Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China

Kai Duan; Yadong Mei; Liping Zhang

doi:10.1007/s12583-016-0625-4

Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China

Kai Duan, Yadong Mei, Liping Zhang

Forest Resources

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	37-46
Number of pages	10
Journal	Journal of Earth Science
Volume	27
Issue number	1
DOIs	https://doi.org/10.1007/s12583-016-0625-4
State	Published - Feb 1 2016

Bibliographical note

Funding 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.

Publisher Copyright:
© 2016, China University of Geosciences and Springer-Verlag Berlin Heidelberg.

Keywords

bivariate distribution
climate change
copulas
flood

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1007/s12583-016-0625-4

Find It

Find It at U of M Libraries

Cite this

@article{e35fe130d59040b083e8410b948b01d9,

title = "Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China",

abstract = "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.",

keywords = "bivariate distribution, climate change, copulas, flood",

author = "Kai Duan and Yadong Mei and Liping Zhang",

note = "Funding 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. Publisher Copyright: {\textcopyright} 2016, China University of Geosciences and Springer-Verlag Berlin Heidelberg.",

year = "2016",

month = feb,

day = "1",

doi = "10.1007/s12583-016-0625-4",

language = "English (US)",

volume = "27",

pages = "37--46",

journal = "Journal of Earth Science",

issn = "1674-487X",

publisher = "China University of Geosciences",

number = "1",

}

TY - JOUR

T1 - Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China

AU - Duan, Kai

AU - Mei, Yadong

AU - Zhang, Liping

N1 - Funding 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. Publisher Copyright: © 2016, China University of Geosciences and Springer-Verlag Berlin Heidelberg.

PY - 2016/2/1

Y1 - 2016/2/1

N2 - 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.

AB - 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.

KW - bivariate distribution

KW - climate change

KW - copulas

KW - flood

UR - http://www.scopus.com/inward/record.url?scp=84957087363&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84957087363&partnerID=8YFLogxK

U2 - 10.1007/s12583-016-0625-4

DO - 10.1007/s12583-016-0625-4

M3 - Article

AN - SCOPUS:84957087363

SN - 1674-487X

VL - 27

SP - 37

EP - 46

JO - Journal of Earth Science

JF - Journal of Earth Science

IS - 1

ER -

Copula-based bivariate flood frequency analysis in a changing climate—A case study in the Huai River Basin, China

Abstract

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

Other files and links

Fingerprint

Cite this