Optimizing Large Scale Particle Image Velocimetry Using a Multi Camera System

Abstract

Hydrometric monitoring plays a vital role in Canada’s environment. It is used for flood monitoring, climate change modelling, water resources engineering design and much more. Environment and Climate Change Canada (ECCC) operates over 2200 hydrometric stations across the country, collecting and publishing near real-time water level and discharge data. Discharge measurements are required to validate rating curves after major rainfall events and periodically throughout the year. Conventional methods can pose safety risks to hydrometric technologists in high flows. Additionally, in flashy streams at remote locations it is challenging to travel to the station in time to obtain the peak discharge. This is where methods such as image velocimetry could be used as an alternative to conventional methods. Numerous surface velocimetry algorithms have been created and adapted for the field, one of which is large scale particle image velocimetry (LSPIV). LSPIV utilizes video recordings of a river surface to estimate the surface velocity field, based on a cross-correlation technique that identifies the displacement between subsequent image frames of surface roughness or scatterers within individual interrogation areas (IA). This study aimed to tackle challenges of both LSPIV and stereoscopic LSPIV. A novel multi-camera LSPIV method was created by combining the best portions of the surface velocity field obtained by each camera. The channel was split in the middle and the portions of the surface velocity field for each half of the channel obtained from the respective adjacent camera were retained and then combined across the cross-section. Single camera LSPIV had absolute percent differences of 11.73%, 26.41%, and 21.05% for the gauge house camera, left bank (bridge) camera and right bank (bridge) camera, respectively, compared to ADCP measurements. The multi-camera method resulted in an improvement compared to the best single camera estimates in four of five surveys and had an absolute average difference of 8.19% compared to the ADCP. The results also reinforced the importance of surface texture and environmental conditions in LSPIV analysis.