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International Journal of

Environmental Research and Public Health Article

A Framework for Flood Risk Analysis and Benefit Assessment of Flood Control Measures in Urban Areas Chaochao Li 1,2, *, Xiaotao Cheng 1 , Na Li 1 , Xiaohe Du 1 , Qian Yu 1 and Guangyuan Kan 1 1

2

*

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; [email protected] (X.C.); [email protected] (N.L.); [email protected] (X.D.); [email protected] (Q.Y.); [email protected] (G.K.) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China Correspondence: [email protected]; Tel.: +86-10-6878-1952; Fax: +86-10-6853-6927

Academic Editor: Miklas Scholz Received: 24 May 2016; Accepted: 22 July 2016; Published: 5 August 2016

Abstract: Flood risk analysis is more complex in urban areas than that in rural areas because of their closely packed buildings, different kinds of land uses, and large number of flood control works and drainage systems. The purpose of this paper is to propose a practical framework for flood risk analysis and benefit assessment of flood control measures in urban areas. Based on the concept of disaster risk triangle (hazard, vulnerability and exposure), a comprehensive analysis method and a general procedure were proposed for urban flood risk analysis. Urban Flood Simulation Model (UFSM) and Urban Flood Damage Assessment Model (UFDAM) were integrated to estimate the flood risk in the Pudong flood protection area (Shanghai, China). S-shaped functions were adopted to represent flood return period and damage (R-D) curves. The study results show that flood control works could significantly reduce the flood risk within the 66-year flood return period and the flood risk was reduced by 15.59%. However, the flood risk was only reduced by 7.06% when the flood return period exceeded 66-years. Hence, it is difficult to meet the increasing demands for flood control solely relying on structural measures. The R-D function is suitable to describe the changes of flood control capacity. This frame work can assess the flood risk reduction due to flood control measures, and provide crucial information for strategy development and planning adaptation. Keywords: flood risk analysis; flood control measures; UFSM; UFDAM; R-D function; EAD (expected annual damage)

1. Introduction With the rapid development of urbanization, flood risks become more and more severe [1]. Since the 1950s, the number of urban flood disasters has been gradually rising in China. The flood damage is higher than it was in the past. According to the 2015 China flood and drought report, more than 100 cities have suffered waterlogging per year since 2006. The numbers of waterlogged cities were 130 in 2008, 258 in 2010, and 234 in 2013, respectively. Nearly 62% of 351 cities were waterlogged between 2008 and 2010. The vast majority of flooding and waterlogging disasters are caused by local extreme rainstorms. Flooding and waterlogging disasters remain one of the main challenges faced by developing counties. They not only cause high mortality and suffering, but also damage local economies that are in process of formation and thwart development achievements [2]. To ensure security, a large number of flood control works and drainage systems have been built to reduce the

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economic losses associated with flood disasters. As shown in area, Figure 1, the lengthcoefficient of drainage drainage pipelines is proportional to urban construction land and the total correlation is pipelines is proportional to urban construction land area, and the correlation coefficient is 0.96. 0.96.

Figure 1. 1. Statistics of total total length length of of drainage drainage pipelines pipelines and and urban urban construction construction area. area. Figure Statistics of

For flood hazard-affected bodies themselves, the subjective reasons for frequent occurrence of For flood hazard-affected bodies themselves, the subjective reasons for frequent occurrence of flooding and waterlogging disasters are analyzed as follows: (1) Cities expand in high flood hazard flooding and waterlogging disasters are analyzed as follows: (1) Cities expand in high flood hazard areas; (2) The development of drainage systems is slower than the city development; (3) Impervious areas; (2) The development of drainage systems is slower than the city development; (3) Impervious areas increase; (4) The large population and property density result in an increased flood areas increase; (4) The large population and property density result in an increased flood vulnerability vulnerability in urban areas. in urban areas. With the continuous expansion of cities, the urban flooding and waterlogging problems are With the continuous expansion of cities, the urban flooding and waterlogging problems are expected to become worse and worse unless more effective measures are adopted. The flood control expected to become worse and worse unless more effective measures are adopted. The flood control work measures can be divided into two types: structural and non-structural. Key elements of work measures can be divided into two types: structural and non-structural. Key elements of structural structural works have been reservoirs, dikes, detention basins, pumping stations, etc. works have been reservoirs, dikes, detention basins, pumping stations, etc. Non-structural measures Non-structural measures include flood forecasting, flood emergency planning and response, and include flood forecasting, flood emergency planning and response, and post-flood recovery. Distinct post-flood recovery. Distinct from technical support services, these activities directly modify the from technical support services, these activities directly modify the vulnerability of communities vulnerability of communities exposed to flood risks. Typically protection measures such as dikes, exposed to flood risks. Typically protection measures such as dikes, levees, seawalls have a certain levees, seawalls have a certain designed capacity. When the flood scale exceeds this capacity, the designed capacity. When the flood scale exceeds this capacity, the structural measures could fail, structural measures could fail, and the flood damage could be more disastrous. The “protection” of and the flood damage could be more disastrous. The “protection” of flood control systems provides flood control systems provides perverse incentives for private individuals and businesses to adapt perverse incentives for private individuals and businesses to adapt on their own devices and may on their own devices and may promote unwanted concentrations of population/businesses in promote unwanted concentrations of population/businesses in hazard-prone areas. Thus it is difficult hazard-prone areas. Thus it is difficult to meet the increasing demands for flood control solely to meet the increasing demands for flood control solely relying on structural measures. Appropriate relying on structural measures. Appropriate non-structural measures provide sound strategies for non-structural measures provide sound strategies for sustainable development. According to Wang’s sustainable development. According to Wang’s research, reasonable land use planning based on research, reasonable land use planning based on flood risk analysis will decrease the future flood flood risk analysis will decrease the future flood risk by 39%–50% in Taihu Basin [3]. The economic risk by 39%–50% in Taihu Basin [3]. The economic losses associated with flood and waterlogging can losses associated with flood and waterlogging can be reduced by scientific flood risk management. be reduced by scientific flood risk management. Reasonable flood risk analysis can provide crucial Reasonable flood risk analysis can provide crucial information for strategy development and information for strategy development and planning adaptation. planning adaptation. Natural disaster risk assessment methods generally are of three types: mathematical statistical Natural disaster risk assessment methods generally are of three types: mathematical statistical methods, index system methods, and dynamic risk evaluation methods based on integrated models. methods, index system methods, and dynamic risk evaluation methods based on integrated models. The disaster trends are summarized based on the first method according to the analysis of historical The disaster trends are summarized based on the first method according to the analysis of historical flood disaster data. Benito provided a scientific method based on palaeoflood and historical data flood disaster data. Benito provided a scientific method based on palaeoflood and historical data for for flood risk analysis [4]. The index system methods focus on the selection of disaster risk indexes, flood risk analysis [4]. The index system methods focus on the selection of disaster risk indexes, optimization, and calculation of their weights. Okazawa established a global flood risk index based optimization, and calculation of their weights. Okazawa established a global flood risk index based on both natural and social factors [5]. In order to estimate the flood risk index at a spatial resolution

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on both natural and social factors [5]. In order to estimate the flood risk index at a spatial resolution of city/county/town units for a river watershed, Seiler used the standardized precipitation index for flood risk monitoring [6]. With the development of hydrological models, hydraulic models and Geographic Information System (GIS) technology, integrated models have been widely used in recent years. Flood inundation information and socio-economic information are analyzed by spatial overlay analysis [7,8]. Detailed geographic information data is needed to construct integrated models based on GIS. For the first two methods, it is difficult to describe the relationship among the main factors, or provide the evolutionary trends of flood risk and time-series inundation information. When the evaluation objects and conditions change, these methods cannot adjust in real time and the uncertainty and dynamics of the disaster system cannot be revealed as well, so the third method has become the mainstream direction of current research on natural disaster risk assessment. Crichton proposed that flood risks depend on three elements: hazard, vulnerability, and exposure [9]. The flood risk can then be represented as follows [10,11]: f lood risk “ f unction phazard, exposure, vulnerabilityq

(1)

The hazard means the threatening natural event including its probability of occurrence, and it generally quantified as the water depth and water flow velocity distribution. The exposure refers the people/assets that are present at the location involved. The vulnerability indicates the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard [12]. Flood disasters are predictable and controllable, which are features that distinguish floods from other natural disasters such as earthquakes and volcanic eruptions. The capacities of flood forecasting, early warming and flood control can be enhanced to mitigate the impact flood disasters. Hence, the capacity should be also considered. The capacity can be quantified as the flood risk reduction. The flood risk reduction comprises the flood damage averted in the future as a result of schemes to reduce the frequency of flooding or reduce the impact of that flooding on the affected property and economic activity, or a combination thereof. Spatially explicit hydrodynamic flood simulation models play an important role in flood risk reduction assessment. However, flood risk assessment in urban areas is more complex than in rural areas because of their closely packed buildings, different kinds of land uses, and large amounts of flood control works and drainage systems [13]. In this study, a framework was developed which combines urban flood simulation and flood damage assessment. A key element of this framework that makes it suitable for risk reduction assessment is the ability to provide objective inundation information with the consideration of buildings, land uses and flood control works. In terms of the concept of the risk triangle, the hazard, exposure, and vulnerability of storm waterlogging were analyzed. The R-D function was proposed to measure the effectiveness of flood control works and provide indications of the changing resilience. The results could be critical for land use planning, for flood control works design, for mapping evacuation egress routes, and for locating suitable emergency shelters to name but a few risk treatments [14]. 2. Study Area Shanghai, which is located in the estuary areas of the Yangtze River, is one of the most important core regions of economic, transportation, industry, science and technology in China (Figure 2). Shanghai has developed rapidly due to its various advantages, such as a highly developed industry, a stable economic base, and a dense population. Shanghai ranked first among Chinese cities in terms of GDP in 2015. Meanwhile, Shanghai is a flat land, which is prone to serious flood disasters caused by “plum rains”, typhoons, and storm surges. The flood risk features in Shanghai are very sensitive to both disaster-causing factors and social economy. Shanghai is divided into four flood protection areas: Pudong, Puxi, Hangjiahu and Yangchengdianmao. The Pudong flood protection area (2722 km2 , hereinafter referred to as Pudong) is a coastal part of Shanghai located in the right bank of the Huangpu

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River, which is taken as the study area in this paper. The estuary of the Yangtze River is situated to the Hangzhou Bay China to theSea south. of Pudong in The Figure 2. The River north, the East to theThe east,location and Hangzhou Bay is to shown the south. location of Huangpu Pudong is shown originates Lake and Dianshan Lake.from It meanders the whole from westthrough to east in Figure from 2. TheTai Huangpu River originates Tai Lake through and Dianshan Lake.city It meanders inthe the upstream. The terrain of Pudong is relatively flat. The ground surface elevation ranges from whole city from west to east in the upstream. The terrain of Pudong is relatively flat. The ground 3.5 m to 4.5 m based on the Wusong Datum, and the northwest is lower than the southeast. The surface elevation ranges from 3.5 m to 4.5 m based on the Wusong Datum, and the northwest is lower Pudong protection area contains administrative districts: New District, Fengxian than theflood southeast. The Pudong flood five protection area contains five Pudong administrative districts: Pudong District, part of Minhang District, part of Songjiang District, and part of Jinsan District. Considering New District, Fengxian District, part of Minhang District, part of Songjiang District, and part of Jinsan the threatConsidering of flooding the caused byoftyphoons, plum rains and riverplum floods, Shanghai has implemented District. threat flooding caused by typhoons, rains and river floods, Shanghai ahas comprehensive flood defense system which consists of dikes, floodwalls, gates, pumps gates, and implemented a comprehensive flood defense system which consists of dikes, floodwalls, drainage pipe networks. pumps and drainage pipe networks.

Figure2.2.The Thelocation locationofofthe thePudong Pudongflood floodprotection protectionarea. area. Figure

Shanghaiisisvulnerable vulnerabletotoflooding floodingdue duetotoits itsgeographic geographiclocation locationon onflat flatand andlow-lying low-lyingterrain, terrain, Shanghai rapidsocio-economic socio-economicdevelopment, development,land landsubsidence, subsidence,and andclimate climatechange. change.InIn2005, 2005,No. No.99Typhoon Typhoon rapid Matsaaffected affectedShanghai. Shanghai.It Itcaused causeda adirect directeconomic economicloss lossofof1.358 1.358billion billionCNY, CNY,which whichincluded included Matsa agriculturallosses lossesofof843 843million millionCNY CNYand andindustrial industriallosses lossesofof158 158million millionCNY. CNY.AArecent recentflood flood agricultural occurredinin2013, 2013,caused causedby byNo. No.2323Typhoon TyphoonFitow. Fitow.ItItwas wasreported reportedthat thatseveral severalriver riverdikes dikesininthe the occurred upstreamwere were overtopped or inundation depth in Songjiang District was upstream or broken, broken,and andthe themaximum maximum inundation depth in Songjiang District estimated at more than 2.5 m [15–17]. The adjacent and residential areas were flooded. was estimated at more than 2.5 m [15–17]. The farmland adjacent farmland and residential areas were flooded. 3. Methodology

3. Methodology 3.1. Framework of Flood Risk Analysis 3.1. Framework of Flood Risk Analysis Urban Flood Simulation Model (UFSM) and Urban Flood Damage Assessment Model (UFDAM) Urban Flood Simulation Model (UFSM) and and Urban Flood Damage are developed by China Institute of Water Resources Hydropower ResearchAssessment (IWHR). TheModel UFSM (UFDAM) are developed Institute of Water and Hydropower Research (IWHR). and UFDAM are coupledbytoChina evaluate the flood risk inResources this study. The flood risk analysis framework The UFDAMmethod are coupled to evaluate the floodare risk in this in study. The flood analysis andUFSM benefitand assessment of flood control measures adopted this paper. The risk methodology framework and benefit assessment of flood control measures are adopted in this paper. The contains a number of steps (Figuremethod 3): methodology contains a number of steps (Figure 3): 1. Flood and waterlogging scenarios are simulated by UFSM. Several model parameters are set up 1. Flood and waterlogging by works. UFSM. Several model parameters are set corresponding to the realscenarios operationare of simulated flood control up corresponding to the real operation of flood control works. 2. Flood damage is evaluated by UFDAM. The index system of flood damage assessment is designed 2. Flood damage is evaluated by UFDAM. The index system of flood damage assessment is according to the indicators of national statistics data. designed according to the indicators of national statistics data. 3. Flood damage curve is constructed based on S-shaped function. The return period-damage function is selected to describe the flood risk.

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3.

Flood damage curve is constructed based on S-shaped function. The return period-damage function selected describe Int. J. Environ. Res.isPublic Healthto 2016, 13, 787 the flood risk. 5 of 18

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Figure 3. 3. A A basic basic framework framework of of flood flood risk risk analysis. analysis. Figure

3.1.1. UFSM UFSM UFSM is a 1D–2D coupled hydrodynamic hydrodynamic model model which which has has been been widely widely used used for for flood and UFSM waterlogging simulation adopted in in this study includes modelling of the waterlogging simulationin inurban urbanareas. areas.The Theapproach approach adopted this study includes modelling of 3.network Aa basic framework of flood risk analysis. riverriver channel and main road Figure as a 1Das nested within the 2D domain the floodplain. the channel and main road 1D network nested within the 2Drepresenting domain representing the It is necessary considertothe impact the of buildings, land use and flood control works on flood floodplain. It isto necessary consider impact of buildings, land use and flood control worksrisk on 3.1.1. UFSM analysis. residential buildings are denselyare distributed in urban areas. Theseareas. buildings would block flood riskThe analysis. The residential buildings densely distributed in urban These buildings and change water flow. As Figure 4, thehas building spaces should be flood deducted would blockthe changeof the direction ofShown water in flow. As Shown inbeen Figure 4, the building spaces UFSM isand adirection 1D–2D coupled hydrodynamic model which widely used for and when we the inundation areas. Thus, a parameter, adjusted rate includes (AAR), ismodelling adopted in should becalculate deducted when weurban calculate the inundation areas. Thus, a area parameter, adjusted area rate waterlogging simulation in areas. The approach adopted in this study of this model, which can be calculated with Equation (2). (AAR), is adopted in this model, which can be calculated with Equation (2). the river channel and main road as a 1D network nested within the 2D domain representing the

floodplain. It is necessary to consider the impact of buildings, land use and flood control works on Ab AAR “ 1 ´ (2) flood risk analysis. The residential buildings are densely distributed in urban areas. These buildings Am would block and change the direction of water flow. As Shown in Figure 4, the building spaces where is the adjusted rate of a mesh; Ab is the area ofThus, building within theadjusted mesh; and Amrate is shouldAAR be deducted whenarea we calculate the inundation areas. a parameter, area the area of the mesh. (AAR), is adopted in this model, which can be calculated with Equation (2).

Figure 4. The distribution of residential buildings and the generated mesh.

=1−

(2)

where AAR is the adjusted area rate of a mesh; is the area of building within the mesh; and Figure 4. The distribution of residential buildings and the generated mesh. is the area of the Figure mesh. 4. The distribution of residential buildings and the generated mesh. Different land use types have different impacts on the runoff process. Parameters related to the runoff process should be set up according to = the land use, such as runoff coefficients and roughness. 1− (2) The initial values of the runoff coefficient and roughness can be given according to the reference values. The reference values of rate roughness for different land ofuse are proposed in mesh; Table and 1. The where AAR is the adjusted area of a mesh; is the area building within the parameters can bemesh. adjusted and identified by comparing simulation results with measured results. is the area of the

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Different land use types have different impacts on the runoff process. Parameters related to the runoff process should be set up according to the land use, such as runoff coefficients and roughness. The initial values of the runoff coefficient and roughness can be given according to the reference values. The reference values of roughness for different land use are proposed in Table 1. The parameters can be adjusted and identified by comparing simulation results with measured results. Int. J. Environ. Res. Public Health 13, reference 787 Table2016, 1. The value of roughness for different land uses.

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The reference value of roughness for different Land Use Table 1.Thicket Dry Farmland Paddy Fieldland uses. Open Space Land Use Roughness Roughness

Thicket 0.065 0.065

Dry Farmland 0.035 0.035

Paddy Field 0.035 0.035

Open Space 0.025–0.035 0.025–0.035

The runoff coefficient of impervious areas is about 0.9, and the runoff coefficient of natural The runoff coefficient of impervious areas is about 0.9, and the runoff coefficient of natural catchments is about 0.5. The runoff coefficient can be calculated by linear interpolation according to catchments is about 0.5. The runoff coefficient can be calculated by linear interpolation according to the impervious area ratio. The impervious area ratio approximates the AAR. Thus: the impervious area ratio. The impervious area ratio approximates the AAR. Thus: R“ (0.9´−0.5q = 0.5 0.5` +p0.9 0.5)ˆ×AAR

(3) (3)

where R R is is the the runoff runoff coefficient. coefficient. where The flood control works in urban generally contain sluices, pipe The flood control works in urban areasareas generally contain dikes, dikes, pumps,pumps, sluices, and pipeand drainage drainage water a riverthan is lower thanelevation, the crest there elevation, no networks.networks. When theWhen waterthe level of a level river of is lower the crest is no there water isflow water flow exchange between the river and the floodplain. When the water level exceeds the crest exchange between the river and the floodplain. When the water level exceeds the crest elevation elevation or a dike the weirisformula used tothe calculate the flow. can be or a dike breaks, thebreaks, weir formula used to is calculate flow. Gates and Gates pumpsand canpumps be simulated simulated according to therules. operation rules.pipe Drainage pipe as several according to the operation Drainage networks arenetworks simplifiedare as simplified several underground underground reservoirs. Details of UFSM can be found in Cheng [18]. reservoirs. Details of UFSM can be found in Cheng [18]. 3.1.2. UFDAM The flood damage assessment method is combined with GIS technology. The direct economic losses can thethe loss ratios of different kinds of hazard-affected bodies. The can be beestimated estimatedaccording accordingtoto loss ratios of different kinds of hazard-affected bodies. hazard-affected bodies are divided into several categories: residential buildings, residential The hazard-affected bodies are divided into several categories: residential buildings, commerce, agriculture agriculture and transportation. transportation. The residential building losses refer properties, industry, commerce, to the cost of rebuilding rebuilding the the structure-damaged structure-damaged and and landscape-damaged landscape-damaged buildings. The residential property losses refer to the cost of replacing furniture and household appliances as TVs, property losses refer to the cost of replacing furniture and household appliances such as such TVs, washing washing and refrigerators. The total directlosses economic of area the affected are equal machinesmachines and refrigerators. The total direct economic of thelosses affected are equalarea to cumulative to cumulative losses of all categories. Theeconomic basic social economic can be acquired from statistics losses of all categories. The basic social data can be data acquired from statistics yearbooks. yearbooks. The flood damage assessment index system is shown in Figure 5. More details The flood damage assessment index system is shown in Figure 5. More details on UFDAM can on be UFDAM can be[19,20]. found in Wang [19,20]. found in Wang

Figure 5. The index system of the direct economic losses.

3.1.3. Flood Damage Function The formulation of the flood damage function must reflect the interaction between the probability of occurrence of a hazardous event and its estimated consequences [21,22]. In recent years, a widely accepted concept of flood risk in a particular region is often termed as expected

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3.1.3. Flood Damage Function The formulation of the flood damage function must reflect the interaction between the probability of occurrence of a hazardous event and its estimated consequences [21,22]. In recent years, a widely accepted concept of flood risk in a particular region is often termed as expected annual damages (EAD) [23–25]. EAD is a cost-based measurement representing the expected average infrastructure Int. J. Environ. Res. Public Health 2016, 13, 787 7 of 18 costs as a result of flooding each year, which is the integration of the complete range of all possible flood events. It can be expressed as: ż1 ( )dp (4) = Risk “ D ppq (4) 0

whereDDisisthe thedamage damage a given flood event, is probability the probability of flood this flood within where ofof a given flood event, andand p is pthe of this eventevent within a year.a year. To calculate risk value (EAD)one value onetoneeds to determine the probability distribution flood To calculate the riskthe (EAD) needs determine the probability distribution of flood of events, events, flood anthe event, and under the damages given depth. In thisapproach study, a the flood the depth in andepth event,inand damages the givenunder depth.the In this study, a practical is to buildcurve a damage return of ispractical to build approach a damage probability based probability on different curve return based periodson of different floods. The returnperiods period R ∈expressed The return period R isp,used instead of probability p =then 1/R, the p ∈risk (0,1), R be (0,∞)and then isfloods. used instead of probability p = 1/R, p P (0,1), R P (0,8)p,and can as: the risk can be expressed as: ż 8

Risk “

=0

D pRq dR ( )

(5) (5)

D(R) can be expressed as an S-shaped function according to previous studies [26–28]. R is the D(R) can be expressed as an S-shaped function according to previous studies [26–28]. R is the independent variable, and D is the dependent variable. As shown in Figure 6, the flood R-D curve independent variable, and D is the dependent variable. As shown in Figure 6, the flood R-D curve can be expressed as an S-shaped function. The characteristics of D(R) function are summarized as can be expressed as an S-shaped function. The characteristics of D(R) function are summarized as follows: (1) the function D(R) increases monotonically; (2) Point C is the inflexion point where the rate follows: (1) the function D(R) increases monotonically; (2) Point C is the inflexion point where the of change of damage begins to decline. It is called the damage transition point. The first derivative rate of change of damage begins to decline. It is called the damage transition point. The first function D’(R) indicates that the slope of the flood damage curve is maximum when R = Rc. derivative function D’(R) indicates that the slope of the flood damage curve is maximum when R = Rc.

Figure Figure6.6.Flood FloodR-D R-Dfunction functioncurve curveand andthe thefirst firstderivative derivativecurve curveof ofR-D R-Dfunction. function.

Themaximum maximum value of flood damage A, critical returnRc,period Rc, and loss integrated loss The value of flood damage A, critical return period and integrated coefficient k coefficient are parameters, the three which main can parameters, which can be determined throughexperimental experiential are the three kmain be determined through experiential judgement, judgement, and experimental simulation, and curve fitting. The maximum flood damage A is related to simulation, curve fitting. The maximum flood damage A is related to flood exposure such as flood exposurefactors. such asThe socio-economic critical return can reflect flood control socio-economic critical returnfactors. period The Rc can reflect floodperiod controlRc capability. The integrated capability. Thek integrated loss coefficient k is used toThe describe flood vulnerability. The historical loss coefficient is used to describe flood vulnerability. historical data or simulation data can be data orfor simulation dataMathematical can be utilized for curve fitting.tools Mathematical tools are utilized curve fitting. statistical analysis are used to statistical determineanalysis the values of the used to determine the values parameters in this study [29]. of the parameters in this study [29]. Figure 77 provides the thethe benefit of flood control measures. The Figure the classic classicdiagram diagramofofcalculating calculating benefit of flood control measures. annual average floodflood damage is theisarea under the graph of flood losseslosses plotted against the return The annual average damage the area under the graph of flood plotted against the periodperiod in years. return in years. r 8 r Dbb(pRqdR R )dR ´ 08D aD (Ra pRqdR )dR EADEAD  EADa a b ´b EAD 00 D 0 r8 EABEAB “  “  EAD D pRqdR b EAD b 0 Db(bR )dR

(6) (6)

0

EAB is the benefit of flood control measures, EADb is the flood risk before taking the measures, EADa is the flood risk after taking the measures, Db(R) is the flood damage function curve before taking the measures, and Da(R) is the flood damage function curve after taking the measures [4].

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Figure Figure 7. 7. The Thebenefit benefit of of flood flood control control measures measures may may have have two two situations: situations: (a) (a)the the flood flood risk risk can can be be reduced under any return period of flood; (b) the flood risk only can be significantly reduced within reduced under any return period of flood; (b) the flood risk only can be significantly reduced within the theflood flood control control capacity. capacity.

3.2. Data EAB is the benefit of flood control measures, EADb is the flood risk before taking the measures, geographic data, social economic datadamage are thefunction foundation the EADThe flood riskdata, after hydrological taking the measures, Db (R) is the flood curvefor before a is the establishment of a flood assessment system. DEM with curve a resolution of 5 m generated taking the measures, andrisk Da (R) is the flood damage function after taking thewas measures [4]. by digitalizing the topographical feature elements. The elevation points in 1:10,000 topographic maps 3.2. Data and other geo-information such as buildings, roads, land uses are provided by the Shanghai Administration of Surveying and Mappingdata, (2012). The economic data of rivers, pumps, and dikes The geographic data, hydrological social data gates, are the foundation for are the adopted according to the Second Shanghai Water Resources Survey (2012). The water surface establishment of a flood risk assessment system. DEM with a resolution of 5 m was generated elevation refers the to the Wusong height datum. The social is obtained from the 2014 by digitalizing topographical feature elements. The economic elevation data points in 1:10,000 topographic statistical yearbooks of Pudong New District, Fengxian District, Minhang District, Songjiang maps and other geo-information such as buildings, roads, land uses are provided by the Shanghai District, and Jinsan District. The design rainfall and design water/tide levels arepumps, calculated Administration of Surveying and Mapping (2012). The data of rivers, gates, andaccording dikes are to the long term data from 1965 to 2013. adopted according to the Second Shanghai Water Resources Survey (2012). The water surface elevation refers to the Wusong height datum. The social economic data is obtained from the 2014 statistical 4. Results and Discussion yearbooks of Pudong New District, Fengxian District, Minhang District, Songjiang District, and Jinsan District. The design rainfall and design water/tide levels are calculated according to the long term 4.1. Model Validation data from 1965 to 2013. According to the above methodology, the flood risk assessment system was established. The 4. Resultswater and Discussion measured level during Typhoon Fitow is set as the upstream and downstream boundary conditions for UFSM. A 150 m × 150 m mesh is considered appropriate in view of both the size of the 4.1. Model Validation river channel and computation time. The total number of meshes is 127,453. A total of 44 gates, 262 pumps, and 31 the drainage areas arethe simulated in the study area. Figure 8 presents the According to abovedivision methodology, flood risk assessment system was established. comparison between the simulation results and measured water levels at the Huangpu Park station. The measured water level during Typhoon Fitow is set as the upstream and downstream boundary The simulation results Ashow good agreement the measured data. The simulation conditions for UFSM. 150 m ˆ 150 m mesh with is considered appropriate in view of bothresults the sizeare of generally lower than the measured water level. The highest simulated water level is 4.94 the the river channel and computation time. The total number of meshes is 127,453. A total of 44m,gates, highest measured level division 5.12 m, areas and the error is 3.5%. lowest simulated water 262 pumps, and 31water drainage arerelative simulated in the studyThe area. Figure 8 presents the level is 1.05 m, the lowest measured water level 1.18 m. The errors at low water level are higher than comparison between the simulation results and measured water levels at the Huangpu Park station. these at high water level. When theagreement water level is low, the gates and along the results Huangpu The simulation results show good with the measured data.pumps The simulation are River release water into the river. As the amount of water from Puxi is ignored, the simulation generally lower than the measured water level. The highest simulated water level is 4.94 m, the highest results are water lower level especially inand a low situation. measured 5.12 m, thetide relative error is 3.5%. The lowest simulated water level is 1.05 m,

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the lowest measured water level 1.18 m. The errors at low water level are higher than these at high water level. When the water level is low, the gates and pumps along the Huangpu River release water into the river. As the amount of water from Puxi is ignored, the simulation results are lower especially Int.aJ.low Environ. Public Health 2016, 13, 787 9 of 18 in tideRes. situation.

Figure 8. The simulation results and measured water levels of the Huangpu Park station during Figure 8. Fitow. The simulation results and measured water levels of the Huangpu Park station during Typhoon Typhoon Fitow.

The surveyed flood damage was 132.12 million CNY, according to the 2013 statistical data of The Songjiang, surveyed flood damageDistrict was 132.12 million CNY, according to the analyzing 2013 statistical data of Jinshan, and Qingpu (Table 2). Through spatial overlay the flooding Jinshan, and Qingpu (Table 2). Through spatial overlay analyzing the(Table flooding data andSongjiang, social economic data,District the flood damage is calculated as 130.60 million 3). data The and social economic data, the flood damage is calculated as 130.60 (Table 3).ofThe simulation simulation result coincides quite well with the surveyed value withmillion a relative error 1.15%. Hence, result coincides welltowith the surveyed with relative error of 1.15%. Hence, the model the model can bequite utilized estimate the floodvalue damage inaShanghai. can be utilized to estimate the flood damage in Shanghai. Table 2. The flood damage based on survey data of Typhoon Fitow (million CNY). Table 2. The flood damage based on survey data of Typhoon Fitow (million CNY). Water Conservancy Facilities Agriculture Industry and Transportation Other 24.00 69.34 21.43 17.35 Water Conservancy Facilities Agriculture Industry and Transportation Other 24.00

69.34 21.43 Table 3. The flood damage simulation results (million CNY).

Total 132.12 Total

17.35

132.12

Residential Residential Table 3. The floodAgriculture damage simulation (million CNY). Industrial Production Losses results Industry Losses Building Losses Property Losses Losses 6.21 17.31 71.26 18.85 9.98 Residential Residential Agriculture Industry Industrial Business Assets Business Direct Economic Building Losses Property Losses Losses Losses Highway Losses Railway Losses Production Losses Losses Revenue Losses Losses 6.21 17.31 71.26 18.85 9.98 1.46 2.41 2.66 0.46 130.60 Business Assets Business Revenue Highway Railway Direct Economic Losses Losses Losses Losses Losses

4.2. Hazard Analysis 1.46

2.41

2.66

0.46

130.60

The 24-h design rainfalls with frequency of 0.001, 0.002, 0.005, 0.01, 0.02, and 0.05 were taken as input data for scenario simulation. Two flood control works conditions are set up. Condition 1: 4.2. Hazard Analysis Flood control works normally operate. Condition 2: All pumps, gates and drainage systems stop The 24-h design rainfalls with frequency of 0.001, 0.002, 0.01, 0.02, and 0.05 were as working. The statistical inundation data is shown in Table 4. 0.005, The benefit of established floodtaken control input data for scenario simulation. Two flood control works conditions are set up. Condition 1: Flood works can be evaluated by comparing the difference between Condition 1 and Condition 2. control works normally operate. Condition 2: All pumps, gates and drainage systems stop working. 4. Theinsimulation results of inundation. The statistical inundation dataTable is shown Table 4. The benefit of established flood control works can be evaluated byReturn comparing the difference between Condition 1 and Condition 2. Inundation Area of The Maximum 24-h Areal Average The Largest Statistical Index

Condition 1

Period (Year) 5

Precipitation (mm) 134.5

Inundation Area (km2) 1287.75

Water Depth (m) 1.09

Water Depth (m) 0.12

10

169.1

1602.62

1.47

0.13

8.93

20

203.1

1805.49

1.79

0.15

16.74

Water Depth ≥0.5 m (km2) 3.71

50

247.5

1984.76

2.01

0.17

32.84

100

280.8

2083.45

2.11

0.19

49.35

200

313.9

2161.28

2.19

0.20

70.81

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Table 4. The simulation results of inundation. Return Period (Year)

Statistical Index

24-h Areal Precipitation (mm)

5 Health 2016, 134.5 Int. J. Environ. Res. Public 13, 787 10 169.1 20 203.1 50 247.5 Condition 1 100 280.8 5 134.5 200 313.9 1000 390.3 10 169.1 Condition 2

Condition 2

520 10 50 20 100 50 200 100 1000 200 1000

203.1 134.5 169.1 247.5 203.1 280.8 247.5 313.9 280.8 390.3 313.9 390.3

Average Water Depth (m)

Inundation Area of Water Depth ě0.5 m (km2 )

1287.75 1.09 1602.62 1.47 1805.49 1.79 1984.76 2.01 Table 4. Cont. 2083.45 2.11 1520.35 1.27 2161.28 2.19 2290.87 2.37 1752.93 1.62

0.12 0.13 0.15 0.17 0.19 0.12 0.20 0.24 0.14

3.71 8.93 16.74 32.84 49.35 2.88 70.81 137.87 8.65

1906.28 1520.35 1752.93 2046.95 1906.28 2123.97 2046.95 2187.96 2123.97 2299.33 2187.96 2299.33

0.15 0.12 0.14 0.18 0.15 0.19 0.18 0.21 0.19 0.25 0.21 0.25

16.66 2.88 8.65 33.20 16.66 50.40 33.20 72.49 50.40 143.70 72.49 143.70

The Largest Inundation Area (km2 )

The Maximum Water Depth (m)

1.90 1.27 1.62 2.07 1.90 2.15 2.07 2.22 2.15 2.40 2.22 2.40

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The water depth and inundation area increase with the growth of the rainfall return period. The The disaster situation of Condition 2 is more serious than Allrainfall inundation areas are water depth and inundation area increase with theCondition growth of1.the return period. accumulated together as the largest inundation area. The largest inundation area of a 5-year flood in The disaster situation of Condition 2 is more serious than Condition 1. All inundation areas are 2, the largest inundation area of a 5-year flood in Condition 2 is 1520.35 Condition 1 is 1287.75 km accumulated together as the largest inundation area. The largest inundation area of a 5-year flood in 2 , the largest 2, km2, and 1the relativekmdifference is inundation 18%. The largest of a 1000-year flood Condition is 1287.75 area of ainundation 5-year floodarea in Condition 2 is 1520.35 kmin 2 Condition 1 is 2290.87 km is , the largest inundation area ofarea 1000-year flood in flood Condition 2 is 2299.33 and the relative difference 18%. The largest inundation of a 1000-year in Condition 1 is 2, and the 2 km relative difference is 0.37%. The inundation areas of different return periods are 2290.87 km , the largest inundation area of 1000-year flood in Condition 2 is 2299.33 km2 , and the compared in Figure relative difference is 9. 0.37%. The inundation areas of different return periods are compared in Figure 9.

Figure 9. Flood inundation areas under different recurrence interval storm events. Figure 9. Flood inundation areas under different recurrence interval storm events.

The total inundation area increases with rainfall return period. With the increase of return The total inundation area increases with areas rainfall return period. With the increase of return period, period, the differences between inundation between Condition 1 and Condition 2 decrease. It the differences inundation areas between Condition and Condition means that means that thebetween flood risk reduction due to flood control 1works decreases2 decrease. with the Itflood return the floodThe riskflood reduction to flood control worksnon-significant decreases with the flood return storm period.conditions. The flood period. risk due reduction effect becomes under extreme risk effect non-significant The reduction inundation areabecomes is reduced by 15.76%. under extreme storm conditions. The inundation area is reduced 15.76%. water depth was set as hazard index of flood disaster, which was divided into Theby maximum Theaccording maximum depth was set as m, hazard index ofm,flood which divided into grades to water field surveys: 0 m–0.15 0.15 m–0.30 0.30 disaster, m–0.50 m, over was 0.55 m [30]. Table grades according to field surveys: 0 m–0.15 of m, the 0.15 flood m–0.30 m, 0.30 m–0.50 over 0.55 m [30]. 5 5 presents the normalization thresholds depth. The floodm,hazard maps are Table drawn presents thetonormalization thresholds of the flood depth. The flood maps areand drawn according according Table 5. Under Condition 1, the flood hazard mapshazard of 1/10, 1/100, 1/1000 storm events are shown in Figure 10. With the increase of flood return period, the flood hazard increases. According to the flood hazard analysis, the hazard is higher in the upstream of Huangpu River and the coastal area. The terrain of Jinshan and Songjiang District is low-lying, thus the flood hazards of these areas are higher. Land reclamation projects were carried out in coastal area recently, for example, Lingang New City Project. The areas reclaimed from the sea are prone to flooding as well.

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Table 5. Normalization thresholds of the flood depth. Int. J. Environ. Res. Public Health 13, 787 Grade Water2016, Depth

11 of 18 Impact Hazard The curb is usually 15 cm, thus it has hardly effect I 0 m < h ≤ 0.15 m None on production and life. It can be maps able to invade simpler house, with to Table 5. IIUnder Condition 1, the flood hazard of 1/10, 1/100, and 1/1000 storm events are 0.15 m < h ≤ 0.30 m Low doors-still next to the level of the sidewalk. shown in Figure 10. With the increase of flood return period, the flood hazard increases. According to It can interrupt traffic of vehicles and mainly the flood hazard analysis, hazard the coastal area. III 0.30 m < the h ≤ 0.50 m is higher in the upstream of Huangpu River andMedium of people. The terrain of Jinshan and Songjiang District is low-lying, thus the flood hazards of these areas are The water will very probably have already invaded higher. Land reclamation projects were carried out in coastal area recently, for example, Lingang New IV h > 0.5 m the interior of houses, causing damages to their High structure and content. City Project. The areas reclaimed from the sea are prone to flooding as well.

(a)

(b)

(c) Figure 10. (a) 1 in 10 years flood hazard map; (b) 1 in 100 years flood hazard map; (c) 1 in 1000 years

Figure 10. (a) 1 in 10 years flood hazard map; (b) 1 in 100 years flood hazard map; (c) 1 in 1000 years flood hazard map. flood hazard map.

4.3. Vulnerability Analysis Table 5. Normalization thresholds of the flood depth. The damage caused by flooding is related to flooding characteristics, such as depth, velocity and duration of flooding. In this paper the damage is estimated based on the depth of flooding Grade Water Depth Impact Hazard which is important and also relatively easy to determine. The loss rate is set up according to the The curb is usually 15 cm, thus it has hardly research of Nanjing I 0 m
0.30 m < h ď 0.50 m

It can interrupt traffic of vehicles and mainly of people.

Medium

IV

h > 0.5 m

The water will very probably have already invaded the interior of houses, causing damages to their structure and content.

High

4.3. Vulnerability Analysis The damage caused by flooding is related to flooding characteristics, such as depth, velocity and duration of flooding. In this paper the damage is estimated based on the depth of flooding which is important and also relatively easy to determine. The loss rate is set up according to the research of

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Nanjing Institute of Geography & Limnology and the disaster survey data of typhoons and torrential J. Environ. Res. Public Health 13, 787 are shown in Figure 11. 12 of 18 rainsInt. [31]. The damage rate 2016, relations Int. J. Environ. Res. Public Health 2016, 13, 787

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Figure different water depth. Figure11. 11.Loss Loss rate rate relations relations ofofdifferent water depth.

4.4. Exposure Analysis 4.4. Exposure Analysis

Figure 11. Loss rate relations of different water depth.

Exposure is often used to describe the people and assets that would be subjected to the threat

4.4. Exposure Exposure is Analysis often used to describe the people and assets that would be subjected to the threat of floods, such as population, buildings, crops and lifelines. In this study, exposure presented as the is often usedbuildings, to describe crops the people and assetsIn that would beexposure subjected presented to the threatas the of floods,Exposure such as population, and lifelines. this study, number of external structure and internal property value of residential buildings. Residential areas of floods, such as population, buildings, crops and lifelines. In this study, exposure presented as the number external internal property value of residential Residential and of land uses arestructure extractedand based on the 1:10,000 topographic map of the buildings. study area in 2012. With areas number of external structure and internal property value of residential buildings. Residential areas and land uses are extracted 1:10,000 topographic of the study area in 2012. With the the increase flood return based period,on thethe flood exposure grows. Themap residential building density is high and land uses are extracted based on the 1:10,000 topographic map of the study area in 2012. With increase flood return period, the flood exposure grows. The residential building density is high in in Minhang District, and its exposure map is drawn in Figure 12. A large proportion of the the increase flood return period, the flood exposure grows. The residential building density is high residential buildings exposed tomap the is inundation depth below m. The cultivated land Minhang District, and itsis exposure drawn inwater Figure 12. A large0.5 proportion of the residential in Minhang District, and its exposure map is drawn in Figure 12. A large proportion of the density is high in Songjiang District, anddepth its exposure is drawn in Figure 13. A large buildings is exposed to the inundation water below 0.5map m. The cultivated land density is high in residential buildings is exposed to the inundation water depth below 0.5 m. The cultivated land proportion of the cultivated landsmap are exposed toininundation water depths below 0.5 m. Some Songjiang District, and its exposure is drawn Figure 13. A large proportion of the cultivated density is high in Songjiang District, and its exposure map is drawn in Figure 13. A large landstonear both banks of thedepths river are submerged higher water depths. landscultivated are exposed water below m. at Some cultivated banks of proportion of theinundation cultivated lands are exposed to 0.5 inundation water depthslands belownear 0.5 both m. Some the river are submerged higher cultivated lands near at both bankswater of the depths. river are submerged at higher water depths.

(a)

(b)

(c)

Figure 12. (a) (a)The exposure of residential building (b) under 1 in 10 years extreme flood; (c) (b) The exposure of residential building under 1 in 100 years extreme flood; (c) The exposure of residential Figure 12. (a) The exposure of residential building under 1 in 10 years extreme flood; (b) The buildings 1 in 1000 of years extreme flood. Figure 12. (a) under The exposure residential building under 1 in 10 years extreme flood; (b) The exposure exposure of residential building under 1 in 100 years extreme flood; (c) The exposure of residential of residential building under 1 in 100 years extreme flood; (c) The exposure of residential buildings buildings under 1 in 1000 years extreme flood.

under 1 in 1000 years extreme flood.

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(a) (b) (c) (a) (b) (c) Figure 13. (a) The exposure of cultivate land under 1 in 10 years extreme flood; (b) The exposure of

Figure 13. (a) The exposure of cultivate land under 1 in 10 years extreme flood; (b) The exposure cultivate under 1 in 100ofyears extreme (c)1 The exposure of cultivate land under 1 in 1000 Figure 13.land (a) The exposure cultivate landflood; under in years extreme (b) The of of cultivate land under 1 in 100 years extreme flood; (c)10 The exposure offlood; cultivate landexposure under 1 in years extreme flood. 1 in 100 years extreme flood; (c) The exposure of cultivate land under 1 in 1000 cultivate land under 1000 years extreme flood. years extreme flood.

We assume that the population distribution of administration district is uniform. Then the We assume that can the population distribution of administration district is uniform. Then the affected affected be population calculated with Equation We population assume that the distribution of(7): administration district is uniform. Then the population can be calculated with Equation (7): affected population can be calculated with Equation (7): n m =ÿ (7) , ÿ Pe “ d Ai,j, (7) i = (7) i “1

j “1

is the total where, is the affected population, is the number of the administration district, number of the administration district within the study area, is the number of the grid cell within isthe theaffected affected population, is number the number the administration district, is the total where, Pe is where, population, i is the of theofadministration district, n is the total number anumber certain district, is the total number the within the residential ofresidential the administration district within the study iscells theofnumber ofcell the grid cell within of the administration district within the study area, jofisarea, the grid number the grid within adistrict, certain is the residential population density of is the administration district , and isresidential the the inundation residential a certain the totalofnumber ofcells the grid cells residential ,within residential district, mdistrict, is the total number the grid within the district, ddistrict, i is the building of the gridadministration j within administration . The population under is the area population of the the administration , and theresidential inundation residential population density of density the district i, anddistrict Ai,jdistrict is the inundation building area , is affected different return periods is shown in Figure 14. building area of the grid j within the administration district . The affected population under of the grid j within the administration district i. The affected population under different return periods different periods is shown return in Figure 14. is shown in Figure 14.

Figure 14. Flood affected population under different recurrence interval storm events. Figure under different different recurrence recurrence interval interval storm storm events. events. Figure 14. 14. Flood Flood affected affected population population under

4.5. R-D Function Construction 4.5. R-D R-D Function Function Construction Construction 4.5. The direct economic losses of the classified assets depending on flood water depth in each sub-district can be estimated according to depending the values on of flood classified The direct direct economic economic losses lossesseparately of the the classified classified assets waterassets, depth relevant in each each The of assets depending on flood water depth in inundated area of flood eventsseparately with different returntoperiods and the damage rates. The flood sub-district can be estimated according the values of classified assets, relevant sub-district can be estimated separately according to the values of classified assets, relevant inundated damage result isofshown inevents Table with 6. The losses ofreturn temporary production and service cessation are inundated area flood different theThe damage rates. The flood area of flood events with different return periods and theperiods damageand rates. flood damage result is ignored this paper. These kinds losses areofrelated to the inundatedand shut-down time, so the damageinin result in Table 6. of The losses temporary production service cessation are shown Table is 6. shown The losses of temporary production and service cessation are ignored in this paper. losses of in thethis industrial commercial are related probably in this paper. ignored paper. and These kinds of sectors losses are tounderestimated the inundated shut-down time, so the losses of the industrial and commercial sectors are probably underestimated in this paper.

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These kinds of losses are related to the inundated shut-down time, so the losses of the industrial and commercial sectors are probably underestimated in this paper. Table 6. The simulation results of flood damage (million CNY). Statistical Index

Return Period

Building

Property

Agriculture

Industry

Commerce

Road

Railway

Total

Condition 1

5 10 20 50 100 200 1000

49.74 103.86 172.77 309.73 452.96 615.90 1086.34

84.60 165.69 327.59 515.66 694.48 888.56 1409.14

104.25 207.69 512.68 716.81 877.44 1022.86 1331.91

104.86 296.52 452.13 702.35 929.16 1181.11 1841.77

14.55 36.19 55.95 89.18 120.21 155.18 249.47

5.44 14.12 20.71 31.29 39.90 48.82 75.76

0.22 0.79 1.15 1.86 2.41 3.04 4.54

363.66 824.85 1542.99 2366.87 3116.57 3915.47 5998.94

Condition 2

5 10 20 50 100 200 1000

53.54 118.41 199.39 350.30 547.43 673.53 1154.46

97.92 193.09 376.88 574.33 831.52 958.04 1483.33

160.41 277.38 613.29 823.56 106.224 1104.33 1389.57

141.66 265.99 539.47 802.45 1132.48 1283.01 1945.60

19.74 36.45 67.43 103.94 150.15 171.36 269.95

6.02 10.99 23.40 33.80 46.20 51.99 77.84

0.29 0.55 1.26 1.88 2.80 3.07 4.46

479.57 902.86 1821.13 2690.26 3772.82 4749.18 6325.21

Two functions are selected to fit flood damage: Gompertz function and Logistics function. (1)

Gompertz function: D “ Ae´e

(2)

´kpR´Rc q

(8)

where, D is the flood damage, R is the return period of flood, A is the maximum damage, Rc is the critical period, and k is the integrated loss coefficient. Function characteristics: the minimum value is 0, the maximum value is A, the abscissa of critical point is Rc, and the maximum slope is Ak/e. Logistics function: A D“ (9) 1 ` e ´k p R´ R c q

Function characteristics: the minimum value is 0, the maximum value is A, the abscissa of critical point is Rc, and the maximum slope is Ak/e. The fitting results are shown in Table 7. The Adjusted R2 is particularly useful in the feature selection stage of model building. The closer that this value is to 1, the better fitting the model is. The Gompertz function is more suitable than the Logistic function to present flood damage. The critical period Rc of Condition 1 is bigger than Condition 2. However, the integrated loss coefficient k of Condition 1 is smaller than Condition 2. It means that the flood control works have an impact on both flood control capacity and flood vulnerability. The fitting results are shown in Figure 15, where the flood risk reduction is represents as the shade of gray. Table 7. Fitting result of two functions. Index

Function

A

Rc

k

Adjusted R2

Condition 1

Gompertz Logistic

5859.27 5857.20

66.00 115.18

0.0092 0.013

0.92 0.89

Condition 2

Gompertz Logistic

6047.61 5886.97

47.97 76.52

0.013 0.019

0.94 0.91

Int. Int.J.J.Environ. Environ. Res. Res. Public Public Health Health 2016, 2016, 13, 13, 787 787

15 15of of18 18

Figure 15. The fitting results of flood damage function. Figure 15. The fitting results of flood damage function.

Equation thethe benefit of flood control measure. It oftenItused to assess feasibility Equation(6) (6)expresses expresses benefit of flood control measure. often used the to assess the of new constructions. In this paper, it towe assess risk due to the established feasibility of new constructions. In we thisuse paper, use the it toflood assess thereduction flood risk reduction due to the flood controlflood system. The system. flood risk reduction is calculated ascalculated follows: as follows: established control The flood risk reduction is r 1000 r 1000 1000 1000 DD2 pRqdR ´ 0 DD1(pRqdR ( R ) dR R )dR “ 7.14% 0 2r 1 Riskreduction “ 0 0 1000 Riskreduction   7.14% 0 1000D2 pRqdR





Riskreduction1 “

r 66 0



D2(R )dR

0

r 66

D2 p RqdR´

r 66

66

0

D1 p RqdR

D2 p RqdR 66

(10) (10)

“ 15.59%

(11)  r DD(pRRq)dR  r D (DR )rpdR RqdR´  dR “7.06% 15.59% D p RqdR D (R )dR  The damage transition point C corresponds to the parameter Rc (critical return period), which (11) Risk Risk  “0 reduction2 reduction 1

0

1000 2 66

661000 66

0



1000 1 1 0 66

2

1000

2

2



1000

is associated with the integrated flood defense orD flood D2(Rcapability )dR  (R )dRcontrol standard. Rc = 66.00 in 1 66 66 Condition 1, and Rc = 47.97 in Condition 2. We choose the larger one. Then the benefit can be divided Riskreduction   7.06% 2 1000 into two parts. Within the flood control capacity, flood D2(Rrisk )dR is reduced by 15.59% and the effect of 66 flood risk reduction is significant. Once flood magnitude exceeds the flood control standard, the flood Thewill damage transition C corresponds to of themutability parameterand Rc the (critical which damage sharply increasepoint and show the feature floodreturn risk isperiod), only reduced is associated with the integrated flood defense capability or flood control standard. Rc = 66.00 in by 7.06%. Condition 1, and Rc = 47.97 in Condition 2. We choose the larger one. Then the benefit can be 5. Conclusions divided into two parts. Within the flood control capacity, flood risk is reduced by 15.59% and the effectDeveloping of flood appropriate risk reduction is significant. Once flood magnitude the floodrequires control emergency responses to address and preventexceeds flood inundation standard, the flood damage will sharply increase and show the feature of mutability and the a reliable flood risk analysis including hazard, vulnerability and exposure. This paper exploresflood the risk is only reduced of byflooding, 7.06%. the vulnerability of different kind of assets and the flood prevention spatial distributions



capacity, particularly concerning buildings, land uses and flood control works in Pudong (Shanghai, 5. Conclusions China). A baseline framework was established that could be used to assess the flood disaster risks and flood Developing risk reduction. We reach the following responses conclusions: appropriate emergency to address and prevent flood inundation

requires a reliable flood risk analysis including hazard, vulnerability and exposure. This paper 1. Scenario modelling of flood inundation from extreme rainfall events is a challenge, particularly in explores the spatial distributions of flooding, the vulnerability of different kind of assets and the urban areas. In order to address the impact of buildings, land uses and flood control works issues flood prevention capacity, particularly concerning buildings, land uses and flood control works in on flood risk, the specific model parameters are required. The flood inundation modelling using Pudong (Shanghai, China). A baseline framework was established that could be used to assess the UFSM indicated that AAR is an important parameter. It can be utilized to describe the impact flood disaster risks and flood risk reduction. We reach the following conclusions: of buildings and land uses. The model was validated using a historical flood event: Typhoon

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2.

3.

4.

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Fitow, which was the most serious flood in recent years. The simulation results match well with the surveyed inundation depths and inundated areas. However, the model was only tested for a single observed event. It is recommended that the model also be tested and evaluated for more storm events to enhance the reliability of the model outputs. The flood hazard, vulnerability and exposure were analyzed. A flood high risk may be caused by elevation, land uses and buildings present in the area. The results indicate that the upstream of Huangpu River and the coastal area are prone to floods. The UFDAM were used in this case study. The evaluation index system of UFDAM is able to describe the indirect loss of urban disaster caused by flooding or waterlogging. These indexes should be easily adopted by the administrative department for emergency management. Hence, they are consistent with the indicators of local statistics yearbooks. The convenience of calculation and feasibility of obtaining are improved. The indirect loss should be taken in to account, and the damage rates of classified assets at different level of water depth should be properly modified according to the duration of inundation and the vulnerabilities of the assets in the future. The flood risk is expressed as R-D function, which contains probability and consequence of flood. The 24-h design rainfalls with frequency of 0.001, 0.002, 0.005, 0.01, 0.02, and 0.05 were taken as input data for scenario modeling. The simulation results are utilized as data sample for flood damage function construction. The flood risk reduction is expressed as the area between the two curves. The S-shaped R-D function can describe the impact of flood capability well. The result shows that within the flood control capacity, flood risk is reduced by 15.59% and flood risk reduction is significant. Once flood magnitude exceeds the flood control standard, the flood damage will increase sharply. The flood risk is only reduced by 7.06%. It means that the flood prevention measures may cease to be effective when the flood scale exceeds the flood control standard. It is difficult to meet the increasing demand for flood control solely relying on structural measures. Rc (critical return period) is associated with the integrated flood defense capability or flood control standard of the study area. Resistance is related to the system’s ability to prevent floods, while resilience determines the ease with which the system recovers from floods [32]. Within the defense standard, the flood control works operate well. Structural measures are taken as the main resistance strategies, which are aimed at flood prevention. When the flood scale exceeds the defense capability, the situation gets out of control. The non-structural measures are taken as the main resilience strategies to minimize the flood impacts and enhance the recovery from those impacts. Under the impact of climate changes and urbanization, the flood risk increases and the flood mitigation become more difficult, complex and long-term. More emphasis should be put on flood forecasting, flood emergency planning and response, and post-flood recovery. A reasonable flood risk analysis is important, which can be utilized for land use planning, for flood control works design, and for emergency response decision making.

Acknowledgments: The work described in this publication was supported by (1) Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (No. 2012BAC21B02); (2) Public Welfare Special Research Projects of Ministry of Water Resources (No. 201401038, No. 201501014); (3) IWHR Research & Development Support Program (No. JZ0145B052016). Author Contributions: Xiaotao Cheng and Chaochao Li contributed ideas concerning the structure and content of the article. The analysis was carried out by Chaochao Li under supervision of Xiaotao Cheng and the technical help of Na Li. The manuscript was further improved and revised by Qianyu, Guangyuan Kan and Xiaohe Du. Conflicts of Interest: The authors declare no conflict of interest.

References 1.

Dawson, R.J.; Speight, L.; Hall, J.W.; Djordjevic, S.; Savic, D.; Leandro, J. Attribution of flood risk in urban areas. J. Hydroinf. 2008, 10, 275–288. [CrossRef]

Int. J. Environ. Res. Public Health 2016, 13, 787

2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

12. 13. 14. 15.

16. 17. 18. 19. 20. 21.

22. 23. 24. 25. 26.

17 of 18

Al-Nammari, F.; Alzaghal, M. Towards local disaster risk reduction in developing countries: Challenges from Jordan. Int. J. Disaster Risk Reduct. 2015, 12, 34–41. [CrossRef] Benito, G.; Ouarda, T.B.M.J.; Bárdossy, A. Applications of palaeoflood hydrology and historical data in flood risk analysis. J. Hydrol. 2005, 313, 1–2. [CrossRef] Wang, Y.Y.; Han, S.; Yu, C.Q.; Hu, C.W. The flood risk and flood alleviation benefit of land use management in Taihu Basin. J. Hydraul. Eng. 2013, 44, 327–335. (In Chinese) Okazawa, Y.; Yeh, P.J.F.; Kanae, S.; Oki, T. Development of a global flood risk index based on natural and socio-economic factors. Hydrol. Sci. J. 2011, 56, 789–804. [CrossRef] Seiler, R.A.; Hayes, M.; Bressan, L. Using the standardized precipitation index for flood risk monitoring. Arthrosc. J. Arthrosc. Relat. Surg. 2002, 22, 1365–1376. [CrossRef] Ding, Z.X.; Li, J.R.; Li, L. Method for flood submergence analysis based on GIS grid model. J. Hydraul. Eng. 2004, 6, 56–60. (In Chinese) Qi, H.; Qi, P.; Altinakar, M.S. GIS-based spatial Monte Carlo analysis for integrated flood management with two-dimensional flood simulation. Water Resour. Manag. 2013, 27, 3631–3645. [CrossRef] Crichton, D. The risk triangle. In Natural Disaster Management; Ingleton, J., Ed.; Tudor Rose: Leicester, UK, 1999; pp. 102–103. Kron, W. Flood Risk = Hazard ˆ Exposure ˆ Vulnerability. Available online: http://www.civil.ist.utl.pt/ ~joana/DFA-riscos-net/2007-08/kron%20-%20flod%20risk%20=%20hazard.pdf (accessed on 24 May 2016). Terante, D.C.; Lamberte, A.E.; Sevilla, M.E.P. Risk-based Analysis of a Flood Plain: A Case in the City of Malabon, Philippines. In Proceedings of the 5th Civil Engineering Conference in the Asian Region and Australasian Structural Engineering Conference 2010, Sydney, Australia, 8–12 August 2010; pp. 859–867. UNISDR (United Nations International Strategy for Disaster Reduction). Terminology on Disaster Risk Reduction. Available online: http://www.unisdr.org/we/inform/erminology (accessed on 22 July 2015). Cheng, X.T.; Li, C.C. The evolutionary trend, key features and countermeasures of urban flood risk. China Flood Drought Manag. 2015, 25, 6–9. (In Chinese) Zerger, A.; Wealands, S. Beyond Modelling: Linking Models with GIS for Flood Risk Management. Nat. Hazards 2004, 33, 191–208. [CrossRef] Cao, X.G.; Wang, H.; Qi, L.B. The combined effects of typhoon Fitow and Danas together with cool air on an excessive heavy rain from 7 to 8 October in 2013 in Shanghai. Torrential Rain Disasters 2014, 33, 351–362. (In Chinese) Li, J.Y.; Xue, J.J.; Wang, W.G.; Gao, S.Z.; Zhang, Y.H. Analysis the typhoon decision-making meteorological service in 2013. J. Inst. Disaster Prev. 2014, 16, 62–68. (In Chinese) Bao, X.; Davidson, N.E.; Yu, H.; Hankinson, M.C.N.; Sun, Z.; Rikus, L.J. Diagnostics for an extreme rain event near Shanghai during the landfall of typhoon Fitow (2013). Mon. Weather Rev. 2015, 143, 1–6. [CrossRef] Cheng, X.T. Urban Flood Prediction and Its Risk Analysis in the Coastal Areas of China; Water & Power Press: Beijing, China, 2009. Wang, Y.Y.; Lu, J.K.; Zheng, X.Y.; Lin, Z. The development of urban flood damage assessment system for Shanghai. J. Catastrophol. 2001, 16, 7–13. (In Chinese) Wang, Y.Y.; Lu, J.K.; Chen, H. The application of flood risk assessment system. Water Resour. Hydropower Eng. 2002, l33, 30–33. (In Chinese) Zonensein, J.; Miguez, M.G.; Magalhães, L.P.; Valentin, C.D.; Mascarenhas, M.G.; Mascarenhas, F.C.B. Flood risk index as an urban management tool. In Proceedings of the 11th International Conference on Urban Drainage, Edinburgh, UK, 5 September 2008; pp. 1–10. Yu, C.Q.; Hall, J.W.; Cheng, X.T.; Evans, E.P. Broad scale quantified flood risk analysis in the Taihu Basin, China. J. Flood Risk Manag. 2013, 6, 57–68. [CrossRef] Hall, J.W.; Dawson, R.J.; Sayers, P.B.; Rosu, C.; Chatterton, J.B.; Deakin, R. A methodology for national-scale flood risk assessment. Proc. Inst. Civil Eng. Water Marit. Eng. 2003, 156, 235–247. [CrossRef] Hoes, O.; Schuurmans, W. Flood standards or risk analyses for polder management in The Netherlands. Irrig. Drain. 2006, 55, 113–119. [CrossRef] Hall, J.W.; Solomatine, D. A framework for uncertainty analysis in flood risk management decisions. Int. J. River Basin Manag. 2010, 6, 85–98. [CrossRef] Li, J.X.; Su, S.L. Calculation model of water pollution induced economic loss for river basin. J. Hydraul. Eng. 2003, 10, 68–74. (In Chinese)

Int. J. Environ. Res. Public Health 2016, 13, 787

27.

28. 29. 30. 31. 32.

18 of 18

Li, J.R.; Ding, Z.X.; Huang, S.F.; Hu, Y.L. Research of flood and waterlogging loss assessment model based on spatial distribution social-economic database. J. China Inst. Water Resour. Hydropower Res. 2003, 1, 104–110. (In Chinese) Chen, M.; Ma, J.; Hu, Y.; Zhou, F.; Li, J.; Yan, L. Is the S-shaped curve a general law? An application to evaluate the damage resulting from water-induced disasters. Nat. Hazards 2015, 78, 1–19. [CrossRef] Li, C.C.; Cheng, X.T.; Li, N.; Liang, Z.M.; Wang, Y.Y.; Han, S. A Three-Parameter S-Shaped Function of Flood Return Period and Damage. Adv. Meteorol. 2016, 2016, 1–11. [CrossRef] Quan, R.S. Research on Risk Assessment of Rainstorm Waterlogging Disaster in Typical Coastal City. Ph.D. Thesis, East China Normal University, Shanghai, China, 12 May 2012. Yin, Z.E.; Xu, S.Y.; Yin, J.; Wang, J. Small-scale Based Scenario Modeling and Disaster Risk Assessment of Urban Rainstorm Water-logging. Acta Geogr. Sin. 2010, 65, 554–562. (In Chinese) Bruijn, K.D. Resilience and flood risk management. Water Policy 2004, 6, 53–66. © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).