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The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined.Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations,and point-and surface-average factors are introduced to revise radar data successively.Hydrologic simulations are then performed with a distributed hydrologic model,called basin pollution calculation center(BPCC) with both raingauge observations and revised NEXRAD estimates inputs.The BPCC model is applied to Clear Creek Watershed,IA,USA,on an hourly scale,and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings.Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs.Coefficient statistics reveal that NEXRAD contributes to model performance,indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.
The next-generation weather radar (NEXRAD) can generally capture the spatial variability of rainfall fields, but fails to provide accurate depth measurements. A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined. evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations, and point-and surface-average factors were introduced to revise radar data successively. Hydrologic simulations are then performed with a distributed hydrologic model center (BPCC) with both raingauge observations and revised NEXRAD estimates inputs.The BPCC model is applied to Clear Creek Watershed, IA, USA, on an hourly scale, and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings. Results show that hydrographs generated from both gauge and NEXRAD are in good agr eement with observed flow hydrographs. Coefficient statistics reveal that NEXRAD contributes to model performance, indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.