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Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain

Author

Listed:
  • Jianzhu Li

    (Tianjin University)

  • Yuming Lei

    (Tianjin University)

  • Senming Tan

    (Pearl River Comprehensive Technology Center (Information Center) of Pearl River Water Resources Commission of the Ministry of Water Resources)

  • Colin D. Bell

    (Purdue University)

  • Bernard A. Engel

    (Purdue University)

  • Yixuan Wang

    (Tianjin University)

Abstract

Flood frequency analysis for practical application is traditionally based on the assumption of stationarity, but this assumption has been open to doubt in recent years. A number of studies have focused on the nonstationary flood frequency analysis, and the associated causes of nonstationarity. In this study, the annual maximum flood peak and flood volume of Wangkuai reservoir watershed were used, and several univariate and bivariate models were established to investigate the nonstationary flood frequency, with the distribution parameters changing over the climate indices (NPO, Niño3) and the check dam indices (CDIp, CDIv). In the univariate models, the Weibull distribution performed best and exhibited an undulate behavior for both flood peak and volume, which tended to describe the nonstationarity reasonably well. The bivariate models were constructed using copulas, of which the optimal Weibull distribution in the univariate flood frequency analysis was considered as marginal distributions within the joint distribution. The results showed that the Gumbel-Hougaard copula offered the best joint distribution, and most of the probability isolines crossed each other, which demonstrated the possibility that the occurrence of combinations of the flood peak and volume may be the same under multiple effects of phase changes in the climate patterns and certain human activities (i.e. soil and water conservation). The most likely events were elaborated in diagrams, and the associated combinations of the flood peak and volume were smaller than that estimated by the fixed parameters (i.e. stationary condition) during most of the study period, while it was the opposite in 1956, 1959 and 1963. The results highlight the necessity of nonstationary flood frequency analysis under various conditions in both univariate and multivariate domains.

Suggested Citation

  • Jianzhu Li & Yuming Lei & Senming Tan & Colin D. Bell & Bernard A. Engel & Yixuan Wang, 2018. "Nonstationary Flood Frequency Analysis for Annual Flood Peak and Volume Series in Both Univariate and Bivariate Domain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4239-4252, October.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:13:d:10.1007_s11269-018-2041-2
    DOI: 10.1007/s11269-018-2041-2
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    References listed on IDEAS

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    1. L. Vasiliades & P. Galiatsatou & A. Loukas, 2015. "Nonstationary Frequency Analysis of Annual Maximum Rainfall Using Climate Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 339-358, January.
    2. Runtong Zhang & Zhenji Zhang & Kecheng Liu & Juliang Zhang (ed.), 2015. "Liss 2013," Springer Books, Springer, number 978-3-642-40660-7, September.
    3. Khodayar Abdollahi & Pablo Guzmán & Marijke Huysmans & Okke Batelaan, 2016. "Rainfall-runoff modelling using a spatially distributed electrical circuit analogue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1279-1300, June.
    4. Jianzhu Li & Senming Tan, 2015. "Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5533-5550, December.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    6. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    7. Antonino Cancelliere, 2017. "Non Stationary Analysis of Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3097-3110, August.
    8. Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), 2015. "Liss 2014," Springer Books, Springer, edition 127, number 978-3-662-43871-8, September.
    9. J. Rolf Olsen & James H. Lambert & Yacov Y. Haimes, 1998. "Risk of Extreme Events Under Nonstationary Conditions," Risk Analysis, John Wiley & Sons, vol. 18(4), pages 497-510, August.
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    3. Rongrong Li & Lihua Xiong & Cong Jiang & Wenbin Li & Chengkai Liu, 2023. "Quantifying multivariate flood risk under nonstationary condition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 1161-1187, March.

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