A GNN routing module is all you need for LSTM Rainfall–Runoff models

Abstract

Rainfall–Runoff (R–R) modeling is crucial for hydrological forecasting and water resource management, yet traditional deep learning approaches, such as Long Short-Term Memory (LSTM) networks, often overlook explicit runoff routing, leading to inaccuracies in complex river basins. This study introduces a novel LSTM-Graph Neural Network (GNN) framework that integrates LSTM for local runoff generation with GNN for spatial flow routing, leveraging river network topology as a directed graph. Applied to the Upper Danube River Basin using the LamaH-CE dataset (1987–2017), the model partitions the basin into 530 subbasins and evaluates four GNN architectures: Graph Convolutional Network (GCN), Graph Attention Network (GAT), Graph SAmple and aggreGatE (GraphSAGE), and Chebyshev Spectral Graph Convolutional Network (ChebNet). Results demonstrate that all LSTM-GNN architectures outperform the baseline LSTM, with LSTM-GAT achieving the highest performance (mean NSE = 0.61, KGE = 0.65, Correlation Coefficient = 0.84, RMSE reduction of ∼ 35 %). Improvements are most evident in downstream stations with high connectivity and large contributing areas, where adaptive attention in GAT effectively captures heterogeneous upstream influences. These findings underscore the potential of GNN-based approaches for large-scale, spatially aware hydrological modelling and provide a foundation for future applications in flood forecasting and climate adaptation.