Abstract
The processing speed of computers and availability of spatial hydrologic data make distributed watershed models a viable approach for many applications, including peak flow forecasting. This study demonstrates the feasibility of using radar precipitation data with a distributed watershed model to obtain increased lead-time for peak flow forecasting. The CASC2D watershed model is applied to the Hassyampa River watershed in central Arizona using radar-based rainfall estimates from the National Weather Service WSR-88D weather radar. An application of radar rainfall data as input to the CASC2D model is then presented by which precipitation forecasts are generated by extrapolation of precipitation patterns from radar images. The calibrated model is run for two rainfall events: with and without linear precipitation forecasts. Results of this study confirm that the precipitation forecasts based on extrapolation of rainfall patterns from radar can produce increases in the forecast lead-time. For the two events presented, the forecast lead-time was increased by four to five hours, which can be significant when issuing flood warnings.
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