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79 Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport

Part 6. Soils

  1. James D Brown1,
  2. Gerard B M Heuvelink2

Published Online: 15 APR 2006

DOI: 10.1002/0470848944.hsa081

Encyclopedia of Hydrological Sciences

Encyclopedia of Hydrological Sciences

How to Cite

Brown, J. D. and Heuvelink, G. B. M. 2006. Assessing Uncertainty Propagation through Physically Based Models of Soil Water Flow and Solute Transport. Encyclopedia of Hydrological Sciences. 6:79.

Author Information

  1. 1

    Universiteit van Amsterdam, Nieuwe Achtergracht, Amsterdam, The Netherlands

  2. 2

    Wageningen University, Wageningen, The Netherlands

Publication History

  1. Published Online: 15 APR 2006
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Figure 1. Example probability models for (a) a continuous numerical variable, (b) a categorical variable, and (c) two statistically dependent (cross-correlated) continuous numerical variables. Inset (d) shows the spatial patterns of uncertainty (autocorrelation) for a continuous numerical variable in the form of a “variogram”

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Figure 2. Uncertainties in model inputs combine with uncertainties in the model (parameters, structure, and solution) and propagate through the model leading to uncertainties in model predictions. Model parameter values may be calibrated against uncertain observations through “inverse modeling”. Dependencies are not shown

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Figure 3. Uncertainty propagation with the TSM for a single input x. The curved line represents the function g, which is approximated by a linear function (dark solid line). The linear function has the same value and gradient as g when evaluated at the mean of x. After linearization, pdfy is easily computed from pdfx. In this example, pdfy is much wider than pdfx because g is steep near the mean of x