The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. The study characterized rainfall variability across the Lake Kariba catchment area, followed by describing prediction models for seasonal rainfall totals over the catchment and for inflows into Lake Kariba. The thesis therefore improved our understanding of rainfall variations over central southern Africa and provided evidence on how seasonal forecasts can be applied in order to potentially improve decision making in dam management.
The prediction of the seasons in which floods or droughts are most likely to occur involves studying the characteristics of rainfall and inflows within these extreme seasons. The study started off by analyzing monthly rainfall data through statistical analysis. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used low-level atmospheric circulation of a fully coupled ocean-atmosphere general circulation model over southern Africa, statistically downscaled to seasonal rainfall totals over the catchment. The verification of hindcasts showed that rainfall over the catchment is predictable at extended lead-times.
Seasonal climate forecasts need to be integrated into application models in order to help with decision-making processes. The use of hydro-meteorological models may be proven effective for reservoir operations since accurate and reliable prediction of reservoir inflows can provide balanced solution to the problems faced by dam or reservoir managers. In order to reliably predict reservoir inflows for decision-making, the study investigated the use of a combination of physical and empirical models to predict seasonal inflows into the Lake. Two predictions systems were considered. First, antecedent seasonal rainfall totals over the upper Zambezi catchment were used as predictors in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method used predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model downscaled to the inflows. Inflow hindcasts performed best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season).