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An enhanced inundation method for urban flood hazard mapping at the large catchment scale
Journal of Hydrology 571 (2019) 873–882
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Research papers
An enhanced inundation method for urban flood hazard mapping at the large catchment scale
T
⁎
Gang Zhaoa,b, Zongxue Xua, , Bo Panga, Tongbi Tuc, Liyang Xud, Longgang Due a
College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK c J. Amorocho Hydraulics Laboratory, Dept. of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA d School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China e Hydrographic Station of Beijing, Beijing 100089, China b
A R T I C LE I N FO
A B S T R A C T
This manuscript was handled by G. Syme, Editor-in-Chief, with the assistance of Hazi Mohammad Azamathulla, Associate Editor
Urban flooding occurs frequently in the world and urban hydrological models are widely applied in urban flood management and disaster mitigation. In this study, an enhanced inundation method (EIM) for urban flood hazard mapping at the large catchment scale is proposed. EIM can be easily coupled with urban hydrological models and the coupled framework can consider both source flooding and non-source flooding in floodwater generation. In EIM, the floodwater spreading order in the positive process is based on the topological relationship between depression outlets; the floodwater from lower depression elements is considered as a feedback process. These improvements make this proposed method suitable for inundation estimation in large urban catchments. Dahongmen (DHM) catchment in Beijing, China was selected as the case study area to illustrate the applicability of the proposed method. Historical inundation records during one heavy storm were applied to test the performance of the method. EIM is compared with USISM (urban storm-inundation simulation method) on the flood hazard map in the DHM catchment, which reveals the effectiveness of the improvements. The results show that all inundation locations are successfully identified by EIM and are distributed in flooding areas (water depth greater than 0.15 m) in the catchment. The average relative error of simulated inundation depths is 15%, which indicates that EIM can successfully simulate flooding scopes and depths in the study area. The results revealed that EIM can be a valuable tool for mapping urban flood hazards at the large catchment scale based on GIS techniques.
Keywords: Enhanced inundation method Flood hazard map Large urban catchment GIS SWMM
1. Introduction Urban flood is regarded as a type of flash flood, which can result in serious loss of lives and property (Smith, 2006). For example, flooding caused by Hurricane Harvey in the city of Houston (Texas, USA) in 2017 led to thousands of homes flooded; the cost of reconstruction was estimated to be as high as $200 billion (BBC News, 2017). The flood hazard in urban areas is predicted to increase with the warmer climate and urban sprawl (Ashley et al., 2005; Huong and Pathirana, 2013; Li et al., 2013; Ntelekos et al., 2010). Urban flood hazard mapping at the large catchment scale can give a comprehensive understanding of the flood-prone areas of a city and is urgently required for city planning and disaster mitigation (Bathrellos et al., 2012; Fernandez and Lutz, 2010). The preferred method to reliably map flood hazard in urban areas is to use hydrodynamic models, such as MIKE URBAN (Lee et al., 2015), Info Works ICM (Cheng et al., 2017) and LISFLOOD-FP (Wu et al., ⁎
2017). These models are based on shallow water equations that describe the physical process of flooding and have been widely applied in small catchments (1–10 km2). The main challenge of applying the above hydrodynamic models in a large catchment (more than 100 km2) is the difficulty in preparing the required datasets (Fewtrell et al., 2011; Salvadore et al., 2015). For example, the detailed information of sewer systems for large urban catchments is difficult to acquire. This problem can be simplified by deducting drainage volume from runoff (Chu et al., 2014; Zhang and Pan, 2014). However, this deduction ignores the distribution of sewer systems and the drainage capacity is set by the modeler’s prior experience. Another challenge is the great computational cost of the hydrodynamic models that use high-resolution terrain datasets in large catchments. High-resolution terrain datasets, such as LiDAR-based Digital Elevation Model (DEM), have been successfully used for hydrodynamic modelling in small urban catchments (Fewtrell et al., 2011; Ozdemir et al., 2013). However, these datasets are seldom
Corresponding author at: Jingshi Building, Xinjiekouwai St., Haidian District, Beijing, China. E-mail address: [email protected] (Z. Xu).
https://doi.org/10.1016/j.jhydrol.2019.02.008 Received 7 April 2018; Received in revised form 6 February 2019; Accepted 8 February 2019 Available online 15 February 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. (a). River system, hydro-meteorological stations, inundation sites and digital elevation of study area; (b) Model structure of SWMM in the study area.
upper depression will receive floodwater from the lower depression when the water level of lower depression is higher than contiguous depressions. The structure of the proposed model in this study better describes urban flooding in a large catchment by adding the feedback process of floodwater from lower depression elements. In this study, an enhanced inundation method (EIM) was proposed for urban hazard mapping at the large catchment scale. This method introduced the SWMM model for floodwater generation, and floodwater spreading was based on the topological relation of depression outlets. A large catchment, Dahongmen (DHM), in Beijing, China, was selected as a case study area to apply and validate the EIM. Two representative areas in the DHM catchment were selected to compare the corresponding flood hazard mapping performance based on EIM and USISM. Historical inundation datasets were used to test the performance of EIM in terms of inundation locations and water depths.
used in large catchments because of the high computational demand (Neal et al., 2010). These challenges limit the applicability of hydrodynamic models for urban flood hazard mapping at the large catchment scale. Geographic Information System (GIS) is an effective tool to map flood hazard at the large catchment scale. The most popular method is the multi-criteria decision approach (MCDA) (Fernandez and Lutz, 2010; Tang et al., 2018). MCDA identifies flood-prone areas using different explanatory factors based on GIS techniques. However, MCDA cannot provide the scope and depth of flooding. Some inundation methods based on GIS such as RFIM (Krupka and Wallis, 2007), RFSM (Gouldby et al., 2008) and FCDC (Zhang et al., 2014) have been proposed for mapping the scope and depth of flooding. These methods can simulate the spreading process of floodwater, but they do not consider the process of floodwater generation. To consider the floodwater generation process, Zhang and Pan (2014) proposed an urban flood hazard mapping framework named urban storm-inundation simulation method (USISM) and successfully applied it to a small catchment in Harbin, Helongjiang Province, China. However, USISM only included a simple floodwater generation model (SCS-CN model), and floodwater spreading order is based on flow accumulation (FA). Furthermore, this method focused only on non-source flooding in urban areas. Consequently, the applicability of the method proposed by Zhang and Pan (2014) is limited when applied to a large urban catchment. To improve the simulation of urban flooding in a large catchment, two improvements were further introduced in this study based on the USISM: the first was to introduce an urban hydrological model (SWMM) to provide the volume of floodwater in floodwater generation. In the USISM method, runoff calculation was based on the SCS-CN model (McCuen, 1982) and the sewer system runoff was represented by one parameter in the water balance model. The exact value of this parameter is very difficult to obtain because of the complexity of the urban rainfall-runoff process and the very limited sewer system data (Zhang and Pan, 2014). USISM also only accounts for non-source flooding and ignores floodwater from rivers. The SWMM model is a popular urban hydrological model and has been widely used for urban rainfall-runoff modelling in large catchment case studies (Barco et al., 2008; Bisht et al., 2016). SWMM, which can provide the volume of floodwater in the drainage system, shows advantages in urban runoff simulation (Hsu et al., 2000). The second improvement was that the floodwater spreading order was determined by the elevation and location of depression outlets, but not by the value of FA. The value of FA reflects the order of water spreading (from upper to lower) in filled DEM. In urban areas, the elevation of contiguous depression changes significantly and some local depressions are easily flooded. These areas are usually ignored in hydrological modelling because of the depression filling process. The order of water spreading in these areas does not strictly follow the order of the FA value (Maksimović et al., 2009). Furthermore, the
2. Materials and methods 2.1. Study area description DHM catchment (39°48′–39°55′N, 116°9′–116°24′E) is located upstream of the Liangshui river in the metropolitan area of Beijing, China. The annual precipitation of this area is about 520 mm and 80% of the precipitation is dispersed during heavy summer storms. The terrain of this catchment shows a downtrend from the northwest to the southeast (Fig. 1(a)) with a total area of 131 km2. DHM catchment has been urbanized rapidly in the past decades and has gradually become a Beijing metropolitan transport hub. About 90% of this catchment area has been modified to be waterproof. This catchment is one of the flood-prone areas in the city and 30% of the inundation sites during the storm of 21 July 2012 were located in this catchment. This 2012 storm caused the heaviest urban flood in the past two decades; it produced 200 mm excess rainfall in the urban area, causing 79 casualties and 11.6 billion Chinese Yuan (nearly $2 billion) in damage to Beijing city (Shi et al., 2017; Zhang et al., 2013). 2.2. Data description EIM spreads floodwater based on the DEM data. In this study, the DEM dataset was provided by Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 1 (GDEM). The resolution of GDEM used in this study is 30-metre and the resolution can successfully identify the depressions for a large catchment by comparing them with sunken areas in the DHM catchment. One historical inundation dataset in the DHM catchment was used for model validation. The dataset comprised 14 inundation sites during the heavy storm of 21 July 2012 in the DHM catchment. These inundation sites were published online (http://map.sogou.com/special/jishui/) 874
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and geo-referenced by GF-2 images. The locations of inundation sites during the 2012 storm are shown in Fig. 1(a). Water depth observations of four inundation sites were provided by the National Disaster Reduction Centre of China.
volume of sourced floodwater (Vs) in SWMM is directly reflected by the volume of overflow at the river or sewer junction points. The volume of non-sourced floodwater (Vn) and total floodwater (VT) for each depression element in one sub-catchment is calculated by Eqs. (1) and (2) respectively.
2.3. Floodwater generation method
Vn = (P − I − D) × Ad
(1)
where P is the depth of precipitation (mm) in the sub-catchment; I is the depth of infiltration (mm) in the sub-catchment; D is the depth of drainage water (mm) in the sub-catchment and Ad is the area of one depression element (m2) in the sub-catchment.
The Storm Water Management Model (SWMM) was selected to generate the volume of floodwater for EIM. SWMM was developed by the Environmental Protection Agency (EPA) of America for water planning and management in urban areas (Huber et al., 1989). SWMM has been widely used in urban water management in many cities of China (Jiang et al., 2015; Liao et al., 2015; Su et al., 2014; Yu et al., 2014). The model applied in the DHM catchment of Beijing city was originally developed and validated in terms of discharge process by Zhao et al. (2014), and improved by considering the impact of urbanization on the rainfall runoff process (Xu and Zhao, 2016). The uncertainty of the SWMM application in urban catchments was studied by Morris and GLUE methods (Knighton et al., 2016; Shi et al., 2014; Shi et al., 2016; Sun et al., 2014). The model structure of SWMM in this study is shown in Fig. 1(b). The DHM catchment was delineated with 79 sub-catchments in SWMM and each sub-catchment comprised several depression elements (Fig. 2(a)). The EIM and SWMM were coupled by the volume of floodwater. SWMM generates runoff in each sub-catchment and transports this runoff through drainage and river system. It can quantitatively calculate the volume of sourced and non-sourced floodwater. The
M
VT =
∑ Vsm + Vn m=1
(2)
where M is the number of junction points in one depression element and Vsm is the volume of floodwater of junction m. In the SWMM model, the Horton model and Kinematic wave model were selected for estimating the amount of infiltration and drainage water (Xu and Zhao, 2016). Four hourly precipitation gauges, Dahongmen (DHM), Youanmen (YAM), Shijingshan (SJS) and Longyuanzha (LYZ) were used as model driven data. Hourly discharge data of the DHM station was used for validation in terms of water balance in this catchment. Observed precipitation and discharge data were provided by the Hydrographic Station of Beijing. The river and sewer system datasets were provided by the Hydrographic Station of Beijing and the Beijing Municipal Institute of City Planning and Design, respectively. These datasets include locations, section shapes and
Fig. 2. The sketch map of depression elements. (D1, D2 and D3 are depressions; E1, E2 and E3 are depression elements which represent the catchment of these depressions. O1 and O2 are depression outlets). 875
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conveyance capacities of the river and sewer system in the study area. The performance of SWMM in terms of drainage flow was calibrated and validated with four real storms (including the storm of 21 July 2012) in previous studies (Xu and Zhao, 2016). 2.4. Floodwater spreading method Depressions are sunken areas below the surrounding terrain and can be easily flooded during heavy storms. Inundation models based on GIS techniques are widely used for flooding scopes and depths estimation (Gouldby et al., 2008; Krupka and Wallis, 2007; Zhang et al., 2014). This section describes the floodwater spreading process of the enhanced inundation model (EIM) in detail. The general idea of floodwater spreading in EIM is described as follows: the catchment is firstly delineated with several depression elements (Fig. 2(a)) based on GIS and each depression element has its own relationship between water level and water volume (named as H-V Curve). The flooding depth of depression elements can be rapidly estimated based on the volume boundary from SWMM and H-V Curve. In previous studies (Zhang and Pan, 2014), flow spreading only follows the positive order of flow accumulation value. In this study, spreading order was based on the topological relationship between depression outlets, and water volume from the lower depression elements was considered as feedback process in the EIM (Fig. 2(a)). The calculation process of EIM is described as follows:
Fig. 3. H-V curve of a depression element.
started to stabilize when the value of ΔH was less than 0.1 m. Therefore, ΔH was set to 0.1 in this study. (2) Determination of flow spreading order The spreading order of water between depression elements is mainly based on the location and elevation of the maximal water level of each depression (HL). The flow routes from the upper depression element with higher HL value to the contiguous depression element that has lower HL value in the positive process. To simplify the problem, each depression element has multiple inputs from upper depression elements and only one output to the lower element. The flow spreading order of a catchment can be described as several tree structures (Fig. 4). For a depression element Emn , the spreading order can be related to two parameters m and n. m is the level number of the tree and water transfer from high level to the root in the positive process. n is the node number in each level and is ordered by the maximum water level of each depression HL (from the lowest to the highest).
(1) Depression elements delineation and H-V Curve calculation Each depression element includes the depression area and the catchment of this depression. It can be extracted by using ArcGIS software with DEM. Depressions are calculated by the elevation difference between the filled DEM map and the original DEM map, and the catchments of depression are delineated based on the flow direction map with D8 algorithm (Fairfield and Leymarie, 1991). These procedures are processed with module of hydrology in ArcGIS software (Maidment, 2002). The sketch map of a depression element is shown in Fig. 2(a). Each depression element has a H-V Curve that reflects the relationship between the water level and volume of this depression element. The height of the depression element can be divided into J parts as given by Eq. (3).
(Hmax − Hmin ) J= ΔH
(3) The positive process The positive process calculated from the highest levels of depression elements to the root node. The floodwater volume changes at a certain time step t in a depression element (Emn ) can be described as Eq. (7).
ΔVmn, t = Vmn,,tp + Vmn,,tT + Vmt , u
where m is the level of depression elements, and n is the number of m= depression elements in each level. 1, 2, ...,M − 1, M , n = 1, 2, ...,N − 1, N , ΔVmn, t is the water volume
(3)
where Hmin is the minimal elevation of the depression element, in metre. Hmax is the maximal elevation of the depression element, in metre. ΔH is the water level interval during H-V curve calculation. ΔVHj is the water volume of each j height and can be estimated by Eq. (4).
ΔVHj = ΔH × S j
(4) j
where j is one part of the total J parts, j = 1, 2, 3, ...,J − 1, J . S is the depression area that elevation is less than H (j ) , in m2. H (j ) is the elevation at j th height, in metre.
H (j ) = Hmin + ΔH × j
(5)
There is a corresponding V (j ) for each H (j ) : J
V (j ) =
∑ ΔVHj 1
(7)
(6)
where V (j ) is the volume of water at j th height. The H-V Curve can be estimated by H (j ) as x-axis and V (j ) as y-axis (Fig. 3). HL and VL are maximal water level and volume of the depression in positive process. For each depression element, the elevation of depression outlet (Ho) is equal to HL. In this study, the H-V curve
Fig. 4. Spreading orders of depression elements in one tree structure. 876
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Fig. 5. Sketch map of water spreading in feedback process.
change of depression Emn at time t. Vmn,,tp is the water volume of surface interception at time t. Vmn,,tT is the volume of total floodwater in depression Emn at time t. Vmt , u is the water volume from upper depression element at time t. The water volume of a depression element at time t can be described in Eq. (8) as:
Vmn, t = Vmn, t − 1 + ΔVmn, t The water level of
⎧ ⎨Vmn, t > ⎩
The water level of these depressions in the feedback process can be interpolated with H-C curve ( f )̂ in Eq. (12). n−1 n Hf = f^ (V1,1 u ), s. t . V^m < V1,1 u < V^m
where Hf is the water level after the feedback process; V1,1 u is the water volume of root depression after positive process.
(8)
Emn
Vmn, t ≤ Vmn , L, Hmn, t n Vm, L, Hmn, t = Hmn , L,
at time t can be calculated as: 2.5. Method validation
= fmn (Vmn, t ) Vmn,,td
=
Vmn, t
−
Vmn , L
The EIM was compared with USISM and validated in the real storm of 21 July 2012 in the DHM catchment. According to the Code for the design of outdoor drainage systems of China (GB 50014-2006, 2016), the catchment can be divided into flooding areas and non-flooding areas by the critical water depth (0.15 m). Flooding areas are defined as the water depth greater than 0.15 m. Since water depth observations are only available at certain inundation sites in the study area, two methods were adopted for model validation to quantify the overall model performance. For inundation sites with observed water depth, relative errors (RE) was applied to evaluate the model performance. RE can be described as Eq. (13).
(9)
where Vmn , L is the maximum volume of Emn . Hmn , L is the maximum water level of Emn . fmn is the H-V curve function of Emn in positive process. Hmn, t is the water level that is interpolated with H-V curve. Vmn,,td is the water volume of overflow to the lower element. (4) The feedback process
V1,1 L is the maximum volume of the root depression in positive process. If V1,1 u ≤ V1,1 L , the calculation will stop (Fig. 5(a)) and the water level of this depression element will be interpolated with H-V curve of root element ( f11). Otherwise, the feedback process will start and the water level of E11 can exceed the maximum water level of this depression element and follow a new H-C curve ( f )̂ (Fig. 5(d)). The H-C curve in the feedback process can be calculated with Eq. (8) and one tree structure has only one feedback curve. f^ =
M
RE =
i=1 k=1
(10)
where f ̂ is the H-C curve in the feedback process of one tree structure. The maximum volume of each depression element will be modified as in Eq. (11). n Vm̂ = f ̂ (Hmn , L)
|Hs − Ho | × 100% Ho
(13)
where Hs is the simulated water depth and Ho is the observed water depth. In addition to RE metric, prediction rate curve (PRC) is applied to test the performance of model results because some inundation sites do not have water depth records. PRC is widely used for model validation in binary (1 and 0, i.e., inundated and non-inundated areas in this study) problems (Tehrany et al., 2013; Zhao et al., 2018). PRC is constructed by plotting the cumulative percentage of flood depth areas from the highest value to the lowest against the cumulative percentage of flooding evidence. A higher AUC value (area under the PRC curve) represents more inundation events falling in flooding areas with higher water depth.
N
∑ ∑ fik (H )
(12)
(11)
n Vm̂
is the maximum volume of the depression element in the where feedback process. 877
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Fig. 6. Floodwater generation by two methods during the storm of 21 July 2012.
3. Results and discussions 3.1. Method development and comparison Fig. 7. Distribution of floodwater in two methods (a: SCS-CN-D; b: SWMM model).
3.1.1. Floodwater generation Floodwater generation processes in USISM and EIM were compared in this study. In USISM, the floodwater was generated with the SCS model and average drainage velocity (SCS-CN-D) (Zhang and Pan, 2014). The floodwater generation process during the storm event of 21 July 2012 is shown in Fig. 6. The curve number (CN) was extracted from the NRCS CN global dataset (Zeng et al., 2017), with value ranges from 49 to 85 in the DHM catchment. For the whole catchment, the depth of floodwater in the SCS-CN-D model was about 76 mm, by subtracting the depth of drainage water (97 mm) from the total runoff depth (173 mm). In the EIM, the floodwater and drainage water depths were calculated by SWMM. The depth of total runoff (125 mm) and floodwater (60 mm) in the SWMM model were less than the corresponding results in the USISM. In the SCS-CN-D model, the maximum drainage capacity was set as 655 m3/s (18 mm/h) by experience (Zhang and Pan, 2014). The SWMM model can reflect the practical distribution of the drainage system, and the capacity was calibrated and validated with real storm events. Compared with observed drainage flow from the DHM gauge, the drainage capacity was overestimated by USISM in the DHM catchment. The distribution of floodwater by the two methods is shown in Fig. 7. In the USISM, the volume of floodwater was highly dependent on the area of each depression. USISM ignored the spatial distribution of the drainage process and did not reflect the surcharge from the river system (source flooding). By using USISM, the flood hazard would be underestimated near rivers, especially during heavy rainfall events and large urban catchment modelling. SWMM showed advantages on the drainage system modelling. In Fig. 7(b) the surcharges from rivers are successfully identified.
to downstream (higher FA value). Accordingly, the maximum water depth of each depression is HL and floodwater will flow to the next depression element when floodwater exceeds the maximum volume of this depression. However, some depression elements (root elements) in urban areas do not have lower depression elements in unfilled DEM. Thus, HL value and outlet location were used to determine spreading order in the proposed EIM to better represent the flow spreading process in urban areas. For example, E1 is the root depression of the Tree1 structure in the unfilled DEM (Fig. 8(b)), and floodwater should flow from E3 to E2, then to E1 in the unfilled DEM. However, the spreading order with flow accumulation was from E1 to E2 to E3 because these depressions were filled (Fig. 8(c)). The correct order of floodwater spreading can be achieved by introducing HL value and outlet location in the proposed EIM. When the volume of floodwater exceeds the VL of E2, floodwater will spread from E2 to E1 via HL2 (56 m) firstly but not HL3 (59 m). When floodwater of E1 exceeds the maximum volume of this depression, feedback process will work and floodwater can flow from E1 to E2 to E4 (HL4 = 58 m). The DHM catchment was delineated with 159 tree structures and 781 depression elements respectively in EIM. The Tree structures and depression elements are shown in Fig. 9. 3.1.3. Flood hazard map comparison This section compares the flood hazard map by USISM and EIM in two representative areas in the DHM catchment. The locations of the two areas are shown in Fig. 10(a). Tree1 is upstream of the catchment and inundations in Tree1 are mainly caused by non-source flooding. As shown in Fig. 10(b) and (c), the simulated flood scopes by the two methods in Tree1 are similar, and observed inundation site (A) can be successfully identified. Tree2 is midstream of the catchment and the
3.1.2. Flow spreading As mentioned earlier, flow spreading in USISM is only based on the flow accumulation (FA) value or order from upstream (lower FA value) 878
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Fig. 8. Flow spreading order of the Tree1 structure in the catchment. (a: Location of Tree1; b: DEM of the Tree1; c: Spreading order based on FA value; d: Spreading order based on HL value and outlet).
simulated by the two methods are shown in Fig. 11 and the model performance is evaluated with a PRC curve in Fig. 12. Specifically, 7.4% and 7% of the catchment area were simulated as flooding areas in both the EIM and UISIM. The EIM overperformed USISM in the study area with a higher AUC value (0.94). Compared with USISM, some depressions along the river are simulated as flooding areas in EIM. These depressions can reflect the inundation caused by source flooding. All inundation sites were successfully identified by EIM. However, two inundation sites were misclassified to non-flooding areas by USISM. Some sites in the study area have information about inundation depth during the storm and such information is valuable for model validation. Compared with the inundation location, the inundation depth information has more uncertainties, since the inundation depth was usually obtained by investigation after the storm and the obtained depth may not accurately represent the actual inundation during the storm. The results of observed and simulated depth from the two methods in this study area are shown in Table 1. From Table 1, it is clear that there was an obvious underestimation of inundation depth by USISM. The average relative errors of EIM and USISM are 15% and 34% respectively, which shows that EIM can better simulate inundation depth in the DHM catchment.
Fig. 9. Model structure of EIM. (Ho represents the elevation of depression outlet).
observed water depth of inundation site (B) is 2 m. The simulated water depth at site B is underestimated by USISM and can be correctly simulated by EIM (Figs. 13(e) and (f)) in Tree2. The reason for this is that source flooding occurred in Tree2 (Fig. 7(b)) that cannot be represented in USISM (Fig. 7(a)). Feedback process did not work in Tree1 because all the floodwater could be filled in the positive process. The flood scopes of Tree2, both before and after the feedback process, are shown in Figs. 13(e) and (f). Following the positive process, about 190,000 m3 of floodwater left in root depression needed to be filled with neighbour depressions in Tree2. Therefore, the flood hazard will be significantly underestimated by USISM, which does not have the feedback process; EIM shows superiority in such cases.
4. Conclusions and recommendations Most cities in China are suffering from urban flooding and the urban hydrological model is a useful tool for urban water management (Jiang et al., 2018; Salvadore et al., 2015). These models are efficient tools for flood prediction but show limitations on estimating flooding scopes. The proposed method in this paper can be easily coupled with the water volume boundary, which can be obtained from the urban hydrological model, to estimate the urban flooding scopes rapidly and accurately. The comparison in this study clearly demonstrates that EIM is superior to USISM in terms of flood hazard mapping in a large urban catchment. The EIM presents advantages as follows: (1) floodwater generation is based on the urban hydrological model in EIM. It considers source and non-source flooding and is suitable for mapping flood hazard in large urban catchments; (2) floodwater spreading is based on the topological relationship among depression
3.2. Method validation Drainage flow simulated by SWMM was validated with observed discharge data in a previous study (Xu and Zhao, 2016), which ensures the accurate simulation of the water balance in the catchment. In this section, we compared the observed inundation sites and the simulated flood depths and scopes by EIM and USISM. The inundation depth maps 879
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Fig. 10. Flood hazard map of two tree structures in the catchment. (a: Location of two tree structures; b: USISM of Tree1; c: EIM of Tree1; d: USISM of Tree2; e: EIM of Tree2 (without feedback process); f: EIM of Tree2 (with feedback process)).
(1) GDEM was used for model development in this study. GDEM is global coverage dataset that helps the application of EIM to other regions. However, GDEM is a type of DTM (Digital Terrain Model) which cannot accurately reflect buildings and other detailed urban landscapes. Thus, high-resolution datasets are necessary to properly model flooding in urban areas (Aktaruzzaman, 2011). However, these datasets are commercially sensitive in the study areas and GDEM is the finest resolution currently available. EIM is quite suitable for flood hazard mapping with high-resolution DEM because the computation consumption will not significantly grow with the increase of the data resolution. High-resolution datasets are desired to test the model robustness and accuracy in future works. (2) Water spreading between tree structures should be considered during extreme rainfall events. Due to data limitations, a representative storm was selected for model validation. The return time of this storm is about 10–20 years (Bai et al., 2012; Xu and Zhao, 2016) and there is no floodwater left after the feedback process. However, in some extreme rainfall events, such as 100-year return period storm, the floodwater may remain after all depression elements in one tree structure are filled after the feedback process. In that case, the floodwater will flow to downstream tree structures and this should be considered in modelling. Thus, the model performance should be further tested with more storms, especially extreme events when the data is available.
Conflict of interest None.
Fig. 11. Inundation depth maps of rainfall on 21 July 2012.
outlets; and (3) the feedback process of floodwater from the lower depression element was introduced in the calculation. It is more suitable for the inundation estimation in complex elevation areas, such as urban areas. There are also recommendations from this study:
Acknowledgements This study was financially supported by the National Key Research and Development Program of China (No.2017YFC1502701), the National Natural Science Foundation of China (No.51879008) and the China Scholarship Council (No.201700260088). 880
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Fig. 12. Model performances validated with the PRC curve. Table 1 The depth of inundation sites on 21 July 2012. Inundation sites
Lianhua Bridge Beijing West Railway Station Passage Kefeng Bridge Zhengyang Bridge Average RE
Observed depth (m)
2.00 1.00 1.00 0.50
EIM
USISM
Simulated depth (m)
RE (%)
Simulated depth (m)
RE (%)
2.02 0.86 0.84 0.36
1 14 16 28 15
1.36 0.40 0.77 0.39
32 60 23 22 34
Jiang, L., Chen, Y., Wang, H., 2015. Urban flood simulation based on the SWMM model. Proc. Int. Assoc. Hydrol. Sci. 368, 186–191. Jiang, Y., Zevenbergen, C., Ma, Y.C., 2018. Urban pluvial flooding and stormwater management: a contemporary review of China's challenges and “sponge cities” strategy. Environ. Sci. Policy 80, 132–143. Knighton, J., Lennon, E., Bastidas, L., White, E., 2016. Stormwater detention system parameter sensitivity and uncertainty analysis using SWMM. J. Hydrol. Eng. 21 (8), 05016014. Krupka, M., Wallis, S., Pender, G., Neélz, S., 2007. Some practical aspects of flood inundation modelling. Transport phenomena in hydraulics, Publications of the Institute of Geophysics. Polish Acad. Sci. E-7 (401), 129–135. Lee, K.S., Cho, W.S., Hwang, J.W., Byeon, S.J., Joo, K.H., 2015. Numerical simulation for reducing the flood damage of Green Park using MIKE URBAN. Int. J. Control Autom. 8 (1), 37–54. Li, G.F., Xiang, X.Y., Tong, Y.Y., Wang, H.M., 2013. Impact assessment of urbanization on flood risk in the Yangtze River Delta. Stoch. Env. Res. Risk A 27 (7), 1683–1693. Liao, Z.L., Zhang, G.Q., Wu, Z.H., He, Y., Chen, H., 2015. Combined sewer overflow control with LID based on SWMM: an example in Shanghai, China. Water Sci. Technol. 71 (8), 1136–1142. Maidment, D.R., 2002. Arc Hydro: GIS for Water Resources. ESRI Inc. Maksimović, Č., Prodanović, D., Boonya-Aroonnet, S., Leitão, J.P., Djordjević, S., Allitt, R., 2009. Overland flow and pathway analysis for modelling of urban pluvial flooding. J. Hydraul. Res. 47 (4), 512–523. McCuen, R.H., 1982. A Guide to Hydrologic Analysis Using SCS Methods. Prentice-Hall Inc. Neal, J.C., Fewtrell, T.J., Bates, P.D., Wright, N.G., 2010. A comparison of three parallelisation methods for 2D flood inundation models. Environ. Modell Softw. 25 (4), 398–411. Ntelekos, A.A., Oppenheimer, M., Smith, J.A., Miller, A.J., 2010. Urbanization, climate change and flood policy in the United States. Climatic Change 103 (3–4), 597–616. Ozdemir, H., Sampson, C.C., de Almeida, G.A.M., Bates, P.D., 2013. Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrol. Earth Syst. Sci. 17 (10), 4015–4030. Salvadore, E., Bronders, J., Batelaan, O., 2015. Hydrological modelling of urbanized catchments: a review and future directions. J. Hydrol 529, 62–81. Shi, H., Chen, J., Li, T., Wang, G., 2017. A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data. J. Hydro-Environ. Res. Shi, R., Pang, B., Zhao, G., Du, L.G., Zhong, Y.D., Zuo, P., 2014. Analyzing parameter sensitivity for the SWMM model in urban storm water simulation. J. Beijing Normal Univ. (Natural Sci.) 50 (5), 456–460 (in Chinese). Shi, R., Zhao, G., Pang, B., Jiang, Q.G., Zhen, T.T., 2016. Uncertainty analysis of SWMM model parameters based on GLUE method. Hydrology 36 (2), 1–6 (in Chinese). Smith, M.B., 2006. Comment on 'Analysis and modeling of flooding in urban drainage
References Aktaruzzaman, M., 2011. The use of LiDAR-derived high-resolution DSM and intensity data to support modelling of urban flooding. Proc. SPIE – Int. Soc. Opt. Eng. 8174 (1), 817406–817409. Ashley, R.M., Balmforth, D.J., Saul, A.J., Blanskby, J.D., 2005. Flooding in the future – predicting climate change, risks and responses in urban areas. Water Sci. Technol. 52 (5), 265–273. Bai, G.Y., Du, L.G., Zang, M., Zhao, H.Y., Zhong, Y.D., Liang, L.J., Wang, M.R., 2012. Rainfall runoff analysis of sotrm '7.21' in central urban area of Beijing. Beijing Shuiwu 5, 3–7 (in Chinese). Barco, J., Wong, K.M., Stenstrom, M.K., 2008. Automatic calibration of the US EPA SWMM model for a large urban catchment. J. Hydraul. Eng.-Asce. 134 (4), 466–474. Bathrellos, G.D., Gaki-Papanastassiou, K., Skilodimou, H.D., Papanastassiou, D., Chousianitis, K.G., 2012. Potential suitability for urban planning and industry development using natural hazard maps and geological-geomorphological parameters. Environ. Earth Sci. 66 (2), 537–548. News, B.B.C., 2017. In maps: Houston and Texas Flooding. BBC. Bisht, D.S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., Panda, A., 2016. Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Nat. Hazards 84 (2), 749–776. Cheng, T., Xu, Z.X., Hong, S.Y., Song, S.L., 2017. Flood risk zoning by using 2D hydrodynamic modeling: a case study in Jinan City. Math. Probl. Eng. Chu, Q., Peng, D.Z., Xu, Z.X., Meng, D.J., Zhen, T.T., Jiang, Q.G., 2014. Risk analysis of urban flood by using MIKE11 and MIKE21. J. Beijing Normal Univ. (natural Sci. 5, 446–451 (in Chinese). Fairfield, J., Leymarie, P., 1991. Drainage networks from grid digital elevation models. Water Resour. Res. 27 (5), 709–717. Fernandez, D.S., Lutz, M.A., 2010. Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Eng. Geol. 111 (1–4), 90–98. Fewtrell, T.J., Duncan, A., Sampson, C.C., Neal, J.C., Bates, P.D., 2011. Benchmarking urban flood models of varying complexity and scale using high resolution terrestrial LiDAR data. Phys. Chem. Earth 36 (7–8), 281–291. GB 50014-2006. Code for design of outdoor wastewater engineering (version 2016). (in Chinese). Gouldby, B., Sayers, P., Mulet-Marti, J., Hassan, M.A.A.M., Benwell, D., 2008. A methodology for regional-scale flood risk assessment. P I Civil Eng.-Wat. M 161 (3), 169–182. Hsu, M.H., Chen, S.H., Chang, T.J., 2000. Inundation simulation for urban drainage basin with storm sewer system. J. Hydrol 234 (1–2), 21–37. Huber, W.C., Dickinson, R.E., Barnwell, T.O., 1989. The USEPA SWMM4 Stormwater Management Model: Version 4 User's Manual. 4(4): 206-207. Huong, H.T.L., Pathirana, A., 2013. Urbanization and climate change impacts on future urban flooding in Can Tho city Vietnam. Hydrol. Earth Syst. Sci. 17 (1), 379–394.
881
Journal of Hydrology 571 (2019) 873–882
G. Zhao, et al.
to a piedmont city: a case study of Jinan City China. Water Sci. Technol. 70 (5), 858–864. Zeng, Z.Y., Tang, G.Q., Hong, Y., Zeng, C., Yang, Y., 2017. Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote Sens. Lett. 8 (6), 528–536. Zhang, D.L., Lin, Y., Zhao, P., Yu, X., Wang, S., Kang, H., Ding, Y., 2013. The Beijing extreme rainfall of 21 July 2012: “Right results” but for wrong reasons. Geophys. Res. Lett. 40 (7), 1426–1431. Zhang, S.H., Pan, B.Z., 2014. An urban storm-inundation simulation method based on GIS. J. Hydrol. 517, 260–268. Zhang, S.H., Wang, T.W., Zhao, B.H., 2014. Calculation and visualization of flood inundation based on a topographic triangle network. J. Hydrol. 509, 406–415. Zhao, G., Pang, B., Xu, Z.X., Du, L.G., Zhong, Y.D., 2014. Simulation of urban storm at Dahongmen drainage area by SWMM. J. Beijing Normal Univ. (Natural Sci.) 50 (5), 452–455 (in Chinese). Zhao, G., Pang, B., Xu, Z.X., Yue, J.J., Tu, T.B., 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Sci. Total Environ. 615, 1133–1142.
systems'. J. Hydrol. 317 (3–4), 355–363. Su, W.Z., Ye, G.B., Yao, S.M., Yang, G.S., 2014. Urban Land Pattern Impacts on Floods in a New District of China. Sustainability-Basel 6 (10), 6488–6508. Sun, N., Hong, B., Hall, M., 2014. Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation (GLUE) framework for a high-resolution urban sewershed. Hydrol. Process. 28 (6), 3018–3034. Tang, Z.Q., Yi, S.Z., Wang, C.H., Xiao, Y.F., 2018. Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment. Stoch Environ. Res. Risk A 32 (3), 701–714. Tehrany, M.S., Pradhan, B., Jebur, M.N., 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 504, 69–79. Wu, X.S., et al., 2017. Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: a case study in Dongguan City, China. J. Hydrol. 547, 428–442. Xu, Z., Zhao, G., 2016. Impact of urbanization on rainfall-runoff processes: case study in the Liangshui River Basin in Beijing, China. Proc. Int. Assoc. Hydrol. Sci. 373, 7–12. Yu, H.J., Huang, G.R., Wu, C.H., 2014. Application of the stormwater management model
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Research papers
An enhanced inundation method for urban flood hazard mapping at the large catchment scale
T
⁎
Gang Zhaoa,b, Zongxue Xua, , Bo Panga, Tongbi Tuc, Liyang Xud, Longgang Due a
College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK c J. Amorocho Hydraulics Laboratory, Dept. of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA d School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China e Hydrographic Station of Beijing, Beijing 100089, China b
A R T I C LE I N FO
A B S T R A C T
This manuscript was handled by G. Syme, Editor-in-Chief, with the assistance of Hazi Mohammad Azamathulla, Associate Editor
Urban flooding occurs frequently in the world and urban hydrological models are widely applied in urban flood management and disaster mitigation. In this study, an enhanced inundation method (EIM) for urban flood hazard mapping at the large catchment scale is proposed. EIM can be easily coupled with urban hydrological models and the coupled framework can consider both source flooding and non-source flooding in floodwater generation. In EIM, the floodwater spreading order in the positive process is based on the topological relationship between depression outlets; the floodwater from lower depression elements is considered as a feedback process. These improvements make this proposed method suitable for inundation estimation in large urban catchments. Dahongmen (DHM) catchment in Beijing, China was selected as the case study area to illustrate the applicability of the proposed method. Historical inundation records during one heavy storm were applied to test the performance of the method. EIM is compared with USISM (urban storm-inundation simulation method) on the flood hazard map in the DHM catchment, which reveals the effectiveness of the improvements. The results show that all inundation locations are successfully identified by EIM and are distributed in flooding areas (water depth greater than 0.15 m) in the catchment. The average relative error of simulated inundation depths is 15%, which indicates that EIM can successfully simulate flooding scopes and depths in the study area. The results revealed that EIM can be a valuable tool for mapping urban flood hazards at the large catchment scale based on GIS techniques.
Keywords: Enhanced inundation method Flood hazard map Large urban catchment GIS SWMM
1. Introduction Urban flood is regarded as a type of flash flood, which can result in serious loss of lives and property (Smith, 2006). For example, flooding caused by Hurricane Harvey in the city of Houston (Texas, USA) in 2017 led to thousands of homes flooded; the cost of reconstruction was estimated to be as high as $200 billion (BBC News, 2017). The flood hazard in urban areas is predicted to increase with the warmer climate and urban sprawl (Ashley et al., 2005; Huong and Pathirana, 2013; Li et al., 2013; Ntelekos et al., 2010). Urban flood hazard mapping at the large catchment scale can give a comprehensive understanding of the flood-prone areas of a city and is urgently required for city planning and disaster mitigation (Bathrellos et al., 2012; Fernandez and Lutz, 2010). The preferred method to reliably map flood hazard in urban areas is to use hydrodynamic models, such as MIKE URBAN (Lee et al., 2015), Info Works ICM (Cheng et al., 2017) and LISFLOOD-FP (Wu et al., ⁎
2017). These models are based on shallow water equations that describe the physical process of flooding and have been widely applied in small catchments (1–10 km2). The main challenge of applying the above hydrodynamic models in a large catchment (more than 100 km2) is the difficulty in preparing the required datasets (Fewtrell et al., 2011; Salvadore et al., 2015). For example, the detailed information of sewer systems for large urban catchments is difficult to acquire. This problem can be simplified by deducting drainage volume from runoff (Chu et al., 2014; Zhang and Pan, 2014). However, this deduction ignores the distribution of sewer systems and the drainage capacity is set by the modeler’s prior experience. Another challenge is the great computational cost of the hydrodynamic models that use high-resolution terrain datasets in large catchments. High-resolution terrain datasets, such as LiDAR-based Digital Elevation Model (DEM), have been successfully used for hydrodynamic modelling in small urban catchments (Fewtrell et al., 2011; Ozdemir et al., 2013). However, these datasets are seldom
Corresponding author at: Jingshi Building, Xinjiekouwai St., Haidian District, Beijing, China. E-mail address: [email protected] (Z. Xu).
https://doi.org/10.1016/j.jhydrol.2019.02.008 Received 7 April 2018; Received in revised form 6 February 2019; Accepted 8 February 2019 Available online 15 February 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. (a). River system, hydro-meteorological stations, inundation sites and digital elevation of study area; (b) Model structure of SWMM in the study area.
upper depression will receive floodwater from the lower depression when the water level of lower depression is higher than contiguous depressions. The structure of the proposed model in this study better describes urban flooding in a large catchment by adding the feedback process of floodwater from lower depression elements. In this study, an enhanced inundation method (EIM) was proposed for urban hazard mapping at the large catchment scale. This method introduced the SWMM model for floodwater generation, and floodwater spreading was based on the topological relation of depression outlets. A large catchment, Dahongmen (DHM), in Beijing, China, was selected as a case study area to apply and validate the EIM. Two representative areas in the DHM catchment were selected to compare the corresponding flood hazard mapping performance based on EIM and USISM. Historical inundation datasets were used to test the performance of EIM in terms of inundation locations and water depths.
used in large catchments because of the high computational demand (Neal et al., 2010). These challenges limit the applicability of hydrodynamic models for urban flood hazard mapping at the large catchment scale. Geographic Information System (GIS) is an effective tool to map flood hazard at the large catchment scale. The most popular method is the multi-criteria decision approach (MCDA) (Fernandez and Lutz, 2010; Tang et al., 2018). MCDA identifies flood-prone areas using different explanatory factors based on GIS techniques. However, MCDA cannot provide the scope and depth of flooding. Some inundation methods based on GIS such as RFIM (Krupka and Wallis, 2007), RFSM (Gouldby et al., 2008) and FCDC (Zhang et al., 2014) have been proposed for mapping the scope and depth of flooding. These methods can simulate the spreading process of floodwater, but they do not consider the process of floodwater generation. To consider the floodwater generation process, Zhang and Pan (2014) proposed an urban flood hazard mapping framework named urban storm-inundation simulation method (USISM) and successfully applied it to a small catchment in Harbin, Helongjiang Province, China. However, USISM only included a simple floodwater generation model (SCS-CN model), and floodwater spreading order is based on flow accumulation (FA). Furthermore, this method focused only on non-source flooding in urban areas. Consequently, the applicability of the method proposed by Zhang and Pan (2014) is limited when applied to a large urban catchment. To improve the simulation of urban flooding in a large catchment, two improvements were further introduced in this study based on the USISM: the first was to introduce an urban hydrological model (SWMM) to provide the volume of floodwater in floodwater generation. In the USISM method, runoff calculation was based on the SCS-CN model (McCuen, 1982) and the sewer system runoff was represented by one parameter in the water balance model. The exact value of this parameter is very difficult to obtain because of the complexity of the urban rainfall-runoff process and the very limited sewer system data (Zhang and Pan, 2014). USISM also only accounts for non-source flooding and ignores floodwater from rivers. The SWMM model is a popular urban hydrological model and has been widely used for urban rainfall-runoff modelling in large catchment case studies (Barco et al., 2008; Bisht et al., 2016). SWMM, which can provide the volume of floodwater in the drainage system, shows advantages in urban runoff simulation (Hsu et al., 2000). The second improvement was that the floodwater spreading order was determined by the elevation and location of depression outlets, but not by the value of FA. The value of FA reflects the order of water spreading (from upper to lower) in filled DEM. In urban areas, the elevation of contiguous depression changes significantly and some local depressions are easily flooded. These areas are usually ignored in hydrological modelling because of the depression filling process. The order of water spreading in these areas does not strictly follow the order of the FA value (Maksimović et al., 2009). Furthermore, the
2. Materials and methods 2.1. Study area description DHM catchment (39°48′–39°55′N, 116°9′–116°24′E) is located upstream of the Liangshui river in the metropolitan area of Beijing, China. The annual precipitation of this area is about 520 mm and 80% of the precipitation is dispersed during heavy summer storms. The terrain of this catchment shows a downtrend from the northwest to the southeast (Fig. 1(a)) with a total area of 131 km2. DHM catchment has been urbanized rapidly in the past decades and has gradually become a Beijing metropolitan transport hub. About 90% of this catchment area has been modified to be waterproof. This catchment is one of the flood-prone areas in the city and 30% of the inundation sites during the storm of 21 July 2012 were located in this catchment. This 2012 storm caused the heaviest urban flood in the past two decades; it produced 200 mm excess rainfall in the urban area, causing 79 casualties and 11.6 billion Chinese Yuan (nearly $2 billion) in damage to Beijing city (Shi et al., 2017; Zhang et al., 2013). 2.2. Data description EIM spreads floodwater based on the DEM data. In this study, the DEM dataset was provided by Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 1 (GDEM). The resolution of GDEM used in this study is 30-metre and the resolution can successfully identify the depressions for a large catchment by comparing them with sunken areas in the DHM catchment. One historical inundation dataset in the DHM catchment was used for model validation. The dataset comprised 14 inundation sites during the heavy storm of 21 July 2012 in the DHM catchment. These inundation sites were published online (http://map.sogou.com/special/jishui/) 874
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and geo-referenced by GF-2 images. The locations of inundation sites during the 2012 storm are shown in Fig. 1(a). Water depth observations of four inundation sites were provided by the National Disaster Reduction Centre of China.
volume of sourced floodwater (Vs) in SWMM is directly reflected by the volume of overflow at the river or sewer junction points. The volume of non-sourced floodwater (Vn) and total floodwater (VT) for each depression element in one sub-catchment is calculated by Eqs. (1) and (2) respectively.
2.3. Floodwater generation method
Vn = (P − I − D) × Ad
(1)
where P is the depth of precipitation (mm) in the sub-catchment; I is the depth of infiltration (mm) in the sub-catchment; D is the depth of drainage water (mm) in the sub-catchment and Ad is the area of one depression element (m2) in the sub-catchment.
The Storm Water Management Model (SWMM) was selected to generate the volume of floodwater for EIM. SWMM was developed by the Environmental Protection Agency (EPA) of America for water planning and management in urban areas (Huber et al., 1989). SWMM has been widely used in urban water management in many cities of China (Jiang et al., 2015; Liao et al., 2015; Su et al., 2014; Yu et al., 2014). The model applied in the DHM catchment of Beijing city was originally developed and validated in terms of discharge process by Zhao et al. (2014), and improved by considering the impact of urbanization on the rainfall runoff process (Xu and Zhao, 2016). The uncertainty of the SWMM application in urban catchments was studied by Morris and GLUE methods (Knighton et al., 2016; Shi et al., 2014; Shi et al., 2016; Sun et al., 2014). The model structure of SWMM in this study is shown in Fig. 1(b). The DHM catchment was delineated with 79 sub-catchments in SWMM and each sub-catchment comprised several depression elements (Fig. 2(a)). The EIM and SWMM were coupled by the volume of floodwater. SWMM generates runoff in each sub-catchment and transports this runoff through drainage and river system. It can quantitatively calculate the volume of sourced and non-sourced floodwater. The
M
VT =
∑ Vsm + Vn m=1
(2)
where M is the number of junction points in one depression element and Vsm is the volume of floodwater of junction m. In the SWMM model, the Horton model and Kinematic wave model were selected for estimating the amount of infiltration and drainage water (Xu and Zhao, 2016). Four hourly precipitation gauges, Dahongmen (DHM), Youanmen (YAM), Shijingshan (SJS) and Longyuanzha (LYZ) were used as model driven data. Hourly discharge data of the DHM station was used for validation in terms of water balance in this catchment. Observed precipitation and discharge data were provided by the Hydrographic Station of Beijing. The river and sewer system datasets were provided by the Hydrographic Station of Beijing and the Beijing Municipal Institute of City Planning and Design, respectively. These datasets include locations, section shapes and
Fig. 2. The sketch map of depression elements. (D1, D2 and D3 are depressions; E1, E2 and E3 are depression elements which represent the catchment of these depressions. O1 and O2 are depression outlets). 875
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conveyance capacities of the river and sewer system in the study area. The performance of SWMM in terms of drainage flow was calibrated and validated with four real storms (including the storm of 21 July 2012) in previous studies (Xu and Zhao, 2016). 2.4. Floodwater spreading method Depressions are sunken areas below the surrounding terrain and can be easily flooded during heavy storms. Inundation models based on GIS techniques are widely used for flooding scopes and depths estimation (Gouldby et al., 2008; Krupka and Wallis, 2007; Zhang et al., 2014). This section describes the floodwater spreading process of the enhanced inundation model (EIM) in detail. The general idea of floodwater spreading in EIM is described as follows: the catchment is firstly delineated with several depression elements (Fig. 2(a)) based on GIS and each depression element has its own relationship between water level and water volume (named as H-V Curve). The flooding depth of depression elements can be rapidly estimated based on the volume boundary from SWMM and H-V Curve. In previous studies (Zhang and Pan, 2014), flow spreading only follows the positive order of flow accumulation value. In this study, spreading order was based on the topological relationship between depression outlets, and water volume from the lower depression elements was considered as feedback process in the EIM (Fig. 2(a)). The calculation process of EIM is described as follows:
Fig. 3. H-V curve of a depression element.
started to stabilize when the value of ΔH was less than 0.1 m. Therefore, ΔH was set to 0.1 in this study. (2) Determination of flow spreading order The spreading order of water between depression elements is mainly based on the location and elevation of the maximal water level of each depression (HL). The flow routes from the upper depression element with higher HL value to the contiguous depression element that has lower HL value in the positive process. To simplify the problem, each depression element has multiple inputs from upper depression elements and only one output to the lower element. The flow spreading order of a catchment can be described as several tree structures (Fig. 4). For a depression element Emn , the spreading order can be related to two parameters m and n. m is the level number of the tree and water transfer from high level to the root in the positive process. n is the node number in each level and is ordered by the maximum water level of each depression HL (from the lowest to the highest).
(1) Depression elements delineation and H-V Curve calculation Each depression element includes the depression area and the catchment of this depression. It can be extracted by using ArcGIS software with DEM. Depressions are calculated by the elevation difference between the filled DEM map and the original DEM map, and the catchments of depression are delineated based on the flow direction map with D8 algorithm (Fairfield and Leymarie, 1991). These procedures are processed with module of hydrology in ArcGIS software (Maidment, 2002). The sketch map of a depression element is shown in Fig. 2(a). Each depression element has a H-V Curve that reflects the relationship between the water level and volume of this depression element. The height of the depression element can be divided into J parts as given by Eq. (3).
(Hmax − Hmin ) J= ΔH
(3) The positive process The positive process calculated from the highest levels of depression elements to the root node. The floodwater volume changes at a certain time step t in a depression element (Emn ) can be described as Eq. (7).
ΔVmn, t = Vmn,,tp + Vmn,,tT + Vmt , u
where m is the level of depression elements, and n is the number of m= depression elements in each level. 1, 2, ...,M − 1, M , n = 1, 2, ...,N − 1, N , ΔVmn, t is the water volume
(3)
where Hmin is the minimal elevation of the depression element, in metre. Hmax is the maximal elevation of the depression element, in metre. ΔH is the water level interval during H-V curve calculation. ΔVHj is the water volume of each j height and can be estimated by Eq. (4).
ΔVHj = ΔH × S j
(4) j
where j is one part of the total J parts, j = 1, 2, 3, ...,J − 1, J . S is the depression area that elevation is less than H (j ) , in m2. H (j ) is the elevation at j th height, in metre.
H (j ) = Hmin + ΔH × j
(5)
There is a corresponding V (j ) for each H (j ) : J
V (j ) =
∑ ΔVHj 1
(7)
(6)
where V (j ) is the volume of water at j th height. The H-V Curve can be estimated by H (j ) as x-axis and V (j ) as y-axis (Fig. 3). HL and VL are maximal water level and volume of the depression in positive process. For each depression element, the elevation of depression outlet (Ho) is equal to HL. In this study, the H-V curve
Fig. 4. Spreading orders of depression elements in one tree structure. 876
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Fig. 5. Sketch map of water spreading in feedback process.
change of depression Emn at time t. Vmn,,tp is the water volume of surface interception at time t. Vmn,,tT is the volume of total floodwater in depression Emn at time t. Vmt , u is the water volume from upper depression element at time t. The water volume of a depression element at time t can be described in Eq. (8) as:
Vmn, t = Vmn, t − 1 + ΔVmn, t The water level of
⎧ ⎨Vmn, t > ⎩
The water level of these depressions in the feedback process can be interpolated with H-C curve ( f )̂ in Eq. (12). n−1 n Hf = f^ (V1,1 u ), s. t . V^m < V1,1 u < V^m
where Hf is the water level after the feedback process; V1,1 u is the water volume of root depression after positive process.
(8)
Emn
Vmn, t ≤ Vmn , L, Hmn, t n Vm, L, Hmn, t = Hmn , L,
at time t can be calculated as: 2.5. Method validation
= fmn (Vmn, t ) Vmn,,td
=
Vmn, t
−
Vmn , L
The EIM was compared with USISM and validated in the real storm of 21 July 2012 in the DHM catchment. According to the Code for the design of outdoor drainage systems of China (GB 50014-2006, 2016), the catchment can be divided into flooding areas and non-flooding areas by the critical water depth (0.15 m). Flooding areas are defined as the water depth greater than 0.15 m. Since water depth observations are only available at certain inundation sites in the study area, two methods were adopted for model validation to quantify the overall model performance. For inundation sites with observed water depth, relative errors (RE) was applied to evaluate the model performance. RE can be described as Eq. (13).
(9)
where Vmn , L is the maximum volume of Emn . Hmn , L is the maximum water level of Emn . fmn is the H-V curve function of Emn in positive process. Hmn, t is the water level that is interpolated with H-V curve. Vmn,,td is the water volume of overflow to the lower element. (4) The feedback process
V1,1 L is the maximum volume of the root depression in positive process. If V1,1 u ≤ V1,1 L , the calculation will stop (Fig. 5(a)) and the water level of this depression element will be interpolated with H-V curve of root element ( f11). Otherwise, the feedback process will start and the water level of E11 can exceed the maximum water level of this depression element and follow a new H-C curve ( f )̂ (Fig. 5(d)). The H-C curve in the feedback process can be calculated with Eq. (8) and one tree structure has only one feedback curve. f^ =
M
RE =
i=1 k=1
(10)
where f ̂ is the H-C curve in the feedback process of one tree structure. The maximum volume of each depression element will be modified as in Eq. (11). n Vm̂ = f ̂ (Hmn , L)
|Hs − Ho | × 100% Ho
(13)
where Hs is the simulated water depth and Ho is the observed water depth. In addition to RE metric, prediction rate curve (PRC) is applied to test the performance of model results because some inundation sites do not have water depth records. PRC is widely used for model validation in binary (1 and 0, i.e., inundated and non-inundated areas in this study) problems (Tehrany et al., 2013; Zhao et al., 2018). PRC is constructed by plotting the cumulative percentage of flood depth areas from the highest value to the lowest against the cumulative percentage of flooding evidence. A higher AUC value (area under the PRC curve) represents more inundation events falling in flooding areas with higher water depth.
N
∑ ∑ fik (H )
(12)
(11)
n Vm̂
is the maximum volume of the depression element in the where feedback process. 877
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Fig. 6. Floodwater generation by two methods during the storm of 21 July 2012.
3. Results and discussions 3.1. Method development and comparison Fig. 7. Distribution of floodwater in two methods (a: SCS-CN-D; b: SWMM model).
3.1.1. Floodwater generation Floodwater generation processes in USISM and EIM were compared in this study. In USISM, the floodwater was generated with the SCS model and average drainage velocity (SCS-CN-D) (Zhang and Pan, 2014). The floodwater generation process during the storm event of 21 July 2012 is shown in Fig. 6. The curve number (CN) was extracted from the NRCS CN global dataset (Zeng et al., 2017), with value ranges from 49 to 85 in the DHM catchment. For the whole catchment, the depth of floodwater in the SCS-CN-D model was about 76 mm, by subtracting the depth of drainage water (97 mm) from the total runoff depth (173 mm). In the EIM, the floodwater and drainage water depths were calculated by SWMM. The depth of total runoff (125 mm) and floodwater (60 mm) in the SWMM model were less than the corresponding results in the USISM. In the SCS-CN-D model, the maximum drainage capacity was set as 655 m3/s (18 mm/h) by experience (Zhang and Pan, 2014). The SWMM model can reflect the practical distribution of the drainage system, and the capacity was calibrated and validated with real storm events. Compared with observed drainage flow from the DHM gauge, the drainage capacity was overestimated by USISM in the DHM catchment. The distribution of floodwater by the two methods is shown in Fig. 7. In the USISM, the volume of floodwater was highly dependent on the area of each depression. USISM ignored the spatial distribution of the drainage process and did not reflect the surcharge from the river system (source flooding). By using USISM, the flood hazard would be underestimated near rivers, especially during heavy rainfall events and large urban catchment modelling. SWMM showed advantages on the drainage system modelling. In Fig. 7(b) the surcharges from rivers are successfully identified.
to downstream (higher FA value). Accordingly, the maximum water depth of each depression is HL and floodwater will flow to the next depression element when floodwater exceeds the maximum volume of this depression. However, some depression elements (root elements) in urban areas do not have lower depression elements in unfilled DEM. Thus, HL value and outlet location were used to determine spreading order in the proposed EIM to better represent the flow spreading process in urban areas. For example, E1 is the root depression of the Tree1 structure in the unfilled DEM (Fig. 8(b)), and floodwater should flow from E3 to E2, then to E1 in the unfilled DEM. However, the spreading order with flow accumulation was from E1 to E2 to E3 because these depressions were filled (Fig. 8(c)). The correct order of floodwater spreading can be achieved by introducing HL value and outlet location in the proposed EIM. When the volume of floodwater exceeds the VL of E2, floodwater will spread from E2 to E1 via HL2 (56 m) firstly but not HL3 (59 m). When floodwater of E1 exceeds the maximum volume of this depression, feedback process will work and floodwater can flow from E1 to E2 to E4 (HL4 = 58 m). The DHM catchment was delineated with 159 tree structures and 781 depression elements respectively in EIM. The Tree structures and depression elements are shown in Fig. 9. 3.1.3. Flood hazard map comparison This section compares the flood hazard map by USISM and EIM in two representative areas in the DHM catchment. The locations of the two areas are shown in Fig. 10(a). Tree1 is upstream of the catchment and inundations in Tree1 are mainly caused by non-source flooding. As shown in Fig. 10(b) and (c), the simulated flood scopes by the two methods in Tree1 are similar, and observed inundation site (A) can be successfully identified. Tree2 is midstream of the catchment and the
3.1.2. Flow spreading As mentioned earlier, flow spreading in USISM is only based on the flow accumulation (FA) value or order from upstream (lower FA value) 878
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Fig. 8. Flow spreading order of the Tree1 structure in the catchment. (a: Location of Tree1; b: DEM of the Tree1; c: Spreading order based on FA value; d: Spreading order based on HL value and outlet).
simulated by the two methods are shown in Fig. 11 and the model performance is evaluated with a PRC curve in Fig. 12. Specifically, 7.4% and 7% of the catchment area were simulated as flooding areas in both the EIM and UISIM. The EIM overperformed USISM in the study area with a higher AUC value (0.94). Compared with USISM, some depressions along the river are simulated as flooding areas in EIM. These depressions can reflect the inundation caused by source flooding. All inundation sites were successfully identified by EIM. However, two inundation sites were misclassified to non-flooding areas by USISM. Some sites in the study area have information about inundation depth during the storm and such information is valuable for model validation. Compared with the inundation location, the inundation depth information has more uncertainties, since the inundation depth was usually obtained by investigation after the storm and the obtained depth may not accurately represent the actual inundation during the storm. The results of observed and simulated depth from the two methods in this study area are shown in Table 1. From Table 1, it is clear that there was an obvious underestimation of inundation depth by USISM. The average relative errors of EIM and USISM are 15% and 34% respectively, which shows that EIM can better simulate inundation depth in the DHM catchment.
Fig. 9. Model structure of EIM. (Ho represents the elevation of depression outlet).
observed water depth of inundation site (B) is 2 m. The simulated water depth at site B is underestimated by USISM and can be correctly simulated by EIM (Figs. 13(e) and (f)) in Tree2. The reason for this is that source flooding occurred in Tree2 (Fig. 7(b)) that cannot be represented in USISM (Fig. 7(a)). Feedback process did not work in Tree1 because all the floodwater could be filled in the positive process. The flood scopes of Tree2, both before and after the feedback process, are shown in Figs. 13(e) and (f). Following the positive process, about 190,000 m3 of floodwater left in root depression needed to be filled with neighbour depressions in Tree2. Therefore, the flood hazard will be significantly underestimated by USISM, which does not have the feedback process; EIM shows superiority in such cases.
4. Conclusions and recommendations Most cities in China are suffering from urban flooding and the urban hydrological model is a useful tool for urban water management (Jiang et al., 2018; Salvadore et al., 2015). These models are efficient tools for flood prediction but show limitations on estimating flooding scopes. The proposed method in this paper can be easily coupled with the water volume boundary, which can be obtained from the urban hydrological model, to estimate the urban flooding scopes rapidly and accurately. The comparison in this study clearly demonstrates that EIM is superior to USISM in terms of flood hazard mapping in a large urban catchment. The EIM presents advantages as follows: (1) floodwater generation is based on the urban hydrological model in EIM. It considers source and non-source flooding and is suitable for mapping flood hazard in large urban catchments; (2) floodwater spreading is based on the topological relationship among depression
3.2. Method validation Drainage flow simulated by SWMM was validated with observed discharge data in a previous study (Xu and Zhao, 2016), which ensures the accurate simulation of the water balance in the catchment. In this section, we compared the observed inundation sites and the simulated flood depths and scopes by EIM and USISM. The inundation depth maps 879
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Fig. 10. Flood hazard map of two tree structures in the catchment. (a: Location of two tree structures; b: USISM of Tree1; c: EIM of Tree1; d: USISM of Tree2; e: EIM of Tree2 (without feedback process); f: EIM of Tree2 (with feedback process)).
(1) GDEM was used for model development in this study. GDEM is global coverage dataset that helps the application of EIM to other regions. However, GDEM is a type of DTM (Digital Terrain Model) which cannot accurately reflect buildings and other detailed urban landscapes. Thus, high-resolution datasets are necessary to properly model flooding in urban areas (Aktaruzzaman, 2011). However, these datasets are commercially sensitive in the study areas and GDEM is the finest resolution currently available. EIM is quite suitable for flood hazard mapping with high-resolution DEM because the computation consumption will not significantly grow with the increase of the data resolution. High-resolution datasets are desired to test the model robustness and accuracy in future works. (2) Water spreading between tree structures should be considered during extreme rainfall events. Due to data limitations, a representative storm was selected for model validation. The return time of this storm is about 10–20 years (Bai et al., 2012; Xu and Zhao, 2016) and there is no floodwater left after the feedback process. However, in some extreme rainfall events, such as 100-year return period storm, the floodwater may remain after all depression elements in one tree structure are filled after the feedback process. In that case, the floodwater will flow to downstream tree structures and this should be considered in modelling. Thus, the model performance should be further tested with more storms, especially extreme events when the data is available.
Conflict of interest None.
Fig. 11. Inundation depth maps of rainfall on 21 July 2012.
outlets; and (3) the feedback process of floodwater from the lower depression element was introduced in the calculation. It is more suitable for the inundation estimation in complex elevation areas, such as urban areas. There are also recommendations from this study:
Acknowledgements This study was financially supported by the National Key Research and Development Program of China (No.2017YFC1502701), the National Natural Science Foundation of China (No.51879008) and the China Scholarship Council (No.201700260088). 880
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Fig. 12. Model performances validated with the PRC curve. Table 1 The depth of inundation sites on 21 July 2012. Inundation sites
Lianhua Bridge Beijing West Railway Station Passage Kefeng Bridge Zhengyang Bridge Average RE
Observed depth (m)
2.00 1.00 1.00 0.50
EIM
USISM
Simulated depth (m)
RE (%)
Simulated depth (m)
RE (%)
2.02 0.86 0.84 0.36
1 14 16 28 15
1.36 0.40 0.77 0.39
32 60 23 22 34
Jiang, L., Chen, Y., Wang, H., 2015. Urban flood simulation based on the SWMM model. Proc. Int. Assoc. Hydrol. Sci. 368, 186–191. Jiang, Y., Zevenbergen, C., Ma, Y.C., 2018. Urban pluvial flooding and stormwater management: a contemporary review of China's challenges and “sponge cities” strategy. Environ. Sci. Policy 80, 132–143. Knighton, J., Lennon, E., Bastidas, L., White, E., 2016. Stormwater detention system parameter sensitivity and uncertainty analysis using SWMM. J. Hydrol. Eng. 21 (8), 05016014. Krupka, M., Wallis, S., Pender, G., Neélz, S., 2007. Some practical aspects of flood inundation modelling. Transport phenomena in hydraulics, Publications of the Institute of Geophysics. Polish Acad. Sci. E-7 (401), 129–135. Lee, K.S., Cho, W.S., Hwang, J.W., Byeon, S.J., Joo, K.H., 2015. Numerical simulation for reducing the flood damage of Green Park using MIKE URBAN. Int. J. Control Autom. 8 (1), 37–54. Li, G.F., Xiang, X.Y., Tong, Y.Y., Wang, H.M., 2013. Impact assessment of urbanization on flood risk in the Yangtze River Delta. Stoch. Env. Res. Risk A 27 (7), 1683–1693. Liao, Z.L., Zhang, G.Q., Wu, Z.H., He, Y., Chen, H., 2015. Combined sewer overflow control with LID based on SWMM: an example in Shanghai, China. Water Sci. Technol. 71 (8), 1136–1142. Maidment, D.R., 2002. Arc Hydro: GIS for Water Resources. ESRI Inc. Maksimović, Č., Prodanović, D., Boonya-Aroonnet, S., Leitão, J.P., Djordjević, S., Allitt, R., 2009. Overland flow and pathway analysis for modelling of urban pluvial flooding. J. Hydraul. Res. 47 (4), 512–523. McCuen, R.H., 1982. A Guide to Hydrologic Analysis Using SCS Methods. Prentice-Hall Inc. Neal, J.C., Fewtrell, T.J., Bates, P.D., Wright, N.G., 2010. A comparison of three parallelisation methods for 2D flood inundation models. Environ. Modell Softw. 25 (4), 398–411. Ntelekos, A.A., Oppenheimer, M., Smith, J.A., Miller, A.J., 2010. Urbanization, climate change and flood policy in the United States. Climatic Change 103 (3–4), 597–616. Ozdemir, H., Sampson, C.C., de Almeida, G.A.M., Bates, P.D., 2013. Evaluating scale and roughness effects in urban flood modelling using terrestrial LIDAR data. Hydrol. Earth Syst. Sci. 17 (10), 4015–4030. Salvadore, E., Bronders, J., Batelaan, O., 2015. Hydrological modelling of urbanized catchments: a review and future directions. J. Hydrol 529, 62–81. Shi, H., Chen, J., Li, T., Wang, G., 2017. A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data. J. Hydro-Environ. Res. Shi, R., Pang, B., Zhao, G., Du, L.G., Zhong, Y.D., Zuo, P., 2014. Analyzing parameter sensitivity for the SWMM model in urban storm water simulation. J. Beijing Normal Univ. (Natural Sci.) 50 (5), 456–460 (in Chinese). Shi, R., Zhao, G., Pang, B., Jiang, Q.G., Zhen, T.T., 2016. Uncertainty analysis of SWMM model parameters based on GLUE method. Hydrology 36 (2), 1–6 (in Chinese). Smith, M.B., 2006. Comment on 'Analysis and modeling of flooding in urban drainage
References Aktaruzzaman, M., 2011. The use of LiDAR-derived high-resolution DSM and intensity data to support modelling of urban flooding. Proc. SPIE – Int. Soc. Opt. Eng. 8174 (1), 817406–817409. Ashley, R.M., Balmforth, D.J., Saul, A.J., Blanskby, J.D., 2005. Flooding in the future – predicting climate change, risks and responses in urban areas. Water Sci. Technol. 52 (5), 265–273. Bai, G.Y., Du, L.G., Zang, M., Zhao, H.Y., Zhong, Y.D., Liang, L.J., Wang, M.R., 2012. Rainfall runoff analysis of sotrm '7.21' in central urban area of Beijing. Beijing Shuiwu 5, 3–7 (in Chinese). Barco, J., Wong, K.M., Stenstrom, M.K., 2008. Automatic calibration of the US EPA SWMM model for a large urban catchment. J. Hydraul. Eng.-Asce. 134 (4), 466–474. Bathrellos, G.D., Gaki-Papanastassiou, K., Skilodimou, H.D., Papanastassiou, D., Chousianitis, K.G., 2012. Potential suitability for urban planning and industry development using natural hazard maps and geological-geomorphological parameters. Environ. Earth Sci. 66 (2), 537–548. News, B.B.C., 2017. In maps: Houston and Texas Flooding. BBC. Bisht, D.S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., Panda, A., 2016. Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Nat. Hazards 84 (2), 749–776. Cheng, T., Xu, Z.X., Hong, S.Y., Song, S.L., 2017. Flood risk zoning by using 2D hydrodynamic modeling: a case study in Jinan City. Math. Probl. Eng. Chu, Q., Peng, D.Z., Xu, Z.X., Meng, D.J., Zhen, T.T., Jiang, Q.G., 2014. Risk analysis of urban flood by using MIKE11 and MIKE21. J. Beijing Normal Univ. (natural Sci. 5, 446–451 (in Chinese). Fairfield, J., Leymarie, P., 1991. Drainage networks from grid digital elevation models. Water Resour. Res. 27 (5), 709–717. Fernandez, D.S., Lutz, M.A., 2010. Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Eng. Geol. 111 (1–4), 90–98. Fewtrell, T.J., Duncan, A., Sampson, C.C., Neal, J.C., Bates, P.D., 2011. Benchmarking urban flood models of varying complexity and scale using high resolution terrestrial LiDAR data. Phys. Chem. Earth 36 (7–8), 281–291. GB 50014-2006. Code for design of outdoor wastewater engineering (version 2016). (in Chinese). Gouldby, B., Sayers, P., Mulet-Marti, J., Hassan, M.A.A.M., Benwell, D., 2008. A methodology for regional-scale flood risk assessment. P I Civil Eng.-Wat. M 161 (3), 169–182. Hsu, M.H., Chen, S.H., Chang, T.J., 2000. Inundation simulation for urban drainage basin with storm sewer system. J. Hydrol 234 (1–2), 21–37. Huber, W.C., Dickinson, R.E., Barnwell, T.O., 1989. The USEPA SWMM4 Stormwater Management Model: Version 4 User's Manual. 4(4): 206-207. Huong, H.T.L., Pathirana, A., 2013. Urbanization and climate change impacts on future urban flooding in Can Tho city Vietnam. Hydrol. Earth Syst. Sci. 17 (1), 379–394.
881
Journal of Hydrology 571 (2019) 873–882
G. Zhao, et al.
to a piedmont city: a case study of Jinan City China. Water Sci. Technol. 70 (5), 858–864. Zeng, Z.Y., Tang, G.Q., Hong, Y., Zeng, C., Yang, Y., 2017. Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications. Remote Sens. Lett. 8 (6), 528–536. Zhang, D.L., Lin, Y., Zhao, P., Yu, X., Wang, S., Kang, H., Ding, Y., 2013. The Beijing extreme rainfall of 21 July 2012: “Right results” but for wrong reasons. Geophys. Res. Lett. 40 (7), 1426–1431. Zhang, S.H., Pan, B.Z., 2014. An urban storm-inundation simulation method based on GIS. J. Hydrol. 517, 260–268. Zhang, S.H., Wang, T.W., Zhao, B.H., 2014. Calculation and visualization of flood inundation based on a topographic triangle network. J. Hydrol. 509, 406–415. Zhao, G., Pang, B., Xu, Z.X., Du, L.G., Zhong, Y.D., 2014. Simulation of urban storm at Dahongmen drainage area by SWMM. J. Beijing Normal Univ. (Natural Sci.) 50 (5), 452–455 (in Chinese). Zhao, G., Pang, B., Xu, Z.X., Yue, J.J., Tu, T.B., 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Sci. Total Environ. 615, 1133–1142.
systems'. J. Hydrol. 317 (3–4), 355–363. Su, W.Z., Ye, G.B., Yao, S.M., Yang, G.S., 2014. Urban Land Pattern Impacts on Floods in a New District of China. Sustainability-Basel 6 (10), 6488–6508. Sun, N., Hong, B., Hall, M., 2014. Assessment of the SWMM model uncertainties within the generalized likelihood uncertainty estimation (GLUE) framework for a high-resolution urban sewershed. Hydrol. Process. 28 (6), 3018–3034. Tang, Z.Q., Yi, S.Z., Wang, C.H., Xiao, Y.F., 2018. Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment. Stoch Environ. Res. Risk A 32 (3), 701–714. Tehrany, M.S., Pradhan, B., Jebur, M.N., 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 504, 69–79. Wu, X.S., et al., 2017. Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: a case study in Dongguan City, China. J. Hydrol. 547, 428–442. Xu, Z., Zhao, G., 2016. Impact of urbanization on rainfall-runoff processes: case study in the Liangshui River Basin in Beijing, China. Proc. Int. Assoc. Hydrol. Sci. 373, 7–12. Yu, H.J., Huang, G.R., Wu, C.H., 2014. Application of the stormwater management model
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