Unsupervised image velocimetry for automated computation of river flow velocities

Abstract

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 (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.