Generalized Pareto processes for simulating space-time extreme events: an application to precipitation reanalyses

被引:0
作者
F. Palacios-Rodríguez
G. Toulemonde
J. Carreau
T. Opitz
机构
[1] Universidad Complutense de Madrid,Departamento de Estadística e Investigación Operativa, Facultad de Ciencias Matemáticas
[2] Université de Montpellier,IMAG, CNRS, Inria
[3] Université de Montpellier,HydroSciences Montpellier, CNRS/IRD
[4] INRAE,Biostatistics and Spatial Processes
来源
Stochastic Environmental Research and Risk Assessment | 2020年 / 34卷
关键词
Extreme-value theory; Precipitation; Risk analysis; Space-time Pareto processes; Stochastic simulation;
D O I
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中图分类号
学科分类号
摘要
To better manage the risks of destructive natural disasters, impact models can be fed with simulations of extreme scenarios to study the sensitivity to temporal and spatial variability. We propose a semi-parametric stochastic framework that enables simulations of realistic spatio-temporal extreme fields using a moderate number of observed extreme space-time episodes to generate an unlimited number of extreme scenarios of any magnitude. Our framework draws sound theoretical justification from extreme value theory, building on generalized Pareto limit processes arising as limits for event magnitudes exceeding a high threshold. Specifically, we exploit asymptotic stability properties by decomposing extreme event episodes into a scalar magnitude variable (that is resampled), and an empirical profile process representing space-time variability. For illustration on hourly gridded precipitation data in Mediterranean France, we calculate various risk measures using extreme event simulations for yet unobserved magnitudes, and we highlight contrasted behavior for different definitions of the magnitude variable.
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页码:2033 / 2052
页数:19
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