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A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process

Author

Listed:
  • Quansen Wang

    (Huazhong University of Science and Technology)

  • Jianzhong Zhou

    (Huazhong University of Science and Technology)

  • Kangdi Huang

    (Huazhong University of Science and Technology)

  • Ling Dai

    (Huazhong University of Science and Technology)

  • Benjun Jia

    (Huazhong University of Science and Technology)

  • Lu Chen

    (Huazhong University of Science and Technology)

  • Hui Qin

    (Huazhong University of Science and Technology)

Abstract

In this study, a new method is developed to generate multi-site streamflow series. First, a correlated sampling method based on spectral decomposition is employed to simulate a large number of individual years of multi-site streamflow series. Then, combined with historical design flood data, flood characteristic series considering historical floods are generated. Finally, a subset of the streamflow series corresponding to these flood characteristics is selected from the pool of multi-site simulated streamflow series. The proposed model is applied to multi-site streamflow simulations in the upper Yangtze River basin. Experimental results show that the simulated data not only ensure the fundamental statistical characteristics of the observed data, but also provide good preservation of high order self-dependent, cross-dependent at finer and coarser time scales with any marginal distributions and any correlation structures. In addition, the simulated extreme situations are more reliable for risk and vulnerability analysis of multi-reservoir joint operation systems due to the consideration of the historical design flood data.

Suggested Citation

  • Quansen Wang & Jianzhong Zhou & Kangdi Huang & Ling Dai & Benjun Jia & Lu Chen & Hui Qin, 2021. "A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1011-1027, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-021-02769-8
    DOI: 10.1007/s11269-021-02769-8
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    References listed on IDEAS

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    1. Yongqi Liu & Hui Qin & Li Mo & Yongqiang Wang & Duan Chen & Shusen Pang & Xingli Yin, 2019. "Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 337-354, January.
    2. Shahnoor Shanta & Visakan Kadirkamanathan, 2015. "A bootstrap-based approach for parameter and polyspectral density estimation of a non-minimum phase ARMA process," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 418-428, February.
    3. Đurica Marković & Jasna Plavšić & Nesa Ilich & Siniša Ilić, 2015. "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4787-4801, October.
    4. Mohammad Mondal & Jahir Chowdhury & Md. Ferdous, 2010. "Risk-Based Evaluation for Meeting Future Water Demand of the Brahmaputra Floodplain Within Bangladesh," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 853-869, March.
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