Advancing ungauged catchment hydrology through regionalized ML-based post-processing

Post-processing large-scale hydrological models remains a significant challenge, particularly in ungauged basins, where limited observations hinder accurate representation of local hydrological conditions. In this study, we propose a machine learning (ML)-based approach for regionalizing and post-processing simulated streamflow from the E-HYPE hydrological model across the pan-European domain. Using Long Short-Term Memory (LSTM) models, we explored E-HYPE post-processing with two different regionalization strategies: (1) individual models trained for basins belonging to clusters of hydrological similarity (Cluster-Specific Model), and (2) a single model incorporating the hydrological clusters as categorical variables (Cluster-Informed Global Model). Performance was evaluated using multiple evaluation metrics (Mean Absolute Error, MAE; Nash-Sutcliffe Efficiency, NSE; and log transformed NSE, log-NSE) under a K-fold cross-validation framework allowing for spatial and temporal testing. Furthermore, the improvements at each location were assessed by examining different hydrological signatures, including mean, high (Q90) and low (Q20) streamflow situations, using the E-HYPE simulations as benchmark. Results show that both regionalization strategies achieve improvements in performance over raw simulations, including the ungauged basins (e.g. those that are excluded from the training dataset). The Cluster-Informed Global Model effectively balances regionalization and accuracy, outperforming the Cluster-Specific Model in both spatial and temporal testing, and it also shows enhanced representation of hydrological signatures. Building on these results, the Cluster-Informed Global Model was applied to all the catchments in E-HYPE, providing an updated pattern of hydrological signatures across the European domain. These findings highlight the potential of ML-based regionalization strategies to enhance hydrological model outputs and hence process understanding, particularly in data-scarce regions, potentially providing a framework for AI-enhancement of large-scale hydro-climate services.