Investigating Severe and Extreme Wind Experience in Ontario to improve disaster resilience

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Description of Research Project

This research effort aims to examine social aspects of residential severe and extreme wind experience in Ontario during the 2026 tornado season (May to October) to enhance disaster resilience. Specifically, our objectives are to improve understanding surrounding: how residents become aware of potential threatening winds, how they perceive risk towards the hazard, how they prepare for and respond to wind hazard exposure, as well as their current mitigation practices and their willingness to take up wind mitigation so as to reduce their overall risk. Alongside Canadian Severe Storms Laboratory (CSSL) summer interns (Western University) who are conducting damage surveys in Ontario communities that have incurred structural wind damage, the York U undergraduate student would travel with me and conduct one-on-one interviews with residents to understand these social dimensions of severe and extreme wind experience. It is approximated that 3-6 trips in southwestern Ontario (radius of 100km from York U campus) would take place between May and July of 2026.

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Undergraduate Student Responsibilities

The undergraduate student would be responsible for travelling with me to the Ontario neighbourhoods that incurred structural damage. The student would interview the residents with me, analyze the interview data, and assist in providing results to residents and to the CSSL.

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Qualifications Required

The undergraduate student should be in the social or related sciences (DEM, anthropology, sociology, geography). They should have some knowledge of the environment (this will be helpful for understanding the meteorological and risk context); knowledge of severe wind impacts, vulnerability and resilience frameworks would be helpful. The student should have exceptional organization skills. The student should have the ability to transform research results into clear, evidence-based conclusions. Training in conducting semi-structured interviews, including ethical training, as well as familiarity with coding (i.e. categorizing data) would be a bonus.