Evolving extreme events caused by climate change: A tail based Bayesian approach for extreme event risk analysis

Abstract

Natural hazards are of significant concern for engineering development in the offshore environment. Climate change phenomena are making these concerns even greater. The frequency and extent of natural hazards are undesirably evolving over time; so risk estimation for such events require special consideration. In most cases the existing extreme models (based on the extreme value theory) are unable to capture the changing frequency and extremeness of natural hazards. To capture the evolving frequency and extremeness of natural hazards and their effects on offshore process operations, an advanced probabilistic approach is proposed in this paper. The approach considers a heavy right tail probability model. The model parameter is estimated through the Bayesian inference. Hill and the SmooHill estimators are used to evaluate the lowest and highest exponent of the probability model. The application of the approach is demonstrated through extreme iceberg risk analysis for the Jeanne d’Arc basin. This study shows climate change or global warming is causing to appear a significant number of icebergs every year in the study area. Offshore structures are often designed to withstand the impact of 1 MT icebergs weight; however, the study observes large icebergs (10 MT weight) are sighted in recent years (14% of the total number of cited icebergs for the period of 2002–2017). As a result, the design philosophy needs to be revised. The proposed risk-based approach provides a robust design criterion for offshore structures.