Assessing different imaging velocimetry techniques to measure shallow runoff velocities during rain events using an urban drainage physical model

Abstract. Although surface velocities are key in the calibration of physically based urban drainage models, the shallow water depths developed during non-extreme precipitations and the potential risks during flood events limit the availability of this type of data in urban catchments. In this context, imaging velocimetry techniques are being investigated as suitable non-intrusive methods to estimate runoff velocities, when the possible influence of rain has yet to be analyzed. This study carried out a comparative assessment of different seeded and unseeded imaging velocimetry techniques: Large Scale Particle Image Velocimetry (LSPIV); Surface Structure Image Velocimetry (SSIV); and Bubble Image Velocimetry (BIV), through six realistic but laboratory-controlled experiments where the runoff generated by three different rain intensities was recorded. First, the use of naturally-generated bubbles and water shadows and glares as tracers allows the unseeded techniques (SSIV and BIV) to measure extremely shallow flows, but these are more affected by raindrop impacts, which even lead to erroneous velocities in the case of the highest rain intensities. At the same time, better results were obtained with techniques that use artificial particles for high intensities and in complex flows. Finally, the study highlights the feasibility of these imaging techniques to be used in measuring surface velocities in real field applications and the importance of considering rain properties to interpret and assess the results obtained.