  Projections of storm surge return levels are a basic requirement for
effective management of coastal risks. A common approach to estimate hazards
posed by extreme sea levels is to use a statistical model, which may use a time
series of a climate variable as a covariate to modulate the statistical model
and account for potentially nonstationary storm surge behavior. Previous work
using nonstationary statistical approaches, however, has demonstrated the
importance of accounting for the many inherent modeling uncertainties.
Additionally, previous assessments of coastal flood hazard using statistical
modeling have typically relied on a single climate covariate, which likely
leaves out important processes and leads to potential biases. Here, I employ
upon a recently developed approach to integrate stationary and nonstationary
statistical models, and examine the effects of choice of covariate time series
on projected flood hazard. Furthermore, I expand upon this approach by
developing a nonstationary storm surge statistical model that makes use of
multiple covariate time series: global mean temperature, sea level, North
Atlantic Oscillation index and time. I show that a storm surge model that
accounts for additional processes raises the projected 100-year storm surge
return level by up to 23 centimeters relative to a stationary model or one that
employs a single covariate time series. I find that the total marginal model
likelihood associated with each set of nonstationary models given by the
candidate covariates, as well as a stationary model, is about 20%. These
results shed light on how best to account for potential nonstationary coastal
surge behavior, and incorporate more processes into surge projections. By
including a wider range of physical process information and considering
nonstationary behavior, these methods will better enable modeling efforts to
inform coastal risk management.
