Paper Info
Title
Regional frequency analysis of rainfall extremes in the Pearl River Basin using L-moments method
Abstract
This paper presents a method for regional frequency analysis of rainfall-extreme regimes in the Pearl River Basin (PRB) using the well-known L-moments approach. Results indicate that: (1) The entire Pearl River Basin (40 sites) can be categorized into 6 regions by cluster analysis together with consideration of the topography and spatial patterns of mean precipitation in the basin. The results of goodness-of-fit measures indicate that the GNO, GLO, GEV, and PE3 distributions fit well for most of the basin for different HOM regions, but their performances are slightly different in term of curve fitting; (2) The estimated quantiles and their biases approximated by Monte Carlo simulation demonstrate that the results are reliable enough for the return periods of less than 100 years; (3) The spatial variations of precipitation in different return periods (Return period=1, 10, 50 and 100 years) increase from the upstream to downstream at the regional scale. To the best of our knowledge, this study is the first attempt to conduct a systematic regional frequency analysis on various annual precipitation extremes in the Pearl River Basin and even in China. These findings are expected to contribute to exploring the complex spatio-temporal patterns of extreme rainfall in this basin in order to reveal the underlying linkages between precipitation and floods from a broad geographical perspective.
Year
DOI
Venue
2010
10.1109/FSKD.2010.5569801
FSKD
Keywords
Field
DocType
topography,regional frequency analysis,annual precipitation extremes,glo distribution,statistical analysis,monte carlo simulation,hom regions,china,rivers,pearl river basin,curve fitting,rainfall-extreme regimes,rain,spatial patterns,gno distribution,topography (earth),cluster analysis,return periods,complex spatiotemporal patterns,monte carlo methods,l-moments approach,mean precipitation,gev distribution,pe3 distribution,spatial variations,floods,regional scale,goodness of fit,return period,river basin,water resources,frequency analysis,spatial pattern,spatial variation,estimation,artificial neural networks,systematics
Drainage basin,Generalized extreme value distribution,Pattern recognition,Computer science,Return period,Quantile,Artificial intelligence,Water resources,Climatology,Spatial ecology,Structural basin,Precipitation
Conference
Volume
ISBN
Citations 
6
978-1-4244-5931-5
0
PageRank 
References 
Authors
0.34
1
4
Name
Order
Citations
PageRank
Tao Yang101.35
Xiaoyan Wang26418.23
Xiaobo Hao300.68
Huihui Li400.68