ABSTRACT:Improving the reliability of parametric hydrologic models (sometimes called cenceptual rainfall‐runoff models) in the continuous simulation of runoff from ungaged catchments has been frustrated by difficulties in estimating model parameters from catchment characteristics. An underlying problem is that these models use parameters to represent catchments as a whole, whereas data on catchment characteristics are collected at multiple field locations and are difficult to transform into one measure of collective impact. Subdividing the catchment and calibrating a stochastic parametric model to estimate distributions for the parameters that covered the range of observed streamflow values was found to improve the simulations. This paper presents an optimization of the amount of subdivision to use in simulation with a version of the Stanford Watershed Model using available climatological data. The calibration process assumes that catchment heterogeneity introduces errors that can be reduced by calibrating parameters as spatial distributions rather than single values. Calibrations for three diverse small gaged catchments located in California and in Virginia found the optimal number of subdivisions to range from 4 to 25 and the optimal scale to range from 0.3 to 2.1 mi
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