Akademik Veri Yönetim Sistemi
Water (Switzerland), cilt.18, sa.2, 2026 (SCI-Expanded, Scopus)
The pressure of climate change on water resources has made the development of reliable hydrological models increasingly important, especially for data-scarce regions. However, due to the limited availability of ground-based observations, it considerably affects the accuracy of models developed using these inputs. This also limits the ability to investigate future hydrological behavior. Satellite-based data sources have emerged as an alternative to address this challenge and have received significant attention. However, the transferability of these datasets across different model classes has not been widely explored. This paper evaluates the transferability of satellite-derived inputs to eleven types of models, including process-based (SWAT), data-driven methods (XGBoost and WGAN), and hybrid model structures that utilize SWAT outputs with AI models. SHAP has been applied to overcome the black-box limitations of AI models and gain insights into fundamental hydrometeorological processes. In addition, uncertainty analysis was performed for all models, enabling a more comprehensive evaluation of performance. The results indicate that hybrid models using SWAT combined with WGAN can achieve better predictive accuracy than the SWAT model based on ground observation. While the baseline SWAT model achieved satisfactory performance during the validation period (NSE ≈ 0.86, KGE ≈ 0.80), the hybrid SWAT + WGAN framework improved simulation skill, reaching NSE ≈ 0.90 and KGE ≈ 0.89 during validation. Models forced with satellite-derived meteorological inputs additionally performed as well as those forced using station-based observations, validating the feasibility of using satellite products as alternative data sources. The future hydrological status of the basin was assessed based on the best-performing hybrid model and CMIP6 climate projections, showing a clear drought signal in the flows and long-term reductions in average flows reaching up to 58%. Overall, the findings indicate that the proposed framework provides a consistent approach for data-scarce basins. Future applications may benefit from integrating spatio-temporal learning frameworks and ensemble-based uncertainty quantification to enhance robustness under changing climate conditions.