Climate change is having increasing impacts on human activities and communities. For example, coastal flooding events seem to occur more often. It is essential that society develops the capability to estimate the magnitude and impact of these extreme events in order to adapt to them and their changing tendencies, and thus to try to mitigate their future impacts. 

 

In response to this need, this session is proposed to stimulate discussion and exchange related to statistical methodologies and various relevant areas of environmental science, with a focus on maritime studies. Statistical modeling of complex extreme events has developed rapidly in recent years; theoretical analysis has transitioned to statistical tools with many environmental applications. We hope to examine the state of the art in present statistical tools, to identify challenges and opportunities, and to motivate new research interactions and developments. 

 

Presentations from all areas of environmental science are welcome. For example, marine mesoscale extreme events are the result of the combination of environmental factors that can be related. Coastal flooding can result from nonlinear interactions of multiple oceanographic, hydrological, geological, and meteorological processes (e.g. tides, sea-level anomalies, storm surge, waves, winds, fluvial discharges, precipitation, and land subsidence). Moreover, although the exceptional extreme variation of a single process (e.g. storm surge) can result in coastal flooding, the more normal situation is that a combination of notably high values of more than one process constitutes the precursor mechanism, thereby making a compound extreme event. Additional topics can consider extremes related to lake-effect (or lake modified) systems and coastal extreme weather.  Moreover, the relevant processes are not stationary, but vary with season, and on time scales of years, decades, etc., with changing climate. Thus, characteristics of an extreme event may be described as the extreme nature of the impact rather than the individual component factors, the multivariate nature of the impact, and the statistical dependence of component factors.