Input data for short-term water level forecasting at 3 stations near HWY 37, Sonoma/Marin County, California

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.