A 60-day streamflow forecasting framework coupling deep learning bias correction with process-based hydrological modeling in the Upper Yangtze River Basin

Reliable medium- and long-term streamflow forecasting is critical for water resources management and hydropower generation. This study proposes a 60-day streamflow forecasting framework that systematically integrates a convolutional neural network (CNN) for bias correction of precipitation forecasts from the UK Met Office (UKMO) numerical weather prediction model, the Geomorphology-Based Eco-Hydrological Model (GBEHM) for streamflow simulation, and an autoregressive with exogenous input (ARX) model for statistical post-processing. Applying the proposed framework to the Upper Yangtze River Basin, results indicate that the CNN model reduces the areal-averaged precipitation root mean square error (RMSE) by around 35% and elevates the temporal correlation coefficient (TCC) from 0.62 to 0.74 against raw UKMO forecasts across the 60-day horizon, with performance gains amplifying at longer lead times. Subsequently, when driving the GBEHM with corrected precipitation and applying ARX post-processing, the streamflow forecasts exhibit substantial enhancements with a reduction in RMSE of 36%, a decrease in relative error (RE) from 48.2% to 17.4%, and an increase in Nash–Sutcliffe efficiency (NSE) from 0.33 to 0.72 compared to those driven by raw forecasts in terms of 60-day mean performance. Error decomposition identifies precipitation forecast errors which intensify with lead time as the dominant source of uncertainty for medium- and long-term streamflow forecasting, while confirming that hydrological model uncertainty remains a significant component, highlighting that the selection of a robust hydrological model is crucial for enhancing the reliability and predictive skill of the streamflow forecasts. By systematically leveraging the CNN to mitigate drifting meteorological biases, the GBEHM to capture physical catchment dynamics, and the ARX to minimize residual errors, the proposed framework yields volumetrically accurate and temporally consistent forecasts across an extended 60-day horizon, providing valuable decision support and sufficient lead time for regional water management.