Quantifying the Contribution of Global Precipitation Product Uncertainty to Ensemble Discharge Simulations and Projections: A Case Study in the Liujiang Catchment, Southwest China

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets.