An interdisciplinary approach to volcanic risk reduction under conditions of uncertainty: a case study of Tristan da Cunha

Abstract

Abstract. The uncertainty brought about by intermittent volcanic activity is fairly common at volcanoes worldwide. While better knowledge of any one volcano's behavioural characteristics has the potential to reduce this uncertainty, the subsequent reduction of risk from volcanic threats is only realised if that knowledge is pertinent to stakeholders and effectively communicated to inform good decision making. Success requires integration of methods, skills and expertise across disciplinary boundaries.This research project develops and trials a novel interdisciplinary approach to volcanic risk reduction on the remote volcanic island of Tristan da Cunha (South Atlantic). For the first time, volcanological techniques, probabilistic decision support and social scientific methods were integrated in a single study. New data were produced that (1) established no spatio-temporal pattern to recent volcanic activity; (2) quantified the high degree of scientific uncertainty around future eruptive scenarios; (3) analysed the physical vulnerability of the community as a consequence of their geographical isolation and exposure to volcanic hazards; (4) evaluated social and cultural influences on vulnerability and resilience; and (5) evaluated the effectiveness of a scenario planning approach, both as a method for integrating the different strands of the research and as a way of enabling on-island decision makers to take ownership of risk identification and management, and capacity building within their community.The paper provides empirical evidence of the value of an innovative interdisciplinary framework for reducing volcanic risk. It also provides evidence for the strength that comes from integrating social and physical sciences with the development of effective, tailored engagement and communication strategies in volcanic risk reduction.

Preface: Approaches and methods to improve risk management in volcanic areas

“…Gehl et al (2013, in this special issue) have devised a tool to calculate damage caused by a given potential eruption, using eruptive scenarios fed by a hypothesized chronology of adverse events. Then, they use state of the art fragility functions (e.g., Jenkins et al, 2014) to evaluate the damage to vulnerable infrastructures (buildings, agriculture, roads, water or electrical networks). The tool was implemented at Mount Cameroon volcano, using data previously collected by Thierry et al (2008), who formerly showed that risk scenarios can help authorities in charge of crisis management to prepare for future events.…”

Risk communication films: Process, product and potential for improving preparedness and behaviour change

“…This underpinning data included: (i) historical analyses of previous eruptions in St Vincent (Pyle, Barclay and Armijos 2017); (ii) group discussion data from earlier STREVA 'forensic' 6 workshops held in St Vincent and Colombia in 2014 ; (iii) interview data with project partners and communities (Armijos and Few 2016, Wilkinson et al 2016, and (iv) learning from other volcanic settings (e.g. Montserrat and Ecuador) (Hicks and Few 2015, Wilkinson 2015, Stone et al 2014, Hicks et al 2014, Few, Armijos and Barclay 2017. The breadth and depth of data, as well as our strong relationships with communities in both settings, was essential to position the films within each specific socio-cultural context and to ensure the aims of the films were both relevant to, and met the needs of the audience.…”

Trajectories of social vulnerability during the Soufrière Hills volcanic crisis

“…the likelihood of physical exposure to the hazard) and, while this is an important component of volcanic risk analysis, it needs to be supported by further research to identify the differentiation and dynamics of societal vulnerability to volcanic hazards (e.g., Dibben and Chester 1999;Wisner et al 2004). Further, empirical research that integrates vulnerability data into volcanic risk assessments is virtually absent (as an exception, see Hicks et al 2014). This is likely a function of, a) the complexity of integrating qualitative and quantitative data sets; b) the challenges of effectively working in an interdisciplinary team to produce new knowledge, when disciplinary methodologies and epistemologies are seemingly incompatible, and c) a disciplinary mismatch of required time for data gathering (social scientific data, for example, usually requires a longer time to obtain than many forms of physical scientific data).…”