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Waterlogging Risk Assessment of the Beijing-Tianjin- Hebei Urban Agglomeration in the Past 60 Years

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Waterlogging Risk Assessment of the Beijing-Tianjin- Hebei Urban Agglomeration in the Past 60 Years Waterlogging Risk Assessment of the Beijing- Tianjin-Hebei Urban Agglomeration in the Past 60 Years Yujie Wang Nanjing University of Information Science and Technology JIANQING ZHAI ( [email protected] ) National Climate Center, CMA https://orcid.org/0000-0001-7793-3966 Lianchun Song National Climate Center, CMA Research Article Keywords: Hazard, Exposure, Vulnerability, Waterlogging risk, Beijing-Tianjin-Hebei Posted Date: February 10th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-162526/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Waterlogging Risk Assessment of the Beijing-Tianjin- 2 Hebei Urban Agglomeration in the Past 60 Years 3 4 Yujie Wang1,2, Jianqing Zhai 3, Lianchun Song3 5 1 Key Laboratory of Meteorological Disaster, Ministry of Education/International Joint Research 6 Laboratory on Climate and Environment Change/Collaborative Innovation Center on Forecast 7 and Evaluation of Meteorological Disasters, Nanjing University of Information Science and 8 Technology, Nanjing 210044, China 9 2 School of Atmospheric Sciences, Nanjing University of Information Science and Technology, 10 Nanjing 210044, China 11 3 National Climate Center, CMA, Beijing 100081, China 12 13 Corresponding author: Jianqing Zhai E-mail: [email protected] 1 14 ABSTRACT 15 In the context of global climate change and rapid urbanization, the risk of urban 16 waterlogging is one of the main climate risks faced by the Beijing-Tianjin-Hebei (BTH) 17 urban agglomeration. In this study, we obtain the urban waterlogging risk index of the 18 BTH urban agglomeration and assess waterlogging risks in the built-up area of the BTH 19 for two time periods (1961–1990 and 1991–2019). We analyze the economic and social 20 data as well as the climate data from 149 meteorological observation stations in Beijing, 21 Tianjin, and Hebei provinces and consider the hazard, exposure, and vulnerability 22 factors. The results showed that for the two time periods considered, the areas with the 23 lowest (Level-1) waterlogging risk have decreased by nearly 50%, and the second- 24 lowest (Level-2) ones have increased by nearly 55%. Although the areas at Level-3 and 25 above have decreased by 17%, the variations in each city were quite different. Among 26 them, the areas at Level-3 have increased by 52% and 51% in Beijing and Handan, 27 respectively. During the years 1991–2019, the areas at Level-3 risk and above were 28 mainly found in Beijing, Tianjin, Shijiazhuang, Qinhuangdao, Handan, and Xingtai. 29 Among them, Beijing had the highest waterlogging risk index. In particular, the areas 30 with Level-3 risk and above have increased by 76% in the past 30 years. The areas with 31 the highest risk level (Level-4) of waterlogging in Beijing were mostly found in the 32 downtown areas (Haidian, Chaoyang, Dongcheng, and Xicheng districts). This study 33 provides a scientific background for urban waterlogging risk management and 34 implementation of the national strategy for the development of the BTH region. 35 Keywords: Hazard, Exposure, Vulnerability, Waterlogging risk, Beijing-Tianjin-Hebei 2 36 1. Introduction 37 China is undergoing rapid urbanization. By the end of 2016, there were 88 large cities 38 in the country with over 1 million inhabitants. The cities with dense population, 39 concentrated assets, high exposure, and vulnerability are at high risk for climate change 40 (Zhai et al., 2018). In the context of climate change and urbanization, the changes and 41 impacts of extreme precipitation in cities have attracted more and more attention (Li et 42 al., 2015; Zhou et al., 2017; Bai et al., 2018; Gu et al., 2019). Due to the hardening of 43 the underlying urban surface and poor drainage facilities, heavy rainfalls will increase 44 the risk of urban waterlogging (Kaźmierczak et al., 2011; Wang et al., 2014; Yu et al., 45 2018a). 46 The Beijing-Tianjin-Hebei (BTH) region is one of the three fastest-growing megacity 47 clusters in China, with an area of 216,000 km2 and a population of 110 million, 48 including 13 cities (Beijing, Tianjin, Shijiazhuang, Baoding, Tangshan, Langfang, 49 Handan, Qinhuangdao, Zhangjiakou, Chengde, Cangzhou, Xingtai, and Hengshui). The 50 precipitation in the BTH cluster is uneven during the year, varies from year to year, and 51 features heavy rainstorm intensity (Zheng et al., 2018). Recent decades have witnessed 52 the increase of the average temperature, the decrease of the annual precipitation, and 53 the number of precipitation days (Zhai et al., 2005; Li et al., 2011; Wang et al., 2020). 54 More specifically, the frequency and intensity of extreme summer precipitation have 55 increased (Zhang et al., 2010; Liang et al., 2018). In particular, the hour-scale extreme 56 heavy rainfall occurs more often, leading to prominent urban waterlogging problems 57 (Yin et al., 2011; Yang et al., 2014; Song et al., 2014; Liang et al., 2017). For example, 58 on July 21, 2012, the 18-hour maximum rainfall in Beijing reached 460mm, which 59 caused serious urban flooding. The disaster covered 16,000 km2, affected 1.9 million 60 populations, left 79 people dead, and paralyzed the traffic (NCC, 2013). For the 61 continuous global warming, the intensity of extreme precipitation events in the BTH 62 urban agglomeration will further increase (Yu et al., 2018b; Shi et al., 2019) and will 63 cause serious risks of urban waterlogging. 64 Recently, urban waterlogging risks have been extensively studied (Saeed et al., 2010; 65 Li et al., 2015; Hu, 2016; Wang et al., 2017; Wang et al., 2018). Various problems have 3 66 been considered ranging from urban rainwater runoff modeling to the construction of 67 urban waterlogging simulation system (Li, 2002) and the two-dimensional 68 hydrodynamic evaluation model (Xie et al., 2018). However, most of the researchers 69 focused on assessing the waterlogging risks in a single city. The same problem for 70 multiple cities at the regional scale was commonly addressed using the data from the 71 weather station located in a given urban area. This approach could not reflect the spatial 72 difference in the urban waterlogging risk at the regional scale (Zheng et al., 2018; Sun 73 et al., 2020). Therefore, there is currently a lack of a regional-scale urban waterlogging 74 risk assessment system featuring high spatial resolution and even fewer studies on 75 waterlogging risks and changes in the BTH urban agglomeration. 76 This paper addresses the problem mentioned above for the urban built-up BTH area. 77 More specifically, we consider hourly precipitation, daily precipitation, and 5-day 78 continuous precipitation as the hazard factors of a rainstorm and waterlogging, 79 population density and GDP per unit area as exposure indicators, and adaptability to 80 heavy rainfall and waterlogging such as drainage network density, vegetation coverage 81 and river network density as vulnerability indicators. The waterlogging risks in the 82 built-up area of the BTH urban agglomeration during the two periods of 1961 to 1990 83 and 1991 to 2019 were assessed, aiming to reveal the characteristics of waterlogging 84 risk changes in the past 60 years. Thus, we aimed to provide scientific background for 85 the implementation of the national strategy for the coordinated development of the BTH 86 region. 87 88 2. Data and Methods 89 2.1 Data 90 The meteorological data used in this paper came from 149 meteorological 91 observation stations in the BTH region provided by the National Meteorological 92 Information Center of the China Meteorological Administration. The hourly and daily 93 precipitation data from 1961 to 2019 (Figure 1) have undergone quality control and 94 uniformity inspection (Ren et al., 2012). 95 The population and GDP grid data at 1km resolution of the BTH region in 2015 came 4 96 from the Data Center for Resources and Environmental Sciences of Chinese Academy 97 of Sciences (http://www.resdc.cn/Default.aspx). We developed the population and GDP 98 grid data at 1km resolution of the BTH region in 1990(Fu et al., 2014) based on the 99 population and GDP statistics of the BTH region from China City Statistical Yearbook 100 1991, and referring to the data processing method of the Data Center for Resources and 101 Environmental Sciences of Chinese Academy of Sciences. 102 The spatial distribution, land usage, and river network density data of 2015 in 13 103 cities of the BTH came from Tsinghua University with a spatial resolution of 30 meters 104 (http://data.ess.tsinghua.edu.cn), and drainage data including pipeline length and 105 vegetation coverage area was obtained from the China City Statistical Yearbook 2016. 106 107 Figure 1 Administrative divisions and meteorological stations in the BTH. 108 2.2 Methods 109 2.2.1 Normalization method 110 To normalize all the factors considered in this study, we used the following 111 expression: 112 I=5+(Xi-Xa)/σ （1） 113 Here, I is the normalized value, Xi is the value of the i-th factor, Xa is the mean value 5 114 of this factor, and σ stands for its standard deviation. 115 2.2.2 Waterlogging risk index 116 We followed IPCC AR5 (IPCC, 2014) and used a three-determinants risk model. We 117 consider hazard, exposure, and vulnerability as model parameters and accounted for the 118 recently suggested method of Sun et al. (2020). The resulting equation for waterlogging 119 risk assessment reads as: 120 R=H×E×(10-A) （2） 121 here, H is the hazard index, E is the exposure index, and A is the adaptability index. 122 The waterlogging risk was divided into four grades according to the standard 123 deviation (σ) ranging from 1 to 4, with values from low to very high, respectively.
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