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Soil Science Society of America Journal Abstract -

Statistical Parameters Characterizing the Spatial Variability of Selected Soil Hydraulic Properties


This article in SSSAJ

  1. Vol. 54 No. 6, p. 1537-1547
    Received: Nov 16, 1989

    * Corresponding author(s):
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  1. K. Ünlü ,
  2. D. R. Nielsen,
  3. J. W. Biggar and
  4. F. Morkoc
  1. Center for Environmental and Hazardous Materials Studies, 245 Smyth Hall, Virginia Polytechnic Inst. and State Univ., Blacksburg, VA 24061-0404
    Dep. of Land, Air, and Water Resources, Univ. of California, Davis, CA 95616



The knowledge of the statistical parameters of the variance, σ2, and the correlation scale, λ, characterizing the spatial structures of the log of the saturated hydraulic conductivity, lnKs, pore size distribution parameter α, and the specific water capacity, C, is required in stochastic modeling in order to understand the overall response of large-scale heterogeneous unsaturated flow systems. These parameters are estimated assuming second-order stationarity and an exponential semivariogram model with nugget effect. Methods of ordinary least squares (OLS), maximum likelihood (ML), and restricted maximum likelihood (RML) are used for estimating σ2 and λ, while methods of cross-validation (kriging) and uncorrelated residuals are used to validate the semivariogram model with estimated σ2 and λ. The objectives of this study were to evaluate the sensitivity of σ2 and λ to the estimation methods and to discuss the implications of the analysis in view of the stochastic modeling. The significance of the results of parameter estimation and model validation in relation to the stochastic modeling of large-scale transient unsaturated flow is demonstrated with two examples involving the variance of soil-water pressure head, σ2h, and the vertical component of effective hydraulic conductivity, K*11. Results show that the estimated values of σ2 and λ are highly dependent on the estimation method. Although the majority of the estimated parameters pass the validation tests, the RML estimates of σ2 and λ used in estimating σ2h and K*11 significantly reduce the prediction uncertainties.

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