Application of Attention-Based Graph Neural Networks for Spatial Distribution Prediction of Streamflow

Accurate streamflow estimation is crucial for effective water resource management and flood forecasting. However, physics-based hydrological models fail to respond promptly to rapid hydrological events due to lack efficiency in model calibration and computing time for large-scale catchment , while existing deep learning models tend to neglect the physical processes of runoff transfer, failing to account for the spatial and temporal dependencies inherent in runoff dynamics. In this study, we propose a topological process-based model that integrates Graph Attention Networks (GAT) to capture the spatial topology of runoff transfer and Long Short-Term Memory (LSTM) networks to simulate the temporal transfer between upstream and downstream runoff. The model was applied to the Yangtze River Basin which is the largest river basin in China to predict streamflow at 10 km spatial resolution. Validation results show that our model achieves a median Nash-Sutcliffe Efficiency (NSE) value of 0.783 at secondary outlet stations across the basin and effectively simulates the streamflow peak due to flooding. Additionally, the model is capable of simulating the spatial distribution of daily streamflow for an entire year within 10 seconds, providing a significant computational speedup compared to physical process-based river confluence models. This work represents a step towards more efficient and responsive prediction of extreme hydrological events using deep learning model.