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While there is high certainty that chronic coastal hazards like floodingand erosion, are increasing due to climate change induced sea-levelrise, there is high uncertainty surrounding the timing, intensity, andlocation of future hazard impacts. Assessments that quantify theseaspects of future hazards are critical for adaptation planning under achanging climate and can reveal new insights into the drivers of coastalhazards. In particular, probabilistic simulations of future hazardimpacts can improve these assessments by explicitly quantifyinguncertainty and by better simulating dependence structures between thecomplex multivariate drivers of hazards. In this study, a regional-scaleprobabilistic assessment of climate change induced coastal hazards isconducted for the Cascadia region, USA during the 21st century. Threeco-produced hazard proxies for beach safety, erosion, and flooding arequantified to identify areas of high hazard impacts and determine hazarduncertainty under three sea-level rise scenarios. A novel chroniccoastal hazard hotspot indicator is introduced that identifies areasthat may experience significant increases in hazard impacts compared topresent day conditions. We find that Southern Cascadia and NorthernWashington have larger hazard impacts and hazard uncertainty due totheir morphologic setting. Erosional hazards, relative to beach safetyand coastal flooding, will increase the most in Cascadia during the 21stcentury under all sea-level rise scenarios. Finally, we find that hazarduncertainty associated with wave and water level variability exceeds theuncertainty associated with sea-level-rise until the end of the century. 

Regional scale assessments of future chronic coastal hazard impacts are critical tools for adaptation planning under a changing climate. Probabilistic simulations of hazard impacts can improve these assessments by explicitly attempting to quantify uncertainty and by better simulating dependence between complex multivariate drivers of hazards. In this study, probabilistic future timeseries of total water levels (TWLs) are generated from a stochastic climate emulator (TESLA; Anderson et al., 2019) for the Cascadia region, USA for use in a chronic hazard impact assessment. This assessment focuses on three hazard metrics: collision, overtopping, and beach safety, and also introduces a novel hotspot indicator to identify areas that may experience dramatic changes in hazard impacts compared to present day conditions. Results are presented for a subset of the Cascadia region (Rockaway Beach Littoral Cell, Oregon) to demonstrate the power of the probabilistic impact assessment approach. The results highlight how useful spatially varying, scenario-based hazard impacts assessments and hotspot indicators are for identifying which areas and types of hazards may require increased adaptation support. This approach enables us to piece apart the relative uncertainty of hazards as driven by SLR versus natural variability (caused by variation in climate, weather, and hydrodynamic drivers). 

Increasing exposure to coastal flood hazards will potentially induce an enormous socio‐economic toll on vulnerable communities. To accurately characterize the hazard, we must consider both natural water level variability and climate change‐induced sea‐level rise. In this study, we develop a paleo‐proxy‐based reconstruction of coastal flood events over the last 500 yr to capture natural water level variability and superimpose that reconstruction onto expected sea‐level rise to explore interannual and multidecadal variability in plausible future coastal flood risk. We first develop reconstructions of leading principal components (PCs) of sea surface temperature anomalies from 1500 CE onwards, using tree‐ring, coral, and sclerosponge chronology‐based El Niño Southern Oscillation reconstructions as predictors in a wavelet autoregression model. These reconstructions of PCs are then used in a stochastic water level emulator to develop ensemble simulations of hourly still water levels (SWLs) in the San Francisco Bay. The emulator accounts for multiple relevant processes, including monthly mean sea level (MMSL) anomalies, storm surge, and tide, all varying at different timescales. Accounting for natural variability in water levels over 1500–2000 CE increases coastal flood risk beyond that suggested by instrumental records alone. When superimposed on 0.22 m of sea‐level rise (approximately the amount experienced over the previous century), the simulations show that while high tides and large storm surges cause the smaller extreme SWLs, the larger extreme SWLs occur during concurrent high MMSL, high tides, and significant storm surges. Our findings thus highlight the need to consider natural water level variability for coastal adaptation and planning.