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Modelling temperature extremes in the Limpopo province: bivariate time-varying threshold excess approach
Daniel Maposa1, Anna M Seimela1, Caston Sigauke2
1Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga Polokwane, South Africa.
Natural Hazards (Dordrecht, Netherlands)
|February 22, 2021
Summary
This study models extreme temperature dependence in South Africa using bivariate conditional extremes and time-varying thresholds. It reveals significant positive and negative temperature dependencies between meteorological stations, crucial for climate change adaptation.
Area of Science:
- Extreme value theory
- Climate science
- Meteorology
Background:
- Assessing multivariate extremal dependence and threshold selection are critical challenges in extreme value theory.
- Extreme temperatures and heat waves pose significant economic and health risks, particularly in developing countries like South Africa.
- Climate change necessitates advanced methods for understanding and predicting extreme weather events.
Purpose of the Study:
- To apply bivariate conditional extremes modelling with a time-varying threshold to analyze extreme temperature dependence in South Africa's Limpopo province.
- To capture climate change effects on extreme temperature data.
- To model extremal dependence of maximum temperatures across four meteorological stations.
Main Methods:
- Utilized bivariate conditional extremes modelling.
- Implemented a time-varying threshold approach.
- Analyzed monthly maximum temperature data from Mara, Messina, Polokwane, and Thabazimbi stations (1994-2009).
Main Results:
- Identified significant positive and negative extremal temperature dependencies between pairs of meteorological stations.
- Found strong positive dependence of Thabazimbi's extreme temperatures on Mara's.
- Observed strong negative dependence of Polokwane's extreme temperatures on Messina's.
Conclusions:
- The combined bivariate conditional extremes and time-varying threshold model effectively captures extremal temperature dependence.
- Findings provide crucial information for meteorologists, climatologists, agriculturalists, and energy sector planners in South Africa.
- The study highlights the importance of understanding localized temperature extremes in the context of climate change.
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