Flood Risk andClimate Change
Hokkaido
WP3 Risk assessment

ClientPartners for Water

Flood Risk andClimate Change
Hokkaido
WP3 Risk assessment
Final report

Authors
Maaskant, Bob (HKV)
Tomura, Sho (RIC / Kai Koei)
Omura, Noriaki (Docon Co.,Ltd)
Masuya, Shigekazu (Docon Co.,Ltd)
Yoshida, Takatoshi (Docon Co.,Ltd)
Rongen, Guus (HKV)

PR3983.10
December 2021

Table of contents
1

Introduction1

1.1

Objective1

1.2

Structure report1

2

Method3

2.1

Flood probability

3

2.2

Flood consequences3

2.3

Flood Risk

4

3

Flood scenarios

53.1

Introduction

5

3.2

Breach locations

5

3.3

Hydrographs

6

3.4

Flood characteristics

8

3.5

Uncertainties in flood scenarios

11

4

Damage13

4.1

Calculation method

13

4.2

Asset

14

4.3

Damage16

4.4

Damage case Obihiro

21

4.5

Uncertainties in calculating damage

21

5

Evacuation

23

5.1

Evacuation Rate of Target Area

23

5.2

Method to Set up Evacuation Rate

23

5.3

Available data and information

25

5.4

Analysis and Results

26

5.5

Results27

5.6

Evacuation Rate

30

5.7

Uncertainties in evacuation

30

5.8

Proposal improve evacuation model

31

5.9

References

32

6

Loss of Life

33

6.1

Loss of Life due to Recent Floods in Target Area

33

6.2

Method of Loss of Life Estimation

33

6.3

Loss of Life case Obihiro

36

6.4

Uncertainties in Loss of Life calculation

39

6.5

Proposal new loss of life model

40

6.6

References

48

7

Flood probabilities

51

7.1

Overview of steps leading to failure probability per section

51

7.2

From section probability to scenario probability

52

7.3

Scenario probabilities

53

8

Flood Risk

55

8.1

Economic Risk

55

8.2

Risk Loss of Life

58

8.3

Uncertainties in Flood Risk

62

9

Reflection, conclusions and recommendations

63

9.1

Economic damage

64

9.2

Evacuation and loss of life

64

9.3

Flood risk

65

1

Introduction

This report gives an overview of the activities from work package 3 (WP3). This work package is
one of the three work packages that examine the flood risk for Obihiro, the case study location on
Hokkaido. WP1 gives insight into the impact of climate change on extreme discharges, WP2
focusses on the probability of levee failure and WP3 determines the flood risks.
The flood risks result from the combination of flood probabilities and flood consequences. WP3 uses
the results from WP2, in which the flood probabilities are determined for the flood scenarios. The
method and analysis on flood probabilities are described in the WP2 report. In this WP3 report only
a summary of the flood probability calculation is included.
For determining the consequences of a flood, the economic damage (direct & indirect) and loss oflife including evacuation are used. Different water levels and shapes of hydrographs are used todetermine consequences, creating a bandwidth that covers a range of possible flood events.

1.1

Objective

The objective of work package 3 is to determine the flood risk for the Obihiro case and give insight
into the uncertainties in flood risk calculation.

1.2

Structure report

The report starts with a brief description of the method which is used to determine the flood risk
(Chapter 2). Chapter 3 gives an overview of the available flood scenarios and the selection of
scenarios used in the flood risk calculation. In chapters 4, 5 and 6 the consequences (damage,evacuation, loss of life) of the flood scenarios are described. In chapter 7 a summary is given forthe scenario probabilities from work package 2. The scenario probabilities (chapter 7) and theconsequences (chapter 4, 5 and 6) are combined in chapter 8 to determine the flood risk. The
report will end with a reflection on the results, conclusions and recommendations (chapter 9) basedon the executed analyses.

PR3983.10 • December 2021

1

2

MethodRisk is the combination of probability and consequences. To determine flood risk, it is therefore
important to know the probability of a flood occurring and the impact it will have. For this purpose,the failure probabilities of the various elements of the flood defence system (various segments oflevee) are determined (WP2). The consequences (economic damage and loss of life) of failures ofthe flood defences are also determined. The failure probability and consequences of a levee breachare not the same at each potential failure location within a levee system. Therefore, different
breach locations are assessed and the flood probability per breach location is combined with theconsequences of those breach locations. This approach gives the flood risks for the considered
area.
Figure 1 Brief overview of risk approach

2.1

Flood probability

The calculation of the flood probabilities is done within work package 2 (WP2). The results fromthat work package are used in this work package to determine the flood risk. In chapter 7 a shortsummary is given on the flood probability. More details on determining flood probabilities can befound in the report on WP2.

2.2

Flood consequences

The flood consequences give insight in the amount of damage and loss of life in a certain area. It
gives a spatial distribution of the loss of life and economic damage. To determine the flood
consequences the brief method is as follows:•

Define consequence sections with a breach location•

Producing flood propagation models•

Defining flood scenarios•

Consequence estimates for each scenario•

Economic damage•

Evacuation•

Loss of Life

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2.3

Flood Risk

The flood risk is the product of the flood consequences and the flood probability. In the figure
below the flood risk calculation is visualized.
Figure 2 Overview of risk calculation

Next to the expected value of the damage and fatalities (loss of life) also the risk is expressed by
the local individual risk. This provides insight in the probability that a person at a certain location
will lose his live due to a flood.

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3

Flood scenarios

3.1

Introduction

An area can be flooded from different breach locations. However, for a sufficiently accurate risk
analysis it is not necessary to identify the consequences of flooding for every possible breachlocation. A system can be divided into sections where the flood pattern will be virtually the same,irrespective of the precise location of the breach within that section. In this chapter different
breaches are grouped in sections for the Obihiro case study.

3.2

Breach locations

In order to calculate the flooded area and the flood characteristics breach locations have to be
determined for which flood simulations must be derived. The choice of breach locations for acertain area depends on the hydraulic loads and the characteristics of the threatened area. For
example, if there is a high element present in the area the flood pattern is influenced. Therefore,
different breach locations must be determined.
For the Obihiro case study there is a database of flood simulations available with breach locations
every 200 meter, see Figure 3. As explained earlier, it is not necessary to include all breachlocations for determining the flood risk due to largely overlapping flood extents of nearby breachlocations.
Figure 3 Available breach location along the Obihiro case study area

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5

All breach locations and accompanying inundation patterns were analysed and a selection has been
made for breach locations used in the flood risk analysis. In the analysis, levee sections have been
determined for which the breach locations are representative. To select the representative breachlocations mainly flood extent and flood characteristics (water depth, flow velocity, rise rate) havebeen used. In the figure below the sections and breach locations are shown.
Figure 4 Levee sections and selected breach locations

The given breach locations are used for the flood risk analysis. For the Tokachi River 5 locations
have been selected and for the Satsunai river 4 locations. The inundation maps and theaccompanying hydrographs for these locations are available.

3.3

Hydrographs

For the selected breach locations different inundation scenarios have been identified that describe
the bandwidth of the possible flood extent per breach location. This is done by linking theinundation scenarios to the associated hydrographs. The hydrograph describes the shape of thedischarge and the local water height over time.

3.3.1

Climate change

For the Obihiro case study there is a database available with hydrographs. These hydrographs are
divided into two sets, one set describes the current and past situation (“past”) and one set
describes the future situation (“future”). The difference between these two sets is that in thesituation “future” climate change is incorporated. This means that discharges can be much higher,
leading to larger flood extents and water depths. So for the further analysis two datasets areavailable, one for the current situation and one including climate change.

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3.3.2

Hydrographs and flood scenarioFirst the hydrographs can be used to gain insight in the hydrograph patterns by generating plots
such as shown the figure below. Only the hydrographs that lead to overtopping of the levee height
are shown in the figure.
Figure 5 Example of a collection of hydrographs for location KP4.2 along the Satsunai river, for both the ‘Past’(HPB) and ‘Future’ (HFB) hydrographs

Second it can be used for selecting the hydrograph and flood extent to be used in the flood risk
analysis. For a flood risk analysis, it is often unnecessary to use each individual flood scenario
because differences between flood scenarios tend to be small. Therefore, it is more efficient toselect a number of hydrographs that represent the whole set of flood scenarios. An example toselect these scenarios is shown in Figure 6. Here, three scenarios were selected:•

The MAX scenario: the scenario with the highest peak water level•

The MIN scenario: the scenario with the lowest peak water level•

The MAXVOL scenario: the scenario with the largest surface of the hydrograph above the proxydike height (representing the potential inflow volume).

If only one scenario leads to overtopping of the levee height, this scenario is used for all threesituations.
Figure 6 Example of the selected MAX, MIN and MAXVOL hydrographs for location KP4.2 along the Satsunai
river, for both the ‘Past’ (HPB) and ‘Future’ (HFB) hydrographs

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For each selected breach location, the three scenarios are listed in Table 1.
Table 1 Scenarios max, min and maxvol for each location
Past - min

Past - max

Past - maxvol

Future - min

Future - max

Future - maxvol

Satsunai Riv. KP4.2

HPB_m045_1960 HPB_m045_1960 HPB_m045_1960 HFB_GF_m111_2082 HFB_MR_m104_2078 HFB_HA_m108_2057

Satsunai Riv. KP5.2

HPB_m045_1960 HPB_m045_1960 HPB_m045_1960 HFB_GF_m114_2092 HFB_MR_m104_2078 HFB_MP_m103_2085

Satsunai Riv. KP6.4

HPB_m022_1980 HPB_m045_1960 HPB_m045_1960 HFB_GF_m107_2097 HFB_MR_m104_2078 HFB_MR_m108_2069

Satsunai Riv. KP7.0

HPB_m069_2006 HPB_m045_1960 HPB_m045_1960 HFB_CC_m104_2099 HFB_MR_m104_2078 HFB_HA_m108_2057

Tokachi Riv. KP56.4 HPB_m021_1979 HPB_m067_1978 HPB_m064_1987 HFB_CC_m108_2101 HFB_MR_m104_2078 HFB_MR_m108_2069
Tokachi Riv. KP58.0 HPB_m004_2000 HPB_m067_1978 HPB_m064_1987 HFB_CC_m108_2058 HFB_MR_m104_2078 HFB_MR_m108_2069
Tokachi Riv. KP59.6 HPB_m063_1968 HPB_m067_1978 HPB_m064_1987 HFB_CC_m112_2090 HFB_MR_m104_2078 HFB_MR_m108_2069
Tokachi Riv. KP61.4 HPB_m024_1997 HPB_m067_1978 HPB_m064_1987 HFB_MP_m105_2105 HFB_MR_m104_2078 HFB_MR_m108_2069
Tokachi Riv. KP62.4 HPB_m063_1968 HPB_m067_1978 HPB_m067_1978 HFB_GF_m112_2094 HFB_MI_m102_2109 HFB_MI_m108_2094

3.4

Flood characteristics

The flood characteristics are determined by flood simulations.

3.4.1

Flood simulation

The inundation depth of the floodplain is calculated by a two-dimensional unsteady flow calculation
model. The water level in the river channel was calculated from the discharge, and the calculation
was performed on the assumption that the embankment broke and the discharge flowed into thefloodplain at the moment when the water level in the river channel exceeded the design waterlevel. A conceptual diagram of the calculation model is shown in Figure 7. The calculation
conditions are shown in Table 2.
Figure 7 Conceptual diagram of computational model

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Table 2 Calculation condition list
Items

Contents

Grid Size, Ground Elevation

100m Grid size, Grid Ground Elevation is based on the latest LP data.

River Channel Status

River Channel of 2016

Flood Control Reservoir

Current Reservoir ( Tokachi Dam, Satsunaigawa Dam, and Sahoro
Dam)

Point of Dike Break

Flood Simulation per flood zone, based on 1 point where the maximum
damage is estimated within zone.

Conditions of Dike BreakIn case of the water level exceeds the set point which dike starts tobreak(HWL for completed dike).

Discharge Redcuction Due to Floodat the Upstream of Dike Break

Reduce the discharge when water level exceeds the height of dike orground elevation.

3.4.2

Water depth

For all selected breach locations the flood characteristics are available. One of those characteristics
is the flood depth. For locations KP61.4 along the Tokachi river and KP6.4 along the Satsunai river,the flood patterns are shown in Figure 8 and Figure 9. The difference between the figures showsthe effect of the combination of hydrograph and flood extent. The hydrograph with the highest local
water level, Figure 4, gives the largest flood pattern. This information is available for all locations
and selected hydrographs.
Figure 8 Future scenarios for location KP61.4 along the Tokachi river (MAX, MIN, MAXVOL)

PR3983.10 • December 2021

9Figure 9 Future scenarios for location KP6.4 along the Satsunai river (MAX, MIN, MAXVOL)

3.4.3

Rise rate

The rise rate describes the speed at which the water rises, calculated in meters per hour. The riserate is an important flood characteristic for determining the mortality. In case of higher rise rates
in combination with larger water depths this leads to a significant increase in mortality compared towater depths with lower rise rates (for more detail see chapter 6). Therefore the rise rate isavailable for all flood scenarios.

3.4.4

Flow velocity

The flow velocity is another flood characteristic, important for building stability. A higher flow
velocity combined with larger water depths increases the probability that a building will collapse,
impacting mortality. For more detailed information see chapter 6 and specifically section 6.5. The
flow velocity is also available for all flood scenarios.

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3.5

Uncertainties in flood scenarios

Regarding the flood scenarios there are several aspects that have an influence on the flood extent
and the flood characteristics. In general the uncertainties are:•

Breaching can occur before or after the top of the discharge peak. In the current approach themoment of breaching has been chosen as the moment the water level exceeds the design
water level. Especially for the scenarios “Max” and “Maxvol” this leads to a breach before thepeak, which leads to a relatively long period of inflow of water.

•

When a levee is more resistant to overflow, the breach can occur at a later moment in time andthe total inflow will be smaller, which reduces the flood extent and flood characteristics. But on
the other hand the breach can also occur even earlier due to breaching before the levee isoverflowing. This can be induced by geotechnical failure mechanisms. In that case the total
inflow will be larger increasing the flood characteristics.

•

The shape of the hydrograph is also an aspect that influences the flooding. So insight in the
different shapes of discharge waves increases the insight in the potential flood patterns thatcan occur.

•

Breach modelling; there are several methods to model the breach growth in flood modelling.
The faster a breach grows the larger the inflow. So the speed at which the breach grows in
depth and width affects the flooding.

So in general there are several uncertainties in determining the flood extents and flood
characteristics. But with the chosen approach which incorporates different flood scenarios these
uncertainties are properly included in the analysis.
An important research question that remains is the probability that different type of discharge
waves occur. What is the distribution between the different types of discharge waves, narrow andpeaked versus long and wide.

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4

DamageBased on the results of the inundation simulation described in the previous section, the expected
damage is calculated.

4.1

Calculation method

The calculation of damage was based on the "Manual for Flood Control and Economic Survey(Draft)", which is a common method for expressing the effects of flood control projects in Japan interms of cost-effectiveness. The” Manual for Flood Control and Economic Survey (Draft)" provides a
specific method for determining the economically assessable effects of flood control projects fromthe basic quantities and damage rates published nationwide. Currently, the latest version is theone published in April 2020, but in this study, each damages are calculated according to the April
2005 version.
There are two types of damage caused by flood inundation: direct damage caused by theinundation of houses, offices, and crops, and indirect damage caused by the cessation of
production at flooded offices and the cutting off of lifelines, etc. In this report, 12 items are listed
as damage that can be economically evaluated at this stage.
Table 3 Items for which damage is recorded
Classification

Description of damage

Directly damage

Asset

GeneralHouse

Damage to residential and business buildings

damageasset

Household articles

Flooding damage to furniture, automobiles, etc.

Office amortization

Flood damage to amortization assets, excluding land

assetsand buildings, among fixed assets of businessdamageestablishments.
Office inventory assets

Flooding damage to office inventory

Agricultural and fishery

Flooding damage to amortization assets of households

amortization assets

involved in agricultural and fishery production,
excluding land and buildings.

Agricultural and fishery

Flooding damage to agricultural and fishery inventory

inventory assets
Agricultural product damage

Flooding damage to crops

Public civil facilities damage

Flooding damage to public civil facilities, utility
facilities, farmland, and agricultural facilities

Indirectly damage

Operational

Damagedamagecaused bybusiness

Business officeSuspension or stagnation of production at flooded
business office (decrease in production)

Public and utility services

Suspension or stagnation of public and utility services

suspension
After theCost ofHousehold economy

Post-event activities such as cleanup of flooded

fact

emergency

Business officehouseholds, new investments in purchasing alternative

Damagemeasures

PR3983.10 • December 2021

supplies such as drinking water, and other damages

13

4.2

Asset

4.2.1

Examining directly affected assets and asset data

The basic quantities of assets, households, and employees in the inundation area, which arenecessary for calculating the amount of damage, are calculated for each computational mesh of theinundation simulation. Assets to be surveyed are as follows, and the basic quantity will be surveyedusing regional mesh statistics published by the Statistics Bureau of the Ministry of Internal Affairs
and Communications.
Assets under investigation•

House(floor area)•

Household articles (number of households)•

Office Amortization/Inventory Assets (number of employees)•

Agricultural and fishery Amortization/Inventory Assets (number of agricultural and fisheryhouseholds)•

Agricultural product (area of rice paddies and fields)

Basic quantity survey
Table 4 Basic asset quantity survey
Items

Units

Regional mesh statistics

SourcesPopulation

people2010 Census

Statistics Bureau of the Ministry of Internal
Affairs and Communications

Housesfloor area2010 Total Floor Area Data

Japan Construction Information Center

Householdarticlesnumber ofhouseholds

2010 Census

Statistics Bureau of the Ministry of Internal
Affairs and Communications

Officesnumber of
employees

2009 Economic Census

Statistics Bureau of the Ministry of Internal
Affairs and Communications

Agriculturaland fisheries

number ofhouseholds

2010 Census

Statistics Bureau of the Ministry of Internal
Affairs and Communications

Agricultural
products

area of fields

2009 National Land
Information

Geographical Survey Institute, Ministry of Land,
Infrastructure, Transport and Tourism

4.2.2

Calculation of asset amountThe asset value is calculated by multiplying the asset quantity of the calculation mesh unit by theasset valuation unit costs. For the asset valuation unit costs, the items except for agricultural andfishery, and fields were based on the "Flood Control Economic Survey Manual (Draft): Various
Asset Valuation Unit Costs and Deflators (River Planning Division, River Bureau, Ministry of Land,
Infrastructure, Transport and Tourism, revised March 2016)" published annually. The valuation unit
costs of agricultural and fishery, and fields were set according to the regional characteristics, basedon the fact that the scale of agricultural management in the area is 16.5 times larger than thenational average.

PR3983.10 • December 2021

14

Table 5 Unit costs of asset value (1000 yen)
Asset items

2015 Unit costs (1000 yen)

Remarks

Houses

186.2

per 1m2 of house

Household articles

13,230per household

Offices

Amortization
assetsInventory
assetsmining, quarrying, sand mining

14,4182,255

construction

1,452

2,669

manufacturing

4,803

4,691

electricity/gas/heat/water supply

113,483

5,267

information and communication

5,150

991

transportation, postal services

5,719

1,015

wholesale/retail trade

2,158

1,741

finance, insurance

1,115

210

real estate、goods leasing

21,437

7,405

academic research, professional
and technical services

1,767

445

accommodations, restaurants

1,525

92

life related services, entertainment

3,652

233

education and learning support

1,052

267

medical care, welfare

1,386

58

complex service industry

1,115

210

service industry

1,115

210

public affairs

1,115

210

9,267

1,070

Industrial
Classification

Agricultural and fisheries
Agricultural
products

per employee

per household

rice paddies997

per hafields853

per haPR3983.10 • December 2021

15

4.3

Damage4.3.1

Amount of direct damage

In order to calculate the amount of damage to general assets and agricultural products, apply theamount of assets and the damage rate based on the inundation depth. Public engineering facilities,
etc. are calculated by applying the rate of damage to general assets.
General asset damage rate
Table 6 General asset damage rate (Damage to house)
Inundation
under
depth
less than 50
50～
floor
Groundcm
99
level
gradient
Group A
0.032
0.092
0.119
Group B
0.044
0.126
0.176
Group C
0.050
0.144
0.205

above floor level
100～

200～

more than

199

299

300 cm

0.266
0.343
0.382

0.580
0.647
0.681

0.834
0.870
0.888

Ground slope A: Less than 1/1000 , B: 1/1000~1/500, C: more than 1/500
Notes:
1.

Damage rates based on the available "Survey of Actual Flood Damage" for the period 1993 to 1996.

2.

The figures also take into account the total destruction of houses.

Table 7 General asset damage rate (Damage to household articles)
above floor level
Inundation
under floor
less than 50
50～
100～
depth
level
cm
99
199
Damage rate

0.021

0.145

0.326

0.508

299

more than
300 cm

0.928

0.991

200～

Notes: Damage rates based on the available "Survey of Actual Flood Damage" for the period 1993 to 1996.

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Table 8 General asset damage rate (Damage to office amortization/inventory assets)
above floor level
Inundation
under floor
less
than
50
50～
100～
depth
level
Assetscm
99
199
Amortization
0.099
0.232
0.453
0.789
Inventory
0.056
0.128
0.267
0.586

200～

more than

299
0.966
0.897

300 cm0.995
0.982

Notes: Damage rates based on the available "Survey of Actual Flood Damage" for the period 1993 to 1996.
Table 9 General asset damage rate (Damage to agricultural and fishery amortization/inventory assets)
above floor level
Inundation
under floor
less than
50～
100～
200～
more than
depth
level
50 cm
99
199
299
300 cmAmortization
0.0
0.156
0.237
0.297
0.651
0.698
Inventory
0.0
0.199
0.370
0.491
0.767
0.831
Notes: Damage rates based on the available "Survey of Actual Flood Damage" for the period 1993 to 1996.
Table 10 General asset damage rate (Damage to agricultural product)
subject
Crown inundation

wet-land
rice

4

6

3

5

～

2

1

～

5

～

～
6

3

～

～
4

1

～

～
2

7

2

4

6

7

7

21

30

36

50

24

44

50

71

37

54

64

74

20

34

47

60

31

40

50

60

44

60

72

82

11

30

50

50

27

40

75

88

38

63

95

100

42

50

70

83

58

70

83

97

47

75

100

100

greens

19

33

46

59

20

44

48

75

44

38

71

84

root
vegetable

32

46

59

62

43

57

100

100

73

87

100

100

cucurbits

22

30

42

56

31

38

51

100

40

50

63

100

beans

23

41

54

67

30

44

60

73

40

50

68

81

field
average

27

42

54

67

35

48

67

74

51

67

81

91

uplandrice
sweet
potatoChinesecabbagefields5

1.0ｍ

more thanrice
paddies30.5～0.99ｍ

more than

crop
type

1

more thaninundationdays

less than 0.5m

～

crown
inundation
water
depth

Note) "greens" includes green onions, spinach, and others; "root vegetable" includes Japanese radish, taro,
burdock, and carrot; "melon" includes cucumber, melon, and watermelon; and "beans" includes red beans,soybeans, peanuts, and onions.

Damage rate of public civil engineering facilities, etc.
The damage rate of public engineering facilities is defined as the rate of damage to the amount ofdamage to general assets (the total amount of damage to houses, household articles, offices, andagricultural and fisheries), and was determined based on the "Flood Statistics" (River Bureau,
Ministry of Land, Infrastructure, Transport and Tourism (Ministry of Construction)) and "Disaster
Records" (Hokkaido) for the past 40 years (1975-2014) of flood damage in Hokkaido. For the
calculation, each year was converted to the 2014 value based on the Comprehensive Price Index(flood damage deflator).

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Table 11 Damage rate of public engineering facilities in Hokkaido calculated from flood statistics and disasterrecords.
Facilityroadbridgeseweragesystemurban
facilities

publicwelfarefarm landindustrial
facilities

subtotal

Damage
rate

84.9

11.3

0.1

1.3

15.5

15.5

117.2

245.8

Value calculated based on flood statistics and disaster records for floods in Hokkaido from 1975 to 2014.
Source 1: "Flood Statistics" (1975-2014), Ministry of Land, Infrastructure, Transport and Tourism (Ministry ofConstruction), Water Management and Land Conservation Bureau (River Bureau)
Source 2: "Disaster Records" (1975-2011), Hokkaido

4.3.2

Amount of indirect damage

Of the inundation damage except for direct damage, three items that can be economicallyevaluated at this stage were calculated as indirect damage based on the "Flood Control andEconomic Survey Manual (Draft)" (River Bureau, Ministry of Land, Infrastructure and Transport,
April 2005). They are 1) business interruption losses, 2) household emergency measures costs,
and 3) office emergency measures costs.
Business interruption losses
Business interruption loss was calculated by multiplying the number of employees in the flooded
area by the total number of days lost due to business suspension or stagnation, and thenmultiplying by the value added per person for each day.
The amount of damage, D, was calculated for each major industrial category using the following
equation, and the business interruption loss was calculated as the total of them.

Di = Mi×(n0＋n1/2)×Pi
i: broad industry category, M: number of employees, P: value-added （yen/（person・day））,
n0:business suspended days, n1:business stagnant days

The suspended days are shown in the table below, and the stagnant days are twice the suspended
days.
Table 12 Days of suspension or stagnation (days)

inundation depth

Under floor
level

above floor level

less than
45cm

less than
50cm

50～99cm

100～
199cm

200～
299cm

more than
300cm

days of suspension

3.0

4.4

6.3

10.3

16.8

22.6

days of stagnation

6.0

8.8

12.6

20.6

33.6

45.2

PR3983.10 • December 2021

18

Table 13 Added value per employee for each day (2015 assessed value, yen/(person・day))Industrial Classification
Added value
mining, quarrying, sand mining

115,235

construction

23,254

manufacturing

31,460electricity/gas/heat/water supply

93,098

information and communication

38,749transportation, postal services

24,543wholesale/retail trade26,464

finance, insurance

20,439real estate、goods leasing

46,152academic research, professional and technicalservices
accommodations, restaurants

35,924life related services, entertainment

22,317

education and learning support

24,046

medical care, welfare

16,969complex service industry

20,216

service industry

20,924

public affairs

20,924

21,594

Source: "Various Asset Valuation Unit Costs and Deflators: Flood Control Economic Survey Manual (Draft)(Revised in March 2016, Ministry of Land, Infrastructure, Transport and Tourism, Water Management and Land
Conservation Bureau)

Household emergency measures costs•

Cleaning labor compensation

The amount of damage caused by household cleaning labor costs was calculated by multiplying thenumber of households by the unit labor cost per household and the total days of cleaning requiredin the table below.
Table 14 Unit labor costs: 10,731 yen per day (assessed in 2015) Days of cleaning (days)
inundation
depth

Under floor
level

days

above floor level

less than
45cm

less than
50cm

50～99cm

100～199cm

200～299cm

more than
300cm

4.0

7.5

13.3

26.1

42.4

50.1

Note: The total number of days required for cleaning and clearing up was obtained from the "Questionnaire
Survey on Flood Disasters" conducted in 1995 and 1996.
Source: "Flood Control Economy Research Manual (Draft)" (April 2005, River Bureau, Ministry of Land,
Infrastructure and Transport)•

Expenditures for alternative activities, etc.

The amount of damage caused by alternative activities, such as the purchasing of drinking waterand alternative transportation for commuting, was calculated by multiplying the number ofhouseholds by the damage unit costs shown in the table below.

PR3983.10 • December 2021

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Table 15 Alternative activity expenses (at household) (Unit cost: 1000 yen per household)
Under floor
above floor level
level
inundation
less
depth
less than 45cm
50～99cm 100～199cm 200～299cm
than50cm
unit cost

82.5

147.6

206.5

275.9

more than
300cm

326.1

343.3

Note: The unit costs were obtained from the "Questionnaire Survey on Flood Disasters" conducted in 1995 and1996.
Source: "Flood Control Economy Research Manual (Draft)" (April 2005, River Bureau, Ministry of Land,
Infrastructure and Transport)

Office emergency measures costs
The amount of damage caused by alternative activities, etc. was calculated by multiplying thenumber of inundated business offices by the damage unit costs shown in the table below.
Table 16 Alternative activity expenses (at office) (Unit cost: 1000 yen per household)inundation
depth

Under floor level

above floor level

less than 45cm

less than50cm

50～99cm

470

925

1,714

unit cost

100～199cm 200～299cm
3,726

6,556

more than
300cm
6,619

Note: The unit costs were obtained from the "Questionnaire Survey on Flood Disasters" conducted in 1995 and1996.
Source: "Flood Control Economy Research Manual (Draft)" (April 2005, River Bureau, Ministry of Land,
Infrastructure and Transport)

4.3.3

Result of economic damage

For the Obihiro case study the damage is calculated for both the “past” set and the “future” set. In
the figure below the spatial distribution of the economic damage for a breach at Satsunai Riverlocation KP4.2 is shown. The top figures describe the damage for the “past” situation and the lower
figures for the “future”. The difference is that in the “future” situation larger discharges occur
leading to floodings with a larger flood extent and greater water depths.
Figure 10 Damage (Satsunai Riv. KP4.2)
Max Discharge

Min Discharge

Max Volume

Past

FuturePR3983.10 • December 2021

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4.4

Damage case Obihiro

The method of determining economic damage is described in section 4.1 to 4.3. When applying thisto the selected breach locations the economic damage is calculated. In section 0 the breachlocations are given and for each hydrograph the economic damage is shown. The table gives
insight in the extent of the flood damage and the difference between the different locations and theimpact of the different hydrographs. In Figure 10 section 4.3.3 the distribution of the damage over
the Obihiro area is shown for breach location KP4.2. The table below shows that for the “past”
scenarios there is no difference in the calculated damage, for the “future” scenario the flood
extents are different and that leads to a variation in calculated damage. The damage varies
between approximately 80,000 million Yen and 269,000 million Yen for the minimum andmaximum scenario. The difference is approximately a factor 3.
When looked at all scenarios, the bandwidth between the different flood scenarios is in general afactor 1.5 to 2.5. Exception is breach location Tokachi KP56.4, the difference for that location islarger, approximately a factor 6. So for that breach location the calculated damage is moresensitive to the type of discharge wave that runs through the river.
Table 17 Economic damage for “past” and “future” scenarios (unit: million Yen)1
Location
Max
Min
MaxVolSatsunai_KP4_2
83200
83200
83200
Satsunai_KP5_2
170400170400170400Satsunai_KP6_4
362700201800362700Satsunai_KP7_0
308000224800308000
Past
Tokachi_KP56_4
311300143500290200Tokachi_KP58_0
465600309300450200Tokachi_KP59_6
568200488200541200Tokachi_KP61_4
641300597600637400Tokachi_KP62_4
606800547800606800Satsunai_KP4_2
26900077800
177500Satsunai_KP5_2
330400141000324300Satsunai_KP6_4
483200193000380100Satsunai_KP7_0
480600226400430700FutureTokachi_KP56_4
834000129000479400Tokachi_KP58_0
855900294200598300Tokachi_KP59_6
865000483000697000Tokachi_KP61_4
917000583800778000Tokachi_KP62_4
798400532300837400

4.5

Uncertainties in calculating damage

Given the flood scenarios the economic damage is calculated. Determining the damage is also
subject to uncertainty. The following aspects are a first overview of uncertainties:•

Important factor is the flood extent and the vulnerability of vital and high value infrastructure.
When flood extent varies and these type of assets are on the edge of the flood extent it can
influence the amount of damage significantly.

•

The relation between direct and indirect economic damage. The direct damage is the damage
to existing assets. But the indirect damage due to failure of the economic activities can have asignificant impact especially when vital infrastructure is flooded.

1 When the damage is the same for the different scenarios it means that there is only one or two scenarios for thatbreach location that lead to a flood.

PR3983.10 • December 2021

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5

Evacuation

5.1

Evacuation Rate of Target Area

When an evacuation Advisory/Order was issued to roughly 54,000 people [1] in the target area ofthe entire Obihiro city in the event of Hokkaido Heavy Rain Disaster 2016, only 1,954 residents [2](3.6%) actually evacuated. It is necessary to set up a population parameter with population within
the flooded area, in order to apply an actual evacuation rate to estimate fatality of the flooded
area. It is, however, not appropriate to use an actual evacuation rate, since Obihiro city did not
suffer from flood damage due to overflowing in Hokkaido Heavy Rain Disaster 2016. This project,
therefore, suggests a new method of setting up an evacuation rate, based on the existing Japaneseand Dutch method to apply for estimating fatality of large-scale flood in Japan.

5.2

Method to Set up Evacuation Rate

5.2.1

Japanese Method

In order to estimate fatality of the flooded area in Japan, an evacuation rate is determined basedon the past 6 floods in Japan and the United States [3]. In reference to the evacuation rates of thepast 6 floods indicated in Table 18, the basic setup is 0%, 40% and 80%, to estimate theremaining population (exposure) in flooded area. The evacuation rates of large-scale floods inrecent years [4][5], however, are not included. It can be considered that there might be a gap
from an actual evacuation rate under the impact of ongoing climate change. Few cases haveapplied such actual evacuation rates in risk analysis of large-scale floods, though there are somecases in an observational study by means of Evacuation Simulator by Kawashima et al. [6] anddiverse experimental studies of evacuations by Baba et al.[7].
Table 18 Evacuation rate in the past floods
Year of
Disaster Name
Disaster

Evacuation Rate（％）

Heavy Rain in Nagano

1982

13

Heavy Rain in Tokai Region

2000

44

Typhoon No.9, Kitakami River

2002

32

Heavy Rain in Niigata and Fukui

2004

19（Mitsuke City, Niigata Prefecture）
23（Sanjo City, Niigata Prefecture）
36（Nakanoshima Town, Niigata Prefecture）

Typhoon No.23, Toyooka Flood

2004

33

Hurricane Katrina

2005

About 80（New Orleans City）

PR3983.10 • December 2021

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5.2.2

Dutch Method

In the Netherlands, evacuation rate is determined, based on interviews with experts to estimate
fatality of the flooded area.8）Expected value of evacuation rate per dike ring is calculated byestimating evacuation rate of diverse scenarios. These scenarios are classified by the below
evacuation process. Especially “Detection and recognition of the threat” is defined by so-called“Lead Time,” available time to evacuate, which means evacuation rate varies due to whether they
have enough lead time, or whether they need to rush because of a short lead time.
1. Detection and recognition of the threat.
2. Organization and strategy choice: decision making and strategy choice by government andsociety.
3. Execution: manner and route of evacuation.
In order to estimate evacuation rate per scenario, an evacuation model is used, taking into accountvarious possibilities, such as road congestion and traffic regulations. Uncertainty and bandwidth is,therefore, included in evacuation rate as indicated in Figure 11. Finally an event tree is created,
including all the relevant events associated as parameters to estimate an evacuation rate.
Challenge here is that the model is not verified in terms of applicability and calibration because,
fortunately, flood disasters have not occurred in recent years.
Figure 11 Evacuation rate per dike ring

PR3983.10 • December 2021

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5.2.3Collaboration between Japanese and Dutch Method

In reference to the above, this project collects large amount of published flood questionnaire
survey results, and creates a simple event tree based on many flood cases in Japan to estimate
evacuation rate (expected value) including uncertainty. Event tree is created by estimating
evacuation rate, on the ground of lead time to evacuate given to residents right before floods,
according to three scenarios with No lead time, Short lead time (less than 3 hours) and Long leadtime (3 hours and more).

5.3

Available data and information

Majority of questionnaire surveys in Japan are conducted by the central government, municipalities
and research institutes to the residents hit by floods. The information given by these surveys
suggests behavioral psychology of residents during floods, and contributes to consider effective
flood control measures, crisis management, and local disaster prevention plan. This project createsan event tree, collecting data of 86 cases of flood questionnaire surveys in Japan (Table 19). It is,however, necessary to note that each survey employs its own population parameter.
Table 19 Collected flood cases in Japan

PR3983.10 • December 2021

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5.4

Analysis and Results

Definition•

Evacuation rate
Evacuation rate is defined to be a ratio of “move out” (horizontal evacuation) in this project.
The destinations are not only shelters, but also other safe places, such as higher ground andrelatives / friends' home.

•

Lead time to evacuate
Lead time in this project is determined by a standard throughout all cases, taking into accountdistance from home to shelters and walking speed, though it should have been better if it were
determined per area/river, dependent on water rising rate of each river/flood. Definition of leadtime to evacuate safely is time after an evacuation information is announced until a disasteroccurs, such as dike failure, overflowing and flood, or until the highest water level is reached(Figure 12). Lead time required for safe evacuation is estimated by distance between home andshelters [9] and walking speed while evacuating [10]. As a result, necessary lead time turned
out to be about 1.4 hours during daytime for people who need a longer time to walk, and 0.6
hours for others (Table 20). Because walking speed is almost halved during night [11], leadtime for people who need a longer time to walk is about 2.8 hours, and about 1.2 hours forothers (Table 21).

PR3983.10 • December 2021

26

Necessary lead time is, therefore, assumed to be approximately 3 hours for residents toevacuate safely, including those need a longer time to walk. Further research is, however,required with more details in the future, because many residents chose car to evacuate in the
recent floods and then the speed to evacuate can be quite different.
Figure 12 Concept of lead time

Water
Level
overflowing etc.
Lead Time to Evacuate
Evacuation Order or Advisory
Evacuation Preparation Information
Lead TimeTime
Table 20 Draft of lead time for citizens (daytime)Target person
Distance
People who need a longer time towalkMax. 2,000m [9]OthersWalking speed（daytime）
0.4m/s(Assumed to be about 40% of
the below based on references[10])
1.0m/s [10]Table 21 Draft of lead time for citizens (night, Walking speed halved
Walking speed（daytime）
Target person
Distance
People who need a longer time
to walk
Others5.5

Max. 2,000m [9]0.2m/s(Assumed to be about 40% of
the below based on references[10])
0.5m/s [10]Time required
1.4 hours
0.6 hours

Time required
2.8 hours
1.2 hours

Results

Based on 3 hours of necessary lead time determined as the above, evacuation rates of collected 86
cases are classified. The cases with Short lead time (less than 3hours) are spread out in wide range
between minimum 7.1％ and maximum 81% as shown in Figure 13 and Table 22. Evacuation ratewith long lead time (3 hours and more) is higher by 10-15%, between minimum 17.3％ andmaximum 96%. Average rate of Long lead time is 43.8%, slightly higher than 40.9% with Shortlead time.

PR3983.10 • December 2021

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Figure 13 Classification of evacuation rates based on lead time

Table 22 Evacuation rate when lead time is short or long
Lead Time
Minimum
Short lead time
7.1％
（less than 3hours）
Long lead time
（3 hours and more）

PR3983.10 • December 2021

17.3％

Average

Maximum

40.9％

81％

43.8％

96％

28

Relationship between Evacuation Rate and Flood Experience
This project provides a correlation between evacuation rate and various elements. Amongthem, this project introduces the relationship between the evacuation rate and the percentage of
people who have experienced floods before, where a positive correlation appeared.
Here, the percentage of people who have experienced floods before indicates the percentage
of damage experience and evacuation experience in past floods. The reason why there are few
applicable cases is that many questionnaire surveys do not have question items related to flood
experience. When the relationships between the evacuation rate and the percentage of people
who have experienced floods before were grasped in all 32 cases, positive correlation(correlation coefficients r = 0.60) was obtained as shown in Figure. This indicates that residents
with a damage experience or evacuation experience have a crisis awareness of floods, and tend
to have a higher possibility to evacuate. Based on this, it is possible to derive an evacuation
function (linear function) with the flood experience as a parameter as shown below.
𝑦 = 0.5869𝑥 + 19.625
Where, x: the percentage of flood experience (%), y: evacuation rate (%). From the intercept of
this function, it can be seen that an evacuation rate of at least about 20% can be expected inareas that have experienced floods. With this evacuation function, it is possible to set theevacuation rate that more reflects the regional characteristics by estimating the evacuation rate
corresponding to any x (percentage of people who have experienced floods).

Figure 14 Evacuation function with the flood experience as a parameter

PR3983.10 • December 2021

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5.6

Evacuation Rate

Figure 15 shows a simple event tree, created according to lead time. Each ratio of the middle layer,
No lead time, Short lead time (less than 3 hours) and Long lead time (3 hours and more), indicatesratio per scenario with population parameters of cases collected for this project. The lower layershows evacuation rate per scenario. Here, if there is no lead time or the evacuation information isannounced late, it is assumed that the residents cannot evacuate safely, and the evacuation rate is
set to 0%. The evacuation rates were 41% and 44%, respectively, when the lead time was shortand when there was long lead time. Overall Average rate (expected value) is 26%. This project
estimates fatality of the flooded area, by calculating remaining population (exposure) in floodedarea, determined by the evacuation rate. In order to improve the evacuation rate, actual record of
Hurricane Katrina Disaster at New Orleans city, 80%, is added. This project proposes the above
method of determining evacuation rate and values of evacuation rates.
Figure 15 Simple event tree based on lead time

5.7

Uncertainties in evacuation

Evacuation is an aspect that is subject to several uncertainties, because human behaviour plays amajor role in the effectiveness. Communication, previous experiences, available time, people’s
awareness, preparedness, road capacity, are all aspect that influence the outcome of theeffectiveness of the evacuation. Also the difference between preventive evacuation outside thethreatened area and evacuation to shelters nearby have a large impact on the determination of thepercentage of people that reach a save location. Therefore, this is a topic that needs further
investigation to improve understanding of the evacuation effectiveness and the potential it canhave in reducing the flood risks.

PR3983.10 • December 2021

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5.8

Proposal improve evacuation model

Further detailed study and examination are required as future issues. As the main study content in
the future, since large-scale floods occur frequently in Japan, the latest information (evacuation
rate, lead time, etc.) is added, and the event tree and loss of life due to floods are estimated basedon it. It is necessary to update the evacuation rate used for. In addition, in this project, we focusedon lead time as an element that can be estimated in common for all cases, but in reality, we haveto focus on elements that correlate with the evacuation rate, such as flood experience, andestimated the evacuation rate immediately before the occurrence of a disaster. The contents offuture studies including these are shown below.
•

Create an event tree based on the latest information.

•

Reexamination of lead time that allows residents to evacuate safely based on the results of theGreat East Japan Earthquake etc.

•

Understanding the correlation between elements other than lead time (flood damage
experience, hazard map understanding rate, participation rate in disaster prevention drills, etc.)
and evacuation rate. Analyze which element (uncertainty) contributes most to the evacuation
rate.

•

Based on the above, create an event tree that further reflects regional characteristics.

•

Examining how to set the target evacuation rate.

Figure 16 Improved evacuation model

PR3983.10 • December 2021

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5.9

References[1]

Tokachi Mainichi News (Local newspaper), ”7,300 people evacuated in 13 municipalities, and
1,954 people evacuated in Obihiro City”, 2016,
https://kachimai.jp/article/index.php?no=2016831133129 . (Japanese only)[2]
Obihiro City, Verification results of disaster response by Typhoon No. 10 in 2016, 2017,https://www.city.obihiro.hokkaido.jp/_res/projects/default_project/_page_/001/002/382/zen
bun.pdf . (Japanese only)[3]
Water and Disaster Management Bureau, Ministry of Land, Infrastructure and Transport, The
Guide for Analysis of Flood Damage Indicators(2013 trial version), 2013,https://www.mlit.go.jp/river/basic_info/seisaku_hyouka/gaiyou/hyouka/pdf/higaisihyou_h25.
pdf . (Japanese only)[4]
Morooka,Y. et al., Survey Report on Acquisition of Disaster Information and Evacuation
Behavior in Heavy Rainfall Disaster in Kanto-Tohoku Regions, September 2015., Advances inriver engineering，Vol.22, 2016, http://library.jsce.or.jp/jsce/open/00906/2016/220345.pdf . (Japanese only)[5]
Gifu Prefecture, tec，Residents' Evacuation Behavior Survey in the Heavy Rain Disaster, July
2020, 2021, https://www.pref.gifu.lg.jp/uploaded/attachment/243366.pdf . (Japanese only)[6]
Kawashima, K. et al., A Flood Risk Communication Support System to Promote Safe
Autonomous Evacuation, Civil engineering planning research and dissertation collection
No.23, no.2, pp.309-318, 2006,
https://www.jstage.jst.go.jp/article/journalip1984/23/0/23_0_309/_article/-char/ja/ .
(Japanese only)[7]
Baba, Y. et al., Experimental Studies on Difficulty of Evacuation from Underground Spaces
under inundated Situations using Regal Scale Models, Journal of Japan Society of Civil
Engineers. Ser. F2, Hydraulic engineering, Underground Space Research, Vol. 67, No.1,
pp.12-27, 2011, https://www.jstage.jst.go.jp/article/jscejusr/67/1/67_1_12/_article/char/ja . (Japanese only)[8]
Maaskant,B. et al., Evacuatieschattingen Nederland, Rapport HKV lijn in water, 2009.
[9]Urban Disaster Prevention Policy Office, City and Regional Development Bureau, MLIT,[Revised] Urban Disaster Prevention Practice Handbook: The Guide for Earthquake-ResistantCommunity Planning and Development, pp.58-59, 174-175, 2005. (Japanese only)[10] Urban Safety Affairs Division and Urban Transport Facilities Division, City Bureau, MLIT, The
guideline of community planning for Tsunami disaster mitigation (First Edition), 2013,https://www.mlit.go.jp/common/001000488.pdf . (Japanese only)[11] Mizuno,R. and Nihei,Y., Laboratory experiments on walking speed in evacuation of flood
disaster under various environmental conditions, 2016, Journal of Japan Society of Civil
Engineers. Ser. B1, Hydraulic engineering, Vol.72, No.4, pp.1333-1338, 2016,
https://www.jstage.jst.go.jp/article/jscejhe/72/4/72_I_1333/_article/-char/ja/ . (Japanese
only)

PR3983.10 • December 2021

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6Loss of Life

6.1

Loss of Life due to Recent Floods in Target Area

As described above, the center of Obihiro city, the target area of this project, did not suffer fromdamage due to overflowing in the event of Hokkaido Heavy Rain Disaster 2016. The record highwater level at Tokachi River Obihiro Reference Point (1955-2015) [1] shows that 2016 was thesecond highest. It also indicates that the record high water level before 2016 was 1981, 35 years
back in time. This is, therefore, an area with no flood damage in recent years, nor fatalities due toflooding.
Table 23 Tokachi River Obihiro Reference Point, record high water level [1]Ranking

Flood date

Highest water level(m)11962.8.4

38.12

2

2016.8.31

38.07

3

1981.8.5

37.54

4

1957.9.19

37.05

5

1961.7.26

37.00

5

1972.9.17

37.00

7

1955.9.8

36.88

8

1958.8.28

36.60

9

1964.8.26

36.49

10

1962.9.9

36.22

6.2

Method of Loss of Life Estimation

6.2.1

Japanese Method

In order to estimate loss of life in the flooded area in Japan, LIFESim model [2] is employed, bywhich United States Army Corps of Engineers (USACE) verified loss of life around New Orleans citywhen Hurricane Katrina hit there [3][4]. This model determines mortality dependent on water
depth, taking into account age of victims (65 years and older/younger than 65 years). When avictim is 65 years and older, it is assumed that they can evacuate vertically to their top floor, andwhen younger than 65 years, the ceiling of the top floor is assumed to be reached by verticalevacuation (Figure 17). Fatality is then estimated by multiplying mortality classified into 3 areas(Table 24), Danger Water Level with maximum water depth from floor, Near Critical Water Level
and Safety Water Level, by number of people in danger dependent on which floor in building.
Ikeuchi et al. [3] estimated loss of life in large-scale flood of Arakawa river etc. based on aconfirmation that there is no significant difference between Japan and the United States, in termsof floor height of residence, height of each story, or average height of people.

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Figure 17 Overview of LIFESim model [3][4]Table 24 Mortality according to water depth [3][4]Classification of danger level

Mortality

Danger Water Level

91.75

Near Critical Water Level

12.00

Safety Water Level

0.023

6.2.2

Dutch Method

There is a model to estimate loss of life of the flooded area in the Netherlands, with mortality
function related to flood characteristics [5][6]. S.N. Jonkman [5] divided flooded area into threehazard zones (breach zone, zone with rapidly rising water, remaining zone) and employedmortality function with parameter of water depth in each zone to estimate mortality in the event offlood. B. Maaskant et al. [6] improved the model of S.N. Jonkman , dividing flooded area into fourzones, since majority of fatality was classified/assumed in two zones (zone with rapidly rising waterand remaining zone) in the risk analysis of “The National Flood Risk Analysis for the Netherlands“VNK2 (Floris2)” [7]. Boundary conditions and mortality function per zone are explained as follows.
The breach zoned∙v≥7m^2/s and v≥2m/s
F =1
D,B

The zone with rapidly rising water（𝑑 ∙ 𝑣 < 7𝑚2 /𝑠 and 𝑣 < 2𝑚/𝑠） and （𝑑 ≥ 2.1𝑚 or w≥ 4𝑚/ℎ𝑟）
FD,Rise (𝑑)= 𝛷𝑁 (ln(𝑑)−𝜇𝑁
𝜎𝑁) 𝜇N=1.46, 𝜎N=0.28

The transition zone（𝑑 ∙ 𝑣 < 7𝑚2 /𝑠 or 𝑣 < 2𝑚/𝑠）
FD = FD,Remain + (w − 0.5)

PR3983.10 • December 2021

and （𝑑 ≥ 2.1𝑚 and 0.5𝑚/ℎ𝑟 ≤ w < 4𝑚/ℎ𝑟）

FD,Rise-FD,Remain
3.5

34

The remaining zone
（w< 0.5𝑚/ℎ𝑟 or (w ≥ 0.5𝑚/ℎ𝑟 and 𝑑 < 2.1𝑚）) and (𝑑 ∙ 𝑣 < 7𝑚2 /𝑠 or 𝑣 < 2𝑚/𝑠）
FD,Remain (𝑑)= 𝛷𝑁 (ln(𝑑)−𝜇𝑁
𝜎𝑁) 𝜇N=7.60, 𝜎N=2.75

where, d: flood depth(m), v: flow velocity(m/s), w: rise rate of water(m/h), F: mortality, Φ:cumulative probability density function of standard normal distribution, 𝜇: average value of h, σ:
standard deviation of h.
Figure 18 Dutch mortality functions

6.2.3

Selection of application method

Rivers in Japan are steeper than in the Netherlands, and their basins are generally steeper. For thisreason, when a river floods, Figure 19 indicates that houses around the breaching point are
frequently washed away or collapsed by a significant fluid force in Japan [8][9]. According to theresearch by Nihei and Shinohara [10][11], there are cases that people died during evacuation or
before taking an action of evacuation due to rapidly rising water（Two examples are as follows:
Max. water rise rate: 0.5m/h in Joso city, Ibaraki prefecture in the event of Heavy Rain Disaster in
Kanto-Tohoku Regions in September, 2015. Max. water rise rate: higher than 1.0m/h in Mabi-cho,Kurashiki city, Okayama Prefecture in the event of Heavy Rain Disaster in July, 2018.) Sato et al.
[12] and Tsukada and Ikeuchi [13] report that majority of the victims were senior citizens or
person requiring support. An accurate loss of life caused by flood in Japan should be estimated,dependent on flooding and damage characteristics, taking into account hydraulic conditions, suchas flow velocity, fluid force, and water rise rate, and age of victims.

PR3983.10 • December 2021

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Figure 19 (Left) Damaged houses collapsed near the breaching point of Chikuma River（in Tsuno, Nagano city,
Nagano prefecture, Typhoon No.19 in October, 2019）（Edited based on references [8]), (Right) Damaged
houses near the breaching point of Suemasa River (Mabi-cho, Kurashiki city, Okayama prefecture in July, 2018)(Edited based on references [9])1st floor of own house
House where
people diedHouse washed away
2nd floor of own house, other

Breach Point
Flow

Washed away
2nd floor damaged1st
floor damaged
Partially damaged
Inundation only

Based on recent flood damage in Japan, loss of life estimation of urban area in Obihiro city needs
to take into account hydraulic conditions, such as flood depth, flow velocity, fluid force and waterrise rate. In addition, vulnerability of victims should be also considered. As described above, themethod to apply mortality function is effective to reflect diverse hydraulic conditions. On the other
hand, LIFESim model is remarkable in giving mortality per flood depth (classification of danger
level), though concerned hydraulic condition is limited only to flood depth, and its feasibility ofvertical evacuation, including age of victims (65 years and older/younger than 65 years.).
There are still significant challenges to apply the above model in practice to existing rivers inJapan, while mortality function is applied considering horizontal load condition, such as flow
velocity and fluid force around the breaching point, and rapidly rising water caused byaccumulating water in low-lying area or being surrounded by dike. This project, therefore,
introduces an improvement of the loss of life estimation model (section 6.5).

6.2.4

Mortality

Based on the complexity of the Japanese situation and the challenges ahead, the choice has been
made to use the Dutch mortality functions for the Obihiro case study. When the improved method
is available an update can be made using the new mortality functions. The next section describes
the loss of life using the Dutch mortality functions.

6.3

Loss of Life case Obihiro

Based on the method described in section 6.2 and the choices that have been made the loss of life
for the Obihiro case can be determined. In the following section the calculation of loss of life isdescribed.

PR3983.10 • December 2021

36

6.3.1

Population

The total population in the case study area is approximately 28,800. These are the inhabitants of
the area at risk. In the figure below the distribution of the population of the area is shown.
Figure 20 Spatial distribution of the population

6.3.2

Mortality

To determine the mortality the water depth, rise rate and flow velocity are combined using the
mortality functions from section 6.2.4. The mortality is calculated for all breach locations and allinundation patterns. In this section one locations is described to illustrate the determination of themortality. In the figure below the water depth is shown for breach location KP61.4 for the Max
Scenario.
Figure 21 Water depth MAX scenario KP61.4

PR3983.10 • December 2021

37

This maximum water depth in combination with the maximum rise rate during the flood scenario
are used to calculate the mortality. This leads to a mortality rate per meshcode shown in Figure 22,
the mortality varies between less than 0,1% to more than 10% in the deeper parts. This can be
explained by the fact that, in general, the greater water depths lead to a higher mortality.
Figure 22 Mortality MAX scenario KP61.4

6.3.3

Loss of life

Loss of life for each breach scenario is calculated by multiplying the mortality by the population
including evacuation. Incorporating evacuation can lead to different numbers for loss of life basedon the included evacuation percentages. In the table below the loss of life is given for the different
flood scenarios (Max, Min, MaxVol) and three evacuation percentages, one for the situation with noevacuation (0%), one for the overall average percentage (expected value) 26%, and one upper
limit of 80 percent. Because of the different flood extents between the flood scenarios also the
exposed population varies, this is also included in the table.
Table 25 Overview Loss of Life for all scenarios
Location

Past

Exp

Max
0%
26%

80%

Exp

0%

Min
26%

Satsunai_KP4_2

4076

17

13

3

4076

17

Satsunai_KP5_2

7830

23

17

5

7830

Satsunai_KP6_4

15594

56

42

11

Satsunai_KP7_0

13930

28

21

Tokachi_KP56_4

13411

186

Tokachi_KP58_0

21555

Tokachi_KP59_6

26884

Tokachi_KP61_4
Tokachi_KP62_4

PR3983.10 • December 2021

80%

Exp

MaxVol
0%
26%

13

3

4076

17

13

3

23

17

5

7830

23

17

5

9358

9

7

2

15594

56

42

11

6

10445

2

2

0

13930

28

21

6

139

37

5985

27

20

5

12427

118

88

24

162

121

32

14200

47

35

9

20769

127

94

25

340

253

68

23204

134

100

27

25533

225

168

45

30421

337

251

67

28893

218

162

44

30283

289

215

58

28880

179

133

36

26558

76

57

15

28880

179

133

36

80%

38

FutureSatsunai_KP4_2

11289

40

30

8

3906

14

11

3

7619

36

27

7

Satsunai_KP5_2

13861

58

43

12

6638

17

13

3

13575

57

42

11

Satsunai_KP6_4

20556

100

74

20

8946

6

5

1

16335

53

39

11

Satsunai_KP7_0

20179

67

50

13

10539

3

2

1

18326

51

38

10

Tokachi_KP56_4

35769

365

272

73

5366

22

17

4

21006

165

123

33

Tokachi_KP58_0

36646

566

422

113

13526

41

31

8

26538

169

126

34

Tokachi_KP59_6

37052

879

655

176

23014

115

86

23

30825

310

231

62

Tokachi_KP61_4

39174

834

622

167

28380

164

122

33

33886

352

262

70

Tokachi_KP62_4

34274

414

308

83

25887

60

45

12

35913

514

383

103

The loss of life analysis shows that largest loss of life is caused by levee breaches along the Tokachi
river. This is due to the fact that the Tokachi river has a larger discharge than the Satsunai riverand therefore leads to a larger flood extent and more people exposed.
In comparison to the calculated damage the loss of life is more sensitive to the hydrograph shapeof the discharge that runs through the river. The difference between the “max” scenario and“maxvol” is relatively small, factor 2. But the difference with the “min” scenarios are relatively
large, it can be up to a factor 20.
In the figure below the distribution of the loss of life over the case study area is shown for the
KP61.4 Max scenario for the situation without evacuation. This gives information on the location
where the loss of life is largest.
Figure 23 Spatial distribution of the loss of life for the max scenario KP61.4

6.4

Uncertainties in Loss of Life calculation

Determining loss of life is also subject to many uncertainties. In the case study the Dutch loss oflife model is used. This method is based on the 1953 flooding in the Netherlands and is mostly
applicable to larger flooded areas and “average” out differences between states. Other methods
aiming to capture more local processes and effects, which will lead to differences in outcomes.

PR3983.10 • December 2021

39

The methods are also sensitive to peoples location during a flood event. If a large percentage ofthe population is in a shelter this has an impact on the use of the loss of life model. So the
combination and interaction between the evacuation and loss of life models is essential for a good
estimation and understanding of the loss of life calculation.
Local situations also have a large impact on the loss of life determination, type of building
constructions, age distribution of population, weather conditions.
Given these uncertainties it is necessary to further research this topic to get a better insight in thepotential loss of life and the flood risks.

6.5

Proposal new loss of life model

During this project, engineers and experts from both countries joined diverse discussions
concerning loss of life estimation model and estimation method, targeting Japan. As a result, thisproject employed a method to apply mortality function, to take into account fluid force in the eventof dike failure and rapidly rising water due to accumulation. In order this method to be applied inJapan, however, requires model calibration, with adjustment to flooding and damage
characteristics in Japan. This is why a new loss of life estimation model is proposed as a result of
this project. In the future, applicability of this model has to be verified.

6.5.1

Point of View

According to Tomura and Masuya et al. [14], no area was classified in (The breach zone), even byexternal force considering climate change (+4K simulation) as indicated in Figure 24. As describedabove, however, damaged houses in recent years are significantly washed away/ collapsed aroundthe breaching point. It is necessary to correctly assess loss of life classified in (The breach zone)
to estimate loss of life of the flooded area. This project focuses on (The breach zone).
Figure 24 +4K simulation: Mortality zone map [14]Concept of [The breach zone] is based on the assumption that people die 100% (FD,B= 1) whenmasonry and brick houses totally disrupt. Boundary condition, therefore, employs collapse criteria
of masonry and brick houses (Figure 25) [15][16], which is implemented in majority of European
countries including the Netherlands.

PR3983.10 • December 2021

40

On the other hand, most of the houses in Japan is made of wood (Figure 26) [17]. Wooden houses
are considered to be more vulnerable to fluid forces than masonry and brick houses, and are
expected to suffer more damage. In order to estimate house damage in the event of a large-scale
earthquake, houses are classified dependent on structure (wooden/non-wooden) and age(old/middle/new) based on fixed assets book data, which provides estimation of total destruction
rate of houses, using damage rate function [18]. It is obvious that vulnerabilities are caused bydifferent structure and age of houses. Especially in assessment of house damage in the event of alarge-scale earthquake, old wooden houses are the most vulnerable. This can be explained byactually reported many cases of extremely severe and wide range of damage caused byearthquake, such as Great Hanshin-Awaji Earthquake in 1995 and Great East Japan Earthquake in2011, which made it possible to assess vulnerability of houses based on actuality. House damage
due to floods, however, is by far less than house damage due to large-scale earthquake, though it
tends to increase in recent years. This is why this project focuses on boundary condition of [Thebreach zone] and determines collapse condition of wooden houses based on review of past studies.
Figure 25 Collapse criteria of masonry and brick houses15)

Figure 26 Percentage of houses to structure in Japan, (Left) All over Japan, (Right) Hokkaido17.
Percentage of houses to be built by structure (Japan, 1988-2018)
1988
1993
1998
2003
2008
2013
2018
0%
Wood

10%

20%

30%

40%

Steel framed reinforced concrete

50%

60%

Reinforced concrete

70%
Steel

80%

90%

Concrete100%
OthersPercentage of houses to be built by structure (Hokkaido, 1988-2018)
1988
1993
1998
2003
2008
2013
2018
0%
Wood

10%

20%

30%

40%

Steel framed reinforced concrete

PR3983.10 • December 2021

50%

60%

Reinforced concrete

70%
Steel

80%

90%

Concrete100%
Others41

The Focus of This Study
Do you know the fairy tale “The Three Pigs” [19] published in the late 18th century? The outline of the story
is as follows. One day, Three Pigs decided to build their own houses. The first little pig built his house out ofstraw. The second little pig built his house out of sticks (wood). Then the third little pig built his house withbricks.
A hungry wolf arrives there and blows away each house one after another, but the brick house made by thethird little pig did not collapse. Eventually, the wolf enters the brick house from the chimney and gets hurt.
In proposing a new loss of life estimation model in this project, we got important inspiration from this story.
The force a wolf blows houses can be replaced by flow velocity or fluid force (a horizontal force,) etc. Only
the brick house made by the third little pig did not break under almost the same horizontal force. European
houses, including the Netherlands, can be seen as the third little pig’s house, and Japanese houses can beseen as the second little pig's house.
In order to estimate loss of life due to floods in Japan, we thought it was necessary to evaluate whether thehouse of the second little pig’s house would be destroyed. This is the focus of this study.

6.5.2

Comparison of Collapse Condition

In order to determine appropriate boundary conditions of 【The breach zone】 in Japan, results ofstudies and literature were collected, related to collapse condition of wooden houses in Japan.
Table 33 indicates collapse condition of houses derived from damage in the past floods. In Japan, alot of studies assess washed away/collapsed houses by index of fluid force of flood flow (v2d, v:
flow velocity and d: flood depth). On the other hand, in the Netherlands, the same index of fluid
force (v2d) is reflected, which is derived from flood depth product (vd) related to moment and flow
velocity (v), a horizontal force causing houses to collapse.
Though a lot of house damage due to Tsunami is reported in Japan, a Tsunami causes significant
fluid force twice: incoming wave and dilatational wave. This is the reason why Table 33 does notinclude Tsunami, which is a different phenomenon from river flooding causing house damage.
Table 26 Collapse condition of wooden house (d: flood depth(m)、v: flow velocity(m/s))
No.

Target housestructure0≤d<2.6, 𝑣 = √1

SourceCollapse condition

35.76
𝑑

Two storied wooden
house （Sliding）
2.6≤d<3.2, 𝑣 = √3.2≤d, 𝑣 = √

𝑑

15.65

2

Two storied wooden
house (new seismic
criterion since 1981)

1.65<d, 𝑣 = √3

Two storied wooden
house (old seismic
criterion since 1950)

1.65<d, 𝑣 = √PR3983.10 • December 2021

122.95−33.53ℎ

Flood Control Planning Room, River Environment
Division and Flood Disaster Prevention Division,
River Department, National Institute for Landand Infrastructure Management, Ministry ofLand,
Infrastructure,
Transportation
andTourism, Manual for Creating a Flood Forecast
Area Map（4th edition）, 2017. 20)

𝑑5.83(𝑑−1.650)

1.56(𝑑−1.650)

Flood Control Planning Room, River Environment
Division and Flood Disaster Prevention Division,
River Department, National Institute for Landand Infrastructure Management, Ministry ofLand,
Infrastructure,
Transportation
andTourism, Manual for Creating a Flood Forecast
Area Map（4th edition）, 2017. 20)
Flood Control Planning Room, River Environment
Division and Flood Disaster Prevention Division,
River Department, National Institute for Landand Infrastructure Management, Ministry ofLand,
Infrastructure,
Transportation
andTourism, Manual for Creating a Flood Forecast
Area Map（4th edition）, 2017. 20)

42

4

5

One-story
houseswoodenUnknown

Large damage: 𝑣 2𝑑 = 10
Washed away: 𝑣 2𝑑 = 20
Damage occurs: 𝑣 2𝑑 = 1.5

Yagi,
R.
and
Kure,
S.,
ProposalofVertical/Horizontal
Evacuation
Zone
Classification based on Flood Inundation
Analysis and Risk Rank Evaluation for the Joganji
River and Jinzu River, 2020. 8)
Kawata, Y. and Nakagawa, H, Flood Disasters in
the Misumi River, 1984. 21)

An uninhabitable house appears: 𝑣 2𝑑 = 2.5
Houses may be damaged（based on
6

Wooden houses

houses survey results in New Zealand）

Sato, S. et al., Numerical Simulation of Flood andDamage to Houses -A Case of the Yoshida Riverdue to Typhoon No.8610, 1989. 22)

𝑣𝑑 = 1
Houses are washed away and large-scale
7

Unknown

damage occurs

Tabata, K. et al., On Flood Risk Reduction byIntegrating Analysis of Flood Flow andInundation in the Kinugawa River during 2015
Large Flood, 2018. 23)

2 < 𝑣 and 2 < 𝑑Collapse conditions in Table 33 are compared in Figure 27. It is quite clear that Dutch masonry andbrick houses（total disruption）and Japanese wooden houses（houses that complied with the newseismic criterion*, collapse）are similar [19]. This is because houses that complied with the newseismic criterion has the similar ultimate shear strength（It collapses when fluid force equals toultimate shear strength.） as Dutch masonry and brick houses.
Aging of Japanese wooden house is, however, not taken into account, this can be, therefore,
considered that new or comparable structure is assumed. This could be applicable if loss of life isestimated targeting new residential area, but it could underestimate damage in the area with many
old houses. Collapse conditions of houses that complied with the old seismic criterion [19] areverified by Yagi and Kure [20] with the case of flood in recent years, targeting damaged houses
due to dike failure at Chikuma river in the event of Typhoon No.19 in October, 2019. It is,however, possible to underestimate damage because aging is not taken into consideration just likethe above mentioned houses that complied with the new seismic criterion.
Concerning a study of a border of “uninhabitable houses” by Sato et al. [22], it is verified by
Tabata et al. [23] targeting damaged houses due to dike failure at Kinugawa river in the event of
Heavy Rainfall Disaster in Kanto-Tohoku Regions, September 2015. What Sato et al. named aborder of “uninhabitable houses” is described by Tabata et al., “risk of house washed away,”
which suggests a possibility to determine conditions of house washed away and collapse in Japan.
Concerning a border of “houses may be damaged” based on houses survey results in New
Zealand [24], its applicability is verified by Iino [25], targeting 4 rivers in Japan (wooden houses,over 20 years old). A border of “houses may be damaged” derived from practice in New Zealandmight overestimate house collapse as Figure 27 indicates, because damage status of house is notclear enough.
Japanese seismic criterion;
In Japan, seismic criterion for houses are set so that they can withstand a certain amount of
external force in the event of an earthquake. The Building Standards Law has revised the seismic
criterion by verifying the damaged houses after the occurrence of a large earthquake in the past.
The Building Standards Law was enacted in 1950, and the seismic criterion was revised in 1971,
1981, and 2000. The current seismic criterion was mainly revised in 1981 and is generally called
the "new seismic criterion". On the other hand, before 1981, it was called the "old seismic
criterion".

PR3983.10 • December 2021

43Figure 27 Comparison of house collapse conditions derived from past studies

6.5.3

Proposal for the Future

In comparison of collapse conditions in past studies, the results shown in Table 27 were obtained.
It is considered that the most suitable condition is currently a border of “uninhabitable house”
v2d=2.5 studied by Sato et al. [22], because there are descriptions of “house washed away” and“collapse” and it is verified by flood damage in recent years.
Table 27 Collapse conditions of Japanese houses and consideration
No.

Target housestructure1

Two storied
wooden house(Sliding)

Collapse condition0≤d<2.6, 𝑣 = √

2

3

4

Two storied
wooden house(new seismic
criterion since
1981)
Two storied
wooden house(old seismic
criterion since1950)
One-story
wooden houses

Verificationin recentfloodsOnly for new
construction

Sliding×

Only for new
construction

Collapse×

Only for new
construction

Collapse〇

Partially (Including
other than new
construction)

Washed away×

Remarks𝑑
122.95−33.53ℎ
𝑑

15.65

1.65<d, 𝑣 = √1.65<d, 𝑣 = √𝑑5.83(𝑑−1.650)

1.56(𝑑−1.650)

Large damage: 𝑣 2𝑑 = 10
Washed away: 𝑣 2𝑑 = 20

PR3983.10 • December 2021

Targetphenomenon

35.76

2.6≤d<3.2, 𝑣 = √3.2≤d, 𝑣 = √Evaluation of
aging
deterioration

44

Unknown

Damage occurs: 𝑣 2𝑑 = 1.5
An uninhabitable houseappears: 𝑣 2𝑑 = 2.5

Partially (Including
other than new
construction)

6

Wooden houses

Houses may be damaged（
based on houses survey
results in New Zealand）
𝑣𝑑 = 1

Partially (Including
other than new
construction)

Damaged〇7

Unknown

Houses are washed away andlarge-scale damage occurs
2 < 𝑣 and 2 < 𝑑Partially (Including
other than new
construction)

Washed away
and largescale damage〇

5

Most suitable

Washed away〇
The target
phenomenon(assumed damage
to the house) is
unclear.
Collapse conditions
derived from theresults of Typhoon
No. 19 in 2019

In addition, the collapse conditions of Sato et al. (An uninhabitable house appears: v2d = 2.5)
have already been verified for the damaged houses due to dike failure at Kinugawa river in theevent of Heavy Rainfall Disaster in Kanto-Tohoku Regions, September 2015, but they are detailed.
It was compared with the reported fluid force (based on flood simulation) and damage situation[26] due to dike failure at Chikuma river during the heavy rain of Typhoon No. 19 in 2019. As
shown in Figure 7.7, it can be seen that the blue range where the fluid force v2d is 2.5 or more andthe location where the house was washed away shown in the red plot are almost the same. Basedon this, it is considered that the collapse conditions of Sato et al. (An uninhabitable house appears:
v2h = 2.5) are generally appropriate from the two recent floods.

Figure 28 Maximum fluid force and damaged houses in Nagano city, Nagano prefecture (Typhoon No. 19 in2019) [26]If collapse condition by Sato et al. is supposed to be a representative collapse condition of Japanand compared to Dutch collapse criteria of masonry and brick houses, it is obvious that there is asignificant difference due to structure. Further research and verifications are required in the future.

PR3983.10 • December 2021

45

Figure 29 Comparison of collapse conditions of Japanese and Dutch houses

As a result of this project, a new loss of life estimation model is proposed for future study as
follows.
【The breach zone】
𝑣 2 ∙ 𝑑 ≥ 2.5𝑚3 /𝑠 2
F =1
D,B

【The zone with rapidly rising water】
𝑣 2 ∙ 𝑑 < 2.5𝑚3 /𝑠 2 and （𝑑 ≥ 2.1𝑚 or w≥ 4𝑚/ℎ𝑟）
FD,Rise (𝑑)= 𝛷𝑁 (ln(𝑑)−𝜇𝑁
𝜎𝑁

𝜇N=1.46, 𝜎N=0.28)【The transition zone】
𝑣 2 ∙ 𝑑 < 2.5𝑚3 /𝑠 2 and（𝑑 ≥ 2.1𝑚 and 0.5𝑚/ℎ𝑟 ≤ w < 4𝑚/ℎ𝑟）
FD = FD,Remain + (w − 0.5)

FD,Rise-FD,Remain
3.5【The remaining zone】（w< 0.5𝑚/ℎ𝑟 or (w ≥ 0.5𝑚/ℎ𝑟 and 𝑑 < 2.1𝑚）) and (𝑣 2 ∙ 𝑑 < 2.5𝑚3 /𝑠 2 ）FD,Remain (𝑑)= 𝛷𝑁 (ln(𝑑)−𝜇𝑁
𝜎𝑁)

𝜇N=7.60, 𝜎 N=2.75

where, d: flood depth(m), v: flow velocity(m/s), w: rise rate of water(m/h), F: mortality, Φ:cumulative probability density function of standard normal distribution, 𝜇: average value of h, σ:
standard deviation of h.

6.5.4

Future activities

The following study is required as activities in the future. In order to verify reproducibility, it isnecessary to grasp applicability to Japan by study on damage cases and large/full scale modelexperiment of, for example, Heavy Rain Disaster in July, 2018 (Mabi-cho, Kurashiki city, Okayama
prefecture), Typhoon No.19 (Nagano city, Nagano prefecture), and Heavy Rain Disaster in July,
2020 (Kuma village, Kumamoto Prefecture).

PR3983.10 • December 2021

46

【The breach zone】•

Verification of reproducibility in recent multiple floods. Verification of applicability to Japan.

•

Elucidation of the house collapse mechanism from a mechanical point of view based on paststudies.

•

Understanding the relationship between collapsed houses and age. Consideration of age forhouse collapse conditions.

•Introduction of house damage function based on actual damage in recent years.

•

Construction of a mortality function derived from past floods.

【The zone with rapidly rising water】【The remaining zone】•

Understanding and organizing the mechanism of death occurrence in each zone.

•

Setting of boundary conditions and Construction of mortality function for each zone based on
past floods.

•

Consideration of water rise rate observed or calculated by flood simulation.

•

Consideration of victim age.

•

Consideration of differences in height of house and person between the two countries.

How to consider age of houses
This project provides a comparison of houses that complied with the old seismic criterion（wooden）andcollapse condition determined by Sato et al. in order to understand aging of houses. If the target of collapse
condition determined by Sato et al. is wooden house, both conditions are wooden houses. The study of Sato et
al. targets Yoshida River Basin in the event of Flood in August, 1986. It is highly possible that these houses arethose that complied with the old seismic criterion (1950) or those built before implementing seismic criterion,
since majority of these houses are older than 10 years old, according to the relationship between maximum
fluid force and aging (Figure 30). On the assumption that it complies with old seismic criterion, collapse
condition of newly built houses that complied with the old seismic criterion can be comparable to rather aged
collapse condition of Sato et al.. The difference (Figure 31) can be assumed to be the change of yield strength
due to aging.
Figure 30 Relationship between Max. fluid force and age of houses (Edited based on references22）)

Maximum fluid force (m3/s2)

Small damage
Medium damage
Large damage
Renovated house

Elapsed years (year)

PR3983.10 • December 2021

47

Figure 31 Comparison of wooden houses that complies with the old seismic criterion

6.6
[1]
[2]

[3]

[4]

[5]

[6]
[7]

[8]

[9]

References
Obihiro Development and Construction Department, Hokkaido Regional Development Bureau,
Report of flood in August, 2016. (Japanese Only)
Interagency Performance Evaluation Task Force: Performance Evaluation of the New Orleans
and Southeast Louisiana Hurricane Protection System – Final Report, Volume VII, pp.108109, US Army Corps of Engineers, 2007.
Water and Disaster Management Bureau, Ministry of Land, Infrastructure and Transport, The
Guide for Analysis of Flood Damage Indicators(2013 trial version), 2013,https://www.mlit.go.jp/river/basic_info/seisaku_hyouka/gaiyou/hyouka/pdf/higaisihyou_h25.pdf . (Japanese only)
Ikeushi, K. et al., Inundation Patterns and Fatality Analysis on Large-scale Flood, Journal ofJapan Society of Civil Engineers. Ser. B1, Hydraulic engineering, Vol.67, No.3, pp133-144,
2011. https://www.jstage.jst.go.jp/article/jscejhe/67/3/67_3_133/_article/-char/ja/(Japanese Only)
Jonkman, S.N., Loss of life estimation in flood risk assessment: Theory and applications,
2007, https://repository.tudelft.nl/islandora/object/uuid%3Abc4fb945-55ef-4079-a606ac4fa8009426
Maaskant, B., et al., Analyse slachtofferaantallen VNK-2 en voorstellen voor aanpassingen
van slachtofferfuncties, 2007.
Rijkswaterstaat VNK Project Office, The National Flood Risk Analysis for the Netherlands FINAL REPORT, 2016, https://www.helpdeskwater.nl/publish/pages/131663/vnk-rapporteng-lr.pdf .
Yagi, R. and Kure, S., Proposal of Vertical/Horizontal Evacuation Zone Classification based on
Flood Inundation Analysis and Risk Rank Evaluation for the Joganji River and Jinzu River,
Journal of Japan Society of Civil Engineers. Ser. B1, Hydraulic engineering, Vol.76, No.2,
I_715-I_720, 2020. (Japanese Only)
Flood Control Subcommittee, Committee on Hydroscience and Hydraulic Engineering, WestJapan Heavy Rain Disaster Survey Report (China), pp91, 2019,https://committees.jsce.or.jp/report/system/files/%e8%a5%bf%e6%97%a5%e6%9c%ac%

PR3983.10 • December 2021

48

[10]

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e8%b1%aa%e9%9b%a8%e7%81%bd%e5%ae%b3%e8%aa%bf%e6%9f%bb%e5%a0%b1%e5%91%8a%e6%9b%b8%ef%bc%88%e4%b8%ad%e5%9b%bd%e5%9c%b0%e5%8c%
ba%ef%bc%89.pdf . (Japanese Only)
Nihei,Y., Heavy Rain in July, 2018: River flooding in Oda River, Firefighting disasterprevention science, 2019, http://www.isad.or.jp/wp/wpcontent/uploads/2019/06/no136_12p.pdf . (Japanese Only)
Shinohara, A. and Nihei, Y., Flood Process in Oda River due to the 2018Western Japan Heavy
Rain, Advances in river engineering, Vol.25, 2019. (Japanese Only)
Sato, Y. et al., Experimental Study on Potential Risk in House under Flood Inundation,
Journal of Japan Society of Civil Engineers. Ser. B1, Hydraulic engineering, Vol.75, 2019.
(Japanese Only)
Tsukada, F. and Ikeuchi, K., Analysis on the Human Damage Caused by 2018 Japan Floods
in Mabi-cho, Kurashiki City and Investigation on Improvement Measures for Human Damage
Estimation Methods, Advances in river engineering, Vol.26, 2020. (Japanese Only)
Sho Tomura et al., Fatality Estimation by Life Loss Evaluation Model for the Large-scale
Floods under Future Climate, Proceedings of the 22nd IAHR-APD Congress 2020, Sapporo,
Japan, 2020.
Clausen, L.K., Potential Dam Failure: Estimation of Consequences, and Implications forPlanning. Unpublished M.Phil. thesis at the School of Geography and Planning at Middlesex
Polytechnic collaborating with Binnie and Partners, Redhill, 1989.
Karvonen, R.A.,et al., The use of physical models in dambreak analysis, RESCDAM Final
Report, Helsinki University of Technology, Helsinki, Finland, 2000.
Ministry of Land, Infrastructure, Transport and Tourism, Survey Report of Construction Start
Statistics, 【Houses】Number of Houses by Usage Relationship, by Structure, byConstruction, by prefecture, 2020,
https://www.mlit.go.jp/sogoseisaku/jouhouka/sosei_jouhouka_tk4_000002.html .
(Japanese Only)
Cabinet Office, About Damage Estimation Method related to the Earthquakes Occurring
Beneath the Tokyo Metropolitan Area, http://www.bousai.go.jp/jishin/syuto/pdf/shiryou3.pdf. (Japanese Only)
The Three Little Pigs, https://americanliterature.com/childrens-stories/the-three-little-pigs
Flood Control Planning Room, River Environment Division and Flood Disaster Prevention
Division, River Department, National Institute for Land and Infrastructure Management,
Ministry of Land, Infrastructure, Transportation and Tourism, Manual for Creating a Flood
Forecast Area Map（4th edition）, pp24-25, viii, 2017,
http://www.mlit.go.jp/river/bousai/main/saigai/tisiki/syozaiti/pdf/manual_kouzuishinsui_171
006.pdf . (Japanese Only)
Kawata, Y. and Nakagawa, H, Flood Disasters in the Misumi River, Disaster Prevention
Research Institute Annuals, No.27 B-2, 1984, http://www.dpri.kyotou.ac.jp/nenpo/no27/27b2/a27b2p12.pdf . (Japanese Only)
Sato, S. et al., Numerical Simulation of Flood and Damage to Houses -A Case of the Yoshida
River due to Typhoon No.8610, Proceedings of the 33rd Hydraulic Lecture, 1989,
https://web.pref.hyogo.lg.jp/hnk09/documents/000137945.pdf . (Japanese Only)
Tabata, K. et al., On Flood Risk Reduction by Integrating Analysis of Flood Flow andInundation in the Kinugawa River during 2015 Large Flood, Journal of Japan Society of Civil
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Only)
Priyambodoho, B. et al., Relationship between Flood Inundation Flow and Building Damage
due to Flood Disaster at Chikuma River in Nagano Prefecture in 2019, Journal of Japan
Society of Civil Engineers. Ser. B1, Hydraulic engineering, Vol.76, No.2, I_619-I_624, 2020.
(Japanese Only)

PR3983.10 • December 2021

49

7

Flood probabilities

In WP2 the flood probabilities are determined, which are used in the flood risk calculation. In theseflood probabilities the dependence between the levee section is incorporated, the “scenario floodprobability”. The table below shows the difference between section and scenario flood probability.
The latter is used for the flood risk assessment, as it is corrected for potential upstream flooding.
The next sections give an overview of how these flood probabilities are calculated, and how thedependencies are taken into account.
Table 28 Section and scenario flood probabilities, for the average discharge statistics under future climate. The
most upstream sections are highlighted as they are unaffected by upstream flooding and therefore have thesame failure probability
Location

Section flood

Scenario floodprobability

probability (Future)

Satsunai_KP4_2

1/16358

1/29708500

Satsunai_KP5_2

1/3063

1/363030

Satsunai_KP6_4

1/703

1/1530Satsunai_KP7_0

1/11301/1130Tokachi_KP56_4

1/13

1/60

Tokachi_KP58_0

1/73

1/3900Tokachi_KP59_6

1/65

1/480

Tokachi_KP61_4

1/105

1/174820

Tokachi_KP62_4

1/180

1/180

7.1

Overview of steps leading to failure probability per section

The method for calculating the flood probability is extensively described in the report WP2. In thissection, a short overview of the steps is given to provide context for the flood probabilities used in
the flood risk calculation.
•

In WP1, the exceedance probabilities of the discharges for Satsunai and Tokachi river are
determined. This information is combined with rating curves, to determine the exceedance
probabilities of water level for all considered breach locations.

•

Fragility curves give a relation between a water level and a failure probability, conditional tothat water level. It is therefore a measure of the strength of the dike. In WP2 these curves arecalculated for the failure mechanism overtopping. We take into account:oThe duration of overtopping, which affects the failure probability.

o

The uncertainty in the revetment quality, revetment height, and the water levels. Witha Monte Carlo simulation these uncertainties are incorporated in the fragility curve.

Integrating these fragility curves with the water level exceedance probabilities gives a failure
probability per dike segment. Calculating these is the main goal of WP2.

PR3983.10 • December 2021

51

•

A lot of dike segments will cause similar flooding when breached. These segments with similarconsequences are grouped into dike sections, for which a failure probability is determined. We
assume that the different dike segments belonging to a section are dependent, so the segmentwith the highest failure probability is normative for the dike section.

•

Nine dike sections are considered in this study, five along Tokachi river and four along Satsunai
river. If an upstream sections fails, water will flow from the river, into the area, reducing thedownstream water levels. We assume that the reduction of the water levels is of such amagnitude that downstream sections will no longer fail during that event. This dependency of
the failure probability to upstream sections is the last step from section failure probability toscenario failure probability. The next section will explain this further.

7.2

From section probability to scenario probability

Last section briefly explained how the failure probability per dike section is calculated. The failure
probability of downstream section is however lower than the section failure probability itself, as
upstream breaches will prevent downstream failures. We call this updated failure probability the
scenario failure probability, or 𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜 . This probability is used to avoid overestimating theconsequences of a flood.
To calculate this failure probability, some assumptions are made on the dependence between flood
characteristics. We assume:•

The discharge per river branch (Satsunai and Tokachi) are dependent. This means that during
an event, the same peak discharge occurs at every dike along a river branch. Note that Tokachi
river has some confluences along Obihiro. We assume that the discharge on these tributaries isalso dependent on the main branch. For example, a once per 100 year discharge will remain aonce per 100 year discharge downstream of the confluences (which means the discharge
increases).

•

The segment failure probabilities are dependent, as explained in the section 7.1.

•

Failure probabilities of sections (conditional to the discharge) are considered independent, but
we consider the order of the sections. When an upstream section has failed, we assume thewater level to be reduced such that the downstream dikes cannot fail anymore during thisevent.

Let 𝑃𝑓,𝑠𝑒𝑐𝑡𝑖𝑜𝑛 be the section failure probability, and |𝑞 conditional to discharge 𝑞. The scenario failure
probability of section 𝑛 during in event, is calculated in upstream to downstream order (1, 2, 3, …,𝑛):
𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,1 |𝑞 = 𝑃𝑓,𝑠𝑒𝑐𝑡𝑖𝑜𝑛,1 |𝑞
𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,2 |𝑞 = 𝑃𝑓,𝑠𝑒𝑐𝑡𝑖𝑜𝑛,2 |𝑞 ⋅ [1 − (𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,1 |𝑞)]𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,3 |𝑞 = 𝑃𝑓,𝑠𝑒𝑐𝑡𝑖𝑜𝑛,3 |𝑞 ⋅ [1 − (𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,1 |𝑞 + 𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,2 |𝑞)]𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,𝑛 |𝑞 = 𝑃𝑓,𝑠𝑒𝑐𝑡𝑖𝑜𝑛,𝑛 |𝑞 ⋅ [1 − 𝑠𝑢𝑚𝑖𝑛−1 (𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,𝑛 |𝑞)]𝑃𝑓,𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜,𝑛 |𝑞 is the scenario failure probability, which is corrected for potential upstream failures.
This number is used to calculate the flood risk, by multiplying it with the consequences of a flood
scenario.

PR3983.10 • December 2021

52

For the most upstream section, the scenario and independent failure probability are equal, as no
flooding further upstream is considered. For the second section, the failure probability is reduced,
since failure can only take place when the upstream section has not failed. For the third section,
both upstream sections reduce the flood risk, etcetera.

7.3

Scenario probabilities

In the basic scenario for the flood probabilities are calculated with the average exceedance
probability of the discharges for the “future” situation, so including climate change. These
probabilities are shown in Table 29.
Table 29 Scenario flood probability, Future average
Location

Scenario flood probability(Future average)

Satsunai_KP4_2

1/29708500

Satsunai_KP5_2

1/363030

Satsunai_KP6_4

1/1530Satsunai_KP7_0

1/1130Tokachi_KP56_4

1/60

Tokachi_KP58_0

1/3900Tokachi_KP59_6

1/480

Tokachi_KP61_4

1/174820

Tokachi_KP62_4

1/180

The impact of climate change can be analysed by comparison the “future” situation with the “past”
situation. In Table 30 the flood probabilities for the “past” situation are shown. The floodprobabilities are smaller than the “future” situation.
Table 30 Scenario flood probability, Past average
Scenario flood probability
Location(past average)
Satsunai_KP4_2

1/1533756680

Satsunai_KP5_2

1/6906020

Satsunai_KP6_4

1/26380

Satsunai_KP7_0

1/39570

Tokachi_KP56_4

1/90

Tokachi_KP58_0

1/22290

Tokachi_KP59_6

1/1870Tokachi_KP61_4

1/1007420

Tokachi_KP62_4

1/7100PR3983.10 • December 2021

53

Another aspect that influences the flood probability is the uncertainty in the exceedance probabilityof the discharges. In the basic analysis the average exceedance probability is used, when the 95%
upper limit of the exceedance probability is used the flood probabilities will increase. In Table 31
the flood probabilities are shown for the 95% upper limit of the exceedance probability of thedischarges. Note that the 95% upper limit is the upper limit of the 95% confidence interval, whichis actually the 97.5th percentile in the probability distribution.
Compared to the flood probabilities in Table 29 the probabilities are significantly larger due to theincrease in exceedance probabilities of higher discharges.
Table 31 Scenario flood probability, Future 95%
Scenario flood probability
Location(Future 95%)
Satsunai_KP4_2

1/2863600

Satsunai_KP5_2

1/35000

Satsunai_KP6_4

1/160

Satsunai_KP7_0

1/70

Tokachi_KP56_4

1/50

Tokachi_KP58_0

1/1770Tokachi_KP59_6

1/290

Tokachi_KP61_4

1/63760

Tokachi_KP62_4

1/20

PR3983.10 • December 2021

54

8

Flood Risk

The flood risk is determined by multiplying the calculated flood probability by the flood
consequences. The risks are expressed in economic risk and risk of loss of life. In this chapter theflood risks are calculated and shown for the Obihiro case study area. In the risk analysis thedifference in risks between “past” and “future” are shown and also the impact of the differenthydrographs shapes on the results.

8.1

Economic Risk

8.1.1

Expected value of economic damage

The expected value of the economic damage is calculated by multiplying the flood probability per
scenario by the economic damage and then combing it over the flood scenarios. To determine theimpact of climate change the results are shown for the “past” and “future” scenarios. In Table 32
the economic risk for the “past” scenarios is given.
The economic risk is calculated by the following formula:𝑛𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑟𝑖𝑠𝑘 = ∑ 𝑃𝑓,𝑠𝑐𝑒𝑛,𝑛 ∙ 𝐷𝑎𝑚𝑎𝑔𝑒𝑠𝑐𝑒𝑛,𝑛1

n = breach locations
The expect value of the economic damage is approximately 3,700 million Yen/year. This economicrisk is dominated by breach location KP56.4 and is caused by a relatively high probability of failure.
When climate change is taken into account, the failure probability and the amount of damage
increases and therefore the economic risk increases as well, Table 33. The expect value of theeconomic damage increases to 20,464 million Yen/year. This is an increase by a factor 5.5, 50% of
the difference is caused by the increase in failure probability and 50% by the increase in economic
damage.
In the basic analyses the “Max” hydrographs are used as flood scenarios. As an sensitivity analysis
the different hydrographs shapes are used to calculate the economic risk. For the “past” scenarios
the impact is relatively small, max 10 percent between “Max” and “Min”. This is due to the fact that
there are a limited number of hydrographs in the “past” dataset that lead to overtopping andfailure of the levee, so there is a limited variation in the economic damage. This is consistent withthe damage calculation in section 4.4.

PR3983.10 • December 2021

55

Table 32 Economic Risk for each breach location and the total economic risk for the “past” situation
Location
DamageEconomic Risk (million
Flood probability (Pf)(million Yen)
Yen/yr)Satsunai_KP4_2
1/1533756680
83200
0.00
Satsunai_KP5_2
1/6906020
1704000.02
Satsunai_KP6_4

1/26380362700Satsunai_KP7_0

1/395703080007.78

Tokachi_KP56_4

1/90

3113003296.58

Tokachi_KP58_0

1/2229046560020.89

Tokachi_KP59_6

1/1870568200304.38Tokachi_KP61_4

1/1007420

6413000.64

Tokachi_KP62_4

1/7100606800

85.51
3,729

Total

13.75

For the “future” scenarios the variation between the different hydrographs “Max”, “Min” and“MaxVol” are larger so the impact on the economic risk will also be larger than for the “past”
scenarios. As stated above the economic risk in the table below is calculated with the “Max”
hydrographs. When the “Min” scenarios are used for the “future” situation the damage will
decrease to approximately million 6,400 Yen/year, this is a reduction with a factor 3.
Table 33 Economic Risk for each breach location and the total economic risk for the “future” situation
Location
Flood probability (Pf)
DamageEconomic Risk (million(million Yen)
Yen/yr)Satsunai_KP4_2
1/29708500
2689830.01
Satsunai_KP5_2
1/363030
3304380.91
Satsunai_KP6_4

1/1530483160314.83

Satsunai_KP7_0

1/1130480609426.26

Tokachi_KP56_4

1/60

83398013346.00

Tokachi_KP58_0

1/3900855874219.20Tokachi_KP59_6

1/4808650331791.73

Tokachi_KP61_4

1/174820

9170395.25

Tokachi_KP62_4

1/180

7984074359.83
20,464Total

The economic risk shown above are calculated with the average exceedance probability of thedischarges. When the upper limit of exceedance probability (95%) is used for the flood probability
calculation, the flood probabilities and flood risk increase. In Table 36 the economic risk is shownfor that situation. The economic risk increases by a factor 3.5, to an expected value of theeconomic risk of 71,618 million Yen/year.
Table 34 Economic Risk for each breach location and the total economic risk for the “future” situation and 95%
upper limit exceedance probability of the discharges
Location
Flood probability (Pf)
DamageEconomic Risk (million(million Yen)
Yen/yr)Satsunai_KP4_2
1/2863600
2689830.09
Satsunai_KP5_2
1/35000
330438
9.44
Satsunai_KP6_4

1/160

483160Satsunai_KP7_0

1/70

4806097082.86

Tokachi_KP56_4

1/50

83398015709.33

Tokachi_KP58_0

1/1770855874484.17

Tokachi_KP59_6

1/2908650332993.99

Tokachi_KP61_4

1/63760

91703914.38

Tokachi_KP62_4

1/20

79840742294.25

Total

PR3983.10 • December 2021

3029.52

71,61856

8.1.2

Spatial distribution of expected value economic damage

The expected value of the damage (section 8.1.1) is a total risk for the entire area. This expectedvalue of economic risk can also be distributed over the area. In Figure 32 the spatial distribution of
the “past” scenario is given. It shows the location with higher economic risk. The impact of climatechange is shown in Figure 33. It clearly shows the increase in economic risk. This increase is acombination of the larger flood probability and the larger flood extent and flood characteristics.
Figure 32 Spatial distribution of the “past” economic risk (million Yen/yr).

Figure 33 Spatial distribution of the “future” economic risk (million Yen/yr).

PR3983.10 • December 2021

57

Figure 33 shows that the economic risks are highest in the areas with the largest water depths.
This can be explained by the fact that that area is the lowest part and that all inundation scenarios
have the largest water depth in that area. It is also interesting that some local areas in the central
part of Obihiro have a higher economic risk, this can indicate that high value infrastructure ispresent. An example of such a location is shown in Figure 34.
Figure 34 One of the locations with higher economic risk

8.2

Risk Loss of Life

8.2.1

Expected value of Loss of LifeThe expected value of loss of life gives insight in the average number of fatalities per year due toflooding. In all analysis regarding loss of life the average evacuation percentage of 26 percent isused. To determine the impact of climate change the results are shown for the “past” and “future”
scenarios. In Table 35 the the expected value of loss of life for the “past” scenarios is given. The
expect value is approximately 1.63 fatalities/year. This risk of loss of life is dominated by breachlocation KP56.4 and is caused by a relatively high probability of failure.
When climate change is taken into account, the failure probability and the loss of life increases andtherefore the risk of loss of life increases as well, Table 36. The expect value of loss of life
increases to 7.6 Fatalities/year. This is an increase by a factor 4.5, 50% of the difference is causedby the increase in failure probability and 50% by the increase in loss of life.

PR3983.10 • December 2021

58

Table 35 Expected value of loss of life for each breach location and the total expected value of loss of life forthe “past” situation
Location
Flood probability (Pf)
Loss of Life
LoL RiskSatsunai_KP4_2
1/1533756680
13
0.00
Satsunai_KP5_2

1/6906020

17

0.00

Satsunai_KP6_4

1/2638042

0.00

Satsunai_KP7_0

1/39570210.00

Tokachi_KP56_4

1/90

139

1.47

Tokachi_KP58_0

1/22290121

0.01

Tokachi_KP59_6

1/1870253

0.14Tokachi_KP61_4

1/1007420

251

0.00

Tokachi_KP62_4

1/7100

133

0.02
1.63

Total

Table 36 Expected value of loss of life for each breach location and the total expected value of loss of life forthe “future” situation
Location
Flood probability (Pf)
Loss of Life
LoL RiskSatsunai_KP4_2
1/29708500
30~0Satsunai_KP5_2

1/363030

43~0Satsunai_KP6_4

1/153074

0.05

Satsunai_KP7_0

1/113050

0.04Tokachi_KP56_4

1/60

272

4.35

Tokachi_KP58_0

1/3900422

0.11Tokachi_KP59_6

1/480

655

1.36

Tokachi_KP61_4

1/174820

622

0.00

Tokachi_KP62_4

1/180

308

1.68

Total

7.60

The risk of loss of life shown above are calculated with the average exceedance probability of thedischarges. When the upper limit of exceedance probability (95%) is used for the flood probability
calculation, the flood probabilities and flood risk increase. In Table 37 the risk of loss of life isshown for that situation. The risk increases by a factor 3.5, to an expected value of the economicrisk of 25.16 fatalities/year.
Table 37 Expected value of loss of life for each breach location and the total expected value of loss of life forthe “future” situation and 95% upper limit exceedance probability of the discharges
Location
Flood probability (Pf)
Loss of Life
LoL RiskSatsunai_KP4_2
1/286360030
0.00
Satsunai_KP5_2

1/35000

43

0.00

Satsunai_KP6_4

1/160

74

0.46

Satsunai_KP7_0

1/70

50

0.74

Tokachi_KP56_4

1/50

272

5.12

Tokachi_KP58_0

1/1770422

0.24Tokachi_KP59_6

1/290

655

2.27

Tokachi_KP61_4

1/63760622

0.01

Tokachi_KP62_4

1/20

308

16.32
25.16

Total

PR3983.10 • December 2021

59

8.2.2

Local Individual Risk

The local individual risk gives insight in the annual probability that an individual will die at aparticular location, including the effect of evacuation. The local individual risk is a combination of
the scenario flood probabilities, the mortality and the evacuation percentage. For the evacuation
percentage the average value of 26 percent is used.
The local individual risk is calculated by the following formula:𝑛𝐿𝑜𝑐𝑎𝑙 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑟𝑖𝑠𝑘 = ∑ 𝑃𝑓,𝑠𝑐𝑒𝑛,𝑛 ∙ 𝑀𝑜𝑟𝑡𝑠𝑐𝑒𝑛,𝑛 ∙ (1 − 𝑒𝑣𝑎𝑐. 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒)1

n = breach locations
The figure below shows the local individual risk for the “past” situation. The riskier places are
situated in the deeper parts of the case study area and are consistent with economic risks.
Figure 35 Local individual risk for the “past” situation

When climate change is included the scenario flood probabilities increase and also the flood extent
and flood characteristics increases. The increase in flood extent and flood characteristics lead to anincrease in mortality. Figure 36 shows the local individual risk for the “future” situation. The flood
extent is larger than the “past” situation and also the risks are higher.

PR3983.10 • December 2021

60

Figure 36 Local individual risk for the “future” situation

The local individual risk shown above is determined with the average exceedance probability of thedischarges. When the upper limit of exceedance probability (95%) is used for the flood probability
calculation, the flood probabilities and flood risk increase. In Figure 37 the local individual risk isshown for that situation. It leads to a significant increase in local individual risk, in some parts the
local individual risk exceeds 1/1,000 per year.
Figure 37 Local individual risk for the “future” situation and 95% upper limit probability of the discharges

PR3983.10 • December 2021

61

The local individual risk is in the basic analysis calculated with the average evacuation percentage
of 26%. When this percentage increases the local individual risk will decrease. In Figure 38 thelocal individual risk is shown for the situation with an 80 percent evacuation. The decrease in risk isequal to the increase in evacuation percentage, from 26% to 80%, a factor 3.
Figure 38 Local individual risk for the “future” situation, 80 percent evacuation, left is the average exceedance
probability of the discharges, right is with the 95% upper limit.

8.3

Uncertainties in Flood Risk

Uncertainties are inextricably linked to determining flood risk. In every step in determining theflood risk choices must be made on how to deal with uncertainties. The uncertainties indetermining the flood risk are therefore a direct translation of the uncertainties of all steps indetermining the flood risks.
Throughout the whole process of determining the flood risk the largest uncertainties are in
determining the flood probability and in particular in the exceedance frequencies of the hydraulicloads.

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9

Reflection, conclusions andrecommendations

The results show that the flood consequences, economic damage and loss of life, and therefore also
the flood risk are subject to uncertainties.
For the economic damage we have seen that the different type of discharge waves can have alarge impact on the calculated damage. The flood extent differs between the different discharge
waves. Especially when high value assets flood in one scenario and not in another scenario, thiscan lead to significant differences in the calculated damage. It is therefore important to conduct a
proper analysis on the damage calculation and how the economic damage may be affected by local
high value assets.
The average evacuation rate in the Obihiro case study is set at 26 percent. This is a relatively low
percentage and is mainly caused by the short lead times. Due to the extreme rainfall the riverdischarges increase quite fast, potentially resulting in flash floods.
The current evacuation method uses only two destinations, people are either at a safe shelter
location or people are at risk at home. In reality people are at different locations with differences invulnerability. People outside the threatened area are not vulnerable, people at shelters in thethreatened area are still vulnerable but less vulnerable than people staying at home.
•

An improvement of the evacuation modelling will lead to better insight into the location where
people will evacuate to, given a flood threat. A first draft of the improved evacuation method
has already been described in the report of WP3.

•

The improved method shows where people will, most likely, go to when an evacuation order isissued. Given the location where people are the vulnerabilities are different and the loss of life
modelling has to be adjusted to these differences. Therefore also the loss of life modelling has
to be improved to translate the evacuation strategies to the impact on the loss of life.

•

Next to improvements of the loss of life modelling through considering the different locations
where people evacuate to, the model can also be improved by incorporating the Japanese typeof constructions. The current Dutch method is based on the Dutch type of constructions(concrete), the Japanese building construction consists mostly of wood, which can impact thepossibility of building collapse, leading to a higher probability of casualties. This is also an
aspect that can be improved in the model, to make them more suitable for the Japanesesituation.

The flood risks are the product of the flood probabilities and the flood consequences. The resultsshows that the economic risk is highest in the locations with the larger water depths, but also highvalue assets have a large impact on the economic damage. It is wise to further analyse thelocations in their vulnerability to flooding.

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The impact of climate change is large, on the one hand the flood extent and flood characteristics
increase. On the other hand the flood probability increases leading to a significant increase ineconomic risk.
The risk of loss of life is expressed by the expected value of loss of life and the local individual risk.
The expected number of loss of life is dominated by the breach location with the highest floodprobability. The scenarios with in potential larger number of fatalities are less important for the riskof loss of life due to a relatively low flood probability. In accordance with the economic risk, theimpact of climate change is large.
The local individual risk gives insight in the annual probability that an individual will die at aparticular location, including the effect of evacuation. For the Obihiro case study area the risks arein the deeper parts of the area larger than 1/10,000 per year and at some locations even larger
than 1/1,000 per year. This is mostly due to a relatively large failure probability of some of thebreach locations. In comparison, in the Dutch flood risk policy the probability of fatality due toflooding must not exceed 1 in 100,000 per year (or 0.001%) for every resident living behind thelevees. This is the so-called tolerable individual risk.

9.1

Economic damage

In the case study the economic damage is determined with the Japanese method. The method isbased on the value of different assets and the inundation depth, based on the inundation depth thedamage rate is determined which is multiplied with the value of the asset. This approach is quite
similar to the Dutch method of determining the economic damage.
The potential economic damage has a large variation between the different breach locations. The
difference in economic damage can go up to a factor 8. The difference of the potential damage
within one breach location due to differences in discharge waves is in the order of a factor 1.5 to
3.5.
Climate change has also an impact on the potential economic damage, due to the increase indischarges the flood probability, flood extent and flood characteristics increase as well. The impact
on the potential damage varies between breach locations but is in the order of a factor 1.5 to 3.

9.2

Evacuation and loss of life

Given the data from 86 cases in Japan the average evacuation rate is set at 26 percent, with abandwidth of 0 percent in case of no lead time up to 44 percent in case of “long” lead time (morethan 3 hours).
Loss of life is determined with the Dutch mortality functions in combination with the evacuation
rate. The mortality functions uses the flood characteristics (water depth, rise rate and flow
velocity) to determine the mortality at all locations. The case study shows that, in accordance withthe economic damage, that there is a large variation in potential loss of life between different
breach locations. In several scenarios the number of casualties is in the hundreds, even when the
expected value of the evacuation rate is taken into account. The difference in loss of life between
the different breach locations can go up to a factor 20.

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The difference in the loss of life within one breach location due to differences in discharge waves isalso relatively large and is in the order of a factor 2.5 to 20. The factor 20 is caused by the factthat there is a large difference between the discharge waves.
Climate change also has an impact on the potential loss of life, due to the increase in discharges
the flood probability, flood extent and flood characteristics increase as well. The impact on thepotential loss of life varies between breach locations but is in the order of a factor 2 to 3.5.
So for the determination of loss of life the shape and height of discharge wave has a significant
impact on the loss of life.

9.3

Flood risk

9.3.1

Economic Risk

The economic risk is calculated for the current situation (“past”) and for the situation including
climate change (“future”). The impact of climate change to the economic risk is approximately afactor 5.5, for which 50% of the difference is caused by the increase in failure probability and 50%
by the increase in economic damage.
The basic economic risk calculation is done with the use of the flood probability which isdetermined with the average exceedance probability of the discharges. When the upper limit of theexceedance probability of the 95 percent confidence interval is used, the flood probability
increases. This larger flood probability increases the economic risk by a factor 3.5.
When looked at the spatial distribution of the economic risk, in general the larger risks are in the
deeper parts of the case study area. What stands out is that at some locations apparently highvalue assets are present because at these locations the economic risk is relatively high.
Therefore a further analysis of these high value assets and their vulnerability to floods isrecommended.

9.3.2

Risk loss of life

In accordance with the economic risk the risk of loss of life is determined for the current situation(“past”) and for the situation including climate change (“future”). The impact of climate change tothe risk of loss of life is approximately a factor 4.5, for which 50% of the difference is caused by
the increase in failure probability and 50% by the increase in economic damage.
When the 95 percent upper limit of the exceedance probability is used, the flood probability
increases. This larger flood probability increases the risk of loss of life by a factor 3.5.
The local individual risk gives insight in the annual probability that an individual will die at aparticular location, including the effect of evacuation. The Obihiro case study shows that the risks
are in the deeper parts of the area larger than 1/10,000 per year and at some locations even larger
than 1/1,000 per year. This is mostly due to a relatively large failure probability of some of thebreach locations.

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