Streamflow Observations From Cameras: Large‐Scale Particle Image Velocimetry or Particle Tracking Velocimetry?

• Published in: Water resources research 2017-12, Vol.53 (12), p.10374-10394
• Main Authors: Tauro, F., Piscopia, R., Grimaldi, S.
• Format: Article
• Language: English
• Subjects: Algorithms; Cameras; Data processing; Environmental monitoring; Evaluation; Field of view; High flow; Mathematical models; Methods; Natural streams; Particle image velocimetry; Particle tracking; Particle tracking velocimetry; Rivers; Stream discharge; Stream flow; Streamflow measurements; Surface velocity; Temporal resolution; Visibility; Visibility conditions
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1002/2017WR020848

Image‐based methodologies, such as large scale particle image velocimetry (LSPIV) and particle tracking velocimetry (PTV), have increased our ability to noninvasively conduct streamflow measurements by affording spatially distributed observations at high temporal resolution. However, progress in optical methodologies has not been paralleled by the implementation of image‐based approaches in environmental monitoring practice. We attribute this fact to the sensitivity of LSPIV, by far the most frequently adopted algorithm, to visibility conditions and to the occurrence of visible surface features. In this work, we test both LSPIV and PTV on a data set of 12 videos captured in a natural stream wherein artificial floaters are homogeneously and continuously deployed. Further, we apply both algorithms to a video of a high flow event on the Tiber River, Rome, Italy. In our application, we propose a modified PTV approach that only takes into account realistic trajectories. Based on our findings, LSPIV largely underestimates surface velocities with respect to PTV in both favorable (12 videos in a natural stream) and adverse (high flow event in the Tiber River) conditions. On the other hand, PTV is in closer agreement than LSPIV with benchmark velocities in both experimental settings. In addition, the accuracy of PTV estimations can be directly related to the transit of physical objects in the field of view, thus providing tangible data for uncertainty evaluation. Image‐based streamflow observations are rarely implemented in practice due to Large‐Scale Particle Image Velocimetry sensitivity to tracers Applied to images in controlled conditions and from a gauge‐cam, LSPIV underestimates velocity while PTV is in agreement with benchmark data A modified PTV that implements a trajectory‐based filtering procedure offers tangible data to evaluate the accuracy of velocity estimations