Enhancing SWAT-based ecosystem modeling through satellite data and hybrid AI under climate change
Environmental Modelling and Software, cilt.204, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 204
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.envsoft.2026.107111
- Dergi Adı: Environmental Modelling and Software
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Environment Index, Geobase, Greenfile, INSPEC, Public Affairs Index, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Climate change, Ecosystem modeling, Hybrid modeling, Satellite-based meteorological data, Streamflow simulation, SWAT
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
As climate change intensifies, reliable ecosystem models are becoming important for sustainable water management. This study aims to improve SWAT-based ecosystem modeling by minimizing predictive uncertainties in SWAT simulations and providing more reliable streamflow predictions. To assess data source effects, the SWAT model was driven by observational and satellite-based meteorological data, demonstrating the effectiveness of satellite inputs in data-scarce regions. To reduce discrepancies between simulated and observed flows, SWAT-TCN and SWAT-CatBoost models were integrated into a Regime-Adaptive Ensemble (RAE)-based SWAT + TCN–CatBoost framework. Model performance was evaluated using statistical metrics and TOPSIS and VIKOR, while SHAP was used for interpretation. The results indicate that regime-adaptive hybrid approach improved SWAT performance, with RAE Hybrid Model giving the best validation results (NSE = 0.90, KGE = 0.87). Subsequently, the framework was applied to future projections using three GCMs and two SSP scenarios, demonstrating more reliable results under climate change.