Thesis (M.Sc. (Statistics )) -- University of Limpopo, 2021 / Temperature extremes have a crucial impact on agricultural, economic, health
and energy sectors due to the occurrence of climate extreme events such as
heat waves and cold waves. Limpopo province is among the hottest provinces
of South Africa and experiences little rainfall which affect the water availabil ity, food production and biodiversity. In the Limpopo province, temperature
extremes are expected to become more frequent as a result of climate change.
The aim of this study was to model temperature extremes in the Limpopo
province of South Africa using extreme value theory (EVT). The stationarity of
the data was tested using augmented Dickey-Fuller (ADF), Phillips-Peron (PP)
and Kwiatkowski-Phillips-Schmit-Shin (KPSS). Four candidate parent distri butions: normal, log-normal, gamma and Weibull distributions, were fitted to
the average monthly maximum and minimum daily temperatures. Prior to the
selection of the parent distributions, the data set at each station was subjected
to normality test using the Shapiro-Wilk (SW) and Jarque-Bera (JB) tests. The
stationarity and normality tests revealed that the maximum and minimum
temperature data series at all the stations are neither stationary nor normally
distributed. Akaike information criterion (AIC) and Bayesian information cri terion (BIC) were used to select the best fitting distribution at a particular site.
The findings revealed that both maximum and minimum temperatures series
at all the stations belong to the Weibull domain of attraction. The findings from
the Mann-Kendall (M-K) test and time series plots trend analyses showed that
there is a monotonic downward and upward long-term trend in minimum and
maximum temperature data, respectively. Two fundamental approaches of EVT, block maxima and peaks-over-threshold
(POT), were used in this dissertation. The generalised extreme value (GEV),
generalised Pareto (GP) and Poisson point process distributions were fitted to
the data set for each station. In order to account for climate change impact,
non-stationary models were considered with Seasonal Oscillation Index (SOI)
as covariates of the parameters of the GEV distribution. The findings revealed
that both the maximum and minimum temperature data can be modelled by
the Weibull family of distribution. The EVT return level analysis findings of
above 400C for maximum temperature suggests impending heat waves and
droughts in the Limpopo province. The bivariate conditional extremes ap proach with a time-varying threshold was used. The findings revealed both
significant positive and negative extremal dependence in some pairs of meteo rological stations. The findings of this study play an important role in revealing
information useful to meteorologists, climatologists, agriculturalists and plan ners in the energy sector where temperature extremes play an important role.
The scientific contribution of this study was to reduce the risk and impact
of temperature extremes on agricultural, energy and health sectors in the
Limpopo province. An understanding of temperature extremes will help gov ernment and other stakeholders to formulate mitigation strategies that will
minimise the negative impact resulting from temperature extremes in the Limpopo
province. Among the major contributions of the study was the use of a pe nalised cubic smoothing spline to perform a nonlinear detrending of the tem perature data, before fitting bivariate time-varying threshold excess models
based on Laplace margins, to capture the climate change effects in the data.
Future studies may consider exploring the use of extreme value copulas, as
well as spatio-temporal dependence between temperature extremes using the
conditional extremes model of Heffernan and Tawn (2004). / DST
National Research Foundation (NRF)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3426 |
| Date | January 2021 |
| Creators | Seimela, Anna Mamodupi |
| Contributors | Maposa, D., Lesaoana, M. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xxii, 173 leaves |
| Relation |
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