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Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Author

Listed:
  • Yang Peng

    (North China Electric Power University)

  • Xianliang Yu

    (North China Electric Power University)

  • Hongxiang Yan

    (Hydrology Technical Group, Pacific Northwest National Laboratory)

  • Jipeng Zhang

    (North China Electric Power University)

Abstract

An estimation of daily suspended sediment concentration (SSC) is required for water resource and environmental management. The traditional methods for simulating daily SSC focus on modeling the SSCs themselves, whereas the cross-correlation structure between SSC and streamflow has received only minor attention. To address this issue, we propose a stochastic method to generate long-term daily SSC using multivariate copula functions that account for temporal and cross dependences in daily SSCs. We use the conditional copula method to construct daily multivariate distributions to alleviate the complications and workload of parameter estimations using high-dimensional copulas. The observed daily streamflow and SSC data are normalized using the normal quantile transform method to relax the computationally intensive model of building daily marginal distributions. Daily SSCs can thus be simulated through the multivariate conditional distribution using previous daily SSC and concurrent daily streamflow values. The proposed method is rigorously examined by application to a case study at the Pingshan station in the Jinsha River Basin, China, and compared with the bivariate copula method. The results show that the proposed method has a high degree of accuracy, in preserving the statistics and temporal correlation of daily SSC observations, and better preserves the lag-0 cross correlation compared with the bivariate copula method. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resource and environmental management, which play a critical role in accurately estimating the frequency and magnitude of extreme SSC events.

Suggested Citation

  • Yang Peng & Xianliang Yu & Hongxiang Yan & Jipeng Zhang, 2020. "Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3913-3932, September.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:12:d:10.1007_s11269-020-02652-y
    DOI: 10.1007/s11269-020-02652-y
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    References listed on IDEAS

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    1. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    2. Vinod, Hrishikesh D., 2006. "Maximum entropy ensembles for time series inference in economics," Journal of Asian Economics, Elsevier, vol. 17(6), pages 955-978, December.
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    Cited by:

    1. Haoyu Jin & Xiaohong Chen & Ruida Zhong & Yingjie Pan & Tongtiegang Zhao & Zhiyong Liu & Xinjun Tu, 2022. "Spatiotemporal distribution analysis of extreme precipitation in the Huaihe River Basin based on continuity," 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. 114(3), pages 3627-3656, December.
    2. Yuming Huang & Yanjie Li & Min Liu & Liang Xiao & Fuwan Gan & Jian Jiao, 2022. "Uncertainty Analysis of Flood Control Design Under Multiple Floods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1175-1189, March.

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