Abstract

Quantitative assessment of the hydrological response to urbanization has been a major concern in hydrology and water resources management. In this study, a procedure combining different statistical methods and a hydrological model to quantify annual runoff response to spatial and temporal variations of impervious surface areas was proposed and applied to the Qinhuai River basin, an urbanized basin located in southeastern China, over the period from 1986 to 2013. The landscape indicators, such as impervious area and number of impervious patches were used to measure the spatial configuration of urbanization and quantify the runoff response to urbanization. Impervious area data were derived from the Landsat images using superior ensemble learning method of rotation forest. The Mann–Kendall test, Sen's estimator, Pettitt test and double mass curve method were applied to examine gradual trend and abrupt changes for hydro-meteorological data series. A hydrological model based on stepwise regression analysis was built and used to explore the relation among annual runoff and precipitation, potential evapotranspiration and landscape indicators. The results showed that annual runoff and runoff coefficient had significant increasing trends and an abrupt change after year 2001 when the watershed impervious area reached 8.6%. The average annual runoff increased by 60%, of which, urbanization was responsible for 59% of the increase, while precipitation changes were responsible for the remaining 1% in the study region. The annual runoff response to impervious area showed a nonlinear relationship, and was more sensitive in dry years than wet years. The changes of impervious area were more remarkable in connecting the existed impervious patches than in developing the new ones beginning in the early 2000s, which increased the watershed's drainage capacity and resulted in the abrupt change of runoff response. The study demonstrated that the proposed procedure was efficient to quantify the runoff responses to urbanization using landscape metrics of impervious surface.

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