A study on flooding scenario simulation of future extreme precipitation in Shanghai

doi:10.1007/s11707-018-0730-z

Front. Earth Sci.

2018, Vol. 12

Issue (4) : 834-845 https://doi.org/10.1007/s11707-018-0730-z

RESEARCH ARTICLE

A study on flooding scenario simulation of future extreme precipitation in Shanghai

Xiaoting WANG, Zhan’e YIN(

), Xuan WANG, Pengfei TIAN, Yonghua HUANG

Department of Geography, Shanghai Normal University, Shanghai 200234, China

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Abstract

In the context of climate warming and urbanization, predictions and inundation simulations for future extreme precipitation have become highly active research topics. In this paper, using daily precipitation recorded at 10 meteorological stations in Shanghai for the period 1961–2010, the daily precipitation of each station during the period 2011–2099 was simulated by the statistical downscaling model (SDSM). And we examined the varying tendencies of future precipitation by the Mann-Kendall test. Further, the Soil Conservation Service (SCS) model and Pearson-III distribution curve were used to simulate the waterlogging duration and depth of future extreme precipitation in different scenarios with 3-, 5-, 10-, 20-, 50-, and 100-year return periods. The results show that: 1) Precipitation in Shanghai before the 2050s shows a trend of increasing and decreasing alternations, followed by a trend of decreasing and a marked decrease in about the 2070s. 2) In the 21st century, the waterlogging duration with return periods of 3, 5, and 10 years in Shanghai is predicted to last for less than 30 minutes, while the return periods of 20, 50, and 100 years last for less than 45 minutes. From the spatial distribution, the waterlogging duration to the east and south of the Huangpu River is predicted to be shorter than that of the west and north. 3) With the increase of the return periods, the depth of waterlogging is predicted to increase. The deepest inundated areas are Jinshan to the south-west of Shanghai, the east side of the Huangpu River, and Chongming Island.

Keywords flooding scenario simulation future extreme precipitation Shanghai

Corresponding Author(s): Zhan’e YIN

Just Accepted Date: 12 October 2018 Online First Date: 07 November 2018 Issue Date: 20 November 2018

Cite this article:

Xiaoting WANG,Zhan’e YIN,Xuan WANG, et al. A study on flooding scenario simulation of future extreme precipitation in Shanghai[J]. Front. Earth Sci., 2018, 12(4): 834-845.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0730-z
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/834

Fig.1 The spatial distribution of 10 weather stations across Shanghai.

Tab.1 CN values in SCS model

Tab.2 Predictor screening results

Tab.3 Coefficient of association (SDSM)

Fig.2 Trend analysis of future precipitation amounts.

Fig.3 Waterlogging duration in the first half of the 21st century under different return periods.

Fig.4 Waterlogging depth in the first half of the 21st century under different return periods (left 15 minutes, right 30 minutes).

Fig.5 Waterlogging duration in the second half of the 21st century under different return periods.

Fig.6 Waterlogging depth in the second half of the 21st century under different return periods (left 15 minutes, right 30 minutes).

1	Brown C, Wilby R L (2012). An alternate approach to assessing climate risks. Eos (Wash DC), 93(41): 401–402 https://doi.org/10.1029/2012EO410001
2	Chen Y D, Zhang Q, Xiao M Z, Singh V P, Leung Y, Jiang L G (2014). Precipitation extremes in the Yangtze River Basin, China: regional frequency and spatial–temporal patterns. Theor Appl Climatol, 116(3–4): 447–461 https://doi.org/10.1007/s00704-013-0964-3
3	Chi X X, Yin Z E, Wang X, Sun Y K (2016). Spatiotemporal variations of precipitation extremes of China during the past 50 years (1960–2009). Theor Appl Climatol, 124(3–4): 555–564 https://doi.org/10.1007/s00704-015-1436-8
4	Fan L J, Fu C B, Chen D L (2005). Review on creating future climate change scenarios by statistical downscaling techniques. Advances in Earth Science, 20(3): 320–329 (in Chinese)
5	Fu C B, Wang Q (1992). The definition and detection of the abrupt climatic change. Chin J Atmos Sci, 16(4): 482–493
6	Gao T, Xie L (2016). Spatiotemporal changes in precipitation extremes over Yangtze River Basin, China, considering the rainfall shift in the late 1970s. Global Planet Change, 147: 106–124 https://doi.org/10.1016/j.gloplacha.2016.10.016
7	Guan Y H, Zheng F L, Zhang X C, Wang B (2017). Trends and variability of daily precipitation and extremes during 1960–2012 in the Yangtze River Basin, China. Int J Climatol, 37(3): 1282–1298 https://doi.org/10.1002/joc.4776
8	Hang R H, Chen J L, Zhou L T, Zhang Q Y (2003). Studies on the relationship between the severe climatic disasters in China and the East Asia climate system. Chin J Atmos Sci, 27(4): 770–787
9	He F F, Gu X D, Xu J L (2006). Studies on radiation resource change in Shanghai since the 1990s. Journal of Natural Resources, 20(2): 210–216 (in Chinese)
10	Hu Z, Yang S, Wu R (2003). Long-term climate variations in China and global warming signals. J Geophys Res D Atmospheres, 108(D19): 4614 https://doi.org/10.1029/2003JD003651
11	IPCC (2013). Climate Change 2013: The Physical Science Basis. Cambridge University Press
12	Li Q P, Ding Y H (2005). Multi-year simulation of the East Asian monsoon and precipitation in China using a regional climate model and evaluation. Acta Meteorol Sin, 19(3): 302–316
13	Liao Y F, Nie C J, Yang L S, Li H R (2012). An overview of the risk assessment of flood disaster. Progress in Geography, 31(3): 361–367 (in Chinese)
14	Liu B J, Chen J F, Chen X H, Lian Y Q, Wu L L (2013). Uncertainty in determining extreme precipitation thresholds. J Hydrol (Amst), 503(11): 233–245 https://doi.org/10.1016/j.jhydrol.2013.09.002
15	Lu H, He H, Chen S R (2010). Spatiotemporal variation of extreme precipitation frequency in summer over South China in 1961–2008. Journal of Ecology, 29(6): 1213–1220 (in Chinese)
16	McCuen R H (1982). A Guide to Hydrologic Analysis Using SCS Method. Englewood Cliffs: Prentice-Hall
17	Novotny V, Chesters G, Shannon J (1981). Handbook of Non-Point Pollution Source and Management. New York: Van Nostrand Reinhold Company
18	Qian W H, Fu J L, Zhang W W, Lin X (2007). Changes in mean climate and extreme climate in China during the last 40 years. Advances in Earth Science, 22(7): 673–683 (in Chinese)
19	Quan R S, Liu M, Hou L J, Lu M, Zhang L J, Ou D N, Xu S Y, Yu L Z (2009). Impact of land use dynamic change on surface runoff: a case study on Shanghai Pudong new district. Journal of Catastrophology, 24(1): 44–49 (in Chinese)
20	Schreider S Y, Smith D I, Jakeman A J (2000). Climate change impacts on urban flooding. Clim Change, 47(1–2): 91–115 https://doi.org/10.1023/A:1005621523177
21	Shanghai Municipal Drainage Administration (2007). Shanghai urban rainwater system professional planning. (accessed 16 August 2017)
22	Shanghai Municipal Statistics Bureau (2015). Shanghai Statistical Yearbook 2015. Beijing: China Statistics Press (in Chinese)
23	SL44 (1993). Design flood code for water conservancy and hydropower projects. (accessed 26 June 2017)
24	Sukovich E M, Ralph F M, Barthold F E, Reynolds D W, Novak D R (2014). Extreme quantitative precipitation forecast performance at the weather prediction center from 2001 to 2011. Weather Forecast, 29(4): 894–911 https://doi.org/10.1175/WAF-D-13-00061.1
25	Tao H, Bai Y G, Mao W Y (2012). Assessment of CMIP3 climate models and projected climate changes in northern Xinjiang. Geogr Res, 31(4): 589–596
26	Tao S Y, Wei J (2007). Correlation between monsoon surge and heavy rainfall causing flash-flood in southern China in summer. Meteorol Monogr, 33(3): 10–18
27	Wilby R L, Dawson C W (2013). The statistical down scaling model (SDSM): insights from one decade of application. Int J Climatol, 33(7): 1707–1719 https://doi.org/10.1002/joc.3544
28	Xu L J, Li J Q, Li T, Liu X H (2007). On rainstorm intensity formula during little time in Shanghai. China Municipal Engineering, 4: 46–48
29	Xu S Y, Su J, Wang Z, Liu M, Zhang Z L, Wu J P, Huang Y M, Shi C, Lu L H (2004). Atlas of Shanghai urban physical geography. Shanghai: Chinese Map Publishing House (in Chinese)
30	Yin J, Yu D P, Wilby R (2016). Modelling the impact of land subsidence on urban pluvial flooding: a case study of downtown Shanghai, China. Sci Total Environ, 544: 744–753 https://doi.org/10.1016/j.scitotenv.2015.11.159
31	Yuan Z, Yang Z Y, Yan D H, Yin J (2017). Historical changes and future projection of extreme precipitation in China. Theor Appl Climatol, 127(1–2): 393–407 https://doi.org/10.1007/s00704-015-1643-3
32	Zheng T F, Guo J M, Yin J F, Wang Q, Wu W (2012). DFA-based research on spatiotemporal distribution of extreme precipitations in Jiangsu Province. Journal of Natural Disaster, 21(4): 76–83 (in Chinese)
33	Zhou L Y, Yang K (2001). Variation of precipitation in Shanghai during the last one hundred years and precipitation differences between city and suburb. Acta Geogr Sin, 56(4): 467–476
34	Zhu Y Y, Wang Y (2006). Frequency distribution statistical test of urban short-duration rainstorm in Fujian Province. Journal of Fuzhou University (Natural Science Edition), 34(3): 415–419 (in Chinese)

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