Effects of urbanization on direct runoff characteristics in urban functional zones

As urbanization processes, the increasing direct runoff caused by land use change has become a major challenge for urban hydrological system. In this study, the impact of urbanization on direct runoff in the Shenyang urban area was investigated using a modified Soil Conservation Service Curve Number model combined with remote sensing. Urban functional zone (UFZ) was used as the basic unit for hydrological analysis. The hydrological changes in runoff were analyzed by calculating the runoff difference between the current condition and the pre-urbanization condition. Moran's I was used to estimate the spatial autocorrelation of the entire area. Then we assessed the relative influence and marginal effects of factors affecting direct runoff using boosted regression trees (BRT). Our results showed that direct runoff was significantly related to urbanization. Under current conditions, direct runoff increment depth affected by urbanization in the study area was 68.02 mm. For different UFZs, high-density residential, business and industrial zones tended to have large runoff volumes and high runoff coefficients. Through flooding hazard analysis, we found about 6.53% of the study area fell into a significant hazard category. The industrial zone had largest area of significant hazard land (40.97 km²) and the business zone had the largest significant hazard percentage (21.19%). Moran's I results illustrated that the high-high clusters in Shenyang were mainly concentrated in the urban center. BRT analysis indicated that runoff had the strongest correlation with rainfall (52.07%), followed by impervious ratio (27.28%), normalized difference vegetation index (14.31%), antecedent 5-day rainfall (3.02%), and UFZs (1.70%). The industrial zone, business zone and high-density residential zone tend to have greater influence on runoff. Our study could present method for recognizing hotspots of direct runoff in large city, and may provide potential implications for green infrastructure selection and urban planning.