Exploring the Transferability of Knowledge In Deep Learning-Based Streamflow Models Across Global Catchments

Accurate streamflow prediction is critical for flood forecasting and water resource management, particularly in data-scarce regions like Central Asia (CA), where traditional hydrological models struggle due to insufficient discharge data. Deep learning models, such as Long Short-Term Memory (LSTM), have demonstrated the potential for global hydrologic regionalization by leveraging both climate data and catchment characteristics. We used a transfer learning (TL) approach to improve streamflow predictions by first pretraining LSTM models on catchments from data-rich regions like Switzerland, Scotland, and British Columbia (source regions). These deep learning models were then fine-tuned on the data scarce target region (CA basins). This approach leverages the knowledge gained from the source regions to adapt the model to the target region, enhancing prediction accuracy despite the data scarcity in CA. Incorporating lagged streamflow alongside ERA-5 climate data boosted prediction accuracy, particularly in snowmelt and glaciers influenced basins like Switzerland (median NSE=0.707 to 0.837), British Columbia (median NSE= 0.775 to 0.923) and CA (median NSE=0.693 to 0.798). K-Means algorithm was applied to categorize catchments from four global locations into five clusters (labeled 0–4) based on their specific attributes. The predictive performance of fine-tuned LSTM model has significantly enhanced when leveraging a pre-trained model with cluster 2, as demonstrated by higher median metrics (NSE=0.958, KGE=0.905, RMSE=10.723, MSE=115.055) compared to both the locally trained model (NSE=0.851, KGE=0.792, RMSE=20.377, MSE=415.579) and individual basin-based training approaches (NSE=0.69, KGE=0.692, RMSE=25.563, MSE=676.110). These results highlight the effectiveness of pretraining the LSTM model on diverse clusters (0, 1, 2, and 4) before fine-tuning on the target region (cluster 3). Moreover, pretraining the LSTM model with clusters 0 and 4 resulted in enhanced performance by increasing the number of basins, whereas the impact was minimal or even declined when using clusters 1 and 2, as well as when all basins from the four clusters were included. These findings demonstrate the feasibility of transfer learning in addressing data scarcity challenges and underscore the importance of diverse and high-quality training data in developing robust, regionalized hydrological models. This approach bridges the gap between data-rich and data-scarce regions, offering a pathway to improved flood prediction and water resource management.