This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the
hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate
the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted
by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF
for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS
considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin.
As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the
timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated
pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved
through the adjustment technique and blending with NWP.