New Volcanic Futures

Graphical Methods for Hazards to Wellbeing

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Wellbeing is a concept that has gained a lot of traction in global policy agendas in the last few years as it has proven to be a more comprehensive framework, compared to narrower socio-economic indicators, to measure the “aspects that matter the most to people and that, together, shape their lives” (Stiglitz et al., 2009). In the specific case of New Zealand, the wellbeing concept has been promoted by the New Zealand Treasury under the Living Standards Framework banner. The purpose of the Living Standards Framework is to assess “policy impacts across the different dimensions of wellbeing” (King, 2018).

In the context of the TTVF project and New Zealand, the Living Standards Framework could be used to assess the impacts from volcanic hazards across the different dimensions of wellbeing in the Taranaki region. However, being a relatively new concept, the wellbeing concept still has not been applied not only in the volcanic hazards space but also across natural hazards in general. Hence, the need was identified for a transdisciplinary and integrative approach to develop a common understanding of the causal links between natural hazards and impacts on wellbeing.

The authors of the journal article were aware that visual tools have helped in areas such as operations research (Rosenhead and Mingers, 2001) and System Dynamics (SD) (Vennix, 1999) to conceptualise complex problems by engaging stakeholders with scientists of various disciplines in model building processes and by integrating individuals’ partial mental models into a holistic graphical system description (Lane and Husemann, 2010).Graphical tools such as diagrams, maps and graphs have been used qualitatively in the decision and social sciences as an intermediate stage between a verbal description and quantitative expert models (Lane, 2008; Lane and Husemann, 2010).

In their most basic forms, graphical tools such as causal maps and networks consist of nodes and edges as depicted in the following figure. The nodes represent concepts, events, and processes while the edges represent correlations and causalities (Lane and Husemann, 2010; Nadkarni and Shenoy, 2001).

Hence, the objective sought in the journal article in question was to review the literature on natural hazards that has relied on graphical methods to graphically represent, structure and model different segments of the hazard-wellbeing risk chain and assess their strengths and weaknesses depending on data availability and objectives sought.

A thorough review of the literature on natural hazards was performed using a set of keywords and filters that resulted in a total of 94 articles, which were then categorised based on the graphical methods used, broad families, properties, hazard types, and segments along the risk chain considered as shown in the following figure.

Out of the review it was identified that the most widely used methodologies in the natural hazards space are probabilistic graphs (e.g. Bayesian networks) representing the random nature of hazards while mapping methods based on System Dynamic principles (SD) (e.g. causal loop diagrams) are used to characterise the dynamically emergent behaviours of socio-economic agents.

Bayesian networks, logic trees and event trees are in essence probabilistic trees showing sequences of events and have been used in the past to estimate and communicate the probabilities of possible cascading events from a diversity of initial triggering events such as volcanic eruptions, floods, droughts, tsunamis, landslides, and earthquakes (Simpson et al., 2016). The following figure shows a generic event tree graphically representing the evolution of an eruption into various types of volcanic hazards across space and time.

SD-based diagrams have been used in the literature to graphically represent risk as a continuous dynamic interaction between the natural hazard and the socio-economic systems, represented by reinforcing feedback loops, enabling the emergence of long-term behaviours such as the adaptation to previous events. The following figure, obtained from Di Baldassarre et al. (2015), shows the dynamic interaction between the natural hazard and the socio-economic system.

While studies linking hazards to wellbeing using graphs are scarce, there is a nascent literature on the characterisation of wellbeing’s multi-dimensionality using networks and SD diagrams. Bayesian networks have been used as an intuitive method to graphically model the dependence structure among the different dimensions of wellbeing (Ceriani and Gigliarano, 2020; Onori and Jona Lasinio, 2020; Ospina-Forero et al., 2020). SD-based diagrams have also been used to map the links between different types of capitals affecting wellbeing using balancing feedback loops, e.g., the growth of the economic capital reduces the natural capital through resource depletion, which in turn affects human wellbeing via pollution (Collins et al., 2014).

This literature review is a first attempt at achieving transdisciplinarity by identifying the graphical methods, respective properties, and potential combinations that could be used as a ‘common ground’ by researchers and stakeholders of different disciplines and backgrounds. This literature review will substantially add to the current literature on the simulation of impacts from natural hazards as graphical methods are becoming an ideal alternative to co-create agile and distilled simulation models of potential impacts. This will be specifically relevant to propagate the risk and uncertainty implicit in natural hazards to impacts on wellbeing as it will open new research avenues to assess risk mitigation policies using decision-making protocols based on probabilistic and multi-criteria wellbeing metrics.

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