The performance of the two-component extreme value distribution in regional flood frequency analysis

1988 ◽  
Vol 24 (6) ◽  
pp. 879-887 ◽  
Author(s):  
Nigel W. Arnell ◽  
Salvatore Gabriele
Keyword(s):  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Flood Frequency Analysis ◽  
Regional Flood Frequency Analysis ◽  
Two Component ◽  
Regional Flood Frequency

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 How to Explain and Predict the Shape Parameter of the Generalized Extreme Value Distribution of Streamflow Extremes Using a Big Dataset

2018 ◽  
Author(s):  
Hristos Tyralis ◽  
Georgia Papacharalampous ◽  
Sarintip Tantanee
Keyword(s):  
Frequency Analysis ◽  
Shape Parameter ◽  
Predictive Performance ◽  
Extreme Value ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Climatic Indices ◽  
Explanatory Variables ◽  
Regional Flood Frequency Analysis ◽  
Regional Flood Frequency

The finding of important explanatory variables for the location parameter and the scale parameter of the generalized extreme value (GEV) distribution, when the latter is used for the modelling of annual streamflow maxima, is known to have reduced the uncertainties in inferences, as estimated through regional flood frequency analysis frameworks. However, important explanatory variables have not been found for the GEV shape parameter, despite its critical significance, which stems from the fact that it determines the behaviour of the upper tail of the distribution. Here we examine the nature of the shape parameter by revealing its relationships with basin attributes. We use a dataset that comprises information about daily streamflow and forcing, climatic indices, topographic, land cover, soil and geological characteristics of 591 basins with minimal human influence in the contiguous United States. We propose a framework that uses random forests and linear models to find (a) important predictor variables of the shape parameter and (b) an interpretable model with high predictive performance. The process of study comprises of assessing the predictive performance of the models, selecting a parsimonious predicting model and interpreting the results in an ad-hoc manner. The findings suggest that the shape parameter mostly depends on climatic indices, while the selected prediction model results in more than 20% higher accuracy in terms of RMSE compared to a na&iuml;ve approach. The implications are important, since incorporating the regression model into regional flood frequency analysis frameworks can considerably reduce the predictive uncertainties.

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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