@article{fdi:010079834,
  title = {{G}eneralized {P}areto processes for simulating space-time extreme events : an application to precipitation reanalyses},
  author = {{P}alacios-{R}odriguez, {F}. and {T}oulemonde, {G}. and {C}arreau, {J}ulie and {O}pitz, {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}o 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. {W}e 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. {O}ur framework draws sound theoretical justification from extreme value theory, building on generalized {P}areto limit processes arising as limits for event magnitudes exceeding a high threshold. {S}pecifically, 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. {F}or illustration on hourly gridded precipitation data in {M}editerranean {F}rance, 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.},
  keywords = {{E}xtreme-value theory ; {P}recipitation ; {R}isk analysis ; {S}pace-time {P}areto processes ; {S}tochastic simulation},
  booktitle = {},
  journal = {{S}tochastic {E}nvironmental {R}esearch and {R}isk {A}ssessment},
  volume = {34},
  numero = {12},
  pages = {2033--2052},
  ISSN = {1436-3240},
  year = {2020},
  DOI = {10.1007/s00477-020-01895-w},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079834},
}