Parameter estimation of the transient storage model by a routing method for river mixing processes

Cheong, Tae Sung; Seo, Il Won

doi:10.1029/2001wr000676

Cited by 34 publications

(25 citation statements)

References 35 publications

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“…An important issue is that calibration fits that account for transient storage effects (see e.g. Cheong and Seo [12]) may provide significantly different values for the Fickian longitudinal dispersion coefficient. This observation reminds us again that dispersion coefficient values depend on the simulation method used, and that ideally they should be calibrated using the same model in which they are to be used, as was done in this study.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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show abstract

“…A detailed review, and also a proposal for an alternative method, are given, for example, in Cheong & Seo (2003). In the present study, the model calibration problem is formulated as optimization:…”

Section: Background Theorymentioning

confidence: 99%

Response to the slug injection of a tracer—a large-scale experiment in a natural river / Réponse à l'injection impulsionnelle d'un traceur—expérience à grande échelle en rivière naturelle

Rowiński

Guymer

Kwiatkowski

2008

Hydrological Sciences Journal

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“…The database is composed of 58 measurements, obtained for river reaches which vary significantly in time and space scales, as well as in hydrological regime. The database, as published by Cheong & Seo (2003), contains information on four hydraulic and morphometric variables, namely river depth averaged over cross-section, H (m), river width, W (m), mean water velocity, U (m/s) and channel slope, S. The complementary fifth variable, used in the present study-the river sinuosity index (Sin)-may be found in the recent study of Cheong et al (2007). These databases include the values of three TSM parameters obtained from tracer experiments: K f , ε and T. The parameter values from these databases are referred to herein as the "original" ones and are denoted ORIG.…”

Section: Databasementioning

confidence: 99%

“…Although many tracer tests in river reaches have been performed around the world, the parameters of TSM have been identified and published in only a few of them. The database used in this paper was put together by Cheong & Seo (2003). They collected the measurements from the studies of Fisher (1968), Godfrey & Frederick (1970), Nordin & Sabol (1974), Graf (1995) and Czernuszenko et al (1998).…”

Section: Databasementioning

confidence: 99%