Statistical Modeling of Spatial Extremes through Max-Stable Process Models: Application to Extreme Rainfall Events in South Africa
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 10
Abstract
A quantification of the spatial dependence among extremes of rainfall events is important for investigating the properties of intense, extreme weather-related hazards. Extreme value theory has been widely applied to weather variables, and rigorous approaches have also been employed to investigate dependence structures among extreme values in space. To investigate the joint dependence of extreme rainfall events in space, spatial dependence modeling through max-stable process models has been considered to analyze extreme rainfall data across selected weather stations in South Africa. The analysis was also used to illustrate how the geographic and temporal covariates can affect the extreme rainfall field and subsequently the distribution of spatial random variables. The results revealed significant trends in the time-heterogeneous spatially fitted generalised extreme value (GEV) distribution. In addition, the max-stable process model predicted the probability of annual maximum rainfall and spatial contrasts of extreme rainfall characteristics across selected weather stations in South Africa. The results indicated that the annual extreme rainfall across selected weather stations in South Africa exhibits noticeable spatial variability. This study also depicted the significance of spatial max-stable process models over the univariate modeling and how models of spatial extremes with dependence can be used to better understand the probability of extreme rainfall events and to account for the influence of temporal covariates. Results obtained in this study have essential scientific and practical applications in monitoring hydrological-related risks for mitigation and adaptation strategies.
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Data Availability Statement
Some or all models or code that support the findings of this study are available from the corresponding author upon reasonable request. Some or all data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgements.
Acknowledgments
The first author is very indebted to the University of South Africa for the financial support. The authors also would like to thank the South African Weather Service for providing the data.
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Journal of Hydrologic Engineering
Volume 26 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
History
Received: Feb 8, 2021
Accepted: Jun 9, 2021
Published online: Aug 13, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 13, 2022
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