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An Adaptive Ensemble Framework for Flood Forecasting and Its Application in a Small Watershed Using Distinct Rainfall Interpolation Methods

Author

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  • Yichao Xu

    (Huazhong University of Science and Technology)

  • Zhiqiang Jiang

    (Huazhong University of Science and Technology)

  • Yi Liu

    (Huazhong University of Science and Technology)

  • Li Zhang

    (Huazhong University of Science and Technology)

  • Jiahao Yang

    (Huazhong University of Science and Technology)

  • Hairun Shu

    (Huazhong University of Science and Technology)

Abstract

Runoff prediction has a pivotal role in the flood warning system. For mountainous small-sized watersheds, establishing a reliable and efficient model to forecast flood is multifarious and disorderly work. The ensemble framework for flash flood forecasting (EF5) provides a new opportunity to model simply and practically. However, the EF5 has not successfully verified its feasibility in mountainous small-sized basins and without satellite rainfall products. This paper used the framework to structure a flood forecast model without any satellite rainfall support for a small watershed in China where flash floods occur frequently. The evaluation indicated that the EF5 model performs well in flood prediction cases, with over 0.9 Pearson's linear correlation coefficient (PCC) values and over 0.85 Nash–Sutcliffe coefficient of efficiency (NSE) values during the validation. In addition, statistical results revealed that the EF5 model can maintain a PCC of more than 0.9, NSE of more than 0.7, and flood peak bias (FPB) of more than -0.2 when the forecast lead time exceeds 3 h. Numerous indicators and plots proved the excellent effect of the model forecasting. Considering the convenience and validity of this framework, the research and verification of the EF5 model in the mountainous small-sized basin are of significance to flood prediction. Graphical Abstract

Suggested Citation

  • Yichao Xu & Zhiqiang Jiang & Yi Liu & Li Zhang & Jiahao Yang & Hairun Shu, 2023. "An Adaptive Ensemble Framework for Flood Forecasting and Its Application in a Small Watershed Using Distinct Rainfall Interpolation Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2195-2219, March.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:5:d:10.1007_s11269-023-03489-x
    DOI: 10.1007/s11269-023-03489-x
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    References listed on IDEAS

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    1. Ayan Fleischmann & Walter Collischonn & Rodrigo Paiva & Carlos Eduardo Tucci, 2019. "Modeling the role of reservoirs versus floodplains on large-scale river hydrodynamics," 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. 99(2), pages 1075-1104, November.
    2. James Charalambous & Ataur Rahman & Don Carroll, 2013. "Application of Monte Carlo Simulation Technique to Design Flood Estimation: A Case Study for North Johnstone River in Queensland, Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4099-4111, September.
    3. Louis de Mesnard, 2013. "Pollution models and inverse distance weighting: some critical remarks," Post-Print hal-00778417, HAL.
    4. Yichao Xu & Yi Liu & Zhiqiang Jiang & Xin Yang & Xinying Wang & Yunkang Zhang & Yangyang Qin, 2022. "Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6149-6168, December.
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    Cited by:

    1. Yichao Xu & Xinying Wang & Zhiqiang Jiang & Yi Liu & Li Zhang & Yukun Li, 2023. "An Improved Fineness Flood Risk Analysis Method Based on Digital Terrain Acquisition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3973-3998, August.
    2. Chao Wang & Zhiqiang Jiang & Pengfei Wang & Yichao Xu, 2024. "A Fast Local Search Strategy Based on the Principle of Optimality for the Long-Term Scheduling of Large Cascade Hydropower Stations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 137-152, January.

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