Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation and effective watershed management for sustainable water resource use. The Soil and Water Assessment Tool (SWAT) is widely utilized for hydrological modeling but shows limitations in baseflow simulation due to its uniform application of the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial and temporal variability. To address these challenges, this study integrated SWAT with the Tree-Based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) framework, to predict HRU-specific alpha factors. Furthermore, a user-friendly web-based program was developed to improve the accessibility and practical application of these optimized alpha factors, supporting more accurate baseflow predictions, even in ungauged watersheds. The proposed HRU-specific alpha factor approach in the study area significantly enhanced the recession and baseflow predictions compared to the traditional uniform alpha factor method. This improvement was supported by key performance metrics, including the Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), the percent bias (PBIAS), and the mean absolute percentage error (MAPE). This integrated framework effectively improves the accuracy and practicality of hydrological modeling, offering scalable and innovative solutions for sustainable watershed management in the face of increasing water stress.