Abstract:
In this paper, monthly, maximum seasonal, and maximum annual
hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data
series from the Kariba catchment area of the Zambezi River basin, Zimbabwe,
have been analyzed in order to determine appropriate probability distribution
models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the
Kolmogorov–Sminorv (KS), Anderson–Darling (AD), and chi-square (x2)
goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution
model for each hydrometeorological data series. A ranking metric that
uses the test statistic from the three GoF tests was formulated and used to select
the most appropriate probability distribution model capable of reproducing the
statistics of the hydrometeorological data series. Results showed that, for each
hydrometeorological data series, the best-fit probability distribution models
were different for the different time scales, corroborating those reported in the
literature. The evaporation data series was best fit by the Pearson system, the
Lake Kariba water levels series was best fit by theWeibull family of probability
distributions, and the rainfall series was best fit by the Weibull and the generalized
Pareto probability distributions. This contribution has potential applications
in such areas as simulation of precipitation concentration and
distribution and water resources management, particularly in the Kariba
catchment area and the larger Zambezi River basin, which is characterized by
(i) nonuniform distribution of a network of hydrometeorological stations,
(ii) significant data gaps in the existing observations, and (iii) apparent inherent
impacts caused by climatic extreme events and their corresponding variability.
