###### Abstract  
This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies--improving the Nash-Sutcliffe Efficiency (NSE) from 0.55 to 0.84, the Kling-Gupta Efficiency (KGE) from 0.67 to 0.89, and reducing the RMSE from 1.084 to 0.301 m\({}^{3}\)/s. The corrected discharge is used as input to a Temporal Fusion Transformer (TFT) trained on hourly meteorological data to predict streamflow at 24-, 48-, and 72-h horizons. In a semi-arid, irrigated basin in Northern Greece, the TFT achieves NSEs of 0.84, 0.78, and 0.71 and RMSEs of 0.301, 0.743, and 0.980 m\({}^{3}\)/s, respectively. Probabilistic forecasts deliver uncertainty bounds with coverage near nominal levels. In addition, the model's built-in interpretability reveals temporal and meteorological influences--such as precipitation--that enhance predictive performance. This framework demonstrates the synergistic benefits of combining physically based modeling with state-of-the-art deep learning to support robust, multi-horizon forecasts in irrigation-influenced, data-scarce environments. hybrid modeling; streamflow forecasting; hydrological simulation; deep learning in hydrology; temporal fusion transformer; HEC-HMS; probabilistic forecasting +
Footnote †: journal: Applied sciences  
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across the Mediterranean region, including Greece, Turkey, Libya, and Bulgaria. It was the deadliest Mediterranean tropical-like cyclone ever recorded, resulting in significant loss of life and widespread infrastructure damage. Additionally, Storm Daniel became the most expensive tropical cyclone ever recorded outside the North Atlantic. These events highlight the impacts that extreme flooding can impose on societies, economies, and ecosystems. It is therefore clear that the need for effective hydrological models is more evident than ever. The severe impacts of recent extreme weather events, including both devastating floods and prolonged droughs, emphasize the pressing need for innovation in this domain. While traditional hydrological forecasting systems remain important, they often fall short in delivering timely and precise predictions. These systems face difficulties in responding to emerging hazards, supporting efficient disaster management, and adapting to the growing complexity of environmental conditions driven by climate change. In parallel, a particular challenge in hydrological prediction is the limitation of available streamflow measurements. The available data in a plethora of basins are insufficient, inconsistent, or biased due to human activity. This \"ungauged basin problem\" is especially critical in regions prone to flash floods or in developing areas where monitoring infrastructure is limited [7]. Researchers have traditionally addressed this issue using physical models, which can simulate watershed processes based on fundamental hydrological principles. This work addresses the middle ground where a framework partially dependent on real data can be utilized to fill in the gaps and deliver operational streamflow forecasts. The integration of physical modeling approaches with deep learning techniques presents a promising path forward for hydrological prediction. The simulated streamflow data from these physical models could potentially serve as additional training data for deep learning approaches. Since neural networks' performance typically scales with the amount of training data available, the synthetic data generated by physical models could enhance the deep learning model's predictive capabilities by providing additional training examples that are physically consistent with watershed processes.