Pre-event impact assessment

Pre-event impact assessment (pre-event IA) is designed to forecast the probabil-ity of the potential expected impact on exposed elements with certain vulnerability conditions, varying in function of hazard intensity (Fig. 1.1). Pre-event impact as-sessments result of a combination of hazard, exposure and vulnerability asas-sessments in a given location and at a specific time of analysis. As previously discussed, within the context of volcanic risk, the complexity of both multi-hazard scenarios and the multiple dimensions of vulnerability make difficult to achieve reliable vulnerability assessments that account for both physical and systemic vulnerabilities.

One of the most applied strategies to quantify physical vulnerability is to identify and describe the potential physical damage caused to an element at risk due to a

given hazard intensity, through the so-calledfragility orvulnerability curves [Wilson et al., 2017; Menoni et al., 2017]. In other words, fragility curves describe the proba-bility of failure of an element at risk depending on the hazard intensity level, due to its degree of weakness. Based on a classification of the different elements exposed, one can categorize their degree of vulnerability as a function of thresholds. Fragility curves are generally derived from real damage data, as well as from analytical and numerical modelling combined with experiments [Wilson et al., 2017; Menoni et al., 2017] (Fig. 1.1). In volcanology, important progress on fragility curves related to tephra fallout, pyroclastic density currents and lahars have been accomplished in the last three decades. Pioneering studies of Blong [1981]; Booth et al. [1983]; Blong [1984]; Spence et al. [1996] and Pomonis et al. [1999] contributed to the develop-ment of fragility curves for roof collapse due to tephra fallout load [Spence et al., 2005]; and window glazing due to pressure, and failure of reinforced concrete frames due to lateral load, both related to pyroclastic density currents [Spence et al., 2004;

Petrazzuoli and Zuccaro, 2004]. Damage scales for dynamic pressure impact of py-roclastic density currents have also been developed by Baxter et al. [2005]. For the case of lahars, Jenkins et al. [2015a] developed fragility curves of masonry buildings due to the impact pressure, and more recently, Daga et al. [2018] have investigated failure models and developed fragility curves associated with lahar depth to road bridges. Experimental and empirical studies of Wardman et al. [2012, 2014]; López et al. [2016] and Lopez Chachalo [2017] elucidated the physical processes behind the flashover of electrical insulators in the power network due to the contamination of tephra; however, specific fragility curves do not exist yet. Finally, fragility curves of the transportation network (i.e. road, rail, airports, maritime transportation) due to the loss of visibility because of the ash settling have been developed by Blake et al.

[2016, 2017b]. Additionally, some experimental advances on the causes of skid resis-tance of roads due to the accumulation of tephra have also been conducted by Blake et al. [2017a]. Further readings can be found in the exhaustive review conducted by Bonadonna et al. [in press].

It is important to highlight that physical vulnerability studies focused on i) the identification of controlling vulnerability factors, ii) the modelling of failure processes

that lead to an impact, based on various physical measures, such as pressure, load or visibility, iii) the categorization of the studied element in vulnerability classes, and iv) the definition of damage/impact scales or states. This analysis is essentially complex due to the multi-hazard character of volcanic eruptions (few sophisticated studies have combined multi-hazard scenarios of pyroclastic density currents, tephra fallout and earthquakes, [Zuccaro and Ianniello, 2004; Esposti Ongaro et al., 2007, 2008; Zuccaro et al., 2004]), and the scarcity of statistically meaningful and real damage data, which does not allow a regular improvement of fragility curves [Wilson et al., 2014], as it is the case in seismic risk, that relies on substantially larger damage datasets [Menoni et al., 2017].

Furthermore, when trying to constrain systemic vulnerability, the issue becomes particularly challenging. According to Sapountzaki et al. [2009] and Van Der Veen and Logtmeijer [2005], systemic vulnerability can be characterized in terms of inter-dependence, transferability and redundancy. Interdependence is the degree to which an activity or system relates to each other (e.g. pumps of the water system depend on electricity to keep functioning); redundancy is the degree of criticality of an ele-ment in a network and it is intrinsically related to the ability to respond by using substitutes (e.g. a secondary road if the main road is impacted); and transferability refers to the capacity to transfer or relocate a function if the system is not able to supply it (e.g. the use of helicopters if the roads are blocked). Systemic vulnerability is therefore intrinsically associated with the functionality of CI.

At this time there is not any guideline, framework or approach to efficiently quan-tify systemic vulnerability in a volcanic context. The pioneering study of Wilson et al. [2014] summarized the impact to critical CI due to tephra fallout, pyroclastic density currents, lava flows and lahars, based on an extensive catalogue of histor-ical observations from several eruptions. Owing to difficulties in identifying clear impact trends as a function of a single hazard parameter (as it is done for physical vulnerability), the authors proposed a conceptual model with a continuum impact scale, from tolerance, through disruption and right up to damage, with increasing hazard intensity. Tolerance means that CI retain all functions and continue to op-erate uninterrupted throughout volcanic eruption. Disruption refers to impacted

Level Level 0 Level 1 Level 2 Level 3

Description No damage Cleaning required Repair required Replacement or fi-nancially expensive

Table 1.1: Disruption and damage levels for expected impacts on the power supply system as a function of tephra-fallout thickness (mm), from Wilson et al. [2014].

CI by volcanic hazards causing it to operate at reduced function until restoration is undertaken. Finally, damage is reserved when physical damage occurs until re-pair is undertaken. Based on this approach, Wilson et al. [2014] proposed scales of Disruption and Damage States (DDS) for expected impacts to CI due to tephra fallout (i.e. DDS as a function of tephra thickness), pyroclastic density currents (i.e. DDS as a function of dynamic pressure), lava flows (i.e. DDS as a function of flow depth), and lahar velocity (i.e. DDS as a function of flow velocity). These DDS scales are specified for power and water supply, wastewater network, airports, roads, rail, marine transportation, vehicles, communications, buildings and critical components. An example of the most refined scale associated with the tephra fall-out over electrical power supply system is shown in the Table 1.1. In a similar way, Jenkins et al. [2015b] categorised the impacts on CI per sector in five DDS levels (D0 to D5), where D0 corresponds tono damage, and D5 to a level beyond economic repair. Other authors have also proposed different metrics for the loss of functional-ity of CI (e.g. full service, rolling outages, no electricfunctional-ity service) [Hayes et al., 2016;

Deligne et al., 2017; Blake et al., 2017c].

Although these DDS scales are a first attempt to correlate both physical damage and systemic impact to a given hazard intensity metric, the intricated relations of a

complex chain of impacts, that are commonly associated with CI, is not really con-sidered. The complexity of impacts on CI cannot only be related to a single hazard parameter (e.g. tephra thickness), or to a single vulnerability source (e.g. physical aspects of components), disregarding key contributions of systemic vulnerability and system capacity to respond. This was demonstrated by Craig et al. [2016a], when trying to apply both Wilson et al. [2014] and Jenkins et al. [2015b] DDS scales to the real case study of the 2011-2012 eruption of the Cordón Caulle volcano (Chile).

Craig et al. [2016a] found that impacts were mainly related to important systemic disruptions rather than long-term physical damage, and, that most systems recover after clean-up and rapid response measures, all these factors independent of tephra thickness. Therefore, impact dynamics is clearly underestimated by using these scales.

Whilst it is true that the proposed scales represent an important step on the classification of impacts on CI, they are still an inventory with a mixture of var-ious physical damages and disruptions which are not clearly interconnected (See Table 1.1). The concepts of physical (e.g. CI design), and systemic (e.g. inter-dependency) vulnerabilities, as well as the effects of rapid response and mitigation measures, crucial for CI functionality, are not considered. Furthermore, the effect of secondary hazards on the long-term disruption of CI, particularly associated with wind-remobilisation of tephra, is not considered either. As a consequence, the use of these DDS scales to forecast expected impacts in pre-event IA is still not suitable.

To summarize, pre-event IA requires reliable and comprehensive hazard, expo-sure and vulnerability assessments. However, these assessments need to be fed by empirical (real) impact data, analytical and numerical modelling, experimental data and experts judgement (Fig. 1.1). It is the reason why post-event impact assess-ments are indispensable to capture the complexity of real events and to improve pre-event IA.