Editorial Type:
Article
Article Type:
Research Article

Characteristics and Changes of Extreme Precipitation in the Yellow–Huaihe and Yangtze–Huaihe Rivers Basins, China

Quan Dong School of Atmospheric Sciences, Nanjing University, Nanjing, and National Meteorological Center, China Meteorological Administration, Beijing, China

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Xing Chen School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Tiexi Chen School of Atmospheric Sciences, Nanjing University, Nanjing, China

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Print Publication:
15 Jul 2011
DOI:
https://doi.org/10.1175/2010JCLI3653.1
Page(s):
3781–3795
Restricted access

Abstract

Many works suggest that the intensity of extreme precipitation might be changing under the background of global warming. Because of the importance of extreme precipitation in the Yellow–Huaihe and Yangtze–Huaihe River basins of China and to compare the spatial difference, the generalized Pareto distribution (GPD) function is used to fit the daily precipitation series in these basins and an estimate of the extreme precipitation spatial distribution is presented. At the same time, its long-term trends are estimated for the period between 1951 and 2004 by using a generalized linear model (GLM), which is based on GPD. High quality daily precipitation data from 215 observation stations over the area are used in this study. The statistical significance of the trend fields is tested with a Monte Carlo experiment based on a two-dimensional Hurst coefficient, H2.

The spatial distribution of the shape parameter of GPD indicates that the upper reaches of the Huaihe River (HuR) basin have the largest probability of extreme rainfall events, which is consistent with most historical flood records in this region. Spatial variations in extreme precipitation trends are found and show significant positive trends in the upper reaches of Poyang Lake in the Yangtze River (YaR) basin and a significant negative trend in the mid- to lower reaches of the Yellow River (YeR) and Haihe River (HaR) basins. The trends in the HuR basin and the lower reaches of Poyang Lake in the YaR basin are nearly neutral. All trend fields are significant at the 5% level of significance from the Monte Carlo experiments.

Corresponding author address: Xing Chen, School of Atmospheric Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, Jiangsu Province, China. E-mail: xchen@nju.edu.cn

Abstract

Many works suggest that the intensity of extreme precipitation might be changing under the background of global warming. Because of the importance of extreme precipitation in the Yellow–Huaihe and Yangtze–Huaihe River basins of China and to compare the spatial difference, the generalized Pareto distribution (GPD) function is used to fit the daily precipitation series in these basins and an estimate of the extreme precipitation spatial distribution is presented. At the same time, its long-term trends are estimated for the period between 1951 and 2004 by using a generalized linear model (GLM), which is based on GPD. High quality daily precipitation data from 215 observation stations over the area are used in this study. The statistical significance of the trend fields is tested with a Monte Carlo experiment based on a two-dimensional Hurst coefficient, H2.

The spatial distribution of the shape parameter of GPD indicates that the upper reaches of the Huaihe River (HuR) basin have the largest probability of extreme rainfall events, which is consistent with most historical flood records in this region. Spatial variations in extreme precipitation trends are found and show significant positive trends in the upper reaches of Poyang Lake in the Yangtze River (YaR) basin and a significant negative trend in the mid- to lower reaches of the Yellow River (YeR) and Haihe River (HaR) basins. The trends in the HuR basin and the lower reaches of Poyang Lake in the YaR basin are nearly neutral. All trend fields are significant at the 5% level of significance from the Monte Carlo experiments.

Corresponding author address: Xing Chen, School of Atmospheric Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, Jiangsu Province, China. E-mail: xchen@nju.edu.cn
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