<![CDATA[This study evaluates the performance of Global Precipitation Measurement (GPM) satellite-based rainfall data in comparison to Automatic Rainfall Recorder (ARR) data in forming rainfall distribution patterns and assessing its impact on flood discharge simulation using the HEC-HMS model. Statistical validation was conducted using the Pearson Correlation Coefficient, the ratio of standard deviation of observations to RMSE (RSR), Percent Bias (PBIAS), and Mean Absolute Percentage Error (MAPE). The results show that GPM has a strong correlation with ARR (r = 0.875) and a low RSR value (RSR= 0.256), yet it exhibits a notable negative bias (PBIAS = –24.41%), indicating an underestimation of rainfall values. In contrast, simulations using ARR rainfall patterns produce peak discharges that closely match actual discharge records at the Jatigede Dam outlet, with an average deviation of less than 3% and a MAPE of 1.17%, categorized as very good. The GPM simulation produces peak discharges 13–16% higher than actual observations, with a MAPE of 14.53%, which still falls into the good category. These results suggest that while ARR provides higher accuracy, GPM remains a viable alternative source, especially in data-scarce areas, provided that appropriate calibration methods such as bias correction are applied. This study supports future research in satellite data calibration using machine learning and multivariate statistical approaches.]]>
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