Unsupervised image velocimetry for automated computation of river flow velocities
<p>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...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-08-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/3727/2025/hess-29-3727-2025.pdf |
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| author | M. T. Perks B. Hortobágyi B. Hortobágyi N. Everard S. Manson J. Rowland A. Large A. J. Russell |
| author_facet | M. T. Perks B. Hortobágyi B. Hortobágyi N. Everard S. Manson J. Rowland A. Large A. J. Russell |
| author_sort | M. T. Perks |
| collection | DOAJ |
| description | <p>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 274 flow gauging measurements obtained using standard operating procedures. This analysis was undertaken at a complex monitoring site with a partial view of the channel and river flows spanning nearly 2 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 (<span class="inline-formula"><i>r</i><sup>2</sup>=0.95</span>–0.97). Similarly, the index-velocity approach enables KLT-IV surface velocities to be precisely related to the section-averaged velocity measurements (<span class="inline-formula"><i>r</i><sup>2</sup>=0.98</span>). These data are subsequently used to estimate river flow discharge. When compared to reference flow gauging data, <span class="inline-formula"><i>r</i><sup>2</sup></span> 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.</p> |
| format | Article |
| id | doaj-art-4cff7a6fcaac4ba69563a7c723a05cb2 |
| institution | Kabale University |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-4cff7a6fcaac4ba69563a7c723a05cb22025-08-20T03:36:35ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-08-01293727374310.5194/hess-29-3727-2025Unsupervised image velocimetry for automated computation of river flow velocitiesM. T. Perks0B. Hortobágyi1B. Hortobágyi2N. Everard3S. Manson4J. Rowland5A. Large6A. J. Russell7School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United KingdomUMR5600 EVS, CNRS, ENS Lyon, Lyon, FranceUK Centre for Ecology and Hydrology, Wallingford, United KingdomFlood and Coastal Risk Management, Environment Agency, Crosskill House, Mill Lane, Beverley, United KingdomEnvironment Agency, Manley House, Kestrel Way, Exeter, UKSchool of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United KingdomSchool of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United Kingdom<p>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 274 flow gauging measurements obtained using standard operating procedures. This analysis was undertaken at a complex monitoring site with a partial view of the channel and river flows spanning nearly 2 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 (<span class="inline-formula"><i>r</i><sup>2</sup>=0.95</span>–0.97). Similarly, the index-velocity approach enables KLT-IV surface velocities to be precisely related to the section-averaged velocity measurements (<span class="inline-formula"><i>r</i><sup>2</sup>=0.98</span>). These data are subsequently used to estimate river flow discharge. When compared to reference flow gauging data, <span class="inline-formula"><i>r</i><sup>2</sup></span> 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.</p>https://hess.copernicus.org/articles/29/3727/2025/hess-29-3727-2025.pdf |
| spellingShingle | M. T. Perks B. Hortobágyi B. Hortobágyi N. Everard S. Manson J. Rowland A. Large A. J. Russell Unsupervised image velocimetry for automated computation of river flow velocities Hydrology and Earth System Sciences |
| title | Unsupervised image velocimetry for automated computation of river flow velocities |
| title_full | Unsupervised image velocimetry for automated computation of river flow velocities |
| title_fullStr | Unsupervised image velocimetry for automated computation of river flow velocities |
| title_full_unstemmed | Unsupervised image velocimetry for automated computation of river flow velocities |
| title_short | Unsupervised image velocimetry for automated computation of river flow velocities |
| title_sort | unsupervised image velocimetry for automated computation of river flow velocities |
| url | https://hess.copernicus.org/articles/29/3727/2025/hess-29-3727-2025.pdf |
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