Flood runoff analysis by the storage function models normally requires an estimate of the effective rainfall as an input, which is computed by use of runoff coefficient or filtering of runoff-component separations . The present study proposes a new storage routing model which can accommodate a nonlinear relationship between the storage and discharge as well as loss mechanisms.The model can calculate the total hydrograph from the observed rainfall instead of using the effective rainfall . The loss mechanisms take into account infiltration, evaporation and transpiration from the river basin . An unknown parameter of the loss component is identified at the same time as the other parameters involved in the storage function model. The proposed model has the advantage of the real-time flood forecasting , because the hydrologic data are directly processed.The model developed in this study was applied to more than 70 flood records from the rivers in Hokkaido . The Newton-Raphson method was used to optimize the model parameters in which the sensitivity coefficients were theoretically derived and the technique of the lower triangular Cholesky factorization was employed to search the optimized values as fast as possible. The results clearly show that the proposed model appears to provide better reproduction of the hydrograph than the hyetograph of effective rainfall patterns is used.

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A physically based model which predicts rainfall distribution using three-dimensionally scanning Doppler radar is proposed.Most of short-range rainfall prediction models using three-dimensionally scanning radar are based on time-extrapolating method. Since physical processes of rainfall phenomenon are not included i those models, the accuracy of prediction is difficult to be more improved.So we offer the method which takes into account the water balance and its phase change in the atomosphere as well as the wind components estimated by VAD analysis using three-dimensionally scanning Doppler radar. The results from a numerical experiment indicate that this method is more effective for short-range rainfall prediction than time-extrapolating method.

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