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Risk assessment of rainstorm disasters under different return periods: A case study of Bohai Rim, China
Ocean and Coastal Management 187 (2020) 105107
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Risk assessment of rainstorm disasters under different return periods: A case study of Bohai Rim, China Ying Li a, b, *, Zhiru Zhang a, Shiyu Gong a, Meijiao Liu a, Yiqin Zhao c a
School of Geography, Liaoning Normal University, Dalian, China Liaoning Key Laboratory of Physical Geography and Geomatics, Liaoning Normal University, Dalian, China c Faculty of Arts, University of Alberta, Edmonton, Alberta, Canada b
A R T I C L E I N F O
A B S T R A C T
Keywords: Rainstorm disaster Return period Risk Vulnerability Exposure
Rainstorms are extreme events that have a serious impact on the safety of people’s lives and property, and on socioeconomic development. Coastal areas have a large population and a high urban concentration tend to be areas that experience high levels risk of extreme precipitation. The present study uses the Bohai Rim, China, as a case study, and applies the copula joint function to daily precipitation data to calculate the probability of rainstorms under different return periods such that the hazard presented by each rainstorm disaster could be analyzed. Grid data of gross domestic product (GDP) per land area (km2) and population density were selected to calculate and assess the associated respectively levels of exposure and vulnerability. The risk of occurrence of a rainstorm disaster was analyzed according to the disaster risk assessment paradigm of the United Nations Office for Disaster Risk Reduction (UNDRR). The results showed that the risk of a rainstorm disaster under different return periods in the Bohai Rim gradually decreased from the southeast coastal region towards the northwest inland area. Our study provides a scientific basis for governments to improve disaster prevention and reduction, and can assist in the formulation of emergency management countermeasures.
1. Introduction Global and local extreme precipitation events have occurred frequently in recent years due to climate change (Canters et al., 2014; Silva et al., 2017; Kang et al., 2018). As extreme precipitation events, rainstorms have a significant impact on the safety of people’s lives and property, as well as socioeconomic development (Demirdjian et al., 2017). Meteorological disasters (e.g., floods and mudslides) that are caused by rainstorms have a serious impact on developing countries in particular (Li et al., 2016; Zhou et al., 2017; Lyu et al., 2018). Coastal areas usually represent important areas in terms of a country’s socio economic development. The rapid development of cities has increased the frequency and intensity of rainstorm events to a certain extent, and has therefore increased the hazard of meteorological disasters (Yin et al., 2017). The vulnerability and exposure of coastal areas are further aggravated by factors such as land-use cover change, rising sea-levels, ecological and environmental deterioration, and infrastructure con struction (Reguero et al., 2015; Vousdoukas et al., 2018). In this context,
coastal areas are often characterized by more risks including urban waterlogging, farmland inundation, and infrastructure collapse, which are caused by extreme precipitation events. Rainstorm disaster risk refers to the expected lost value of human property and socioeconomic development, which are caused by a rain storm disaster during a certain period of time in a given area. Scholars have made numerous achievements in the study of rainstorm disaster risk evaluation, and have mainly focused on the mechanisms of disaster risk formation (UNDHA, 1991), risk evaluation research methods (Saeed et al., 2010), and risk evaluation and zoning amongst others (Hu, 2016). With regards to the mechanisms of disaster risk formation, Maskrey (1989) proposed that natural disaster risk is the sum of the hazard and vulnerability. Smith (1996), on the other hand, stated that disaster risk is the product of the disaster occurrence probability and disaster loss. The UNDRR (2019) proposed that disaster risk is the product of hazard, exposure and vulnerability. There are many research methods involved in risk evaluation research, for example, the comprehensive evaluation method (Kazmierczak and Cavan, 2011), uncertainty method (Nayak
Abbreviations: GDP, gross domestic product; UNDRR, United Nations Office for Disaster Risk Reduction. * Corresponding author. School of Geography, Liaoning Normal University, Dalian, China. E-mail addresses: [email protected] (Y. Li), [email protected] (Z. Zhang), [email protected] (S. Gong), [email protected] (M. Liu), yiqin2@ ualberta.ca (Y. Zhao). https://doi.org/10.1016/j.ocecoaman.2020.105107 Received 5 September 2019; Received in revised form 30 December 2019; Accepted 13 January 2020 Available online 31 January 2020 0964-5691/© 2020 Elsevier Ltd. All rights reserved.
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et al., 2005; Wang et al., 2017a), historical disaster data method (Moel et al., 2011; Quan, 2014), hydrological hydrodynamics method (Saeed et al., 2010), and hidden Markov model (Wang et al., 2018). Most studies have been analyzed from the perspective of a rainstorm as a single element; however, rainstorm disasters are usually the result of the joint action of multiple elements. Traditional single element analysis can therefore reduce the accuracy of results, whereas a multi-element combination can improve the risk evaluation of a rainstorm disaster (Li et al., 2015). The present study takes the Bohai Rim as a case study, and uses daily precipitation data along with a copula joint function to calculate the probability of rainstorm occurrence in different return periods (5, 10, 20, and 50 years) as a means of analyzing the hazard levels of rainstorm disasters. Population and GDP per land area (km2) are selected to analyze the associated respectively levels of exposure and vulnerability, and are subsequently used together with the hazard assessment to determine the risk of rainstorm disasters in the study region. A further objective is to improve the accuracy of rainstorm probability evaluation by using a multi-element combination of rainstorm, and by refining the spatial difference of disaster vulnerability by using grid data. Overall, the present study aims to provide a reference for the risk assessment of rainstorm disasters, and a scientific basis for disaster prevention and mitigation.
dense, unevenly distributed population and has experienced rapid eco nomic development over recent years, but also great loss as a result of rainstorms and flood disasters. 2.2. Hazard analysis of rainstorm disasters 2.2.1. Data source Daily precipitation data from 1967 to 2016 for the Bohai Rim was provided by the Chinese Meteorological Date Service Center (http://dat a.cma.cn/). According to the principle of the maximum time period and continuity, data from 60 stations were selected after missing measure ment values were replaced, and a spatial anomaly test and a homoge neity test were performed. We categorized �50 mm precipitation in 24 hours as a rainstorm according to the National Meteorological Standard of China. Based on this standard, the present study selected various necessary rainstorm elements, including the rainstorm volume, number of rainstorm days, rainstorm intensity, and rainstorm contribution rate. 2.2.2. Copula joint function and return period This study uses the copula joint function to calculate the probability of a rainstorm under different return periods (5, 10, 20, and 50 years) as a means of analyzing the risk of rainstorm disasters. The copula function is used to fit the edge distribution functions through correlation (Sklar, 1959; Salvadori and Michele, 2010; Liu et al., 2016), and has been widely applied in hydrology, meteorology, and other fields (Shiau, 2006; Renard and Lang, 2007). We have also successfully used the copula function in related studies for the return period of disasters, such as drought and rainstorms (Li et al., 2015; Wang et al., 2017b; Feng et al., 2019). The edge distribution function is the basis for constructing the copula joint function, and was determined for each rainstorm element (i.e., rainstorm volume, number of rainstorm days, rainstorm intensity, and rainstorm contribution rate). Firstly, Spearman’s rank correlation coef ficient was used to measure the correlation of various rainstorm element, the maximum likelihood method was used to estimate the parameters, and the edge function was used to fit each rainstorm
2. Methodology 2.1. Research area China’s Bohai Rim includes Beijing City, Tianjin City, Liaoning Province, Hebei Province, and Shandong Province (Fig. 1). The region is located on coasts of the Bohai and Yellow seas, and is largely charac terized by a warm temperate semi-humid monsoon climate. Due to the unique geographical location of the study area, relatively high levels of precipitation mean that the region is prone to flooding and water logging. The Bohai Rim is an important political, economic, cultural, scientific, and technological center in northern China. The region has a
Fig. 1. Regional location map of the Bohai Rim. 2
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Ocean and Coastal Management 187 (2020) 105107
element. The optimal edge function was determined by the KolmogorovSmirnov test. LðθÞ ¼
Yn
Fðxi ; θÞ ¼ i¼1
Yn ∂Fðxi ; θÞ i¼1 ∂xi
2.4.1. Data source GDP per land area (km2) grid data was obtained for the Bohai Rim using a 1 km grid gross domestic product (GDP) dataset for 2010, which was provided by the National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China (http://www.geodata.cn/).
(1)
n X
ln½LðθÞ� ¼
2.4. Vulnerability analysis
(2)
ln½Fðxi ; θÞ� i¼1
∂ln½LðθÞ� ¼0 ∂θ
2.4.2. Vulnerability calculation Vulnerability is determined by external factors such as social, eco nomic, environmental and etc. (UNDRR, 2019). Therefore, in the risk analysis of rainstorm disasters, GDP per land area (km2) was selected as the main index of vulnerability. The dataset was imported into ArcGIS and was divided into five levels by the Jenks natural breaks classifica tion method: Lower, Low, Middle, High, and Higher. Distribution plots of the vulnerability data was subsequently determined.
(3)
In the formula, LðθÞ is the likelihood function, Fðxi ; θÞ is a function of the edge distribution density, and θ is the parameter to be estimated. Based on the determination of the edge distribution function of the single elements of a rainstorm event, the three-dimensional copula function was combined for all rainstorm elements. The maximum like lihood method was used to estimate the parameters, and the root-meansquare error and Akaike information criterion were used to determine the optimal copula function. pffiffiffiffiffiffiffiffiffi 8 RMSE ¼ MSE > > > n < 1 X MSE ¼ ðPei Pi Þ2 (4) > n 1 > i¼1 > : AIC ¼ n logðMSEÞ þ 2l
2.5. Risk analysis of rainstorm disasters The UNDRR proposed that disaster risk is the product of three major factors: hazard, exposure, and vulnerability (UNDRR, 2019). Based on this, the determination of risk can be expressed as formula (6): Risk ¼ Hazard � Exposure � Vulnerability
In formula (4), Pei is the empirical probability of the threedimensional combination of the rainstorm multi-elements, Pi is the joint distribution value, and l is the number of parameters contained in the model. According to the return period theory (Feng et al., 2019), the three-dimensional joint return period of the copula function is expressed by formula (5): Ta ¼
N ¼ nPðD � dU S � sU M � mÞ n½1
N Cðu; v; wÞ�
(6)
Hence, the present study calculated the risk of rainstorm disasters under different return periods at different sites in the study using for mula (6). The Jenks natural breaks optimization method was then used to divide risk into five levels in ArcGIS: Lower, Low, Middle, High, and Higher, and the area occupied by different levels of risk can be measured. 3. Results
(5)
3.1. Rainstorm disaster hazard analysis
Where Ta is the joint return period, N is the sample length, Cðu; v; wÞ is the joint distribution function, u; v; w is the edge distribution function of the rainstorm elements, and n is the number of times a certain value is exceeded during the study period. The joint accumulative probability of multiple elements of a rain storm at each site in the Bohai Rim is calculated for four return periods (5, 10, 20, and 50 years) according to the formula of the return period. Based on the probability results, spatial interpolation is executed using the Kriging interpolation method, and is subsequently divided into five grades, from low to high: Lower, Low, Middle, High, and Higher, to analyze the spatial distribution difference of rainstorm disaster hazards under the different return periods.
Fig. 2 presents the distribution plots for the rainstorm disaster haz ards in different joint return periods, from which the spatial character istics of rainstorm hazards in the Bohai Rim can be analyzed. Firstly, we report on the 5-year return period analysis results. Zhangjiakou and Chengde West, in the northwestern region of Hebei Province (Fig. 1), are located on the leeward slope and far away from the ocean. This region is not conducive to water vapor transportation and experienced relatively low precipitation; thus, the rainstorm disaster hazard level was found to be Lower (Fig. 2a). The majority of the central region of the Bohai Rim was determined to be associated with a Lowlevel hazard, whereas the southeast coastal areas and eastern Liaoning Province were assigned a Middle-level hazard rating. Among the southeast coastal areas, Dandong City was determined to be of a High hazard level. Most of these areas are located in coastal areas with lower elevations. In summer, they are affected by the convergence of warm and humid ocean air currents and cold air from the north, thus resulting in more precipitation. During the 10-year return period, the hazard levels of rainstorm disaster increased in comparison to the 5-year return period; the Lowerlevel hazard area in the northwestern region of Hebei Province increased to a Low-level hazard and extended to the northwest where there is less precipitation. Moreover, the High-level hazard expanded from Dandong City towards the southeast coastal region, and the rest of the central region increased to a Middle-level hazard rating (Fig. 2b). For the 20-year return period rainstorm disasters hazard ratings, Low-level and Middle-level hazard areas were mainly distributed in the northwestern region of Hebei Province. In comparison to the 10-year return period, the High-level hazard area gradually expanded to the northwest, and a Higher-level hazard appeared in some parts of the
2.3. Exposure analysis 2.3.1. Data source Population density grid data was obtained for the Bohai Rim using a 1 km grid population dataset for 2010, which was provided by the Na tional Earth System Science Data Sharing Infrastructure, National Sci ence and Technology Infrastructure of China (http://www.geodata. cn/). 2.3.2. Exposure calculation In the risk analysis of rainstorm disasters, the population density was selected as the main index of exposure (UNDRR, 2019). The dataset was imported into ArcGIS and was divided into five levels by the Jenks natural breaks classification method: Lower, Low, Middle, High, and Higher. Distribution plots of the population density exposure data was subsequently determined.
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Fig. 2. Distribution of rainstorm disaster hazards in different return periods in the Bohai Rim.
Areas of Lower-level exposure were mainly distributed in the northwestern region of Hebei Province and in the eastern and western regions of Liaoning Province (Fig. 3). These areas have relatively small populations because of their high altitude, and the northwestern region of Hebei Province is located on the boundary of the monsoon area. Fig. 3 also shows that most of the remaining Bohai Rim was found to be of either Low-level or Middle-level exposure. High-level and Higher-level exposure were mainly associated with large urban areas and surround ing county centers, such as Beijing, Tianjin, Shijiazhuang, and Shenyang (Fig. 3). Most of these areas are located in the plain area, which has a monsoon climate and is relatively densely populated due to nature, policy and transportation amongst other reasons.
southeast coastal area (Fig. 2c). During the 50-year return period, the hazard levels of rainstorm di sasters increased, and there were no Lower-level or Low-level hazard areas. Middle-level and High-level hazard areas were indented to the northwest, and most of the southeastern region was assigned as a Higher-level hazard area (Fig. 2d). Overall, our results showed that the Lower-level hazard of rainstorm disasters in the Bohai Rim was mainly distributed in the northwestern region of Hebei Province, the hazard level associate with the southeast coastal areas was High, with a transition area in between these two re gions. This was consistent with the spatial distribution of precipitation in the Bohai Rim. From the 5-year to 50-year return periods, the range of Lower-level and Low-level hazard areas gradually decreased to the northwestern region, until the 50-year return period, when there were no Lower-level and Low-level hazard areas in the Bohai Rim. The Middle-level, High-level, and Higher-level hazard areas of rainstorm disaster gradually expanded towards the northwestern region; the range of Middle-level and High-level hazards first increased and then decreased and the Higher-level hazard area gradually expanded. Thus, only Middle-level, High-level, and Higher-level hazards of rainstorm disaster were determined for the 50-year return period.
3.3. Vulnerability analysis Fig. 4 presents the distribution plots for vulnerability for the Bohai Rim, from which the different spatial characteristics can be analyzed. The areas with Higher-level and High-level vulnerability were mainly distributed in Beijing, Tianjin, provincial capital cities, and sublevel prefecture-level urban cities. Fig. 4 shows that most of the urban centers were determined as areas of a Higher-level vulnerability, while the peripheries were found to be of a High-level vulnerability. Most of these areas are located in the monsoon climate zone and have flat terrain, convenient transportation, dense population, rapid urbaniza tion, more developed economy, and hence High-level of vulnerability. We determined a periphery of High-level areas, some county centers, the
3.2. Exposure analysis Fig. 3 presents the distribution plots for exposure for the Bohai Rim, from which the different spatial characteristics can be analyzed. 4
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Fig. 3. Exposure in the Bohai Rim.
Fig. 4. Vulnerability in the Bohai Rim.
coastal areas of the Bohai and Yellow seas were all found to be a Middlelevel vulnerability. The area of Lower-level vulnerability was located in the northwestern region of Hebei Province and the northwestern and eastern regions of Liaoning Province (Fig. 4). This included Zhangbei, which is located at the edge of the Inner Mongolia Plateau and far from the ocean; thus, there is a relatively low economic level because of the local topography and climate amongst other factors. Therefore, this area was associated with a Lower-level of vulnerability. The rest of the region was determined as being of a Low-level of vulnerability.
3.4. Risk analysis of rainstorm disasters According to the distribution plots of rainstorm disaster risk shown in Fig. 5, the spatial characteristics of rainstorm disaster risk in the Bohai Rim can be analyzed. Approximately 92% of the Bohai Rim was determined as being of Lower-level and Low-level risk of rainstorm disasters (Fig. 5). The northwestern region of Hebei Province, northwestern Beijing, and the western region of Liaoning Province had Lower-level of risk due to relatively lower precipitation and long distance from the ocean. The Low-level risk areas were found to be located in the central and eastern regions of Liaoning Province, the eastern and southern regions of Hebei 5
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Fig. 5. Risk distribution of rainstorm disasters in the Bohai Rim.
Province, Tianjin, and the majority of Shandong Province. The Higherlevel and High-level risk areas were distributed in the centers of large cities and the surrounding suburban centers, for example, Beijing, Tianjin, Shijiazhuang, Shenyang, and Qingdao. The urban centers in these regions were associated with a Higher-level of risk, which gradu ally reduced with distance to the peripheries. The level of risk of a rainstorm disaster was higher in urban centers in the study region, which related in part to them being relatively densely populated, ur banized, and economically developed areas. The remaining scattered small urban areas were determined to be transitional areas with a Middle-level risk of rainstorm disasters. Areas associated with a Higher-level risk of rainstorm disaster in the Bohai Rim were concentrated in the centers of large cities and around the surrounding suburban centers. The Lower-level risk areas were mainly concentrated in the northwestern region of Hebei Province. Most of the remaining areas were found to be of the risk transition area. As the
return period increased, the Lower-level risk area reduced by 43% to wards the northwest, the Low-level risk area expanded by 38% to the northwest, the Middle-level risk area expanded by 2.8%, the High-level risk area expanded by 1.55%, and the Higher-level risk area expanded by 1.31% (Table 1). The boundaries of the rainstorm disaster risk, which included Lower-level and Low-level, roughly coincided with the 400 mm equal precipitation line. 3.5. Comparison of rainstorm disaster risks in different scale cities Four cities with different scales were selected within the same pre cipitation belt (semi-humid areas with 400–800 mm annual precipita tion) in order to compare and analyze the differences in the risk of rainstorm disasters. These were: Beijing (municipalities directly under the central government), Shenyang (provincial capital city), Dalian (deputy provincial city), and Shouguang (county-level city).
Table 1 The area of different levels of risk under different return periods and their proportion of the total area. Level Lower Low Middle High Higher
5-year
10-year
20-year
50-year
Area (km2)
Proportion
Area (km2)
Proportion
Area (km2)
Proportion
Area (km2)
Proportion
330235.6 159094.3 29353.7 12232.9 2510.3
61.91% 29.82% 5.5% 2.3% 0.47%
233235.6 242187.8 40311.5 13083.0 4608.93
43.72% 45.4% 7.56% 2.45% 0.86%
115541.9 344912.4 50060.6 16177.7 6734.1
21.66% 64.66% 9.38% 3.03% 1.26%
99337.6 359881.4 44203.2 20521 9483.5
18.62% 67.47% 8.29% 3.85% 1.78%
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Fig. 6 illustrates that within the same precipitation zone, the hazard level of rainstorm disaster was determined to be approximately the same. Among the four cities, most of the population is concentrated in urban centers, which are characterized by rapid economic development. The urban peripheries are generally sparsely populated and economic development is relatively slow. Therefore, the risk levels of rainstorm
disasters within the cities were found to be quite different to the pe ripheries, whereby the risk level from urban centers towards the pe ripheries gradually reduced. Beijing and Shenyang are inland cities. Beijing’s terrain is high in the northwest and low in the southeast. The population is concentrated in the southeast whereas the northwest population is sparse, and thus the economy of the southeast is more
Fig. 6. Risk of rainstorm disasters under different return periods in cities of different grades. 7
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developed. Therefore, the risk of rainstorm disasters in Beijing was found to be high in the southeast and low in the northwest. However, the exposure and vulnerability associated with the Shenyang suburbs was determined to be higher than of that associated with northwestern Beijing; hence, the risk level of rainstorm disasters in the suburbs was higher than of that in northwestern Beijing. Dalian and Shouguang are coastal cities, and the difference in annual precipitation is small. Dalian is surrounded by the sea on three sides; its geographical position is su perior and its level of exposure and vulnerability is higher than that of Shouguang. The risk level of rainstorm disaster in Dalian was therefore found to be relatively higher than of that determined for Shouguang.
calculated based on the copula joint function, and the hazard of rain storm disasters was analyzed. Population and economic grid data were selected to analyze exposure and vulnerability. On the basis of these data and according to the formula of disaster risk evaluation, the risk of rainstorm disasters was then analyzed. The results can be summarized as follows: (1) From the coastal areas of the Bohai and Yellow seas towards the northwestern area, the hazard levels of rainstorm disasters gradually decreased. As the return period increased, the range of the Lower-level and Low-level hazard areas gradually narrowed towards the northwest, while the Middle-level, High-level, and Higher-level hazard areas advanced towards the northwest. The Middle-level and High-level hazard areas showed a tendency to increase and then decrease, and the areas associated with a Higher-level hazard gradually expanded. (2) The urban centers had Higher-level exposure. The Lower-level exposure areas were found to be located in the northwestern re gions of Hebei Province, the eastern and western regions of Liaoning Province, and the remaining area was the exposure transition area. (3) Areas of Higher-level and High-level vulnerability were found to be concentrated in some major cities, such as Beijing, Tianjin, and Shijiazhuang. The northwestern region of Hebei Province and the western and eastern regions of Liaoning Province were deter mined as Lower-level vulnerability areas, while the remaining areas were assigned as a transition zones of vulnerability. (4) From the southeastern coastal area to the northwestern area in the Bohai Rim, the rainstorm disaster risk levels gradually decreased under different return periods. As the return period increased, the Lower-level risk area reduced by 43% to the northwestern, the Low-level risk area expanded by 38% to the northwestern, and the Middle-level, High-level, and Higher-level risk areas expanded by 5.7%.
4. Discussion Most studies on rainstorm disaster risk in the past have been limited by statistical data. Many coastal disaster risk analyses have used administrative units for spatial analyses, whereby the associated statis tical data were evenly distributed within administrative units, which could not reflect the internal differences of an administrative unit (He and Gong, 2014; Shao et al., 2014). However, the use of grid data can make up for this limitation; the replacement of the administrative units with grid units that can reflect the internal differences of each admin istrative unit allows a refined evaluation of disaster risk. This approach can provide a scientific basis for formulating disaster prevention and mitigation as well as emergency management strategies. By taking Beijing as an example, if traditional statistical data is used to map the vulnerability of heavy rain disasters, the disaster risk within the administrative unit is the same level, and internal small differences are not reflected. However, in the actual administrative unit, the population is often unevenly distributed, the urban central economy is relatively developed, and the regional differences are large. Therefore, in the present study, the use of 1 km grid data for the disaster spatial analysis, showed that the Lower-level risk areas were located in northern, west ern, and southeastern Beijing. The Low-level risk was distributed in small areas of the central and eastern regions, whereas the periphery of the urban area was determined as Middle-level risk. The High-level risk was associated with the central urban area, and a small area to the south of the central urban area was associated with a Higher-level of risk. The difference between the Lower-level risk areas in the west and north of Beijing and the Higher-level risk area in the southeast was therefore more precisely determined. There are some shortcomings in the present study, which are aspects that need to be improved in the future. Due to the influence of the weather system, terrain, and geographical location, the spatial distri bution of precipitation in various regions is uneven. Annual precipita tion rates across the Bohai Rim are quite different (Song et al., 2019). Dandong, in eastern Liaoning Province, has the largest annual precipi tation, which can reach more than 1000 mm, while Zhangbei, in northwestern Hebei Province, has less than 300 mm. The unified stan dard of �50 mm precipitation in 24 h as stipulated by the National Meteorological Standard of China was selected as the critical threshold of rainstorm disaster in this study. The northwestern region of Hebei Province was determined as a low-level risk area, and most of the remaining regions in the southeast of the Bohai Rim were assigned as regions of lower-level risk. The small difference in the rainstorm disaster risk levels in the region may be due to the use of a uniform rainstorm threshold. Therefore, future studies of large and medium spatial scales could attempt to use different rainstorm thresholds according to actual, local conditions, for example, by using the percentile threshold method (Chi et al., 2015; Abatan et al., 2016) or detrended fluctuation analysis (Liu et al., 2013).
Funding This research was funded by National Natural Science Foundation of China, No. 41601114; Science and Technology Research Program Sup ported by the Education Department of Liaoning Province, China, No. L201683677, No. H201783626; Social Science Planning Fund Project of Liaoning Province, China, No. L19CJL005. Contributors Ying Li conceived and designed the studies. Zhiru Zhang and Meijiao Liu collected and analyzed data. Zhiru Zhang and Shiyu Gong drafted the article. Yiqin Zhao contributed to the writing and polishing of the article. All Authors revised it critically for important intellectual content. Declaration of competing interest No potential conflict of interest was reported by the authors. Acknowledgements We acknowledge the use of data support from “National Earth Sys tem Science Data Center, National Science and Technology Infrastruc ture of China. (http://www.geodata.cn)” and “National Meteorological Information Centre of the Chinese Meteorological Administration (htt p://data.cma.cn/)”. Cordial thanks should give to the editors and anonymous reviewers for their constructive suggestions and comments that improved the manuscript.
5. Conclusions According to datasets for precipitation, population density, and GDP per land area (km2) for the Bohai Rim, the rainstorm probability was 8
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References
Quan, R., 2014. Risk assessment of flood disaster in Shanghai based on spatial–temporal characteristics analysis from 251 to 2000. Environ. Earth Sci. 72, 4627–4638. https://doi.org/10.1007/s12665-014-3360-0. Reguero, B.G., Losada, I.J., Díazsimal, P., Mendez, F.J., 2015. Effects of climate change on exposure to coastal flooding in Latin America and the Caribbean. PloS One 10, e0133409. https://doi.org/10.1371/journal.pone.0133409. Renard, B., Lang, M., 2007. Use of a Gaussian copula for multivariate extreme value analysis: some case studies in hydrology. Adv. Water Resour. 30, 897–912. https:// doi.org/10.1016/j.advwatres.2006.08.001. Saeed, G., Bahram, S., Reza, M., 2010. Derivation of probabilistic thresholds of spatially distributed rainfall for flood forecasting. Water Resour. Manag. 24, 3547–3559. https://doi.org/10.1007/s11269-010-9619-7. Salvadori, G., Michele, C.D., 2010. Multivariate multiparameter extreme value models and return periods: a copula approach. Water Resour. Res. 46, 219–233. https://doi. org/10.1029/2009WR009040. Shao, M., Gong, Z., Xu, X., 2014. Risk assessment of rainstorm and flood disasters in China between 2004 and 2009 based on gray fixed weight cluster analysis. Nat. Hazards 71, 1025–1052. https://doi.org/10.1007/s11069-013-0664-4. Shiau, J.T., 2006. Fitting drought duration and severity with two-dimensional copulas. Water Resour. Manag. 20, 795–815. https://doi.org/10.1007/s11269-005-9008-9. Silva, S.F., Martinho, M., Capit~ ao, R., Reis, T., Fortes, C.J., Ferreira, G.C., 2017. An indexbased method for coastal-flood risk assessment in low-lying areas (Costa de Caparica, Portugal). Ocean Coast Manag. 144, 90–104. https://doi.org/10.1016/j. ocecoaman.2017.04.010. Sklar, A., 1959. Fonctions de repartition � a n dimensions et leurs marges, vol. 8. Publication de l’Institut de Statistique l’Universite Paris, pp. 229–231. Smith, K., 1996. Environmental Hazards: Assessing Risk and Reducing Disaster. Routledge, New York, pp. 12–38. Song, X., Zou, X., Zhang, C., Zhang, J., 2019. Multiscale spatio-temporal changes of precipitation extremes in Beijing-Tianjin-Hebei region, China during 1958–2017. Atmosphere 10, 462. https://doi.org/10.3390/atmos10080462. United Nations Department of Humanitarian Affairs (UNDHA), 1991. Mitigating Natural Disasters Phenomena, Effects and Options: a Manual for Policy Makers and Planners. United Nations, New York, pp. 1–164. United Nations Office for Disaster Risk Reduction (UNDRR), 2019. Global Assessment Report on Disaster Risk Reduction. United Nations Office for Disaster Risk Reduction (UNDRR), Geneva, Switzerland, pp. 137–157. Vousdoukas, M.I., Lorenzo, M., Evangelos, V., Bianch, A., Dottori, F., Feyen, L., 2018. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nat. Clim. Change 8, 776–780. https://doi.org/10.1038/s41558-018-0260-4. Wang, C., Ren, X., Li, Y., 2017b. Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example. Theor. Appl. Climatol. 128, 169–179. https://doi.org/10.1007/s00704015-1692-7. Wang, X.F., Wang, S., Ren, L., Zeng, Z.Z., 2017a. Spatial distribution of rainstorm hazard risk based on EW-AHP in mountainous scenic area of China. Hum. Ecol. Risk Assess. 23, 925–943. https://doi.org/10.1080/10807039.2017.1292845. Wang, C., Wu, J., Wang, X., He, X., 2018. Application of the hidden Markov model in a dynamic risk assessment of rainstorms in Dalian, China. Stoch. Environ. Res. Risk Assess. 32, 2045–2056. https://doi.org/10.1007/s00477-018-1530-4. Yin, H., Sun, Y., Wan, H., Zhang, X.B., 2017. Detection of anthropogenic influence on the intensity of extreme temperatures in China. Int. J. Climatol. 37, 3. https://doi.org/ 10.1002/joc.4771. Zhou, L., Wu, X.H., Ji, Z.H., Ge, G., 2017. Characteristic analysis of rainstorm-induced catastrophe and the countermeasures of flood hazard mitigation about Shenzhen city. Geomatics, Nat. Hazards Risk 8, 1886–1897. https://doi.org/10.1080/ 19475705.2017.1392368.
Abatan, A., Abiodun, B., Lawal, K.A., Gutowski, W.J., 2016. Trends in extreme temperature over Nigeria from percentile-based threshold indices. Int. J. Climatol. 36, 2527–2540. https://doi.org/10.1002/joc.4510. Canters, F., Vanderhaegen, S., Khan, A.Z., Engelen, G., Uljee, L., 2014. Land-use simulation as a supporting tool for flood risk assessment and coastal safety planning: the case of the Belgian coast. Ocean Coast Manag. 101, 102–113. https://doi.org/ 10.1016/j.ocecoaman.2014.07.018. Chi, X., Yin, Z., Wang, X., Sun, Y., 2015. A comparison of methods for benchmarking the threshold of daily precipitation extremes in China. J. Catastrophology 30, 186–190 (In Chinese). Demirdjian, L., Zhou, Y., Huffman, G.J., 2017. Statistical modeling of extreme precipitation with TRMM data. J. Appl. Meteorol. Climatol. 57, 15–30. https://doi. org/10.1175/JAMC-D-17-0023.1. Feng, Y., Li, Y., Zhang, Z., Gong, S., Liu, M., Peng, F., 2019. Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China. Geomatics, Nat. Hazards Risk 10, 1988–2008. https://doi.org/10.1080/ 19475705.2019.1660727. He, Y., Gong, Z., 2014. China’s regional rainstorm floods disaster evaluation based on grey incidence multiple-attribute decision model. Nat. Hazards 71, 1125–1144. https://doi.org/10.1007/s11069-013-0655-5. Hu, H.B., 2016. Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing. Nat. Hazards 83, 485–500. https://doi.org/10.1007/ s11069-016-2325-x. Kang, Y., Park, C.S., Park, J.Y., Cho, D., 2018. Spatial differences in the heavy precipitation risk intensity in South Korea. Hum. Ecol. Risk Assess. 24, 1579–1594. https://doi.org/10.1080/10807039.2017.1418655. Kazmierczak, A., Cavan, G., 2011. Surface water flooding risk to urban communities: analysis of vulnerability, hazard and Exposure. Landsc. Urban Plann. 103, 185–197. https://doi.org/10.1016/j.landurbplan.2011.07.008. Li, W., Liu, C., Hong, Y., Manabendra, S., Sun, W.W., Yao, D.J., Chen, W., 2016. Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, China. Geomatics, Nat. Hazards Risk 7, 1908–1918. https://doi.org/ 10.1080/19475705.2016.1179685. Li, Y., Gu, W., Cui, W., Chang, Z., Xu, Y., 2015. Exploration of copula function use in crop meteorological drought risk analysis: a case study of winter wheat in Beijing, China. Nat. Hazards 77, 1–15. https://doi.org/10.1007/s11069-015-1649-2. Liu, B., Chen, J., Chen, X., Lian, Y., Wu, L., 2013. Uncertainty in determining extreme precipitation thresholds. J. Hydrol 503, 233–245. https://doi.org/10.1016/j. jhydrol.2013.09.002. Liu, L., Li, X., Xia, G., Jin, J., Chen, G., 2016. Spatial fuzzy clustering approach to characterize flood risk in urban storm water drainage systems. Nat. Hazards 83, 1469–1483. https://doi.org/10.1007/s11069-016-2371-4. Lyu, H.M., Sun, W.J., Shen, S.L., Arulrajah, A., 2018. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci. Total Environ. 626, 1012–1025. https://doi.org/10.1016/j.scitotenv.2018.01.138. Maskrey, A., 1989. Disaster Mitigation: a Community Based Approach. Oxfam, Oxford, pp. 1–100. Moel, H.D., Aerts, J.C.J.H., Koomen, E., 2011. Development of flood exposure in The Netherlands during the 20th and 21st century. Global Environ. Change 21, 620–627. https://doi.org/10.1016/j.gloenvcha.2010.12.005. Nayak, P.C., Sudheer, K.P., Ramasastri, K.S., 2005. Fuzzy computing based rainfallrunoff model for real time flood forecasting. Hydrol. Process. 19, 955–968. https:// doi.org/10.1002/hyp.5553.
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Ocean and Coastal Management journal homepage: http://www.elsevier.com/locate/ocecoaman
Risk assessment of rainstorm disasters under different return periods: A case study of Bohai Rim, China Ying Li a, b, *, Zhiru Zhang a, Shiyu Gong a, Meijiao Liu a, Yiqin Zhao c a
School of Geography, Liaoning Normal University, Dalian, China Liaoning Key Laboratory of Physical Geography and Geomatics, Liaoning Normal University, Dalian, China c Faculty of Arts, University of Alberta, Edmonton, Alberta, Canada b
A R T I C L E I N F O
A B S T R A C T
Keywords: Rainstorm disaster Return period Risk Vulnerability Exposure
Rainstorms are extreme events that have a serious impact on the safety of people’s lives and property, and on socioeconomic development. Coastal areas have a large population and a high urban concentration tend to be areas that experience high levels risk of extreme precipitation. The present study uses the Bohai Rim, China, as a case study, and applies the copula joint function to daily precipitation data to calculate the probability of rainstorms under different return periods such that the hazard presented by each rainstorm disaster could be analyzed. Grid data of gross domestic product (GDP) per land area (km2) and population density were selected to calculate and assess the associated respectively levels of exposure and vulnerability. The risk of occurrence of a rainstorm disaster was analyzed according to the disaster risk assessment paradigm of the United Nations Office for Disaster Risk Reduction (UNDRR). The results showed that the risk of a rainstorm disaster under different return periods in the Bohai Rim gradually decreased from the southeast coastal region towards the northwest inland area. Our study provides a scientific basis for governments to improve disaster prevention and reduction, and can assist in the formulation of emergency management countermeasures.
1. Introduction Global and local extreme precipitation events have occurred frequently in recent years due to climate change (Canters et al., 2014; Silva et al., 2017; Kang et al., 2018). As extreme precipitation events, rainstorms have a significant impact on the safety of people’s lives and property, as well as socioeconomic development (Demirdjian et al., 2017). Meteorological disasters (e.g., floods and mudslides) that are caused by rainstorms have a serious impact on developing countries in particular (Li et al., 2016; Zhou et al., 2017; Lyu et al., 2018). Coastal areas usually represent important areas in terms of a country’s socio economic development. The rapid development of cities has increased the frequency and intensity of rainstorm events to a certain extent, and has therefore increased the hazard of meteorological disasters (Yin et al., 2017). The vulnerability and exposure of coastal areas are further aggravated by factors such as land-use cover change, rising sea-levels, ecological and environmental deterioration, and infrastructure con struction (Reguero et al., 2015; Vousdoukas et al., 2018). In this context,
coastal areas are often characterized by more risks including urban waterlogging, farmland inundation, and infrastructure collapse, which are caused by extreme precipitation events. Rainstorm disaster risk refers to the expected lost value of human property and socioeconomic development, which are caused by a rain storm disaster during a certain period of time in a given area. Scholars have made numerous achievements in the study of rainstorm disaster risk evaluation, and have mainly focused on the mechanisms of disaster risk formation (UNDHA, 1991), risk evaluation research methods (Saeed et al., 2010), and risk evaluation and zoning amongst others (Hu, 2016). With regards to the mechanisms of disaster risk formation, Maskrey (1989) proposed that natural disaster risk is the sum of the hazard and vulnerability. Smith (1996), on the other hand, stated that disaster risk is the product of the disaster occurrence probability and disaster loss. The UNDRR (2019) proposed that disaster risk is the product of hazard, exposure and vulnerability. There are many research methods involved in risk evaluation research, for example, the comprehensive evaluation method (Kazmierczak and Cavan, 2011), uncertainty method (Nayak
Abbreviations: GDP, gross domestic product; UNDRR, United Nations Office for Disaster Risk Reduction. * Corresponding author. School of Geography, Liaoning Normal University, Dalian, China. E-mail addresses: [email protected] (Y. Li), [email protected] (Z. Zhang), [email protected] (S. Gong), [email protected] (M. Liu), yiqin2@ ualberta.ca (Y. Zhao). https://doi.org/10.1016/j.ocecoaman.2020.105107 Received 5 September 2019; Received in revised form 30 December 2019; Accepted 13 January 2020 Available online 31 January 2020 0964-5691/© 2020 Elsevier Ltd. All rights reserved.
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et al., 2005; Wang et al., 2017a), historical disaster data method (Moel et al., 2011; Quan, 2014), hydrological hydrodynamics method (Saeed et al., 2010), and hidden Markov model (Wang et al., 2018). Most studies have been analyzed from the perspective of a rainstorm as a single element; however, rainstorm disasters are usually the result of the joint action of multiple elements. Traditional single element analysis can therefore reduce the accuracy of results, whereas a multi-element combination can improve the risk evaluation of a rainstorm disaster (Li et al., 2015). The present study takes the Bohai Rim as a case study, and uses daily precipitation data along with a copula joint function to calculate the probability of rainstorm occurrence in different return periods (5, 10, 20, and 50 years) as a means of analyzing the hazard levels of rainstorm disasters. Population and GDP per land area (km2) are selected to analyze the associated respectively levels of exposure and vulnerability, and are subsequently used together with the hazard assessment to determine the risk of rainstorm disasters in the study region. A further objective is to improve the accuracy of rainstorm probability evaluation by using a multi-element combination of rainstorm, and by refining the spatial difference of disaster vulnerability by using grid data. Overall, the present study aims to provide a reference for the risk assessment of rainstorm disasters, and a scientific basis for disaster prevention and mitigation.
dense, unevenly distributed population and has experienced rapid eco nomic development over recent years, but also great loss as a result of rainstorms and flood disasters. 2.2. Hazard analysis of rainstorm disasters 2.2.1. Data source Daily precipitation data from 1967 to 2016 for the Bohai Rim was provided by the Chinese Meteorological Date Service Center (http://dat a.cma.cn/). According to the principle of the maximum time period and continuity, data from 60 stations were selected after missing measure ment values were replaced, and a spatial anomaly test and a homoge neity test were performed. We categorized �50 mm precipitation in 24 hours as a rainstorm according to the National Meteorological Standard of China. Based on this standard, the present study selected various necessary rainstorm elements, including the rainstorm volume, number of rainstorm days, rainstorm intensity, and rainstorm contribution rate. 2.2.2. Copula joint function and return period This study uses the copula joint function to calculate the probability of a rainstorm under different return periods (5, 10, 20, and 50 years) as a means of analyzing the risk of rainstorm disasters. The copula function is used to fit the edge distribution functions through correlation (Sklar, 1959; Salvadori and Michele, 2010; Liu et al., 2016), and has been widely applied in hydrology, meteorology, and other fields (Shiau, 2006; Renard and Lang, 2007). We have also successfully used the copula function in related studies for the return period of disasters, such as drought and rainstorms (Li et al., 2015; Wang et al., 2017b; Feng et al., 2019). The edge distribution function is the basis for constructing the copula joint function, and was determined for each rainstorm element (i.e., rainstorm volume, number of rainstorm days, rainstorm intensity, and rainstorm contribution rate). Firstly, Spearman’s rank correlation coef ficient was used to measure the correlation of various rainstorm element, the maximum likelihood method was used to estimate the parameters, and the edge function was used to fit each rainstorm
2. Methodology 2.1. Research area China’s Bohai Rim includes Beijing City, Tianjin City, Liaoning Province, Hebei Province, and Shandong Province (Fig. 1). The region is located on coasts of the Bohai and Yellow seas, and is largely charac terized by a warm temperate semi-humid monsoon climate. Due to the unique geographical location of the study area, relatively high levels of precipitation mean that the region is prone to flooding and water logging. The Bohai Rim is an important political, economic, cultural, scientific, and technological center in northern China. The region has a
Fig. 1. Regional location map of the Bohai Rim. 2
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element. The optimal edge function was determined by the KolmogorovSmirnov test. LðθÞ ¼
Yn
Fðxi ; θÞ ¼ i¼1
Yn ∂Fðxi ; θÞ i¼1 ∂xi
2.4.1. Data source GDP per land area (km2) grid data was obtained for the Bohai Rim using a 1 km grid gross domestic product (GDP) dataset for 2010, which was provided by the National Earth System Science Data Sharing Infrastructure, National Science and Technology Infrastructure of China (http://www.geodata.cn/).
(1)
n X
ln½LðθÞ� ¼
2.4. Vulnerability analysis
(2)
ln½Fðxi ; θÞ� i¼1
∂ln½LðθÞ� ¼0 ∂θ
2.4.2. Vulnerability calculation Vulnerability is determined by external factors such as social, eco nomic, environmental and etc. (UNDRR, 2019). Therefore, in the risk analysis of rainstorm disasters, GDP per land area (km2) was selected as the main index of vulnerability. The dataset was imported into ArcGIS and was divided into five levels by the Jenks natural breaks classifica tion method: Lower, Low, Middle, High, and Higher. Distribution plots of the vulnerability data was subsequently determined.
(3)
In the formula, LðθÞ is the likelihood function, Fðxi ; θÞ is a function of the edge distribution density, and θ is the parameter to be estimated. Based on the determination of the edge distribution function of the single elements of a rainstorm event, the three-dimensional copula function was combined for all rainstorm elements. The maximum like lihood method was used to estimate the parameters, and the root-meansquare error and Akaike information criterion were used to determine the optimal copula function. pffiffiffiffiffiffiffiffiffi 8 RMSE ¼ MSE > > > n < 1 X MSE ¼ ðPei Pi Þ2 (4) > n 1 > i¼1 > : AIC ¼ n logðMSEÞ þ 2l
2.5. Risk analysis of rainstorm disasters The UNDRR proposed that disaster risk is the product of three major factors: hazard, exposure, and vulnerability (UNDRR, 2019). Based on this, the determination of risk can be expressed as formula (6): Risk ¼ Hazard � Exposure � Vulnerability
In formula (4), Pei is the empirical probability of the threedimensional combination of the rainstorm multi-elements, Pi is the joint distribution value, and l is the number of parameters contained in the model. According to the return period theory (Feng et al., 2019), the three-dimensional joint return period of the copula function is expressed by formula (5): Ta ¼
N ¼ nPðD � dU S � sU M � mÞ n½1
N Cðu; v; wÞ�
(6)
Hence, the present study calculated the risk of rainstorm disasters under different return periods at different sites in the study using for mula (6). The Jenks natural breaks optimization method was then used to divide risk into five levels in ArcGIS: Lower, Low, Middle, High, and Higher, and the area occupied by different levels of risk can be measured. 3. Results
(5)
3.1. Rainstorm disaster hazard analysis
Where Ta is the joint return period, N is the sample length, Cðu; v; wÞ is the joint distribution function, u; v; w is the edge distribution function of the rainstorm elements, and n is the number of times a certain value is exceeded during the study period. The joint accumulative probability of multiple elements of a rain storm at each site in the Bohai Rim is calculated for four return periods (5, 10, 20, and 50 years) according to the formula of the return period. Based on the probability results, spatial interpolation is executed using the Kriging interpolation method, and is subsequently divided into five grades, from low to high: Lower, Low, Middle, High, and Higher, to analyze the spatial distribution difference of rainstorm disaster hazards under the different return periods.
Fig. 2 presents the distribution plots for the rainstorm disaster haz ards in different joint return periods, from which the spatial character istics of rainstorm hazards in the Bohai Rim can be analyzed. Firstly, we report on the 5-year return period analysis results. Zhangjiakou and Chengde West, in the northwestern region of Hebei Province (Fig. 1), are located on the leeward slope and far away from the ocean. This region is not conducive to water vapor transportation and experienced relatively low precipitation; thus, the rainstorm disaster hazard level was found to be Lower (Fig. 2a). The majority of the central region of the Bohai Rim was determined to be associated with a Lowlevel hazard, whereas the southeast coastal areas and eastern Liaoning Province were assigned a Middle-level hazard rating. Among the southeast coastal areas, Dandong City was determined to be of a High hazard level. Most of these areas are located in coastal areas with lower elevations. In summer, they are affected by the convergence of warm and humid ocean air currents and cold air from the north, thus resulting in more precipitation. During the 10-year return period, the hazard levels of rainstorm disaster increased in comparison to the 5-year return period; the Lowerlevel hazard area in the northwestern region of Hebei Province increased to a Low-level hazard and extended to the northwest where there is less precipitation. Moreover, the High-level hazard expanded from Dandong City towards the southeast coastal region, and the rest of the central region increased to a Middle-level hazard rating (Fig. 2b). For the 20-year return period rainstorm disasters hazard ratings, Low-level and Middle-level hazard areas were mainly distributed in the northwestern region of Hebei Province. In comparison to the 10-year return period, the High-level hazard area gradually expanded to the northwest, and a Higher-level hazard appeared in some parts of the
2.3. Exposure analysis 2.3.1. Data source Population density grid data was obtained for the Bohai Rim using a 1 km grid population dataset for 2010, which was provided by the Na tional Earth System Science Data Sharing Infrastructure, National Sci ence and Technology Infrastructure of China (http://www.geodata. cn/). 2.3.2. Exposure calculation In the risk analysis of rainstorm disasters, the population density was selected as the main index of exposure (UNDRR, 2019). The dataset was imported into ArcGIS and was divided into five levels by the Jenks natural breaks classification method: Lower, Low, Middle, High, and Higher. Distribution plots of the population density exposure data was subsequently determined.
3
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Fig. 2. Distribution of rainstorm disaster hazards in different return periods in the Bohai Rim.
Areas of Lower-level exposure were mainly distributed in the northwestern region of Hebei Province and in the eastern and western regions of Liaoning Province (Fig. 3). These areas have relatively small populations because of their high altitude, and the northwestern region of Hebei Province is located on the boundary of the monsoon area. Fig. 3 also shows that most of the remaining Bohai Rim was found to be of either Low-level or Middle-level exposure. High-level and Higher-level exposure were mainly associated with large urban areas and surround ing county centers, such as Beijing, Tianjin, Shijiazhuang, and Shenyang (Fig. 3). Most of these areas are located in the plain area, which has a monsoon climate and is relatively densely populated due to nature, policy and transportation amongst other reasons.
southeast coastal area (Fig. 2c). During the 50-year return period, the hazard levels of rainstorm di sasters increased, and there were no Lower-level or Low-level hazard areas. Middle-level and High-level hazard areas were indented to the northwest, and most of the southeastern region was assigned as a Higher-level hazard area (Fig. 2d). Overall, our results showed that the Lower-level hazard of rainstorm disasters in the Bohai Rim was mainly distributed in the northwestern region of Hebei Province, the hazard level associate with the southeast coastal areas was High, with a transition area in between these two re gions. This was consistent with the spatial distribution of precipitation in the Bohai Rim. From the 5-year to 50-year return periods, the range of Lower-level and Low-level hazard areas gradually decreased to the northwestern region, until the 50-year return period, when there were no Lower-level and Low-level hazard areas in the Bohai Rim. The Middle-level, High-level, and Higher-level hazard areas of rainstorm disaster gradually expanded towards the northwestern region; the range of Middle-level and High-level hazards first increased and then decreased and the Higher-level hazard area gradually expanded. Thus, only Middle-level, High-level, and Higher-level hazards of rainstorm disaster were determined for the 50-year return period.
3.3. Vulnerability analysis Fig. 4 presents the distribution plots for vulnerability for the Bohai Rim, from which the different spatial characteristics can be analyzed. The areas with Higher-level and High-level vulnerability were mainly distributed in Beijing, Tianjin, provincial capital cities, and sublevel prefecture-level urban cities. Fig. 4 shows that most of the urban centers were determined as areas of a Higher-level vulnerability, while the peripheries were found to be of a High-level vulnerability. Most of these areas are located in the monsoon climate zone and have flat terrain, convenient transportation, dense population, rapid urbaniza tion, more developed economy, and hence High-level of vulnerability. We determined a periphery of High-level areas, some county centers, the
3.2. Exposure analysis Fig. 3 presents the distribution plots for exposure for the Bohai Rim, from which the different spatial characteristics can be analyzed. 4
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Fig. 3. Exposure in the Bohai Rim.
Fig. 4. Vulnerability in the Bohai Rim.
coastal areas of the Bohai and Yellow seas were all found to be a Middlelevel vulnerability. The area of Lower-level vulnerability was located in the northwestern region of Hebei Province and the northwestern and eastern regions of Liaoning Province (Fig. 4). This included Zhangbei, which is located at the edge of the Inner Mongolia Plateau and far from the ocean; thus, there is a relatively low economic level because of the local topography and climate amongst other factors. Therefore, this area was associated with a Lower-level of vulnerability. The rest of the region was determined as being of a Low-level of vulnerability.
3.4. Risk analysis of rainstorm disasters According to the distribution plots of rainstorm disaster risk shown in Fig. 5, the spatial characteristics of rainstorm disaster risk in the Bohai Rim can be analyzed. Approximately 92% of the Bohai Rim was determined as being of Lower-level and Low-level risk of rainstorm disasters (Fig. 5). The northwestern region of Hebei Province, northwestern Beijing, and the western region of Liaoning Province had Lower-level of risk due to relatively lower precipitation and long distance from the ocean. The Low-level risk areas were found to be located in the central and eastern regions of Liaoning Province, the eastern and southern regions of Hebei 5
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Fig. 5. Risk distribution of rainstorm disasters in the Bohai Rim.
Province, Tianjin, and the majority of Shandong Province. The Higherlevel and High-level risk areas were distributed in the centers of large cities and the surrounding suburban centers, for example, Beijing, Tianjin, Shijiazhuang, Shenyang, and Qingdao. The urban centers in these regions were associated with a Higher-level of risk, which gradu ally reduced with distance to the peripheries. The level of risk of a rainstorm disaster was higher in urban centers in the study region, which related in part to them being relatively densely populated, ur banized, and economically developed areas. The remaining scattered small urban areas were determined to be transitional areas with a Middle-level risk of rainstorm disasters. Areas associated with a Higher-level risk of rainstorm disaster in the Bohai Rim were concentrated in the centers of large cities and around the surrounding suburban centers. The Lower-level risk areas were mainly concentrated in the northwestern region of Hebei Province. Most of the remaining areas were found to be of the risk transition area. As the
return period increased, the Lower-level risk area reduced by 43% to wards the northwest, the Low-level risk area expanded by 38% to the northwest, the Middle-level risk area expanded by 2.8%, the High-level risk area expanded by 1.55%, and the Higher-level risk area expanded by 1.31% (Table 1). The boundaries of the rainstorm disaster risk, which included Lower-level and Low-level, roughly coincided with the 400 mm equal precipitation line. 3.5. Comparison of rainstorm disaster risks in different scale cities Four cities with different scales were selected within the same pre cipitation belt (semi-humid areas with 400–800 mm annual precipita tion) in order to compare and analyze the differences in the risk of rainstorm disasters. These were: Beijing (municipalities directly under the central government), Shenyang (provincial capital city), Dalian (deputy provincial city), and Shouguang (county-level city).
Table 1 The area of different levels of risk under different return periods and their proportion of the total area. Level Lower Low Middle High Higher
5-year
10-year
20-year
50-year
Area (km2)
Proportion
Area (km2)
Proportion
Area (km2)
Proportion
Area (km2)
Proportion
330235.6 159094.3 29353.7 12232.9 2510.3
61.91% 29.82% 5.5% 2.3% 0.47%
233235.6 242187.8 40311.5 13083.0 4608.93
43.72% 45.4% 7.56% 2.45% 0.86%
115541.9 344912.4 50060.6 16177.7 6734.1
21.66% 64.66% 9.38% 3.03% 1.26%
99337.6 359881.4 44203.2 20521 9483.5
18.62% 67.47% 8.29% 3.85% 1.78%
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Fig. 6 illustrates that within the same precipitation zone, the hazard level of rainstorm disaster was determined to be approximately the same. Among the four cities, most of the population is concentrated in urban centers, which are characterized by rapid economic development. The urban peripheries are generally sparsely populated and economic development is relatively slow. Therefore, the risk levels of rainstorm
disasters within the cities were found to be quite different to the pe ripheries, whereby the risk level from urban centers towards the pe ripheries gradually reduced. Beijing and Shenyang are inland cities. Beijing’s terrain is high in the northwest and low in the southeast. The population is concentrated in the southeast whereas the northwest population is sparse, and thus the economy of the southeast is more
Fig. 6. Risk of rainstorm disasters under different return periods in cities of different grades. 7
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developed. Therefore, the risk of rainstorm disasters in Beijing was found to be high in the southeast and low in the northwest. However, the exposure and vulnerability associated with the Shenyang suburbs was determined to be higher than of that associated with northwestern Beijing; hence, the risk level of rainstorm disasters in the suburbs was higher than of that in northwestern Beijing. Dalian and Shouguang are coastal cities, and the difference in annual precipitation is small. Dalian is surrounded by the sea on three sides; its geographical position is su perior and its level of exposure and vulnerability is higher than that of Shouguang. The risk level of rainstorm disaster in Dalian was therefore found to be relatively higher than of that determined for Shouguang.
calculated based on the copula joint function, and the hazard of rain storm disasters was analyzed. Population and economic grid data were selected to analyze exposure and vulnerability. On the basis of these data and according to the formula of disaster risk evaluation, the risk of rainstorm disasters was then analyzed. The results can be summarized as follows: (1) From the coastal areas of the Bohai and Yellow seas towards the northwestern area, the hazard levels of rainstorm disasters gradually decreased. As the return period increased, the range of the Lower-level and Low-level hazard areas gradually narrowed towards the northwest, while the Middle-level, High-level, and Higher-level hazard areas advanced towards the northwest. The Middle-level and High-level hazard areas showed a tendency to increase and then decrease, and the areas associated with a Higher-level hazard gradually expanded. (2) The urban centers had Higher-level exposure. The Lower-level exposure areas were found to be located in the northwestern re gions of Hebei Province, the eastern and western regions of Liaoning Province, and the remaining area was the exposure transition area. (3) Areas of Higher-level and High-level vulnerability were found to be concentrated in some major cities, such as Beijing, Tianjin, and Shijiazhuang. The northwestern region of Hebei Province and the western and eastern regions of Liaoning Province were deter mined as Lower-level vulnerability areas, while the remaining areas were assigned as a transition zones of vulnerability. (4) From the southeastern coastal area to the northwestern area in the Bohai Rim, the rainstorm disaster risk levels gradually decreased under different return periods. As the return period increased, the Lower-level risk area reduced by 43% to the northwestern, the Low-level risk area expanded by 38% to the northwestern, and the Middle-level, High-level, and Higher-level risk areas expanded by 5.7%.
4. Discussion Most studies on rainstorm disaster risk in the past have been limited by statistical data. Many coastal disaster risk analyses have used administrative units for spatial analyses, whereby the associated statis tical data were evenly distributed within administrative units, which could not reflect the internal differences of an administrative unit (He and Gong, 2014; Shao et al., 2014). However, the use of grid data can make up for this limitation; the replacement of the administrative units with grid units that can reflect the internal differences of each admin istrative unit allows a refined evaluation of disaster risk. This approach can provide a scientific basis for formulating disaster prevention and mitigation as well as emergency management strategies. By taking Beijing as an example, if traditional statistical data is used to map the vulnerability of heavy rain disasters, the disaster risk within the administrative unit is the same level, and internal small differences are not reflected. However, in the actual administrative unit, the population is often unevenly distributed, the urban central economy is relatively developed, and the regional differences are large. Therefore, in the present study, the use of 1 km grid data for the disaster spatial analysis, showed that the Lower-level risk areas were located in northern, west ern, and southeastern Beijing. The Low-level risk was distributed in small areas of the central and eastern regions, whereas the periphery of the urban area was determined as Middle-level risk. The High-level risk was associated with the central urban area, and a small area to the south of the central urban area was associated with a Higher-level of risk. The difference between the Lower-level risk areas in the west and north of Beijing and the Higher-level risk area in the southeast was therefore more precisely determined. There are some shortcomings in the present study, which are aspects that need to be improved in the future. Due to the influence of the weather system, terrain, and geographical location, the spatial distri bution of precipitation in various regions is uneven. Annual precipita tion rates across the Bohai Rim are quite different (Song et al., 2019). Dandong, in eastern Liaoning Province, has the largest annual precipi tation, which can reach more than 1000 mm, while Zhangbei, in northwestern Hebei Province, has less than 300 mm. The unified stan dard of �50 mm precipitation in 24 h as stipulated by the National Meteorological Standard of China was selected as the critical threshold of rainstorm disaster in this study. The northwestern region of Hebei Province was determined as a low-level risk area, and most of the remaining regions in the southeast of the Bohai Rim were assigned as regions of lower-level risk. The small difference in the rainstorm disaster risk levels in the region may be due to the use of a uniform rainstorm threshold. Therefore, future studies of large and medium spatial scales could attempt to use different rainstorm thresholds according to actual, local conditions, for example, by using the percentile threshold method (Chi et al., 2015; Abatan et al., 2016) or detrended fluctuation analysis (Liu et al., 2013).
Funding This research was funded by National Natural Science Foundation of China, No. 41601114; Science and Technology Research Program Sup ported by the Education Department of Liaoning Province, China, No. L201683677, No. H201783626; Social Science Planning Fund Project of Liaoning Province, China, No. L19CJL005. Contributors Ying Li conceived and designed the studies. Zhiru Zhang and Meijiao Liu collected and analyzed data. Zhiru Zhang and Shiyu Gong drafted the article. Yiqin Zhao contributed to the writing and polishing of the article. All Authors revised it critically for important intellectual content. Declaration of competing interest No potential conflict of interest was reported by the authors. Acknowledgements We acknowledge the use of data support from “National Earth Sys tem Science Data Center, National Science and Technology Infrastruc ture of China. (http://www.geodata.cn)” and “National Meteorological Information Centre of the Chinese Meteorological Administration (htt p://data.cma.cn/)”. Cordial thanks should give to the editors and anonymous reviewers for their constructive suggestions and comments that improved the manuscript.
5. Conclusions According to datasets for precipitation, population density, and GDP per land area (km2) for the Bohai Rim, the rainstorm probability was 8
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References
Quan, R., 2014. Risk assessment of flood disaster in Shanghai based on spatial–temporal characteristics analysis from 251 to 2000. Environ. Earth Sci. 72, 4627–4638. https://doi.org/10.1007/s12665-014-3360-0. Reguero, B.G., Losada, I.J., Díazsimal, P., Mendez, F.J., 2015. Effects of climate change on exposure to coastal flooding in Latin America and the Caribbean. PloS One 10, e0133409. https://doi.org/10.1371/journal.pone.0133409. Renard, B., Lang, M., 2007. Use of a Gaussian copula for multivariate extreme value analysis: some case studies in hydrology. Adv. Water Resour. 30, 897–912. https:// doi.org/10.1016/j.advwatres.2006.08.001. Saeed, G., Bahram, S., Reza, M., 2010. Derivation of probabilistic thresholds of spatially distributed rainfall for flood forecasting. Water Resour. Manag. 24, 3547–3559. https://doi.org/10.1007/s11269-010-9619-7. Salvadori, G., Michele, C.D., 2010. Multivariate multiparameter extreme value models and return periods: a copula approach. Water Resour. Res. 46, 219–233. https://doi. org/10.1029/2009WR009040. Shao, M., Gong, Z., Xu, X., 2014. Risk assessment of rainstorm and flood disasters in China between 2004 and 2009 based on gray fixed weight cluster analysis. Nat. Hazards 71, 1025–1052. https://doi.org/10.1007/s11069-013-0664-4. Shiau, J.T., 2006. Fitting drought duration and severity with two-dimensional copulas. Water Resour. Manag. 20, 795–815. https://doi.org/10.1007/s11269-005-9008-9. Silva, S.F., Martinho, M., Capit~ ao, R., Reis, T., Fortes, C.J., Ferreira, G.C., 2017. An indexbased method for coastal-flood risk assessment in low-lying areas (Costa de Caparica, Portugal). Ocean Coast Manag. 144, 90–104. https://doi.org/10.1016/j. ocecoaman.2017.04.010. Sklar, A., 1959. Fonctions de repartition � a n dimensions et leurs marges, vol. 8. Publication de l’Institut de Statistique l’Universite Paris, pp. 229–231. Smith, K., 1996. Environmental Hazards: Assessing Risk and Reducing Disaster. Routledge, New York, pp. 12–38. Song, X., Zou, X., Zhang, C., Zhang, J., 2019. Multiscale spatio-temporal changes of precipitation extremes in Beijing-Tianjin-Hebei region, China during 1958–2017. Atmosphere 10, 462. https://doi.org/10.3390/atmos10080462. United Nations Department of Humanitarian Affairs (UNDHA), 1991. Mitigating Natural Disasters Phenomena, Effects and Options: a Manual for Policy Makers and Planners. United Nations, New York, pp. 1–164. United Nations Office for Disaster Risk Reduction (UNDRR), 2019. Global Assessment Report on Disaster Risk Reduction. United Nations Office for Disaster Risk Reduction (UNDRR), Geneva, Switzerland, pp. 137–157. Vousdoukas, M.I., Lorenzo, M., Evangelos, V., Bianch, A., Dottori, F., Feyen, L., 2018. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nat. Clim. Change 8, 776–780. https://doi.org/10.1038/s41558-018-0260-4. Wang, C., Ren, X., Li, Y., 2017b. Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example. Theor. Appl. Climatol. 128, 169–179. https://doi.org/10.1007/s00704015-1692-7. Wang, X.F., Wang, S., Ren, L., Zeng, Z.Z., 2017a. Spatial distribution of rainstorm hazard risk based on EW-AHP in mountainous scenic area of China. Hum. Ecol. Risk Assess. 23, 925–943. https://doi.org/10.1080/10807039.2017.1292845. Wang, C., Wu, J., Wang, X., He, X., 2018. Application of the hidden Markov model in a dynamic risk assessment of rainstorms in Dalian, China. Stoch. Environ. Res. Risk Assess. 32, 2045–2056. https://doi.org/10.1007/s00477-018-1530-4. Yin, H., Sun, Y., Wan, H., Zhang, X.B., 2017. Detection of anthropogenic influence on the intensity of extreme temperatures in China. Int. J. Climatol. 37, 3. https://doi.org/ 10.1002/joc.4771. Zhou, L., Wu, X.H., Ji, Z.H., Ge, G., 2017. Characteristic analysis of rainstorm-induced catastrophe and the countermeasures of flood hazard mitigation about Shenzhen city. Geomatics, Nat. Hazards Risk 8, 1886–1897. https://doi.org/10.1080/ 19475705.2017.1392368.
Abatan, A., Abiodun, B., Lawal, K.A., Gutowski, W.J., 2016. Trends in extreme temperature over Nigeria from percentile-based threshold indices. Int. J. Climatol. 36, 2527–2540. https://doi.org/10.1002/joc.4510. Canters, F., Vanderhaegen, S., Khan, A.Z., Engelen, G., Uljee, L., 2014. Land-use simulation as a supporting tool for flood risk assessment and coastal safety planning: the case of the Belgian coast. Ocean Coast Manag. 101, 102–113. https://doi.org/ 10.1016/j.ocecoaman.2014.07.018. Chi, X., Yin, Z., Wang, X., Sun, Y., 2015. A comparison of methods for benchmarking the threshold of daily precipitation extremes in China. J. Catastrophology 30, 186–190 (In Chinese). Demirdjian, L., Zhou, Y., Huffman, G.J., 2017. Statistical modeling of extreme precipitation with TRMM data. J. Appl. Meteorol. Climatol. 57, 15–30. https://doi. org/10.1175/JAMC-D-17-0023.1. Feng, Y., Li, Y., Zhang, Z., Gong, S., Liu, M., Peng, F., 2019. Multi-factor joint return period of rainstorms and its agricultural risk analysis in Liaoning Province, China. Geomatics, Nat. Hazards Risk 10, 1988–2008. https://doi.org/10.1080/ 19475705.2019.1660727. He, Y., Gong, Z., 2014. China’s regional rainstorm floods disaster evaluation based on grey incidence multiple-attribute decision model. Nat. Hazards 71, 1125–1144. https://doi.org/10.1007/s11069-013-0655-5. Hu, H.B., 2016. Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing. Nat. Hazards 83, 485–500. https://doi.org/10.1007/ s11069-016-2325-x. Kang, Y., Park, C.S., Park, J.Y., Cho, D., 2018. Spatial differences in the heavy precipitation risk intensity in South Korea. Hum. Ecol. Risk Assess. 24, 1579–1594. https://doi.org/10.1080/10807039.2017.1418655. Kazmierczak, A., Cavan, G., 2011. Surface water flooding risk to urban communities: analysis of vulnerability, hazard and Exposure. Landsc. Urban Plann. 103, 185–197. https://doi.org/10.1016/j.landurbplan.2011.07.008. Li, W., Liu, C., Hong, Y., Manabendra, S., Sun, W.W., Yao, D.J., Chen, W., 2016. Rainstorm-induced shallow landslides process and evaluation – a case study from three hot spots, China. Geomatics, Nat. Hazards Risk 7, 1908–1918. https://doi.org/ 10.1080/19475705.2016.1179685. Li, Y., Gu, W., Cui, W., Chang, Z., Xu, Y., 2015. Exploration of copula function use in crop meteorological drought risk analysis: a case study of winter wheat in Beijing, China. Nat. Hazards 77, 1–15. https://doi.org/10.1007/s11069-015-1649-2. Liu, B., Chen, J., Chen, X., Lian, Y., Wu, L., 2013. Uncertainty in determining extreme precipitation thresholds. J. Hydrol 503, 233–245. https://doi.org/10.1016/j. jhydrol.2013.09.002. Liu, L., Li, X., Xia, G., Jin, J., Chen, G., 2016. Spatial fuzzy clustering approach to characterize flood risk in urban storm water drainage systems. Nat. Hazards 83, 1469–1483. https://doi.org/10.1007/s11069-016-2371-4. Lyu, H.M., Sun, W.J., Shen, S.L., Arulrajah, A., 2018. Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach. Sci. Total Environ. 626, 1012–1025. https://doi.org/10.1016/j.scitotenv.2018.01.138. Maskrey, A., 1989. Disaster Mitigation: a Community Based Approach. Oxfam, Oxford, pp. 1–100. Moel, H.D., Aerts, J.C.J.H., Koomen, E., 2011. Development of flood exposure in The Netherlands during the 20th and 21st century. Global Environ. Change 21, 620–627. https://doi.org/10.1016/j.gloenvcha.2010.12.005. Nayak, P.C., Sudheer, K.P., Ramasastri, K.S., 2005. Fuzzy computing based rainfallrunoff model for real time flood forecasting. Hydrol. Process. 19, 955–968. https:// doi.org/10.1002/hyp.5553.
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