A regionalisation approach for rainfall based on extremal dependence

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, t...

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Bibliographic Details
Published in:Extremes (Boston) 2021-06, Vol.24 (2), p.215-240
Main Authors: Saunders, K. R., Stephenson, A. G., Karoly, D. J.
Format: Article
Language:English
Subjects:
Civil Engineering
Clustering
Dependence
Economics
Environmental Management
Finance
Hydrogeology
Insurance
Management
Mathematics and Statistics
Model testing
Quality Control
Rainfall
Reliability
Safety and Risk
Statistical models
Statistics
Statistics for Business
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Summary: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.
ISSN:1386-1999
1572-915X
DOI:10.1007/s10687-020-00395-y