UBC Theses and Dissertations
Streamflow monitoring in a time of change : using image velocimetry methods on citizen videos of the November 2021 flooding in Merritt, British Columbia Yang, Jian Song
The atmospheric river event in November 2021 is one of the costliest natural disasters in Canadian history. Among others, the Coldwater River in Merritt, British Columbia breached its banks on November 15th, 2021, resulting in extensive damage to the infrastructure and total evacuation of the residents. Estimating the magnitude of this flood is difficult, as it damaged the local flow monitoring station and altered the surrounding landscape. Parts of this flooding event, including the flow close to its peak, were filmed by local residents using mobile devices or drones. Though with significant perspective distortion and imprecision, they still provide valuable information on this extreme event, which would have otherwise been neglected. This study aims to apply image velocimetry techniques to some of these videos, with limited resources and outdated geodata, for reconstructing surface velocities and discharges during the flood. The analysis method consists of using Large Scale Particle Image Velocimetry and Farneback optical flow on the original clips where possible. The extreme and post-event nature of the flood requires changes to many aspects of the conventional image velocimetry workflow. Ground Control Points are identified in the videos, then geolocated or surveyed after the flood, for rectification of raw velocities from image to real-world coordinates. This conservative measure allows unlimited iterations in orthorectification. Discharges are then calculated using surveyed transects, with water surface elevations estimated from the video frames. Results from both methods show a maximum of 20% difference against estimates from from the Water Survey of Canada, proving the versatility of image velocimetry under adverse conditions. Uncertainties in one standard deviation of all four transect discharges, at a maximum of 47%, are higher than expected but still reasonable, likely due to deviations in estimating the stage directly from the videos of poor quality. Extensive testing on the Farneback method show a different response on velocity estimation, especially when surface features are not as rich as those from flooding. Edge pixels are tested and proven to be a promising metric for quantifying natural surface features, without the need for image binarization which does not work well with dense optical flow.
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