Improving Daily Streamflow Forecasting under Non-stationarity with a Physics-Informed, Decomposition-Enhanced Deep Learning Model

Abstract

Accurate and reliable streamflow forecasting is essential for hydropower generation, aquatic ecosystem management, and integrated water resource use. However, with ongoing climate change, streamflow series are increasingly exhibiting pronounced non-stationarity and non-linearity, which poses significant challenges for traditional forecasting models. Traditional physical models, despite having clear hydrological mechanisms, have limited accuracy under complex conditions. In contrast, deep learning models offer strong predictive capabilities but lack physical interpretability. Consequently, existing single-modeling approaches struggle to balance predictive accuracy with physical plausibility and show significant deficiencies in simulating extreme hydrological events. To address these issues, this study developed a multi-stage hybrid modeling framework integrating physical mechanisms, signal decomposition, and self-attention deep learning. This framework combines the Hydrologiska Byråns Vattenbalansavdelning (HBV) conceptual hydrological model, Variational Mode Decomposition (VMD), and a self-attention Bidirectional Long Short-Term Memory network (att-BiLSTM) to systematically enhance predictive performance for complex hydrological processes. Applied to the upper Heihe River Basin, the developed HBV-VMD-att-BiLSTM hybrid model demonstrates excellent testing set performance. It achieved Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) values of 0.978 and 0.985, improvements of 29.0% and 15.5% over the standalone HBV model. Regarding extreme flow simulation, the hybrid model markedly reduced prediction biases for high flows (FHV improved from −13.4% to −1.7%) and low flows (FLV reduced from 9.2% to 2.6%), while maintaining high stability and robustness across different hydrological seasons. These findings provide critical technical support for refined water resource management and risk mitigation of extreme hydrological events in catchments facing significant hydrological non-stationarity.