Statistical analysis of annual maximum daily rainfall for Nelspruit and its environs

Jàmbá

卷:10

Jacques Snyman¹  Eric M. Masereka¹  George M. Ochieng² 

[1] Department of Civil Engineering, Tshwane University of Technology;

[2] Department of Civil Engineering, Vaal University of Technology;

关键词: annual maximum daily rainfall;    generalized extreme value distribution;    generalized logistic distribution;    goodness of fit tests;    empirical frequency analysis;    best fit distribution function;   

DOI  :  10.4102/jamba.v10i1.499

来源: DOAJ

【 摘 要 】

Nelspruit and its environs frequently experience extreme high annual maximum daily rainfall (AMDR) events resulting in flood hazards. These flood hazards have caused flood disasters that have resulted in loss of property and lives. The main objective of this study was to carry out statistical analysis of extreme high AMDR events that have caused flood hazards, which in turn have caused flood disasters in Nelspruit and its environs. Empirical continuous probability distribution functions (ECPDF) and theoretical continuous probability distribution functions (TCPDF) were applied to carry out the statistical analysis of the extreme high AMDR events. Annual maximum daily rainfall event of magnitude 100 mm was identified as a threshold. Events > 100 mm were considered as extreme high events resulting in flood disasters. The results of empirical frequency analysis showed that the return period of flood disasters was 10 years. The occurrence probability of flood disaster event at least once in 1, 2, 3, 4 and 5 years was 0.10, 0.19, 0.27, 0.34 and 0.41, respectively. Generalised logistic PDF was identified as the best-fit theoretical PDF for statistical analysis of the extreme high AMDR events in Nelspruit and its environs. The results of this study contributed to the understanding of frequency and magnitude of extreme high AMDR events that could lead to flood disasters. The results could be applied in developing flood disaster management strategies in Nelspruit and its environs.

【 授权许可】

Unknown