Preprints
https://doi.org/10.5194/egusphere-2022-735
https://doi.org/10.5194/egusphere-2022-735
 
02 Sep 2022
02 Sep 2022
Status: this preprint is open for discussion.

Adaptively monitoring streamflow using a stereo computer vision system

Nicholas Reece Hutley1, Ryan Beecroft1, Daniel Wagenaar2, Josh Soutar3, Blake Edwards4, Nathaniel Deering1, Alistair Grinham1, and Simon Albert1 Nicholas Reece Hutley et al.
  • 1School of Civil Engineering, The University of Queensland, Brisbane, 4072, Australia
  • 2Xylem Water Solutions, Newcastle, 2292, Australia
  • 3Xylem Water Solutions, Brisbane, 4174, Australia
  • 4Leading Edge Engineering Solutions, Albury, 2640, Australia

Abstract. The gauging of free surface flows in waterways provides the foundation for monitoring and managing the water resources of built and natural environments. A significant body of literature exists around the techniques and benefits of optical surface velocimetry methods to estimate flows in waterways without intrusive instruments or structures. However, to date the operational application of these surface velocimetry methods has been limited by site configuration and inherent challenging optical variability across different natural and constructed waterway environments. This work demonstrates a significant advancement in the operationalisation of non-contact stream discharge gauging applied in the computer vision stream gauging (CVSG) system through the use of methods for remotely estimating water levels and adaptively learning discharge ratings over time. A cost-effective stereo camera-based stream gauging device (CVSG device) has been developed for streamlined site deployments and automated data collection. Evaluations between reference state-of-the-art discharge measurement technologies using DischargeLab (using surface structure image velocimetry), Hydro-STIV (using space-time image velocimetry), ADCPs (acoustic doppler current profilers), and gauging station discharge ratings demonstrated that the optical surface velocimetry methods were capable of estimating discharge within best available measurement error margins of 5–15 %. Furthermore, results indicated model machine learning approaches leveraging data to improve performance over a period of months at the study sites produced a marked 5–10 % improvement in discharge estimates, despite underlying noise in stereophotogrammetry water level or optical flow measurements. The operationalisation of optical surface velocimetry technology, such as CVSG, offers substantial advantages towards not only improving the overall density and availability of data used in stream gauging, but also providing a safe and non-contact approach for effectively measuring high flow rates while providing an adaptive solution for gauging streams with non-stationary characteristics.

Nicholas Reece Hutley et al.

Status: open (until 28 Oct 2022)

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Nicholas Reece Hutley et al.

Nicholas Reece Hutley et al.

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Short summary
Measuring flows in streams allows us to manage crucial water resources. This work shows the automated application of a dual camera computer vision stream gauging (CVSG) system for measuring streams. Comparing between state-of-the-art technologies demonstrated that camera-based methods were capable of performing within the best available error margins. CVSG offers significant benefits towards improving stream data and providing a safe way for measuring floods while adapting to changes over time.