Disentangling the Source of Uncertainty in Monthly Streamflow Predictions: A Case Study of Riu Mannu di Narcao Basin, Sardinia Region, Italy

This study quantifies the uncertainty in monthly streamflow predictions under future climate scenarios in two periods (near and far future) for the Riu Mannu di Narcao basin in Sardinia, Italy. The sources of uncertainty include the hydrological model structure, model parameters, and variability in climatic inputs derived from global and regional climate models (GCM-RCM coupling) and representative concentration pathways (RCPs). Three conceptual and lumped hydrological models (GR3M, ABCD, and IHACRES) were combined with four climate models and two RCPs (RCP 4.5 and RCP 8.5) to assess future streamflow. Monte Carlo simulations were performed to evaluate parameter uncertainty, and the analysis of variance (ANOVA) method was applied to quantify the different sources of uncertainty. The results reveal that, as a single source, GCM-RCM coupling is the largest contributor, accounting for 47.32% (54.64%) of total near (far) future monthly streamflow projection uncertainties, followed by the hydrological model structure at 16.02% (21.09%), RCP scenarios at 15.35% (8.54%), and parameter uncertainty at 0.79% (1.39%). A consistent decline in median monthly streamflow is projected, especially during winter months (December to February), raising a concern about water availability in the region. Our study quantified different sources of uncertainty in monthly streamflow predictions under climate change, disentangling the roles of the hydrological model, model parameters, climate model, and climate scenario for reliable future streamflow projections.