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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. 

Increasing coastal flooding threatens urban centers worldwide. Projections of physical damages to structures and their contents can characterize the monetary scale of risk, but they lack relevant socioeconomic context. The impact of coastal flooding on communities hinges not only on the cost, but on the ability of households to pay for the damages. Here, we repurpose probabilistic risk assessment to analyze the monetary and social risk associated with coastal flooding in the San Francisco Bay Area for 2020–2060. We show that future coastal flooding could financially ruin a substantial number of households by burdening them with flood damage costs that exceed discretionary household income. We quantify these impacts at the census block group scale by computing the percentage of households without discretionary income, before and after coastal flooding costs. We find that for several coastal communities in San Mateo County more than 50% of households will be facing financial instability, highlighting the need for immediate policy interventions that target existing, socially produced risk rather than waiting for potentially elusive certainty in sea level rise projections. We emphasize that the percentage of financially unstable households is particularly high in racially diverse and historically disadvantaged communities, highlighting the connection between financial instability and inequity. While our estimates are specific to the San Francisco Bay Area, our granular, household‐level perspective is transferable to other urban centers and can help identify the specific challenges that different communities face and inform appropriate adaptation interventions.