This study presents an integrated approach to evaluate inundation
risks, in which an algorithm is proposed to integrate the storm water
management model (SWMM) into a geographical information system (GIS). The
proposed algorithm simulates the flood inundation of overland flows and in
metro stations for each designed scenario. It involves the following stages:
(i) determination of the grid location and spreading coefficient and (ii) an
iterative calculation of the spreading process. In addition, an equation is
proposed to calculate the inundation around a metro station and to predict
the potential inundation risks of the metro system. The proposed method is
applied to simulate the inundation risk of the metro system in the urban
centre of Shanghai under 50-year, 100-year, and 500-year rainfall
intensities. Both inundation extent and depth are obtained and the proposed
method is validated with records of historical floods. The results
demonstrate that in the case of a 500-year rainfall intensity, the inundated
area with a water depth excess of 300 mm covers up to 5.16 km
With rapid urbanization, numerous urban constructions (such as underground metro systems, malls, infrastructural systems, and parks) have been built (Wu et al., 2016, 2019; Peng and Peng, 2018). Underground constructions (Shen et al., 2014, 2017; Tan et al., 2017; Wang et al., 2019) render the environment susceptible to certain natural hazards, such as floods, tornados, and typhoons (Lyu et al., 2016, 2017). Recently, climate change has resulted in various rainstorm events in China (Zhou et al., 2012; Yin et al., 2017, 2018; Xu et al., 2018). Many metropolitan areas have frequently suffered from inundation owing to urban flooding, which is one of the most severe hazards and causes the catastrophic submerging of ground surfaces and severe inundation of underground facilities. Numerous metro lines were inundated during the flood season (May–September) in 2016 in China, for instance, the metro lines in Guangzhou and Wuhan. The Shanghai Station of metro line no. 1 was inundated on 3 October 2016 (Lyu et al., 2018a, b, 2019a, b, c). Thus, the prediction and prevention of inundation in metro systems must be integrated into current urban layouts (Huong and Pathirana, 2013).
In general, four methods have been developed to predict the inundation risk:
(1) a statistical analysis based on historical disaster records (Nott,
2006), (2) geographical information system and remote sensing (GIS–RS)
techniques (Sampson et al., 2012; Meesuk et al., 2015), (3) multi-criteria
index analysis (Jiang et al., 2009; Kazakis et al., 2015), and (4) scenario-based
inundation analysis (Willems, 2013; Naulin et al., 2013). Although
assessment results based on historical disaster records can predict the
inundation risk of an area, the method requires high numbers of data (Nott,
2006). The GIS–RS method can provide the technological support for an
inundation risk evaluation (Sampson et al., 2012; Meesuk et al., 2015).
However, it requires high investments and high-resolution data sources. The
multi-criteria index analysis has a few limitations regarding the
determination of subjective indices (Jiang et al., 2009; Kazakis et al., 2015). The
scenario-based inundation analysis predicts the inundation risk for
different scenarios (Willems, 2013; Naulin et al., 2013; Wu et al., 2018)
and requires the topography, land use, and urban drainage system data. Owing
to the complex interaction between the drainage system and overland surface
in urban regions, scenario-based models can only simulate inundation for a
small range (e.g. below 3 km
Numerical simulation is a useful tool to analyse urban flooding. Xia et al. (2011) integrated an algorithm into a two-dimensional (2-D) hydrodynamic model to assess flood risks. Szydlowski et al. (2013) proposed a numerical flood model in which a mathematical model was incorporated into a 2-D hydrological model to estimate inundation risks. Furthermore, Chen et al. (2015) used numerical simulations to predict the inundation risk in a flood-prone coastal zone. Morales-Hernandez et al. (2016) presented coupled one-dimensional (1-D) and 2-D models (1-D–2-D models) for the fast computation of large-river floods. However, these numerical models have the following deficiencies: (i) the characteristics of the landform (e.g. the topographical elevation, slope, and river system) are difficult to model; (ii) a numerical simulation is typically used to estimate the inundation risk in a small area, whereas flooding hazards often occur on a regional scale. The existing methods can only simulate inundation for small ranges (e.g. several square kilometres) (Naulin et al., 2013; Wu et al., 2018). Therefore, a new tool (e.g. the GIS technique) is required to consider variations in topographical elevations. Moreover, an integrated method is required to simulate regional-scale flooding.
The storm water management model (SWMM) is a dynamic hydrological model,
which is widely used to simulate the rainfall–runoff processes in urban
catchments (Shen and Zhang, 2015; Bisht et al., 2016; Zhao et al., 2019). However, the SWMM cannot be used to
determine surface water flows directly. The existing studies have been
applied to only small regions of several square kilometres (Wu et al., 2018;
Chen et al., 2018; Kumar et al., 2019). For example, Zhu et al. (2016)
applied the SWMM and a multi-index system to evaluate the inundation risks
in south-western Guangzhou, China (area of 0.43 km
Few researchers have focused on the inundation risks of metro systems. Yanai (2000) and Hashimoto et al. (2003) analysed the flood event in Fukuoka in 1999 which led to a serious inundation of the metro station. Based on previous research, Aoki et al. (2016) proposed anti-inundation measures for the Tokyo Metro. Herath and Dutta (2004) created an urban flooding model and included the underground space. Furthermore, Suarez et al. (2005) conducted a flooding risk assessment for the Boston metro area. Ishigaki et al. (2009) presented a method for the safety assessment of a Japanese metro. Nevertheless, research and literature on the inundation risks of metro systems are insufficient.
The objectives of this study are as follows: (i) propose a method for the prediction of the potential inundation risk on a regional scale with a new algorithm that integrates the SWMM into a GIS for overland flow simulations; (ii) propose an evaluation method for the potential inundation risk of a metro system. Then, the proposed method is adopted to simulate urban inundations and inundation depths for the Shanghai Metro system under 50-year, 100-year, and 500-year rainstorm events. The proposed method assumes that the runoff on the surface flows from one subcatchment to another.
Shanghai is located at 31
Metro line distribution in the study area of Shanghai.
Precipitation is the external driving force behind flood disasters. The
Chicago design storm (Yin et al., 2016a, b) is widely applied to generate
rainfall hyetographs, which are used to calibrate the peak intensity and
precipitation before and after the peak for different return periods of the
rainfall. The equations for the Chicago design storm are as follows:
Based on investigations, the IDF of the Shanghai municipal rainstorm can be
expressed as follows (Jiang, 2015; Yin et al., 2016a):
To consider temporal variations, the parameter
The digital elevation model (DEM) with a 30 m resolution was obtained from a
geospatial data cloud. To simulate the reality of the study area, the DEM
was further modified with buildings based on field surveys and documents
(Yin et al., 2016a). The building heights were rebuilt in the DEM to
reproduce the blockage effects of surface flows. During the reprocess of
elevation data, the investigated area was divided into grids with 20 m
The SWMM was incorporated into the GIS model to predict the inundation
depth. The following phases must be performed during its incorporation.
The investigated area was classified into different subcatchments in the
GIS. Each subcatchment was provided with the corresponding geographical
information (e.g. elevation, slope, area, and width). The information of
each subcatchment was stored in the GIS database. The information of each subcatchment was exported from the GIS database
and reproduced to produce a “. The “. The calculated water volume of each subcatchment was converted into the
average water depth with the water volume and area of each subcatchment. Each subcatchment was divided into 20 m The grids with all information were applied to perform the spreading
process with the proposed algorithm in the GIS until the water level of each
grid was stable. During the spreading process, a spreading coefficient was used
to move the runoff between neighbouring grids.
Finally, the water depth of each grid was exported to visualize the
distribution of the inundation depth of the investigated area. The details
of the proposed algorithm are presented in Sect. 3.2.
The SWMM is widely applied to simulate the runoff quantity produced in each subcatchment in a simulated period. The results obtained with the SWMM approximate the measured value and indicate that the runoff reaches a peak in the shortest time possible (Lee et al., 2010). In addition, researchers have reported that the SWMM is one of the best hydrologic models (Tan et al., 2008; Cherqui et al., 2015). It is assumed that under extreme rainfall scenarios, the runoff concentrates at the outlet of each catchment, and the function of the drainage network is negligible. In this case, the overland flow is more likely to move in multiple directions rather than through the predefined flow paths and outlets. Therefore, a coefficient was included in the spreading process algorithm to determine the flow paths on the surface. The spreading coefficient was used to move the runoff between neighbouring subcatchments. Moreover, the function of the drainage network was reflected by the drainage capacity of each drainage station (see Fig. 2). The water quantity of each subcatchment calculated in the SWMM was reduced by the capacity of the drainage station. Detailed information on the algorithm is introduced in Sect. 3.2.
A subcatchment is the basic calculation cell in the SWMM. The two types of
subcatchment divisions are based on Shen and Zhang (2015): (i) the
subcatchment partition and (ii) drainage system. In this study, a
subcatchment was initially divided with the Thiessen polygon method based on
the spatial distribution of the drainage stations (Shen and Zhang, 2015; Zhu
et al., 2016). The drainage capacity of each subcatchment was determined
with the service range of the drainage stations. In addition, the boundary
was a fixed boundary. Thus, the water level at the boundary was not
considered to spread because the water volume of the grids located at the
boundary have less effect on the spreading process. Figure 2 shows the
characteristics of subcatchments and grids in the SWMM and GIS. The study area
was classified into different subcatchments based on the drainage capacity
of the drainage station (Fig. 2a). The drainage capacity of each drainage
station was obtained from the existing publication (Yin et al., 2016b). Each
subcatchment was meshed into grids with 20 m
Calculated subcatchments and grids in the SWMM and GIS:
Based on the previously mentioned subcatchment division, each subcatchment
was assigned its topographical characteristics. The model included 195 subcatchments and 204 junctions. Each subcatchment in the SWMM
included the width, area, and permeability. The width and area can be
calculated with the Spatial Analyst Tools in the GIS. Table 1 lists the
parameters of the subcatchments in the SWMM. The impervious parameter was
determined according to the land use types. The study area is located in the
urban centre, where the land use has no big changes. The dense distribution
of buildings leads to an impervious surface of more than 80 % of the total
surface. Owing to the existence of road pavements, subgrades, and many
municipal pipelines under the roads, water infiltration through the road and
subsurface is very low. Thus, roads can be considered impervious, and soil
infiltration and evapotranspiration have slight effects on the surface
runoff concentration during short-term flash flooding during rainstorms. The
soil infiltration mainly depends on green land (e.g. lawns, flower beds, and
groves) and the water bodies within the study area. The geotechnical
information in Shanghai is as follows: the groundwater table is higher than
2 m below the ground surface. The soil type at a depth of 2 m is mixed soil
with sand (5 %), silt (55 %), and clay (40 %) according to the
Shanghai Geotechnical Investigation Code (DGJ08-37-2012). The sand content
is 15 % at the surface. Thus, the soil has a hydraulic conductivity of
Parameters of the subcatchments in the SWMM.
After the calibration of the runoff volume of each subcatchment, the
spreading procedure of the calibrated runoff must be performed. The
spreading procedure algorithm was used to exchange the data between the GIS and
SWMM. Figure 3 presents the spreading procedure of the runoff. Furthermore,
Fig. 3a illustrates the determination of the grid location and spreading
coefficient. Figure 3b presents an iterative calculation of the spreading
process. First, the study area was meshed into 113 810 grids with 20 m
Description of the spreading process:
The inundation depth around a metro station is used to evaluate the
inundation risk of metro lines. Therefore, Eq. (6) is proposed:
During the establishment of the storm water model in the SWMM, the rainfall intensity was set to return periods of 50-year, 100-year, and 500-year. In addition, the rainfall duration was set to 2 h. The water volume of each subcatchment can be computed in the SWMM. The calculated water volume in the SWMM was input into the GIS model to update the water level of each grid with the proposed algorithm. The stable water level of each grid in the GIS was used to reflect the inundation depth in the study area. Subsequently, the inundation depth was used to evaluate the flood risks. By using the inundation depth of the ground surface, the inundation depth around a metro station can be determined with Eq. (6). Furthermore, the spatial distribution of the inundation depth can be visualized with the GIS. The calibration of the proposed model is based on a comparison between the predicted results and historic inundation locations.
Figure 4 shows the runoff volume of each subcatchment at different rainfall
intensities. The runoff increases with increasing rainfall intensity.
Furthermore, the area of each subcatchment is presented. Most subcatchments
cover approximately 2 km
Runoff volume of each subcatchment in the corresponding area under different rainfall intensities.
The inundation depth across the study area was computed with the proposed algorithm (see Fig. 3). Figure 5 displays the distribution of the inundation extent and depth for the previously mentioned rainfall scenarios. The floodwater profiles of the three scenarios are similar. However, an increasing rainfall intensity exacerbates the inundation depth and areal extent. Figure 5a and b exhibit similarities in inundation depths and extents for the different scenarios. Figure 5c presents the maximal inundation depth and extent for the 500-year rainfall intensity. The maximal depth for each scenario first occurs in certain places in the Changning, Huangpu, and Yangpu districts. The maximal inundation depth exceeds 400 mm.
Distribution of the potential inundation extent and depth under
different rainfall intensities:
To analyse the inundation risks of different scenarios, the inundated area
and ratio were determined with the GIS. The inundation ratio is represented by
the ratio of the inundated area to the total area (120 km
Statistical inundation area with the corresponding ratio at different depths.
Based on the spatial distribution of the inundation depth of a ground surface, the potential inundation depths around metro stations can be obtained with Eq. (6). Figure 7 shows the potential inundation depths around metro stations for the 50-year rainfall intensity (Fig. 7a), 100-year rainfall intensity (Fig. 7b), and 500-year rainfall intensity scenarios (Fig. 7c). Most inundated metro stations lie in the region with a deeper flood depth. As shown in Fig. 7, the increasing rainfall intensity exacerbates the inundation depths and extents. Regarding the 50-year rainfall intensity, the Xinjiangwan Cheng Station, Yingao East Station, Yangshupu Road Station, and Longyao Road Station possibly become inundated with a depth of 100 mm. Regarding the 100-year rainfall intensity, the inundation depth of the four stations increases by 200–300 mm and the inundation expands to other central regions. In the 500-year rainfall intensity scenario, the largest inundation depth exceeds 300 mm. Other metro stations also experience inundation, with depths of 100–300 mm in the central region. In all three scenarios, the inundation initially occurs in the metro stations of Xinjiangwan Cheng, Yingao East, Yangshupu Road, and Longyao Road. Moreover, the depths increase with increasing rainfall intensity.
The number of inundated stations is presented in Fig. 7. The number of inundated metro stations increases significantly with increasing rainfall intensity. In the 500-year rainfall intensity scenario, the inundation depths of the stations of Xinjiangwan Cheng, Yingao East, Yangshupu Road, and Longyao Road are above 300 mm (see Fig. 7c).
Potential inundation depth around the metro stations under
different scenarios:
To validate the proposed model, the results of RS inundation maps (aerial or
satellite) and reliable field surveys must be compared with those of the
calculated inundated areas. However, the observed inundation maps of
historical flood events in Shanghai are unavailable. Nevertheless, public
sources can provide some historical data of inundation depths in several
locations in Shanghai. Thus, the proposed model was validated by comparing
the simulated data and these records. The records were provided by the
following two sources: (1) flood incidents reported by public sources via
websites (e.g. Google and Baidu) or in (2) publications (Huang et al., 2017;
Yin et al., 2016b). The public sources provide sufficient information and
include the locations of affected roads and buildings and an estimate of the
inundation depth. Figure 8 depicts the location of the recorded flood. As
presented in Fig. 8a, the records cover historical floods with deep
inundation depths. Figure 8b shows the flooding of Xujingdong Road
(
Furthermore, publications can be used to collect recorded flood data. The
official records of the rainstorm which occurred with Typhoon “Matsa” in
2005 presented by Huang et al. (2017) are similar to those of the simulated
100-year intensity, which caused serious inundations in the districts of
Yangpu, Hongkou, Changning, Putuo, and Xuhui. In addition, Yin et al. (2016b) recorded the flood of 12 August 2011. However, they investigated
an area of only 3.25 km
Distribution of the recorded flood locations:
Comparison of the inundation depths obtained from the simulated results and recorded data.
In this study, the open-source inundation SWMM, combined with the GIS, was adopted to evaluate inundation risks. To improve the approach, a new algorithm is proposed to simulate the overland flow on the ground surface. The algorithm can integrate the SWMM into the GIS. This approach can predict the inundation risks on a regional scale, whereas the existing methods can only evaluate small areas. It is assumed that the rainwater flows from one grid to another. Moreover, a spreading coefficient is used to move the runoff between neighbouring grids. During the surface flow, the rainwater is redistributed between the ground surface and drainage stations. However, the existing drainage network is not directly considered in this method owing to its complexity for a regional scale. Alternatively, the capacity of the drainage station (see Fig. 2) was used to reduce the water quantity of each subcatchment calculated in the SWMM. The function of the drainage network is reflected by the drainage capacity of each drainage station. In reality, a short-term rainstorm easily induces flash floods in urban areas. The existing studies paid more attention to flash floods induced by short-term rainstorms within 2 h or 3 h (Yin et al., 2016a, b; Wu et al., 2017). Therefore, in this study, a rainfall duration of 2 h was selected for simulation. During the simulation, the rainwater is supposed to flow on the ground surface and in the drainage stations. As already mentioned, the effects of the underground drainage network are not considered. During a short flash flood, the rainwater mainly flows on the surface (the spreading process occurs in a domain). Thus, the proposed approach is suitable for the simulation of rainstorms with short durations. Because of a lack of recorded data for the inundation depths of the metro stations, only the inundation depth on the ground surface was validated through a comparison between the simulated results and records of historical floods. The comparison reveals that the model can capture the surface flow dynamics of rainwater. However, the calculated inundation depths and validated results exhibit some differences. This can be ascribed to the uncertainties originating from various assumptions for the parametric values, data quality, and modelling conditions. These uncertainties result in a larger inundation depth than in the recorded data. Overall, the simulated results provide a relatively reliable prediction of inundation risks. Although the simulated results reveal various uncertainties, the deviations are acceptable, and the model is suitable for urban inundation predictions.
The simulated results show a spatio-temporal distribution of the inundation profiles. The inundation profiles are characterized by a consistency in the rainfall scenarios with larger inundation depths and extents corresponding to higher rainfall intensities. In the scenario of the 500-year rainfall intensity, various regions within the study area are predicted to suffer catastrophic inundation, particularly those regions near the Huangpu River. This phenomenon may be due to the backwater effect, which is well known to be stronger and more apparent at riversides than that in inland regions. Therefore, there is a need to improve the drainage facilities (e.g. sewer system, manhole, and outlet) along the Huangpu River. Inundation of the metro system primarily occurred in the regions with a deep inundation depth. To mitigate the damage caused by inundation in the metro system, the drainage capacity of the ground surface around the metro station should be increased (Suarez et al., 2005; Aoki et al., 2016). In addition, the height of the step of the metro station with a high inundation risk should be increased. Drainage facilities within the metro station should also be allocated for the emergency of flooding. In the future, more flooding adaptation measures should be taken to mitigate the catastrophic damages caused by urban flooding.
This study presents a method for the evaluation of inundation risks through
the integration of a hydraulic model and GIS-based analysis via a proposed
algorithm. The proposed approach was used to predict the inundation risk of
the metro system in Shanghai. The results were verified by recorded flooding
events. According to the results, major conclusions were drawn as follows.
A new algorithm was proposed to simulate the inundation extent and depth
on the ground surface. This algorithm included two aspects: (i) the
determination of the grid location and (ii) an interactive calculation of the
spreading process. With the proposed algorithm, the incorporated SWMM and
GIS are adopted to yield a spatial–temporal distribution of the inundation
risk on the ground surface. The study area was classified into subcatchments, and their
corresponding information was stored in the GIS database. The information of
each subcatchment was exported and input in the SWMM to calculate the
water volume of each subcatchment. Each subcatchment was meshed
into grids. The calculated water volume was adopted to update the water
level of each grid using iterative calculation with the proposed algorithm.
Finally, the stable water level of each grid in the GIS was used to determine
the inundation depth. Based on the inundation depth on the ground surface, an equation was
proposed to calculate the inundation depth of the metro system
qualitatively. The proposed equation provides a quantitative evaluation of
the metro system by considering the drainage capacity and characteristics of
each metro station. The proposed approach was used to simulate the inundation risks of the
metro stations in Shanghai under 50-year, 100-year, and 500-year
intensities. The results show that the stations of Xinjiangwan Cheng, Yingao
East, Yangshupu Road, and Longyao Road might become inundated. In the
50-year rainfall intensity, these four stations will be inundated with a
depth of 100 mm. In the 100-year rainfall intensity, the inundation depth of
the four stations increases by 200–300 mm, whereas the inundation expands
to other central regions. In the 500-year rainfall intensity, the highest
inundation depth exceeds 300 mm. Moreover, other metro stations experience
inundation with depths of 100–300 mm in the central region.
The topographical data were obtained from a
geospatial data cloud (
This paper represents a result of collaborative teamwork. SLS developed the concept; HML drafted the manuscript; JY provided constructive suggestions and revised the manuscript; ZYY collected the data and revised the manuscript. The four authors contributed equally to this work.
The authors declare that they have no conflict of interest.
The research work described herein was funded by the National Natural Science Foundation of China (NSFC) (grant no. 41672259) and the Innovative Research Funding of the Science and Technology Commission of Shanghai Municipality (grant no. 18DZ1201102). These financial supports are gratefully acknowledged.
This research has been supported by the National Natural Science Foundation of China (grant no. 41672259) and the Innovative Research Funding of the Science and Technology Commission of Shanghai Municipality (grant no. 18DZ1201102).
This paper was edited by Nadia Ursino and reviewed by Malek Alshorman and two anonymous referees.