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Soil Science Society of America Journal Abstract -

Model for Nonreactive Solute Transport in Structured Soils with Continuous Preferential Flow Paths


This article in SSSAJ

  1. Vol. 62 No. 4, p. 881-886
    Received: Feb 3, 1997

    * Corresponding author(s): wallach@agri.huji.ac.il
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  1. R. Wallach  and
  2. T. S. Steenhuis
  1. Dep. of Soil and Water Sciences, Faculty of Agricultural, Food and Environmental Quality Sciences, Hebrew Univ. of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
    Dep. of Agricultural and Biological Engineering, Cornell Univ., Ithaca, NY 14853



A mathematical model for solute movement in a structured soil with well-defined and continuous preferential paths was developed. The model divides the soil profile into one mobile and one “stagnant” pore group. For well-structured soils, the mobile pore group consists of a few well-connected pores that conduct the nonreactive solute downward very rapidly. Only a narrow matrix layer of stagnant solute along the interface between the two pore groups takes part in solute exchange with the preferential paths. Due to differences in time scales between convection and chemical transfer, the rate of chemical exchange between the preferential path and the active matrix layer for short and moderate times after chemical application is controlled mainly by the preferential flow concentration and, to a lesser extent, by the concentration in the matrix active layer. This is expressed mathematically by multiplying the concentration in the active matrix layer in the mass balance equation by a small dimensionless parameter, ɛ ≪ 1. A regular small perturbations problem is then obtained whose solution is expressed as the sum of the zeroth- and first-order approximations. The simulated breakthrough curve (BTC) is made up of piecewise linear lines and shows a good fit to the measured solute concentration in the outflow of undisturbed columns that have significant preferential flow. Due to the model's simplicity, the transport parameters are estimated directly by fitting the model output to the measured BTCs.

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