Effect of Temporal and Spatial Rainfall Resolution on HSPF Predictive Performance and Parameter Estimation

Abstract

Watershed-scale rainfall-runoff models are used for environmental management and regulatory modeling applications, but their effectiveness is limited by predictive uncertainties associated with model input data. This study evaluated the effect of temporal and spatial rainfall resolution on the predictive performance of Hydrological Simulation Program—Fortran (HSPF) using manual and automatic calibration procedures. Furthermore, the effect of automatic parameter estimation on the physical significance of calibrated parameter values was evaluated. Temporal resolutions examined included 15 min, 30 min, 1 h, and 2 h, and spatial resolution effects evaluated included the effect of a spatially averaged network of four rain gauges and Next-Generation Radar (NEXRAD) for selected rain events. Model efficiencies ranged from 0.31 to 0.86 when individual rain gauges (RG71, RG73, RG74, and RG77) were used one at a time. Model efficiency improved and ranged from 0.86 to 0.94 when a spatially averaged network of four rain gauges was used. The effect of temporal resolution on model performance varied with rain gauge location in the watershed and with use of a single gauge or spatially averaged rain gauges for model calibration. Rainfall resolution has a strong influence on parameter estimation because, to achieve high model performance, parameter values must shift whenever the resolution of the rainfall data is changed. Despite a shift in parameter values as a result of changes in rainfall resolution, the results showed that Parameter Estimation Software (PEST)-calibrated values remained within their parameter bounds. In summary, results obtained from a medium-sized Piedmont watershed in Georgia, USA, revealed that model performance was more sensitive to spatial resolution than temporal resolution.