Specification of realistic environmental design conditions for marine structures is of fundamental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, H S . This analysis is complicated by two effects. Firstly, H S exhibits temporal dependence. Secondly, the characteristics of H S sp are non-stationary with respect to multiple covariates, particularly wave direction and season. We develop directional-seasonal design values for storm peak significant wave height ( H S sp ) by estimation of, and simulation under a non-stationary extreme value model for H S sp . Design values for significant wave height ( H S ) are estimated by simulating storm trajectories of H S consistent with the simulated storm peak events. Design distributions for individual maximum wave height ( H max ) are estimated by marginalisation using the known conditional distribution for H max given H S . Particular attention is paid to the assessment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

Careful modelling of covariate effects is critical to reliable specification of design criteria. We present a spline based methodology to incorporate spatial, directional, temporal and other covariate effects in extreme value models for environmental variables such as storm severity. For storm peak significant wave height events, the approach uses quantile regression to estimate a suitable extremal threshold, a Poisson process model for the rate of occurrence of threshold exceedances, and a generalised Pareto model for size of threshold. Multidimensional covariate effects are incorporated at each stage using penalised tensor products of B-splines to give smooth model parameter variation as a function of multiple covariates. Optimal smoothing penalties are selected using cross-validation, and model uncertainty is quantified using a bootstrap resampling procedure. The method is applied to estimate return values for a large spatial neighbourhood of locations off the North West Shelf of Australia, incorporating spatial and directional effects.

Characterising the joint distribution of extremes of significant wave height and wind speed is critical for reliable design and assessment of marine structures. The extremal dependence of pairs of oceanographic variables can be characterised using one of a number of summary statistics, which describe the two different types of extremal dependence. Quantifying the type of extremal dependence is an essential pre-requisite to joint or spatial extreme value modelling, and ensures that appropriate model forms are employed. We estimate extremal dependence between storm peak significant wave height and storm peak wind speed ( H s , WS ) for locations in a region of the northern North Sea. However, since the extremal dependence itself may vary with storm direction, we introduce new covariate-dependent forms of the extremal dependence measures that account for the direction of the storm. We discuss the implications of all of the estimates for marine design, including specification of joint design criteria for extended spatial domains, and statistical downscaling to incorporate the effects of climate change on design specification.