Abstract:
Haulage trucks and cross border traders cross through Beitbridge border post from
landlocked countries such as Zimbabwe and Zambia for the sake of trading. Because of
global warming, South Africa has lately been experiencing extreme weather patterns
in the form of very high temperatures and heavy rainfall. Evidently, in 2013 tra c
could not cross the Limpopo River because water was
owing above the bridge.
For planning, its important to predict the likelihood of such events occurring in
future. Extreme value models o er one way in which this can be achieved. This study
identi es suitable distributions to model the annual maximum heights of Limpopo
river at Beitbridge border post. Maximum likelihood method and the Bayesian
approach are used for parameter estimation.
The r -largest order statistics was also used in this dissertation. For goodness of
t, the probability and quantile- quantile plots are used. Finally return levels are
calculated from these distributions.
The dissertation has revealed that the 100 year return level is 6.759 metres using
the maximum likelihood and Bayesian approaches to estimate parameters. Empirical
results show that the Fr echet class of distributions ts well the
ood heights data at
Beitbridge border post.
The dissertation contributes positively by informing stakeholders about the socio-
economic impacts that are brought by extreme
flood heights for Limpopo river at Beitbridge border post
