Low-lying coastal highways are susceptible to flooding as the sea level rises. Flooding events already impact some highways, like Highway 37 which runs across the lowlands at the northern end of San Francisco Bay and is crossed by several creeks/rivers. Short-term operational forecasts are required to enable planning for traffic disruption, evacuation, and protection of property and infrastructure. Traditional physically based numerical models have great predictive capability but require extensive datasets and are computationally expensive which limits their ability to do short-term forecasting. Here we develop a data-driven, site-specific method that can be implemented at multiple vulnerable sites throughout San Francisco Bay and other low-lying coastal areas across the State of California. This method is based on direct observations of the water level at the site and is independent of large computer simulations. For this study, we use a relatively simple statistical model (multiple-linear regression) combined with a forecast error correction inspired by an autoregressive moving average method (ARMA) commonly used in time-series forecasting. The model is then used to produce a 4-day water level forecast at 3 stations near HWY 37, Sonoma/Marin County, California.
