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An Improved Fineness Flood Risk Analysis Method Based on Digital Terrain Acquisition

Author

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  • Yichao Xu

    (Huazhong University of Science and Technology)

  • Xinying Wang

    (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)

  • Yukun Li

    (China University of Geosciences)

Abstract

Flood risk analysis plays an essential role in flood warning systems and disaster prevention. The design flood is one flood event that probably occurs locally, drawn up for flood control design. Generally, the inundation of design floods under different return periods is simulated to determine the risk distribution of the area and to provide guidance for flood mitigation efforts. However, previous flood risk analysis studies have been stagnant at a large scale and crude level, with less practical guidance for regional or even individual flood prevention. In this study, we applied the digital terrain acquisition method to flood risk analysis, and finely analysed flood risk distribution for three disaster prevention object towns in China. The oblique photography technology of unmanned aerial vehicle flight was utilized innovatively in this paper and the one-two-dimensional coupling hydrodynamic model for design floods inundation was established based on the digital terrain results. Finally, the simulation results were superimposed on the high-definition orthophotos to meticulously analyse flood risk at the scale of the houses and to obtain fineness risk distribution, and a total of 42 high-risk houses, 866 medium-risk houses, and 600 low-risk houses were identified in their locations. This research is innovative, the results are reasonable and reliable, and the methods are of significant importance for both flood risk analysis and disaster prevention management. In addition, such approaches are easily transferable to other catchments or towns. Graphical Abstract

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:10:d:10.1007_s11269-023-03535-8
    DOI: 10.1007/s11269-023-03535-8
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Bosch, J.L. & Batlles, F.J. & Zarzalejo, L.F. & López, G., 2010. "Solar resources estimation combining digital terrain models and satellite images techniques," Renewable Energy, Elsevier, vol. 35(12), pages 2853-2861.
    4. 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.
    5. 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.
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