Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998–2063) and mid-future (2018–2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950–2015) if adaptation measures are not implemented.