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This study introduces a Directed Temporal Graph Convolutional Network (D\u2010TGCN)\u2014a flow\u2010aware architecture that explicitly incorporates upstream\u2010downstream connectivity as a physical prior. Evaluated in the Poyang Lake Basin, D\u2010TGCN reduced RMSE by up to 19.1% for Total Nitrogen and averaged 13.4% for Dissolved Oxygen compared to undirected models, with maximal improvements in stable unidirectional river sections. The model also demonstrated effective cross\u2010basin transferability. These results confirm that embedding flow direction substantially improves watershed\u2010scale prediction accuracy and spatial explicitness. 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