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Stochastic Flood Simulation Method Combining Flood Intensity and Morphological Indicators

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  • Xiaodi Fu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
    China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Research Center on Flood & Drought Disaster Prevention and Reduction, Ministry of Water Resources, Beijing 100038, China)

  • Xiaoyan He

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
    China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Research Center on Flood & Drought Disaster Prevention and Reduction, Ministry of Water Resources, Beijing 100038, China)

  • Liuqian Ding

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China
    China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

Abstract

The existing flood stochastic simulation methods are mostly applied to the stochastic simulation of flood intensity characteristics, with less consideration for the randomness of the flood hydrograph shape and its correlation with intensity characteristics. In view of this, this paper proposes a flood stochastic simulation method that combines intensity and morphological indicators. Using the Foziling and Xianghongdian reservoirs in the Pi River basin in China as examples, this method utilizes a three-dimensional asymmetric Archimedean M6 Copula to construct stochastic simulation models for peak flow, flood volume, and flood duration. Based on K-means clustering, a multivariate Gaussian Copula is employed to construct a dimensionless flood hydrograph stochastic simulation model. Furthermore, separate two-dimensional symmetric Copula stochastic simulation models are established to capture the correlations between flood intensity characteristics and shape variables such as peak shape coefficient, peak occurrence time, rising inflection point angle, and coefficient of variation. By evaluating the fit between the simulated flood characteristics and the dimensionless flood hydrograph, a complete flood hydrograph is synthesized, which can be applied in flood control dispatch simulations and other related fields. The feasibility and practicality of the proposed model are analyzed and demonstrated. The results indicate that the simulated floods closely resemble natural floods, making the simulation outcomes crucial for reservoir scheduling, risk assessment, and decision-making processes.

Suggested Citation

  • Xiaodi Fu & Xiaoyan He & Liuqian Ding, 2023. "Stochastic Flood Simulation Method Combining Flood Intensity and Morphological Indicators," Sustainability, MDPI, vol. 15(18), pages 1-25, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:14032-:d:1245063
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    References listed on IDEAS

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    1. Hongjun Lei & Jie Yu & Hongwei Pan & Jie Li & Shah Jahan Leghari & Chongju Shang & Zheyuan Xiao & Cuicui Jin & Lili Shi, 2023. "A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    2. Changjiang Xu & Jiabo Yin & Shenglian Guo & Zhangjun Liu & Xingjun Hong, 2016. "Deriving Design Flood Hydrograph Based on Conditional Distribution: A Case Study of Danjiangkou Reservoir in Hanjiang Basin," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, March.
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    Cited by:

    1. Huaqing Zhao & Hao Wang & Yuxuan Zhang & Ranhang Zhao & Zhen Qi & Haodong Zhang, 2024. "Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir," Sustainability, MDPI, vol. 16(12), pages 1-23, June.
    2. Xianli Wang & Zhigang Zhao & Feilong Jie & Jingjing Xu & Sheng Li & Kun Hao & Youliang Peng, 2024. "A Copula Function–Monte Carlo Method-Based Assessment of the Risk of Agricultural Water Demand in Xinjiang, China," Agriculture, MDPI, vol. 14(11), pages 1-23, November.

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