EGU2020-15525
https://doi.org/10.5194/egusphere-egu2020-15525
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Image-velocimetry techniques under particle aggregation for streamflow monitoring: a numerical approach

Alonso Pizarro1, Silvano Fortunato Dal Sasso2, and Salvatore Manfreda3
Alonso Pizarro et al.
  • 1Department of European and Mediterranean Cultures (DICEM), University of Basilicata, Matera, Italy (alonso.pizarro@unibas.it)
  • 2Consortium of Italian Universities for Hydrology (CINID), Potenza, Italy (silvano.dalsasso@cinid.it)
  • 3Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Napoli, Italy (salvatore.manfreda@unibas.it)

Monitoring extreme events with high accuracy and consistency is still a challenge, even by using up-to-date approaches. On the one side, field campaigns are in general expensive and time-consuming, requiring the presence of high-qualified personnel and forward planning. On the other side, non-contact approaches (such as image velocimetry, radars, and microwave systems) have had promising signs of progress in recent years, making now possible real-time flow monitoring. This work focuses on the estimation of surface flow velocities for streamflow monitoring under particle aggregation, in which tracers are not necessarily uniformly distributed across the entire field of view. This issue is extremely relevant for the computing stream flows since velocity errors are transmitted to river discharge estimations. Ad-hoc numerical simulations were performed to consider different levels of particle aggregation, particle colour and shapes, seeding density, and background noise. Particle Tracking Velocimetry (PTV) and Large-Scale Particle Image Velocimetry (LSPIV) were used for image velocimetry estimations due to their widely used worldwide. Comparisons between the theoretical and computed velocities were carried out to determine the associated uncertainty and optimal experimental setup that minimises those errors.

How to cite: Pizarro, A., Dal Sasso, S. F., and Manfreda, S.: Image-velocimetry techniques under particle aggregation for streamflow monitoring: a numerical approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15525, https://doi.org/10.5194/egusphere-egu2020-15525, 2020

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