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, 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.

Automated summary

Statistical models that accurately capture extreme rainfall events in a large geographical scale need to consider non-stationary dependence structures, and clustering stations into regions of similar extremal dependence can be an effective approach. By partitioning stations and fitting models to each region, researchers can visualize and evaluate the effectiveness of the approach in addressing the challenges of spatial dependence on a large scale. This work serves as a foundation for future projects dealing with non-stationary spatial dependence in extreme events.