A geostatistical extreme-value framework for fast simulation of natural hazard events.
We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student'st-process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements.
College of Engineering, Mathematics and Computer Science , University of Exeter , Laver Building, North Park Road, Exeter, EX4 4QE, UK.
Benjamin D
0000-0003-0215-8189
BD
Youngman
College of Engineering, Mathematics and Computer Science , University of Exeter , Laver Building, North Park Road, Exeter, EX4 4QE, UK.
David B
DB
Stephenson
Student’s -process; generalized Pareto distribution; quantile regression; spatial statistics; statistics of extremes; wind gust data