Abstract. Accurate streamflow forecasting is essential for water resource management, yet remains challenging in data-scarce regions. This study develops and evaluates a hybrid modeling framework for the Ouémé River basin (Benin) that combines the conceptual GR4J hydrological model with machine learning (ML) via a residual-correction strategy. The approach integrates GR4J with three ML techniques Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks to correct systematic errors in simulated streamflow. Models were trained on daily data from 1996–2012 and validated on an independent period (2013–2020) using performance metrics including Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). Results show that all hybrid models (GR4J–RF, GR4J–XGB, and GR4J–LSTM) outperform the standalone GR4J model. The tree-based hybrids (GR4J–RF and GR4J–XGB) achieved the highest performance gains, with validation KGE values of 0.80 and 0.79, respectively, compared to 0.75 for GR4J alone. While the GR4J–LSTM hybrid also improved baseline results, it exhibited comparatively lower performance, attributed to the data-intensive nature of deep learning in a limited-data context. The study demonstrates that hybrid residual-correction frameworks, particularly those employing efficient tree-based ML, offer a robust and practical pathway for enhancing streamflow prediction in data-scarce catchments.
Ahouandjinou et al. (Wed,) studied this question.