Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous
damages to humans and properties. Therefore, it is important to understand the spatial-temporal features of rainfall.
In this study, RADAR rainfall was used to calculate gridded areal rainfall which reflects the spatial-temporal
variability. In addition, Kalman-filter method, a stochastical technique, was used to combine ground rainfall network
with RADAR rainfall network to calculate areal rainfall. Thiessen polygon method, Inverse distance weighting
method, and Kriging method were used for calculating areal rainfall, and the calculated data was compared with
adjusted areal RADAR rainfall measured using the Kalman-filter method. The result showed that RADAR rainfall
adjusted with Kalman-filter method well-reproduced the distribution of raw RADAR rainfall which has a similar
spatial distribution as the actual rainfall distribution. The adjusted RADAR rainfall also showed a similar rainfall
volume as the volume shown in rain gauge data. Anseong-Cheon basin was used as a study area and the RADAR
rainfall adjusted with Kalman-filter method was applied in VfloTM model, a physical-based distributed model, and
ModClark model, a semi-distributed model. As a result, VfloTM model simulated peak time and peak value similar
to that of observed hydrograph. ModClark model showed good results for total runoff volume. However, for
verifying the parameter, VfloTM model showed better reproduction of observed hydrograph than ModClark model.
These results confirmed that flood runoff simulation is applicable in domestic settings(in South Korea) if highly accurate areal rainfall is calculated by combining gauge rainfall and RADAR rainfall data and the simulation is
performed in link to the distributed hydrological model.
Keywords : RADAR rainfall, kalman filter, distributed hydrologic model, flood