## 5 Conclusions  
This study introduced a hybrid hydrological forecasting framework that integrates physically based modeling (HEC-HMS), machine learning-based bias correction, and a Temporal Fusion Transformer (TFT) to generate accurate, multi-horizon streamflow predictions in a semi-arid, irrigation-impacted basin in Northern Greece.  
By correcting irrigation-induced discrepancies in synthetic discharge data, the proposed framework significantly improved forecast accuracy, with an NSE increasing from 0.55 to 0.84 and an RMSE decreasing from 1.084 to 0.301 m\({}^{3}\)/s. When used as input to the TFT, the bias-corrected data enabled robust 24-, 48-, and 72-h streamflow forecasts with high reliability and well-calibrated uncertainty bounds. The model's interpretability further uncovered key temporal and meteorological drivers, such as seasonal irrigation patterns and rainfall events, contributing to both scientific insight and operational transparency.  
Despite relying primarily on synthetic inputs, the hybrid model achieved performance comparable to observed-data-driven baselines. Its modular design--separating physical simulation, bias correction, and deep learning--allows for flexible adaptation across different basins and data availability levels. This makes the framework particularly suitable for ungauged or data-scarce environments, where conventional monitoring may fall short.  
Overall, this work demonstrates that combining the physical realism of hydrological simulation with the pattern recognition capabilities of deep learning leads to reliable, explainable, and transferable streamflow forecasts. Future research should explore scaling this approach to larger catchments, automating data ingestion pipelines, and incorporating diverse data sources to further enhance performance and generalizability.  
Conceptualization, H.A. and V.A.; Data curation, V.A.; Formal analysis, H.A.; Methodology, H.A. and I.R.; Resources, V.A.; Supervision, I.R.; Writing--original draft, H.A.; Writing--review and editing, I.R. and V.A. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge that the HEC-HMS model utilized in this study was initially developed within the framework of the \"Nature-Based Solutions for Water Management in the Mediterranean\" (NATMED) project, funded from the European Union's PRIMA Research and Innovation Programme under Grant Agreement No. 2221. The project's developments provided valuable foundational resources, and the completion of this work was further supported by additional institutional and academic contributions.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to restrictions imposed by the Greek regional authorities, as access to the raw data requires prior approval.  
The authors acknowledge the Department of Agriculture of the University of Western Macedonia for managing and providing access to the data from the Western Macedonia regional administration's hydrometeorological station network, which was essential for training the models, as well as for offering valuable regional expertise and counsel. The authors would also like to thank the organizers of SETN 2024 for the opportunity to present this work and for granting permission to submit this extended version for publication.  
The authors declare no conflicts of interest.