With the acceleration of urbanization process, waterlogging problems in urban area are becoming more and more serious due to climate change. In this context, it is very important to reduce disaster risk through urban planning. The pre-condition for urban planning is to simulate future land use and to analyze its hydrological effect. Taking Shanghai as a case study, this study predicted land use in 2030 by using Terrset CA- Markov model and analyzed hydrological response to land use change based on the land use data of Shanghai in 2000, 2003 and 2006. The results revealed that the simulation accuracy of CA-Markov model reached 0.85, which met the required simulation accuracy for the prediction of land use in 2030. The proportion of urban impermeable land, which consists of industrial and commercial land, residential land and road and square, increased from 26.54% in 2000 to 59.19% in 2030. Meanwhile, the proportion of nonconstruction land, which consists of arable land, garden, forestry and green space, decreased from 73.46% in 2000 to 40.81% in 2030. Moreover, the surface runoff depth showed an increasing trend from 2000 to 2030, but the spatial and temporal difference among districts in Shanghai was remarkable due to the land use change. Under the assumption of daily maximum precipitation at 200.5 mm, the surface runoff depth increased 3.86 mm during 2000-2006 and 9.66 mm during 2006-2030, respectively. Generally, the increase of surface runoff depth in suburb is more than that in central urban area in Shanghai, which results from high and stable proportion of impermeable land in central urban area and significantly increased proportion of impermeable land in suburb in Shanghai during 2000- 2030. The study shows that the rapid increase of impermeable surface area increased the surface runoff depth, which could increase waterlogging risk in Shanghai. In addition, to reduce the exposure and vulnerability of urban system to rainstorm waterlogging, urban planning should focus on improving drainage system and optimizing the structure and layout of land use with the consideration of eco- environmental protection. These results provide important information for local government to improve urban risk management and urban planning.