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.

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.

scholarly journals Regional Rainfall Frequency Analysis on Seven Sites of Punjab, Pakistan using L-moments

2020 ◽  
Vol 2 (2) ◽  
pp. 25-35
Author(s):  
Uzma Nawaz ◽  
Zamir Hussain ◽  
Tooba Nihal ◽  
Saira Usman
Keyword(s):  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Regional Distribution ◽  
Return Periods ◽  
Goodness Of Fit Test ◽  
Rainfall Frequency ◽  
Pearson Type ◽  
L Moments ◽  
Rainfall Frequency Analysis ◽  
Regional Rainfall

The hydro-meteorological variables of extreme rainfall are not easy to explain due to unexpected changes in climate and varied usage of water with growing population. Regional rainfall frequency analysis is the one such method that is useful for the requirement of more accurate estimates of rainfall yearly or desineally for the regions having lack of fresh water resources. The series of Annual Maximum Monthly Rainfall Totals (AMMRT) has been used for the seven sites of northern Punjab, Pakistan using L-moments. The results of different test, the run test, lag-1 correlation and Mann-Whitney U test illustrate that the data series of the seven sites of northern Punjab were found random and independently and identically distributed and have no serial correlation. Heterogeneity measure exposed that the region is homogeneous and discordancy measure gives the evidence that no site is discordant among the seven. The result of goodness of fit test including L-moment Ratio diagrams, ZDIST statistic and Mean Absolute Deviation Index exposed the Pearson Type III (PE3), Generalized Normal (GNO) and Generalized Extreme Value(GEV) are best suitable of the regional distribution for the quantiles estimation. The quantiles estimates obtained for different return periods. A linear regression model was developed with good fit between the at site characteristics and the mean of the AMMRT of the sites. The estimates of the study may be used for the estimation of the rainfall quantiles of the seven sites for different return periods. The estimates will be useful to design future preventive measures for the harmful impact of hydro meteorological events at these sites in Punjab Pakistan.

scholarly journals Extreme rainfall events over Odisha state, India

MAUSAM ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 131-138
Author(s):  
S. PASUPALAK ◽  
G. PANIGRAHI ◽  
T. PANIGRAHI ◽  
S. MOHANTY ◽  
K. K. SINGH
Keyword(s):  
Heavy Rainfall ◽  
Hot Spot ◽  
Daily Rainfall ◽  
India Meteorological Department ◽  
Extreme Rainfall ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Rainfall Frequency ◽  
Before And After ◽  
Block Level

Extreme rainfall events are a significant cause of loss of life and livelihoods in Odisha. Objectives of the present study are to determine the trend of the extreme rainfall events during 1991-2014 and to compare the events between two periods before and after 1991. Block level daily rainfall data were used in identifying the extreme rainfall events, while district level aggregation was used in analysing the trend   in three categories, viz., heavy, very heavy and extremely heavy rainfall as per criteria given by India Meteorological Department (IMD). The state as a whole received one extremely heavy, nine very heavy, and forty heavy rainfall events in a year. When percentage of occurrence of each category out of the total extreme events over different districts was considered, maximum % of extremely heavy rainfall occurred in Kalahandi (5.8%), very heavy rainfall in Bolangir (23.8%) and heavy rainfall in Keonjhargarh (85.4%). Trend analysis showed that number of extreme rainfall events increased in a few districts, namely, Bolangir, Nuapada, Keonjhargarh, Koraput, Malkangiri, and Nawarangapur and did not change in other districts. In Puri district, extremely heavy rainfall frequency decreased. New all-time record high one-day rainfall events were observed in twenty districts during 1992 to 2014, surpassing the earlier records, which could be attributed to  climate change induced by  global warming. Interior south Odisha was found as the hot spot for extreme rainfalls.

scholarly journals Best-Fit Probability Distribution Model for Rainfall Frequency Analysis of Three Cities in South Eastern Nigeria

2017 ◽  
Vol 1 (1) ◽  
pp. 34-42 ◽  
Author(s):  
A. I. Agbonaye ◽  
O. C. Izinyon
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Distribution Model ◽  
Hydraulic Structures ◽  
Annual Maximum ◽  
Rainfall Frequency ◽  
Pearson Type ◽  
Rainfall Frequency Analysis ◽  
Best Fit ◽  
Annual Maximum Series

Rainfall frequency analysis is the estimation of how often rainfall of specified magnitude will occur. Such analyses are helpful in defining policies relating to water resources management. It serves as the source of data for flood hazard mitigation and the design of hydraulic structures aimed at reducing losses due to floods action. In this study rainfall frequency analysis for three (3) cities in South Eastern Nigeria were carried out using annual maximum series of daily rainfall data for the stations. The objective of the study was to select the probability distribution model from among six commonly used probability distribution models namely: Generalized Extreme value distribution (GEV), Extreme value type I distribution (EVI), Generalized Pareto distribution (GPA), Pearson Type III (PIII), log Normal (LN) and Log Pearson Type III (LP111) distributions. These distributions were applied to annual maximum series of daily precipitation data at each station using the parameters of the distributions estimated by the method of moments. The best fit probability distribution model at each location was selected based on the results of seven goodness of fit tests entry: root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute deviation index (MADI) and probability plot correlation coefficient (PPCC), Maximum Absolute Error (MAE), Chi square test and D- Index and a scoring and ranking scheme. Our results indicate that the best fit probability distribution model at all study locations is GEV and this was used to forecast rainfall return values for the stations for return periods of between 5years and 500years. The values obtained are useful for planning, design and management of hydraulic structures for flood mitigation and prevention of flood damage at the location.

scholarly journals Climate change impacts on extreme rainfall frequency prediction

2019 ◽  
Vol 11 (4) ◽  
pp. 935-943
Author(s):  
Fahad S. Alahmadi ◽  
Norhan Abd Rahman
Keyword(s):  
Climate Change ◽  
Goodness Of Fit ◽  
Regional Growth ◽  
Growth Curves ◽  
Daily Rainfall ◽  
Climate Change Impacts ◽  
Extreme Rainfall ◽  
Distribution Functions ◽  
Rainfall Frequency ◽  
Pearson Type

Abstract The full integration between the computation of climate change effects and the prediction of extreme rainfall frequency is not yet well developed. In this study, the maximum daily rainfall of 26 stations in the western Kingdom of Saudi Arabia (KSA) are extracted covering an area of 180,000 km2 for processing and analyzing. Discordancy test (Di) showed that some stations are discordant, and the selected study area needs to be subdivided in order to reduce the inherent discordance. The rainfall stations are subdivided into three sub-regions based on a new approach by using L-Skewness parameter value (low, moderate, and high). Five probability distribution functions (PDFs) are evaluated using goodness of fit (Zdist) test and L-moment ratios diagram (LMRD). It was found that for sub-regions A, B, and C, the best fits are generalized Pareto (GPA), Pearson type III (PE3) and generalized extreme value (GEV) PDFs, respectively. Regional growth curves for each sub-region are developed and the predicted extreme rainfall for 100 years' return periods are computed for each station. Finally, climate change impact is evaluated using the emission scenario A2 which is about +40% and the predicted extreme rainfall frequency is computed taking into consideration the climate change impacts.

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