Estimation of the dispersion coefficient in rivers with riparian vegetation

Estimación del coeficiente de dispersión longitudinal en ríos de la región central de Argentina utilizando ADCP

“…Una vez aproximados los perfiles de velocidad y fondo, se procedió a estimar el coeficiente de dispersión transversal en cada progresiva mediante la ecuación empírica desarrollada por Smeithlov [13] y utilizada por Deng et al [12], Carr y Rehmann [2], Perucca [14] y Shen [10] (ecuación 4). …”

Effect of sorption process on cadmium transport

“…Although the dispersion of solutes is an important process in water bodies and many research studies are available in the literature (including recent ones by Perucca et al (2009), Deng et al (2010, Shen et al (2010) and Tealdi et al (2010) for example), extensive experimental investigations into the effect of sorption on the dispersion coefficient have not been reported. Sorption is a relatively slow reaction, and therefore a long flume is usually needed to investigate the effect of sorption on the dispersion coefficient experimentally.…”

Pollutant Dispersion Modeling in Natural Streams Using the Transmission Line Matrix Method

“…The method of moments (Equation (4)) [13][14][15] is used to determine, experimentally, the longitudinal dispersion coefficient DL:…”

“…The values of Fa2, MRSE, and E indicate that the model representation of the experimental longitudinal dispersion coefficients is fairly acceptable. Moreover, the numerical estimation of this coefficient is very difficult; in the literature [7][8][9]14,17,38,[43][44][45][46] for natural rivers, there is a great disparity between the values of longitudinal dispersion coefficients calculated by different methods and empirical formulas. This shows that such a coefficient is difficult to estimate, its experimental determination is the fairest approach because it is based on spatio-temporal monitoring of pollutant concentrations in streams with the real effect of skewness, topography, and water storage areas.…”

“…The statistical indices are the basic tools for analyzing the model quality [6,14,[35][36][37]. The following tests have been chosen: The coefficient of divergence Rdiv with Rdiv >1 (Rdiv < 1) indicates an over-prediction (under-prediction) of a value; the mean percentage error E(%) representing a convenient model when it is small; the Mean Relative Square Error MRSE indicating a successful model if it is close to 0; the Factor Of EXcedence FOEX(%), signifying an overestimation (underestimation and/or correct estimation) of all values when it is equal to 100% (0%); the factor of two FA2(%) representing a reliable model if it is close to 100% and finally the scatter diagram indicating a situation of overestimation (underestimation) when the point is above (below) "y = x" line.…”