A regionalisation approach for rainfall based on extremal dependence

K. R. Saunders*, A. G. Stephenson, D. J. Karoly

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
17 Downloads (Pure)


To mitigate the risk posed by extreme rainfall events, we require statistical models that reliably capture extremes in continuous space with dependence. However, assuming a stationary dependence structure in such models is often erroneous, particularly over large geographical domains. Furthermore, there are limitations on the ability to fit existing models, such as max-stable processes, to a large number of locations. To address these modelling challenges, we present a regionalisation method that partitions stations into regions of similar extremal dependence using clustering. To demonstrate our regionalisation approach, we consider a study region of Australia and discuss the results with respect to known climate and topographic features. To visualise and evaluate the effectiveness of the partitioning, we fit max-stable models to each of the regions. This work serves as a prelude to how one might consider undertaking a project where spatial dependence is non-stationary and is modelled on a large geographical scale.

Original languageEnglish
Pages (from-to)215-240
Number of pages26
Issue number2
Publication statusPublished - 2020


  • 60G70
  • 62D05
  • 62G32
  • 62P12
  • Climate extremes
  • Clustering
  • Extremal dependence
  • Spatial dependence


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