Modelling Extreme Temperature Using Extreme Value Theory: A Case Study Northern Kenya
International Journal of Data Science and Analysis
Volume 6, Issue 5, October 2020, Pages: 130-136
Received: Sep. 28, 2020;
Accepted: Oct. 9, 2020;
Published: Oct. 17, 2020
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Morris Mbithi Wambua, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph Kyalo Mung’atu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Jane Akinyi Aduda, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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.
Morris Mbithi Wambua,
Joseph Kyalo Mung’atu,
Jane Akinyi Aduda,
Modelling Extreme Temperature Using Extreme Value Theory: A Case Study Northern Kenya, International Journal of Data Science and Analysis.
Vol. 6, No. 5,
2020, pp. 130-136.
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