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Application of LiDAR UAV for High-Resolution Flood Modelling

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Abstract

To overcome the difficulty of collecting fine-grained terrain data that is important for flood modelling, this work presents a method for the application of UAV-based LiDAR techniques to drive high-resolution flood propagation and inundation modelling. This paper comprehensively introduces the UAV platform, LiDAR sensor and data processing techniques required and proposes the approach for obtaining refined DEM for flood management using which the DEM accuracy can reach ±3 cm. Accordingly, two kinds of terrains, a small mountain area and a large urban area, have been measured and the time requirements for the method are 5 h and 2 days respectively. Based on the collected data, a full hydrodynamic numerical flood model is applied to simulate a flash flood in the mountain catchment and an urban flood at high-resolution. The results show that the water depth and velocity affected by key micro terrain features, such as tiny channels and roads, can be captured and considered, indicating that LiDAR UAV techniques are an efficient and reliable method for surveying terrain making them highly important for creating high accurate flood simulation.

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Data Availability

The data and code that support the study are available from the corresponding author upon reasonable request.

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Funding

This work is partly supported by the National Natural Science Foundation of China (No. 52079106), National Key Research and Development Program of China (No. 2016YFC0402704) and Visiting Researcher Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science (No. 2016HLG01).

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Conceptualization and Methodology: J. Hou, B. Li; Writing-original draft preparation: B. Li; Material preparation, collection and analysis: B. Li, D. Li, D. Yang, H. Han, X. Bi, X. Wang; Supervision: R. Hinkelmann, J. Xia; Funding acquisition: J. Hou.

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Correspondence to Jingming Hou.

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Li, B., Hou, J., Li, D. et al. Application of LiDAR UAV for High-Resolution Flood Modelling. Water Resour Manage 35, 1433–1447 (2021). https://doi.org/10.1007/s11269-021-02783-w

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  • DOI: https://doi.org/10.1007/s11269-021-02783-w

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  1. Bingyao Li