title: Impacts of representing sea-level rise uncertainty on future flood risks: An example from San Francisco Bay.
abstract: Rising sea levels increase the probability of future coastal flooding. Many decision-makers use risk analyses to inform the design of sea-level rise (SLR) adaptation strategies. These analyses are often silent on potentially relevant uncertainties. For example, some previous risk analyses use the expected, best, or large quantile (i.e., 90%) estimate of future SLR. Here, we use a case study to quantify and illustrate how neglecting SLR uncertainties can bias risk projections. Specifically, we focus on the future 100-yr (1% annual exceedance probability) coastal flood height (storm surge including SLR) in the year 2100 in the San Francisco Bay area. We find that accounting for uncertainty in future SLR increases the return level (the height associated with a probability of occurrence) by half a meter from roughly 2.2 to 2.7 m, compared to using the mean sea-level projection. Accounting for this uncertainty also changes the shape of the relationship between the return period (the inverse probability that an event of interest will occur) and the return level. For instance, incorporating uncertainties shortens the return period associated with the 2.2 m return level from a 100-yr to roughly a 7-yr return period (∼15% probability). Additionally, accounting for this uncertainty doubles the area at risk of flooding (the area to be flooded under a certain height; e.g., the 100-yr flood height) in San Francisco. These results indicate that the method of accounting for future SLR can have considerable impacts on the design of flood risk management strategies.
authors.affiliation: Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
authors.forename: Kelsey L
authors.identifier: http://orcid.org/0000-0001-9513-2636
authors.initials: KL
authors.lastname: Ruckert
authors.affiliation: Department of Geosciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
authors.forename: Perry C
authors.initials: PC
authors.lastname: Oddo
authors.affiliation: Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America.|Department of Geosciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America.|Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Klaus
authors.initials: K
authors.lastname: Keller
mesh_terms: D000465:Algorithms; D061006:Bays; D057231:Climate Change; D004189:Disaster Planning / Q000379:methods* / Q000639:trends; D055868:Floods; D005544:Forecasting; D005843:Geography; D008962:Models, Theoretical; D009792:Oceans and Seas; D018570:Risk Assessment / Q000379:methods* / Q000639:trends; D012307:Risk Factors; D012308:Risk Management / Q000379:methods* / Q000639:trends; D012495:San Francisco; D013997:Time Factors; D035501:Uncertainty