Exploring the Controlling Factors of Watershed Streamflow Variability Using Hydrological and Machine Learning Models

Abstract

Abstract Studying streamflow processes and controlling factors is crucial for sustainable water resource management. This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), based on the input and output data from the Soil and Water Assessment Tool (SWAT) and comparing their streamflow simulation performances. The Shapley Additive exPlanations (SHAP) method identified the controlling factors and their interactions in streamflow variation, whereas scenario simulations quantified the relative contributions of climate and land use changes. The results showed that when integrated with the SWAT model, XGBoost demonstrated better streamflow simulation performance than RF. Among the key factors influencing streamflow variation, area was the most important, with precipitation having a stronger impact than temperature, positively affecting streamflow when exceeding 550 mm. Different land use types exerted nonlinear impacts on streamflow, with notable differences and threshold effects. Specifically, grassland, cropland, and forest positively contributed to streamflow when their proportions were below 50%, above 20%, and between 30% and 50%, respectively. Nonlinear interaction effects on streamflow between land use types resulted in positive or negative contributions at specific proportion thresholds. Furthermore, precipitation was not dominant in the interaction with land use. Streamflow changes were primarily driven by drastic land use changes, which contributed 55.71%, while climate change accounted for 44.27%. This integration of hydrological models with machine learning revealed the complex impacts of climate and land use changes on streamflow, offering scientific insights for watershed water resource management.

Keywords

• streamflow variability
• climate change
• land use change
• machine learning
• hydrological modeling