Conditional Modelling of Spatio-Temporal Extremes for Red Sea Surface Temperatures

Emma S. Simpson, Jennifer L. Wadsworth

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Recent extreme value theory literature has seen significant emphasis on the modelling of spatial extremes, with comparatively little consideration of spatio-temporal extensions. This neglects an important feature of extreme events: their evolution over time. Many existing models for the spatial case are limited by the number of locations they can handle; this impedes extension to space-time settings, where models for higher dimensions are required. Moreover, the spatio-temporal models that do exist are restrictive in terms of the range of extremal dependence types they can capture. Recently, conditional approaches for studying multivariate and spatial extremes have been proposed, which enjoy benefits in terms of computational efficiency and an ability to capture both asymptotic dependence and asymptotic independence. We extend this class of models to a spatio-temporal setting, conditioning on the occurrence of an extreme value at a single space-time location. We adopt a composite likelihood approach for inference, which combines information from full likelihoods across multiple space-time conditioning locations. We apply our model to Red Sea surface temperatures, show that it fits well using a range of diagnostic plots, and demonstrate how it can be used to assess the risk of coral bleaching attributed to high water temperatures over consecutive days.
Original languageEnglish (US)
Pages (from-to)100482
JournalSpatial Statistics
StatePublished - Feb 11 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-02-16
Acknowledged KAUST grant number(s): OSR-CRG2017-3434
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3434.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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