GIS and flood inundation model-based flood risk assessment in urbanized floodplain

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1 GI and R in Hydrology, ater Resources and nvironment, Volume 1, Cheng et al. (eds)2003, un Yat-sen University Press GI and flood inundation model-based flood risk assessment in urbanized floodplain Jiquan Zhang College of Urban and nvironmental cience, ortheast ormal University, Changchun , China, zhangjiquan652@hotmail.com Tomoharu HORI Department of Civil ngineering ystems, Kyoto University, Kyoto , Japan Hirokazu Tatano and orio Okada Disaster Prevention Research Institute, Kyoto University, Uji , Japan Chao Zhang ational Key Lab. of Resources & nvironment Information ystem, Institute of Geographical ciences and atural Resources Research, China Academy of ciences, Beijing Takuya Matsumoto Disaster Prevention Research Institute, Kyoto University, Uji , Japan Key words: flood risk, flood risk assessment, flood inundation model, urbanized floodplain, GI Abstract: This paper presents the methodology and procedure for flood risk assessment using GI and flood inundation model (FIM) based on the micro-zonation concept in which an entire floodplain considered is divided into numerous unit areas (meshes or grids). In this study, hinkawa-river floodplain around agoya City of Aichi Prefecture in Japan - an urbanized lowland area, which was greatly damaged by heavy rainfall on eptember 11 to 12, 2000 is chosen as the study area. 1 ITRODUCTIO Risk assessment of natural disasters is defined as the assessment on both the probability of natural disaster occurrence and the degree of danger caused by natural disasters. It can be said that natural disasters result from the interaction between physical impact (hazard) and human and environmental vulnerability. The risk associated with flood for any region is a product of both the region s exposure to the hazard (natural event) and the vulnerability of objects (society) to the hazard. It suggests that three main factors contribute to a region s flood risk: hazard, exposure, and vulnerability. It is very important, especially in mega-cities, to take measures to avoid the situation where a large-scale flood event does not lead to catastrophic loss directly. Hence, in order to cope with the increasing vulnerability especially in urbanized area and avoid/mitigate catastrophic flood risk, it is very important to introduce flood risk management in an integrated manner, so called, integrated flood disaster risk management which deals with not only controlling disaster risk (mitigation measures taken in flood plain) but also financing the risk (risk finance 92

2 measures). However, both of the floodplain-oriented mitigation and flood insurance designs need the detailed risk assessment in floodplain. From this viewpoint, this paper presents the methodology and procedure for flood risk assessment using GI and flood inundation model (FIM). Taking up the floodplain as a target field instead of a river channel, micro-zonation concept should be incorporated where an entire floodplain considered is divided into numerous unit areas and several risk measures associated with flood loss are computed through two-dimensional inundation analyses. 2 TUDY ARA In this study, hinkawa river basin around agoya City of Aichi Prefecture in central area of Japan, which was greatly damaged by heavy rainfall on eptember 11 to 12, 2000 (Tokai heave rainfall) is chosen as the study area (figure 1). The study area includes Kita-ku, agoya, ishi-ku agoya, hnikawa Town and ishibiwajima Town of Aichi Prefecture. Figure 1: Location and range of the study area Heavy rainfall occurred in the Toukai district around agoya City (Central area of Japan) on 11 to 12 eptember 2000 caused by Typhoon o.14 and a stationary front, this heavy rainfall brought great damages to various areas in Tokai district. Aichi Prefecture was damaged to the extent that it composed 92% of all the inundated houses in Japan. An embankment of about 100m was destroyed in the hinkawa river in the ishi ard of agoya City. Besides this, embankments were destroyed at ten places in Aichi Prefecture and much flood damage occurred surrounding agoya City and the northern part of the Chita Peninsula. 3 DATA T AD PRPROCIG There are generally two types of data sets have been used in this study: spatial and nonspatial data. patial data include digital elevation model (DM), channel network, dams or floodgates, watershed boundary and buildings/properties. onspatial data are discharge, land/building values, weather data and channel geometric data. (1) Topography data Digital 50m Grid ize for levation Data from Japan Map Center (JMC). (2) Property data Digital Map 2500 (patial Data Framework from Japan Map Center (JMC). 93

3 Digital Grid quare tatistics of Population Census from tatistical Information Institute for Consulting and Analysis (IFOICA) Digital grid quare tatistics of 1996 stablishment and nterprise Census from tatistical Information Institute for Consulting and Analysis (IFOICA). Digital Grid quare tatistics of Floor Area Data from Japan Construction Information Center (JACIC). (3) conomic data Flood damage statistics from River Bureau, Ministry of Land, Infrastructure and Transport. Manual for conomic Research of Flood control (draft) from River Bureau, Ministry of Land, Infrastructure and Trans. DM is critical and most important among the spatial data sets. For the study area, 50m grid size digital elevation data was transformed to ArcView GI to create digital elevation model (DM) as shown in figure 2. GojoKawa river hinkawa river honai river hinkawa river honai river Figure 2: DM of the study area generated from 50m grids The property database is the key element describing land use types in the study area. Properties may be represented as points or polygons. The above data sets were transformed to ArcView GI and the entire property database was generated. Figure 3 shows the house property polygons and attributes. Figure 3: House property polygons and attributes with 500m mesh 4 MTHOD AD PROCDUR 4.1 Procedures for flood risk assessment illustrated The blueprint for flood risk assessment is as follows: (1) stimate the flood behaviour (depth (s), duration(s), velocities etc) for different return periods of the flood event. (2) Relate the flood behaviour to a property database. (3) Read the flood damage amount from an appropriate damage curve for each flood-affected 94

4 h M + + = 0 t x y M ( um) ( vm) H τ + + = gh bx t x y x ρ ( u) ( v) H τby + + = gh t x y y ρ V(PD)=f(h) Percent damage (PD) PD FH = D FH/PV Floodwater depth (FH) area in the property database. (4) um the damages for the area of interest for the different flood surfaces generated under (1). (5) Develop a Risk vs. Damage relationship. (6) Annualise the damage estimate. (7) Calculate the net present worth of the average annual damage for benefit cost comparison with the cost of flood mitigation options. The assessment and quantification of flood risk will be based on the standard formulation of risk as a product of hazard (natural event), the region s exposure to the hazard and the vulnerability of objects (society) to the hazard. The concept framework and analytic procedure of flood risk assessment based on GI and flood inundation model were developed (figure 4). Flood risk assessment includes five separate but related stages viz., (1) predict flooding area and identify flood hazard based on FIM (Hazard identification), (2) evaluate the value of properties exposed to flood hazard using GI (xposure evaluation), (3) estimate vulnerability of the properties to flood hazard based on damage estimation model (Vulnerability estimation), (4) assess and rank flood risk (Risk assessment), and (5) to interpret the numerical findings and present the results in a variety of easily understandable graphical forms using GI (tandardization of Visualization of the Results). Data collection and literatures Field survey Hazard Identification Module Identifying flood hazard xposure valuation Module valuating properties exposed to the hazard Flood inundation model Floodplain Remote sensing data patial data Attribute data imulated floodplain with floodwater depth, flow velocity etc. Mesh (Grid) layout Building prope rties (umber, floor area structure and content value of building, etc.) stablishment properties (umber of persons engaged, depreciation and stock values, etc.) Agricultural and fishery household properties (umber of households, depreciation and stock values, etc.) GIdatabase Floodwater depth (h) Property category Vulnerability Assessment Module Assessing Vulnerability to the exposure Hazard identification and assessment Loss estimation Vulnerability analysis and assessment Flood Probability Property values (PV) Inundation model Loss estimation model Vulnerability assessment model Maximum inundation depth Maximum velocity Reaching time of inundation water Inundation on duration Frequency of the inundation tage damage curves Depth damage curve Velocity damage curve ocioeconomic development level Population density aterproof potential Properties of exposures etc. Depth percent damage (damage ratios) functions for structure and content of buildings Depth percent damage (damage ratios) functions for content of residential buildings Depth percent damage (damage ratios) functions for depreciation and stock properties of establishments Depth percent damage (damage ratios) functions for depreciation and stock properties of agricultural and fishery households Percent damage of property values (Damage ratios) (DR) xpected losses Flood risk curves Flood Risk assessment model Flood risk assessment Flood risk management Public policy analysis Mitigation countermeasures Defense countermeasures Damage correspondences Risk Assessment Module Assessing risk to the properties exposed to the hazard P(PD) = P (Damage > PD) = 1-F(PD xpected annual damage (AD) Risk distribution (patial and temporal, incidental structure etc.) Risk curve, etc. Figure 4: The concept framework (left-hand side) and analytic procedure (right-hand side) 4.2 Creation of different kinds of grid square (mesh) Because the original data format is mesh format, so in this study the spatial analysis via the methods of grid square is applied. According to the basic concept of grid square, the different kinds of grid square (mesh) have been created by using GI. 4.3 cenario generation 95

5 The study was applied to a hypothetical dike break case study due to the different return period rainfalls, obtaining the evaluation of the expected damages to different properties, so scenario generating is necessary, which includes basic (design) flood estimation and dike break condition analysis. For the analysis, the location of left-rank of shinkawa river was assumed as the dike break location and three scenarios were selected based on the basic (design) flood estimation approaches and dike break condition analysis methods as following: Tokai heavy rainfall with 20-year return period, 30-year return period rainfall and 100-year return period rainfall (figure 5). cenario 1:Tokai heavy rainfall =1/20 Dike break (Left-bank 16.8km) Precipitation (mm) ater level (T.P.m) cenario 2: =1/30 Precipitation in hinkawa-river basin Danger water level T.P 5.2m Cumulative Precipitation (mm) Precipitation (mm) ater level (T.P.m) cenario 3:=1/100 Precipitation in hinkawa-river basin Danger water level T.P 5.2m Cumulative Precipitation (mm) Time (h) Time (h) 5 FLOOD HAZARD IDTIFICATIO Figure: 5 cenarios in shinkawa river for analysis Flood risk assessment was carried out by dividing the risk into vulnerability associated with the land use pattern and hazard associated with hydrological and hydraulic parameters. Flood hazard identification and assessment are based on information on the intensity and the frequency of flood events. Usually the spatial distribution of both flood flow depth and flow velocity has to be considered. In the study, the flood flow depth was selected as indicator of flood identification and assessment. Dynamics of inundation flow on lowland area can be modeled as a shallow flow where the velocity distribution are integrated over the vertical direction. Therefore the definition of flood hazard was based on two-dimensional hydraulic model (flood inundation model) for the assumed flood events with the various return periods. ith the GI, flood hazard map was produced. Figure 6 shows the simulated flood flow after the dike break in hinkawa-river flood plain in agoya, Japan, which was struck by severe flooding in Flood Inundation Depth at 30 Minutes after Dike Break Flood Inundation Depth at 120 minutes after Dike Break Ñ ####### ########################## ######## ########################## ######## ######################### Railraod Public Building Administrative Division ater Maximum Inundation Depth (m) # # # # # # # 3- Ñ ######################### ################# Railraod Public Building Administrative Division ater Maximum Inundation Depth (m) # # # # # # # 3 - Figure 6: xamples of flood inundation simulation in hinkawa-river floodplain 96

6 6 XPOUR VALUATIO xposure describes the size of a region, all the structures, people, and activities subject to the demands imposed by the hazard. xposure evaluation refers to evaluation of the value of properties exposed to hazard. The following properties (general properties) have been considered: buildings including structure properties of residential, commercial, industrial and public buildings, and content properties of residential buildings, establishments including depreciation and stock properties and agricultural and fishery households including depreciation and shock properties The outcome of this task was a spreadsheet, linked to the GI, for each grid or mesh, the values of the properties were stored. Figure 7 shows a example of spatial distributions of the estimated property values. tructure Property V alue for Buildings Railroad Public B uilding Adm inistrative Division ater Property Value (100 million yen) Figure 7: patial distribution of structure property value for buildings 7 VULRABILITY AMT Vulnerability represents how easily and severely a region exposed properties can be affected by a flood event. Vulnerability is the percentage of the value of properties exposed to risk that could be lost if the event should occur. Commonly, flood damage is related to the depth, velocity, duration and extent of flooding. Therefore, the basic concept of assessing vulnerability is to develop the relationship between flood behavior and economic data (vulnerability function). The relationship often is referred as a Flood Damage Curve. In this study, flood damage curves are expressed as a function of Damage Amount (%property value) vs. Flood Depth. xamples of vulnerability functions for residential wooden buildings given by data on Tokai heave rainfall disaster on eptember 11 to 12, 2000 investigated by Property and Casualty Insurance Rating Organization Of Japan (PCIRO) are represented in figure 8. imilar relations have been derived for the other properties. mage Percent da 30% 25% 20% 15% 10% 5% 0% Floodwater depth (cm) e Percent damag Floodwater depth (cm) Figure 8: Vulnerability functions for structure (left-hand side) and contents (right-hand side)of residential wooden buildings 97

7 The vulnerability of each mesh of the territory for any type of properties was calculated by applying the above described vulnerability functions and floodwater depths simulated by flood inundation model (FIM) in a GI program. An example of spatial distribution of calculated vulnerability values is shown in figure 9. Vulnerability For tructure Property of Buildings Railroad Public B uilding Administrative Division ater V ulnerability (Dam age Ratio) Figure 9: patial distribution of vulnerability value for structure of buildings 8 RIK AMT The risk caused by the flood will depend on the actual damages to the properties. Therefore, risk assessment model (Risk = Hazard * xposure * Vulnerability) was developed, which evaluate the expected damages (losses) to properties. The risk considered in this study was direct damages to properties. The flood damage may be estimated under the assumption that, for given social and economic conditions, damage is a function of floodwater depth. ince the flood inundation model is based on grid (mesh) network used in finite difference schemes, for dynamically linking of output parameters of flood inundation model as input, the damage estimation model is also formulated as grid (mesh) based model. Based on the above-mentioned research, the grid (mesh) based mathematical models for expected damages to properties are shown in table 1. Table 1: Mathematical models for expected damages to properties Damage to houses (buildings) Damage to repay ment and stock Damage to repayment and stock properties of agricultural and fishery properties of establishments households tructure damage Repayment damage Repayment damage Dsh ( i, = PVshi (. * VVshi (, Dre ( i, = PVrei (. * VVrei (, Draf ( i, = PVraf( i. * VVraf( i, Content damage tock to damage tock to damage Dch ( i, = PVchi (. * VVchi (, Dse ( i, = PVsei (. * VVsei (, Dse ( i, = PVsaf( i. * VVsaf( i, here for any grid (mesh) (i,, D = Flood damage for respective property category; PV = Property value for respective category; VV = Vulnerability value for respective property category (Percent damage or damage ratio) According to table 1, the expected damages to properties were calculated for each mesh of the flooded area. Figure 10 shows an example of spatial distribution of expected damages to properties in 120 minutes after the dike break in hinkawa-river flood plain in agoya, Japan, which was struck by severe flooding in

8 Total Loss Legend Railroad Public B uilding Adm inistrative Division ater Property Value (100 million yen) Figure 10: patial distribution of expected total damages to properties 9 GRAPHICAL RULT PRTATIO AD VIUALIZATIO GI is considered as an essential tool to gather, store, handle, update, output and display spatial data, so graphical results can be presented by the GI-view as above shown. In addition, in order to obtain the scene of the process of flood inundation, the methods of three dimensions creation and dynamic display are being used. The results of animation are shown in the figure 11 as follow. 10 COCLUIO Figure 11: The simulation of flood inundation process based on the methods of three dimensions creation and dynamic display An urbanized floodplain oriented methodology and procedure for flood risk assessment based on flood inundation model and GI has been developed. The method was applied to a hypothetical dike break case study due to different return period rainfall, obtaining the evaluation of the expected damages to general properties. Although needing further refining, it may give acceptable results especially in analyzing in relative terms the different levels of risk associated to properties in diverse locations. This method could be of great usefulness in cost benefit evaluation of risk mitigation programs and in deciding priorities of intervention. In addition, the results can be applied in other aspects, such as flood hazard map, regional planning, flood disaster insurance, flood control planning, etc.. This study may provide reference to the disaster risk assessment of similar situation. RFRC Tomoharu HOR I, Ji-quan Zhang, Hirokazu TATAO, orio OKADA and huichi IIKBUCHI, Micro-zonation-based Flood Risk Assessment in Urbanized Floodplain, Proceedings of The econd Annual IIAA-DPRI Meeting ITGRATD DIATR RIK MAAGMT: Megacity Vulnerability and Resilience, IIAA, A-2361 Laxenburg, Austria. 99