The economic impact of a severe storm event stems from correlated random occurrences of losses, where each loss depends primarily on the local intensity of the event (measured in terms of wind speed, runoff, river flow, etc.) at the local level. However, the total outcome from the event is in fact more complex and is determined by the spatial clustering of the separate local extremes and the evolution of these extremes and their dependence over time. Consequently, while the risk to a single location can be estimated by analyzing the local extreme series using existing statistical tools, a collective risk assessment (especially over large domains such as the United States, Western Europe, China, etc.) requires not only studying the local extremes, but also their coherence in the space-time continuum. Despite the fact that collective risk assessment lies at the heart of natural catastrophe risk management and has been the focus of the catastrophe modeling community for at least two decades, the science of quantifying and modeling spatially and temporally-dependent extremes for robust assessment of collective risk is yet to be developed.
We seek presentations relevant to the statistical analysis and modeling of coherent hydrometeorological extremes and, in general, to the assessment of collective risk related to hydrometeorological phenomena. Areas of special interest include, but are not limited to: 1) Statistical means of qualifying and quantifying coherent hydrometeorological extremes in space and time; 2) Geostatistical models for spatially and temporally dependent extremes (abstracts reporting the geospatial use of copulas are particularly encouraged); 3) Simulations and practical implementations of statistical models for spatially-temporally dependent extremes.
Abstracts are sought from those involved in both the theoretical and practical aspects of the above or related topics.