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Reconstructing depth-averaged open-channel flows using image velocimetry and photogrammetry

Abstract : Steep streams with massive sediment supply are among the most complex systems to study, even in the laboratory. Their shallow sediment-laden flows create self-adjusting bed geometries that evolve rapidly. Often, morphological changes and flow processes cannot be dissociated. Because these very shallow and unstable flows cannot be equipped with measurement sensors, image analysis techniques, such as photogrammetry (e.g., structure-from-motion, SfM) and large-scale particle image velocimetry (LSPIV), are interesting options for capturing the characteristics of these systems. The present work describes a complete procedure using both techniques to measure spatially distributed surface velocity and bed properties (deposit patterns, channel slope, local roughness). The velocity data are used to assess the local flow directions along which the channel slope and roughness are extracted from the SfM digital elevation models. Ferguson's "variable power equation" friction law, having been previously validated by comparison with approximately 100 local flow depth measurements, was used in a second step with the collected data to reconstruct a complete mapping of the depth-averaged flows, thereby enabling a comprehensive analysis of the hydro-geomorphic system where shallow water equations apply. The assumptions, details, use of the friction law with roughness standard deviation rather than diameter as parameter and limitations of the procedure as well as possible sources of errors are discussed here, along with possibilities for improvements. This affordable and simple-to-implement procedure can provide a large amount of data, allowing for a more comprehensive analysis of complex hydraulic systems.
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Guillaume Piton, Alain Recking, Jerome Le Coz, Hervé Bellot, Alexandre Hauet, et al.. Reconstructing depth-averaged open-channel flows using image velocimetry and photogrammetry. Water Resources Research, American Geophysical Union, 2018, 54 (6), pp.4164-4179. ⟨10.1029/2017WR021314⟩. ⟨hal-01855840⟩

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