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Abstract. Typical hydrogeologic data sets consisting of information from boreholes provide excellent information on vertical variability of sedimentary deposits but very limited information on lateral distribution and variability. In cases where surface geomorphic features reflect processes similar to those responsible for past deposition, the soil survey offers a resource for assessing the lateral sediment variability. Facies mean length and transition probability measurements of C horizon textures from the soil maps on the Kings River alluvial fan, California, provide a basis for Markov chain models of spatial variability in the principal lateral directions and facies orientation information for the horizontal plane. Incorporation with a Markov chain model of vertical-direction transitions based on well data yields a three-dimensional Markov chain model of sediment variability which includes cross correlation between sediment types and representation of asymmetry (e.g., fining upward tendencies). Use of the model in geostatistical conditional simulation and simulated annealing produces a detailed, geologically plausible image of the subsurface hydrofacies distribution. The use of previously recorded C horizon (approximately !.5 m depth) soil data may provide considerable insight for deriving geostatistical correlation structure in undersampled, lateral directions, assuming that the modern geomorphologic processes are similar to those responsible for formation of the aquifer system. In these cases the soil survey may be used as a "training image" [Deutsch and Journel, 1992; Koltermann and for understanding the spatial distribution of facies in lateral directions. We present a new application of soil survey data in conjunction with subsurface data (e.g., well logs and core) and a structure-imitating method that employs a transition probability and Markov chain geostatistical approach to create geologically plausible, three-dimensional characterizations of a heterogeneous, fluvial aquifer system.