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Soil depth is an important input parameter in hydrological and ecological modeling. Presently, the soil depth data available in national soil databases (STATSGO, SSURGO) is provided as averages within generalized map units. Spatial uncertainty within these units limits their applicability for spatially distributed modeling. This work reports a statistical model for prediction of soil depth in a semi-arid mountainous watershed that is based upon topographic and other landscape attributes. Soil depth was surveyed by driving a rod into the ground until refusal at geo-referenced locations selected to represent the range of topographic and land cover variations in Dry Creek Experimental Watershed, Boise, Idaho, USA. The soil depth survey consisted of a model calibration set, measured at 819 locations over 8 sub-watersheds, and a model testing set, measured at 130 locations randomly distributed over the remainder of the watershed. Topographic attributes were derived from a Digital Elevation Model. Land cover attributes were derived from Landsat TM remote sensing images and high resolution aerial photographs. A Generalized Additive Model was developed to predict soil depth over the watershed from these attributes. This model explained about 50% of the soil depth spatial variation and is an important improvement towards solving the need in distributed modeling for distributed soil depth input data.