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Among the various factors that contribute to flood risk, heavy storms, inadequate storm drainage systems, and the concentration of population and assets have usually been considered to be fundamental factors affecting flood damage. 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 risk assessment framework. The method is applied to the Kiba drainage area in Tokyo, Japan. The study gives a quantitative evaluation of the changes in flood risk due to risk factors such as increase in asset values, flood control, and climate change using a flood risk impact factor (FRIF). The FRIF is introduced as an index to evaluate the impact of various sources of increased or reduced flood risk to society.

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. Concentrated population and assets are also important social aspects of flood risk. Climate change is now considered an important factor that increases flood risk, with an increase in the frequency and their intensity of torrential storms [

There are many different definitions of flood risk. For risk evaluation, Wilson [

Climate change will likely increase flood risks, especially the frequency and severity of future storms. Risk management studies have been carried out within the context of climate change [

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 sewer simulation software XP-SWMM 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 Kiba drainage area in Tokyo, Japan. The study reports a quantitative evaluation of the changes in flood risk due to four flood risk factors, urbanization, flood control projects, non-structural measures, and climate change, using a flood risk impact factor (FRIF). The concept of the FRIF was first introduced in Morita [

This study’s methodology has three stages: FDPM simulation, flood risk analysis, and flood risk assessment. It uses the results of FDPM simulations and provides a basis for flood risk assessment to calculate flood risk costs and FRIFs.

FDPM simulations provide three curves: an inundation characteristic curve, a damage characteristic curve, and an inundation-damage characteristic curve (

Flood damage prediction model (upper) and three curves for flood damage characteristics (lower): (

Urbanization concentrates population and assets, increasing the potential for damage in urban catchments. This means urbanization, or the concentration of assets, shifts the damage characteristic curve upwards as shown by the dashed line in

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 (

For Model 1, XP-SWMM, a stormwater modeling software package for urban drainage [

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 multiplied by a damage rate determined from the depth-damage rate curve. The asset values of all the items in drainage area are obtained by GIS database. Indirect damage is calculated with the relation between inundation depth and the number of business interruption days for each business. Multiplying the number of days by the employee’s added value per day for each place of business, the indirect damage is obtained for each place.

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 per unit depth for different return period storms. The curves are shown for only one return period in _{1} to d_{2}, we then integrate area of inundation per unit depth between depth d_{1} and d_{2}. In the FDPM calculation, the integral of the curve equals the total area inundated in the catchment.

For the inundation characteristic curve, the solid line shows the inundation for present catchment conditions as in

The monetary costs of inundation are estimated for any given inundation depth using Model 2 as shown in

Monetary inundation damage is calculated by multiplying a value from the inundation characteristic curve by the appropriate value from the damage characteristic curve. This produces a new curve called the inundation-damage characteristic curve, which shows the relationship between inundation depth and monetary damage per unit depth over the whole catchment for one return period storm (

The inundation and damage characteristics for a catchment are described by the three curves. These curves reflect the natural and social aspects of the catchment.

The flood risk analysis uses three curves: a storm probability curve, a damage potential curve, and an annual risk density curve (

Three curves used in flood risk analysis: (

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

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/T^{2}, as given in

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 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

The three curves describe the flood risk characteristics for a catchment.

FDPM simulations for different return period storms provide inundation and damage characteristic curves. Both characteristic curves are linked to the inundation-damage characteristic curve. The 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.

The three curves obtained by the FDPM simulations shift with changes in catchment conditions. Similarly, the damage potential curve shifts upward due to urban development, global climate change, and increased assets, and downward as flood control projects and flood damage reduction measures are implemented, as shown in

The six curves are, as stated above, interconnected and reflect the effects of urbanization, climate change, structural and non-structural flood prevention measures, and other positive or negative factors.

In this study, risk cost is used to mean the risk as defined in monetary terms for the annually averaged expenditure over time incurred for flood inundation damage. 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 as flood control projects are implemented 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 RC_{0} and RC, respectively. The change in flood risk cost should be the difference between the two, ∆RC = RC − RC_{0}.

Urban river basins are exposed to various flood risks such as increased impervious area due to urban development and the effects of global climate change. Increased impervious area does not necessarily represent a flood risk itself, but a factor that may affect the flood risk of a specific urban area. Conversely, flood control projects are effective in reducing the flood risk in urban catchments.

The factors that affect risk cost can be evaluated with a single non-dimensional scale of one simple index, FRIF. This is computed as FRIF = (RC − RC_{0})/RC_{0}. A positive FRIF value indicates increased flood risk, while 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.

This flood risk assessment method was applied to the Kiba drainage area located in Koto Ward in the Tokyo metropolis. The Kiba drainage area has an area of 6 km^{2} characterized by a combined sewer network with a flat topography facing the Tokyo bay. The drainage area has a high population density and numerous assets. The land use and the distribution of population and assets in the drainage area are almost homogeneous.

Kiba drainage area and calculated inundation depth for a 15-year return period storm under present catchment condition with GIS data superposed.

The FDPM simulation uses a set of design storm hyetographs having various return periods. Each hyetograph input into the FDPM is created form the IDF relationship for its corresponding return period using the alternating block method [

Variation of Intensity-Duration-Frequency relationships and return period.

Design storms for the simulations have return periods of 3, 5, 15, 30, 50, 70, 100, 150, 200, 300, and 500 years. Input hyetographs with a duration of 12 h were created based on IDF curves for storms with these 11 return periods using the alternating block method.

For Model 1, we used XP-SWMM to calculate the inundation depths two-dimensionally on a 10-m square grid for a given hyetograph. The time increment ∆t was set to be 1.0 s for calculation stability. There were 392 nodes and 404 grid cells. The inundation water was assumed to flow on the natural surface according to the Geographical Survey Institute of Japan’s 10 m digital elevation model (DEM). The roughness coefficients were set be 0.03 for pervious area and 0.01 for impervious area. The water’s pathways and detailed land use were taken into account by adjusting the roughness coefficients up to 0.1. The hydraulic model (Model 1) using XP-SWMM was calibrated by comparing the calculated inundation depths with the inundation depths under a historical storm shown in the hazard map of Koto Ward in the Tokyo metropolis [^{−4}. If a drainage area has complicated topography with steep slopes in some areas, we have to consider the velocities calculated by Model 1 for the flood risk evaluation.

Monetary damages are calculated by Model 2, using the relationship between inundation depth and damage rate for the eleven different types: four types for building structures and seven types for movables. The calculation methods of Model 2 are explained in Morita [

Model 2 uses a relationship between inundation depth and damage rate to estimate flood damage in monetary basis as stated above. This basic idea for flood damage estimation seems to be internationally accepted as the standard approach to assess urban flood losses [

The change in urban catchment conditions is likely to bring about more flooding and social impacts to cities and the people living in them. Hammond

Inundation damage in the Kiba drainage area could be reduced if steps were taken to improve inland flooding. It is extremely urbanized, with 65% of its area being impervious. Flood control projects such as storm storage reservoirs and infiltration facilities would be equivalent to reducing the impervious area from 65% to lower rates, such as 55% and 45%. The FDPM simulations deal with two cases, in which flood control projects reduce the impervious area to 55% and 45% of the total catchment.

If the assets in the area were concentrated even more, the cost of flood inundation damage would increase. In the simulations, the case where assets increase by 20% is also calculated. The damage characteristic curve shown in

There is now much meteorological research working on predicting changes in the characteristics of storms due to global warming. Little, however, focuses on the changes in magnitude and frequency of heavy storms in the Tokyo region in terms of IDF relationships. Only two studies have produced such results, both based on general circulation models (GCMs): the National Institute for Land and Infrastructure Management (NILIM) [

To assess the impact of climate change, risk assessment using FDPM simulations needs not only current design hyetographs but also predicted hyetographs after global warming. This means IDF relationships should be estimated for global climate change. Nguyen

The change in flood risk cost due to global warming is determined by the increase in inundated areas as shown by the inundation characteristic curves in the flood risk assessment (

The RPS method can also be used to create a damage potential curve for post-global climate change conditions directly from the present damage potential curve for non-homogeneous drainage areas [

Return periods for 20th and 21st centuries (Oki [

The results of the FDPM simulations produced the three inundation and damage characteristic curves shown in

The inundation characteristic curve gives the relationship between inundation depth and its area per unit depth.

The inundation characteristic curves are calculated for different return period storms.

Inundation characteristic curves for present catchment condition, a flood control project, and climate change.

Inundation characteristic curves for different return period storms and present catchment conditions.

The damage characteristic curve is shown in

Damage characteristic curves for present catchment conditions, damage reduction measures (−20% in assets), and with assets increased by 20%.

Inundation-damage characteristic curves for present catchment conditions, flood control projects, damage reduction measures, asset increases, and climate change.

The damage potential curve shows the relationship between storm levels expressed by return period and their cost of monetary inundation damage. The curve is created by integrating the inundation-damage characteristic curve for each return period and then plotting them for different return periods.

The RPS method was applied to the inundation characteristic curve using the relationship shown in

The annual risk density curves were computed by multiplying the damage potential and storm probability curves shown in

Storm probability curve and damage potential curves for present catchment conditions, flood control projects, damage reduction measures, asset increases, and climate change.

Annual risk density curves for present catchment conditions, flood control projects, damage reduction measures, asset increases, and climate change.

Flood risk costs were calculated by integrating the annual risk density curves for the five conditions (FC45%, FC55%, DRM0.8, AI1.2, and Present), with and without global climate change.

Annual risk costs for present catchment conditions, flood control projects, damage reduction measures, increased assets, and climate change. FC45% and FC55%: flood control projects; DRM0.8: damage reduction measures; and AI1.2: asset increase.

The risk cost grows as assets increase, but decrease when flood control projects are undertaken. The risk cost for current catchment conditions increases by a remarkable 70% due to global warming and the risk costs for the other catchment conditions (FC45%, FC55%, DRM0.8, AI1.2) have similar trends.

FRIF is easily computed using the calculated risk costs given in

Flood risk impact factors for present catchment conditions, flood control projects, damage reduction measures, asset increases, and climate change. FC45% and FC55%: flood control projects; DRM0.8: damage reduction measures; and AI1.2: increased assets.

Current conditions have an annual risk cost of about 1 BFP and an FRIF of 0.0. The effect of increasing assets by 20% (AI1.2) has the same value, 0.25, as that of building flood control projects that lower the impervious area to 55% combined with global warming (

The FRIFs calculated in the risk assessment can be used in decision-making processes. The well-informed decision making, however, depends on the quality of the assessment. It is, thus, important to have an understanding of uncertainty related to flood risk assessment [

The objective of the study was to present a risk assessment method for evaluating and comparing the main factors contributing to flood risk in urban drainage areas. The most important results are as follows:

We present a risk assessment method that employs a GIS-based FDPM and the XP-SWMM routine, and can evaluate factors that increase and decrease the risk of urban flooding to serve as a basis for urban drainage management;

The risk assessment method was employed to estimate the reduction in flood risk provided by flood control projects and damage reduction measures and the increased risk due to asset increases and global climate change in the Kiba drainage area of the Tokyo metropolis;

The RPS method was applied as a simple way to estimate the flood damage potential of global warming, based on present conditions without climate change;

FRIF was introduced as an index to evaluate the effectiveness of various sources of increased or reduced flood inundation risk to our society. Risk impact factors calculated from FDPMs may play an important role in urban flood prevention planning and decision-making processes.

The authors declare no conflict of interest.