With sea level rise (SLR) fast becoming one of the most pressing matters for governments worldwide, there has been mass amounts of research done on the impacts of SLR. However, these studies have largely focussed on the ways that SLR will impact both the natural and built environment, along with how the risk to low-lying coastal communities can be mitigated, while the inevitable impacts that this will have on mental well-being has been understudied. This research has attempted to determine the ways in which SLR can impact the mental well-being of those living in a low-lying coastal community, along with how these impacts could be mitigated while remaining adaptable to future environmental change. This was done through conducting an in-depth literature review to understand current SLR projections, the key components of mental well-being and how SLR can influence changes to mental well-being. This literature review then shaped a questionnaire which was distributed to residents of the New Brighton coastline. This questionnaire asked respondents how they interact with the local environment, how much they know about SLR and its associated hazards, whether SLR causes any level of stress or worry along with how respondents feel that these impacts could be mitigated. This research found that SLR impacts the mental well-being of those living in low-lying coastal communities through various methods: firstly, the respondents perceived risk to SLR and its associated hazards, which was found to be influenced by the suburbs that respondents live in, their knowledge of SLR, their main sources of information and the prior experience of the Canterbury Earthquake Sequence (CES). Secondly, the financial aspects of SLR were also found to be drivers of stress or worry, with depreciating property values and rising insurance premiums being frequently noted by respondents. It was found that the majority of respondents agreed that being involved in and informed of the protection process, having more readable and accurate information, and an increased engagement with community events and greenspaces would help to reduce the stress or worry caused by SLR, while remaining adaptable to future environmental change.
Lake Taupō in New Zealand is associated with frequent unrest and small to moderate eruptions. It presents a high consequence risk scenario with immense potential for destruction to the community and the surrounding environment. Unrest associated with eruptions may also trigger earthquakes. While it is challenging to educate people about the hazards and risks associated with multiple eruptive scenarios, effective education of students can lead to better mitigation strategies and risk reduction. Digital resources with user-directed outcomes have been successfully used to teach action oriented skills relevant for communication during volcanic crisis [4]. However, the use of choose your own adventure strategies to enhance low probability risk literacy for Secondary school outreach has not been fully explored. To investigate how digital narrative storytelling can mediate caldera risk literacy, a module “The Kid who cried Supervolcano” will be introduced in two secondary school classrooms in Christchurch and Rotorua. The module highlights four learning objectives: (a) Super-volcanoes are beautiful but can be dangerous (b) earthquake (unrest) activity is normal for super-volcanoes (c) Small eruptions are possible from super-volcanoes and can be dangerous in our lifetimes (d) Super-eruptions are unlikely in our lifetimes. Students will create their digital narrative using the platform Elementari (www.elementari.io). The findings from this study will provide clear understanding of students’ understanding of risk perceptions of volcanic eruption scenarios and associated hazards and inform the design of educational resources geared towards caldera risk literacy.
Recent tsunami events have highlighted the importance of effective tsunami risk management strategies (including land-use planning, structural and natural mitigation, warning systems, education and evacuation planning). However, the rarity of tsunami means that empirical data concerning reactions to tsunami warnings and evacuation behaviour is rare when compared to findings for evacuations from other hazards. More knowledge is required to document the full evacuation process, including responses to warnings, pre-evacuation actions, evacuation dynamics, and the return home. Tsunami evacuation modelling has the potential to inform evidence-based tsunami risk planning and response. However, to date, tsunami evacuation models have largely focused on the timings of evacuations, rather than behaviours of those evacuating. In this research, survey data was gathered from coastal communities in Banks Peninsula and Christchurch, New Zealand, relating to behaviours and actions during the November 14th 2016 Kaikōura earthquake tsunami. Survey questions asked about immediate actions following the earthquake shaking, reactions to tsunami warnings, pre-evacuation actions, evacuation dynamics and details on congestion. This data was analysed to characterise trends and identify factors that influenced evacuation actions and behaviour, and was further used to develop a realistic evacuation model prototype to evaluate the capacity of the roading network in Banks Peninsula during a tsunami evacuation. The evacuation model incorporated tsunami risk management strategies that have been implemented by local authorities, and exposure and vulnerability data, alongside the empirical data collected from the survey. This research enhances knowledge of tsunami evacuation behaviour and reactions to tsunami warnings, and can be used to refine evacuation planning to ensure that people can evacuate efficiently, thereby reducing their tsunami exposure and personal risk.
The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
