Exploring an urban integrated pluvial flood risk assessment using big data

Exploring an urban integrated pluvial flood risk assessment using big data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring an urban integrated pluvial flood risk assessment using big data Hui Zhang, Cheng Li, Zhifeng Wu, Qifei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1550233/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Currently, urban pluvial flood risk management and mitigation strongly require integrated flood risk assessment at a fine scale. In this study, we tried to perform integrated pluvial flood risk assessment in the framework of “hazard-exposure-vulnerability” at the grid scale. First, a simplified pluvial flood inundation was utilized to quickly calculate flood depth for hazard information to find the flood depth under different precipitation conditions. Next, spatial analysis based on Geographic Information Science (GIS) was chosen to analyze the entire exposure degree of land use. Then, point of interest (POI) data together with traditional statistical data were analyzed by principal component analysis (PCA) for vulnerability assessment at the grid scale, and spatial overlay analysis was used to obtain the integrated risk assessment. The results showed that as the intensity of heavy rain increased, the level and intensity of hazard and exposure risk increased accordingly and showed obvious spatial agglomeration. The 20-year return period of a rainstorm was an inflection point for the area change of hazard. However, vulnerability had a significant clustering effect, which was obviously distinct from hazard and exposure risk. As the rainstorm intensity increased, the regional area of pluvial flood risk facing two or three risks gradually increased, and its spatial agglomeration effect also gradually increased. It is worth noting that the highest integrated risk area was mainly distributed near both the midstream and the downstream sides of Donghaochong. Our research can provide scientific reference for the mitigation of urban pluvial flood risk, and case reference for other cities to carry out similar research. Urban pluvial flood integrated risk assessment hazard exposure vulnerability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Urban development and climate change continue to aggravate urban pluvial floods, which are becoming more serious (Bulti et al., 2020), causing socioeconomic problems such as economic losses, traffic congestion, environmental pollution, and resident` travel and health risks (Rahmati et al., 2020 ). Urban land cover change dominated by the increase in impervious surfaces has reduced surface infiltration and increased surface runoff (Arnold and Gibbons, 1996 ). In the process of urbanization, urban infrastructure construction is not in harmony with urban development in most majority countries. For example, drainage networks in artificial standards cannot deal with heavy rainfall, which has led to the frequent occurrence of urban floods. In addition, climate change, characterized by extreme rainfall and sea level rise, is exacerbating this trend. Furthermore, population and assets gradually accumulate in cities during urbanization (Slater and Villarini, 2016 ), resulting in a further increase in the scope and intensity of urban flood disasters. Therefore, urban flooding has become an urgent problem of urban flood management and has attracted increasing attention from government departments and stakeholders. Urban flood risk management, including urban flood risk assessment and risk mitigation, rather than traditional gray engineering measures, is considered the main solution for resolving urban flood problems (Johnson et al., 2007). To date, urban flood risk assessment is an important and effective means to strengthen technological support and improve emergency response capabilities. The purpose of urban flood risk assessment is to analyze current or future flood risk information, identify high-risk areas (Muis et al., 2015 ), and provide decision support for flood mitigation measures, as well as assess the effectiveness of their mitigation measures (Zhou et al., 2012 ). Numerous related scholars (Haynes et al., 2008 ; Koks et al., 2015 ; Foudi et al., 2015 ; Fakhruddin et al., 2020 ) and research institutes have used the frame of “hazard-exposure-vulnerability” for flood risk assessment. Flood risk = Hazard (H) × Exposure (E) × Vulnerability (V) Hazard can be described as the flood location, depth, and flow rate. Exposure is mainly used to analyze the affected land-use types, people, buildings, and infrastructure in certain flood events. Vulnerability can be defined as the susceptibility of the elements (people, buildings, infrastructure and so on) at risk of suffering from flood damage. This framework not only focuses on the hazard and exposure of a disaster but also considers the vulnerability of various disaster-bearing bodies. Previous studies on urban flood risk have mainly focused on fluvial or coastal floods at relatively large scales, such as regional scales or basin scales (Vojinovic et al., 2015; Ganguli and Merz, 2019 ). With urban development, although the damage or cost of urban pluvial floods is greater than that of coastal or river floods, the frequency of pluvial floods increases (Muthusamy et al., 2019 ) with urban growth, thus having a greater adverse impact on people, transportation and economic activities. Hence, urban pluvial floods deserve more attention. Urban pluvial floods usually occur in local areas of the city due to short-term heavy rainfall, and their impact is very extensive. Therefore, urban pluvial flood risk urgently needs a comprehensive assessment (Kulkarni et al., 2014 ; Vercruysse et al., 2019 ) on a fine scale (Casiano Flores and Crompvoets, 2020 ). However, most of the research content has focused on the hazard or exposure to risk, and less attention has been given to vulnerability. There are few comprehensive flood risk assessment studies. Hazards mainly rely on hydrological, hydraulic models or simplified models (Bulti et al., 2020; Feng et al., 2020 ) to obtain inundation information. Exposure based on the hazard requires spatial analysis to obtain information on the different land-use types, infrastructure, population, etc., exposed to inundation in the region. An inadequate understanding of the comprehensive risk of urban flood has caused researchers to neglect vulnerability. Since urban flooding cannot be managed in isolation, an integrated approach is required (Zevenbergen et al., 2008 ). Urban pluvial flood risk assessment pays more attention to larger scales, such as the catchment (Rouillard et al., 2015 ; Zhou et al., 2019 ), urban (Mebarki et al., 2012 ; Di Salvo et al., 2018 ), and regional scales (Prokić et al., 2019; Speight et al., 2017 ); in contrast, less attention has been given to small scales in the inner city, such as the subcatchment (Pathak et al., 2020 a), community (Azizi and Meier, 2021 ) or street (Yin et al., 2016 ) scales. Therefore, the research content and scale do not meet the needs of urban pluvial flood management and prevention. The main reasons for these patterns are as follows: (1) Refined scale assessment requires more accurate data; (2) the complex surface characteristics of different natural watersheds within the city cause the current flooding mechanism to be further explored. A finer scale usually requires more sufficient and accurate data, such as data on the high-precision terrain, buildings, commercial facilities, infrastructure, the population distribution and drains network data. Traditional demographic and economic statistics used in vulnerability assessments are based on administrative divisions that are not sufficient for urban inner pluvial flood assessments, especially in relation to vulnerability. To date, drainage network data and computing power are still the main limiting factors in two-dimensional hazard hydraulic modeling in large urban areas. The progress of big data technology provides a wealth of data sources that offer very large application potential and new opportunities for urban flood risk assessment at finer scale. At present, different types of big data are starting to be used in studies related to urban flooding. Social media or POI (point of interest) data were used for urban pluvial flood early warning (Young et al., 2021 ), flood risk assessment (Coletti et al., 2020 ), and flood monitoring and mapping (Rosser et al., 2017 ; Helmrich et al., 2021 ). POI data contains information on business, education, transportation, and medical care at an urban internal fine scale, which can break through the administrative division restrictions of traditional statistical data. Therefore, POI data can be considered a new type of data for conducting a refined vulnerability assessment. In this paper, we presented an integrated urban pluvial flood risk assessment at a grid scale. First, limited by the municipal drainage network data, we could not establish a two-dimensional urban hydraulic model at the district level of the city; thus, a simplified hydrological model was used as a substitute to obtain hazard information. Second, spatial analysis was performed to map the exposure degree of land-use types on the basis of hazard information. Then, POI data together with traditional socioeconomic data were used to assess the vulnerability map on a finer scale with the help of statistical and spatial analysis methods. Finally, an integrated waterlogging risk (H-E-V) assessment was achieved. This study could provide a scientific reference and decision basis for urban flood mitigation and urban planning. 2 Materials And Methods 2.1 Study Area Guangzhou, located in southern China (22°26'–23°56'N and 112°57'–114°03'E), is the capital of Guangdong Province and the central urban area of Guangdong-Hong Kong-Macao Greater Bay Area. Guangzhou comprises 11 districts, such as Yuexiu, Tianhe, and Haizhu, covering an area of approximately 7434 km 2 (see Fig. 1 ). Guangzhou, which is dominated by subtropical monsoons, is one of the cities with the most rainfall in China, with an annual precipitation of 1164 ~ 1899 mm and an annual average of 149 rainy days. Since 1978, this city has experienced dramatic urbanization, and its urban pluvial flood has been worsening in the process. Hallegatte et al. ( 2013 ) showed that Guangzhou has the highest flood risk ranking among 138 major coastal cities in the world. Yuexiu District is the oldest downtown area in Guangzhou. Moreover, as the administrative, commercial, financial, and cultural center of Guangzhou, it has formed an industrial structure with the tertiary industry as the main body, the characteristic economy as the driving force, and the commercial service industry as the support. Its pluvial flood problem is particularly serious due to its dense population and old infrastructure. Therefore, we choose Yuexiu District as the study area (Fig. 1 ). 2.2 Data A variety of data were used in this study for hazard and exposure analysis. Land-use maps, precipitation data, catchment data and terrain data were used in hazard analysis. The land use/ cover map was digitized based on the 1:2000 aerial remote sensing images with orthogonal projection (spatial resolution 0.1 m) and 1:500 terrain data obtained from the field survey conducted in 2013 (Zhang et al., 2018 ). The original classification system contained 10 land cover types and 46 subtypes. We reclassified the land cover map from 46 subtypes into 10 primary-level land cover types in ArcGIS. Primary-level classification included (1) cultivated land; (2) garden plot; (3) forestland; (4) grassland; (5) building area; (6) road; (7) structure; (8) artificial heap-excavation land; (9) desert and bare surface; and (10) water (Zhang et al., 2018 ). Then, we reclassified building area, road, and structure as impervious surface and others as pervious surface. Precipitation data of different recurrence periods were calculated by using the Chicago rainstorm calculation formula published by the Guangzhou Water Bureau (see Formula 1). P= \(\frac{3618.427(1+0.438Lg q)}{{(t+11.259)}^{0.750}}\) (1) Unit: L/S*ha; Relative position of rain peak: r = 0.367. Additionally, land-use maps were the main data source for exposure. In the vulnerability analysis, population attribute data were derived from China's sixth census, and population density data were from http://www.worldpop.org.uk/data/data_sources/ . POI refers to describing the characteristics of residential infrastructure (Fig. 2 ), and these data were obtained by Baidu Map by Network reptiles using Python. Considering previous research (Kulkarni et al., 2014 ; Tanaka et al., 2020 ; Pricope et al., 2019 ) and the data availability in our study area, we selected these indicators. Descriptions of specific indicators and their data sources are shown in Table 1 . Table 1 Description of specific indicators and their data sources Indicator Primary Secondary Description/positive(-) and negative (-) correlation Data source Hazard Depth Inundation depth in certain rainfall event (+) From model Exposure Exposure Total exposure of flooded land type (+) Vulnerability Population P01 Percent of female population (+) China's sixth census P02 Percent of children population ( = 75 year) (+) China's sixth census P04 Illiteracy ratio (Illiteracy as a percentage of population over 15 year people) (+) China's sixth census P05 Percent of population with low education level (Population ratio of high school and junior high school) (+) China's sixth census P06 Percent of population with high education level (Percent of population with college degree or above) (-) China's sixth census P07 Percent of unhealthy old people (Percent of the elderly who live on their own) (+) China's sixth census P08 Percent of divorced and widowed population (Percent of population over 15 years) (+) China's sixth census P09 Migrant population ratio (Percent of population with household registration in other places) (+) China's sixth census Den_pep People density (+) http://www.worldpop.org.uk/data/data_sources/ Residential characteristics B01 Percent of brick-wood and mixed-structure houses (+) China's sixth census B02 Percent of living houses (-) China's sixth census Infrastructure indicators I1 Index of indoor emergency shelter (T) (-) 2017 Baidu POI I2 Spatial distribution index of medical services (Y) (-) 2017 Baidu POI I3 Distribution space index for commercial services (C) (-) 2017 Baidu POI I4 index of bus station spatial distribution (B) (+) 2017 Baidu POI I5 Index of spatial distribution of kindergartens in primary and secondary schools (T) (+) 2017 Baidu POI I6 Green space area (L) (-) Land-use map in 2015 year 2.3 Urban pluvial flood assessment method 2.3.1 A simplified urban pluvial flood model A simplified urban pluvial flood model was built to obtain inundation depth in different rainstorm return periods. The urban pluvial flooding process includes rainfall, runoff, pipe network drainage and surface waterlogging. In this study, the Soil Conservation Service (SCS) model and reservoir capacity curve were used to build a simplified urban flood model for simulating the above mentioned urban pluvial flood process with ArcGIS 10.7. As an experienced hydrological model, SCS was developed in the 1950s by the U.S. Department of Agriculture Water and Soil Conservation. It was based on more than 20 years of measured runoff data and was used to assess the hydrological characteristics of small watersheds. The model mainly determines the total runoff depending on the soil and rainfall conditions, in which the soil factors are determined by the soil permeation characteristics, the soil pre-water content and the land type. Because the SCS has good performance in few parameters, simple calculations and better precision, urban runoff calculations and related floods have also begun to be used in recent years (Zhao et al., 2019 ; Wang et al., 2021 ; Bouvier et al., 2018 b). The reservoir capacity curve is the relationship curve between the reservoir water level and its corresponding reservoir capacity. It is mainly used for reservoir planning design and management scheduling in hydraulics (Pandey et al., 2016 ; Issa et al., 2017 ). In this paper, we used the principle of the reservoir capacity curve to establish the relationship between the flooding height and the amount of water accumulation in each subcatchment. The SCS, as a simplified runoff model, was adopted to simulate the runoff yield and concentration process (Bouvier et al., 2018 a). In addition, we simplified the drainage process. In the study area, most of the standard drainage network standard was once-a-year rainfall, so we considered that the drainage capacity of the drainage network was 42.5 mm per hour. After that, we subtracted the runoff yield from the water discharge in each subcatchment and obtained the final amount of water accumulation. Then, we used the principle of the reservoir capacity curve to establish the equation between the depth of flooding and the amount of water accumulation. Water accumulation calculations at different depths were implemented by ArcGIS 10.7 using the Surface Volume tool. Then, the final inundation depth was calculated. The simplified modeling process was as follows: (1) First, the Chicago rainstorm calculation formula and SCS were used to model rainfall runoff, and the calculation Formula (2) is as follows: Q r = \({(\text{P}-{I}_{a})}^{2}\) /(P + S- \({I}_{a}\) ) \(\text{P}\) ≥ \({I}_{a}\) Q r =0 \(\text{P}\) < \({I}_{a}\) \({I}_{a}\) =0.2S S= \(\frac{25400}{CN}\) – 254 CN = 0.24X + 74 (2) where Q r is the total runoff (m 3 ) produced in the subcatchment area; P is the rainfall (mm) of different rainstorm return periods; CN is a comprehensive parameter that reflects the characteristics of the catchment area before rainfall; and X is the percent of impervious surfaces in each subcatchment. (2) Water accumulation calculation: W a = (Q r -Q d )*Area (3) where W a is the surface water accumulation; Area is the area of each subcatchment area (unit: m 2 ); and Q d is the drainage capacity of the drainage network per hour. 3) Establishment of the reserve capacity curve of each subcatchment: The Surface Volume tool in ArcGIS calculates surface water accumulation under the set horizontal plane elevation. To this end, we established the functional relationship between the plane elevation of each sub-catchment and water accumulation, as shown in Appendix Table 1 , according to the calculation results. Then, according to the water accumulation formula, the set horizontal plane elevations of different subcatchments were calculated. 4) Using the Raster Calculator in ArcGIS, the original topographic map was subtracted from the above mentioned set horizontal plane elevations and differences greater than 0 were the final inundation elevations (Appendix Table 1 ). 2.3.2. Exposure index calculation Yuexiu District is a highly developed area in Guangzhou with densely distributed buildings and populations. Therefore, when assessing exposure risks, we assessed all types of land use without distinguishing specific categories. We calculated the exposure index of land use to characterize exposure. The calculation formula is as follows: E(land-index)= \({\sum }_{i=1}^{m}{g}_{i}\left({u}_{j}\right)\text{*}{W}_{i}\) \({ g}_{i}\left({u}_{j}\right)\) = \(\frac{{f}_{i}\left({u}_{j}\right)}{\sum _{j=1}^{n}{f}_{i}\left({u}_{j}\right)}\) (4) where E (land index) is the exposure index; \({g}_{i}\left({u}_{j}\right)\) represents the area proportion in area j with an exposure level of i as land with the same exposure level in the study area; \({W}_{i}\) is a weight factor for exposure grade i; m = 6; n = 3528; and weight is divided into 6 levels according to the inundation depth. A total of 3528 grids were included in the calculation. Finally, E (land index) was divided into five levels according to the natural breakpoint method, i.e., potential, micro, mild, moderate and severe exposure levels. As a hierarchical classification statistic based on the distribution of numerical statistics, the natural breakpoint method maximizes the differences between classes. 2.3.3 Vulnerability index calculation The selection of indicators and calculation method are the two main questions of vulnerability assessment. In selecting vulnerability indicators, we first should choose enough indicators as soon as possible to reflect the urban pluvial flood vulnerability of the study area. Referring to previous research results (Müller et al., 2011 ; Tapia et al., 2017 ; Pricope et al., 2019 ) and the availability of data, we selected three primary indicators and eighteen secondary indicators (Table 1 ). However, there may be redundancy between indicators. It is better to obtain more information with the fewest variables. Considering the wide application of the PCA method in flood vulnerability (Pricope et al., 2019 ; Wu, 2021 ), PCA was selected for vulnerability assessment in this paper. Before performing PCA, the Kaiser–Mayer–Olkin (KMO) statistic and Bartlett's sphericity test were performed to estimate the suitability of PCA (Aroca-Jimenez et al., 2017 ; Pathak et al., 2020 b). The KMO value in this study was 0.599 (> 0.5), and the df value of the Bartlett test was 153. Therefore, the vulnerability indicators were considered appropriate for PCA. According to the PCA results, the explained variance of each main component was its weight coefficient, which was used to determine its final vulnerability score. Similar to the exposure classification, vulnerability was divided into five levels using the natural breakpoint method: potential, micro, mild, moderate and severe vulnerability. 2.3.4 Integrated flood risk assessment After obtaining the evaluation results of the three elements of risk, due to the subjectivity and uncertainty of the weight setting process, we did not adopt the method of setting weights to obtain the integrated risk assessment. We combined three layers, including hazard, exposure, and vulnerability maps, to produce an integrated flood risk map using ArcGIS 10.7. For hazards, raster grid cells with inundation depths greater than 20 cm are dangerous areas worthy of attention and were assigned a value of 1, and the rest were assigned a value of 0. In the exposure and vulnerability assessment results, the moderate and severe areas were considered to be areas of concern, they were assigned a value of 1, and the remaining areas were assigned a value of 0. After assigning values (0 or 1) for hazard, exposure, and vulnerability according to the rules, a spatial overlay was performed for these three layers in order of hazard, exposure, and vulnerability. Among them, 001, 010 and 001 represented that the area faces one of the risks of hazard, exposure, and vulnerability, respectively, while 011, 110, and 101 represented that the study area faced two risks of hazard, exposure, and vulnerability respectively. Additionally, 111 meant that the area faced three risks at the same time. A flowchart of the integrated pluvial flood risk assessment is shown in Fig. 3 . 3 Results And Discussion 3.1 Hazard and exposure analysis By using the simplified urban pluvial model, we obtained the inundation depth of a series of rainstorm periods for each subcatchment (Appendix Table 2 ). Hazard results, including area and spatial pattern in Yuexiu District, are shown in Appendix Table 2 and Fig. 4 . In general, the inundation area increased with the increase in rainstorm periods, and they showed a high spatial agglomeration effect. In terms of inundation area, it increased significantly in the 10-year return period compared to the 5-year return period; however, it remained relatively stable between the 20 and 50-year periods (Appendix Table 2 ). For the 5-year period, the inundation area was 3.93 km 2 , accounting for 11.13% of the total area. In the following return periods (10–100 years), the proportion of inundation area was 14.6%, 18.48%, 20.44%, and 21.69%, respectively. It is generally believed that a flood depth greater than 20 cm has a greater impact on pedestrians. Based on this, we focused on the statistics of flooded areas with a submerged depth greater than 20 cm. The proportion of inundation area where the flood depth was greater than 20 cm increased with increasing period. The proportion of their inundation area was 5.61%, 8.45%, 12.36%, 15.17%, and 16.61% from the 5-year to the 100-year return periods, respectively. During the 5- to 10-year rainstorm periods, the proportion of the inundation area greater than 20 cm of submerged depth increased by 50.62%, and this proportion slowed slightly to 46.27% during the 10- or 20-year period. However, during the periods of 20–50 years and 50–100 years, the proportion fell sharply to 22.73% and 9.49%, respectively. We found that the 20-year period was the inflection point of the inundation area change and that the growth rate of the inundation area increased gradually in the period less than 20 years but slowed after a 20-year period. The drainage pipe standard of Guangzhou’s new urban area was designed only according to a return period of 3 years, while approximately 83% of the drainage pipe network in the old urban area was designed for the “1-year return period” drainage standard. Taking into account the impact of unfavorable factors such as blockage in the use of the drainage network, in the design of urban drainage network reconstruction, the standards that deal with a 50-year return period may be worthy of reference. Additionally, the Guangzhou Municipal Government document "Guangzhou City Flood Control System Construction Standard Guidelines" noted that in metropolitan areas, including Yuexiu District, the future drainage standards should withstand the 50-year return year rainstorm. Currently, Guangzhou is advancing the construction of sponge cities. Our research results can provide valuable reference information for improving the drainage capacity of sponge cities in old cities. In terms of spatial aggregation, the model simulation results showed that the flooded areas in Yuexiu District were mainly concentrated in the following seven regions: (1) the middle and lower reaches of Donghao Creek; (2) the region between East Lake and the Inner Ring Road; (3) the region around the Xiaobei subway station, (4) the intersection of the People North Road and the LiuHua Road and its surrounding area; (5) the region between Yanxi Station and Tongde Wei North–South viaduct; (6) the region between Zhongshan First intersection and Yangji village, and (7) the region between Yanjiang Road and Yide Road. These regions are associated with the actual flooding point of Yuexiu. The results showed that these severely affected areas were mainly located around important roads or subway stations or on both sides of low-lying rivers. Schools, hospitals, commercial centers, etc., gathered around them. These regions were associated with the actual flooding point of Yuexiu. The results could not simulate all the flooding areas of Yuexiu, probably because this paper simulated a maximum rainfall of 99.6 mm per hour of 100, and some flooded areas may have occurred due to more intense rainfall. Another reason may be that the model homogenized the drainage capacity of each area and prioritized low-lying areas in the distribution of flooded water. In terms of the exposed area (Appendix Table 3 ), the main focus was on potential exposure. The exposed area above a slight degree first increased and then decreased under the 5-100 year return periods rainstorms. Among them, the slightly exposed area ratio increased from 6.32% in 5-year to 13.92% in 20 years and then decreased to 12.98% in 100-years, while the ratio of moderately exposed areas increased from 1.82% in 5 year- to 5.98% in 50-year. After that, it was reduced to 5.66% in 100-year. Since the calculation of exposure assessment depends on the hazard result, the spatial distribution of the exposure of submerged land under different rainfall intensities was similar to the spatial aggregation of hazard (Fig. 5 ). 3.2 Vulnerability analysis We obtained 5 principal components replacing the 16 raw indicators using PCA. The results showed that five components explained approximately 74.116% of the variation in the vulnerability data across the study area (Table 2 ). The first three components (components 1, 2, and 3) explained 54.943%, representing population and residential characteristics with dominant indicators P02 (the proportion of children), P08 (ratio of divorce and widowhood), P05 (low educated population ratio), and B02 (the proportion of population with housing). The last two components (components 4 and 5) explained 19.173% of the variance in the infrastructure indicators. Most indicators were in specific components once, however, P09 (migrant population ratio) and I5 (index of spatial distribution of kindergartens in primary and secondary schools) were shown twice in different components. This result proved that the migrant population and primary and secondary schools were essential for understanding urban pluvial flood vulnerability. The spatial distribution map and area statistics of vulnerability levels are shown in Fig. 6 and Table 3 . The proportion of the area of ​​each vulnerability was mainly at the micro and mild levels, and the two areas together accounted for 59.86% of the total area of ​​Yuexiu District. Special attention was given to the area ratio of moderate and severe exposure reaching 30.02%, which was far greater than the 7% of moderate and severe exposure. The severity of Yuexiu's vulnerability was higher than that of exposure, which showed the importance of vulnerability in urban pluvial flood risk assessment. The spatial distribution map of vulnerability levels showed that vulnerability also had a significant clustering effect. The largest severely vulnerable area was located in the area surrounded by Yanjiangxi Road, Jiefang South Road, Daxin Road, and Renmin Elevated Road. The area that extended to the periphery was the moderately vulnerable region. In terms of spatial distribution, the moderately vulnerable area was more scattered than the severely vulnerable area. It was cross-distributed among mild vulnerability, moderate vulnerability, and severe vulnerability. Micro vulnerability was mainly distributed around Luhu Lake in the north of Yuexiu District and Ersha Island in the south of Yuexiu District. The results showed that the hazard and exposure to the risk factors were close in spatial distribution, while the spatial distribution of the vulnerability levels was quite different. It was observed that vulnerability was a key factor affecting the final comprehensive flood risk assessment. However, in most cities in China, vulnerability is often neglected in the mapping of flood risk, which leads to incomplete final results. Currently, there is not only less assessment of socioeconomic vulnerability in flood assessment in Guangzhou but also less quantitative damage assessment. Because of the absence of a stage-damage curve applicable to Guangzhou, a quantitative vulnerability assessment was not made. The vulnerability assessment in this paper was mainly based on the social vulnerability assessment based on the index method. Therefore, Guangzhou should start to establish a database of flood disaster losses applicable to the region to establish a disaster curve that can quantitatively assess flood losses and obtain a quantitative vulnerability assessment and a comprehensive flood risk assessment. Table 2 Summary of vulnerability components based on PCA results Indicator Component Number 1 2 3 4 5 Den_pep 0.242 0.329 -0.003 0.68 -0.083 P01 0.859 0.414 0.249 -0.024 0.074 P02 0.923 0.089 -0.081 0.066 0.022 P03 0.283 0.856 -0.149 0.086 0.2 P04 0 0.708 0.614 -0.112 0.018 P05 0.07 0.085 0.961 -0.032 0.016 P06 0.805 0.059 -0.513 -0.007 -0.037 P07 0.183 0.734 0.391 0.234 0.039 P08 0.207 0.926 -0.002 0.188 0.122 P09 0.633 0.138 0.69 0.031 -0.128 B01 0.081 0.661 0.133 0.326 -0.182 B02 0.842 0.314 0.404 -0.043 0.059 I1 -0.155 0.068 0.303 0.32 0.035 I2 0.116 0.01 -0.104 0.664 I5 0.196 0.239 -0.276 0.536 0.428 I6 0.075 -0.079 -0.039 -0.589 0.248 %Variance Explained 20.394 19.799 14.751 12.555 6.618 Cumulative 20.394 40.193 54.943 67.498 74.116 Table 3 Scores and areas in different vulnerability classes Vulnerability class Score Area (km 2 ) Percent of area (%) potential [-1.79, -0.49) 3.69 10.46 micro [-0.49, -0.02) 9.76 27.66 mild [-0.02,0.16) 11.24 31.86 moderate [0.16,0.36) 6.79 19.25 severe [0.36,0.89] 3.80 10.77 3.3 The integrated urban pluvial flood assessment The risk assessment framework based on the “H-E-V” framework can include all risk dimensions to comprehensively reflect the risk level of floods in the rainy season. The study results (Appendix Table 4) indicated that with the increase in rainfall intensity, the regional area facing one risk at least gradually increased, and their spatial agglomeration effects also gradually became obvious. Among them, the area facing at least one kind of risk increased from 11.54 km 2 of the 5-year return period to 13.65 km 2 of the 100-year return period, and their area percent was between 26.61 and 28.77%. The area facing two or three types of risks at the same time increased from 1.37 km 2 of the 5-year return period to 3.89 km 2 of the 100-year return period, accounting for 3.89–10.55% of the total area. In areas with a high level of hazard and exposure risk but low vulnerability, flood mitigation measures should focus on improving the drainage capacity of flooded areas. However, areas with low hazard and exposure risk levels but with high vulnerabilities are easily overlooked by decision-makers. The current cities are facing an increase in extreme rainfall (Chen et al., 2017) and urban development (Zhang et al., 2019 ), and the risk level of the area will increase significantly. The Blue Book of China's Climate Change (2021) shows that the precipitation in Guangzhou increased by 20–50 mm/10a from 1961 to 2020. Therefore, reasonable flood planning and management should include climate change and urban change (Moura Rezende et al., 2019 ; Gimenez-Maranges et al., 2020 ). While enhancing the capacity of the drainage network, attention should be given to non-engineering measures and should also strengthen residents’ awareness of flood risk and emergency management of government departments to minimize the adverse effects of flood disasters. Areas with high risks in all three dimensions (H-E-V) should be the areas of greatest concern. Our study found that the region with the highest integrated risk was mainly distributed near both the midstream and the downstream sides of Donghaochong from the 50-year return period (Fig. 7 ). The topographic conditions in this area were prone to waterlogging, and social vulnerability was also high, leading to the highest integrated urban pluvial flood risk. Previous studies (Yu et al., 2018 ; Chan et al., 2021 ) have shown that there are multiple flood hotspots in the Yuexiu area only from the hazard perspective. We adopted a comprehensive flood risk assessment, focusing on both the midstream and the downstream sides of Donghaochong as the region with the highest comprehensive risk. More importantly, flood control measures should focus on areas with high levels of risk among the three dimensions. To solve the urban flooding problem in Yuexiu District, the deep tunnel project is under construction. As the first deep tunnel project in China, it is expected to effectively alleviate the waterlogging problem (Wu et al., 2016 ). However, Huang et al. (Huang et al., 2019 ) found that the best mitigation was in the west and the immediate north of the main tunnel, whereas the east of the creek did not show an obvious mitigation effect. This result is particularly worthy of the attention of the flood control and planning departments. 4 Conclusion In this paper, we evaluated each of the three dimensions of flood risk separately and then conducted a comprehensive flood risk assessment. The integrated flood risk could more fully reflect the spatial distribution of risk and provide a scientific reference for future flood risk mitigation measures. The conclusions are as follows. (1) Hazard and exposure risks: as the intensity of heavy rain increases, the level and intensity of hazard and exposure risk increase accordingly. The 20-year return period of a rainstorm is an inflection point for the area change of hazard. Hazard and exposure risks show obvious spatial agglomeration, and there are approximately seven areas where the flood risk is highly concentrated. (2) Vulnerability: In terms of area, special attention is given to the area ratio of moderate to severe vulnerability in reaching 30.02%, which is far greater than the 7% of moderate to severe exposure. This result shows that the severity of Yuexiu's vulnerability is higher than its exposure, which proves the importance of vulnerability in the risk assessment of urban pluvial floods. In terms of spatial agglomeration, the spatial distribution map of vulnerability levels shows that vulnerability also has a significant clustering effect that is obviously different from hazard and exposure risks. (3) Integrated urban pluvial flood risk: with the increase in rainfall intensity, the region facing two or three risks gradually increases. Meanwhile, its spatial agglomeration effect gradually increases. The area ratio facing two and three risks at the same time increased from 3.89% of the 5-year return period of 10.55% in the 100-year return period. The highest integrated risk area was mainly distributed near both the midstream and the downstream sides of Donghaochong from the 50-year return period. Declarations Acknowledgements This work was supported by National Natural Science Foundation of China (Grant No.42007423), The Team Project of Guangdong Provincial Natural Science Foundation (Grant No.2018B030312004) Conflict of interest The authors declare that they have no conflict of interest. References Arnold CL, Gibbons CJ (1996) Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. J Am Plann Association 62:243–258 Aroca-Jimenez E, Bodoque JM, Garcia JA, Diez-Herrero A (2017) Construction of an integrated social vulnerability index in urban areas prone to flash flooding. Nat Hazards Earth Syst Sci 17(9):1541–1557 Azizi K, Meier CI (2021) Urban Pluvial Flood Risk Assessment: Challenges and Opportunities for Improvement Using a Community-Based Approach. In:World environmental and water resources congress. 7–11, June, pp 350–361 Bouvier C, Bouchenaki L, Tramblay Y (2018) Comparison of SCS and Green-Ampt Distributed Models for Flood Modelling in a Small Cultivated Catchment in Senegal. Geosciences 8(4):122 Bouvier C, Chahinian N, Adamovic M et al (2018) Large-scale GIS-based urban flood modelling: a case study On the City of Ouagadougou. Advances in hydroinformatics. Springer, Singapore, pp 703–717 Bulti DT, Abebe BG (2020) A review of flood modeling methods for urban pluvial flood application. Model earth Syst Environ 6(3):1293–1302 Casiano Flores C, Crompvoets J (2020) Assessing the governance context support for creating a pluvial flood risk map with climate change scenarios: The Flemish subnational case. ISPRS Int J Geo-Information 9(7):460 Chan FKS, Yang LE, Scheffran J et al (2021) Urban flood risks and emerging challenges in a Chinese delta: The case of the Pearl River Delta, vol 122. Environmental Science & Policy, pp 101–115 Coletti A, De Nicola A, Di Pietro A et al (2020) A comprehensive system for semantic spatiotemporal assessment of risk in urban areas. J Contingencies Crisis Manag 28(3):178–193 Di Salvo C, Pennica F, Ciotoli G et al (2018) A GIS-based procedure for preliminary mapping of pluvial flood risk at metropolitan scale. Environ Model Softw 107:64–84 Fakhruddin BSHM, Boylan K, Wild A et al (2020) Assessing vulnerability and risk of climate change. Climate extremes and their implications for impact and risk assessment. Elsevier, pp 217–241 Feng B, Wang J, Zhang Y et al (2020) Urban flood hazard mapping using a hydraulic–GIS combined model. Nat Hazards 100(3):1089–1104 Foudi S, Osés-Eraso N, Tamayo I (2015) Integrated spatial flood risk assessment: The case of Zaragoza. Land Use Policy 42:278–292 Ganguli P, Merz B (2019) Extreme coastal water levels exacerbate fluvial flood hazards in Northwestern Europe. Sci Rep 9(1):1–14 Gimenez-Maranges M, Breuste J, Hof A (2020) Sustainable Drainage Systems for transitioning to sustainable urban flood management in the European Union: A review. J Clean Prod 255:120191 Hallegatte S, Green C, Nicholls RJ et al (2013) Future flood losses in major coastal cities. Nat Clim change 3(9):802–806 Haynes H, Haynes R, Pender G (2008) Integrating socio-economic analysis into decision‐support methodology for flood risk management at the development scale (Scotland). Water and Environment Journal 22(2):117–124 Helmrich AM, Ruddell BL, Bessem K et al (2021) Opportunities for crowdsourcing in urban flood monitoring. Environ Model Softw 143:105124 Huang H, Zhang L, Liu L et al (2019) Assessing the mitigation effect of deep tunnels on urban flooding: a case study in Guangzhou, China. Urban Water Journal 16(4):312–321 Issa IE, Al-Ansari N, Sherwany G et al (2017) Evaluation and modification of some empirical and semi-empirical approaches for prediction of area-storage capacity curves in reservoirs of dams. Int J Sedim Res 32(1):127–135 Johnson C, PENNING-ROWSELL E, Parker D (2007) Natural and imposed injustices: the challenges in implementing ‘fair’flood risk management policy in England. Geogr J 173(4):374–390 Koks EE, Jongman B, Husby TG et al (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47:42–52 Kulkarni AT, Mohanty J, Eldho TI et al (2014) A web GIS based integrated flood assessment modeling tool for coastal urban watersheds. Comput Geosci 64:7–14 Mebarki A, Valencia N, Salagnac JL et al (2012) Flood hazards and masonry constructions: a probabilistic framework for damage, risk and resilience at urban scale. Nat Hazards Earth Syst Sci 12(5):1799–1809 Muis S, Güneralp B, Jongman B et al (2015) Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data. Sci Total Environ 538:445–457 Müller A, Reiter J, Weiland U (2011) Assessment of urban vulnerability towards floods using an indicator-based approach–a case study for Santiago de Chile. Nat Hazards Earth Syst Sci 11(8):2107–2123 Muthusamy M, Rivas Casado M, Salmoral G et al (2019) A remote sensing based integrated approach to quantify the impact of fluvial and pluvial flooding in an urban catchment. Remote Sens 11(5):577 Pandey A, Chaube UC, Mishra SK et al (2016) Assessment of reservoir sedimentation using remote sensing and recommendations for desilting Patratu Reservoir, India. Hydrol Sci J 61(4):711–718 Pathak S, Liu M, Jato-Espino D et al (2020) Social, economic and environmental assessment of urban sub-catchment flood risks using a multi-criteria approach: A case study in Mumbai City, India. J Hydrol 591:125216 Pathak S, Panta HK, Bhandari T et al (2020) Flood vulnerability and its influencing factors. Nat Hazards 104(3):2175–2196 Pricope NG, Halls JN, Rosul LM (2019) Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics. J Environ Manage 237:387–398 Rahmati O, Darabi H, Panahi M et al (2020) Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10(1):1–19 Rezende OM, de Oliveira AKB, Jacob ACP et al (2019) A framework to introduce urban flood resilience into the design of flood control alternatives. J Hydrol 576:478–493 Rosser JF, Leibovici DG, Jackson MJ (2017) Rapid flood inundation mapping using social media, remote sensing and topographic data. Nat Hazards 87(1):103–120 Rouillard JJ, Ball T, Heal KV et al (2015) Policy implementation of catchment-scale flood risk management: learning from Scotland and England. Environ Sci Policy 50:155–165 Slater LJ, Villarini G (2016) Recent trends in US flood risk.Geophysical Research Letters, 43(24): 12,428 – 12,436 Speight LJ, Hall JW, Kilsby CG (2017) A multi-scale framework for flood risk analysis at spatially distributed locations. J Flood Risk Manag 10(1):124–137 Tanaka T, Kiyohara K, Tachikawa Y (2020) Comparison of fluvial and pluvial flood risk curves in urban cities derived from a large ensemble climate simulation dataset: A case study in Nagoya, Japan. J Hydrol 584:124706 Tapia C, Abajo B, Feliu E et al (2017) Profiling urban vulnerabilities to climate change: An indicator-based vulnerability assessment for European cities. Ecol Ind 78:142–155 Vercruysse K, Dawson DA, Glenis V et al (2019) Developing spatial prioritization criteria for integrated urban flood management based on a source-to-impact flood analysis. J Hydrol 578:124038 Vojinovic Z, Hammond M, Golub D et al (2016) Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya. Thail Nat Hazards 81(1):589–616 Wang P, Li Y, Yu P et al (2021) The analysis of urban flood risk propagation based on the modified Susceptible Infected Recovered model. J Hydrol 603:127121 Wu H, Huang G, Meng Q et al (2016) Deep tunnel for regulating combined sewer overflow pollution and flood disaster: a case study in Guangzhou City, China. Water 8(8):329 Wu T (2021) Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis. Ecol Ind 129:108006 Yin J, Yu D, Yin Z et al (2016) Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai. China J hydrology 537:138–145 Young A, Bhattacharya B, Zevenbergen C (2021) A rainfall threshold-based approach to early warnings in urban data‐scarce regions: A case study of pluvial flooding in Alexandria, Egypt. J Flood Risk Manag 14(2):e12702 Yu H, Zhao Y, Fu Y et al (2018) Spatiotemporal variance assessment of urban rainstorm waterlogging affected by impervious surface expansion: A case study of Guangzhou. China Sustain 10(10):3761 Zevenbergen C, Veerbeek W, Gersonius B et al (2008) Challenges in urban flood management: travelling across spatial and temporal scales. J Flood Risk Manag 1(2):81–88 Zhang H, Cheng J, Wu Z et al (2018) Effects of impervious surface on the spatial distribution of urban waterlogging risk spots at multiple scales in Guangzhou, South China. Sustainability 10(5):1589 Zhang H, Wu C, Chen W et al (2019) Effect of urban expansion on summer rainfall in the Pearl River Delta, South China. J Hydrol 568:747–757 Zhao G, Xu Z, Pang B et al (2019) An enhanced inundation method for urban flood hazard mapping at the large catchment scale. J Hydrol 571:873–882 Zhou Q, Mikkelsen PS, Halsnæs K et al (2012) Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits. J Hydrol 414:539–549 Zhou Y, Shen D, Huang N et al (2019) Urban flood risk assessment using storm characteristic parameters sensitive to catchment-specific drainage system. Sci Total Environ 659:1362–1369 Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1550233","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":105602053,"identity":"9347db8c-3da6-4168-ad53-d3808eb42063","order_by":0,"name":"Hui Zhang","email":"","orcid":"","institution":"North-China University of Water Conservancy and Electric Power: North China University of Water Resources and Electric Power","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":105602054,"identity":"6bd690fa-e6f8-46cd-bc61-bf68ef89f86a","order_by":1,"name":"Cheng Li","email":"","orcid":"","institution":"Guangdong Institute of Eco-Environmental Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Li","suffix":""},{"id":105602055,"identity":"b9643b94-446f-4de0-b860-5b80be4a50b9","order_by":2,"name":"Zhifeng Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDAC5gMMDB8YGBJAbAnitLAlMDDOIFkLMw9JWuTbeMykbX7V5RkcYD54m4fBLo+gFkaQltw+tmKDA2zJ1jwMycUEtTDL926Tzu3hSdxwAKiXh+FAYgMhLWxsvNukLXskgFr4vxGnhQekheGHAcgWNuK0SLDxf7bsbUhInHmYzdhyjkEyYS3ybWyJN378qUvsO9788MabCjvCWsCAsQ1IMINYBkSpB4E/RKscBaNgFIyCkQgAId0110LwFxMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3173-4739","institution":"Guangzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Wu","suffix":""},{"id":105602056,"identity":"7455f0b0-1ff5-4a39-a7ed-fa99de5de1d2","order_by":3,"name":"Qifei Zhang","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qifei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-04-12 12:13:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1550233/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1550233/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21711661,"identity":"b49547a8-c9b4-4aca-91b4-e63c5ed45efc","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":224308,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Part of the remote sensing image of Yuexiu district; (b) DEM; (c) Yuexiu administrative boundary, catchment boundary and river map\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/e064deb9e337e8952efb0f8a.jpg"},{"id":21712063,"identity":"112a75d1-8ca8-4dd7-bd9b-03b5525d2e96","added_by":"auto","created_at":"2022-05-20 15:04:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":881802,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the core density of infrastructure service facilities based on POI data and population density maps: (a) emergency shelters; (b) medical services; (c) bus stations; (d) commercial facilities; (e) primary and secondary schools and kindergartens; (f) population density\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/6660c4249a8b33e163ca043c.png"},{"id":21711662,"identity":"6d400153-ac0b-4d88-916d-3145d5fad76f","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265394,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the integrated pluvial flood risk assessment\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/68b3e3f6806ddbb9ce25458a.png"},{"id":21711668,"identity":"5eab5fda-03bd-401d-b838-c43f94f86a97","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6596039,"visible":true,"origin":"","legend":"\u003cp\u003eHazard maps of (a) return period=5 years; (b) return period=10 years; (c) return period=20 years; (d) return period=50 years; (e) return period=100 years.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/dc267948ea18985bf521c6bf.jpg"},{"id":21711667,"identity":"59dd4869-8dbd-4a5f-9e70-28207b4b390a","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5747723,"visible":true,"origin":"","legend":"\u003cp\u003eExposure map: (a) return period=5 years; (b) return period=10 years; (c) return period=20 years; (d) return period=50 years; (e) return period=100 years.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/e01b5f87419e480b2614382c.jpg"},{"id":21711666,"identity":"dfb6ddbb-f34c-435f-9bcd-e61a6b1d16c6","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1494105,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution map of vulnerability level\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/945ffc1e660bc45d61ce96c6.jpg"},{"id":21712223,"identity":"0263b019-70a8-46b1-acc8-8e8ec48d881d","added_by":"auto","created_at":"2022-05-20 15:09:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6727487,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated urban pluvial flood risk: (a) return period=5 years; (b) return period=10 years; (c) return period=20 years; (d) return period=50 years; (e) return period=100 years.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/b05c77f852a0526df3c42f0f.jpg"},{"id":23846996,"identity":"d11d145a-c220-4de5-a66f-df6c66f1a7e4","added_by":"auto","created_at":"2022-07-14 07:18:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1957393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/8b83f9c6-c515-40e2-bee4-d7f85f225a6f.pdf"},{"id":21711663,"identity":"41089758-1472-420b-88e7-05b729655cbb","added_by":"auto","created_at":"2022-05-20 14:59:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35383,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-1550233/v1/83651b109c9ab3c356f17d34.docx"}],"financialInterests":"","formattedTitle":"Exploring an urban integrated pluvial flood risk assessment using big data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eUrban development and climate change continue to aggravate urban pluvial floods, which are becoming more serious (Bulti et al., 2020), causing socioeconomic problems such as economic losses, traffic congestion, environmental pollution, and resident` travel and health risks (Rahmati et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Urban land cover change dominated by the increase in impervious surfaces has reduced surface infiltration and increased surface runoff (Arnold and Gibbons, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In the process of urbanization, urban infrastructure construction is not in harmony with urban development in most majority countries. For example, drainage networks in artificial standards cannot deal with heavy rainfall, which has led to the frequent occurrence of urban floods. In addition, climate change, characterized by extreme rainfall and sea level rise, is exacerbating this trend. Furthermore, population and assets gradually accumulate in cities during urbanization (Slater and Villarini, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), resulting in a further increase in the scope and intensity of urban flood disasters. Therefore, urban flooding has become an urgent problem of urban flood management and has attracted increasing attention from government departments and stakeholders.\u003c/p\u003e \u003cp\u003eUrban flood risk management, including urban flood risk assessment and risk mitigation, rather than traditional gray engineering measures, is considered the main solution for resolving urban flood problems (Johnson et al., 2007). To date, urban flood risk assessment is an important and effective means to strengthen technological support and improve emergency response capabilities. The purpose of urban flood risk assessment is to analyze current or future flood risk information, identify high-risk areas (Muis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and provide decision support for flood mitigation measures, as well as assess the effectiveness of their mitigation measures (Zhou et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Numerous related scholars (Haynes et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Foudi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fakhruddin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and research institutes have used the frame of \u0026ldquo;hazard-exposure-vulnerability\u0026rdquo; for flood risk assessment.\u003c/p\u003e \u003cp\u003eFlood risk\u0026thinsp;=\u0026thinsp;Hazard (H) \u0026times; Exposure (E) \u0026times; Vulnerability (V)\u003c/p\u003e \u003cp\u003eHazard can be described as the flood location, depth, and flow rate. Exposure is mainly used to analyze the affected land-use types, people, buildings, and infrastructure in certain flood events. Vulnerability can be defined as the susceptibility of the elements (people, buildings, infrastructure and so on) at risk of suffering from flood damage. This framework not only focuses on the hazard and exposure of a disaster but also considers the vulnerability of various disaster-bearing bodies.\u003c/p\u003e \u003cp\u003ePrevious studies on urban flood risk have mainly focused on fluvial or coastal floods at relatively large scales, such as regional scales or basin scales (Vojinovic et al., 2015; Ganguli and Merz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With urban development, although the damage or cost of urban pluvial floods is greater than that of coastal or river floods, the frequency of pluvial floods increases (Muthusamy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with urban growth, thus having a greater adverse impact on people, transportation and economic activities. Hence, urban pluvial floods deserve more attention.\u003c/p\u003e \u003cp\u003eUrban pluvial floods usually occur in local areas of the city due to short-term heavy rainfall, and their impact is very extensive. Therefore, urban pluvial flood risk urgently needs a comprehensive assessment (Kulkarni et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vercruysse et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on a fine scale (Casiano Flores and Crompvoets, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, most of the research content has focused on the hazard or exposure to risk, and less attention has been given to vulnerability. There are few comprehensive flood risk assessment studies. Hazards mainly rely on hydrological, hydraulic models or simplified models (Bulti et al., 2020; Feng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to obtain inundation information. Exposure based on the hazard requires spatial analysis to obtain information on the different land-use types, infrastructure, population, etc., exposed to inundation in the region. An inadequate understanding of the comprehensive risk of urban flood has caused researchers to neglect vulnerability. Since urban flooding cannot be managed in isolation, an integrated approach is required (Zevenbergen et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUrban pluvial flood risk assessment pays more attention to larger scales, such as the catchment (Rouillard et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), urban (Mebarki et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Di Salvo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and regional scales (Prokić et al., 2019; Speight et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); in contrast, less attention has been given to small scales in the inner city, such as the subcatchment (Pathak et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea), community (Azizi and Meier, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or street (Yin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) scales. Therefore, the research content and scale do not meet the needs of urban pluvial flood management and prevention. The main reasons for these patterns are as follows: (1) Refined scale assessment requires more accurate data; (2) the complex surface characteristics of different natural watersheds within the city cause the current flooding mechanism to be further explored. A finer scale usually requires more sufficient and accurate data, such as data on the high-precision terrain, buildings, commercial facilities, infrastructure, the population distribution and drains network data. Traditional demographic and economic statistics used in vulnerability assessments are based on administrative divisions that are not sufficient for urban inner pluvial flood assessments, especially in relation to vulnerability. To date, drainage network data and computing power are still the main limiting factors in two-dimensional hazard hydraulic modeling in large urban areas.\u003c/p\u003e \u003cp\u003eThe progress of big data technology provides a wealth of data sources that offer very large application potential and new opportunities for urban flood risk assessment at finer scale. At present, different types of big data are starting to be used in studies related to urban flooding. Social media or POI (point of interest) data were used for urban pluvial flood early warning (Young et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), flood risk assessment (Coletti et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and flood monitoring and mapping (Rosser et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Helmrich et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). POI data contains information on business, education, transportation, and medical care at an urban internal fine scale, which can break through the administrative division restrictions of traditional statistical data. Therefore, POI data can be considered a new type of data for conducting a refined vulnerability assessment.\u003c/p\u003e \u003cp\u003eIn this paper, we presented an integrated urban pluvial flood risk assessment at a grid scale. First, limited by the municipal drainage network data, we could not establish a two-dimensional urban hydraulic model at the district level of the city; thus, a simplified hydrological model was used as a substitute to obtain hazard information. Second, spatial analysis was performed to map the exposure degree of land-use types on the basis of hazard information. Then, POI data together with traditional socioeconomic data were used to assess the vulnerability map on a finer scale with the help of statistical and spatial analysis methods. Finally, an integrated waterlogging risk (H-E-V) assessment was achieved. This study could provide a scientific reference and decision basis for urban flood mitigation and urban planning.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eGuangzhou, located in southern China (22\u0026deg;26'\u0026ndash;23\u0026deg;56'N and 112\u0026deg;57'\u0026ndash;114\u0026deg;03'E), is the capital of Guangdong Province and the central urban area of Guangdong-Hong Kong-Macao Greater Bay Area. Guangzhou comprises 11 districts, such as Yuexiu, Tianhe, and Haizhu, covering an area of approximately 7434 km\u003csup\u003e2\u003c/sup\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Guangzhou, which is dominated by subtropical monsoons, is one of the cities with the most rainfall in China, with an annual precipitation of 1164\u0026thinsp;~\u0026thinsp;1899 mm and an annual average of 149 rainy days. Since 1978, this city has experienced dramatic urbanization, and its urban pluvial flood has been worsening in the process. Hallegatte et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) showed that Guangzhou has the highest flood risk ranking among 138 major coastal cities in the world. Yuexiu District is the oldest downtown area in Guangzhou. Moreover, as the administrative, commercial, financial, and cultural center of Guangzhou, it has formed an industrial structure with the tertiary industry as the main body, the characteristic economy as the driving force, and the commercial service industry as the support. Its pluvial flood problem is particularly serious due to its dense population and old infrastructure. Therefore, we choose Yuexiu District as the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data\u003c/h2\u003e \u003cp\u003eA variety of data were used in this study for hazard and exposure analysis. Land-use maps, precipitation data, catchment data and terrain data were used in hazard analysis. The land use/ cover map was digitized based on the 1:2000 aerial remote sensing images with orthogonal projection (spatial resolution 0.1 m) and 1:500 terrain data obtained from the field survey conducted in 2013 (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe original classification system contained 10 land cover types and 46 subtypes. We reclassified the land cover map from 46 subtypes into 10 primary-level land cover types in ArcGIS. Primary-level classification included (1) cultivated land; (2) garden plot; (3) forestland; (4) grassland; (5) building area; (6) road; (7) structure; (8) artificial heap-excavation land; (9) desert and bare surface; and (10) water (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Then, we reclassified building area, road, and structure as impervious surface and others as pervious surface. Precipitation data of different recurrence periods were calculated by using the Chicago rainstorm calculation formula published by the Guangzhou Water Bureau (see Formula 1).\u003c/p\u003e \u003cp\u003eP=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{3618.427(1+0.438Lg q)}{{(t+11.259)}^{0.750}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eUnit: L/S*ha; Relative position of rain peak: r\u0026thinsp;=\u0026thinsp;0.367.\u003c/p\u003e \u003cp\u003eAdditionally, land-use maps were the main data source for exposure. In the vulnerability analysis, population attribute data were derived from China's sixth census, and population density data were from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldpop.org.uk/data/data_sources/\u003c/span\u003e\u003cspan address=\"http://www.worldpop.org.uk/data/data_sources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. POI refers to describing the characteristics of residential infrastructure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and these data were obtained by Baidu Map by Network reptiles using Python. Considering previous research (Kulkarni et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tanaka et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pricope et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the data availability in our study area, we selected these indicators. Descriptions of specific indicators and their data sources are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of specific indicators and their data sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription/positive(-) and negative (-) correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInundation depth in certain rainfall event (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrom model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal exposure of flooded land type (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003eVulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of female population (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of children population (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;14 year) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of old people population (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;75 year) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIlliteracy ratio (Illiteracy as a percentage of population over 15 year people) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of population with low education level (Population ratio of high school and junior high school) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of population with high education level (Percent of population with college degree or above) (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of unhealthy old people (Percent of the elderly who live on their own) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of divorced and widowed population (Percent of population over 15 years) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMigrant population ratio (Percent of population with household registration in other places) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDen_pep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople density (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldpop.org.uk/data/data_sources/\u003c/span\u003e\u003cspan address=\"http://www.worldpop.org.uk/data/data_sources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidential characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of brick-wood and mixed-structure houses (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of living houses (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChina's sixth census\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eInfrastructure indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndex of indoor emergency shelter (T) (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Baidu POI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial distribution index of medical services (Y) (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Baidu POI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistribution space index for commercial services (C) (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Baidu POI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eindex of bus station spatial distribution (B) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Baidu POI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndex of spatial distribution of kindergartens in primary and secondary schools (T) (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017 Baidu POI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen space area (L) (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLand-use map in 2015 year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Urban pluvial flood assessment method\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 A simplified urban pluvial flood model\u003c/h2\u003e \u003cp\u003eA simplified urban pluvial flood model was built to obtain inundation depth in different rainstorm return periods. The urban pluvial flooding process includes rainfall, runoff, pipe network drainage and surface waterlogging. In this study, the Soil Conservation Service (SCS) model and reservoir capacity curve were used to build a simplified urban flood model for simulating the above mentioned urban pluvial flood process with ArcGIS 10.7.\u003c/p\u003e \u003cp\u003eAs an experienced hydrological model, SCS was developed in the 1950s by the U.S. Department of Agriculture Water and Soil Conservation. It was based on more than 20 years of measured runoff data and was used to assess the hydrological characteristics of small watersheds. The model mainly determines the total runoff depending on the soil and rainfall conditions, in which the soil factors are determined by the soil permeation characteristics, the soil pre-water content and the land type. Because the SCS has good performance in few parameters, simple calculations and better precision, urban runoff calculations and related floods have also begun to be used in recent years (Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bouvier et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe reservoir capacity curve is the relationship curve between the reservoir water level and its corresponding reservoir capacity. It is mainly used for reservoir planning design and management scheduling in hydraulics (Pandey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Issa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this paper, we used the principle of the reservoir capacity curve to establish the relationship between the flooding height and the amount of water accumulation in each subcatchment.\u003c/p\u003e \u003cp\u003eThe SCS, as a simplified runoff model, was adopted to simulate the runoff yield and concentration process (Bouvier et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea). In addition, we simplified the drainage process. In the study area, most of the standard drainage network standard was once-a-year rainfall, so we considered that the drainage capacity of the drainage network was 42.5 mm per hour. After that, we subtracted the runoff yield from the water discharge in each subcatchment and obtained the final amount of water accumulation. Then, we used the principle of the reservoir capacity curve to establish the equation between the depth of flooding and the amount of water accumulation. Water accumulation calculations at different depths were implemented by ArcGIS 10.7 using the Surface Volume tool. Then, the final inundation depth was calculated. The simplified modeling process was as follows:\u003c/p\u003e \u003cp\u003e(1) First, the Chicago rainstorm calculation formula and SCS were used to model rainfall runoff, and the calculation Formula (2) is as follows:\u003c/p\u003e \u003cp\u003eQ\u003csub\u003er\u003c/sub\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(\\text{P}-{I}_{a})}^{2}\\)\u003c/span\u003e\u003c/span\u003e/(P\u0026thinsp;+\u0026thinsp;S-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I}_{a}\\)\u003c/span\u003e\u003c/span\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\)\u003c/span\u003e\u003c/span\u003e\u0026ge;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I}_{a}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eQ\u003csub\u003er\u003c/sub\u003e=0 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I}_{a}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({I}_{a}\\)\u003c/span\u003e \u003c/span\u003e =0.2S\u003c/p\u003e \u003cp\u003eS=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{25400}{CN}\\)\u003c/span\u003e\u003c/span\u003e \u0026ndash; 254\u003c/p\u003e \u003cp\u003eCN\u0026thinsp;=\u0026thinsp;0.24X\u0026thinsp;+\u0026thinsp;74 (2)\u003c/p\u003e \u003cp\u003ewhere Q\u003csub\u003er\u003c/sub\u003e is the total runoff (m\u003csup\u003e3\u003c/sup\u003e) produced in the subcatchment area; P is the rainfall (mm) of different rainstorm return periods; CN is a comprehensive parameter that reflects the characteristics of the catchment area before rainfall; and X is the percent of impervious surfaces in each subcatchment.\u003c/p\u003e \u003cp\u003e(2) Water accumulation calculation:\u003c/p\u003e \u003cp\u003eW\u003csub\u003ea\u003c/sub\u003e = (Q\u003csub\u003er\u003c/sub\u003e -Q\u003csub\u003ed\u003c/sub\u003e)*Area (3)\u003c/p\u003e \u003cp\u003ewhere W\u003csub\u003ea\u003c/sub\u003e is the surface water accumulation; Area is the area of each subcatchment area (unit: m\u003csup\u003e2\u003c/sup\u003e); and Q\u003csub\u003ed\u003c/sub\u003e is the drainage capacity of the drainage network per hour.\u003c/p\u003e \u003cp\u003e3) Establishment of the reserve capacity curve of each subcatchment:\u003c/p\u003e \u003cp\u003eThe Surface Volume tool in ArcGIS calculates surface water accumulation under the set horizontal plane elevation. To this end, we established the functional relationship between the plane elevation of each sub-catchment and water accumulation, as shown in Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, according to the calculation results. Then, according to the water accumulation formula, the set horizontal plane elevations of different subcatchments were calculated.\u003c/p\u003e \u003cp\u003e4) Using the Raster Calculator in ArcGIS, the original topographic map was subtracted from the above mentioned set horizontal plane elevations and differences greater than 0 were the final inundation elevations (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Exposure index calculation\u003c/h2\u003e \u003cp\u003eYuexiu District is a highly developed area in Guangzhou with densely distributed buildings and populations. Therefore, when assessing exposure risks, we assessed all types of land use without distinguishing specific categories. We calculated the exposure index of land use to characterize exposure. The calculation formula is as follows:\u003c/p\u003e \u003cp\u003eE(land-index)=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{m}{g}_{i}\\left({u}_{j}\\right)\\text{*}{W}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ g}_{i}\\left({u}_{j}\\right)\\)\u003c/span\u003e \u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{f}_{i}\\left({u}_{j}\\right)}{\\sum _{j=1}^{n}{f}_{i}\\left({u}_{j}\\right)}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003ewhere E (land index) is the exposure index; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({g}_{i}\\left({u}_{j}\\right)\\)\u003c/span\u003e\u003c/span\u003erepresents the area proportion in area j with an exposure level of i as land with the same exposure level in the study area; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a weight factor for exposure grade i; m\u0026thinsp;=\u0026thinsp;6; n\u0026thinsp;=\u0026thinsp;3528; and weight is divided into 6 levels according to the inundation depth. A total of 3528 grids were included in the calculation. Finally, E (land index) was divided into five levels according to the natural breakpoint method, i.e., potential, micro, mild, moderate and severe exposure levels. As a hierarchical classification statistic based on the distribution of numerical statistics, the natural breakpoint method maximizes the differences between classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Vulnerability index calculation\u003c/h2\u003e \u003cp\u003eThe selection of indicators and calculation method are the two main questions of vulnerability assessment. In selecting vulnerability indicators, we first should choose enough indicators as soon as possible to reflect the urban pluvial flood vulnerability of the study area. Referring to previous research results (M\u0026uuml;ller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tapia et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pricope et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the availability of data, we selected three primary indicators and eighteen secondary indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, there may be redundancy between indicators. It is better to obtain more information with the fewest variables. Considering the wide application of the PCA method in flood vulnerability (Pricope et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), PCA was selected for vulnerability assessment in this paper. Before performing PCA, the Kaiser\u0026ndash;Mayer\u0026ndash;Olkin (KMO) statistic and Bartlett's sphericity test were performed to estimate the suitability of PCA (Aroca-Jimenez et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pathak et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb). The KMO value in this study was 0.599 (\u0026gt;\u0026thinsp;0.5), and the df value of the Bartlett test was 153. Therefore, the vulnerability indicators were considered appropriate for PCA. According to the PCA results, the explained variance of each main component was its weight coefficient, which was used to determine its final vulnerability score. Similar to the exposure classification, vulnerability was divided into five levels using the natural breakpoint method: potential, micro, mild, moderate and severe vulnerability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Integrated flood risk assessment\u003c/h2\u003e \u003cp\u003eAfter obtaining the evaluation results of the three elements of risk, due to the subjectivity and uncertainty of the weight setting process, we did not adopt the method of setting weights to obtain the integrated risk assessment. We combined three layers, including hazard, exposure, and vulnerability maps, to produce an integrated flood risk map using ArcGIS 10.7. For hazards, raster grid cells with inundation depths greater than 20 cm are dangerous areas worthy of attention and were assigned a value of 1, and the rest were assigned a value of 0. In the exposure and vulnerability assessment results, the moderate and severe areas were considered to be areas of concern, they were assigned a value of 1, and the remaining areas were assigned a value of 0.\u003c/p\u003e \u003cp\u003eAfter assigning values (0 or 1) for hazard, exposure, and vulnerability according to the rules, a spatial overlay was performed for these three layers in order of hazard, exposure, and vulnerability. Among them, 001, 010 and 001 represented that the area faces one of the risks of hazard, exposure, and vulnerability, respectively, while 011, 110, and 101 represented that the study area faced two risks of hazard, exposure, and vulnerability respectively. Additionally, 111 meant that the area faced three risks at the same time. A flowchart of the integrated pluvial flood risk assessment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results And Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Hazard and exposure analysis\u003c/h2\u003e \u003cp\u003eBy using the simplified urban pluvial model, we obtained the inundation depth of a series of rainstorm periods for each subcatchment (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hazard results, including area and spatial pattern in Yuexiu District, are shown in Appendix Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In general, the inundation area increased with the increase in rainstorm periods, and they showed a high spatial agglomeration effect.\u003c/p\u003e \u003cp\u003eIn terms of inundation area, it increased significantly in the 10-year return period compared to the 5-year return period; however, it remained relatively stable between the 20 and 50-year periods (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the 5-year period, the inundation area was 3.93 km\u003csup\u003e2\u003c/sup\u003e, accounting for 11.13% of the total area. In the following return periods (10\u0026ndash;100 years), the proportion of inundation area was 14.6%, 18.48%, 20.44%, and 21.69%, respectively.\u003c/p\u003e \u003cp\u003eIt is generally believed that a flood depth greater than 20 cm has a greater impact on pedestrians. Based on this, we focused on the statistics of flooded areas with a submerged depth greater than 20 cm. The proportion of inundation area where the flood depth was greater than 20 cm increased with increasing period. The proportion of their inundation area was 5.61%, 8.45%, 12.36%, 15.17%, and 16.61% from the 5-year to the 100-year return periods, respectively. During the 5- to 10-year rainstorm periods, the proportion of the inundation area greater than 20 cm of submerged depth increased by 50.62%, and this proportion slowed slightly to 46.27% during the 10- or 20-year period. However, during the periods of 20\u0026ndash;50 years and 50\u0026ndash;100 years, the proportion fell sharply to 22.73% and 9.49%, respectively.\u003c/p\u003e \u003cp\u003eWe found that the 20-year period was the inflection point of the inundation area change and that the growth rate of the inundation area increased gradually in the period less than 20 years but slowed after a 20-year period. The drainage pipe standard of Guangzhou\u0026rsquo;s new urban area was designed only according to a return period of 3 years, while approximately 83% of the drainage pipe network in the old urban area was designed for the \u0026ldquo;1-year return period\u0026rdquo; drainage standard. Taking into account the impact of unfavorable factors such as blockage in the use of the drainage network, in the design of urban drainage network reconstruction, the standards that deal with a 50-year return period may be worthy of reference. Additionally, the Guangzhou Municipal Government document \"Guangzhou City Flood Control System Construction Standard Guidelines\" noted that in metropolitan areas, including Yuexiu District, the future drainage standards should withstand the 50-year return year rainstorm. Currently, Guangzhou is advancing the construction of sponge cities. Our research results can provide valuable reference information for improving the drainage capacity of sponge cities in old cities.\u003c/p\u003e \u003cp\u003eIn terms of spatial aggregation, the model simulation results showed that the flooded areas in Yuexiu District were mainly concentrated in the following seven regions: (1) the middle and lower reaches of Donghao Creek; (2) the region between East Lake and the Inner Ring Road; (3) the region around the Xiaobei subway station, (4) the intersection of the People North Road and the LiuHua Road and its surrounding area; (5) the region between Yanxi Station and Tongde Wei North\u0026ndash;South viaduct; (6) the region between Zhongshan First intersection and Yangji village, and (7) the region between Yanjiang Road and Yide Road. These regions are associated with the actual flooding point of Yuexiu. The results showed that these severely affected areas were mainly located around important roads or subway stations or on both sides of low-lying rivers. Schools, hospitals, commercial centers, etc., gathered around them.\u003c/p\u003e \u003cp\u003eThese regions were associated with the actual flooding point of Yuexiu. The results could not simulate all the flooding areas of Yuexiu, probably because this paper simulated a maximum rainfall of 99.6 mm per hour of 100, and some flooded areas may have occurred due to more intense rainfall. Another reason may be that the model homogenized the drainage capacity of each area and prioritized low-lying areas in the distribution of flooded water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of the exposed area (Appendix Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the main focus was on potential exposure. The exposed area above a slight degree first increased and then decreased under the 5-100 year return periods rainstorms. Among them, the slightly exposed area ratio increased from 6.32% in 5-year to 13.92% in 20 years and then decreased to 12.98% in 100-years, while the ratio of moderately exposed areas increased from 1.82% in 5 year- to 5.98% in 50-year. After that, it was reduced to 5.66% in 100-year. Since the calculation of exposure assessment depends on the hazard result, the spatial distribution of the exposure of submerged land under different rainfall intensities was similar to the spatial aggregation of hazard (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Vulnerability analysis\u003c/h2\u003e \u003cp\u003eWe obtained 5 principal components replacing the 16 raw indicators using PCA. The results showed that five components explained approximately 74.116% of the variation in the vulnerability data across the study area (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first three components (components 1, 2, and 3) explained 54.943%, representing population and residential characteristics with dominant indicators P02 (the proportion of children), P08 (ratio of divorce and widowhood), P05 (low educated population ratio), and B02 (the proportion of population with housing). The last two components (components 4 and 5) explained 19.173% of the variance in the infrastructure indicators. Most indicators were in specific components once, however, P09 (migrant population ratio) and I5 (index of spatial distribution of kindergartens in primary and secondary schools) were shown twice in different components. This result proved that the migrant population and primary and secondary schools were essential for understanding urban pluvial flood vulnerability.\u003c/p\u003e \u003cp\u003eThe spatial distribution map and area statistics of vulnerability levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The proportion of the area of ​​each vulnerability was mainly at the micro and mild levels, and the two areas together accounted for 59.86% of the total area of ​​Yuexiu District. Special attention was given to the area ratio of moderate and severe exposure reaching 30.02%, which was far greater than the 7% of moderate and severe exposure. The severity of Yuexiu's vulnerability was higher than that of exposure, which showed the importance of vulnerability in urban pluvial flood risk assessment.\u003c/p\u003e \u003cp\u003eThe spatial distribution map of vulnerability levels showed that vulnerability also had a significant clustering effect. The largest severely vulnerable area was located in the area surrounded by Yanjiangxi Road, Jiefang South Road, Daxin Road, and Renmin Elevated Road. The area that extended to the periphery was the moderately vulnerable region. In terms of spatial distribution, the moderately vulnerable area was more scattered than the severely vulnerable area. It was cross-distributed among mild vulnerability, moderate vulnerability, and severe vulnerability. Micro vulnerability was mainly distributed around Luhu Lake in the north of Yuexiu District and Ersha Island in the south of Yuexiu District.\u003c/p\u003e \u003cp\u003eThe results showed that the hazard and exposure to the risk factors were close in spatial distribution, while the spatial distribution of the vulnerability levels was quite different. It was observed that vulnerability was a key factor affecting the final comprehensive flood risk assessment. However, in most cities in China, vulnerability is often neglected in the mapping of flood risk, which leads to incomplete final results. Currently, there is not only less assessment of socioeconomic vulnerability in flood assessment in Guangzhou but also less quantitative damage assessment. Because of the absence of a stage-damage curve applicable to Guangzhou, a quantitative vulnerability assessment was not made. The vulnerability assessment in this paper was mainly based on the social vulnerability assessment based on the index method. Therefore, Guangzhou should start to establish a database of flood disaster losses applicable to the region to establish a disaster curve that can quantitatively assess flood losses and obtain a quantitative vulnerability assessment and a comprehensive flood risk assessment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of vulnerability components based on PCA results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eComponent Number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDen_pep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.859\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.923\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.856\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.614\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.961\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.805\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.734\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.926\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.633\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.661\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.842\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.664\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.536\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.428\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.589\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%Variance Explained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScores and areas in different vulnerability classes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVulnerability class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent of area (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epotential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-1.79, -0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emicro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.49, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-0.02,0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0.16,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0.36,0.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The integrated urban pluvial flood assessment\u003c/h2\u003e \u003cp\u003eThe risk assessment framework based on the \u0026ldquo;H-E-V\u0026rdquo; framework can include all risk dimensions to comprehensively reflect the risk level of floods in the rainy season. The study results (Appendix Table\u0026nbsp;4) indicated that with the increase in rainfall intensity, the regional area facing one risk at least gradually increased, and their spatial agglomeration effects also gradually became obvious. Among them, the area facing at least one kind of risk increased from 11.54 km\u003csup\u003e2\u003c/sup\u003e of the 5-year return period to 13.65 km\u003csup\u003e2\u003c/sup\u003e of the 100-year return period, and their area percent was between 26.61 and 28.77%. The area facing two or three types of risks at the same time increased from 1.37 km\u003csup\u003e2\u003c/sup\u003e of the 5-year return period to 3.89 km\u003csup\u003e2\u003c/sup\u003e of the 100-year return period, accounting for 3.89\u0026ndash;10.55% of the total area.\u003c/p\u003e \u003cp\u003eIn areas with a high level of hazard and exposure risk but low vulnerability, flood mitigation measures should focus on improving the drainage capacity of flooded areas. However, areas with low hazard and exposure risk levels but with high vulnerabilities are easily overlooked by decision-makers. The current cities are facing an increase in extreme rainfall (Chen et al., 2017) and urban development (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the risk level of the area will increase significantly. The Blue Book of China's Climate Change (2021) shows that the precipitation in Guangzhou increased by 20\u0026ndash;50 mm/10a from 1961 to 2020. Therefore, reasonable flood planning and management should include climate change and urban change (Moura Rezende et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gimenez-Maranges et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While enhancing the capacity of the drainage network, attention should be given to non-engineering measures and should also strengthen residents\u0026rsquo; awareness of flood risk and emergency management of government departments to minimize the adverse effects of flood disasters.\u003c/p\u003e \u003cp\u003eAreas with high risks in all three dimensions (H-E-V) should be the areas of greatest concern. Our study found that the region with the highest integrated risk was mainly distributed near both the midstream and the downstream sides of Donghaochong from the 50-year return period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The topographic conditions in this area were prone to waterlogging, and social vulnerability was also high, leading to the highest integrated urban pluvial flood risk. Previous studies (Yu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have shown that there are multiple flood hotspots in the Yuexiu area only from the hazard perspective. We adopted a comprehensive flood risk assessment, focusing on both the midstream and the downstream sides of Donghaochong as the region with the highest comprehensive risk. More importantly, flood control measures should focus on areas with high levels of risk among the three dimensions. To solve the urban flooding problem in Yuexiu District, the deep tunnel project is under construction. As the first deep tunnel project in China, it is expected to effectively alleviate the waterlogging problem (Wu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, Huang et al. (Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the best mitigation was in the west and the immediate north of the main tunnel, whereas the east of the creek did not show an obvious mitigation effect. This result is particularly worthy of the attention of the flood control and planning departments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eIn this paper, we evaluated each of the three dimensions of flood risk separately and then conducted a comprehensive flood risk assessment. The integrated flood risk could more fully reflect the spatial distribution of risk and provide a scientific reference for future flood risk mitigation measures. The conclusions are as follows.\u003c/p\u003e \u003cp\u003e(1) Hazard and exposure risks: as the intensity of heavy rain increases, the level and intensity of hazard and exposure risk increase accordingly. The 20-year return period of a rainstorm is an inflection point for the area change of hazard. Hazard and exposure risks show obvious spatial agglomeration, and there are approximately seven areas where the flood risk is highly concentrated.\u003c/p\u003e \u003cp\u003e(2) Vulnerability: In terms of area, special attention is given to the area ratio of moderate to severe vulnerability in reaching 30.02%, which is far greater than the 7% of moderate to severe exposure. This result shows that the severity of Yuexiu's vulnerability is higher than its exposure, which proves the importance of vulnerability in the risk assessment of urban pluvial floods. In terms of spatial agglomeration, the spatial distribution map of vulnerability levels shows that vulnerability also has a significant clustering effect that is obviously different from hazard and exposure risks.\u003c/p\u003e \u003cp\u003e(3) Integrated urban pluvial flood risk: with the increase in rainfall intensity, the region facing two or three risks gradually increases. Meanwhile, its spatial agglomeration effect gradually increases. The area ratio facing two and three risks at the same time increased from 3.89% of the 5-year return period of 10.55% in the 100-year return period. The highest integrated risk area was mainly distributed near both the midstream and the downstream sides of Donghaochong from the 50-year return period.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e This work was supported by National Natural Science Foundation of China (Grant No.42007423), The Team Project of Guangdong Provincial Natural Science Foundation (Grant No.2018B030312004)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnold CL, Gibbons CJ (1996) Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. J Am Plann Association 62:243\u0026ndash;258\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAroca-Jimenez E, Bodoque JM, Garcia JA, Diez-Herrero A (2017) Construction of an integrated social vulnerability index in urban areas prone to flash flooding. Nat Hazards Earth Syst Sci 17(9):1541\u0026ndash;1557\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzizi K, Meier CI (2021) Urban Pluvial Flood Risk Assessment: Challenges and Opportunities for Improvement Using a Community-Based Approach. In:World environmental and water resources congress. 7\u0026ndash;11, June, pp\u0026nbsp;350\u0026ndash;361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouvier C, Bouchenaki L, Tramblay Y (2018) Comparison of SCS and Green-Ampt Distributed Models for Flood Modelling in a Small Cultivated Catchment in Senegal. Geosciences 8(4):122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouvier C, Chahinian N, Adamovic M et al (2018) Large-scale GIS-based urban flood modelling: a case study On the City of Ouagadougou. Advances in hydroinformatics. Springer, Singapore, pp 703\u0026ndash;717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulti DT, Abebe BG (2020) A review of flood modeling methods for urban pluvial flood application. Model earth Syst Environ 6(3):1293\u0026ndash;1302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasiano Flores C, Crompvoets J (2020) Assessing the governance context support for creating a pluvial flood risk map with climate change scenarios: The Flemish subnational case. ISPRS Int J Geo-Information 9(7):460\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan FKS, Yang LE, Scheffran J et al (2021) Urban flood risks and emerging challenges in a Chinese delta: The case of the Pearl River Delta, vol 122. Environmental Science \u0026amp; Policy, pp 101\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColetti A, De Nicola A, Di Pietro A et al (2020) A comprehensive system for semantic spatiotemporal assessment of risk in urban areas. J Contingencies Crisis Manag 28(3):178\u0026ndash;193\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Salvo C, Pennica F, Ciotoli G et al (2018) A GIS-based procedure for preliminary mapping of pluvial flood risk at metropolitan scale. Environ Model Softw 107:64\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFakhruddin BSHM, Boylan K, Wild A et al (2020) Assessing vulnerability and risk of climate change. Climate extremes and their implications for impact and risk assessment. Elsevier, pp 217\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng B, Wang J, Zhang Y et al (2020) Urban flood hazard mapping using a hydraulic\u0026ndash;GIS combined model. Nat Hazards 100(3):1089\u0026ndash;1104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoudi S, Os\u0026eacute;s-Eraso N, Tamayo I (2015) Integrated spatial flood risk assessment: The case of Zaragoza. Land Use Policy 42:278\u0026ndash;292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanguli P, Merz B (2019) Extreme coastal water levels exacerbate fluvial flood hazards in Northwestern Europe. Sci Rep 9(1):1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGimenez-Maranges M, Breuste J, Hof A (2020) Sustainable Drainage Systems for transitioning to sustainable urban flood management in the European Union: A review. J Clean Prod 255:120191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallegatte S, Green C, Nicholls RJ et al (2013) Future flood losses in major coastal cities. Nat Clim change 3(9):802\u0026ndash;806\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaynes H, Haynes R, Pender G (2008) Integrating socio-economic analysis into decision‐support methodology for flood risk management at the development scale (Scotland). Water and Environment Journal 22(2):117\u0026ndash;124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelmrich AM, Ruddell BL, Bessem K et al (2021) Opportunities for crowdsourcing in urban flood monitoring. Environ Model Softw 143:105124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Zhang L, Liu L et al (2019) Assessing the mitigation effect of deep tunnels on urban flooding: a case study in Guangzhou, China. Urban Water Journal 16(4):312\u0026ndash;321\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIssa IE, Al-Ansari N, Sherwany G et al (2017) Evaluation and modification of some empirical and semi-empirical approaches for prediction of area-storage capacity curves in reservoirs of dams. Int J Sedim Res 32(1):127\u0026ndash;135\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson C, PENNING-ROWSELL E, Parker D (2007) Natural and imposed injustices: the challenges in implementing \u0026lsquo;fair\u0026rsquo;flood risk management policy in England. Geogr J 173(4):374\u0026ndash;390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoks EE, Jongman B, Husby TG et al (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47:42\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulkarni AT, Mohanty J, Eldho TI et al (2014) A web GIS based integrated flood assessment modeling tool for coastal urban watersheds. Comput Geosci 64:7\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMebarki A, Valencia N, Salagnac JL et al (2012) Flood hazards and masonry constructions: a probabilistic framework for damage, risk and resilience at urban scale. Nat Hazards Earth Syst Sci 12(5):1799\u0026ndash;1809\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuis S, G\u0026uuml;neralp B, Jongman B et al (2015) Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data. Sci Total Environ 538:445\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;ller A, Reiter J, Weiland U (2011) Assessment of urban vulnerability towards floods using an indicator-based approach\u0026ndash;a case study for Santiago de Chile. Nat Hazards Earth Syst Sci 11(8):2107\u0026ndash;2123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuthusamy M, Rivas Casado M, Salmoral G et al (2019) A remote sensing based integrated approach to quantify the impact of fluvial and pluvial flooding in an urban catchment. Remote Sens 11(5):577\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey A, Chaube UC, Mishra SK et al (2016) Assessment of reservoir sedimentation using remote sensing and recommendations for desilting Patratu Reservoir, India. Hydrol Sci J 61(4):711\u0026ndash;718\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak S, Liu M, Jato-Espino D et al (2020) Social, economic and environmental assessment of urban sub-catchment flood risks using a multi-criteria approach: A case study in Mumbai City, India. J Hydrol 591:125216\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathak S, Panta HK, Bhandari T et al (2020) Flood vulnerability and its influencing factors. Nat Hazards 104(3):2175\u0026ndash;2196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePricope NG, Halls JN, Rosul LM (2019) Modeling residential coastal flood vulnerability using finished-floor elevations and socio-economic characteristics. J Environ Manage 237:387\u0026ndash;398\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahmati O, Darabi H, Panahi M et al (2020) Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10(1):1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezende OM, de Oliveira AKB, Jacob ACP et al (2019) A framework to introduce urban flood resilience into the design of flood control alternatives. J Hydrol 576:478\u0026ndash;493\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosser JF, Leibovici DG, Jackson MJ (2017) Rapid flood inundation mapping using social media, remote sensing and topographic data. Nat Hazards 87(1):103\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouillard JJ, Ball T, Heal KV et al (2015) Policy implementation of catchment-scale flood risk management: learning from Scotland and England. Environ Sci Policy 50:155\u0026ndash;165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlater LJ, Villarini G (2016) Recent trends in US flood risk.Geophysical Research Letters, 43(24): 12,428 \u0026ndash; 12,436\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeight LJ, Hall JW, Kilsby CG (2017) A multi-scale framework for flood risk analysis at spatially distributed locations. J Flood Risk Manag 10(1):124\u0026ndash;137\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka T, Kiyohara K, Tachikawa Y (2020) Comparison of fluvial and pluvial flood risk curves in urban cities derived from a large ensemble climate simulation dataset: A case study in Nagoya, Japan. J Hydrol 584:124706\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTapia C, Abajo B, Feliu E et al (2017) Profiling urban vulnerabilities to climate change: An indicator-based vulnerability assessment for European cities. Ecol Ind 78:142\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVercruysse K, Dawson DA, Glenis V et al (2019) Developing spatial prioritization criteria for integrated urban flood management based on a source-to-impact flood analysis. J Hydrol 578:124038\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVojinovic Z, Hammond M, Golub D et al (2016) Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya. Thail Nat Hazards 81(1):589\u0026ndash;616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Li Y, Yu P et al (2021) The analysis of urban flood risk propagation based on the modified Susceptible Infected Recovered model. J Hydrol 603:127121\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Huang G, Meng Q et al (2016) Deep tunnel for regulating combined sewer overflow pollution and flood disaster: a case study in Guangzhou City, China. Water 8(8):329\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T (2021) Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis. Ecol Ind 129:108006\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin J, Yu D, Yin Z et al (2016) Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai. China J hydrology 537:138\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung A, Bhattacharya B, Zevenbergen C (2021) A rainfall threshold-based approach to early warnings in urban data‐scarce regions: A case study of pluvial flooding in Alexandria, Egypt. J Flood Risk Manag 14(2):e12702\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H, Zhao Y, Fu Y et al (2018) Spatiotemporal variance assessment of urban rainstorm waterlogging affected by impervious surface expansion: A case study of Guangzhou. China Sustain 10(10):3761\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZevenbergen C, Veerbeek W, Gersonius B et al (2008) Challenges in urban flood management: travelling across spatial and temporal scales. J Flood Risk Manag 1(2):81\u0026ndash;88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Cheng J, Wu Z et al (2018) Effects of impervious surface on the spatial distribution of urban waterlogging risk spots at multiple scales in Guangzhou, South China. Sustainability 10(5):1589\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Wu C, Chen W et al (2019) Effect of urban expansion on summer rainfall in the Pearl River Delta, South China. J Hydrol 568:747\u0026ndash;757\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao G, Xu Z, Pang B et al (2019) An enhanced inundation method for urban flood hazard mapping at the large catchment scale. J Hydrol 571:873\u0026ndash;882\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Q, Mikkelsen PS, Halsn\u0026aelig;s K et al (2012) Framework for economic pluvial flood risk assessment considering climate change effects and adaptation benefits. J Hydrol 414:539\u0026ndash;549\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Shen D, Huang N et al (2019) Urban flood risk assessment using storm characteristic parameters sensitive to catchment-specific drainage system. Sci Total Environ 659:1362\u0026ndash;1369\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban pluvial flood, integrated risk assessment, hazard, exposure, vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-1550233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1550233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrently, urban pluvial flood risk management and mitigation strongly require integrated flood risk assessment at a fine scale. In this study, we tried to perform integrated pluvial flood risk assessment in the framework of \u0026ldquo;hazard-exposure-vulnerability\u0026rdquo; at the grid scale. First, a simplified pluvial flood inundation was utilized to quickly calculate flood depth for hazard information to find the flood depth under different precipitation conditions. Next, spatial analysis based on Geographic Information Science (GIS) was chosen to analyze the entire exposure degree of land use. Then, point of interest (POI) data together with traditional statistical data were analyzed by principal component analysis (PCA) for vulnerability assessment at the grid scale, and spatial overlay analysis was used to obtain the integrated risk assessment. The results showed that as the intensity of heavy rain increased, the level and intensity of hazard and exposure risk increased accordingly and showed obvious spatial agglomeration. The 20-year return period of a rainstorm was an inflection point for the area change of hazard. However, vulnerability had a significant clustering effect, which was obviously distinct from hazard and exposure risk. As the rainstorm intensity increased, the regional area of pluvial flood risk facing two or three risks gradually increased, and its spatial agglomeration effect also gradually increased. It is worth noting that the highest integrated risk area was mainly distributed near both the midstream and the downstream sides of Donghaochong. Our research can provide scientific reference for the mitigation of urban pluvial flood risk, and case reference for other cities to carry out similar research.\u003c/p\u003e","manuscriptTitle":"Exploring an urban integrated pluvial flood risk assessment using big data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-20 14:59:28","doi":"10.21203/rs.3.rs-1550233/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb5c069d-caa1-4ca6-af7c-50f3ae63a6c3","owner":[],"postedDate":"May 20th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-14T07:18:12+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-20 14:59:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1550233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1550233","identity":"rs-1550233","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}