A Regionalisation Approach for Rainfall based on Extremal Dependence
| Autor: | Alec Stephenson, David J. Karoly, K. R. Saunders |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences 010504 meteorology & atmospheric sciences 60G70 Economics Econometrics and Finance (miscellaneous) 01 natural sciences Statistics - Applications Clustering 010104 statistics & probability Econometrics Applications (stat.AP) 62D05 0101 mathematics Spatial dependence Cluster analysis Engineering (miscellaneous) 0105 earth and related environmental sciences Mathematics Structure (mathematical logic) Hydrogeology Regionalisation Extremal dependence Statistical model 62P12 Climate extremes 60G70 62P12 62G32 62D05 Scale (map) 62G32 |
| Zdroj: | Extremes: statistical theory and applications in science, engineering and economics, 24(2) |
| ISSN: | 1386-1999 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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