4.6 Article

An assessment of large-scale flood modelling based on LiDAR data

Journal

HYDROLOGICAL PROCESSES
Volume 35, Issue 8, Pages -

Publisher

WILEY
DOI: 10.1002/hyp.14333

Keywords

flood management; GIS; hydraulic model; LISFLOOD-FP; validation; water level

Funding

  1. Ministere de la Securite publique du Quebec
  2. Ministere de l'Environnement et de la Lutte contre les changements climatiques du Quebec
  3. Natural Sciences and Engineering Research Council of Canada [RGPIN-2016-05466]

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This study introduces a novel large-scale flood modeling approach using high-resolution LiDAR DEMs and hydrogeomorphological GIS tools, demonstrating significant improvements in simulating flood levels at the watershed scale through validation in four different watersheds in Quebec.
Large-scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25-30 m. Here, high-resolution (1 m) LiDAR DEMs are employed to present a novel large-scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large-scale 1D/2D flood model LISFLOOD-FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS-automated procedure allows to obtain the average width required to run LISFLOOD-FP. The GIS-automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10-m resampled LiDAR DEM. Results show significant improvements to large-scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.

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