Analysis of rainfall extremes in the Ngong River Basin of Kenya: Towards integrated urban flood risk management

Extreme rainfall events are a major cause of highly disruptive flooding in small urban watersheds with limited flood risk management systems. In the Ngong River Basin of Kenya, such floods affect more than 0.5 Million residents within the Kibera informal settlements of Nairobi. However, there is paucity of information about the characteristics of the extreme rainfalls to support flood risk management. This study investigated the best-fit probability distribution models for the extreme rainfalls of the Ngong Basin using Block Maxima approach as a basis for anticipatory flood risk management. Daily rainfall data for the period between 1968 and 2017 were acquired from the existing two rainfall stations to support the analysis at monthly, seasonal and annual timescales. The Gamma, Pearson Type III, Gumbel and Generalized Extreme Value distributions were selected and applied to each timescale. Parameters of the distributions were determined using the Maximum-Likelihood estimator. The validity of the fitted probability models was tested using the Kolomogorov- Smirnov, Anderson-Darling and Cramer von Misses measures for Goodness of Fit. The best-fit probability distributions were subsequently used to establish the rainfall frequencies and return levels at annual timescales. The results show that Pearson Type III provided the best fit at monthly timescales during the dry spell months, while the Generalized Extreme Value distribution provided best results during the wet periods. At seasonal timescales, the Gamma distribution was noted to be the best-fit model. The return levels developed could essentially support the design of urban flood control structures for appropriate flood risk management.