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Advances in Meteorology
Volume 2016 (2016), Article ID 2486928, 15 pages
http://dx.doi.org/10.1155/2016/2486928
Research Article

Response of Extreme Hydrological Events to Climate Change in the Water Source Area for the Middle Route of South-to-North Water Diversion Project

1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2Hubei Collaborative Innovation Center for Water Resources Security, Wuhan University, Wuhan 430072, China
3College of Tourism Culture and Geographical Science, Huanggang Normal University, Huanggang 438000, China

Received 20 February 2015; Revised 15 July 2015; Accepted 30 July 2015

Academic Editor: Yongqiang Zhang

Copyright © 2016 Wei Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the water source area for the middle route of China’s South-to-North Water Diversion Project, the upper Hanjiang basin is of central concern for future management of the country’s water resources. The upper Hanjiang is also one of the most flood-prone rivers in China. This paper explores the process of extreme floods by using multivariate analysis to characterize flood and precipitation event data in combination, for historical data and simulated data from global climate models. The results suggested that the generalized extreme value and Gamma models better simulated the extreme precipitation and flood volume sequence than the generalized Pareto model for the annual maximum series, while the generalized Pareto distribution model was the best-fit model for peaks over threshold series. For the two-dimensional joint distributions of precipitation and flood volume, the Frank Copula was preferred in simulation of the annual maximum flood series whereas the Gumbel Copula was the most appropriate function to simulate the points over threshold flood series. We concluded that, compared with the traditional univariate approach, multivariate statistical analysis produced flood estimates that were more physically based and statistically sound and carried lower risk for flood design purposes.