A large-scale distribution function model was used to investigate the effect of differing parameter mapping schemes on the quality of hydrological predictions. Precipitation was mapped over a large forested catchment area (163 km2) using both one-dimensional linear and three-dimensional non-linear interpolation schemes. Lumped stream flow predictions were found to be particularly sensitive to the different precipitation maps, with the three-dimensional map predicting 12% higher mean annual precipitation, resulting in 36% higher modelled stream flow over a three-yr period. However, spatial predictions of stream flow appeared worse when derived from the three-dimensional map, which is considered the better of the two precipitation maps. This implies uncertainty in either the model's response to precipitation or the precipitation mapping process (the 12% precipitation difference was strongly determined by a single, short term gauge). Leaf area index (LAI) was mapped using both remote sensing and species based methods. The two LAI maps had similar lumped mean values but exhibited significant spatial differences. The resulting lumped predictions of stream flow did not vary. This suggests a linear response of water balance to LAI in the non-water-limited conditions of the study area, and de-emphasizes the importance of quanitfying relative spatial variations in LAI. Topographic maps were created for a small experimental subcatchment (15 ha) using both air photographic interpretation and ground survey. The two maps differed markedly and lead to significantly different spatial predictions of runoff generation, but nearly identical predicted hdyrographs. Thus, at scales of small basins accurate topographic mapping is suggested to be of little importance indistribution function modelling because models are unable to make use of complex spatial data. Predictions of water yield can be very sensitive (in the case of precipitation) or insensitive (in the case of small-scale topography) to changes in spatial parameterization. In either case, increased complexity in spatial parameterization does not necessarily result in better, or more certain prediction of hydrological response.The effect of differing parameter mapping schemes on the quality of hydrological predictions was investigated using a large-scale distribution function model. Precipitation was mapped over a large forested catchment area using one-dimensional linear and three-dimensional nonlinear interpolation schemes. Lumped stream flow predictions were found to be particularly sensitive to the different precipitation maps. Leaf area index was mapped by remote sensing and species-based method. The two maps differed markedly, and lead to significantly-different spatial predictions of runoff generation, but nearly identical predicted hydrographs.
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