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Evaluates satellite rainfall input uncertainty in hydrological models, indicating significant bias reduction.
Uncertainty analysis estimates the range of variability in a model's expected results. A main source of uncertainty in hydrological models is rainfall input data. This study aims to evaluate the uncertainty in satellite-based rainfall data for the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The Upper Bogowonto Watershed, Central Java, is selected to test the method. The results of the study showed that bias correction significantly reduced the bias in satellite rainfall data. The reference model using field observation data demonstrated good performance, with R2 = 0.64 and KGE = 0.80 for the calibration period and R2 = 0.63 and KGE = 0.78 for the validation period. The GLUE analysis revealed that the range of input data uncertainty covered 60% of the observation data during the calibration period and 63% during the validation period. High uncertainty occurred during peak flow and tended to be lower during low flow. The GSMaP satellite data were considered the best rainfall input data for the hydrological model in the Upper Bogowonto Watershed.
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Rinduan et al. (2026) studied this question.