Copula-based bivariate frequency analysis can be used to investigate the changes in flood characteristics in the Huai River Basin that could be caused by climate change. The univariate distributions of historical flood peak, maximum 3-day and 7-day volumes in 1961–2000 and future values in 2061–2100 projected from two GCMs (CSIRO-MK3.5 and CCCma-CGCM3.1) under A2, A1B and B1 emission scenarios are analyzed and compared. Then, bivariate distributions of peaks and volumes are constructed based on the copula method and possible changes in joint return periods are characterized. Results indicate that the Clayton copula is more appropriate for historical and CCCma-CGCM3.1 simulating flood variables, while that of Frank and Gumbel are better fitted to CSIRO-MK3.5 simulations. The variations of univariate and bivariate return periods reveal that flood characteristics may be more sensitive to different GCMs than different emission scenarios. Between the two GCMs, CSIRO-MK3.5 evidently predicts much more severe flood conditions in future, especially under B1 scenario, whereas CCCma-CGCM3.1 generally suggests contrary changing signals. This study corroborates that copulas can serve as a viable and flexible tool to connect univariate marginal distributions of flood variables and quantify the associated risks, which may provide useful information for risk-based flood control.
Bibliographical noteFunding Information:
This work was jointly supported by the General Program of National Natural Science Foundation of China (No. 51479140), the Major Program of National Natural Science Foundation of China (No. 51239004), the Meteorological Research Open Foundation of Huai River Basin (No. HRM201403), and the National Natural Science Foundation of China (No. 41401612). The authors are very grateful for the kind help from the editors and the insightful comments from four anonymous reviewers. The final publication is available at Springer via http://dx.doi.org/10.1007/s12583-016-0625-4.
© 2016, China University of Geosciences and Springer-Verlag Berlin Heidelberg.
Copyright 2016 Elsevier B.V., All rights reserved.
- bivariate distribution
- climate change