scholarly journals REGIONAL RAINFALL FREQUENCY ANALYSIS FOR SAMARAHAN RIVER BASIN

2017 ◽  
Vol 8 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Dayang Nazihah Abang Uthman ◽  
Onni Suhaiza Selaman
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Gumbel Distribution ◽  
Rainfall Analysis ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Frequency Map ◽  
Basin Area ◽  
Regional Rainfall ◽  
Adequate Data

In planning to mitigate flood, it is essential for engineers to determine the magnitude and frequency of rainfall. The rainfall frequency and magnitude can be determined by rainfall frequency analysis. This study analyzes the regional rainfall frequency of the Samarahan River basin. There are 12 rainfall stations over the 508km2 of basin area, of which 11 are included in this study. The rainfall frequency analyses of each individual station in Samarahan River basin are conducted using Gumbel distribution and Weibull plotting position formulas. The curves that are close to each other are grouped into the same region. Other factors such as topography, station elevation, type of rainfall distribution and isohyet are also considered in determining the region. Subsequently, a regional rainfall frequency map of Samarahan River basin is established. The findings show that Samarahan River basin can be divided into three homogenous regions. In comparison to previous research, there are changes in grouping the rainfall stations selected into regions. These changes may be due to different years of data used and number of rainfall stations selected since the data is limited. Dissimilar outcomes may also be caused by other factors such as nature change over time. This research updates the rainfall analysis of the Samarahan River basin using more adequate data compared to previous research.

Regional Rainfall Frequency Analysis for the State of Michigan

2005 ◽  
Vol 10 (6) ◽  
pp. 437-449 ◽  
Author(s):  
Christopher M. Trefry ◽  
David W. Watkins ◽  
Dennis Johnson
Keyword(s):  
Frequency Analysis ◽  
The State ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Regional Rainfall

scholarly journals Regional Rainfall Frequency Analysis by Multivariate Techniques

2008 ◽  
Vol 41 (5) ◽  
pp. 517-525 ◽  
Author(s):  
Woo-Sung Nam ◽  
Tae-Soon Kim ◽  
Ju-Young Shin ◽  
Jun-Haeng Heo
Keyword(s):  
Frequency Analysis ◽  
Multivariate Techniques ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Regional Rainfall

scholarly journals Heavy rainfall frequency analysis in the Benin section of the Niger and Volta Rivers basins: is the Gumbel's distribution a one-size-fits-all model?

2021 ◽  
Vol 384 ◽  
pp. 187-194
Author(s):  
Djigbo Félicien Badou ◽  
Audrey Adango ◽  
Jean Hounkpè ◽  
Aymar Bossa ◽  
Yacouba Yira ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Heavy Rainfall ◽  
Daily Rainfall ◽  
Gumbel Distribution ◽  
Extreme Rainfall ◽  
Type Iii ◽  
Rainfall Frequency ◽  
Pearson Type ◽  
Gumbel’S Distribution ◽  
Rainfall Frequency Analysis

Abstract. West African populations are increasingly exposed to heavy rainfall events which cause devastating floods. For the design of rainwater drainage facilities (to protect populations), practitioners systematically use the Gumbel distribution regardless of rainfall statistical behaviour. The objective of this study is twofold. The first is to update existing knowledge on heavy rainfall frequency analysis in West Africa to check whether the systematic preference for Gumbel's distribution is not misleading, and subsequently to quantify biases induced by the use of the Gumbel distribution on stations fitting other distributions. Annual maximum daily rainfall of 12 stations located in the Benin sections of the Niger and Volta Rivers' basins covering a period of 96 years (1921–2016) were used. Five statistical distributions (Gumbel, GEV, Lognormal, Pearson type III, and Log-Pearson type III) were used for the frequency analysis and the most appropriate distribution was selected based on the Akaike (AIC) and Bayesian (BIC) criteria. The study shows that the Gumbel's distribution best represents the data of 2/3 of the stations studied, while the remaining 1/3 of the stations fit better GEV, Lognormal, and Pearson type III distributions. The systematic application of Gumbel's distribution for the frequency analysis of extreme rainfall is therefore misleading. For stations whose data best fit the other distributions, annual daily rainfall maxima were estimated both using these distributions and the Gumbel's distribution for different return periods. Depending on the return period, results demonstrate that the use of the Gumbel distribution instead of these distributions leads to an overestimation (of up to +6.1 %) and an underestimation (of up to −45.9 %) of the annual daily rainfall maxima and therefore to an uncertain design of flood protection facilities. For better validity, the findings presented here should be tested on larger datasets.

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