Unsupervised image velocimetry for automated computation of river flow velocities

Perks, Matthew T. ORCID: https://orcid.org/0000-0002-6960-5484; Hortobágyi, Borbála ORCID: https://orcid.org/0000-0002-0105-9456; Everard, Nick ORCID: https://orcid.org/0000-0002-9936-9902; Manson, Susan; Rowland, Juliet; Large, Andrew; Russell, Andrew J.. 2025 Unsupervised image velocimetry for automated computation of river flow velocities. Hydrology and Earth System Sciences Discussions, hess-2024-213. 10.5194/hess-2024-213

Before downloading, please read NORA policies.

Preview

Text hess-2024-213.pdf - Submitted Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview

Abstract/Summary

Accurate, long-term, measurements of river flow are imperative for understanding and predicting a broad range of fluvial processes. Modern technological advances are enabling the development of new solutions that are tailored to manage water resources and hazards in a variety of flow regimes. This study appraises the potential of freely available image velocimetry software (KLT-IV) to provide automatic determination of river surface velocity in an unsupervised workflow. In this research, over 11,000 videos are analysed, and these are compared with 1-D velocities derived from 303 flow gauging measurements obtained using standard operating procedures. This analysis was undertaken at a complex monitoring site with a partial view of the channel with river flows spanning nearly two orders of magnitude. Following image velocimetry analysis, two differing approaches are adopted to produce outputs that are representative of the depth-averaged and cross-section averaged flow velocities. These approaches include the utilisation of theoretical flow field distributions to extrapolate beyond the field of view, and an index-velocity approach to relate the image-based velocities to a section averaged (1-D) velocity. Analysis of the section-averaged velocities obtained using KLT-IV, compared to traditional flow gauging, yields highly significant linear relationships (r2 = 0.95–0.97). Similarly, the index-velocity approach enables KLT-IV surface velocities to be precisely related to the section-averaged velocity measurements (r2 = 0.98). These data are subsequently used to estimate river flow discharge. When compared to reference flow gauging data, r2 values of 0.98 to 0.99 are obtained (for a linear model with intercept of 0 and slope of 1). KLT-IV offers an attractive approach for conducting unsupervised flow velocity measurements in an operational environment where autonomy is of paramount importance.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.5194/hess-2024-213
UKCEH and CEH Sections/Science Areas:	Hydroclimate Extremes and Resilience (2025-)
ISSN:	1812-2116
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Electronics, Engineering and Technology Hydrology Data and Information
Related URLs:	Other Other Dataset
Date made live:	10 Jan 2025 12:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538698

Actions (login required)

View Item

Document Downloads

More statistics for this item...