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

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 Fore...

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Vydáno v:	Water resources research Ročník 61; číslo 5
Hlavní autoři:	Ding, Bingbing, Yu, Xinxiao, Jia, Guodong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington John Wiley & Sons, Inc 01.05.2025 Wiley
Témata:	Agricultural land climate Climate and land use Climate change cropland forests Grasslands Hydrologic models hydrological modeling Hydrology Land use land use change Learning algorithms Machine learning Precipitation Resource management Soil and Water Assessment Tool model Stream discharge Stream flow Streamflow changes streamflow variability Streamflow variations temperature water water management Water resources Water resources management Watersheds
ISSN:	0043-1397, 1944-7973
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Shrnutí:	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. Plain Language Summary As global water resources have increased in scarcity, it has become necessary to study the processes underlying streamflow variation and its controlling factors. In this study, it was revealed that combining hydrological models with machine learning methods is a promising approach for addressing this issue. When integrated with the SWAT model, XGBoost demonstrated better streamflow simulation performance than RF. Through the SHAP method, we identified the key factors influencing streamflow variation. Watershed area was the main factor, and precipitation exerted a greater impact on streamflow than temperature. Different land use types had nonlinear impacts on streamflow with significant threshold effects. The interaction effects between land use types revealed that different land use combinations played complex roles in regulating streamflow. Precipitation did not dominate the interaction effects with land use. Instead, drastic land use changes led to abrupt variations in watershed streamflow, although climate changes played contributing roles as well. Key Points Machine learning methods can be effectively integrated with hydrological models, resulting in superior streamflow simulation capabilities Different controlling factors exerted nonlinear influences on streamflow, with notable differences and threshold effects Precipitation does not dominate the interaction effects with land use, with land use change as the primary driver of streamflow variation
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0043-1397 1944-7973
DOI:	10.1029/2024WR039734