Climate change and local environmental change are exerting multiple influences on the probability of inundation of low-lying coastal lands, including flooding of developed areas as well as natural lands. These influences include sea level rise, storm surges, local wind and wave effects, changing fluvial hydrographs, and changing morphology of coastal regions. Inundation threatens the sustainability of coastal communities, infrastructure and ecosystems, requiring forward-looking quantification of the probability of coastal flooding under specific conditions. In addition to planning tools, communities need real-time information and improved short-term projections of flooding events to guide behavior during floods and reduce the negative impacts of events as they occur. One research gap that makes these types of projections very difficult is investigation of the relationships among multiple instantaneous and long-term sources of flooding: storm surge, shoreline fluvial systems, wave/wind action, extreme high tides, and sea level rise. This gap is both in the availability of fine spatial-temporal scale data on water elevations and in modeling how these multiple sources interact to create shoreline inundation and potential structural failure (e.g., of levees and highways). The objective of this project is to develop a candidate explanatory model for the combined effects of multiple sources of flooding at a fine spatial-temporal scale, in order to contribute to short-term projections of flood risk.
Like most coastal U.S. states, California’s shoreline communities and ecosystems have been exposed to flooding related to sea level rise and storms, which jeopardize their persistence. In particular, shoreline transportation is both especially vulnerable in certain places to flooding and failure and, because it is part of a continuously used network with little redundancy, 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 will improve assessment of both overall probability and short-term forecasts for specific locations in the San Francisco Bay that are vulnerable to flooding associated with sea level rise. Locations will be chosen in consultation with coastal managers (optionally may include an open-coast location). The researchers will develop 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). They will collect water level data across fine-scale arrays at fluvial-bay junctures in Sonoma and Marin Counties. One analysis will deconstruct water level records into multiple quasi-independent signals, which can be better predicted and recombined to produce probability of extreme events and also can be combined to produce short-term forecasts during a flooding event based on predicted wind, rain, and tide. Another analysis will focus on the probability of co-occurrence of fluvial and sea level flooding by resolving temporal relationships between forcing due to rain/run-off and wind/wave surges during storms. In addition, real-time data will be available to first responders at critical locations during a flooding event. This is a pilot project that could be replicated at many other vulnerable locations around the San Francisco Bay and elsewhere.