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This study evaluates performance of the neural network based radar rainfall estimation and examines the applicability of the radar rainfall input estimated from the neural network (RNN) in streamflow modeling. The Uono River basin is selected as the study basin. A distributed hydrologic model is driven by using RNN, the radar rainfall input obtained from the Z-R relationship (RZ-R), and the gauge rainfall (RG) respectively. The statistical results of radar rainfall estimation indicate that the radar rainfall product using the neural network is more accurate than that using the operational Z-R relationship. In addition, the streamflow simulation results show that the simulated hydrographs obtained from RNN are more accurate than those obtained from RZ-R. The study concluded that the neural network technique outperforms the existing operational Z-R relationship. The appropriately trained network at the rain gauge sites is accurate, stable, and robust for estimating radar rainfall over the whole basin. The study also suggested that the RNN is an alternative input for hydrologic modeling when the gauge rainfall data is unavailable or insufficient.

Applicability of the Neural Network Based Radar Rainfall Estimation in Streamflow Modeling