The spatial exposure of the Chinese infrastructure system to flooding and drought hazards

Abstract

Recent rapid urbanisation means that China has invested in an enormous amount of infrastructure, much of which is vulnerable to natural hazards. This paper investigates from a spatial perspective how the Chinese infrastructure system is exposed to flooding and drought hazards. Infrastructure exposure across three different sectors—energy, transport, and waste—is considered. With a database of 10,561 nodes and 2863 edges that make up the three infrastructure networks, we develop a methodology assigning the number of users to individual infrastructure assets and conduct hotspot analysis by applying the Kernel density estimator. We find that infrastructure assets in Anhui, Beijing, Guangdong, Hebei, Henan, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Zhejiang—and their 66 cities—are exceptionally exposed to flooding, which affects sub-sectors including rail, aviation, shipping, electricity, and wastewater. The average number of infrastructure users who could be disrupted by the impacts of flooding on these sectors stands at 103 million. The most exposed sub-sectors are electricity and wastewater (20 and 14 % of the total, respectively). For drought hazard, we restrict our work to the electricity sub-sector, which is potentially exposed to water shortages at hydroelectric power plants and cooling water shortage at thermoelectric power plants, where the number of highly exposed users is 6 million. Spatially, we demonstrate that the southern border of Inner Mongolia, Shandong, Shanxi, Hebei, north Henan, Beijing, Tianjin, south-west of Jiangsu—and their 99 cities—are especially exposed. While further work is required to understand infrastructure’s sensitivity to hazard loading, the results already provide evidence to inform strategic infrastructure planning decisions.

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Notes

  1. 1.

    Note that Hunan province has a percentage at 101 % and Jilin province at 109 %, which may be a reflection of data inaccuracy of the Enipedia database. In this case, Enipedia has collected power plant data, which exceed the official database’s output. Data on Taiwan, Hongkong, and Macao do not exist hence exhibit 0 %.

  2. 2.

    The OpenStreetMap data set has rail tracks and station data in separate files. This means that some stations are off the track where others have no tracks nearby. Since our “rail routes” data are stored in station-to-station format, we resort to constructing our own tracks and verify these with the OpenStreetMap tracks.

  3. 3.

    For detailed drought methodology, please refer to the Atlas of Natural Disaster Risk of China (Shi 2011).

  4. 4.

    Exceptionally exposed is defined as provinces that are located in areas where their infrastructure hotspot values are either 7 or 8.

  5. 5.

    Personal communication with the Chinese Ministry of Water Resources indicated that a national-scale flooding risk map should be available by 2017.

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Acknowledgments

This work was supported by the Asian Studies Centre, University of Oxford. JWH and WHL acknowledge the Oxford Martin School for the financial support of this study through the grant OMPORS. We thank Simon Abele at the Environmental Change Institute (ECI), University of Oxford, for his contribution in assembling the OpenStreetMap network data set. We are also grateful to Dr. Raghav Pant for coding the input from the flood results, Scott Thacker at the ECI, and Valerie Bevan for their comments during the development of the paper.

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Correspondence to Xi Hu.

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Appendices

Appendix 1

See Table 7.

Table 7 Route type and carrying capacity

Appendix 2

List of cities exposed to high flooding risks for all infrastructure sub-sectors (rail, aviation, shipping, electricity, and wastewater)

City Province
Chaohu Anhui
Chuzhou Anhui
Hefei Anhui
Ma’anshan Anhui
Suzhou Anhui
Wuhu Anhui
Xuancheng Anhui
Beijing Beijing
Dongguan Guangdong
Foshan Guangdong
Guangzhou Guangdong
Huizhou Guangdong
Jiangmen Guangdong
Qingyuan Guangdong
Zhaoqing Guangdong
Zhongshan Guangdong
Zhuhai Guangdong
Baoding Hebei
Cangzhou Hebei
Handan Hebei
Hengshui Hebei
Langfang Hebei
Shijiazhuang Hebei
Tangshan Hebei
Xingtai Hebei
Anyang Henan
Hebi Henan
Jiaozuo Henan
Kaifeng Henan
Luohe Henan
Puyang Henan
Xinxiang Henan
Xuchang Henan
Zhengzhou Henan
Zhoukou Henan
Changzhou Jiangsu
Nanjing Jiangsu
Nantong Jiangsu
Suzhou Jiangsu
Taizhou Jiangsu
Wuxi Jiangsu
Xuzhou Jiangsu
Yancheng Jiangsu
Yangzhou Jiangsu
Zhenjiang Jiangsu
Anshan Liaoning
Fuxin Liaoning
Jinzhou Liaoning
Liaoyang Liaoning
Panjin Liaoning
Shenyang Liaoning
Binzhou Shandong
Dezhou Shandong
Heze Shandong
Jinan Shandong
Jining Shandong
Liaocheng Shandong
Linyi Shandong
Tai’an Shandong
Zaozhuang Shandong
Zibo Shandong
Shanghai Shanghai
Tianjin Tianjin
Hangzhou Zhejiang
Huzhou Zhejiang
Jiaxing Zhejiang
Ningbo Zhejiang
Shaoxing Zhejiang

Appendix 3

List of cities exposed to high drought risks for the electricity sub-sector

City Province
Weinan Shaanxi
Bengbu Anhui
Bozhou Anhui
Chaohu Anhui
Chuzhou Anhui
Fuyang Anhui
Hefei Anhui
Huaibei Anhui
Huainan Anhui
Lu’an Anhui
Ma’anshan Anhui
Suzhou Anhui
Wuhu Anhui
Xuancheng Anhui
Beijing Beijing
Dongguan Guangdong
Huizhou Guangdong
Jiangmen Guangdong
Yangjiang Guangdong
Bijie Guizhou
Zunyi Guizhou
Chengde Hebei
Handan Hebei
Langfang Hebei
Qinhuangdao Hebei
Shijiazhuang Hebei
Tangshan Hebei
Xingtai Hebei
Zhangjiakou Hebei
Qiqihar Heilongjiang
Qitaihe Heilongjiang
Shuangyashan Heilongjiang
Anyang Henan
Hebi Henan
Jiaozuo Henan
Jiyuan shi Henan
Kaifeng Henan
Luohe Henan
Luoyang Henan
Nanyang Henan
Pingdingshan Henan
Puyang Henan
Sanmenxia Henan
Xinxiang Henan
Xinyang Henan
Xuchang Henan
Zhengzhou Henan
Zhoukou Henan
Zhumadian Henan
Jingmen Hubei
Suizhou Shi Hubei
Xiangfan Hubei
Yichang Hubei
Changde Hunan
Zhangjiajie Hunan
Changzhou Jiangsu
Huai’an Jiangsu
Nanjing Jiangsu
Wuxi Jiangsu
Yangzhou Jiangsu
Zhenjiang Jiangsu
Benxi Liaoning
Fushun Liaoning
Huludao Liaoning
Liaoyang Liaoning
Shenyang Liaoning
Hohhot Nei Mongol
Hulunbuir Nei Mongol
Ordos Nei Mongol
Ulaan Chab Nei Mongol
Yan’an Shaanxi
Yulin Shaanxi
Dezhou Shandong
Heze Shandong
Jinan Shandong
Jining Shandong
Laiwu Shandong
Liaocheng Shandong
Linyi Shandong
Qingdao Shandong
Rizhao Shandong
Tai’an Shandong
Weifang Shandong
Yantai Shandong
Zaozhuang Shandong
Zibo Shandong
Changzhi Shanxi
Datong Shanxi
Jincheng Shanxi
Jinzhong Shanxi
Linfen Shanxi
Luliang Shanxi
Shuozhou Shanxi
Taiyuan Shanxi
Xinzhou Shanxi
Yangquan Shanxi
Yuncheng Shanxi
Tianjin Tianjin

Appendix 4

List of cities that are exceptionally vulnerable in terms of infrastructure alone

City Province
Xuancheng Anhui
Beijing Beijing
Baoding Hebei
Langfang Hebei
Tangshan Hebei
Changzhou Jiangsu
Nantong Jiangsu
Suzhou Jiangsu
Taizhou Jiangsu
Wuxi Jiangsu
Zhenjiang Jiangsu
Shanghai Shanghai
Tianjin Tianjin
Hangzhou Zhejiang
Huzhou Zhejiang
Jiaxing Zhejiang
Ningbo Zhejiang
Shaoxing Zhejiang

Appendix 5

Here, we summarise the verification process as in the Atlas of Natural Disaster Risk in China (Shi 2011). Figure 15 shows the drought hazard map from the Atlas. The red areas demonstrate higher potential for experiencing drought events.

Fig. 15
figure15

Drought hazard map

To verify the results, data were obtained from the “China Natural Disaster Database” which contains a record of natural disasters at county level, reported in Chinese provincial newspapers between 1949 and 2010 (Chinese Academy of Sciences 2015). The database includes information on the start and end times, location, disaster type, impact, and journal sources.

Figure 16 shows the historical records of drought events between 1949 and 2010 at county level. Darker red areas demonstrate higher incidents of flooding events. Blank cells contain no data. As can be seen from the figure below, between 1949 and 2010, drought events occurred mainly in northern China.

Fig. 16
figure16

Drought frequency at county level; for example, the maroon counties have an aggregate drought frequency in the range of 10–28 between 1949 and 2010

As counties contain multiple values of hazard level (Fig. 15), the average hazard level was calculated for each county. The correlation between the average hazard level for that county was then plotted with the historical hazard frequency for that county. Pearson and Spearman correlation tests were conducted, and it was demonstrated that the correlation between the hazard level map (Fig. 15) and the historical map (Fig. 16) is significant at 1 %. Results of the statistical tests are reported in the table below. For more verification details, please refer to the Atlas of Natural Disasters in China (Shi 2011) (Table 8).

Table 8 Correlation between drought hazard map and historical drought map at county level

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Hu, X., Hall, J., Shi, P. et al. The spatial exposure of the Chinese infrastructure system to flooding and drought hazards. Nat Hazards 80, 1083–1118 (2016). https://doi.org/10.1007/s11069-015-2012-3

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Keywords

  • Exposure
  • Flooding
  • Drought
  • Infrastructure (energy, electricity, waste, transport, rail, aviation, shipping)
  • China