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Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical significance, especially for non-Gaussian and serially dependent data. In this paper, the choice of trend estimation and significance testing method is suggested as important for these kinds of studies, as illustrated by quantifying the urbanization effect on trends in seven hot-extreme indices for the megacity of Shanghai during 1961-2013. Both linear and nonlinear trend estimation methods were used. The trends and corresponding statistical significances were estimated by taking into account potential non-Gaussian and serial dependence in the extreme indices. A new method based on adaptive surrogate data is proposed to test the statistical significance of the ensemble empirical mode decomposition (EEMD) nonlinear trend. The urbanization contribution was found to be approximately 34 % (43 %) for the trend in the non-Gaussian distributed heat wave index based on nonparametric linear trend (EEMD nonlinear trend) estimation. For some of the other six hot-extreme indices analyzed, the urbanization contributions estimated based on linear and nonlinear trends varied greatly, with as much as a twofold difference between them. For the linear trend estimation itself, the ordinary least squares fit can give a substantially biased estimation of the urbanization contribution for some of the non-Gaussian extreme indices. [ABSTRACT FROM AUTHOR]

Despite continuing efforts to upgrade the urban storm sewer system since the late 1950s, the City of Shanghai is still vulnerable to persistent rainstorm waterlogging due to excess surface runoff and sewer surcharge, which frequently cause significant damage to buildings and disruption to traffic. Rapid urbanization and associated land cover changes are the major factors contributing to waterlogging. However, it is unclear to what extent changes in rainfall variability over the past few decades are also involved. This paper investigates the combined impacts of land use and land cover change, storm sewer development, and long-term variations in precipitation. Evidence of persistent waterlogging is presented first. We then give an account of land surface modifications during the process of urbanization and the development of the city's urban storm sewer system. Statistical analysis suggests that the increase in runoff coefficient due to conversion of lands from agricultural to industrial, commercial, and residential uses is a major factor driving greater waterlogging risk. In particular, historical analysis of aerial photographs reveals the rate and extent of modification to river networks in the past few decades. The natural drainage network has shrunk by 270 km, significantly reducing the city's capacity to transport excess surface flow. In line with other studies, we find no significant overall trends in annual rainfall totals (at Baoshan and Xujiahui). However, seasonal and monthly rainfall intensities have increased. At the daily scale, we find that compared to pre-1980s: (i) there has been an increase in the number of wet days with precipitation exceeding 25 mm (Heavy Rainfall) and decrease in those below 25 mm and (ii) the number of consecutive wet days with precipitation maximum and average exceeding the threshold known to cause waterlogging shows an increasing trend. Since rainfall intensity is expected to increase under climate change, this could further compound the impacts of land use changes and place even greater pressure on the existing storm sewer system. [ABSTRACT FROM AUTHOR]