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Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth

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Abstract

Mapping floods is important for policy makers to make timely decisions in regards to emergency responses and future planning. It is therefore crucial to develop a rapid inundation modelling framework to map flood inundation. This study develops an airborne scanning laser altimetry (LiDAR) digital elevation model (DEM) based Rapid flood Inundation Modelling framework (LiDAR-RIM) for assessment of inundation extent, depth, volume and duration for flood events. The modelling framework has been applied to the mid-Murrumbidgee region in the southeast Murray-Darling Basin, Australia for two flood events occurred in December 2010 and March 2012. The inundation extents estimated using this methodology compared well to those obtained from two Landsat ETM+ images, demonstrating suitability and applicability of this method. For testing possibility of larger area application, the framework also uses 30-m resolution shuttle radar topography mission (SRTM)-DEM to replace LiDAR-DEM for the same modelling. The inundation extents obtained by using the SRTM-DEM are smaller than those obtained using the LiDAR-DEM, especially for large flood events. A possible reason is that the river cross sections obtained from the SRTM-DEM are not accurate enough for inundation modelling. The LiDAR-RIM has an advantage for process modelling and scenario modelling under future climatic conditions.

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Authors and Affiliations

  1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Yongqiang Zhang

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  1. Yongqiang Zhang
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Correspondence to Yongqiang Zhang.

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Foundation

CAS Talents Program and IGSNRR Supporting Fund, No.YJRCPT2019-101

Author

Zhang Yongqiang, PhD, E-mail: zhangyq@igsnrr.ac.cn

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Zhang, Y. Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth. J. Geogr. Sci. 30, 1649–1663 (2020). https://doi.org/10.1007/s11442-020-1805-9

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  • DOI: https://doi.org/10.1007/s11442-020-1805-9

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