Climate change results in extreme weather events, and flood damage occurs frequently due to local heavy rainfall events, which necessitates quantitative rainfall forecasts using radars. Moreover, the use of MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) forecast rainfall increases because of its capability of observing the real-time behavior of rainfall events. Although the MAPLE forecast rainfall offers spatial-temporal resolutions of high quality, it does

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... produce reliable rainfall data for the analysis of rainfall-runoff. To overcome this problem, calibrations need to be made with the MAPLE forecast rainfall and ground rainfall to create reliable rainfall data. In this study, the Hydro-AMBER model, a calibrated rainfall-forecasting data generator, was developed to create reliable rainfall data for the MAPLE forecast rainfall. The Hydro-AMBER model is suitable for distributional rainfall-runoff models because it divides small basins using meteorological data (DEM (digital elevation model), land cover), creates calibrated radar rainfall data and MAPLE forecast rainfall data using ground rainfall data, and stores generated rainfall data in the form of an ASCII file. With the Inbukcheon, Pyeongchang River, upstream portion of the Namhangang River, and Yangyang Namdaecheon River as target basins applied to the S-RAT model, which is a distributional rainfall-runoff model. Moreover, the accuracy of the MAPLE forecast rainfall was evaluated by comparing the runoffs from the forecast rainfall calibrated by the Hydro-AMBER model and from the ground rainfall.