Flood estimation can be classified into two categories, i.e. flood prediction and flood forecasting. Flood prediction is used for the estimation of design floods, which are floods associated with a degree of risk of being equalled or exceeded. Predictions are needed for the design and construction of infrastructure that are at risk to flowing water. Flood forecasting is used for the estimation of flood flows from an impending and/or occurring rainfall event (i.e. the estimation of the magnitude of future flood flows with reference to a specific time in the future). These are needed by catchment and disaster managers for the mitigation of flood damage. The estimation of flood magnitudes for flood forecasting requires the specific knowledge of prevailing surface conditions which are associated with the processes of rainfall conversion into flood runoff. In order to best achieve this, a distributed model (in order to exploit remotely sensed data and capture the spatial scale of the phenomenon) is used to continuously update the surface conditions that are important in this conversion process. This dissertation focuses on both flood estimation categories. In the first part of the dissertation, attention is given to the improvement of two simple event-based design flood prediction methods currently in use by design practitioners, namely the regional maximum flood (RMF) and the rational formula (RF) by comparison with statistically modelled historical flood data. The second part of the dissertation lays the theoretical and practical foundation for the implementation of a fully distributed physically-based rainfall-runoff model for real-time flood forecasting in South Africa. The TOPKAPI model was chosen for this purpose. This aspect of the research involved assimilating the literature on the model, testing the model and gathering and preparing of the input data required by the model for its eventual application in the Liebenbergsvlei catchment. The practical application of the model is left for a follow-up study. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2007. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2007

MSc (Statistics) / Department of Statistics / 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