Abstract
Climate change has brought about unprecedented new weather patterns, one of which is changes in extreme rainfall. In Kenya, heavy rains and severe flash floods have left people dead and displaced hundreds from their settlements. In order to build a resilient society and achieve sustainable development, it is paramount that adequate inference about extreme rainfall be made. To this end, this research modelled and predicted extreme rainfall events in Kenya using Extreme Value Theory for rainfall data from 1901-2016. Maximum Likelihood Estimation was used to estimate the model parameters and block maxima approach was used to fit the Generalized Extreme Value Distribution (GEVD) while the Peak Over Threshold method was used to fit the Generalized Pareto Distribution (GPD). The Gumbel distribution was found to be the optimal model from the GEVD while the Exponential distribution gave the optimal model over the threshold value. Furthermore, prediction for the return periods of 10, 20, 50 and 100 years were made using the return level estimates and their corresponding confidence intervals were presented. It was found that increase in return periods leads to a corresponding increase in return levels. However, the GPD gave higher return levels for 10 and 20 years compared to GEVD. While, for higher return periods 50 and 100 years, the GEVD gave higher return levels compared to the GPD. Model diagnostics using probability, density, quantile and return level plots indicated that the models provided were a good fit for the data.
Highlights
The East African community is prone to climate and weather extremes with a highly variable climate, and has relatively high levels of population exposure and vulnerability
It was found that increase in return periods leads to a corresponding increase in return levels
For higher return periods 50 and 100 years it can be seen that the Generalized Extreme Value Distribution (GEVD) gives higher return levels compared to the Generalized Pareto Distribution (GPD)
Summary
The East African community is prone to climate and weather extremes with a highly variable climate, and has relatively high levels of population exposure and vulnerability. Kenya is not new to extreme rainfall and the study of Parry, Echeverria, Dekens, and Maitima (2012) found out that Kenya’s exposure to climate risk is high, experiencing major droughts about every 10 years and moderate droughts or floods every 3 to 4 years, and as such regarded as one of the most disaster-prone countries in the world. Huho and Kosonei (2014) found that there is an inverse relationship between economic development and extremes in rainfall that causes disasters. This has been seen in the Long-Rains during March - May of 2018 which was regarded as one of the wettest seasons (Kilavi et al, 2018)
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