Open AccessModelling extreme rainfall events in Kigali city using generalized Pareto distribution
Author(s) -
Singirankabo Edouard,
Iyamuremye Emmanuel
Publication year - 2022
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.2076
Subject(s) - generalized pareto distribution , environmental science , precipitation , extreme weather , extreme value theory , climate extremes , distribution (mathematics) , climatology , meteorology , physical geography , environmental resource management , climate change , statistics , geography , ecology , mathematics , biology , geology , mathematical analysis
Extreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter ( ξ ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events.