A Framework for Classifying and Assessing Sea Level Rise Risk
Population risk assessments of sea level rise are key to understanding the impacts of climate change on coastal communities and necessary for adaptation planning. Future sea level rise exposes coastal populations to a spectrum of risk, but assessments often define exposure narrowly, such as areas experiencing permanent inundation only. We reviewed the most common sea level rise exposure assessment methods and identified three widely used spatial definitions of physical exposure risk: mean
... re risk: mean higher high water, the 100-year floodplain, and the low-elevation coastal zone. Taken individually, each treat risk to sea level rise as binary (affected or not affected), resulting in narrow definitions, homogenizing risk and exposure across space and time. We present a framework that integrates and smooths these classifications under a single continuous metric. To do so, we advance a sophisticated spatiotemporal flood-modeling approach -- expected annual exposure -- based on a probabilistic spatial envelope that unifies spatial extents between the high-tide line and the 10,000-year floodplain. We show that the effects from sea level rise will impact far more people far sooner than previously thought. In particular, our results suggest that single, binary extent assessments either underestimate or overestimate the magnitude of the at-risk populations while also spatially homogenizing the impacts to sea level rise. Our advance on modeling annual exposure provides a more robust and holistic assessment of the populations most at-risk to flooding from sea level rise. This typology can be used to guide new research connecting risk of sea level rise to related adaptation policies and planning.