A review of graphical methods to map the natural hazard-to-wellbeing risk chain in a socio-ecological system
The popular concept of wellbeing has added multiple dimensions to the current socio-economic measures of vulnerability from natural hazards. Due to the wellbeing concept’s relevance in various policy agendas, there is a need for a stronger integration of what is predominantly a socio-economic concept into the natural hazards space. Graphical methods have been used as transdisciplinary engagement tools to translate verbal descriptions of socio-ecological systems into simulation models able to test hypotheses. The purpose of this article is to identify the graphical methods that have been used in the literature to graphically represent, structure and model different segments of the hazard risk chain. 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. A case study on volcanic hazards in Mount Taranaki, New Zealand showcased ways forward by conceptually combining methods to link hazards to impacts on wellbeing. 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. 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. Hence, the possibilities to use common methods, or combinations of these, are numerous potentially enabling the creation of graph-based, distilled simulation models that can be used by experts from different backgrounds to quantitatively model the wellbeing impacts exerted by natural hazards.