Extreme value analysis of annual maximum water levels in the Pearl River Delta, China

Abstract

We analyzed the statistical properties of water level extremes in the Pearl River Delta using five probability distribution functions. Estimation of parameters was performed using the L-moment technique. Goodness-of-fit was done based on Kolmogorov-Smirnov’s statistic D (K-S D). The research results indicate that Wakeby distribution is the best statistical model for description of statistical behaviors of water level extremes in the study region. Statistical analysis indicates that water levels corresponding to different return periods and associated variability tend to be larger in the landward side of the Pearl River Delta and vice versa. A ridge characterized by higher water level can be identified expanding along the West River and the Modaomen channel, showing the impacts of the hydrologic process of the West River basin. Trough and higher grades of water level changes can be detected in the region drained by Xi’nanyong channel, Dongping channel, and mainstream of Pearl River. The Pearl River Delta region is characterized by low-lying topography and a highly-advanced socio-economy, and is heavily populated, being prone to flood hazards and flood inundation due to rising sea level and typhoons. Therefore, sound and effective countermeasures should be made for human mitigation to natural hazards such as floods and typhoons.

Keywords

extreme values / probability distribution functions / annual maximum water level / extreme value analysis / Pearl River estuary

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Acknowledgements

The work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK405308), the National Natural Science Foundation of China (Grant No. 40701015), and the Programme of Introducing Talents of Discipline to Universities—the 111 Project of Hohai University. Cordial thanks should be extended to two anonymous reviewers for their invaluable comments and suggestions which greatly helped to improve the quality of this paper. We were also grateful to Prof. Chen Xiaohong, Dr. Yang Tao, and Dr. Jiang Tao for their kind help in the collection and pre-processing of the data.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg