New communication and digital image technologies have enabled the public to produce large quantities of flood observations and share them through social media. In addition to flood incident reports, valuable hydraulic data such as the extent and depths of inundated areas and flow rate estimates can be computed using messages, photos and videos produced by citizens. Such crowdsourced data help improve the understanding and modelling of flood hazard. Since little feedback on similar initiatives is available, we introduce three recent citizen science projects which have been launched independently by research organisations to quantitatively document flood flows in catchments and urban areas of Argentina, France, and New Zealand. Key drivers for success appear to be: a clear and simple procedure, suitable tools for data collecting and processing, an efficient communication plan, the support of local stakeholders, and the public awareness of natural hazards.

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The probability of the occurrence of urban flash floods has increased appreciably in recent years. Scientists have published various articles related to the estimation of the vulnerability of people and vehicles in urban areas resulting from flash floods. However, most published works are based on research performed using numerical models and laboratory experiments. This paper presents a novel approach that combines the implementation of image velocimetry technique (large-scale particle image velocimetry-LSPIV) using a flash flood video recorded by the public locally and the estimation of the vulnerability of people and vehicles to high water velocities in urban areas. A numerical onedimensional hydrodynamic model has also been used in this approach for water velocity characterization. The results presented in this paper correspond to a flash flood resulting on November 29, 2012, in the city of Asunción in Paraguay. During this flash flood, people and vehicles were observed being carried away because of high water velocities. Various sequences of the recorded flash flood video were characterized using LSPIV. The results obtained in this work validate the existing vulnerability criterion based on the effect of the flash flood and resulting high water velocities on people and vehicles.