  Sea-level rise and associated flood hazards pose severe risks to the millions
of people globally living in coastal zones. Models representing coastal
adaptation and impacts are important tools to inform the design of strategies
to manage these risks. Representing the often deep uncertainties influencing
these risks poses nontrivial challenges. A common uncertainty characterization
approach is to use a few benchmark cases to represent the range and relative
probabilities of the set of possible outcomes. This has been done in coastal
adaptation studies, for example, by using low, moderate, and high percentiles
of an input of interest, like sea-level changes. A key consideration is how
this simplified characterization of uncertainty influences the distributions of
estimated coastal impacts. Here, we show that using only a few benchmark
percentiles to represent uncertainty in future sea-level change can lead to
overconfident projections and underestimate high-end risks as compared to using
full ensembles for sea-level change and socioeconomic parametric uncertainties.
When uncertainty in future sea level is characterized by low, moderate, and
high percentiles of global mean sea-level rise, estimates of high-end (95th
percentile) damages are underestimated by between 18% (SSP1-2.6) and 46%
(SSP5-8.5). Additionally, using the 5th and 95th percentiles of sea-level
scenarios underestimates the 5-95% width of the distribution of adaptation
costs by a factor ranging from about two to four, depending on SSP-RCP pathway.
The resulting underestimation of the uncertainty range in adaptation costs can
bias adaptation and mitigation decision-making.
