Application of Gumbel’s Distribution Method for Flood Frequency Analysis of Lower Ganga Basin (Farakka Barrage Station), West Bengal, India

2021 ◽  
pp. 51-58
Author(s):  
Kajal Kumar Mandal ◽  
K. Dharanirajan ◽  
Sabyasachi Sarkar
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Flood Hazard ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Ganga Basin ◽  
Distribution Method ◽  
Discharge Data ◽  
Gumbel’S Distribution ◽  
Farakka Barrage

The analysis of flood frequency will depend on the historical peak discharge data for at least 10 years. This study has taken into account peak annual maximum discharge data for 72 years (1949 to 2020). The discharge data was collected from the Farakka Barrage Gauging station (24°48'15.10" N and 87°55'52.70" E) situated in the upper part of lower Ganga basin. The flood frequency analysis of the lower Ganga basin’s upper portions has been carried out using Gumbel’s frequency distribution method. Gumbel’s method (XT) is a prediction analysing statistical approach. The discharge data was tabulated in descending order and rank has been assigned based on the discharge volume. The return period was calculated based on Weibull’s formula (P) for this analysis. The flood frequency data was plotted on a graph where X-axis shows the return period and the Yaxis is the discharge value. The R2 value of this graph is 0.9998 which describe Gumbel’s distribution method is best for the flood frequency analysis. The flood frequency analysis is an essential step to assess the flood hazard.

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

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.

scholarly journals Regional flood frequency analysis of Tripura based on L-moment

2012 ◽  
Vol 4 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Abhijit Bhuyan ◽  
Munindra Borah
Keyword(s):  
Frequency Analysis ◽  
Regional Growth ◽  
Growth Curves ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Ungauged Catchments ◽  
Discharge Data ◽  
Pearson Type ◽  
Regional Flood Frequency Analysis ◽  
Regional Flood Frequency

The annual maximum discharge data of six gauging sites have been considered for L-moment based regional flood frequency analysis of Tripura, India. Homogeneity of the region has been tested based on heterogeneity measure (H) using method of L-moment. Based on heterogeneity measure it has been observed that the region consist of six gauging sites is homogeneous. Different probability distributions viz. Generalized extreme value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), Pearson Type III (PE3) and Wakebay (WAK) have been considered for this investigation. PE3, GNO and GEV have been identified as the candidate distributions based on the L-moment ratio diagram and ZDIST -statistics criteria. Regional growth curves for three candidate distributions have been developed for gauged and ungauged catchments. Monte Carlo simulations technique has also been used to estimate accuracy of the estimated regional growth curves and quantiles. From simulation study it has been observed that PE3 distribution is the robust one.

scholarly journals Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model

2019 ◽  
Vol 79 ◽  
pp. 03022
Author(s):  
Shangwen Jiang ◽  
Ling Kang
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Yangtze River ◽  
Flood Frequency ◽  
Additive Models ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
The Yangtze River ◽  
Stationary Model ◽  
Risk Of Failure

Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.

scholarly journals Comparison Between Bivariate and Trivariate Flood Frequency Analysis Using the Archimedean Copula Functions, A Case Study of the Karun River in Iran

2021 ◽  
Author(s):  
Mohamad Haytham Klaho ◽  
Hamid R. Safavi ◽  
Mohamad H. Golmohammadi ◽  
Maamoun Alkntar
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Goodness Of Fit ◽  
Flood Frequency ◽  
Archimedean Copula ◽  
Flood Frequency Analysis ◽  
Cumulative Probability ◽  
Copula Functions ◽  
Flood Peak ◽  
Multiple Variables

Abstract Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak, volume and duration poses more motivation for hydrologists to study. In this paper, the bivariate and trivariate flood frequency analysis and modeling using Archimedean copula functions is focused. For this purpose, the annual flood data over a 55-year historical period recorded at the Dez Dam hydrometric station were used. The results showed that based on goodness of fit criteria, the Frank function built upon the couple of the flood peak-volume and the couple of the flood peak-duration as well as the Clayton function built upon the flood volume-duration were identified to be the best copula families to be adopted. The trivariate analysis was conducted and the Clayton family was chosen as the best copula function. Thereafter, the common and conditional cumulative probability distribution functions were built and analyzed to determine the periodic "and", "or" and "conditional" bivariate and trivariate flood return periods. The results suggest that the bivariate conditional return period obtained for short-term periods is more reliable than the trivariate conditional return period. Additionally, the trivariate conditional return period calculated for long-term periods is more reliable than the bivariate conditional return period.

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