scholarly journals Flood Frequency Analysis of River Niger, Shintaku Gauging Station, Kogi State, Nigeria

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Andy O Ibeje
Keyword(s):  
Frequency Distribution ◽  
Goodness Of Fit ◽  
Climatic Factors ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Optimal Model ◽  
Frequency Curve ◽  
Type Iii ◽  
Pearson Type ◽  
Niger River

The study outlines a frequency distribution study on the highest annual flood statistics in Niger River located at Shintaku hydrologic Station for period of 58years. In order to determine the optimal model for highest annual flood analysis Generalised extreme value, Log normal, Gumbel maximum, Generalised Pareto and Log Pearson III, were tested. Based on error produced by criteria of goodness of Fit tests, the optimal model was determined. Results obtained indicated that Log Pearson type III was best to model maximum flood magnitude of Niger River at Shintaku station. The flood frequency curve was therefore derived using Log Pearson type III frequency distribution. Flood frequency curve showed that return periods of 50 and 100 years with the probabilities of 2% and1% respectively, yielded discharges of 15300m3/s and 15600m3/s respectively. These results were strongly influenced by their topographical, geographical and climatic factors. The findings of this work will be useful for design engineers in deciding the dimension of hydraulic structures such as spillway, dams, canals, bridges and levees among others. Future studies are required to include flood forecasting in the development of inundation maps for Niger River.Keywords—Return period, Frequency Distribution, Flood, Niger River, Flood Modeling

scholarly journals Flood Flow Probability Distribution Model Selection on Niger/Benue River Basins in Nigeria

2021 ◽  
pp. 76-94
Author(s):  
Itolima Ologhadien
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
River Basins ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Distribution Models ◽  
Type Iii ◽  
Pearson Type ◽  
Best Fit

Flood frequency analysis is a crucial component of flood risk management which seeks to establish a quantile relationship between peak discharges and their exceedance (or non-exceedance) probabilities, for planning, design and management of infrastructure in river basins. This paper evaluates the performance of five probability distribution models using the method of moments for parameter estimation, with five GoF-tests and Q-Q plots for selection of best –fit- distribution. The probability distributions models employed are; Gumbel (EV1), 2-parameter lognormal (LN2), log Pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value( GEV). The five statistical goodness – of – fit tests, namely; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR) were used to identify the most suitable distribution models. The study was conducted using annual maximum series of nine gauge stations in both Benue and Niger River Basins in Nigeria. The study reveals that GEV was the best – fit distribution in six gauging stations, LP3 was best – fit distribution in two gauging stations, and PR3 is best- fit distribution in one gauging station. This study has provided a significant contribution to knowledge in the choice of distribution models for predicting extreme hydrological events for design of water infrastructure in Nigeria. It is recommended that GEV, PR3 and LP3 should be considered in the development of regional flood frequency using the existing hydrological map of Nigeria.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

Flood Frequency Analysis of the Mahi Basin by Using Log Pearson Type III Probability Distribution

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Uttam Pawar ◽  
Pramodkumar Hire
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Series Data ◽  
Period Range ◽  
Type Iii ◽  
Pearson Type ◽  
Data Points

Flood frequency analysis is one of the techniques of examination of peak stream flow frequency and magnitude in the field of flood hydrology, flood geomorphology and hydraulic engineering. In the present study, Log Pearson Type III (LP-III) probability distribution has applied for flood series data of four sites on the Mahi River namely Mataji, Paderdi Badi, Wanakbori and Khanpur and of three sites on its tributaries such as Anas at Chakaliya, Som at Rangeli and Jakham at Dhariawad. The annual maximum series data for the record length of 26-51 years have been used for the present study. The time series plots of the data indicate that two largest ever recorded floods were observed in the year 1973 and 2006 on the Mahi River. The estimated discharges of 100 year return period range between 3676 m3/s and 47632 m3/s. The return period of the largest ever recorded flood on the Mahi River at Wankbori (40663 m3/s) is 127-yr. The recurrence interval of mean annual discharges (Qm) is between 2.73-yr and 3.95-yr, whereas, the return period of large floods (Qlf) range from 6.24-yr to 9.33-yr. The magnitude-frequency analysis curves represent the reliable estimates of the high floods. The fitted lines are fairly close to the most of the data points. Therefore, it can be reliably and conveniently used to read the recurrence intervals for a given magnitude and vice versa.

scholarly journals The Effect of Involving Exceptional Outlier Data on Design Flood Magnitude

2015 ◽  
Vol 10 (2) ◽  
pp. 698-706
Author(s):  
Bagher Heidarpour ◽  
Bahram Saghafian ◽  
Saeed Golian
Keyword(s):  
Return Period ◽  
Rare Events ◽  
Flood Frequency ◽  
The Other ◽  
Flood Frequency Analysis ◽  
Data Series ◽  
Design Flood ◽  
Type Iii ◽  
Pearson Type ◽  
Outlier Data

The term "outlier" is generally used to refer to single data points that appear to depart significantly from the trend of the other data. Outliers are classified into three types: incorrect observations, rare events resulting from essentially the same phenomena as the other maxima, and rare events resulting from a different phenomenon. Flood frequency analysis was first performed on complete data series (including the outlier) and then on the series with the outlier removed. Results revealed that omission of the outlier data didn’t affect the probability distribution function (Log-Pearson type III), but the design discharge reduced by 60 percent in 10000 year return period from 3320 (m3/s) to 1340 (m3/s). Furthermore, the method proposed by the U.S. Water Resources Council (WRC), and the HEC-SSP software were applied in order to compose outlier data with other systematic data and to modify the parameters of the statistical distribution. Using WRC method, the estimated 10000-year flood was equaled to 1907 (m3/s) by designating the outlier as the 200-year return period and revising the parameters of Log-Pearson type III distribution; that is about 43 percent decrease over the scenario involving the outlier.

Use of Akaike information criterion for selection of flood frequency distribution

1992 ◽  
Vol 19 (4) ◽  
pp. 616-626 ◽  
Author(s):  
K. C. Ander Chow ◽  
W. Edgar Watt
Keyword(s):  
Frequency Distribution ◽  
Goodness Of Fit ◽  
Information Criterion ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Single Station ◽  
The Cost ◽  
Selection Of

When conducting a conventional single-station flood frequency analysis, an appropriate distribution must be selected. Typically, sample statistics, probability plots, goodness-of-fit tests, etc. are used to facilitate the decision process. For the predominate case of a relatively short record length of flood series, this standard approach leads to undue emphasis on goodness of fit and virtually no consideration of the uncertainty due to additional parameters. The information criterion suggested by Akaike (AIC) is a measure to evaluate the "benefit" of goodness of fit and the "cost" of parameter uncertainty. The criterion is tested for 42 long-term hydrometric stations across Canada and its applicability and limitations are demonstrated in eight samples. The AIC is recommended as an aid in selecting a flood frequency distribution. Key words: flood frequency, goodness of fit, single station, information criterion.

scholarly journals Influences on flood frequency distributions in Irish river catchments

2012 ◽  
Vol 16 (4) ◽  
pp. 1137-1150 ◽  
Author(s):  
S. Ahilan ◽  
J. J. O'Sullivan ◽  
M. Bruen
Keyword(s):  
Goodness Of Fit ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Type I ◽  
Type Ii ◽  
Rock Outcrops ◽  
Frequency Distributions ◽  
Type Iii ◽  
Flood Quantiles ◽  
River Catchments

Abstract. This study explores influences on flood frequency distributions in Irish rivers. A Generalised Extreme Value (GEV) type I distribution is recommended in Ireland for estimating flood quantiles in a single site flood frequency analysis. This paper presents the findings of an investigation that identified the GEV statistical distributions that best fit the annual maximum (AM) data series extracted from 172 gauging stations of 126 rivers in Ireland. Analysis of these data was undertaken to explore hydraulic and hydro-geological factors that influence flood frequency distributions. A hierarchical approach of increasing statistical power that used probability plots, moment and L-moment diagrams, the Hosking goodness of fit algorithm and a modified Anderson-Darling (A-D) statistical test was followed to determine whether a type I, type II or type III distribution was valid. Results of the Hosking et al. method indicated that of the 143 stations with flow records exceeding 25 yr, data for 95 (67%) was best represented by GEV type I distributions and a further 9 (6%) and 39 (27%) stations followed type II and type III distributions respectively. Type I, type II and type III distributions were determined for 83 (58%), 16 (11%) and 34 (24%) stations respectively using the modified A-D method (data from 10 stations was not represented by GEV family distributions). The influence of karst terrain on these flood frequency distributions was assessed by incorporating results on an Arc-GIS platform showing karst features and using Monte Carlo simulations to assess the significance of the number and clustering of the observed distributions. Floodplain effects were identified by using two-sample t-tests to identify statistical correlations between the distributions and catchment properties that are indicative of strong floodplain activity. The data reveals that type I distributions are spatially well represented throughout the country. While also well represented throughout the country, the majority of type III distributions appear in areas where attenuation influences from floodplains are likely. The majority of type II distributions appear in a single cluster in a region in the west of the country that is underlain by karst but importantly, is characterised by shallow of glacial drift with frequent exposures of rock outcrops. The presence of karst in river catchments would be expected to provide additional subsurface storage and in this regard, type III distributions might be expected. The prevalence of type II distributions in this area reflects the finite nature of this storage. For prolonged periods of rainfall, rising groundwater levels will fill karst voids, remove subsurface storage and contribute to recharge related sinkhole flooding. Situations where rainfall intensities exceed karst percolation rates also produce high levels of surface runoff (discharge related flooding) that can promote type II distributions in nearby river catchments. Results therefore indicate that in some instances, assuming type I distributions is incorrect and may result in erroneous estimates of flood quantiles at these locations. Where actual data follows a type II distribution, flood quantiles may be underestimated by in excess of 35% and for type III distributions, overestimates by over 25% can occur.

scholarly journals Flood Frequency Analysis for Burhi Gandak River Basin

2020 ◽  
pp. 82-89
Author(s):  
Kuldeepak Pal ◽  
Kanhu Charan Panda ◽  
Gaurav Sharma ◽  
Suryansh Mandloi
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Stream Flow ◽  
Flood Frequency ◽  
Peak Discharge ◽  
Flood Frequency Analysis ◽  
Discharge Data ◽  
Type Iii ◽  
Pearson Type ◽  
Pearson Type Iii Distribution

The study is aimed at finding the best distribution to match the steam flow and calculation of magnitude and frequency of flow. In the current study, we have used several statistical distributions to find the best fit distribution for stream flow and used flood frequency analysis techniques to find the magnitude and frequency of stream flow and non-exceedance probability of peak discharge. The study has been performed at Sikandarpur and Rosera gauging sites of BurhiGandak River. Historical (50 years) maximum annual peak discharge data of each station are used for statistical analysis for estimating maximum peak discharge in 5, 10, 25, 50, 100 year return period. In this study, Lognormal distribution, Galton distribution, Gamma distribution, Log Pearson Type III distribution, Gumbell distribution, Generalised extreme values distribution have been considered to describe the annual maximum stream flow. Flood frequency analysis methods were used for estimating the magnitude of the extreme flow events and their associated return periods. For both Sikandarpur and Rosera stations, Log Pearson type III distributions showed the lowest value of K–S and Chi-square test statistic. The annual probable peak discharge for 5, 10, 25, 50, and 100 years return period is calculated for each distribution. The most suitable distribution for both the stations is found to be the log-Pearson type III distribution.

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