Effects of the hydraulic conductivity microstructure on macrodispersivity

Dato, Mariaines Di; Barros, Felipe P. J. de; Fiori, Aldo; Bellin, Alberto

doi:10.1002/2016wr019086

Cited by 17 publications

(13 citation statements)

References 71 publications

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“…It aims to explain the dispersivity values identified in controlled field experiments, like for example, Borden Site and Cape Cod, as well as to overcome the lack of prediction capability by existing theories, like the first order approximation in logconductivity variance (see e.g., Attinger et al [2004]). A few possible additional mechanisms have been considered, for example, time varying oscillations of flow (Bellin et al 1996), small scale features of hydraulic conductivity, (Jankovic et al 2009;Di Dato et al 2016) or spatial variations of local anisotropy (Cirpka et al 2015). A few possible additional mechanisms have been considered, for example, time varying oscillations of flow (Bellin et al 1996), small scale features of hydraulic conductivity, (Jankovic et al 2009;Di Dato et al 2016) or spatial variations of local anisotropy (Cirpka et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The main proposed mechanisms are (1) the interplay between large-scale advection, which in turn is ruled by the spatial structure of heterogeneous conductivity, and (2) pore-scale dispersion with primarily its transverse and vertical components (see e.g., Fiori [1996]; Berglund and Fiori [1997]). A few possible additional mechanisms have been considered, for example, time varying oscillations of flow (Bellin et al 1996), small scale features of hydraulic conductivity, (Jankovic et al 2009;Di Dato et al 2016) or spatial variations of local anisotropy (Cirpka et al 2015). However, the sole aim of the present paper is to analyze the available field data, toward providing practitioners with typical values of observed transverse macrodispersivities.…”

Section: Introductionmentioning

confidence: 99%

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A Critical Analysis of Transverse Dispersivity Field Data

et al. 2018

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Transverse dispersion, or tracer spreading orthogonal to the mean flow direction, which is relevant e.g, for quantifying bio‐degradation of contaminant plumes or mixing of reactive solutes, has been studied in the literature less than the longitudinal one. Inferring transverse dispersion coefficients from field experiments is a difficult and error‐prone task, requiring a spatial resolution of solute plumes which is not easily achievable in applications. In absence of field data, it is a questionable common practice to set transverse dispersivities as a fraction of the longitudinal one, with the ratio 1/10 being the most prevalent. We collected estimates of field‐scale transverse dispersivities from existing publications and explored possible scale relationships as guidance criteria for applications. Our investigation showed that a large number of estimates available in the literature are of low reliability and should be discarded from further analysis. The remaining reliable estimates are formation‐specific, span three orders of magnitude and do not show any clear scale‐dependence on the plume traveled distance. The ratios with the longitudinal dispersivity are also site specific and vary widely. The reliability of transverse dispersivities depends significantly on the type of field experiment and method of data analysis. In applications where transverse dispersion plays a significant role, inference of transverse dispersivities should be part of site characterization with the transverse dispersivity estimated as an independent parameter rather than related heuristically to longitudinal dispersivity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Critical Analysis of Transverse Dispersivity Field Data

et al. 2018

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“…T may be thought of as the number of integral scales traveled by the plume centroid in time t . Another approach to this problem has been the so‐called self‐consistent approach (e.g., Cvetkovic et al, ; Dagan et al, ; Di Dato et al, ), which assumes that the aquifer is an effectively homogeneous medium with spheroidal or cuboidal inclusions of different hydraulic conductivity. Subject to a number of approximations, this approach makes it possible to formally write the longitudinal macrodispersivity as a multidimensional integral over the joint pdf of inclusion radius and conductivity and the asymptotic trajectory deflection due to a single such inclusion (Dagan et al, ).…”

Section: Introductionmentioning

confidence: 99%

Direct Breakthrough Curve Prediction From Statistics of Heterogeneous Conductivity Fields

Hansen

Haslauer

Cirpka

et al. 2018

Water Resources Research

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This paper presents a methodology to predict the shape of solute breakthrough curves in heterogeneous aquifers at early times and/or under high degrees of heterogeneity, both cases in which the classical macrodispersion theory may not be applicable. The methodology relies on the observation that breakthrough curves in heterogeneous media are generally well described by lognormal distributions, and mean breakthrough times can be predicted analytically. The log‐variance of solute arrival is thus sufficient to completely specify the breakthrough curves, and this is calibrated as a function of aquifer heterogeneity and dimensionless distance from a source plane by means of Monte Carlo analysis and statistical regression. Using the ensemble of simulated groundwater flow and solute transport realizations employed to calibrate the predictive regression, reliability estimates for the prediction are also developed. Additional theoretical contributions include heuristics for the time until an effective macrodispersion coefficient becomes applicable, and also an expression for its magnitude that applies in highly heterogeneous systems. It is seen that the results here represent a way to derive continuous time random walk transition distributions from physical considerations rather than from empirical field calibration.

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“…It may be noted that Di Dato et al . [] observed increased values of transverse macrodispersivities for strongly heterogeneous materials made of a multitude of elongated inclusions with strong conductivity contrasts. Like Cirpka et al .…”

Section: Introductionmentioning

confidence: 99%

The impact of sedimentary anisotropy on solute mixing in stacked scour‐pool structures

Bennett

Haslauer

Cirpka

2017

Water Resources Research

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The spatial variability of hydraulic conductivity is known to have a strong impact on solute spreading and mixing. In most investigations, its local anisotropy has been neglected. Recent studies have shown that spatially varying orientation in sedimentary anisotropy can lead to twisting flow enhancing transverse mixing, but most of these studies used geologically implausible geometries. We use an object‐based approach to generate stacked scour‐pool structures with either isotropic or anisotropic filling which are typically reported in glacial outwash deposits. We analyze how spatially variable isotropic conductivity and variation of internal anisotropy in these features impacts transverse plume deformation and both longitudinal and transverse spreading and mixing. In five test cases, either the scalar values of conductivity or the spatial orientation of its anisotropy is varied between the scour‐pool structures. Based on 100 random configurations, we compare the variability of velocity components, stretching and folding metrics, advective travel‐time distributions, one and two‐particle statistics in advective‐dispersive transport, and the flux‐related dilution indices for steady state advective‐dispersive transport among the five test cases. Variation in the orientation of internal anisotropy causes strong variability in the lateral velocity components, which leads to deformation in transverse directions and enhances transverse mixing, whereas it hardly affects the variability of the longitudinal velocity component and thus longitudinal spreading and mixing. The latter is controlled by the spatial variability in the scalar values of hydraulic conductivity. Our results demonstrate that sedimentary anisotropy is important for transverse mixing, whereas it may be neglected when considering longitudinal spreading and mixing.

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Effects of the hydraulic conductivity microstructure on macrodispersivity

Cited by 17 publications

References 71 publications

A Critical Analysis of Transverse Dispersivity Field Data

A Critical Analysis of Transverse Dispersivity Field Data

Direct Breakthrough Curve Prediction From Statistics of Heterogeneous Conductivity Fields

The impact of sedimentary anisotropy on solute mixing in stacked scour‐pool structures

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