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Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method

Author

Listed:
  • Shuhui Guo

    (Wuhan University
    Pearl River Comprehensive Technology Center of Pearl River Water Resources Commission)

  • Lihua Xiong

    (Wuhan University
    Pearl River Comprehensive Technology Center of Pearl River Water Resources Commission)

  • Jie Chen

    (Wuhan University
    Pearl River Comprehensive Technology Center of Pearl River Water Resources Commission)

  • Shenglian Guo

    (Wuhan University
    Pearl River Comprehensive Technology Center of Pearl River Water Resources Commission)

  • Jun Xia

    (Wuhan University
    Pearl River Comprehensive Technology Center of Pearl River Water Resources Commission)

  • Ling Zeng

    (Bureau of Hydrology, Changjiang Water Resources Commission)

  • Chong-Yu Xu

    (University of Oslo)

Abstract

Most researches on regional flood frequency analysis (RFFA) have proved that the incorporation of hydrologic information (e.g., catchment attributes and flood records) from different sites in a region can provide more accurate flood estimation than using only the observed flood series at the site of concern. One kind of RFFA is based on the Bayesian method with prior information inferred from regional regression by using the generalized least squares (GLS) model, which is more flexible than other RFFA methods. However, the GLS model for regional regression is a stationary method and not suitable for coping with nonstationary prior information. In this study, in nonstationary condition, the Bayesian RFFA with the prior information inferred from regional regression by using the linear mixed effect (LME) model (i.e. a model that adds random effects to the GLS model) is investigated. Both the GLS-based and LME-based Bayesian RFFA methods have been applied to four hydrological stations within the Dongting Lake basin for comparison, and the results show that the performance of nonstationary LME-based Bayesian RFFA method is better than that of stationary GLS-based Bayesian RFFA method according to the deviance information criterion (DIC). Compared with the stationary GLS-based Bayesian RFFA method, changes in uncertainty of regression coefficients estimation of at-site flood distribution parameters are different from site to site by using the nonstationary LME-based Bayesian RFFA method. The use of nonstationary LME-based Bayesian RFFA method reduces design flood uncertainty, especially for the very small exceedance probability at the tail. This study extends the application of the Bayesian RFFA method to the nonstationary condition, which is helpful for nonstationary flood frequency analysis of ungauged sites.

Suggested Citation

  • Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:2:d:10.1007_s11269-022-03394-9
    DOI: 10.1007/s11269-022-03394-9
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    References listed on IDEAS

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    1. Qianyu Gao & Guofang Li & Jin Bao & Jian Wang, 2021. "Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4435-4456, October.
    2. Soumyashree Dixit & K. V. Jayakumar, 2022. "A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1217-1236, March.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    5. Pezhman Allahbakhshian-Farsani & Mehdi Vafakhah & Hadi Khosravi-Farsani & Elke Hertig, 2020. "Regional Flood Frequency Analysis Through Some Machine Learning Models in Semi-arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2887-2909, July.
    6. 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.
    7. 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).
    8. Homa Razmkhah & Alireza Fararouie & Amin Rostami Ravari, 2022. "Multivariate Flood Frequency Analysis Using Bivariate Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 729-743, January.
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