Predicting Longitudinal Dispersion Coefficient in Natural Streams

Seo, Il Won; Cheong, Tae Sung

doi:10.1061/(asce)0733-9429(1998)124:1(25)

Cited by 252 publications

(278 citation statements)

References 15 publications

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“…As expected, the formula of Elder [16] yields a much lower dispersion values, whereas the formula of Fischer [21] and, a fortiori, the formula of Seo and Cheong [41] yield higher dispersion values. The maximum concentration varies from 2 mg/L for the Elder formula to 1 mg/L for the Seo and Cheong formula.…”

Section: Uncertainty In Modelling a Pollution Dynamics Throughout A Csupporting

confidence: 75%

“…The main difference with the evaluation against literature data is that the formula of Koussis and Rodriguez-Mirasol [32] shows a performance as good as, if not better than, the formula of Liu [34]. On the other hand, the formula of Seo and Cheong [41] yields poorer results. It appears that the formulae for which the fit was mainly based on the parameter W/H [34,27,32] are generally more appropriate for field data.…”

Section: Performance Of Dispersion Predictorsmentioning

confidence: 97%

“…2 used a very similar data set. Seo and Cheong [41] gave a listing of these data, which mainly correspond to measurements in US rivers 3 during the sixties and early seventies. In order to investigate more situations, we added laboratory data from Fischer [19], and additional field data from Carr and Rehmann [9] to the data set.…”

Section: Review Of Predictive Formulae Used In 1-d Modelsmentioning

confidence: 99%

“…However, they systematically overestimate the value of D L (mean(E) > 0). With respect to the four scores, the best results are obtained with the formula from Iwasa and Aya [27], then with the formulae from Liu [34] and Seo and Cheong [41]. The formulae of Fischer [21] and Koussis and Rodriguez-Mirasol [32] show poorer performance for most of the four criteria.…”

Section: Review Of Predictive Formulae Used In 1-d Modelsmentioning

confidence: 99%

“…It appears that the formulae for which the fit was mainly based on the parameter W/H [34,27,32] are generally more appropriate for field data. On the contrary, formulae with a large b-value [21,41] seem to be too sensitive to the calculation of U/U * , which often induces larger uncertainties especially in field data. Seo and Baek [42] showed that the formula of Seo and Cheong [41] would give larger estimates than other formulas when W/H < 40.…”

Section: Performance Of Dispersion Predictorsmentioning

confidence: 99%

See 4 more Smart Citations

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Launay¹,

Coz²,

Camenen³

et al. 2015

Journal of Hydro-environment Research

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The objective of this article is to investigate the major issues associated with the calibration of the pollutant dispersion in 1-D hydraulic models applied to river networks, especially large, complex, artificialized ones where ecological and socio-economical threats are important. Such issues are illustrated and discussed using the results of five fluorescent tracer experiments conducted in contrasted open-channel systems, ranging from a simple trapezoidal canal to a more complex river network. Experimental dispersion values were quantified using both the change of moment method and a simple fit-by-eye procedure for eight river reaches with homogeneous hydraulic conditions and an achieved tracer mixing and dispersive equilibrium. Since dispersion coefficient values depend on the assumed dispersion model, ideally they should be calibrated using the same model in which they are to be used, as was done in this study. We also derived concurrent longitudinal dispersion values using the velocity field measured by hydro-acoustic profilers (ADCP), which appears as a promising and cost-efficient technique for documenting dispersion in large river systems. It appears that the formulae for which the fit was mainly based on the cross-sectional aspect ratio are generally more appropriate for field data than those which are sensitive to the velocity to shear velocity ratio. The interpretation of complex dispersion and mixing processes, along with the selection of relevant dispersion coefficient predictors are key to minimizing errors in the numerical simulation of pollution dynamics in river networks.

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Section: Uncertainty In Modelling a Pollution Dynamics Throughout A Csupporting

confidence: 75%

Section: Performance Of Dispersion Predictorsmentioning

confidence: 97%

Section: Review Of Predictive Formulae Used In 1-d Modelsmentioning

confidence: 99%

Section: Review Of Predictive Formulae Used In 1-d Modelsmentioning

confidence: 99%

Section: Performance Of Dispersion Predictorsmentioning

confidence: 99%

See 3 more Smart Citations

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Launay¹,

Coz²,

Camenen³

et al. 2015

Journal of Hydro-environment Research

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Predictability of tracer dilution in large open channel flows: Analytical solution for the coefficient of variation of the depth‐averaged concentration

Pannone

2014

Water Resources Research

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A large-time analytical solution is proposed for the spatial variance and coefficient of variation of the depth-averaged concentration due to instantaneous, cross sectionally uniform solute sources in pseudorectangular open channel flows. The mathematical approach is based on the use of the Green functions and on the Fourier decomposition of the depth-averaged velocities, coupled with the method of the images. The variance spatial trend is characterized by a minimum at the center of the mass and two mobile, decaying symmetrical peaks which, at very large times, are located at the inflexion points of the average Gaussian distribution. The coefficient of variation, which provides an estimate of the expected percentage deviation of the depth-averaged point concentrations about the section-average, exhibits a minimum at the center which decays like t 21 and only depends on the river diffusive time scale. The defect of crosssectional mixing quickly increases with the distance from the center, and almost linearly at large times. Accurate numerical Lagrangian simulations were performed to validate the analytical results in preasymptotic and asymptotic conditions, referring to a particularly representative sample case for which crosssectional depth and velocity measurements were known from a field survey. In addition, in order to discuss the practical usefulness of computing large-time concentration spatial moments in river flows, and resorting to directly measured input data, the order of magnitude of section-averaged concentrations and corresponding coefficients of variation was estimated in field conditions and for hypothetical contamination scenarios, considering a unit normalized mass impulsively injected across the transverse section of 81 U.S. rivers.

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Roughness, resistance, and dispersion: Relationships in small streams

Noß

Lorke

2016

Water Resources Research

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Although relationships between roughness, flow, and transport processes in rivers and streams have been investigated for several decades, the prediction of flow resistance and longitudinal dispersion in small streams is still challenging. Major uncertainties in existing approaches for quantifying flow resistance and longitudinal dispersion at the reach scale arise from limitations in the characterization of riverbed roughness. In this study, we characterized the riverbed roughness in small moderate‐gradient streams (0.1–0.5% bed slope) and investigated its effects on flow resistance and dispersion. We analyzed high‐resolution transect‐based measurements of stream depth and width, which resolved the complete roughness spectrum with scales ranging from the micro to the reach scale. Independently measured flow resistance and dispersion coefficients were mainly affected by roughness at spatial scales between the median grain size and the stream width, i.e., by roughness between the micro‐ and the mesoscale. We also compared our flow resistance measurements with calculations using various flow resistance equations. Flow resistance in our study streams was well approximated by the equations that were developed for high gradient streams (>1%) and it was overestimated by approaches developed for sand‐bed streams with a smooth riverbed or ripple bed.

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Predicting Longitudinal Dispersion Coefficient in Natural Streams

Cited by 252 publications

References 15 publications

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Calibrating pollutant dispersion in 1-D hydraulic models of river networks

Predictability of tracer dilution in large open channel flows: Analytical solution for the coefficient of variation of the depth‐averaged concentration

Roughness, resistance, and dispersion: Relationships in small streams

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