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. Although surface velocities are key in the calibration ofphysically based urban drainage models, the shallow water depths developedduring non-extreme precipitation and the potential risks during floodevents limit the availability of this type of data in urban catchments. Inthis context, imaging velocimetry techniques are being investigated assuitable non-intrusive methods to estimate runoff velocities, when thepossible influence of rain has yet to be analyzed. This study carried out acomparative assessment of different seeded and unseeded imaging velocimetrytechniques based on large-scale particle image velocimetry (LSPIV) andbubble image velocimetry (BIV) through six realistic butlaboratory-controlled experiments, in which the runoff generated by threedifferent rain intensities was recorded. First, the use ofnaturally generated bubbles and water shadows and glares as tracers allowsunseeded techniques to measure extremely shallow flows. However, thesetechniques are more affected by raindrop impacts, which even lead toerroneous velocities in the case of high rain intensities. At the same time,better results were obtained for high intensities and in complex flows withtechniques that use artificial particles. Finally, the study highlights thepotential of these imaging techniques for measuring surface velocities inreal field applications as well as the importance of considering rainproperties to interpret and assess the results obtained. The robustness ofthe techniques for real-life applications yet remains to be proven by meansof further studies in non-controlled environments.

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