Friday, November 8, 2013: 1:00 p.m.
Analyses of flow and transport in the shallow subsurface require information about spatial and statistical distributions of soil hydraulic properties (water content and permeability, their dependence on capillary pressure) as functions of scale and direction. Measuring these properties is relatively difficult, time consuming and costly. It is generally much easier, faster and less expensive to collect and describe the makeup of soil samples in terms of textural composition (e.g. per cent sand, silt, clay and organic matter), bulk density and other such pedological attributes. Over the last two decades soil scientists have developed a set of tools, known collectively as pedotransfer functions (PTFs), to help translate information about the spatial distribution of pedological indicators into corresponding information about soil hydraulic properties. One of the most successful PTFs is the nonlinear Rosetta neural network model developed by Marcel Schaap. I describe a recent application of Rosetta to soil sample data from an experimental site in southern Arizona and a novel geostatistical scaling analysis of Rosetta inputs and outputs conducted by Alberto Guadagnini, Monica Riva, Marcel Schaap and myself.