Coastal flood hazard zones and the design of coastal defenses are often devised using the maximum recorded water level or a ''design'' event such as the 100 year return level, usually projected from observed extremes. Despite technological advances driving more consistent instrumental records of waves and water levels, the observational record may be short, punctuated with intermittent gaps, and vary in quality. These issues in the record often preclude accurate and robust estimates of extreme return level events. Here we present a total water level full simulation model (TWL-FSM) that simulates the various components of TWLs (waves, tides, and nontidal residuals) in a Monte Carlo sense, taking into account conditional dependencies that exist between the various components. Extreme events are modeled using nonstationary extreme value distributions that include seasonality and climate variability. The resulting synthetic TWLs allow for empirical extraction of return level events and the ability to more robustly estimate and assess present-day flood and erosion hazards. The approach is demonstrated along a northern Oregon, USA littoral cell but is applicable to beaches anywhere wave and water level records or hindcasts are available. Simulations result in extreme 100 year TWL return levels as much as 90 cm higher than those extrapolated from the ''observational'' record. At the Oregon site, this would result in 30% more coastal flooding than the ''observational'' 100 year TWL return level projections. More robust estimates of extreme TWLs and tighter confidence bounds on return level events can aid coastal engineers, managers, and emergency planners in evaluating exposure to hazards.

The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.

To better understand how individual processes combine to cause flooding and erosion events, we investigate the relative contribution of tides, waves, and nontidal residuals to extreme total water levels (TWLs) at the shoreline of U.S. West Coast sandy beaches. Extreme TWLs, defined as the observed annual maximum event and the simulated 100 year return level event, peak in Washington, and are on average larger in Washington and Oregon than in California. The relative contribution of wave‐induced and still water levels (SWL) to the 100 year TWL event is similar to that of the annual maximum event; however, the contribution of storm surge to the SWL doubles across events. Understanding the regional variability of TWLs will lead to a better understanding of how sea level rise, changes in storminess, and possible changes in the frequency of major El Niños may impact future coastal flooding and erosion along the U.S. West Coast and elsewhere.

As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.