The water availability in the river basin possesses essential benefits for human life, habitats, energy, and farming. As the river basin collects and stores inland water runoff from rainfall, it keeps a maintainable river discharge or so-called a dependable flow. As an extensive irrigation system in central java, the Jratunseluna river basin provides water for 257 thousand hectares of irrigation, with the river basin's area 9.896 km2. In this case, the dependable flow used for irrigation intake's design needs to be analyzed. However, as a ground-based instrument is used and a point-based measurement, the accuracy may depend on the number of devices deployed on the ground. On the other hand, the Global Precipitation Measurement (GPM) collects extensive spatial data in one observation cycle at near-real-time. Here, the use of satellite data looks like the main challenge dealing with a large-scale area target. This study aims to analyze the accuracy of satellite-based data related to rainfall amount used as an input to calculate the dependable flow compared to the analyses generated from the ground-based data. The study uses the Mock model with the calibrated parameter obtained from the observed flow data from 2010 to 2020. The model was developed under two scenarios; the rain gauge models and the GPM models. This study uses the GPM-IMERG specification with a time resolution of 1 day and a spatial resolution of 0.1° x 0.1°. The results show that the estimated dependable flows under two conditions are comparable to the observed data regarding the correlation coefficient, RMSE, and NSE scores. The best result of the statistical tests for the correlation coefficient, RMSE, and NSE obtains 0.75 m3/s, 3.60, and 0.70 for the rain gauge model and 0.75 m3/s, 3.30, 0.80 for the GPM model, respectively. Through this study, the use of satellite-based GPM data is expected to be more extensive to support modern agricultural management activities.

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