The two-component extreme value distribution for flood frequency analysis: Derivation of a new estimation method

1987 ◽  
Vol 1 (3) ◽  
pp. 199-208 ◽  
Author(s):  
M. Fiorentino ◽  
K. Arora ◽  
V. P. Singh
Keyword(s):  
Frequency Analysis ◽  
Estimation Method ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Flood Frequency Analysis ◽  
Two Component

Performance of L-moments and nonparametric flood frequency analysis

1994 ◽  
Vol 21 (5) ◽  
pp. 856-862 ◽  
Author(s):  
Denis Gingras ◽  
Kaz Adamowski
Keyword(s):  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Nonparametric Methods ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Flood Frequency Analysis ◽  
Generalized Extreme Value ◽  
L Moments

A simulation study was undertaken to compare parametric L-moments and nonparametric approaches in flood frequency analysis. Data of various sample lengths were generated from a given generalized extreme value distribution and the quantiles estimated using the fixed-kernel nonparametric method and from a generalized extreme value distribution fitted by L-moments. From the resulting root-mean-square errors for various quantiles, it was concluded for unimodal distributions that nonparametric methods are preferable for large return period floods estimated from short (<30 years) samples while parametric methods are preferable in other circumstances. It was also pointed out that nonparametric methods are more suitable for mixed distributions. Key words: frequency analysis, L-moments, nonparametric methods, simulation.

scholarly journals Potential flood frequency analysis and susceptibility mapping using CMIP5 of MIROC5 and HEC-RAS model: a case study of lower Dwarkeswar River, Eastern India

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sadhan Malik ◽  
Subodh Chandra Pal
Keyword(s):  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Susceptibility Mapping ◽  
Peak Discharge ◽  
Flood Frequency Analysis ◽  
Flood Susceptibility ◽  
Pearson Type

AbstractFloods are one of the major concerns in the world today. The lower reaches of the river coming from the western side of West Bengal are often affected by floods. Thereby estimation and prediction of flood susceptibility in the light of climate change have become an urgent need for flood mitigation and is also the objective of this study. The historical floods (1978–2018) of the monsoon-dominated lower Dwarkeswar River, as well as the possibility of future floods (2020–2075), were investigated applying peak flow daily data. The possibilities of future flow and floods were estimated using rainfall data from MIROC5 of CMIP5 Global Circulation Model (GCM). Besides, four extreme value distribution functions like log-normal (LN), Log-Pearson Type III (LPT-3), Gumbel’s extreme value distribution (EV-I) and extreme value distribution-III (EV-III) were applied with different recurrence interval periods to estimate its probability of occurrences. The flood susceptibility maps were analyzed in HEC-RAS Rain-on-grid model and validated with Receiver Operating Characteristic (ROC) curve. The result shows that Log-Pearson-Type-III can be very helpful to deal with flood frequency analysis with minimum value in Kolmogorov–Smirnov (K–S = 0.11676), Anderson–Darling (A–D = 0.55361) and Chi-squared test (0.909) and highest peak discharge 101.9, 844.9, 1322.5, 1946.2, 2387.9 and 2684.3 cubic metres can be observed for 1.5, 5, 10, 25, 50 and 75 years of return period. Weibull’s method of flood susceptibility mapping is more helpful for assessing the vulnerable areas with the highest area under curve value of 0.885. All the applied models of flood susceptibility, as well as the GCM model, are showing an increasing tendency of annual peak discharge and flood vulnerability. Therefore, this study can assist the planners to take the necessary preventive measures to combat floods.

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