Estimation of Extreme Sea Levels in a Tide-Dominated Environment Using Short Data Records

Abstract

A new method for estimating extreme sea levels from short sea-level records in a tide-dominated environment is presented. A short sea-level record is first decomposed into its constituent components such as tide, mean level of the sea, and storm surge. Monte Carlo simulations are then incorporated into an empirical simulation technique to randomly recombine the components to produce an annual series of sea levels at high tide from which the annual maximum is selected. The yearly simulation is repeated many thousands of times to yield robust statistics on extreme values. Comparison of the method with the traditional extreme-value analysis of annual maximum sea levels for a 33-year record shows that the methods give similar results. The method is likely to be most useful for estimation of extreme sea levels at locations where the available sea-level record is short (