Operational Streamflow Forecasting for Cascaded Reservoirs in the Dadu River Basin: A Deep Learning Approach Based on Encoder-Decoder LSTM and Multi-Source Data Integration

This study presents a robust streamflow forecasting framework based on an Encoder-Decoder LSTM architecture designed for the Dadu River Basin, a major tributary of the upper Yangtze River with a drainage area of 77,700 $km^2$ and annual precipitation increasing from 600 to 1500 mm northwest-to-southeast. The model integrates multi-source heterogeneous data, including ERA5-Land reanalysis products, local grid precipitation, and historical runoff observations. A key innovation is the State Transfer Module, which maps compressed historical catchment features into the decoder’s initial state to simulate the transformation from antecedent conditions to future runoff processes. The framework was validated across eight reservoirs on the Dadu River main stem, representing diverse regulation capacities including daily, seasonal (Houziyan), and annual (Pubugou) regulation During the 2024–2025 test period, the model achieved an average Mean Relative Error (MRE) of 18.2%, significantly outperforming traditional deterministic (24.7%) and similarity-based (21.0%) methods. Specifically, Nash-Sutcliffe Efficiency (NSE) values reached 0.89 at Houziyan and 0.88 at Pubugou, demonstrating superior skill in capturing flood peaks and recession trends. With minute-level training and second-level inference efficiency, this deep learning approach provides a reliable core technology for long-lead (10-day) operational forecasting and cascaded reservoir management