<![CDATA[Effective reservoir management can be supported by applying rainfall-runoff hydrological models. However, one of the main challenges of such models lies in the availability of reliable rainfall data. Satellite-based rainfall data offer a viable alternative to address this issue. This study aims to evaluate the reliability of satellite-based rainfall data for hydrological applications, specifically for simulating reservoir inflow using the HBV-96 model in the Selorejo Reservoir. The rainfall data used in this study include satellite-based datasets from TRMM, GPM, and RCM, tested in both raw and corrected forms. The HBV-96 model parameters were calibrated using observed rainfall data from 1998 to 2008, achieving a correlation coefficient and Nash-Sutcliffe Efficiency (NSE) of 0.86 and 0.72, respectively, for simulated streamflow. The model's performance was subsequently verified using observed rainfall data from 2009 to 2016, yielding consistent results with a correlation coefficient and NSE of 0.832 and 0.71, respectively. These calibrated parameters were then applied to the satellite rainfall datasets. The findings reveal that, in general, corrected TRMM satellite rainfall data using regression equations were not suitable for hydrological modelling. However, TRMM data corrected using duration curves significantly reduced deviations by up to 50% compared to raw data and provided better-simulated streamflow results, aligning more closely with observed streamflow. Conversely, RCM rainfall data, whether raw or corrected, performed poorly in the HBV model, with negative NSE values. Meanwhile, the bias-corrected GPM satellite rainfall data demonstrated the best performance in the HBV model, with a maximum deviation of only 5.81%.]]>
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