A hybrid model for streamflow prediction addressing spatial connectivity and non-stationary dynamics with adaptive graph learning and multiscale decomposition

Abstract. Accurate streamflow forecasting remains a challenge due to the pronounced nonlinearity and multiscale variability inherent in hydrological processes. In this paper, a hybrid logarithmically transformed complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-based the spatial graph gated recurrent unit with adaptive graph structure (LCEEMDAN-ASGGRU) model is proposed to improve streamflow forecasting. The hybrid model is validated by forecasting daily streamflow at 14 stations in the Poyang Lake basin, a region characterized by complex river-lake interactions and significant spatial variability in streamflow magnitudes among stations. Results demonstrate that the LCEEMDAN-ASGGRU model shows superior predictive accuracy compared to benchmark models, achieving a mean Nash–Sutcliffe efficiency coefficient of 0.888 and mean root mean squared error of 264. The adaptive graph structure is spatially interpretable, closely aligning with known hydrological flow paths, while simultaneously capturing temporal similarity patterns among stations. In addition, a hidden Markov model with Gaussian Mixture Regression is used to quantify predictive uncertainty. Compared with other models, LCEEMDAN‑ASGGRU yields the most reliable forecasts. This study demonstrates the effectiveness of coupling logarithmic transformation, CEEMDAN decomposition, and adaptive graph learning with graph neural networks, providing a novel integrated approach for improving streamflow forecasting accuracy under complex hydrological conditions.