From precipitation datasets to streamflow simulations: Tracing the propagation of uncertainty in hydrological modelling

Understanding how uncertainty propagates in hydrological modelling, from precipitation inputs to streamflow simulations, is essential for improving model sensitivity and strengthening the interpretation of model outputs. While gridded precipitation products are increasingly developed and widely used in different applications worldwide, their impacts on the uncertainty of simulated streamflows remain largely unexplored. In this work, we tested a variety of state-of-the-art precipitation datasets to explore how their uncertainty cascades through a process-based hydrological model and influences streamflow predictions using the Reno River basin in Italy as a case study. Our results indicate that, although precipitation patterns are broadly consistent across datasets, substantial differences emerge at seasonal and annual scales especially in complex terrains. Moreover, precipitation uncertainties are propagated and also amplified to the streamflow, on average 3.5 times for the dry season. The opposite occurs for the wet season, where uncertainty slightly decreases. The subsequent analysis reveals that the influence of precipitation uncertainty differs among subbasins. As such, our work emphasises the substantial impact of precipitation forcing in hydrological modelling and the significance of evaluating and quantifying uncertainty propagation