Calculation Higher Probability Weighted Moments for Generalized Extreme Value in Hydrology

2012 ◽  
Vol 518-523 ◽  
pp. 4015-4021 ◽  
Author(s):  
Song Bai Song ◽  
Yang Li ◽  
Yan Kang
Keyword(s):  
Frequency Analysis ◽  
Extreme Value ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Frequency Curve ◽  
Generalized Extreme Value ◽  
Gev Distribution ◽  
Probability Weighted Moments

In flood frequency analysis, the observed data of the smaller censored flows have no influence on fitting frequency curve, and the final fitting still depends very mainly on the larger flows. It also exhibits two or more distinct segments. The higher probability weighted moments(HPWMS) showed its potential merit for these estimating flood samples. In this paper, a detail approximating calculation of the parameter for common generalized extreme value (GEV) distribution by HPWMS method is given. This method has no more complication procedures than traditional lower orders of probability weighted moments.

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.

Application of Higher Probability Weighted Moments in Flood Frequency Analysis

2012 ◽  
Vol 518-523 ◽  
pp. 4139-4143
Author(s):  
Yang Li ◽  
Song Bai Song
Keyword(s):  
Frequency Analysis ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Gev Distribution ◽  
Probability Weighted Moments ◽  
Northern Shaanxi ◽  
Flood Analysis ◽  
Better Than

This paper aims to study the use of higher probability moments (PWMs) for flood frequency analysis. By estimating the parameters of GEV distribution and matching higher PWMs to annual maximum flow series in northern Shaanxi. The results show that higher PWMs describe the data reasonably better than lower PWMs in flood analysis. This method involves no more complication than lower PWMs that be commonly used, and is suitable for flood frequency analysis.

scholarly journals Flood Frequency Analyses over Different Basin Scales in the Blue Nile River Basin, Ethiopia

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 44 ◽  
Author(s):  
Getachew Tegegne ◽  
Assefa M. Melesse ◽  
Dereje H. Asfaw ◽  
Abeyou W. Worqlul
Keyword(s):  
River Basin ◽  
Frequency Analysis ◽  
Spatial Scales ◽  
Extreme Value ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Nile River ◽  
Generalized Extreme Value ◽  
Blue Nile ◽  
Blue Nile River Basin

The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.

Sign in / Sign up

Export Citation Format

Share Document