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Uncertainty quantification and providing probabilistic streamflow forecasts are of particular interest for water resource management. The Hydrologic Uncertainty Processor (HUP) is a well-known Bayesian approach used to quantify hydrologic uncertainty based on observations and deterministic forecasts. This uncertainty quantification is model-specific; however, utilizing information from multiple hydrologic models should be advantageous and should lead to better probabilistic forecasts. Using seven, structurally different, conceptual models, this study firstly aims at evaluating the effects of implementing different hydrologic models on HUP performance. Secondly, using the concepts of the Bayesian Model Averaging (BMA) approach, a multi-model HUP-based Bayesian post-processor (HUP-BMA) is proposed where the combination of posterior distributions derived from HUP with different hydrologic models are used to better quantify the hydrologic uncertainty. All post-processing approaches are applied for medium-range daily streamflow forecasting (1 to 14 days ahead) in two watersheds located in Ontario, Canada. The results indicate that the HUP forecasts for short lead-times are negligibly affected by implementing different hydrologic models, while with increasing lead-time and flow magnitude, they significantly depend on the quality of the deterministic forecast. Moreover, the superiority of the proposed HUP-BMA method over HUP is demonstrated based on various verification metrics in both watersheds. Additionally, HUP-BMA outperformed the original BMA in quantifying hydrologic uncertainty for short lead-times. However, by increasing lead-time, considering the effects of initial observed flow on HUP-BMA formulation may be not beneficial. So, its modified version unconditioned on initial observations is preferred.

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