Analysis of Runoff Changes and Their Driving Forces in the Minjiang River Basin (Chengdu Section) in the Last 30 Years

Abstract

Surface runoff is a key component of the hydrological cycle and is essential for water resource management and water ecological balance in river basins. It is important to accurately reveal the spatial and temporal dynamics of regional surface runoff over long time scales and to quantify the impacts of climate change and human activities on surface runoff changes for sustainable water resources management and utilization. In this study, the Minjiang River Basin (Chengdu section) was selected, which has significant natural and anthropogenic variations, and a comprehensive analysis of runoff and its drivers will help to formulate an effective regional water resource management strategy. We mainly used SWAT to simulate the monthly-scale runoff in the Chengdu section of the Minjiang River Basin from 1990 to 2019 and combined SWAT-CUP to perform sensitivity analysis on the model parameters and Partial Least Squares Structural Equation Modeling (PLS-SEM) to quantitatively analyze the main drivers of the changes in surface runoff. The results show that the average multi-year runoff in the Minjiang River Basin (Chengdu section) ranges from 628.96 to 1088.46 mm, with an average value of 834.13 mm, and that the overall annual runoff in the past 30 years shows a fluctuating tendency. The goodness-of-fit of the PLS-SEM model is 0.507; the validity and reliability assessment indicated that the model was reasonable, and its results showed that economic and landscape factors had significant negative impacts on runoff changes, while natural factors had positive impacts on runoff changes, with path coefficients of −0.210, −0.131, and 0.367, respectively. Meanwhile, this study also identified two potential indirect impact pathways, i.e., the economic factors had an indirect negative impact on runoff by changing the distribution of landscapes, and the natural factors had indirect negative impacts on runoff by influencing economic activities, reflecting the complex interactions among economic activities, landscape distribution, and natural factors in influencing surface runoff. This study provides a research framework and methodology for quantitatively modeling surface runoff and the analysis of influencing factors in watersheds, contributing to a deeper scientific understanding of long-term runoff changes and the contribution of their drivers.