Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China

Attributing runoff change to different drivers is vital to better understand how and why runoff varies and to further support decision-makers on water resources planning and management. Previous works focused more on runoff change in the arid and semi-arid areas, runoff change in humid and semi-humid areas was insufficiently investigated. This study aims to quantitatively attribute runoff change in a humid subtropical basin (the Qingliu River basin, east China, dominated by evergreen broad-leaved forest) to climate variability, land use change, and other human activity (namely the human activities other than land use change) at multiple scales over different periods by using a Soil and Water Assessment Tool (SWAT) model. The results show that runoff increased insignificantly during 1960-2012 with an abrupt change occurring in 1984. Annual runoff in the post-change period (1985-2012) increased by 16.05% (38.05 mm) relative to the pre-change period (1960-1984), most of which occurred in winter and early spring (March). On the annual scale, climate variability dominated runoff change. During 1985-2012, climate variability, human activity and land use change (mainly for forest cover decrease) contributed 95.36%, 4.64%, and 12.23% to runoff increase, respectively. On the seasonal scale, human activity dominated runoff change (accounting for 72.11%) in the dry season during 1985-2012, while climate variability contributed most to runoff change in the wet season. On the monthly scale, human activity was the dominant contributor to runoff variation in all months except January, May, July and August during 1985-2012. Impacts of climate variability and human activity on runoff during 2001-2012 both became stronger than that during 1985-2000, but counteracted with each other. The findings should help better understanding of runoff behaviour in the Qingliu River and provide scientific support for local water resources management.