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

Hauet, Alexandre; Morlot, Thomas; Daubagnan, Léa

doi:10.1051/e3sconf/20184006015

Cited by 47 publications

(65 citation statements)

References 9 publications

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“…Particle image and tracking velocimetry has emerged as a method for measuring surface velocity and computing discharge from the ground [12][13][14], from small, unmanned aircraft systems (sUAS) [15][16][17], and from manned aircraft systems [18]. Satellite-based sensors can be used to derive parameters such as water elevation, slope, and top width.…”

Section: Introductionmentioning

confidence: 99%

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

et al. 2020

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Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

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Section: Introductionmentioning

confidence: 99%

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

et al. 2020

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“…The exponent, notated 1/ m ′ , refers to the power model that describes the velocity profile v ( z ) at elevation z above the bed

v false(z false) = v false(h false) {((), \frac{z}{h})}^{1 false/ m'}

where v ( h ) is the velocity measured at elevation h . In rivers, the exponent can range from 1/3 to 1/10 (Hauet et al, ).…”

Section: Experiments and Methodsmentioning

confidence: 99%

“…where v(h) is the velocity measured at elevation h. In rivers, the exponent can range from 1/3 to 1/10 (Hauet et al, 2018).…”

Section: Quality Review Of Raw Discharge Measurements and Choice Of Ementioning

confidence: 99%

Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments

Despax

Coz

Hauet

et al. 2019

Water Resources Research

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Repeated measures experiments can be conducted to empirically estimate the uncertainty of a stream gauging method, such as the widespread moving‐boat acoustic Doppler current profilers (ADCPs) approach. Previous ADCP repeated measures experiments, also known as interlaboratory comparisons, provided a credible range of uncertainty estimates reflecting the quality of the site conditions. However, the method, which is a one‐way analysis of variance, only addresses the uncertainty of one lumped factor that combines several distinct factors: instrument, operator, procedure, and cross‐section effects. To decompose the uncertainty of ADCP streamflow measurements due to cross‐section selection and team effects, a large repeated measures experiment has been conducted in the Taurion River (France). The experiment design was crossed and balanced, with two sets of 24 teams circulated over two sets of 12 cross sections. A constant flow rate was released from a dam, located immediately upstream of the experimental site. Prior to the statistical analysis, a data quality review was performed using the U.S. Geological Survey QRev software to clean the data set from avoidable errors and to homogenize the discharge computations. A two‐way analysis of variance was applied to quantify the cross‐section effect, the team effect, and their interaction, which was found to dominate the pure cross‐section effect. It was then possible to predict the average uncertainty of multiple‐transect ADCP discharge measurements, depending on the number of teams, cross sections, and repeated transects included in the discharge average. The method opens interesting avenues for documenting difficult‐to‐estimate uncertainty sources of stream gauging techniques in other measuring conditions, especially the most adverse ones.

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“…Hauet et al. (2018) observed values of 0.8 with narrow dispersion (5th and 95th percentile values being about 0.7 and 0.9). The authors found that the value of this coefficient increases linearly with hydraulic radius and suggested 0.8 with an uncertainty ±15% at 95% confidence interval for water depths smaller than 2 m. Thus, the uncertainty in this velocity coefficient can lead to uncertainty in discharge of more than 30%.…”

Section: Introductionmentioning

confidence: 96%

A Drone‐Borne Method to Jointly Estimate Discharge and Manning's Roughness of Natural Streams

Bandini

Lüthi²,

Peña‐Haro³

et al. 2021

Water Resources Research

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Image cross‐correlation techniques, such as particle image velocimetry (PIV), can estimate water surface velocity (v surf) of streams. However, discharge estimation requires water depth and the depth‐averaged vertical velocity (U m ). The variability of the ratio U m /v surf introduces large errors in discharge estimates. We demonstrate a method to estimate v surf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any ground control point (GCP): the conversion of velocities from pixels per frame into length per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone‐borne radar. For approximately uniform flow, U m is a function of the Gauckler‐Manning‐Strickler coefficient (K s ) and v surf. We implement an approach that can be used to jointly estimate K s and discharge by informing a system of two unknowns (K s and discharge) and two nonlinear equations: i) Manning's equation and ii) mean‐section method for computing discharge from U m . This approach relies on bathymetry, acquired in situ a priori, and on UAS‐borne v surf and water surface slope measurements. Our joint (discharge and K s ) estimation approach is an alternative to the widely used approach that relies on estimating U m as 0.85·v surf. It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error of 19.1% compared to in situ discharge measurements. K s estimates showed a mean absolute error of 3 m1/3/s compared to in situ measurements.

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

Cited by 47 publications

References 9 publications

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

Decomposition of Uncertainty Sources in Acoustic Doppler Current Profiler Streamflow Measurements Using Repeated Measures Experiments

A Drone‐Borne Method to Jointly Estimate Discharge and Manning's Roughness of Natural Streams

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