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A Cellular Automata Based Rainfall-Runoff Model for Urban Inundation Analysis Under Different Land Uses

Author

Listed:
  • Shuang Yao

    (Wuhan University)

  • Nengcheng Chen

    (Wuhan University
    Collaborative Innovation Center of Geospatial Technology)

  • Wenying Du

    (Wuhan University)

  • Chao Wang

    (Wuhan University)

  • Cuizhen Chen

    (Institute of Water Science Research in Wuhan)

Abstract

Recently many runoff models based on cellular automaton (CA) have been developed to simulate floods; however, the existing models cannot be readily applied to complex urban environments. This study proposes a novel rainfall-runoff model based on CA (RRCA) to simulate inundation. Its main contributions include a fine runoff generation process that considers 12 urban scenarios rather than a single land use type and the confluence process determined by the new transition rules considering water supply and demand (WS-WD transition rules). RRCA was compared with another CA based flood model (E2DCA). With the benchmark model, the results showed that there was good agreement, with an R-squared greater than 0.9, and that RRCA was more sensitive to waterlogging levels than E2DCA. Furthermore, the simulated vegetation interception, infiltration and drainage processes had varying degrees of impact on waterlogging. Corresponding measures can be taken in urban flood management according to the identification of areas experiencing drainage difficulties.

Suggested Citation

  • Shuang Yao & Nengcheng Chen & Wenying Du & Chao Wang & Cuizhen Chen, 2021. "A Cellular Automata Based Rainfall-Runoff Model for Urban Inundation Analysis Under Different Land Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1991-2006, April.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02826-2
    DOI: 10.1007/s11269-021-02826-2
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    References listed on IDEAS

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    1. J. Teng & J. Vaze & S. Kim & D. Dutta & A. J. Jakeman & B. F. W. Croke, 2019. "Enhancing the Capability of a Simple, Computationally Efficient, Conceptual Flood Inundation Model in Hydrologically Complex Terrain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 831-845, January.
    2. Jenna Tyler & Abdul-Akeem Sadiq & Douglas S. Noonan, 2019. "A review of the community flood risk management literature in the USA: lessons for improving community resilience to floods," 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. 96(3), pages 1223-1248, April.
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    Cited by:

    1. Jinping Zhang & Hang Zhang & Hongyuan Fang, 2022. "Study on Urban Rainstorms Design Based on Multivariate Secondary Return Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2293-2307, May.
    2. Bartosz Szeląg & Roman Suligowski & Grzegorz Majewski & Przemysław Kowal & Adrian Bralewski & Karolina Bralewska & Ewa Anioł & Wioletta Rogula-Kozłowska & Francesco Paola, 2022. "Application of Multinomial Logistic Regression to Model the Impact of Rainfall Genesis on the Performance of Storm Overflows: Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3699-3714, August.
    3. Hongfa Wang & Xinjian Guan & Yu Meng & Zening Wu & Kun Wang & Huiliang Wang, 2023. "Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1275-1295, February.
    4. Zhangjun Liu & Jingwen Zhang & Tianfu Wen & Jingqing Cheng, 2022. "Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4981-4993, October.

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