# Multivariate statistical modelling of future marine storms

## TL;DR

This paper develops a multivariate, non-stationary statistical model using generalized Pareto distributions and hierarchical Archimedean copulas to characterize future marine storms and their dependence on climate change, applied to the Catalan Coast.

## Contribution

It introduces a novel non-stationary multivariate model linking storm variables and climate change, with application to a specific coastal scenario.

## Key findings

- Most storm variables decrease over time under climate change.
- Joint distribution of storm variables shows cyclical fluctuations.
- Climate dynamics have a stronger influence than climate itself on storm dependence.

## Abstract

Extreme events, such as wave-storms, need to be characterized for coastal infrastructure design purposes. Such description should contain information on both the univariate behaviour and the joint-dependence of storm-variables. These two aspects have been here addressed through generalized Pareto distributions and hierarchical Archimedean copulas. A non-stationary model has been used to highlight the relationship between these extreme events and non-stationary climate. It has been applied to a Representative Concentration Pathway 8.5 Climate-Change scenario, for a fetch-limited environment (Catalan Coast). In the non-stationary model, all considered variables decrease in time, except for storm-duration at the northern part of the Catalan Coast. The joint distribution of storm variables presents cyclical fluctuations, with a stronger influence of climate dynamics than of climate itself.