Can CNN-LSTM and lumped models improve (extreme) streamflow prediction of semi-distributed models? A comparative analysis of two hybrid frameworks

Water resources management depends heavily on hydrological modeling for reservoir operation and risk mitigation, especially in data-scarce regions. Hybrid approaches that combine artificial intelligence and conceptual models offer great potential for accurate streamflow prediction. However, their implementation can be time-consuming and applied in different configurations. This study comprehensively compares two promising hybrid frameworks: the Conceptual-Data-Driven Approach (CDDA) and the Ensemble Approach. The analysis was conducted in the Upper Blue Nile Basin in Ethiopia over the period from 2002 to 2019. Six baseline models were developed, including CNN-LSTM (data-driven), NAM and HBV-Light (lumped), and SWAT+, WEAP, and HEC-HMS (semi-distributed). All models achieved NSE ≥ 0.85 during the validation period, with CNN-LSTM performing best (NSE = 0.94). Each model was integrated into the two hybrid frameworks using Random Forest (RF) or Artificial Neural Networks (ANN). Results showed that the Ensemble Approach outperformed CDDA by combining two conceptual models. ANN performed better than RF across both frameworks. Hybrid modeling significantly improved semi-distributed models, while lumped and data-driven models showed minimal benefits. In the Ensemble Approach, normal and extreme flows simulated using semi-distributed models performed best when supported by CNN-LSTM or lumped models. Our analysis also demonstrated the robustness of the Ensemble Approach for selecting the supporting model. These findings emphasize the value and feasibility of the Ensemble Approach for improving streamflow prediction and better supporting decision-making in data-scarce regions. Nevertheless, a thorough understanding of the opportunities in hybrid modeling requires further research with a specific focus on operational forecasting.