Movies taken by witnesses of extreme ood events are increasingly available on video sharing websites. They potentially provide highly valuable information on ow velocities and hydraulic processes that can help improve the post-ood determination of discharges in streams and ooded areas. We investigated the troubles and potential of applying the now mature LSPIV technique to such ood movies that are recorded under non-ideal conditions. Processing was performed using user-friendly, free software only, such as Fudaa-LSPIV. Typical issues related to the image processing and to the hydrological analysis are illustrated using a selected example of a pulsed ash-ood ow lmed in a mountainous torrent. Simple corrections for lens distortion (sheye) and limited incoherent camera movement (shake) were successfully applied and the related errors were reduced to a few percents. Testing the dierent image resolution levels oered by YouTube showed that the dierence in time-averaged longitudinal velocity was less than 5% compared to full resolution. A limited number of GRPs, typically 10, is required but they must be adequately distributed around the area of interest. The indirect determination of the water level is the main source of uncertainty in the results, usually much more than errors due to the longitudinal slope and waviness of the ow free-surface. The image-based method yielded direct discharge estimates of the base ow between pulses, of the pulse waves, and of the time-averaged ow over a movie sequence including a series of 5 pulses. A comparison with traditional indirect determination methods showed that the criticaldepth method may produce signicantly biased results for such a fast, unsteady ow, while the 1 Author-produced version of the article published in Hydrological Processes (2016), Volume 30, Issue 1, p 90-105The original publication is available at http://onlinelibrary.wiley.com DOI: 10.1002/hyp.10532 slope-area method seems to be more robust but would overestimate the time-averaged ow rate if applied to the high-water marks of a pulsed ow.

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Abstract. 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 that are 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 reference 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 seven countries across Europe and North America, consisting of videos that have been subjected to a range of pre-processing and image velocimetry analyses (Perks et al., 2020, https://doi.org/10.4121/uuid:014d56f7-06dd-49ad-a48c-2282ab10428e). Reference data are available for 12 of the 13 case studies presented, enabling these data to be used for reference and accuracy assessment.

We present a novel motion estimation technique for image-based river velocimetry. It is based on the so-called optical flow, which is a well developed method for rigid motion estimation in image sequences, devised in computer vision community. Contrary to PIV (Particle Image Velocimetry) techniques, optical flow formulation is flexible enough to incorporate physics equations that govern the observed quantity motion. Over the past years, it has been adopted by experimental fluid dynamics community where many new models were introduced to better represent different fluids motions, (see (Heitz et al., 2010) for a review). Our optical flow is based on the scalar transport equation and is augmented with a weighted diffusion term to compensate for small scale (non-captured) contributions. Additionally, since there is no ground truth data for such type of image sequences, we present a new evaluation method to assess the results. It is based on trajectory reconstruction of few Lagrangian particles of interest and a direct comparison against their manually-reconstructed trajectories. The new motion estimation technique outperformed traditional optical flow and PIV-based methods.