Learning Basin Similarity Through Combined Deep Learning and Random Forest Approaches for Improved Parameter Transfer in Ungauged Basins

Parameter regionalization for ungauged basins remains a critical challenge in hydrological modeling. While traditional approaches rely on physical catchment descriptors or spatial proximity, and recent machine learning applications have focused primarily on direct streamflow prediction, there remains significant potential to leverage machine learning for improved parameter transfer strategies. This study explores novel approaches that combine Long Short-Term Memory (LSTM) networks and Random Forest (RF) models to predict basin similarity and optimize parameter transfer for physically-based hydrologic models. Using case studies from British Columbia's Fraser River Basin and Ontario's Great Lakes region, we test multiple methodologies for integrating deep learning with traditional parameter transfer approaches. Our primary benchmark is established through an exhaustive parameter transfer experiment using the Raven hydrological model, where parameters from each potential donor basin were transferred to every possible receiver basin across 10 independent trials. This benchmark represents the best achievable KGE via parameter transfer methods. Our framework employs a regional LSTM model to capture complex streamflow dynamics and characterize basin similarity, then explores various RF-based approaches to predict optimal donor-receiver basin pairs for parameter transfer. These methods are evaluated against both the exhaustive transfer benchmark and emerging machine learning approaches. Results indicate that thoughtfully combining deep learning and random forest techniques can capture nuanced relationships between basin characteristics and hydrological response similarity, advancing the state-of-the-art in parameter regionalization for ungauged basins while maintaining physical interpretability.