Abstract
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.
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Acknowledgments
Kate Saunders would like to thank Peter Taylor for his support and guidance throughout the course of her Ph.D, during which this research was undertaken. She would also like to thank Phillippe Naveau for his helpful suggestions and guidance during the onset of this work.
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Saunders, K.R., Stephenson, A.G. & Karoly, D.J. A regionalisation approach for rainfall based on extremal dependence. Extremes 24, 215–240 (2021). https://doi.org/10.1007/s10687-020-00395-y
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DOI: https://doi.org/10.1007/s10687-020-00395-y