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Projections of storm surge return levels are a basic requirement foreffective management of coastal risks. A common approach to estimate hazardsposed by extreme sea levels is to use a statistical model, which may use a timeseries of a climate variable as a covariate to modulate the statistical modeland account for potentially nonstationary storm surge behavior. Previous workusing nonstationary statistical approaches, however, has demonstrated theimportance of accounting for the many inherent modeling uncertainties.Additionally, previous assessments of coastal flood hazard using statisticalmodeling have typically relied on a single climate covariate, which likelyleaves out important processes and leads to potential biases. Here, I employupon a recently developed approach to integrate stationary and nonstationarystatistical models, and examine the effects of choice of covariate time serieson projected flood hazard. Furthermore, I expand upon this approach bydeveloping a nonstationary storm surge statistical model that makes use ofmultiple covariate time series: global mean temperature, sea level, NorthAtlantic Oscillation index and time. I show that a storm surge model thataccounts for additional processes raises the projected 100-year storm surgereturn level by up to 23 centimeters relative to a stationary model or one thatemploys a single covariate time series. I find that the total marginal modellikelihood associated with each set of nonstationary models given by thecandidate covariates, as well as a stationary model, is about 20%. Theseresults shed light on how best to account for potential nonstationary coastalsurge behavior, and incorporate more processes into surge projections. Byincluding a wider range of physical process information and consideringnonstationary behavior, these methods will better enable modeling efforts toinform coastal risk management.

An Integration and Assessment of Covariates of Nonstationary Storm Surge Statistical Behavior by Bayesian Model Averaging