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ORIGINAL RESEARCH

A Field Application of the Scaled-Predictive Method for Unsaturated Soil

^a Errol L. Montgomery & Associates, Inc., 1550 East Prince Rd., Tucson, AZ 85719
^b Dep. of Soil, Water and Environmental Science, Univ. of Arizona, Tucson, AZ
^c Dep. of Hydrology and Water Resources, Univ. of Arizona, Tucson, AZ
^* Corresponding author (mthomasson{at}elmontgomery.com)

Received 22 August 2005.

Unsaturated hydraulic conductivity is important to many vadose zone processes. However, there is uncertainty regarding how laboratory- and field-measured hydraulic property measurements can be combined with soil textural data to improve the description of the unsaturated hydraulic conductivity function at the field scale. In this investigation, we examine a scaled-predictive method for defining the hydraulic conductivity function. This approach uses field-measured data to adjust the hydraulic conductivity relationship developed using either laboratory measurements or soil textural data. In addition, both hydraulic property models were scaled using field-measured data collected during a controlled infiltration experiment. Data from a second controlled infiltration experiment were used to evaluate the hydraulic property models based on accuracy of prediction of the arrival time of a wetting front and the water content distribution with time. The unadjusted laboratory-derived parameters provide the best first approximation for predicting the wetting front arrival. Both hydraulic property models gave more accurate predictions of the wetting front arrival after adjustment with field-measured data. Both scaled models predicted the water content distributions poorly. These predictions were improved, especially for the pedotransfer model, if the scaling was applied to only the lower portion of the soil profile.

Abbreviations: LDP, laboratory-derived hydraulic properties • NN, neural network • RMS, residual squared errors • S-P, scaled-predictive