Flood frequency analysis with systematic and historical or paleoflood data based on the two-parameter general extreme value models

1994 ◽  
Vol 30 (6) ◽  
pp. 1653-1664 ◽  
Author(s):  
Felix Frances ◽  
Jose D. Salas ◽  
Duane C. Boes
Keyword(s):  
Frequency Analysis ◽  
Extreme Value ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Two Parameter ◽  
General Extreme Value

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 Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

2021 ◽  
Author(s):  
Lei Yan ◽  
Lihua Xiong ◽  
Gusong Ruan ◽  
Chong-Yu Xu ◽  
Mengjie Zhang
Keyword(s):  
Frequency Analysis ◽  
Location Parameter ◽  
Extreme Value ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Generalized Extreme Value ◽  
Record Length ◽  
Flood Estimation ◽  
The Stability

Abstract In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty; however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

The value of regional information to flood frequency analysis using the method of L-moments

1992 ◽  
Vol 19 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Paul J. Pilon ◽  
K. Adamowski
Keyword(s):  
Nova Scotia ◽  
Frequency Analysis ◽  
Sampling Error ◽  
Extreme Value ◽  
Flood Frequency ◽  
Generalized Extreme Value Distribution ◽  
Flood Frequency Analysis ◽  
Generalized Extreme Value ◽  
Regional Flood Frequency Analysis ◽  
L Moments

The value of regional flood frequency analysis is investigated using the method of L-moments. This study shows that the variability of L-skewness in the province of Nova Scotia is due in large part to sampling error. This implies that a relation between the L-skewness and the basin's characteristics cannot be determined. This study demonstrates that flood data in Nova Scotia can best be described by the generalized extreme value (GEV) distribution. The results also indicate that the regional GEV model is more accurate than the single site analysis. Key words: L-moments, skewness, regional flood analysis, generalized extreme value distribution, simulation.

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