Coastal cities are facing increasing flood risk due to sea-level rise and changing storm activities associated with climate change, as well as increasing population and asset density at coastal areas. There is now a pressing need for communities to develop targeted and robust adaptation strategy to reduce impacts. However, the long-term projections of local sea-level rise and the magnitude and distribution of associated socio-economic impacts, which ultimately shapes the adaptation strategies and help prioritize efforts, are deeply uncertain. This is a result from an accumulation of uncertainties from the climate models’ limited ability to resolve local changes in sea-level and storm activities and uncertainties in long-term societal changes (e.g. population growth). Consequently, sea-level rise adaptation strategies developed based on single or small number of future flood impacts scenarios are unlikely to succeed because the long-term future flood impact is unlikely to be the same as what the strategy was intended to address. Therefore recent research suggests that a broad range of future scenarios of physical and social changes should be considered in adaptation planning to account for more uncertainty in the hopes to avoid surprises. But how can a large number of scenarios be considered in adaptation planning without overwhelming the decision-makers?
My research will apply a machine learning-based method as a novel approach to identify a discrete set of spatially explicit and archetypal coastal flooding impact patterns that are salient across a large number of possible future scenarios. Strategies developed based on these archetypal impact patterns are more likely to perform acceptably well even when the actual climate and impacts digress from the prediction. To demonstrate and evaluate this method, this project will apply the method to identify archetypal patterns of economic (e.g., business disruption), social (e.g., vulnerable population affected), and physical (e.g. debris generated) impacts of coastal flooding in the City of Vancouver for the 2100 timeframe.
Jackie is a PhD Candidate at the Institute for Resources, Environment and Sustainability at the University of British Columbia, working under the supervision of Dr. Stephanie E. Chang. Her overarching goal in research is to use interdisciplinary research approaches to address environmental and public health issues. She holds an MSc in Meteorology from McGill University and BSc in Environmental and Biological Sciences from University of Cape Town, South Africa. Her research interest lies in the intersection of climate adaptation, disaster risk reduction, and decision-making. Besides her dissertation research on sea-level rise impact modeling, she is also involved in the development of new online tools and methods that aim to support hazard risk reduction and adaptation efforts at the community level. In 2013, she worked with the United Nations University’s Institute for Water, Environment, and Health where she was involved in a knowledge mobilization initiative and the development of a climate change literacy online course designed for practitioners in developing nations.