Coupled Water–Sediment Modelling in the Yellow River Basin Using a Physics-Guided LSTM–GNN Framework Incorporating River Network Topology

The Yellow River Basin (YRB) is among the most water-scarce, sediment-laden, and anthropogenically impacted river basins worldwide. Rainfall–runoff and runoff–sediment relationships in the YRB have traditionally been investigated using process-based hydrological models, which are computationally demanding and difficult to apply at large spatial scales. Here, a physics-guided LSTM–GNN (Long Short-Term Memory and Graph Neural Network) framework was proposed to simulate coupled water–sediment processes across the YRB. Using sub-basin delineation and upstream–downstream connectivity derived from the physically based Geomorphology-Based Ecohydrological Model (GBEHM), the framework employs LSTM to learn local runoff and sediment generation within individual sub-basins, and GNN to represent topology-constrained routing along the river network. The coupled model generated monthly streamflow and sediment data for 718 sub-basins over the period 1982–2017. Compared with a baseline model that neglects physical river-network topology (total NSE_flow=0.78, NSE_sediment=0.62; median NSE_flow=0.09, NSE_sediment=0.13), the proposed framework demonstrated significantly improved predictive performance (total NSE_flow=0.89, NSE_sediment=0.85; median NSE_flow=0.42, NSE_sediment=0.32) during the test period (2013–2017), especially at stations in large tributaries and the main stream, with high connectivity and large catchment areas. These results show that the proposed LSTM-GNN framework can effectively serve as a surrogate of the process-based model with high accuracy, highlighting its potential for simulating upstream–downstream coupled hydrological processes in super-large river basins.