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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-188
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-188
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 May 2020

12 May 2020

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A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Spatial distribution of tracers for optical sensing of stream surface flow

Alonso Pizarro1, Silvano F. Dal Sasso1, Matthew Perks2, and Salvatore Manfreda3 Alonso Pizarro et al.
  • 1Department of European and Mediterranean Cultures, University of Basilicata, Matera, 75100, Italy
  • 2School of Geography, Politics and Sociology, Newcastle University, Newcastle-upon-Tyne, NE1 7RU, UK
  • 3Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, 80125, Italy

Abstract. River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and level of aggregation. Therefore, a requirement is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of particle aggregation, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρ1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

Alonso Pizarro et al.

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Alonso Pizarro et al.

Data sets

Data on spatial distribution of tracers for optical sensing of stream surface flow A. Pizarro, S. F. Dal Sasso, M. Perks, and S. Manfreda https://doi.org/10.5281/zenodo.3761859

Alonso Pizarro et al.

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Latest update: 28 Sep 2020
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Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and therefore remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on these results, a dimensionless parameter was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof-of-concept of the proposed framework showing error reductions.
An innovative approach is presented to optimise image-velocimetry performances for surface flow...
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