The objective of this research is to develop and demonstrate a general approach for modeling flow and transport in the heterogeneous vadose zone. The approach uses similar media scaling, geostatistics, and conditional simulation methods to estimate soil hydraulic parameters at unsampled locations from field-measured water content data and scale-mean hydraulic parameters determined from available site characterization data. Neural network methods are being developed to estimate soil hydraulic parameters from more easily measured physical property data such as bulk density, organic matter content, and percentages of sand, silt, and clay (or particle-size distributions). Field water content distributions are being estimated using various geophysical methods including neutron moderation, ground-penetrating radar, and electrical resistance tomography. One of the primary goals of this research is to determine relationships between the type of data used in model parameterization, the quantity of data available, the scale of the measurement, and the uncertainty in predictions of flow and transport using these methods. Evaluation of the relationships between available data, scale, and uncertainty will use primarily existing data from large-scale, controlled experiments.
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