Accurate daily runoff forecast is crucial for water resources management, disaster reduction and power generation. However,traditional single-station runoff forecast lacks spatial information.In order to improve the accuracy of runoff forecast, this study presents a spatiotemporal graph convolutional network(STGCN) modeling with reservoir regulation(RR) and seasonal-trend decomposition procedures based on loess (STL) for Hanjiang River Basin(HRB) multi-station daily rrunoff forecast. First, the topological structure of the relationships between stations in the basin is extracted using a graph neural network. Then, a virtual node is constructed according to the principle of water balance, considering the influence of reservoir regulation and storage, and its runoff data is calculated. Finally, the runoff data of both virtual and real points are processed by STL to form a graphical dataset. This watershed runoff prediction model framework is applied to HRB, and various single and mixed models are benchmarked. The results show that the STGCN model outperforms traditional BP and LSTM models. Notably, the proposed STL-RR-STGCN model in this study significantly improves the accuracy of daily runoff predictions compared to the single STGCN model, especially for peak runoff predictions. For example, using the SMAPE index, the average value across 20 stations improved from 0.15 to 0.10, increasing the accuracy of the average measurement error in the HRB by 33.33%.