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Effect of Temporal and Spatial Rainfall Resolution on HSPF Predictive Performance and Parameter Estimation
Citation:
MOHAMOUD, Y. M. AND L. M. PRIETO. Effect of Temporal and Spatial Rainfall Resolution on HSPF Predictive Performance and Parameter Estimation. JOURNAL OF HYDROLOGIC ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, 17(3):377 - 388, (2012).
Impact/Purpose:
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Description:
Watershed scale rainfall‐runoff models are used for environmental management and regulatory modeling applications, but their effectiveness are 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, we evaluated the effect of automatic parameter estimation on the physical significance of calibrated parameter values. Temporal resolutions examined included 15‐min, 30‐min, 1‐hour, and 2‐hour and spatial resolution effects evaluated included the effect of spatially averaged network of four raingauges and next generation weather radar (NEXRAD) for selected rain events. Model efficiencies ranged from 0.31 to 0.86 when individual raingauges (RG71, RG73, RG74, and RG77) were used one at a time. Model efficiency improved and ranged from 0.86 to 0.94 when spatially averaged network of four raingauges were used. The effect of temporal resolution on model performance varied with raingauge location in the watershed and whether a single gauge or a spatially averaged raingauges were used for model calibration. Rainfall resolution has 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 due to changes in rainfall resolution, our 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.
