engineering

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users:

Engineers, building owners

No definition for

vulnerability is given, it is

however considered to be

the structural damage of

the building following a

rock fall.

The methodology is designed

for individual buildings, it is

however difficult to be

applied on a larger number

of buildings.

31 Mejia-

Navarro et

al. (1994)

Type of disaster:

Subsidence, rock falls,

debris flows, and floods

Scale: Local

Location: Colorado, USA

Research domain: Earth

Science

Vulnerability is defined as

the intrinsic predisposition

of any element to be at risk

of a mental or economic

loss upon the occurrence

of a hazardous event of

intensity i.

In the calculation of the

vulnerability the condition or

the construction type of

building is not taken into

consideration. No final

vulnerability map is

provided.

Section Title

53

Focus: Ecosystem,

economic and social

structure vulnerability

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users: Urban

planners, local authorities

32 Meyer et

al. (2009)

Type of disaster: Floods

Scale: regional

Location: River Mulde,

Germany

Research domain: Natural

science, Engineering

Focus: Economical,

ecological and social risk

Type of assessment:

Qualitative and

quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Local

authorities and engineers

Damaged share of the total

value of the assets,

depending on inundation

depth.

The results are very

dependant on the criteria

chosen and the weights

given to the different

criteria.

33 Michael-

Leiba et

al. (2003)

Type of disaster: Debris

flow

Scale: Regional

Location: Cairns, Australia

Research domain: Natural

science- disaster

management

Focus: People, buildings

and roads

Type of assessment:

Quantitative

Hazard dependant: YES

(type of disaster)

Vulnerability curves: NO

Possible end-users:

Emergency planners, local

authorities

The vulnerability is

considered the probability

of death or destruction

given that a landslide hit

the residence or road.

The methodology assumes

that vulnerability is

independent of landslide

magnitude.

34 Papathom

a-Köhle et

al. (2007)

Type of disaster:

Landslides

Scale: Local

Location: Germany

No vulnerability definition

is given but vulnerability is

considered a dynamic

element that should be

assessed by taking into

The methodology is based

on pre-existing landslide

susceptibility maps that in

some cases might be difficult

to obtain and in others their

Section Title

54

Research domain: Natural

sciences, civil protection

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: NO

Vulnerability curves: NO

Possible end-users: Local

authorities, public, civil

protection services,

insurance companies

consideration temporal

and spatial aspects.

quality can be questionable.

The method is also time-

consuming, as most of the

data have to be collected on

site for each house.

Therefore, the methodology

cannot be applied on large

areas.

35 Papathom

a-Köhle et

al. (2012)

Type of disaster: Debris

flow

Scale: Local

Location: Martell, Italy

Research domain: Natural

sciences, civil protection

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Local

authorities, public, civil

protection services,

insurance companies

A vulnerability curve as a

function of the intensity of

the process and the degree

of loss is presented.

Although the validation

process demonstrated the

reliability of the results, a

new damage assessment

documentation is being

recommended and

presented.

36 Romang

(2004)

Type of disaster: Floods

and debris flow

Scale: Local

Location: Switzerland

Research domain: Natural

science, engineering

Focus: Buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users: Local

authorities

Vulnerability is defined

according to the insurance

sector as follows:

Vulnerability= insured

damage/insured value of

the building

Only the value of the

building is taken into

consideration and not its

shape, construction material,

condition and other

indicators that influence its

vulnerability.

37 Shrestha

(2005)

Type of disaster:

Landslides and floods

Scale: Regional

Location: Nepal

Research domain: Natural

Vulnerability is the degree

to which a system is likely

to experience harm to its

exposure to hazard (Turner

II et al. 2003). It is

determined by the capacity

The indicators used for the

physical vulnerability to

floods and landslides were

not clear. The regional scale

of the study is not

appropriate to use for

Section Title

55

and social science

Focus: Physical and socio-

economic vulnerability

Type of assessment:

Qualitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users:

Government, public and

private organisations,

NGOs, the community,

insurance companies.

of a system to anticipate,

cope with, resist, and

recover from the impact of

hazard (Wisner et al.

2004).

emergency management.

38 Sterlacchi

ni et al.

(2007)

Type of disaster: Debris

flow

Scale: Local

Location: Italy

Research domain: Natural

science

Focus: Built up areas,

infrastructure, Socio-

economic features of the

area

Type of assessment:

Qualitative

Hazard dependant: NO

Vulnerability curves: NO

Possible end-users: Public

administrators, economic

planners, building

managers and owners,

lawmakers, civil protection

and emergency services.

No definition of

vulnerability is given but

vulnerability corresponds

to the physical effects

(aesthetic, functional and

structural damage) due to

the impact of a damaging

event.

It is not clear which

attributes of the buildings

located in the hazardous

area have been taken into

consideration in order to

assess their vulnerability.

There is no map showing

vulnerability’s spatial

pattern.

39 Totschnig

et al.

(2011)

Type of disaster: Debris

flow

Scale: Local

Location: Austria

Research domain: Natural

science

Focus: Built up areas,

residential buildings

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Public

administrators, economic

The vulnerability was

measured using an

economic approach.

Vulnerability was derived

from the quotient between

the loss and the individual

reinstatement value for

each element at risk in the

test site.

The vulnerability assessment

method is designed only for

one kind of building which is

common in alpine countries

but the methodology and

the vulnerability curve could

not be transferred to areas

with different structural

characteristics.

Section Title

56

planners, building

managers and owners,

lawmakers, civil

protection.

40 Totschnig

and Fuchs

(2013)

Type of disaster: Fluvial

sediment transport, debris

flow

Scale: Local

Location: Austria, Italy

Research domain: Natural

science

Focus: Built up areas,

different building

categories

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users: Public

administrators, economic

planners, building

managers and owners,

lawmakers, insurers.

The vulnerability was

measured using an

economic approach.

Vulnerability was derived

from the quotient between

the loss and the individual

reinstatement value for

each element at risk in the

test site.

The vulnerability assessment

method is designed for

multiple building categories

and hazard types. The

methodology and the

vulnerability curve may be

transferred to areas with

different structural

characteristics.

41 Uzielli et

al. (2008)

Type of disaster:

Landslides

Scale: Local

Location: -

Research domain: Natural

science-engineering

Focus: Built environment

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Emergency planners, local

authorities.

Vulnerability V is defined in

terms of both the landslide

intensity I and of the

susceptibility S of the

elements at risk.

V= I x S

The method is too

sophisticated and the data

difficult to collect especially

for larger areas.

42 Wilhelm

(1997)

Type of disaster: Snow

avalanches

Scale: Local

Location: Switzerland

Research domain: Natural

science, economics

Focus: Buildings, people,

traffic lines

The authors do not give

any definition of

vulnerability. The degree

(or susceptibility) of loss

(from 0 to 1) is part of the

calculation for the damage

potential.

The degree of loss is more an

estimation due to only few

detailed event analyses.

Section Title

57

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Regional and local

authorities, civil engineers,

insurance companies

43 Zêzere et

al. (2008)

Type of disaster:

Landslides

Scale: Local

Location: Lisbon, Portugal

Research domain: Natural

Science, geography

Focus: Buildings and roads

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: NO

Possible end-users: Local

authorities, emergency

services, insurance

companies.

Vulnerability is considered

as the degree of loss. It

depends not only on the

structural properties of the

exposed elements but also

on the type of process and

its magnitude, this is why it

cannot be defined in

absolute terms but only

with respect to a specific

process.

For the vulnerability of the

buildings only the

construction type of the

building is taken into

consideration. The

vulnerability to translational

and rotational slides is 1

(total loss) for all the types

of buildings. No map of

vulnerability is provided.

44 Zhai et al.

(2006)

Type of disaster: Floods

Scale: regional

Location: Japan

Research domain: Natural

science - Engineering,

planning

Focus: People

Type of assessment:

Quantitative

Hazard dependant: YES

Vulnerability curves: YES

Possible end-users:

Emergency managers:

efficiency of warnings and

other emergency response

measures.

“Social vulnerability refers

to population, land use,

systems for warning,

emergency assistance,

preparedness, and so on.”

Only the indicator

`inundated buildings’ is

taken into account for the

prediction of the probability

of fatality or injury. The

effect of evacuation

behaviour, natural and

socioeconomic

characteristics were not yet

considered.