A novel approach to uncertainty-conscious long-term streamflow reconstruction in ungauged basin combining machine learning and dendro-hydrological indicators

Study Region: The Abitibi River watershed is a large hydropower-regulated basin located in northeastern Ontario and northwestern Quebec, Canada, where natural streamflow records are unavailable due to long-term flow regulation and reservoir operations. Study Focus: This study presents an uncertainty-aware framework for long-term streamflow reconstruction and trend analysis in a regulated, data-scarce watershed. The framework integrates hydrological modeling, machine-learning-based parameter estimation, and dendrohydrological validation to reconstruct natural streamflow in the Abitibi River watershed. Three machine learning algorithms are used to estimate parameters of the GR4J-CemaNeige model, generating pseudo-observations of natural streamflow. These pseudo-observations are then used to calibrate five conceptual hydrological models within the Raven platform, producing a 15-member ensemble of daily streamflow simulations for 1950–2023. New Hydrological Insights for the Region: The reconstructed ensemble shows strong agreement with flood-ring signature in tree rings of naturally flowing rivers, with significant correlations ranging from 0.35 to 0.72, and is further supported by comparison with scaled natural flows from a nearby watershed (annual CRPS = 49.39 m 3 /s). A hierarchical Bayesian linear model reveals statistically significant increases in annual and spring streamflow, with estimated trends of 1.91 and 2.67 m 3 /s/year, respectively. Overall, the framework provides a robust strategy for reconstructing long-term natural streamflow in regulated watersheds.