Abstract
Abstract. River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated 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 their spatial distribution. Therefore, a principal research goal 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 tracer clustering, 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) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI=ν0.1/ρρcν1, where ν, ρ, and ρcν1 are the spatial-clustering level, the seeding density, and the reference seeding density at ν=1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9 % and 16.1 % were calculated – using PTV and PIV, respectively – by employing the optimal frame window.
Highlights
Streamflow observations are of enormous importance for environmental protection and engineering practice in general (Anderson et al, 2006; Manfreda, 2018; Manfreda et al, 2020; Owe, 1985)
Streamflow measurement campaigns are generally expensive and time-consuming, requiring the presence of highly qualified personnel and forward planning (Tauro et al, 2018). Such approaches are typically based on pointwise measurements performed with flowmeters or acoustic Doppler current profilers (ADCPs) that require the direct placement of the operators or devices into the water
This paper aims to quantify the accuracy of surface flow velocity (SFV) estimates under different seeding densities and spatial-clustering levels
Summary
Streamflow observations are of enormous importance for environmental protection and engineering practice in general (Anderson et al, 2006; Manfreda, 2018; Manfreda et al, 2020; Owe, 1985) Such observations are critical for many hydrological and hydraulic applications. This is necessary to provide a full description of the flow velocity profile, but on the other hand, these field methods might alter the measurements given the potential interaction of these elements with the flow These standard approaches can be challenging and sometimes impossible to perform under flood conditions, when operators and devices are unable to work in situ due to unfavourable circumstances
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