The use of unmanned aerial vehicles in flood hazard assessment

Flood inundation models are central components of any flood risk analysis system because they transform the bulk discharge outputs from flood‐frequency analyses or rainfall‐runoff models into distributed predictions of flood hazard in terms of water depth, inundation extent, and flow velocity. The a...

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Vydáno v:Journal of flood risk management Ročník 13; číslo 4
Hlavní autoři: Karamuz, Emilia, Romanowicz, Renata J., Doroszkiewicz, Joanna
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford, UK Blackwell Publishing Ltd 01.12.2020
John Wiley & Sons, Inc
Wiley
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ISSN:1753-318X, 1753-318X
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Shrnutí:Flood inundation models are central components of any flood risk analysis system because they transform the bulk discharge outputs from flood‐frequency analyses or rainfall‐runoff models into distributed predictions of flood hazard in terms of water depth, inundation extent, and flow velocity. The accuracy of flood hazard maps depends on the availability of distributed observations of inundation outlines. Unfortunately, the acquisition of aerial photographs or satellite images is costly and in addition, their temporal resolution is strongly limited by weather conditions. Remote sensing based on unmanned aerial vehicles (UAV) is becoming increasingly popular due its flexibility and quickly decreasing costs. In particular, UAV can provide precise up to date georeferenced information about the location of a river shorelines, channel geometry, and vegetation. This information is particularly useful for the calibration and validation of distributed flood routing models. The application of cheap, well georeferenced UAV images of river shorelines is an unprecedented source of distributed observations. The aim of this article is to present a procedure for the updating of boundary conditions of the hydrodynamic model, based on UAV‐born data. The approach proposed is also a very effective means of on‐line updating of flood risk maps and their verification.
Bibliografie:Funding information
National Science Centre, Grant/Award Number: 2018/30/Q/ST10/00654; Ministry of Science and Higher Education
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ISSN:1753-318X
1753-318X
DOI:10.1111/jfr3.12622