Memory effects induced by dependence on initial conditions and ergodicity of transport in heterogenous media

Suciu, N.; Knabner, P.; Vereecken, H.; Vamos, C.; Sabelfeld, K.

doi:10.1029/2007WR006740

Journal Article

PreJuSER-62963

Memory effects induced by dependence on initial conditions and ergodicity of transport in heterogenous media

Suciu, N. ; Vamos, C. ; Vereecken, H.FZJ* ; Sabelfeld, K. ; Knabner, P.

2008
AGU Washington, DC

Water resources research 44, W08501 (2008) [10.1029/2007WR006740]

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Please use a persistent id in citations: http://hdl.handle.net/2128/19878 doi:10.1029/2007WR006740

Abstract: For transport in statistically homogeneous random velocity fields with properties that are routinely assumed in stochastic groundwater models, the one-particle dispersion (i.e., second central moment of the ensemble average concentration for a point source) is a "memory-free'' quantity independent of initial conditions. Nonergodic behavior of large initial plumes, as manifest in deviations of actual solute dispersion from one-particle dispersion, is associated with a "memory term'' consisting of correlations between initial positions and displacements of solute molecules. Reliable numerical experiments show that increasing the source dimensions has two opposite effects: it reduces the uncertainty related to the randomness of center of mass, but, at the same time, it yields large memory terms. The memory effects increase with the source dimension and depend on its shape and orientation. Large narrow sources oriented transverse to the mean flow direction yield ergodic behavior with respect to the one-particle dispersion of the longitudinal dispersion and nonergodic behavior of the transverse dispersion, whereas for large longitudinal sources, the longitudinal dispersion behaves nonergodically, and the transverse dispersion behaves ergodically. Such memory effects are significantly large over hundreds of heterogeneity scales and should therefore be considered in practical applications, for instance, calibration of model parameters, forecasting, and identification of the contaminant source.

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