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NOVATECH 2013 Comparative evaluation of the main factors that contribute to flood risk in urban drainage areas Évaluation comparative des principaux facteurs contribuant au risque d’inondation dans les zones de drainage urbain Masaru Morita* Shibaura Institute of Technology 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan [email protected] RÉSUMÉ Parmi les divers facteurs contribuant au risque d’inondation (ou risque inondatoire), les violents typhons et les systèmes de drainage des eaux pluviales inadaptés ainsi que la concentration de population et de biens sont généralement considérés comme des facteurs fondamentaux pour le drainage urbain. Les changements climatiques devraient également constituer une véritable menace avec des typhons à la fréquence et violence accrues. Cette étude présente la méthodologie d'évaluation comparative de l'impact des facteurs de risque d'inondation à l'aide d'un modèle de prévision du dommage inondatoire (MPDI) basé sur un système d’information géographique (SIG). Le MDPI calcule les profondeurs des eaux d’inondation via XPSWMM et les dommages financiers dus aux inondations grâce à un modèle d'estimation des dommages inondatoires pour tous les typhons et conditions de captage donnés. La notion de risque d’inondation dans ce contexte est définie comme le produit entre les dommages dus aux inondations et la probabilité de leur occurrence. L'étude montre une structure du risque d’inondation dans un cadre de gestion de ce risque en milieu urbain. La méthode d'évaluation du risque est appliquée au bassin du fleuve Zenpukuji à Tokyo, au Japon. L'étude montre l'évaluation quantitative des changements dans le risque d’inondation en raison de facteurs de risque d’inondation: l'urbanisation, les projets de contrôle des inondations, les mesures non structurelles et les changements climatiques via un facteur d'impact du risque d’inondation (FIRI). ABSTRACT Among the various factors that contribute to flood risk, heavy storms and inadequate storm drainage systems and the concentration of population and property have usually been considered to be fundamental factors affecting urban drainage. Climate change is also a real threat, bringing heavier and more frequent storms. This study presents a methodology for comparatively evaluating the impact of the flood risk factors using a GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation depths using the XP-SWMM routine and monetary flood damages using a flood damage estimation model for various storms and catchment conditions. The concept of flood risk in this context is defined as the product of flood damage and the probability of its occurrence. This study produces a flood risk structure in a framework of urban flood risk management. The risk assessment method is applied to the Zenpukuji River basin in Tokyo, Japan. The study gives a quantitative evaluation of the changes in flood risk due to flood risk factors such as urbanization, flood control projects, non-structural measures, and climate change using a flood risk impact factor (FRIF). KEYWORDS Climate change, Flood inundation modelling, Flood risk assessment, Flood risk impact factor (FRIF) 1 C6b - RISQUE INONDATION / FLOOD RISK 1 INTRODUCTION Many factors contribute to flood risk in urban catchment areas. Hydrological factors cause rapid flood runoff and flood discharge grows with an increase in impervious areas. Concentration of population and assets is also important as a social aspect of flood risk. Climate change is now considered an important factor increasing flood risk, with its increasingly frequent torrential storms (IPCC, 2007; Patrik Willems et al., 2012). To reduce flood risk, national and local governments have been implementing structural measures, constructing flood control reservoirs and infiltration and storage facilities with their budgets available for flood prevention. Significant non-structural measures must also be employed for flood risk reduction, using such tools as hazard maps and effective forecasting systems. Flood insurance mitigates flood inundation damage and contributes to relieving flood risk. For urban flood risk management, these factors that increase or decrease risk should be compared and evaluated in the decision-making process of urban flood control planning. There are many different definitions of flood risk. Crichton (1999) gives the simplest, a comprehensive definition using the “Crichton Risk Triangle” that consists of hazard, vulnerability, and exposure. Some researchers adopt the narrow definition of flood risk as the probability of failure during a flood event (e.g., National Research Council, 2000). Some other researchers strongly emphasize the mental aspect of flood risk (Baan, 2005). Samuels (2013) broadly discusses flood risk management and gives the definition of flood risk as an evaluation of the combination of the probability of flooding and the adverse consequences that ensures. Klijn et al. (2008) shows the standard definition of flood risk as the ‘product’ of the probability of floods and their consequences. This definition enables us to evaluate flood risk on a monetary basis though it is by no means new. Accordingly, flood risk assessment focuses on the frequency and severity of flooding events (Davis, 2003; Morita, 2011). Whatever the definition of flood risk, quantifying flood risk provides a basis for evaluating flood prevention measures and making appropriate decisions in flood control management. Climate change will likely affect flood risk increases, especially future storms’ frequency and severity. This can be forecast in changes of rainfall intensity-duration-frequency relationships used in urban drainage planning (Nguyen et al., 2007; Patrick, W. et al., 2012). Such a projection, formulated as a return period shift (RPS) method, has been applied to evaluating flood risk increases due to global warming (Morita, 2013). This study presents a methodology to comparatively evaluate the impact of flood risk factors using a GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation depths using the XP-SWMM program and estimates monetary flood damages with a flood damage estimation model for any given storm and its catchment conditions. Flood risk in this context is defined as the product of flood damage and the probability of its occurrence. The risk assessment method is applied to the Zenpukuji River basin in Tokyo, Japan. The study reports a quantitative evaluation of the changes in flood risk due to the following flood risk factors: urbanization, flood control projects, non-structural measures and climate change using a flood risk impact factor (FRIF). It provides a flood risk structure in a framework of urban flood risk management. 2 METHODS This study’s methodology consists of three stages: FDPM simulation, flood risk analysis, and flood risk assessment. This methodology uses the results of FDPM simulations and provides a basis for flood risk assessment to calculate flood risk costs and flood risk impact factors. 2.1 FDPM simulation FDPM simulations provide three curves: an inundation characteristic curve, a damage characteristic curve, and an inundation-damage characteristic curve (Figure 1). 2.1.1 Flood damage prediction model FDPM, a GIS-based flood damage prediction model, consists of two parts, Model 1 and Model 2. Model 1 calculates two-dimensional inundation depths for input hyetographs. Model 2 estimates monetary inundation damages for any given inundation depths (Figure 1). For Model 1, XP-SWMM, a storm water modeling software package for urban drainage (Phillip et al. 2005), is used to calculate the inundation depths on a square grid for a given hyetograph. Other flood inundation simulation models can also be used for Model 1. 2 NOVATECH 2013 Model 2 estimates the monetary cost of inundation damage, including direct and indirect damages, as a function of inundation depth. To calculate direct damages, an asset valuation of each item is Design Storm (T-year Return Period) basically multiplied by the damage rate determined from the depth-damage rate curve. Indirect Intensity-duration-frequency curves damage is calculated using the number of days Alternating Block Method business is interrupted for each place of business. 2.1.2 Three curves for inundation damage characteristics and Design Storm Hyetograph FDPM The inundation depths for any given storm are calculated in two-dimensions using Model 1. The inundation characteristic curve shows the relationship between inundation depth and the flooded area for different return period storms. The curves are shown for only one return period in Figure 1(a). The integral of the curve equals the total area inundated in the catchment for the FDPM calculation. The monetary costs of inundation are estimated for any given inundation depth using Model 2 as shown in Figure 1. The damage characteristic curve gives the relationship between the inundation depth and monetary inundation damage per unit area averaged for the catchment (Figure 1(b)). Catchments actually have various land uses and the population and assets are not distributed uniformly. This curve can be used for catchments that are relatively spatially homogeneous. Model 2 Inundation depth (XP-SWMM) Depth-damage rate curve Effective rainfall Sewer flow Damage Rate GIS Asset valuation (Channel flow) Inundation depth Inundation damage 1000 Urbanization Climate change Inundation area (L**2/L) For the inundation characteristic curve, the solid line shows the inundation for present catchment conditions as in Figure 1(a). Urban development and global warming shift the present inundation upward as shown by the dashed lines. Conversely, flood control projects decrease the inundation as shown by the dotted lines. Model 1 800 600 Present catchment condition 400 Flood control project 200 Urbanization concentrates population and assets and thus increases the potential for damage in 0 0 0.5 Inundation1 depth (L)1.5 2 (a) Inundation characteristic curve. 700 Concentration of population and assets Urbanization etc. 600 Inundation damage ($ /L) Damage per unit area ($ /L**2) 20 15 500 Concentration of population and assets 400 10 300 Present catchment condition 200 5 Present catchment condition 100 0 0 0 0.5 1 1.5 Inundation depth (L) 2 (b) Damage characteristic curve. 2.5 0 0.5 1 1.5 Inundation depth (L) 2 2.5 (c) Inundation-damage characteristic curve. Figure 1. Flood damage prediction model and inundation-damage characteristics. urban catchments. This means urbanization or the concentration of assets shift the damage characteristic curve upwards as shown by the dashed line in Figure 1(b). Conversely, the removal of 3 C6b - RISQUE INONDATION / FLOOD RISK household articles before inundation could reduce the inundation damage as a non-structural measure. Damage Potential The monetary inundation damage is calculated by multiplying a value from the inundation characteristic curve by the appropriate value from the damage characteristic curve. The curve thus obtained is termed the inundation-damage characteristic curve which shows the relationship between inundation depth and the monetary damage over the whole catchment for one return period storm (Figure 1.2 1(c)). The integral of the curves for different return Urbanization periods gives the monetary damages for storms having 1 Climate change different return periods. The results are used to devise etc. the damage potential curve described below. 0.8 The inundation and damage characteristics for a catchment are described by the three curves. These curves reflect the conditions in the catchment. 2.2 Flood risk analysis 0.6 0.4 The flood risk analysis uses three curves: a storm probability curve, a damage potential curve, and an annual risk density curve (Figure 2). In the flood risk analysis, multiplying the storm probability curve by the inundation damage potential curve produces the annual risk density curve. The risk density curve gives useful information on flood risk characteristics and risk cost. 0 10 (a ) Damage potential curve. 1 0.8 The results of integrating the inundation-damage characteristic curves for different return periods are plotted as a damage potential curve, which shows the relationship between the design storm return period and flood damage as in Figure 2(a). The damage potential curve can also be calculated directly from the FDPM using a GIS assets database for cases where the land use in and asset distribution in the catchment are not homogeneous (Morita, 2011). 2.2.2 Interconnected structure of flood risk f(T) = 1/T2 0.6 0.4 0.2 0 The design storms used in the FDPM simulations are specified by their return periods. The return period T and cumulative probability P are related by the equation P = 1 – 1/T. Thus, the probability density is f(T) = 1/T2, as given in Figure 2(b). The three curves describe the characteristics of flood risk for a catchment. 100 Storm Level (Return Period) Three curves for flood risk analysis 1 10 Storm Level (Return Period) (b) Storm probability curve. 8 7 Annual Risk Density In our study, flood risk is defined as the product of flood inundation damage and the probability of its occurrence. Thus, flood inundation risk is quantified using the estimates of monetary damage caused by the design storm and that storm’s probability of occurring. The annual risk density curve, which has a peak in the middle as shown in Figure 2(c), is thus obtained by multiplying the increasing damage potential curve and the decreasing storm probability curve. Flood control project 0.2 Probability Density 2.2.1 Present catchment condition 6 Urbanization, climate change etc. 5 4 3 Flood control project Present catchment conditions 2 1 0 10 100 Storm Level (Return Period) FDPM simulations for different return period storms first (c) Annual Risk density curve. provide the inundation and damage characteristic curves. The two characteristic curves are both linked to Figure 2. Flood risk analysis and the inundation-damage characteristic curve. The its three associated curves. integrals of the inundation-damage characteristic curves for different return periods are transformed into the damage potential curve. The product of the damage potential curve and the storm probability curve produces the annual risk density curve. 4 NOVATECH 2013 The three curves obtained by the FDPM simulations shift with changes in catchment conditions. Similarly, the damage potential curve shifts upwards due to urban development, global climate change, and increased assets, and downwards with the implementation of flood control projects as shown in Figure 2(a). Changes in the damage potential curve are, in turn, linked with the risk density curve as shown in Figure 2(c). The six curves are, as stated above, interconnected and reflect the effects of urbanization, climate change, flood control projects and other positive or negative factors. 2.3 Flood risk assessment 2.3.1 Risk cost In this study, we take flood risk cost to be the annually averaged monetary expenditure over time for flood inundation damages. The risk cost depends on the annual risk density curve and is obtained by integrating the risk density curve with respect to return period. The flood risk cost for present catchment conditions decreases with the implementation of flood control projects and increases with global climate change because heavy storms are expected to become more frequent due to global warming. The risk costs for present catchment conditions and future conditions are RC0 and RC, respectively. The change in flood risk cost should be the difference between the two, RC = RC – RC0. 2.3.2 Flood risk impact factor (FRIF) Urban river basins are exposed to various flood risks such as increasing impervious area caused by urban development and the effects of global climate change. Conversely, flood control projects are effective in reducing the flood risk in urban catchments. The factors that affect risk cost can be evaluated on a single scale of one simple index, FRIF. This is computed as FRIF = (RC – RC0)/RC0. A positive FRIF value indicates increased flood risk, whereas a negative one indicates reduced flood risk. The magnitude of the impact factor indicates the importance of the risk-increasing or risk-decreasing effects of changing conditions in urban catchment areas. 3 APPLICATION OF FLOOD RISK ASSESSMENT This risk assessment method, based on FDPM simulations, was applied to the Zenpukuji River basin in the Tokyo Metropolis. The Zenpukuji River basin, which has an area of 18.3 km2, is characterized by a high population density and numerous assets. Repeated flood inundation disasters prompted the Tokyo Metropolitan Government (TMG) to construct an underground flood control reservoir under Loop 7 Road in 2006. The reservoir has a capacity of 540000 m3, receiving floodwater from the Zenpukuji, Myousyouji and Kanda rivers. Figure 3 shows the outline of the Zenpukuji River basin and the flood control reservoir called the Loop 7 Reservoir. 3.1 FDPM simulation Flood inundation depths and damages were calculated using the FDPM Model 1 and Myousyouji River Model 2 for the present catchment R0 R1 Kanda River Zenpukuji River conditions. A set of design hyetographs having different return periods was used for FDPM simulations. Each hyetograph input to Kanda River R0 the FDPM is created from the IntensityR0 Duration-Frequency (IDF) relationship for its corresponding return period using the 0 1 2 3 km alternating block method (e.g., Ven Te Chow et al., 1988). The IDF curves for the study Figure 3. Outline of Zenpukuji River basin. R0: were determined by the Gumbel distribution Loop 7 Reservoir ; R1: assumed reservoir and have been adopted by the TMG. Figure under Loop 8 Road. 4 shows some examples of the IDF curves for different return periods for present conditions. Design storms for the simulations have return periods of 1, 2, 3, 4, 5, 15, 30, 50, 70, 100, 150, 200, 300, and 500 years. 5 250 C6b - RISQUE INONDATION / FLOOD RISK Rain intensity (mm/hr) T = 3 yr For Model 1, we used XP-SWMM to calculate 200 inundation depths in two-dimensions on a 50 m T = 5 yr square grid for the design storms for a given T = 10 yr hyetograph. The inland flows from the sewer 150 pipes and overflows from the river channel T = 30 yr were both calculated spatially and temporarily for the catchment. The time increment t was T = 100 yr 100 set to be 1.0 s for calculation stability. The inundating water was assumed to flow on the natural surface according to the 50 m digital 50 elevation model (DEM). The water pathways and detailed land use were taken into account by adjusting their roughness coefficients. An 0 20 40 60 80 100 120 140 160 180 example of flood inundation calculations is shown with GIS data superposed in Figure 5 Duration (min) for a 15-year return period storm under present catchment conditions. The calculated results of Figure 4. Variation of intensity-duration-frequency inundation depths are described as an relationships and return period. inundation characteristic curve for each return period storm as shown in Figure 1(a) for the Zenpukuji River basin. The monetary damages are calculated by Model 2 as a function of the inundation depth using a GIS database available from the TMG. The effect of water velocity on the damage can be negligible. The results are expressed as a damage characteristic curve for the catchment as shown in Figure 1(b). Multiplying the damage characteristic curve by the inundation characteristic curves for the different return period storms gives estimated monetary inundation damages to provide a damage potential curve used in the following flood risk analysis. Depth (m) Figure 5. Calculated inundation depths for a 15-year return period storm in the present catchment with GIS data superposed for the onset area in Figure 3. 3.2 Changes in present catchment conditions Various factors increase or decrease the present flood risk in urban areas. Flood control projects such as the construction of flood control reservoirs definitely reduce flooding in the Zenpukuji River basin. In the study, the FDPM simulations deal with one case that assumes that a flood control project (another flood control reservoir under Loop 8 Road) will be built (R1 in Figure 3). Concentration of assets in a catchment increases the cost of flood inundation damages and exacerbates the flood risk. In the simulations, assets are also assumed to increase by 30%. For 6 NOVATECH 2013 example, economic growth of 1% per year is expected to produce 30% growth in a quarter of century. This would shift the damage characteristic curve shown in Figure 1(b) upward. 3.3 Change in storm characteristics due to global climate change Climate change is a significant factor in increased flood risk. Risk assessment needs not only the current design hyetographs but also predicted hyetographs reflecting the effect of global warming. 3.3.1 Changes in IDF relationships 4 Return period (year) after climate change Return period (year) before climate change An IDF relationship should be estimated to evaluate the effect off global climate change on flood risk. Nguyen (2007) presented a detailed spatial and temporal downscaling method based on global climate models (GCMs) and estimates the resulting variation in IDF curves. However, few studies deal directly with the change in magnitude and frequency of heavy storms in the Tokyo area. Only two studies have predicted changes based on General Circulation Models (GCMs): The National Institute for Land and Infrastructure Management (NILIM, 2008) and Oki (2006) of the Institute of Industrial Science (IIS). Oki produced the relationship between return periods before and after climate change shown in Figure 6. 500 500 3.3.2 Return period shift (RPS) method 200 200 Morita (2012) presented a simple way, the 100 100 Return Period Shift (RPS) method, to deal with changes in return period due to global climate change by shifting return periods in 30 30 the damage potential curve. In this study, the return period shift method is further 10 10 simplified to obtain an inundation characteristic curve after climate change as 5 5 shown in Figure 1(a). For example, a return period of 30 years before climate change corresponds to a 15-year return period after 1 10 the change as shown in Figure 6. The 0.2 0.4 0.6 0.8 1 calculated inundation characteristic curve for a return period of 30 years before Figure 6. Return periods before and after climate climate change, therefore, can be change (Oki, 2006). interpreted as that of a 15-year return period after climate change. The inundation characteristic curve for a 15-year return period after climate change thus shifts to the 30-year return period. Accordingly, all of the inundation characteristic curves for various return periods shift to the new corresponding return periods. RESULTS AND DISCUSSION The results of the FDPM simulations produce the three curves for the inundation and damage characteristics of the catchment shown in Figure 1. Those three curves were followed by the three curves used to analyze flood risk: the damage potential curve, storm probability curve, and annual risk density curve. Finally, we used the risk assessment method to compute the flood risk costs and FRIFs to compare and evaluate the main factors contributing to flood risk in the catchment. 4.1 Inundation and damage characteristics The inundation characteristic curves were calculated for different return periods. Figure 7 shows the inundation characteristic curves of some return periods for the present catchment conditions. The inundation depth and area are larger for higher return period storms. The curves for 15-, 50-, and 100year return periods have peaks at 0.9, 1.1, and 1.3 m of inundation, respectively. The peaks of inundation depth move to the right from the 15-year to the 100-year return period. The inundated areas are almost the same, from 100 to 150 ha/m for these peaks. This means there should be a large ponded area in the catchment. 7 C6b - RISQUE INONDATION / FLOOD RISK The inundation characteristic curve for the present changes if flood control measures are taken or if global climate change occurs. Figure 8 shows the inundation characteristic curves for a 15-year return period storm for three cases: present catchment conditions, construction of another flood control reservoir under Loop 8 Road, and global climate change expressed by the curve for a 30-year return period storm. Naturally, shallowly inundated areas are larger than those covered by deeper water. Climate change causes heavier storms and thus increases the inundation area. 500 400 Area of inundation (ha/m) Area of inundation (ha/m) 500 3-year 15-year 300 50-year 100-year 200 100 0 0.5 1 1.5 2 2.5 Inundation depth (m) 400 Present 300 Climate change Flood control project 200 100 0 3 Figure 7. Inundation characteristic curves of different return period storms for present catchment conditions. 0.5 1 1.5 2 2.5 Inundation depth (m) 3 Figure 8. Inundation characteristic curves for present catchment conditions, a flood control project, and climate change. The damage characteristics reflect the asset valuation of the catchment. Figure 9 shows the damage characteristic curves for present conditions and for a 30% increase in assets. The larger the inundation depth, the more inundation damage occurs. 20 15 10 Present 5 Asset increase 1.3 0 0.5 1 1.5 2 2.5 3 Inundation depth (m) Figure 9. Damage characteristic curves for present catchment conditions, increasing assets by 30%. 8 Inundation damage (US $ millions / m) Damage per unit area (US $ millions / ha) Multiplying the inundation characteristic curve by the damage characteristic curve produces the inundation-damage characteristic curve that relates inundation damage to inundation depth as shown in Figure 10. We can easily see that the most damage occurs at an inundation depth of about 1.0 m. The floor level of buildings in the catchment seems to be about 0.5 m. This means that most of the damage is due to flooded household articles in private houses and the loss of depreciable and inventory assets in business buildings. 1200 Climate change Asset increse 1.3 Present Flood control prolect 1000 800 600 400 200 0 0.5 1 1.5 2 Inundation depth (m) 2.5 3 Figure 10. Inundation-damage characteristic curves for present catchment conditions, a flood control project, and climate change. NOVATECH 2013 4.2 Damage potential and annual risk density The damage potential curve relates storm levels expressed in return periods and the monetary cost of flood damage. It is computed by integrating the inundation-damage characteristic curve for each return period, and is plotted for the different return periods. The damage potential curves are shown in Figure 11 for the four cases in Figure 10. The damage potential curves were also calculated for combinations of such as assuming climate change and the construction of a flood control project. The monetary flood damage are divided by the average annual Budget for Flood Prevention (BFP) of the Tokyo Metropolitan Government (approximately 500 million US$) to express them in non-dimensional form. Multiplying the damage potential curves by the storm probability curve produces the annual risk density curves for the four cases as shown in Figure 13. The risk density curves have a peak at 2 years. The risk density rises due to increased assets and climate change and drops due to the effects of the flood control project. The risk density curves were also computed for the same combined cases as the damage potential curves. 1 7 6 5 0.8 0.6 4 0.4 3 2 probability density 1 0 1 0.2 0 10 100 200 Storm level (return period: year) Figure 11. Damage potential curves for present catchment conditions, a flood control project, increased assets, and climate change. 4.3 0.3 Annual risk density (BFP/year) Climate change Asset increase 1.3 Present Flood control project Probability density (1/year) Damage potential (BFP) 8 0.25 Climate change Asset increse 1.3 Present Flood control project 0.2 0.15 0.1 0.05 0 1 10 100 200 Storm level (return period: year) Figure 12. Annual risk density curves for present catchment conditions, a flood control project, increased assets, and climate change. Risk cost and risk cost change Flood risk costs were calculated by integrating the annual risk density curves for the four cases: present catchment conditions, climate change (C.C.), a flood control project (F.C.P.), and asset increase (A.I.). Risk costs of combinations of these cases were also computed. The values of the risk costs are read on the x-axis in Figure 13. The risk cost grows as assets increase, but it decreases when flood control projects are implemented. The risk cost for the present catchment conditions due to global climate change is estimated to increase by approximately 70% and, remarkably, grow by over 110% when the effect of increased assets is included. 4.4 Flood risk impact factors We made a comparative evaluation of the effects of the main factors that contribute to flood risk, computing the flood risk impact factors (FRIFs) for the four cases and their combinations. Flood risk impact factors are easily computed using the calculated risk costs in Figure 13. The values of the FRIFs are read on the y-axis in Figure 13. As a single factor, climate change has the largest flood risk impact factor, 0.70, showing it to have the largest effect. A 30% increase in assets has a positive impact factor of 0.29. Conversely, constructing the flood control reservoir on the Loop 8 Road has a negative effect with an impact factor of -0.25. Combining increased assets and the flood control project balance their negative and positive effects, yielding a risk impact factor of almost zero. From the inundation-damage characteristic curve shown in Figure 10, most of the damage is due to flooded household articles in private houses and the loss of depreciable and inventory assets in business buildings, as mentioned earlier. If people have sufficient warning and can remove some of their possessions from harm’s way, increased assets become less 9 C6b - RISQUE INONDATION / FLOOD RISK 5 CONCLUDING REMARKS The objective of this study was to present a risk assessment method for making comparative evaluations of the main factors contributing to flood risk in urban drainage areas. The important results are as follows: (1) We have developed a risk assessment method incorporating a GIS-based FDPM using the XP-SWMM routine that can evaluate factors that increase and decrease the risk of urban flooding to serve as a basis for urban river management. 1.5 Flood risk impact factor (FRIF) effective and the FRIF of the combination is negative. As a result, the flood control project would be more effective combined with this non-structural measure. C. C. + A. I. No climate change Climate change Present 1 C. C. 0.5 A. I. + F. C. P. 0 0.4 0.6 0.8 C. C. + F. C. P + A. I. 1 1.2 1.4 Risk cost (BFP) 1.6 1.8 + F. C. P -0.5 Figure 13. Flood risk impact factors and risk costs for present catchment conditions, a flood control project, asset increase, and climate change. A.I. : asset increase; C.C.: climate change; F.C.P.: flood control project. (2) The risk assessment method was employed to estimate the reduction in flood risk provided by a flood control project and the increased risk due to asset increases and global climate change for the Zenpukuji River basin in Tokyo. (3) We introduced the FRIF as an index that can be used to evaluate the effectiveness of various factors that could increase or reduce flood inundation risk. Risk impact factors calculated from FDPMs may play an important role in urban flood control planning and decision-making processes in the future. LIST OF REFERENCES Baan, P. (2005). Risk Perceptance and Preparedness and Flood Insurance. In: Urban Flood Management (ed. by A. Szollosi-Nagy and C. Zevenbergen, A.A.Balkerma Publishers. Chow, V.T., Maidment, D. R. and Mays, L.W. (1988). Applied Hydrology, MacGraw-Hill, International Edition, New York Crichton, D. (1999). Role of Insurance in Reducing Flood Risk, Geneva Papers 33, 117-132. Davis, D. W. (2003). Risk analysis in flood damage reduction studies – The Corps Experience, Congress of Environmental and Water Resources Institute, Philadelphia, Pennsylvania, USA. June 23-26, 2003. IPCC (2007). Climate change 2007: Forth Assessment Report Climate Change 2007 Synthesis Report, Topic3, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf [accessed 14 September 2009]. Klijn, F., Samuels, P., Van Os, A. (2008). Towards flood risk management in the EU : State of affairs with examples from various European countries, International Journal of River Basin Management 6(4), 307-321. Morita, M. (2011). Quantification of increased flood risk due to global climate change for urban river management planning. Water Science and Technology 63(12), 2967-2974. Morita, M. (2013). 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