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Abstract
Currently available soil datasets, presenting low resolution and shallow depth, are inadequate for activities that warrant a detailed characterization of the sub-surface. I investigated an alternative method to determine spatially distributed soil texture and developed a high-resolution soil texture map by importing soil electrical resistivity, relative depth of investigation, and weekly antecedent rainfall in an Artificial Neural Network (ANN) framework. I used the predicted textures to investigate the effects of varying domain complexity in simulations of hydrological and chloride transport at the hillslope scale using a Richard’s equation based single porosity model. I compared the outcomes with ones developed using the traditional pedological soil horizon-based division of the sub-surface. In both models, the hillslope received a constant sub-surface flux input of solute and fed into a lake. The ANN accurately predicted soils, but the information did not have a major effect on the single porosity, hydrological simulations.