Abstract
The impacts of extremely high temperatures on plants, human beings and animals’ health have been studied in several parts of the world. However, extreme events are uncommon and have only attracted attention recently. In this study, extreme temperature behavior was modelled through the application of extreme value theory using maximum monthly temperatures over a 36 years period. Data on monthly maximum temperature from the Mandera, Wajir and Lodwar stations was modelled using generalized extreme value (GEV) and generalized Pareto distributions (GPD) models. The results revealed that the GEV model was better in modelling extreme temperature behavior because it had the least AIC and BIC values. Two comparative tests, namely, Anderson-Darling and Kolmogorov-Smirnov confirmed the GEV model to be adequate for the data. Diagnostic checks of the two models using probability-probability (PP) plot, quantile-quantile (QQ) plot, return level plot and mean residual life plot revealed that the GEV fitted the data well. Return periods of 5, 10, 20, 50 and 100 years also revealed an increasing trend for long return periods.
Highlights
We have had some research work on extreme temperatures in several countries across the globe with most researchers interested in developing appropriate statistical methods for extreme events that provide a significant help towards these problems
In the past few years, there have been several researches concerning extreme climatic events such as those by [2-6] Most of the research work is based on Extreme Value Theory (EVT) which is a branch of statistics dealing with asymptotic behavior of extreme events, this theory has been applied in areas of meteorology, hydrology, ecological disturbances and finance with an aim of characterizing rare events and tails of distributions
Extreme value analysis was performed on this study by fitting both the generalized extreme value distribution and Generalized Pareto Distribution using method of maximum likelihood estimates (MLE)
Summary
Recent special reports on climate extremes have shown evidences of changes in the patterns of climate extremes at global, regional and local scales. In the past few years, there have been several researches concerning extreme climatic events such as those by [2-6] Most of the research work is based on Extreme Value Theory (EVT) which is a branch of statistics dealing with asymptotic behavior of extreme events, this theory has been applied in areas of meteorology, hydrology, ecological disturbances and finance with an aim of characterizing rare events and tails of distributions. Extreme Value Theory (EVT) furnishes us with pertinent tools for modelling and predicting extreme temperature in Kenya [7] and this is the focus of this article
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