Towards harmonisation of image velocimetry techniques for river surface velocity observations

Matthew Perks ,Lionel Pénard,Sabine Käfer,Peter Mayr,Salvatore Manfreda,Flavia Tauro,Magali Jodeau,Alain Goulet,Dariia Strelnikova,Silvano Fortunato Dal Sasso,Jérôme Le Coz,Gernot Paulus,Alexandre Hauet,Salvatore Grimaldi,Robert Ljubičić,Leigh Sinclair,Borbála Hortobágyi,Ian Maddock,Sophie Pearce,Alonso Pizarro,James Bomhof,E C Jamieson ,Salvador Peña‐Haro

doi:10.5194/essd-12-1545-2020

Abstract

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.

Highlights

When designing hydrological monitoring networks or acquiring opportunistic measurements for determining openchannel flow, the optimum choice of apparatus is likely to be a compromise between the data requirements, resource availability, and the hydro-geomorphic characteristics of the site (Mishra and Coulibaly, 2009)
As a result of some of these challenges, the potential for implementing alternative, non-contact approaches has been recently explored. Within this field of research, image velocimetry has emerged as an exciting new approach for determining a key hydrological characteristic, namely flow velocity
We present a range of datasets that have been compiled from across seven countries in order to facilitate these inter-comparison studies (Fig. 1, Perks et al, 2020). These data have been independently produced for the primarily purposes of (i) enhancing our understanding of open-channel flows in diverse flow regimes and (ii) testing specific image velocimetry techniques

Summary

Introduction

When designing hydrological monitoring networks or acquiring opportunistic measurements for determining openchannel flow, the optimum choice of apparatus is likely to be a compromise between the data requirements, resource availability, and the hydro-geomorphic characteristics of the site (Mishra and Coulibaly, 2009). We present a range of datasets that have been compiled from across seven countries in order to facilitate these inter-comparison studies (Fig. 1, Perks et al, 2020) These data have been independently produced for the primarily purposes of (i) enhancing our understanding of open-channel flows in diverse flow regimes and (ii) testing specific image velocimetry techniques. The compilation of these diverse datasets offers the research community a resource for addressing key challenges that have been identified in the use of image velocimetry algorithms These include (but are not limited to) the potential for the characteristics of the seeding material (e.g. particle density) to affect the resultant velocity estimates (Dal Sasso et al, 2018; Pizarro et al, 2020), the impact of UAS movement on velocity measurements (Lewis and Rhoads, 2018), and the testing of different image stabilisation approaches to address this. Additional assessments may concern the role of image pre-processing (e.g. background suppression; Thielicke and Stamhuis, 2014) and the role of pixel resolution and video length on errors under differing flow conditions (Tauro et al, 2018b)

Experimental design

Findings

Conclusions