Discharge Measurements of Snowmelt Flood by Space-Time Image Velocimetry during the Night Using Far-Infrared Camera

Fujita, Ichiro

doi:10.3390/w9040269

Cited by 38 publications

(21 citation statements)

References 21 publications

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“…Non-intrusive methods are a promising alternative allowing measurement of flood flow. Surface velocities can be measured using Radar Doppler [3] or image-based methods like Large-Scale Particle Image Velocimetry [4,5], Particle Tracking Velocimetry [6] or Space-Time Image Velocimetry [7]. As remote measurements only measure surface velocity, assumptions regarding the link between surface and depth-averaged velocity have to be made in order to compute discharge for a cross-section of known bathymetry.…”

Section: Non-intrusive Methods For Flood Discharge Measurementmentioning

confidence: 99%

Velocity profile and depth-averaged to surface velocity in natural streams: A review over alarge sample of rivers

2018

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For measuring flood discharge with non-intrusive methods, one should know the depth-averaged to surface velocity ratio α. In this paper, a large sample of gauging (3611 gaugings over 176 sites of EDF-DTG) realized with a current meter are analyzed to compute α with three methods: fitting of a log- and a power-law and using the measured surface velocity. Results show that the three methods give very similar results, with a mean value of α of about 0.8, with few dispersion (5th and 95th percentile values being about 0.7 and 0.9). A link between α and the water depth is established, as α increases with the hydraulic radius. The link between α and the bed roughness, or between α and the relative roughness (Rh/d50) is not clear. Results only shows that the mean value of α is 0.8 for natural rivers (sandy, pebbly or boulders rivers), and 0.9 for artificial concrete channels.

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Section: Non-intrusive Methods For Flood Discharge Measurementmentioning

confidence: 99%

Velocity profile and depth-averaged to surface velocity in natural streams: A review over alarge sample of rivers

2018

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“…Alternatively, two dimensional Fast Fourier transformation (FFT) using a band pass filter can be used to improve the quality of STI by picking up only the pattern related to the flow [11,15]. The conventional STIV has been successfully applied to several flood flow measurements [1,18], but it is difficult to evaluate the quality of STI based on the optical gradient tensor method. Other than the above technique [4,7] developed a similar (1)…”

Section: Conventional Methods Of Stivmentioning

confidence: 99%

Efficient and accurate estimation of water surface velocity in STIV

et al. 2018

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In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space-time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105-114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space-time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

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“…Another limitation is that the surface velocity errors are affected by wind and rain drops, so the measurements are difficult in such weather conditions [72]. The light limitation during nights can be eliminated by using a thermal camera or a far-infrared camera to provide monitoring all time [73,74]. Overall, the main advantages of the imaged-based methods, compared to other traditional methods, are the safety of the recorder, that they are not intrusive to the water body, and they do not require water contact by either the tools or humans.…”

Section: Discussionmentioning

confidence: 99%

UAVs for Hydrologic Scopes: Application of a Low-Cost UAV to Estimate Surface Water Velocity by Using Three Different Image-Based Methods

Koutalakis

Tzoraki

Zaimes

2019

Drones

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Stream velocity and flow are very important parameters that must be measured accurately to develop effective water resource management plans. There are various methods and tools to measure the velocity but, nowadays, image-based methods are a promising alternative that does not require physical contact with the water body. The current study describes the application of a low cost unmanned aerial vehicle that was selected in order to capture a video over a specific reach of Aggitis River in Greece. The captured frames were analyzed by three different software (PIVlab, PTVlab, and KU-STIV) in order to estimate accurately the surface water velocity. These three software also represent three different image-based methodologies. Although there are differences among these three methods, the analysis produced similar trends for all. The velocity ranged between 0.02 and 3.98 m/s for PIVlab, 0.12 and 3.44 m/s for PTVlab, and 0.04 and 3.99 m/s for KU-STIV software. There were parts, especially in the existing vegetation, where differences were observed. Further applications will be examined in the same or different reaches, to study the parameters affecting the analysis. Finally, the image-based methods will be coupled with verification measurements by a current meter to produce more rigorous results.

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Discharge Measurements of Snowmelt Flood by Space-Time Image Velocimetry during the Night Using Far-Infrared Camera

Cited by 38 publications

References 21 publications

Velocity profile and depth-averaged to surface velocity in natural streams: A review over alarge sample of rivers

Velocity profile and depth-averaged to surface velocity in natural streams: A review over alarge sample of rivers

Efficient and accurate estimation of water surface velocity in STIV

UAVs for Hydrologic Scopes: Application of a Low-Cost UAV to Estimate Surface Water Velocity by Using Three Different Image-Based Methods

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