Holistic uncertainty quantification and attribution for real-time seasonal streamflow predictions: Insights from input, parameter and initial condition

Two mesoscale watersheds in the East China, where increased occurrences of hydrometeorological extreme events (drought-flood abrupt alternation) were observed over the last few years. To investigate the impact of different uncertainty sources in seasonal streamflow predictability with lead times from + 1 M to + 9 M, the study proposed an uncertainty quantification and attribution framework leveraged by a three-way ANOVA by considering uncertainties from forcing inputs (CFSs), hydrological model parameters (PARs) and basin initial conditions (ICs). The annual and sub-annual variations in predictability and uncertainty contribution were analyzed and elaborated through four well-designed experiments, by using both deterministic and uncertainty metrics. The results show that CFSs-driven monthly streamflow predictions are effective up to + 3 M. Additionally considering PARs uncertainty enhances the predictability for all lead times, whereas additional consideration of ICs uncertainty exerts a positive impact on streamflow prediction only in shorter lead times (<+2 M). Uncertainty contributions vary in lead times and seasons. For the + 1 M predictions, the uncertainty is influenced by complicated contributors, with 47 % from CFSs, 18 % from ICs, and 17 % from PARs, respectively. Regarding lead times beyond + 1 M, CFSs and PARs are the dominant contributors, with overall contributions exceeding 90 %. Seasonally, ICs and PARs play a dominated role during low-flow period, nevertheless CFSs more pronounced during high-flow period. Our finding provides insights for modelers into the relative impact of individual uncertainty sources in the hydrological forecasting process, and supporting valuable suggestions for related applications. • A holistic uncertainty quantification and attribution is developed for real-time seasonal streamflow prediction. • Uncertainties in CFSs, PARs and ICs and their synergy are analyzed and quantified for sub-annual variations. • Effects of ICs are substantial for lead times <= +3M, for longer lead times the CFSs uncertainty dominated. • Valuable suggestion has been made for regional water resources management.