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This article in VZJ

  1. Vol. 5 No. 2, p. 706-719
     
    Published: May, 2006


    * Corresponding author(s): jrnimmo@usgs.gov
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doi:10.2136/vzj2005.0088

Resolving Structural Influences on Water-Retention Properties of Alluvial Deposits

  1. Kari A. Winfielda,
  2. John R. Nimmo *a,
  3. John A. Izbickib and
  4. Peter M. Martinb
  1. a U.S. Geological Survey, 345 Middlefield Road, MS 421, Menlo Park, CA 94025
    b U.S. Geological Survey, 5735 Kearny Villa Road, San Diego, CA 92123

Abstract

With the goal of improving property-transfer model (PTM) predictions of unsaturated hydraulic properties, we investigated the influence of sedimentary structure, defined as particle arrangement during deposition, on laboratory-measured water retention (water content vs. potential [θ(ψ)]) of 10 undisturbed core samples from alluvial deposits in the western Mojave Desert, California. The samples were classified as having fluvial or debris-flow structure based on observed stratification and measured spread of particle-size distribution. The θ(ψ) data were fit with the Rossi–Nimmo junction model, representing water retention with three parameters: the maximum water content (θmax), the ψ-scaling parameter (ψo), and the shape parameter (λ). We examined trends between these hydraulic parameters and bulk physical properties, both textural—geometric mean, M g, and geometric standard deviation, σg, of particle diameter—and structural—bulk density, ρb, the fraction of unfilled pore space at natural saturation, A e, and porosity-based randomness index, Φs, defined as the excess of total porosity over 0.3. Structural parameters Φs and A e were greater for fluvial samples, indicating greater structural pore space and a possibly broader pore-size distribution associated with a more systematic arrangement of particles. Multiple linear regression analysis and Mallow's C p statistic identified combinations of textural and structural parameters for the most useful predictive models: for θmax, including A e, Φs, and σg, and for both ψo and λ, including only textural parameters, although use of A e can somewhat improve ψo predictions. Textural properties can explain most of the sample-to-sample variation in θ(ψ) independent of deposit type, but inclusion of the simple structural indicators A e and Φs can improve PTM predictions, especially for the wettest part of the θ(ψ) curve.

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