Spatial distribution of tracers for optical sensing of stream surface flow

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 νρ, 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.

Numerical simulation of clustered tracers.

How to cite: Pizarro, A., Dal Sasso, S. F., Perks, M., and Manfreda, S.: Spatial distribution of tracers for optical sensing of stream surface flow, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-188, in review, 2020. [pdf]

New Insights Offered by UAS for River Monitoring

Traditional monitoring approaches are unlikely to provide the level of detail required to advance our understanding and description of the underlying physical processes and mechanisms for both technical and economical limitations (Manfreda and McCabe, 2019). Indeed, our ability to monitor system processes in the face of recent climate and anthropogenic changes is being increasingly compromised by the significant decline in the number of monitoring installations over the last few decades (Shiklomanov et al., 2002). The dynamic nature and inherent variability of many hydrological processes dictates a need for new monitoring technologies and approaches able to increase spatial and temporal resolution of data.

How to cite: Manfreda, S., Dal Sasso, S. F., Pizarro, A., & Tauro, F. New Insights Offered by UAS for River Monitoring. Applications of Small Unmanned Aircraft Systems: Best Practices and Case Studies, 211, 2019.

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Towards harmonization of image velocimetry techniques for river surface velocity observations

Since the turn of the 21st Century, image based velocimetry techniques have become an increasingly popular approach for determining open-channel flow in a range of hydrological settings across Europe, and beyond. Simultaneously, a range of large-scale image velocimetry algorithms have been developed, equipped with differing image pre-processing, and analytical capabilities. Yet in operational hydrometry, these techniques are utilised by few competent authorities. Therefore, imagery collected for image velocimetry analysis, along with validation data is required both to enable inter-comparisons between these differing approaches and to test their overall efficacy. Through benchmarking exercises, it will be possible to assess which approaches are best suited for a range of fluvial settings, and to focus future software developments. Here we collate, and describe datasets acquired from six countries across Europe and Asia, consisting of videos that have been subjected to a range of pre-processing, and image velocimetry analysis.We present both the raw footage and processed imagery along with information about the processing parameters used. Validation data is available for 12 of the 13 case studies presented enabling these data to be used for validation and accuracy assessment.

Figure 1. Distribution of the study sites considered.

How to cite: Perks, M. T., S. Fortunato Dal Sasso, A. Hauet, S. Pearce, S. Peña-Haro, F. Tauro, S. Grimaldi, B. Hortobágyi, M. Jodeau, J. Le Coz, I. Maddock, L. Pénard, and S. Manfreda, Towards harmonization of image velocimetry techniques for river surface velocity observations, Earth System Science Data Discussion, https://www.earth-syst-sci-data-discuss.net/essd-2019-133/, 2019. [pdf]

Exploring the optimal experimental setup for surface flow velocity measurements using PTV

Advances in flow monitoring are crucial to increase our knowledge on basin hydrology and to understand the interactions between flow dynamics and infrastructures. In this context, image processing offers great potential for hydraulic monitoring, allowing acquisition of a wide range of measurements with high spatial resolution at relatively low costs. In particular, the particle tracking velocimetry (PTV) algorithm can be used to describe the dynamics of surface flow velocity in both space and time using fixed cameras or unmanned aerial systems (UASs). In this study, analyses allowed exploration of the optimal particle seeding density and frame rate in different configurations. Numerical results provided useful indications for two field experiments that have been carried out with a low-cost quadrocopter equipped with an optical camera to record RGB videos of floating tracers manually distributed over the water surface. Field measurements have been carried out using different natural tracers under diverse hydraulic and morphological conditions; PTV’s processed velocities have been subsequently benchmarked with current meter measurements. The numerical results allowed rapid identification of the experimental configuration (e.g., required particle seeding density, image resolution, particle size, and frame frequency) producing flow velocity fields with high resolution in time and space with good agreement with the benchmark velocity values measured with conventional instruments.

How to cite: Dal Sasso, S. F., A. Pizarro, C. Samela, L. Mita, and S. Manfreda, Exploring the optimal experimental setup for surface flow velocity measurements using PTVEnvironmental Monitoring and Assessment, 190:460, (doi: 10.1007/s10661-018-6848-3) 2018. [pdf]

Chapter 10: New insights offered by UAS for river monitoring

Traditional monitoring approaches are unlikely to provide the level of detail required to advance our understanding and description of the underlying physical processes and mechanisms for both technical and economical limitations (Manfreda and McCabe, 2019). Indeed, our ability to monitor system processes in the face of recent climate and anthropogenic changes is being increasingly compromised by the significant decline in the number of monitoring installations over the last few decades (Shiklomanov, Lammers, and Vörösmarty, 2002). The dynamic nature and inherent variability of many hydrological processes dictates a need for new monitoring technologies and approaches able to increase spatial and temporal resolution of data.

How to cite: S. Manfreda, S. F. Dal Sasso, A. Pizarro, F. Tauro, Chapter 10: New insights offered by UAS for river monitoring, in press, 2019.