Adaptively monitoring streamflow using a stereo computer vision system
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 w...
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Published in | Hydrology and earth system sciences Vol. 27; no. 10; pp. 2051 - 2073 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Katlenburg-Lindau
Copernicus GmbH
31.05.2023
Copernicus Publications |
Subjects | |
Online Access | Get full text |
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Summary: | 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),
acoustic Doppler current profilers (ADCPs), and gauging station discharge ratings
demonstrated that the optical surface velocimetry methods were capable of
estimating discharge within a 5 %–15 % range between these best available
measurement approaches. 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. |
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ISSN: | 1607-7938 1027-5606 1607-7938 |
DOI: | 10.5194/hess-27-2051-2023 |