Like most coastal states in the U.S., California’s shoreline communities and ecosystems have been exposed to flooding related to sea level rise and storms, which jeopardize their persistence and well-being. Shoreline transportation is especially vulnerable in certain places to flooding and failure, and because it is part of a continuously used network with little redundancy, it transfers its vulnerability to regional transportation networks. Forward-projected inundation/flooding risk is typically modeled at coarse spatial and temporal scales, which are useful at regional and decadal scales, but less useful for coastal managers and flood responders. This project improved assessment of both overall probability and short-term forecasts of water level for specific locations in San Francisco Bay that are vulnerable to flooding associated with sea level rise. The authors have developed probability assessment and forecasts through developing data-based, site-specific, model-independent approaches, which can be compared with and help to improve regional models of coastal flooding (e.g., CoSMoS). Water level data were collected across fine-scale arrays at fluvial-bay junctures in Sonoma and Marin Counties. The primary analysis is based on deconstructing water level records into multiple quasi-independent signals, which can be better predicted and recombined to produce probability of extreme events and to produce short-term forecasts during a flooding event based on predicted weather, wind, rain, and tide. In addition, real-time water level data are now available to first responders at critical locations in Novato Creek and Petaluma River when there is potential for flooding, as well as during a flood event. This is a pilot project that could be replicated at many other vulnerable locations around San Francisco Bay and elsewhere.

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