A Framework for Flood Risk Analysis and Benefit Assessment of Flood

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 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.) 2 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

Int. J. Environ. Res. Public Health 2016, 13, 787; doi:10.3390/ijerph13080787 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2016, 13, 787 2 of 18 Int. J. Environ. Res. Public Health 2016, 13, 787 2 of 18 economicdrainage pipelines losses associated is proportional with ﬂood to urban disasters. construction As shown land in area, Figure and1, thethe totalcorrelation length coefficient of drainage is pipelines0.96. is proportional to urban construction land area, and the correlation coefﬁcient is 0.96.

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

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

Int. J. Environ. Res. Public Health 2016, 13, 787 3 of 18 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 (Figure2). 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 Int. J. Environ. Res. Public Health 2016, 13, 787 4 of 18

Int. J. Environ. Res. Public Health 2016, 13, 787 4 of 18 River, which is taken as the study area in this paper. The estuary of the Yangtze River is situated to the Hangzhounorth, the EastBay Chinato the Sea south. to the The east, location and Hangzhou of Pudong Bay is to shown the south. in TheFigure location 2. The of Huangpu Pudong is shownRiver originatesin Figure 2from. The Tai Huangpu Lake and River Dianshan originates Lake. from It me Taianders Lake andthrough Dianshan the whole Lake. city It meanders from west through to east inthe the whole upstream. city from The west terrain to eastof Pudong in the upstream. is relative Thely flat. terrain The ofground Pudong surface is relatively elevation flat. ranges The ground from 3.5surface m to elevation4.5 m based ranges on fromthe Wuso 3.5 mng to 4.5Datum, m based and onthe the northwest Wusong Datum,is lower and than the the northwest southeast. is lowerThe Pudongthan the flood southeast. protection The Pudongarea contains flood five protection administ arearative contains districts: five Pudong administrative New District, districts: Fengxian Pudong District,New District, part of Fengxian Minhang District, District, part part of of Minhang Songjiang District, District, part an ofd Songjiangpart of Jinsan District, District. and Considering part of Jinsan theDistrict. threat Considering of flooding thecaused threat by of typhoons, flooding causedplum rains by typhoons, and river plum floods, rains Shanghai and river has floods, implemented Shanghai ahas comprehensive implemented flood a comprehensive defense system flood which defense consists system of which dikes, consists floodwalls, of dikes, gates, floodwalls, pumps gates,and drainagepumps and pipe drainage networks. pipe networks.

FigureFigure 2. 2. TheThe location location of of the the Pudo Pudongng flood ﬂood protection protection area. area.

ShanghaiShanghai is is vulnerable vulnerable to to flooding flooding due due to to its its geog geographicraphic location location on on flat flat and and low-lying low-lying terrain, terrain, rapidrapid socio-economic socio-economic development, development, land land subsiden subsidence,ce, and and climate climate change. change. In In 2005, 2005, No. No. 9 9Typhoon Typhoon MatsaMatsa affected affected Shanghai. Shanghai. It Itcaused caused a adirect direct economic economic loss loss of of 1.358 1.358 billion billion CNY, CNY, which which included included agriculturalagricultural losses losses of of 843 843 million million CNY CNY and and industrial industrial losses losses of of 158 158 million million CNY. CNY. A A recent recent flood flood occurredoccurred in in 2013, 2013, caused caused by by No. No. 23 23 Typhoon Typhoon Fitow. Fitow. It It was was reported reported that that several several river river dikes dikes in in the the upstreamupstream were were overtopped oror broken,broken, andand the the maximum maximum inundation inundation depth depth in Songjiangin Songjiang District District was wasestimated estimated at more at more than 2.5than m [152.5– 17m]. [15–17]. The adjacent The farmlandadjacent farmland and residential and residential areas were flooded.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) are developedUrban Flood by ChinaSimulation Institute Mo ofdel Water (UFSM) Resources and andUrban Hydropower Flood Damage Research Assessment (IWHR). The Model UFSM (UFDAM)and UFDAM are developed are coupled by to China evaluate Institute the flood of Water risk in Resources this study. and The Hydropower flood risk analysis Research framework (IWHR). Theand UFSM benefit and assessment UFDAM method are coupled of flood to evaluate control measures the flood are risk adopted in this instudy. this paper.The flood The risk methodology analysis frameworkcontains a numberand benefit of steps assessment (Figure 3method): of flood control measures are adopted in this paper. The methodology contains a number of steps (Figure 3): 1. Flood and waterlogging scenarios are simulated by UFSM. Several model parameters are set up 1. Floodcorresponding and waterlogging to the real scenarios operation are of simulated flood control by works.UFSM. Several model parameters are set 2. upFlood corresponding damage is evaluatedto the real by op UFDAM.eration of The flood index control system works. of flood damage assessment is designed 2. Floodaccording damage to the is indicatorsevaluated ofby national UFDAM. statistics The index data. system of flood damage assessment is 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.

Int. J. Environ. Res. Public Health 2016, 13, 787 5 of 18

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

Int. J. Environ. Res. Public Health 2016, 13, 787 5 of 18

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

3.1.1. UFSM UFSM UFSM is a 1D–2D coupled hydrodynamic model which which has has been widely used for flood flood and waterlogging simulationsimulation inin urbanurban areas. areas. The The approach approach adopted adopted in in this this study study includes includes modelling modelling of theof theriver river channel channel and mainand main road Figure asroad a 1D as3. network Aa basic1D network framework nested withinnested of flood the within risk 2D domainanalysis. the 2D representing domain representing the floodplain. the floodplain.It is necessary It is tonecessary consider to the consider impact the of buildings,impact of buildings, land use and land flood use and control flood works control on floodworks risk on floodanalysis.3.1.1. risk UFSM The analysis. residential The residential buildings are buildings densely are distributed densely distributed in urban areas. in urban These areas. buildings These would buildings block wouldand changeUFSM block the isand a direction 1D–2D change ofcoupled the water direction flow.hydrodynamic As of Shownwater inmodelflow. Figure As which 4Shown, the has building in been Figure spaceswidely 4, the should used building for be deductedflood spaces and shouldwhenwaterlogging we be calculatededucted simulation the when inundation in we urban calculate areas.areas. the Thus,The inundation approach a parameter, areas. adopted adjusted Thus, in thisa areaparameter, study rate includes (AAR), adjusted is modelling adopted area rate inof (AAR),thisthe model,river is adoptedchannel which caninand this be main calculatedmodel, road which as with a can1D Equation benetwork calculated (2). nested with within Equation the 2D(2). domain representing the floodplain. It is necessary to consider the impact of buildings, land use and flood control works on Ab flood risk analysis. The residential buildingsAAR are“ densely1 ´ distributed in urban areas. These buildings(2) Am would block and change the direction of water flow. As Shown in Figure 4, the building spaces whereshouldAAR be deductedis the adjusted when areawe calculate rate of a mesh;the inundationAb is the areaareas. of Thus, building a parameter, within the adjusted mesh; and areaA mrateis the(AAR), area is of adopted the mesh. 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 Figuremesh. 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.where AARThe referenceis the adjusted values area of rateroughness of a mesh; for different is the arealand ofuse building are proposed within the in mesh;Table and1. The parametersis the area ofcan the be mesh. adjusted and identified by comparing simulation results with measured results. 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 2016, 13, 787 6 of 18

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 Table1. The parameters can be adjusted and identified by comparing simulation results with measured results.

Int. J. Environ. Res. Public HealthTable 2016 1. The, 13, reference787 value of roughness for different land uses. 6 of 18

Land UseTable 1. ThicketThe reference value DryFarmland of roughness for Paddy different Field land uses. Open Space RoughnessLand Use Thicket 0.065 Dry Farmland 0.035 Paddy 0.035 Field Open 0.025–0.035 Space Roughness 0.065 0.035 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=0.5+“ 0.5 ` p(0.90.9´ −0.5 0.5q) ˆ×AAR (3)(3) where R isis thethe runoffrunoff coefficient.coefficient. The floodflood control control works works in urbanin urban areas areas generally generall containy contain dikes, dikes, pumps, pumps, sluices, andsluices, pipe and drainage pipe drainagenetworks. networks. When the When water the level water of a level river of is lowera river than is lower the crest than elevation, the crest thereelevation, is no there water is flow no waterexchange flow between exchange the between river and the the river floodplain. and the floodplain. When the When water the level water exceeds level the exceeds crest elevationthe crest elevationor a dike breaks,or a dike the breaks, weir formula the weir is formula used to is calculate used to the calculate flow. Gates the flow. and Gates pumps and can pumps be simulated can be simulatedaccording toaccording the operation to the rules. operation Drainage rules. pipe Drainage networks pipe are networks simplified are as simplified several underground as several undergroundreservoirs. Details reservoirs. of UFSM Details can beof foundUFSM incan Cheng be found [18]. in Cheng [18].

3.1.2. UFDAM The ﬂoodflood damage assessment me methodthod is combined with GIS technology. The direct economic losses can can be be estimated estimated according according to to the the loss loss ratios ratios of different of different kinds kinds of hazard-affected of hazard-affected bodies. bodies. The Thehazard-affected hazard-affected bodies bodies are aredivi dividedded into into several several categories: categories: residential residential buildings, buildings, residential properties, industry, commerce,commerce, agricultureagriculture and transportation.transportation. The residential building losses refer to the cost ofof rebuildingrebuilding thethe structure-damagedstructure-damaged andand landscape-damagedlandscape-damaged buildings.buildings. The residential property losseslosses refer refer to to the the cost cost of replacingof replacing furniture furniture and household and household appliances appliances such as such TVs, washingas TVs, washingmachines machines and refrigerators. and refrigerators. The total direct The total economic direct losses economic of the losses affected of areathe affected are equal area to cumulative are equal tolosses cumulative of all categories. losses of all The categories. basic social The economicbasic social data economic can be data acquired can be from acquired statistics from yearbooks. statistics Theyearbooks. ﬂood damage The flood assessment damage indexassessment system index is shown system in Figure is shown5. More in detailsFigure on5. More UFDAM details can beon UFDAMfound in Wangcan be [found19,20]. in Wang [19,20].

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 annual damages (EAD) [23–25]. EAD is a cost-based measurement representing the expected average infrastructure 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:

Int. J. Environ. Res. Public Health 2016, 13, 787 7 of 18

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: 1 Risk“ = D p(pq)dp (4)(4) ż0 wherewhereD Dis is the the damage damage of of a givena given flood flood event, event, and andp is p the is probabilitythe probability of this of floodthis flood event event within within a year. a Toyear. calculate To calculate the risk the (EAD) risk value(EAD) one value needs one toneeds determine to determine the probability the probability distribution distribution of flood of events, flood theevents, flood the depth flood in an depth event, in and an the event, damages and underthe damages the given under depth. the In thisgiven study, depth. a practical In this approach study, a ispractical to build approach a damage probabilityis to build curvea damage based probabi on differentlity curve return based periods on of different floods. The return return periods period Rof isfloods. used insteadThe return of probability period R isp ,usedp = 1/ insteadR, p P (0,1),of probabilityR P (0,8) p and, p = then1/R, thep ∈ risk (0,1), can R be∈ expressed(0,∞)and then as: the risk can be expressed as: 8 Risk “ D pRq dR (5) =0 () (5) ż 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 Figure6, 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.

FigureFigure 6.6.Flood Flood R-DR-D functionfunction curvecurveand andthe theﬁrst firstderivative derivativecurve curve of of R-D R-D function. function.

TheThe maximummaximum value value of floodof flood damage damageA, critical A, critical return periodreturnRc period, and integrated Rc, and lossintegrated coefficient lossk arecoefficient the three k main are parameters,the three whichmain canparameters, be determined which through can be experiential determined judgement, through experimental experiential simulation,judgement, andexperimental curve fitting. simulation, The maximum and curve flood fitting. damage The maximumA is related flood to flood damage exposure A is related such as to socio-economicflood exposure factors.such as The socio-economic critical return factors. period TheRc can critical reflect return flood period control Rc capability. can reflect The flood integrated control losscapability. coefficient Thek integratedis used to describeloss coefficient flood vulnerability. k is used to The describe historical flood data vulnerability. or simulation The data historical can be utilizeddata or forsimulation curve fitting. data Mathematicalcan be utilized statistical for curve analysis fitting. tools Mathematical are used to statistical determine analysis the values tools of theare parametersused to determine in this study the values [29]. of the parameters in this study [29]. FigureFigure7 7provides provides thethe classicclassic diagram of of calculating calculating the the benefit benefit of offlood flood control control measures. measures. The Theannual annual average average flood flood damage damage is the is area the areaunder under the graph the graph of flood of floodlosses lossesplotted plotted against against the return the returnperiod period in years. in years.

88 EADEAD´ EAD EAD DDbab()p RRq dRdR´ DD () Rap dRRqdR b ba a r000r0 EABEAB“ “ 8 (6)(6) EADEADb  D pRqdR b r0 DRdR()b 0 b

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].

Int. J. Environ. Res. Public Health 2016, 13, 787 8 of 18 Int. J. Environ. Res. Public Health 2016, 13, 787 8 of 18

FigureFigure 7. 7. TheThe benefit 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 reducedreduced under under any any return return period period of of flood; flood; ( (bb)) the the flood flood risk risk only only can can be be significantly significantly reduced reduced within within thethe flood 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, EADThea is thegeographic flood risk data, after hydrological taking the measures, data, socialDb(R) economicis the flood data damage are the function foundation curve for before the establishmenttaking the measures, of a flood and riskDa(R) assessmentis the flood system. damage DEM function with curvea resolution after taking of 5 m the was measures generated [4]. by digitalizing the topographical feature elements. The elevation points in 1:10,000 topographic maps and3.2. Dataother geo-information such as buildings, roads, land uses are provided by the Shanghai AdministrationThe geographic of Surveying data, hydrological and Mapping data, (2012). social The economicdata of rivers, data gates, are the pumps, foundation and dikes for are the adoptedestablishment according of a floodto the riskSecond assessment Shanghai system. Water DEMResources with aSurvey resolution (2012). of 5The m waswater generated surface elevationby digitalizing refers theto the topographical Wusong height feature datum. elements. The social The economic elevation data points is obtained in 1:10,000 from topographic the 2014 statisticalmaps and yearbooks other geo-information of Pudong suchNewas District, buildings, Fengxian roads, landDistrict, uses areMinhang provided District, by the Songjiang Shanghai District,Administration and Jinsan of SurveyingDistrict. The and design Mapping rainfall (2012). and design The data water/tide of rivers, levels gates, are pumps, calculated and according dikes are toadopted the long according term data to from the Second 1965 to Shanghai 2013. 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.yearbooks Results and of Pudong Discussion 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 measured4. Results water and Discussion 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 river4.1. Model channel Validation and computation time. The total number of meshes is 127,453. A total of 44 gates, 262 pumps,According and to31 thedrainage above division methodology, areas are the simulated flood risk in the assessment study area. system Figure was 8 presents established. the comparisonThe measured between water the level simulation during Typhoon results and Fitow meas is setured as water the upstream levels at andthe Huangpu downstream Park boundary station. Theconditions simulation for UFSM.results Ashow 150 mgoodˆ 150 agreement m mesh with is considered the measured appropriate data. The in view simulation of both results the size are of generallythe river channellower than and the computation measured time.water The level. total Th numbere highest of simulated meshes is 127,453.water level A total is 4.94 of 44 m, gates, the highest262 pumps, measured and 31 water drainage level division5.12 m, areasand the are rela simulatedtive error in theis 3.5%. study The area. lowest Figure simulated8 presents water the levelcomparison is 1.05 m, between the lowest the simulationmeasured water results level and 1.18 measured m. The water errors levels at low at water the Huangpu level are Parkhigher station. than theseThesimulation at high water results level. show When good the agreement water level with is low, the measuredthe gates and data. pumps The simulation along the resultsHuangpu are Rivergenerally release lower water than into the measuredthe river. water As the level. amount The highest of water simulated from Puxi water is levelignored, is 4.94 the m, simulation the highest resultsmeasured are waterlower levelespecially 5.12 m, in anda low the tide relative situation. error is 3.5%. The lowest simulated water level is 1.05 m,

Int. J. Environ. Res. Public Health 2016, 13, 787 9 of 18 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 inInt. a J. lowEnviron. tide Res. situation. Public Health 2016, 13, 787 9 of 18

Figure 8. The simulation results and measured water levels of the Huangpu Park station during Figure 8. Typhoon Fitow.The simulation results and measured water levels of the Huangpu Park station during Typhoon Fitow. The surveyed flood damage was 132.12 million CNY, according to the 2013 statistical data of Jinshan,The Songjiang, surveyed floodand Qingpu damage Di wasstrict 132.12 (Table million 2). Through CNY, according spatial overlay to the analyzing 2013 statistical the flooding data of Jinshan,data and Songjiang, social economic and Qingpu data, District the flood (Table damage2). Through is calculated spatial overlay as 130.60 analyzing million the (Table flooding 3). dataThe andsimulation social economicresult coincides data, the quite flood well damage with the is calculatedsurveyed value as 130.60 with million a relative (Table error3). of The 1.15%. simulation Hence, resultthe model coincides can be quite utilized well to with estima thete surveyed the flood value damage with in a Shanghai. relative error of 1.15%. Hence, the model 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 Total 24.00 69.34 21.43 17.35 132.12 Water Conservancy Facilities Agriculture Industry and Transportation Other Total 24.00 69.34 21.43 17.35 132.12 Table 3. The flood damage simulation results (million CNY).

Residential Residential Industrial Production Table 3. The floodAgriculture damage simulation Losses results Industry (million Losses CNY). Building Losses Property Losses Losses 6.21Residential 17.31Residential 71.26Agriculture Industry 18.85 Industrial 9.98 Business Assets Business Direct Economic Building Losses Property LossesHighway LossesLosses RailwayLosses 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 working.The 24-hThe statistical design rainfalls inundation with frequencydata is shown of 0.001, in Table 0.002, 4. 0.005,The benefit 0.01, 0.02,of established and 0.05 were flood taken control as inputworks data can forbe evaluated scenario simulation. by comparing Two the flood diffe controlrence worksbetween conditions Condition are 1 setand up. Condition Condition 2. 1: Flood control works normally operate. Condition 2: All pumps, gates and drainage systems stop working. The statistical inundation dataTable is shown 4. The in simulation Table4. The results benefit of inundation. of established flood control works can be evaluated byReturn comparing 24-h the Areal difference The betweenLargest ConditionThe Maximum 1 and ConditionAverage 2. Inundation Area of Statistical Period Precipitation Inundation Water Depth Water Depth Water Depth Index (Year) (mm) Area (km2) (m) (m) ≥0.5 m (km2) 5 134.5 1287.75 1.09 0.12 3.71 10 169.1 1602.62 1.47 0.13 8.93 20 203.1 1805.49 1.79 0.15 16.74 Condition 1 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 1000 390.3 2290.87 2.37 0.24 137.87

Int. J. Environ. Res. Public Health 2016, 13, 787 10 of 18

Table 4. The simulation results of inundation.

Return 24-h Areal The Largest The Maximum Average Inundation Area of Statistical Period Precipitation Inundation Water Depth Water Depth Water Depth Index (Year) (mm) Area (km2) (m) (m) ě0.5 m (km2)

Int. J. Environ. Res. Public5 Health 2016 134.5, 13, 787 1287.75 1.09 0.12 3.71 10 of 18 10 169.1 1602.62 1.47 0.13 8.93 20 203.1 1805.49 1.79 0.15 16.74 Condition 1 50 247.5 1984.76Table 4. Cont 2.01. 0.17 32.84 100 280.8 2083.45 2.11 0.19 49.35 2005 313.9134.5 2161.281520.35 2.19 1.27 0.20 0.12 70.81 2.88 100010 390.3169.1 2290.87 1752.93 2.37 1.62 0.24 0.14 137.87 8.65 520 134.5203.1 1520.35 1906.28 1.27 1.90 0.12 0.15 16.66 2.88 Condition 2 1050 169.1247.5 1752.93 2046.95 1.62 2.07 0.14 0.18 33.20 8.65 20100 203.1280.8 1906.28 2123.97 1.90 2.15 0.15 0.19 50.4016.66 Condition 2 50 247.5 2046.95 2.07 0.18 33.20 100200 280.8313.9 2123.97 2187.96 2.15 2.22 0.19 0.21 72.4950.40 2001000 313.9390.3 2187.96 2299.33 2.22 2.40 0.21 0.25 143.70 72.49 1000 390.3 2299.33 2.40 0.25 143.70 The water depth and inundation area increase with the growth of the rainfall return period. The Thedisaster water situation depth andof Condition inundation 2 is area more increase serious with than the Condition growth of 1. the All rainfall inundation return areas period. are Theaccumulated disaster situationtogether as of the Condition largest inundation 2 is more seriousarea. The than largest Condition inundation 1. All area inundation of a 5-year areas flood are in accumulatedCondition 1 is together 1287.75 as km the2, largestthe largest inundation inundation area. area The of largest a 5-year inundation flood in area Condition of a 5-year 2 is flood1520.35 in Conditionkm2, and 1the is 1287.75relative km difference2, the largest is inundation18%. The larg areaest of ainundation 5-year flood area in Condition of a 1000-year 2 is 1520.35 flood km in2, andCondition the relative 1 is 2290.87 difference km2 is, the 18%. largest The largestinundation inundation area of area 1000-year of a 1000-year flood in floodCondition in Condition 2 is 2299.33 1 is 2290.87km2, and km the2, the relative largest difference inundation is area0.37%. of 1000-yearThe inundation flood inareas Condition of different 2 is 2299.33 return km periods2, and theare relativecompared difference in Figure is 9. 0.37%. The inundation areas of different return periods are compared in Figure9.

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 period,The the total differences inundation between area increases inundation with areas rainfall between return period.Condition With 1 and the increaseCondition of return2 decrease. period, It themeans differences that the between flood risk inundation reduction areas due between to flood Condition control 1works and Condition decreases 2 decrease.with the Itflood means return that theperiod. flood The risk flood reduction risk duereduction to flood effect control becomes works non-significant decreases with theunder flood extreme return storm period. conditions. The flood riskThe reductioninundation effect area becomes is reduced non-significant by 15.76%. under extreme storm conditions. The inundation area is reducedThe bymaximum 15.76%. water depth was set as hazard index of flood disaster, which was divided into gradesThe according maximum to waterfield surveys: depth was 0 m–0.15 set as m, hazard 0.15 m–0.30 index of m, flood 0.30 disaster,m–0.50 m, which over was0.55 m divided [30]. Table into grades5 presents according the normalization to field surveys: thresholds 0 m–0.15 of m, the 0.15 flood m–0.30 depth. m, 0.30 The m–0.50 flood m, hazard over 0.55 maps m [ 30are]. Tabledrawn5 presentsaccording the to normalization Table 5. Under thresholds Condition of the1, the flood flood depth. hazard The floodmaps hazard of 1/10, maps 1/100, are and drawn 1/1000 according 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.

Int. J. Environ. Res. Public Health 2016, 13, 787 11 of 18

Table 5. Normalization thresholds of the flood depth.

Int. J. Environ.Grade Res. Public HealthWater2016 Depth, 13 , 787Impact Hazard 11 of 18 The curb is usually 15 cm, thus it has hardly effect I 0 m < h ≤ 0.15 m None on production and life. to Table5. Under Condition 1, the flood hazardIt can be maps able to ofinvade 1/10, simpler 1/100, house, and with 1/1000 storm events are II 0.15 m < h ≤ 0.30 m Low shown in Figure 10. With the increase of flooddoors-still return next period, to the level the floodof the sidewalk. hazard increases. According to It can interrupt traffic of vehicles and mainly the flood hazardIII analysis, 0.30 m < theh ≤ 0.50 hazard m is higher in the upstream of Huangpu River andMedium the coastal area. of people. The terrain of Jinshan and Songjiang DistrictThe water is will low-lying, very probably thus have the already flood invaded hazards of these areas are higher. LandIV reclamation h > projects0.5 m were carriedthe interior out of in houses, coastal causing area recently,damages to for their example, High Lingang New City Project. The areas reclaimed from the sea are pronestructure to and flooding content. 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 researchI of Nanjing 0 m 0.5 m invaded the interior of houses, causing High damages to their structure and content.

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 Int. J. Environ. Res. Public Health 2016, 13, 787 12 of 18

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

Int. J. Environ. Res. Public Health 2016, 13, 787 12 of 18

FigureFigure 11. 11.Loss Loss raterate relations of of different different water water depth. depth.

Figure 11. Loss rate relations of different water depth. 4.4. Exposure4.4. Exposure Analysis Analysis Exposure is often used to describe the people and assets that would be subjected to the threat Exposure4.4. 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 of floods,Exposure such as population,is often used buildings,to describe cropsthe people and lifelines.and assets In that this would study, be exposuresubjected presentedto the threat as the 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 numberand ofland external uses are structure extracted andbased internal on the 1:10,000 property topographic value of residential map of the buildings.study area in Residential 2012. With areas number of external structure and internal property value of residential buildings. Residential areas and landthe increase uses are flood extracted return basedperiod,on the the flood 1:10,000 exposure topographic grows. The map residential of the studybuilding area density in 2012. is high With the and land uses are extracted based on the 1:10,000 topographic map of the study area in 2012. With increasein Minhang flood return District, period, and its the exposure flood exposure map is grows.drawn in The Figure residential 12. A large building proportion density of is the high in the increase flood return period, the flood exposure grows. The residential building density is high Minhangresidential District, buildings and its is exposureexposed to map the isinundation drawn in water Figure depth 12. Abelow large 0.5 proportion m. The cultivated of the residential land in Minhang District, and its exposure map is drawn in Figure 12. A large proportion of the buildingsdensity is exposedis high in to theSongjiang inundation District, water and depth its exposure below 0.5 map m. Theis drawn cultivated in Figure land density13. A large is high in residential buildings is exposed to the inundation water depth below 0.5 m. The cultivated land proportion of the cultivated lands are exposed to inundation water depths below 0.5 m. Some Songjiangdensity District, is high and in Songjiang its exposure District, map isand drawn its exposure in Figure map 13. Ais largedrawn proportion in Figure of13. the A cultivatedlarge cultivated lands near both banks of the river are submerged at higher water depths. landsproportion are exposed of tothe inundation cultivated lands water are depths exposed below to 0.5inundation m. Some water cultivated depths lands below near 0.5 bothm. Some banks of the rivercultivated are submerged lands near atboth higher banks water 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 Figurebuildings 12. (a) under The exposure 1 in 1000 ofyears residential extreme flood. 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.

Int. J. Environ. Res. Public Health 2016, 13, 787 13 of 18 Int. J. Environ. Res. Public Health 2016, 13, 787 13 of 18 Int. J. Environ. Res. Public Health 2016, 13, 787 13 of 18

(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 land under 1 in 100 years extreme flood; (c) The exposure of cultivate land under 1 in 1000 ofFigure cultivate 13. (a land) The under exposure 1 in of 100 cultivate years extreme land un flood;der 1 in (c )10 The years exposure extreme of flood; cultivate (b) The land exposure under 1 inof years extreme flood. 1000cultivate years land extreme under flood. 1 in 100 years extreme flood; (c) The exposure of cultivate land under 1 in 1000 years extreme flood. We assume that the population distribution of administration district is uniform. Then the We assume that the population distribution of administration district is uniform. Then the affected affectedWe populationassume that can the be populationcalculated with distribution Equation 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 , (7) P “ d A e =i i,,j (7)(7) i“1 j“1 where, is the affected population, is the ÿnumberÿ of the administration district, is the total where,number P eofis is the the the administration affected affected population, population, districti is within the is numberthe the number study of the area,of administration the administration is the number district, district,ofn theisthe grid total iscell the number within total ofanumber certain the administration of residential the administration district, district withindistrict is the the within total study number the area, study jofis area,the the grid number is cells the of numberwithin the grid the of cell theresidential withingrid cell a district, certainwithin residentiala certainis the residentialpopulation district, m district,densityis the totalof isthe numberthe administration total of number the grid districtof cellsthe grid within, and cells the, withinis residential the theinundation residential district, residential ddistrict,i is the populationbuilding is the areapopulation density of the of density thegrid administration j withinof the theadministration districtadministrationi, and districtA i,districtj is the, and inundation. The,is affectedthe residential inundation population building residential under area ofdifferentbuilding the grid returnareaj within of periods the the administrationgrid is shown j within in Figurethe district administration 14.i. The affected district population . The under affected different population return periods under isdifferent shown return in Figure periods 14. is shown in Figure 14.

Figure 14. Flood affected population under different recurrence interval storm events. Figure 14. Flood affected population under differentdifferent recurrence interval stormstorm events.events. 4.5. R-D Function Construction 4.5. R-D Function Construction 4.5. R-DThe Functiondirect economic Construction losses of the classified assets depending on flood water depth in each sub-district can be estimated separately according to the values of classified assets, relevant The direct economic losses of the classifiedclassified assetsassets depending on floodflood water depth in each inundated area of flood events with different return periods and the damage rates. The flood sub-district cancan be be estimated estimated separately separately according accordin to theg to values the ofvalues classified of classified assets, relevant assets, inundated relevant damage result is shown in Table 6. The losses of temporary production and service cessation are areainundated of flood area events of flood with differentevents with return different periods return and the periods damage and rates. the The damage flood damagerates. The result flood is ignored in this paper. These kinds of losses are related to the inundated shut-down time, so the showndamage in result Table 6is. Theshown losses in ofTable temporary 6. The losses production of temporary and service production cessation and are ignoredservice incessation this paper. are lossesignored of inthe this industrial paper. andThese commercial kinds of sectorslosses are are relatedprobably to underestimatedthe inundated shut-downin this paper. time, so the losses of the industrial and commercial sectors are probably underestimated in this paper.

Int. J. Environ. Res. Public Health 2016, 13, 787 14 of 18

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 Return Building Property Agriculture Industry Commerce Road Railway Total Index Period 5 49.74 84.60 104.25 104.86 14.55 5.44 0.22 363.66 10 103.86 165.69 207.69 296.52 36.19 14.12 0.79 824.85 20 172.77 327.59 512.68 452.13 55.95 20.71 1.15 1542.99 Condition 1 50 309.73 515.66 716.81 702.35 89.18 31.29 1.86 2366.87 100 452.96 694.48 877.44 929.16 120.21 39.90 2.41 3116.57 200 615.90 888.56 1022.86 1181.11 155.18 48.82 3.04 3915.47 1000 1086.34 1409.14 1331.91 1841.77 249.47 75.76 4.54 5998.94 5 53.54 97.92 160.41 141.66 19.74 6.02 0.29 479.57 10 118.41 193.09 277.38 265.99 36.45 10.99 0.55 902.86 20 199.39 376.88 613.29 539.47 67.43 23.40 1.26 1821.13 Condition 2 50 350.30 574.33 823.56 802.45 103.94 33.80 1.88 2690.26 100 547.43 831.52 106.224 1132.48 150.15 46.20 2.80 3772.82 200 673.53 958.04 1104.33 1283.01 171.36 51.99 3.07 4749.18 1000 1154.46 1483.33 1389.57 1945.60 269.95 77.84 4.46 6325.21

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

(1) Gompertz function: ´kpR´Rcq D “ Ae´e (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. (2) Logistics function: A D “ (9) 1 ` e´kpR´Rcq

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 Table7. 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.

2 Index Function ARc k Adjusted R Gompertz 5859.27 66.00 0.0092 0.92 Condition 1 Logistic 5857.20 115.18 0.013 0.89 Gompertz 6047.61 47.97 0.013 0.94 Condition 2 Logistic 5886.97 76.52 0.019 0.91 Int.Int. J.J. Environ.Environ. Res.Res. PublicPublic HealthHealth 20162016,, 1313,, 787787 15 15 ofof 1818

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

EquationEquation (6)(6) expresses expresses the the benefit benefit of flood of flood control co measure.ntrol measure. It often It used often to assessused theto assess feasibility the offeasibility new constructions. of new constructions. In this paper, In wethis use paper, it to we assess use theit to flood assess risk the reduction flood risk due reduction to the established due to the floodestablished control flood system. control The system. flood risk The reduction flood risk is calculatedreduction is as calculated follows: as follows:

100010001000 1000 0 DD2p()R RqdR dR´ 0 D D1 ()pR q dR Risk “ r 0021r “ 7.14% (10) Riskreduction 1000 7.14% reduction 1000D pRqdR (10) r0 DRdR2() 0 2 66 D pRqdR´ 66 D pRqdR r0 2 r0 1 Riskreduction1 “ 66 “ 15.59% 660 D2pRqdR 66 DRdR()r DRdR () (11) 100021D pRqdR´ 1000 D pRqdR 00r66 2 r66 1 RiskRiskreduction2 “ 1000 “ 7.06%15.59% reduction1 66 D pRqdR r66 DRdR()2 0 2 The damage transition point C corresponds to the parameter Rc (critical return period), which 1000 1000 (11) is associated with the integrated flood defense capability or flood control standard. Rc = 66.00 in D21() R dR D () R dR Rc Risk 66 66 7.06% Condition 1, and = 47.97reduction in Condition2  2. We choose1000 the larger one. Then the benefit can be divided into two parts. Within the flood control capacity, floodDRdR() risk is reduced by 15.59% and the effect of 66 2 flood risk reduction is significant. Once flood magnitude exceeds the flood control standard, the flood damageThe will damage sharply transition increase point and showC corresponds the feature to ofthe mutability parameter and Rc the(critical flood return risk is period), only reduced which byis associated 7.06%. with the integrated flood defense capability or flood control standard. Rc = 66.00 in Condition 1, and Rc = 47.97 in Condition 2. We choose the larger one. Then the benefit can be 5.divided Conclusions into two parts. Within the flood control capacity, flood risk is reduced by 15.59% and the effectDeveloping of flood appropriaterisk reduction emergency is significant. responses Once to addressflood magnitude and prevent exceeds flood inundation the flood requires control astandard, reliableflood the flood risk analysisdamage includingwill sharply hazard, increase vulnerability and show andthe feature exposure. of mutability This paper and explores the flood the spatialrisk is only distributions reduced ofby flooding, 7.06%. the vulnerability of different kind of assets and the flood prevention capacity, particularly concerning buildings, land uses and flood control works in Pudong (Shanghai, China).5. Conclusions A baseline framework was established that could be used to assess the flood disaster risks and floodDeveloping risk reduction. appropriate We reach theemergency following responses conclusions: 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

Int. J. Environ. Res. Public Health 2016, 13, 787 16 of 18

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. 2. 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. 3. 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. 4. 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 17 of 18

2. 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] 3. 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] 4. 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) 5. 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] 6. 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] 7. 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) 8. 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] 9. Crichton, D. The risk triangle. In Natural Disaster Management; Ingleton, J., Ed.; Tudor Rose: Leicester, UK, 1999; pp. 102–103. 10. 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). 11. 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. 12. 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). 13. 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) 14. Zerger, A.; Wealands, S. Beyond Modelling: Linking Models with GIS for Flood Risk Management. Nat. Hazards 2004, 33, 191–208. [CrossRef] 15. 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) 16. 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) 17. 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] 18. Cheng, X.T. Urban Flood Prediction and Its Risk Analysis in the Coastal Areas of China; Water & Power Press: Beijing, China, 2009. 19. 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) 20. 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) 21. 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. 22. 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] 23. 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] 24. Hoes, O.; Schuurmans, W. Flood standards or risk analyses for polder management in The Netherlands. Irrig. Drain. 2006, 55, 113–119. [CrossRef] 25. 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] 26. 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 18 of 18

27. 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) 28. 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] 29. 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] 30. 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. 31. 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) 32. 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/).